HK1260281B - Detecting and classifying copy number variation - Google Patents

Description

Detection and classification of copy number variations

This application is a divisional application of the invention patent application with application date of November 7, 2012, application number 201210441134.8, and invention name “Detection and classification of copy number variation”.

Background Art

One of the key efforts in human medical research is the discovery of genetic abnormalities that are crucial for adverse health outcomes. In many cases, specific genes and/or key diagnostic markers have been identified in multiple parts of the genome that are present in abnormal copy numbers. For example, in prenatal diagnosis, extra or lost copies of entire chromosomes are common genetic lesions. In cancer, loss or duplication of copies of entire chromosomes or chromosome segments, as well as higher levels of amplification in specific regions of the genome, are common.

Most of the information about copy number variation has been provided by the cytogenetic resolution that allows the identification of structural abnormalities. A variety of conventional procedures for genetic screening and biological dosimetry have utilized invasive procedures (e.g., amniocentesis) to obtain cells for karyotyping. Recognizing the need for more rapid testing methods that do not require cell culture, fluorescence in situ hybridization (FISH), quantitative fluorescence PCR (QF-PCR), and array-comparative genomic hybridization (array-CGH) have been developed as molecular cytogenetic methods for analyzing copy number variation.

The advent of technologies that allow sequencing of entire genomes in a relatively short period of time, as well as the discovery of circulating cell-free DNA (cfDNA), have provided the opportunity to compare genetic material from one chromosome to be compared with that of another chromosome without the risks associated with invasive sampling procedures. However, limitations of existing methods, including insufficient sensitivity due to limited levels of cfDNA and sequencing biases of the technology due to the inherent nature of genomic information, dictate a continuing need for noninvasive methods that will provide any or all of the specificity, sensitivity, and applicability to reliably determine copy number changes in a variety of clinical settings.

The embodiments disclosed herein satisfy some of the above needs and, in particular, offer an advantage in providing a reliable method at least suitable for performing non-invasive prenatal diagnostics and for diagnosing and monitoring metastatic progression in cancer patients.

Overview

The maternal DNA background in a maternal sample imposes operational limitations on the sensitivity of any test that attempts to distinguish fetal chromosomes from the maternal chromosome set in the sample. Therefore, for diagnostics and routine tests that rely on quantitative differences and/or substantial differences between the fetal and maternal chromosome sets, fetal fraction is an important parameter to consider. The present invention provides a method for determining the fetal fraction in a maternal sample. The method obtains the fetal fraction as a function of normalized chromosome values or normalized chromosome segment values. The method for determining the fetal fraction of the present invention can be combined with other methods, such as a method for obtaining the fetal fraction as a function of allele imbalance information in a polymorphism, to classify copy number variations of fetal chromosomes or chromosome segments in a maternal sample. The present invention also provides devices and kits for implementing the method.

Provide a variety of methods for determining the copy number variation (CNV) of a sequence of interest in a test sample comprising a mixture of nucleic acids, which are known or suspected to be different in the amount of one or more sequences of interest. This method includes a statistical approach that takes into account the cumulative variability from process-related, interchromosomal and intersequence variability. The method is applicable to determining the CNV of any fetal aneuploidy, as well as multiple CNVs known or suspected to be associated with multiple medical conditions. CNVs that can be determined according to this method include trisomy or monosomy of any one or more of chromosomes 1-22, X and Y, polysomy of other chromosomes, and deletion and/or duplication of any one or more segments of these chromosomes, which can be detected by sequencing the nucleic acid of the test sample only once. Any aneuploidy can be determined from the sequencing information obtained by sequencing the nucleic acid of the test sample only once.

In one embodiment, a method is provided for determining the presence or absence of any four or more different, complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The steps of the method include: (a) obtaining sequence information of fetal and maternal nucleic acids in the maternal test sample; (b) using the sequence information to identify a certain number of sequence tags for each of any four or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a certain number of sequence tags for a normalizing chromosome sequence for each of any four or more chromosomes of interest; (c) using the number of sequence tags identified for each of any four or more chromosomes of interest and the number of sequence tags identified for each of the normalizing chromosome sequences to calculate a single chromosome dose for each of any four or more chromosomes of interest; and (d) comparing each of the single chromosome doses for each of any four or more chromosomes of interest with a threshold for each of any four or more chromosomes of interest, and thereby determining the presence or absence of any four or more complete, different fetal chromosomal aneuploidies in the maternal test sample. Step (a) can comprise sequencing at least a portion of the nucleic acids of a test sample to obtain the sequence information for the fetal and maternal nucleic acid molecules of the test sample. In some embodiments, step (c) comprises calculating a single chromosome dose for each chromosome of interest as the ratio of the number of sequence tags identified for each chromosome of interest to the number of sequence tags identified for the normalizing chromosome sequence for each chromosome of interest. In some other embodiments, step (c) comprises: (i) calculating a sequence tag density ratio for each chromosome of interest by relating the number of sequence tags identified for each chromosome of interest in step (b) to the length of each chromosome of interest; (ii) calculating a sequence tag density ratio for each normalizing chromosome sequence by relating the number of sequence tags identified for each normalizing chromosome sequence in step (b) to the length of each normalizing chromosome sequence; and (iii) using the sequence tag density ratios calculated in steps (i) and (ii) to calculate a single chromosome dose for each chromosome of interest, wherein the chromosome dose is calculated as the ratio of the sequence tag density ratio for each chromosome of interest to the sequence tag density ratio for the normalizing chromosome sequence for each chromosome of interest.

In another embodiment, a method is provided for determining the presence or absence of any four or more different, complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The method comprises the steps of: (a) obtaining sequence information for the fetal and maternal nucleic acids in the maternal test sample; (b) using the sequence information to identify a chromosome selected from chromosomes 1-22, X, and Y. (c) calculating a single chromosome dose for each of any four or more chromosomes of interest using the number of sequence tags identified for each of the four or more chromosomes of interest and the number of sequence tags identified for each of the normalizing chromosome sequences; and (d) comparing each of the single chromosome doses for each of any four or more chromosomes of interest to a threshold value for each of the four or more chromosomes of interest, and thereby determining the presence or absence of any four or more complete, different fetal chromosomal aneuploidies in the maternal test sample, wherein the any four or more chromosomes of interest selected from chromosomes 1-22, X, and Y include at least twenty chromosomes selected from chromosomes 1-22, X, and Y, and wherein the presence or absence of at least twenty different, complete fetal chromosomal aneuploidies is determined. Step (a) can comprise sequencing at least a portion of the nucleic acids of the test sample to obtain the sequence information for the fetal and maternal nucleic acid molecules of the test sample. In some embodiments, step (c) comprises calculating a single chromosome dose for each chromosome of interest as the ratio of the number of sequence tags identified for each chromosome of interest to the number of sequence tags identified for the normalizing chromosome sequence for each chromosome of interest. In some other embodiments, step (c) comprises: (i) calculating a sequence tag density ratio for each chromosome of interest by relating the number of sequence tags identified for each chromosome of interest in step (b) to the length of each chromosome of interest; (ii) calculating a sequence tag density ratio for each normalizing chromosome sequence by relating the number of sequence tags identified for each normalizing chromosome sequence in step (b) to the length of each normalizing chromosome sequence; and (iii) using the sequence tag density ratios calculated in steps (i) and (ii) to calculate a single chromosome dose for each chromosome of interest, wherein the chromosome dose is calculated as the ratio of the sequence tag density ratio for each chromosome of interest to the sequence tag density ratio for the normalizing chromosome sequence for each chromosome of interest.

In another embodiment, a method is provided for determining the presence or absence of any four or more different, complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The steps of the method include: (a) obtaining sequence information for the fetal and maternal nucleic acids in the maternal test sample; (b) using the sequence information to identify a certain number of sequence tags for each of any four or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a certain number of sequence tags for a normalizing chromosome sequence for each of any four or more chromosomes of interest; (c) using the number of sequence tags identified for each of any four or more chromosomes of interest and the number of sequence tags identified for each of the normalizing chromosome sequences to identify a certain number of sequence tags for any four or more chromosomes of interest. (d) comparing each of the single chromosome doses for each of the four or more chromosomes of interest to a threshold for each of the four or more chromosomes of interest, and thereby determining the presence or absence of any four or more complete, different fetal chromosomal aneuploidies in the sample, wherein the four or more chromosomes of interest selected from chromosomes 1-22, X, and Y are all chromosomes 1-22, X, and Y, and wherein the presence or absence of complete fetal chromosomal aneuploidies of all chromosomes 1-22, X, and Y is determined. Step (a) may include sequencing at least a portion of the nucleic acids of the test sample to obtain the sequence information for the fetal and maternal nucleic acid molecules of the test sample. In some embodiments, step (c) includes calculating a single chromosome dose for each of the chromosomes of interest as the ratio of the number of sequence tags identified for each of the chromosomes of interest to the number of sequence tags identified for the normalized chromosome sequence of each of the chromosomes of interest. In some other embodiments, step (c) includes: (i) calculating a sequence tag density ratio for each chromosome of interest by relating the number of sequence tags identified for each chromosome of interest in step (b) to the length of each chromosome of interest; (ii) calculating a sequence tag density ratio for each normalizing chromosome sequence by relating the number of sequence tags identified for each normalizing chromosome sequence in step (b) to the length of each normalizing chromosome sequence; and (iii) using the sequence tag density ratios calculated in steps (i) and (ii) to calculate a single chromosome dose for each chromosome of interest, wherein the chromosome dose is calculated as the ratio of the sequence tag density ratio for each chromosome of interest to the sequence tag density ratio for the normalizing chromosome sequence for each chromosome of interest.

In any of the above embodiments, the normalizing chromosome sequence can be a single chromosome selected from chromosomes 1-22, X, and Y. Alternatively, the normalizing chromosome sequence is a group of chromosomes selected from chromosomes 1-22, X, and Y.

In another embodiment, a method is provided for determining the presence or absence of any one or more different, complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The steps of the method include: (a) obtaining sequence information for the fetal and maternal nucleic acids in the sample; (b) using the sequence information to identify a certain number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a certain number of sequence tags for a normalizing chromosome sequence for each of any one or more chromosomes of interest; (c) using the number of sequence tags identified for each of any one or more chromosomes of interest and the number of sequence tags identified for each of the normalizing segment sequences to calculate a single chromosome dose for each of any one or more chromosomes of interest; and (d) comparing each of the single chromosome doses for any one or more chromosomes of interest with a threshold for each of the one or more chromosomes of interest, and thereby determining the presence or absence of any one or more complete, different fetal chromosomal aneuploidies in the sample. Step (a) may comprise sequencing at least a portion of the nucleic acids of the test sample to obtain said sequence information for the fetal and maternal nucleic acid molecules of the test sample.

In some embodiments, step (c) comprises calculating a single chromosome dose for each chromosome of interest as the ratio of the number of sequence tags identified for each chromosome of interest to the number of sequence tags identified for the normalizing chromosome sequence for each chromosome of interest. In some other embodiments, step (c) comprises: (i) calculating a sequence tag density ratio for each of the chromosomes of interest by relating the number of sequence tags identified for each chromosome of interest in step (b) to the length of each chromosome of interest; (ii) calculating a sequence tag density ratio for each normalizing segment sequence by relating the number of sequence tags identified for each normalizing segment sequence in step (b) to the length of each normalizing chromosome; and (iii) calculating a single chromosome dose for each of the chromosomes of interest using the sequence tag density ratios calculated in steps (i) and (ii), wherein the chromosome dose is calculated as the ratio of the sequence tag density ratio for each chromosome of interest to the sequence tag density ratio for the normalizing segment sequence for each chromosome of interest.

In another embodiment, a method is provided for determining the presence or absence of any one or more different, complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The steps of the method include: (a) obtaining sequence information for fetal and maternal nucleic acids in a sample; (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing chromosome sequence for each of the one or more chromosomes of interest; (c) using the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for each normalizing segment sequence to calculate a single chromosome dose for each of the one or more chromosomes of interest; and (d) comparing each of the single chromosome doses for each of the one or more chromosomes of interest to a threshold value for each of the one or more chromosomes of interest, and thereby determining the presence or absence of one or more complete, different fetal chromosomal aneuploidies in the sample, wherein the one or more chromosomes of interest selected from chromosomes 1-22, X, and Y include at least twenty chromosomes selected from chromosomes 1-22, X, and Y, and wherein the presence or absence of at least twenty different complete fetal chromosomal aneuploidies is determined. Step (a) can comprise sequencing at least a portion of the nucleic acids of the test sample to obtain the sequence information for the fetal and maternal nucleic acid molecules of the test sample. In some embodiments, step (c) comprises calculating a single chromosome dose for each chromosome of interest as the ratio of the number of sequence tags identified for each chromosome of interest to the number of sequence tags identified for the normalizing chromosome sequence for each chromosome of interest. In some other embodiments, step (c) comprises: (i) calculating a sequence tag density ratio for each chromosome of interest by relating the number of sequence tags identified for each chromosome of interest in step (b) to the length of each chromosome of interest; (ii) calculating a sequence tag density ratio for each normalizing segment sequence by relating the number of sequence tags identified for each normalizing segment sequence in step (b) to the length of each normalizing chromosome; and (iii) calculating a single chromosome dose for each chromosome of interest using the sequence tag density ratios calculated in steps (i) and (ii), wherein the chromosome dose is calculated as the ratio of the sequence tag density ratio for each chromosome of interest to the sequence tag density ratio for the normalizing segment sequence of each chromosome of interest.

In another embodiment, a method is provided for determining the presence or absence of any one or more different, complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The steps of the method include: (a) obtaining sequence information for fetal and maternal nucleic acids in a sample; (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing segment sequence for each of the one or more chromosomes of interest; (c) using the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for each normalizing segment sequence to calculate a single chromosome dose for each of the one or more chromosomes of interest; and (d) comparing each of the single chromosome doses for each of the one or more chromosomes of interest to a threshold value for each of the one or more chromosomes of interest, and thereby determining the presence or absence of one or more complete, different fetal chromosomal aneuploidies in the sample, wherein the one or more chromosomes of interest selected from chromosomes 1-22, X, and Y are all chromosomes 1-22, X, and Y, and wherein the presence or absence of complete fetal chromosomal aneuploidies of all chromosomes 1-22, X, and Y is determined. Step (a) can comprise sequencing at least a portion of the nucleic acids of the test sample to obtain the sequence information for the fetal and maternal nucleic acid molecules of the test sample. In some embodiments, step (c) comprises calculating a single chromosome dose for each chromosome of interest as the ratio of the number of sequence tags identified for each chromosome of interest to the number of sequence tags identified for the normalizing chromosome sequence for each chromosome of interest. In some other embodiments, step (c) comprises: (i) calculating a sequence tag density ratio for each chromosome of interest by relating the number of sequence tags identified for each chromosome of interest in step (b) to the length of each chromosome of interest; (ii) calculating a sequence tag density ratio for each normalizing segment sequence by relating the number of sequence tags identified for each normalizing segment sequence in step (b) to the length of each normalizing chromosome; and (iii) calculating a single chromosome dose for each chromosome of interest using the sequence tag density ratios calculated in steps (i) and (ii), wherein the chromosome dose is calculated as the ratio of the sequence tag density ratio for each chromosome of interest to the sequence tag density ratio for the normalizing segment sequence of each chromosome of interest.

In any of the above embodiments, the different complete chromosomal aneuploidies are selected from complete chromosomal trisomy, complete chromosomal monosomy, and complete chromosomal polysomy. The different chromosomal aneuploidies are selected from complete aneuploidies of any one of chromosomes 1-22, X, and Y. For example, the different complete fetal chromosomal aneuploidies are selected from trisomy 2, trisomy 8, trisomy 9, trisomy 20, trisomy 21, trisomy 13, trisomy 16, trisomy 18, trisomy 22, 47,XXX, 47,XYY, and monosomy X.

In any of the above embodiments, steps (a)-(d) are repeated for test samples from different maternal subjects, and the method includes determining the presence or absence of any four or more different whole fetal chromosomal aneuploidies in each test sample.

In any of the above embodiments, the method can further comprise calculating a normalized chromosome value (NCV), wherein the NCV relates the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

where and are the estimated mean and standard deviation, respectively, for the jth chromosome dose in a set of qualified samples, and x _ij is the observed jth chromosome dose for test sample i.

In another embodiment, a method is provided for determining the presence or absence of different, partial fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids. The steps of the method include: (a) obtaining sequence information for fetal and maternal nucleic acids in the sample; (b) using the sequence information to identify a certain number of sequence tags for each of any one or more segments of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y and a certain number of sequence tags for the normalizing segment sequence of any one or more segments of any one or more chromosomes of interest; (c) using the number of sequence tags identified for any one or more segments of any one or more chromosomes of interest and the number of sequence tags identified for each normalizing segment sequence to calculate a single chromosome dose for each of any one or more segments of any one or more chromosomes of interest; and (d) comparing each of the single segment doses for any one or more segments of any one or more chromosomes of interest to a threshold value for any one or more segments of any one or more chromosomes of interest, and thereby determining the presence or absence of one or more different, partial fetal chromosomal aneuploidies in the sample. Step (a) may comprise sequencing at least a portion of the nucleic acids of the test sample to obtain said sequence information for the fetal and maternal nucleic acid molecules of the test sample.

In some embodiments, step (c) comprises calculating a single segment dose for each of the one or more segments of any one or more chromosomes of interest as the ratio of the number of sequence tags identified for each of the one or more segments of any one or more chromosomes of interest to the number of sequence tags identified for the normalizing segment sequence for each of the one or more segments of any one or more chromosomes of interest. In some other embodiments, step (c) includes: (i) calculating a sequence tag density ratio for each segment of interest by relating the number of sequence tags identified in each segment of interest in step (b) to the length of each segment of interest; (ii) calculating a sequence tag density ratio for each normalizing segment sequence by relating the number of sequence tags identified in step (b) to the length of each normalizing segment sequence; and (iii) using the sequence tag density ratios calculated in steps (i) and (ii) to calculate a single chromosome dose for each segment of interest, wherein the segment dose is calculated as the ratio of the sequence tag density ratio for each segment of interest to the sequence tag density ratio for the normalizing segment sequence for each segment of interest. The method can further include calculating a normalized segment value (NSV), wherein the NSV relates the segment dose to the average of the corresponding segment doses in a set of qualified samples as:

where and are the estimated mean and standard deviation, respectively, for the jth bin dose in a set of qualified samples, and x _ij is the observed jth bin dose for test sample i.

In various embodiments of the described methods, whereby a normalizing segment sequence is used to determine a chromosome dose or segment dose, the normalizing segment sequence can be a single segment of any one or more of chromosomes 1-22, X, and Y. Alternatively, the normalizing segment sequence can be a group of segments of any one or more of chromosomes 1-22, X, and Y.

The step (a)-(d) of the method for determining the presence or absence of part fetal chromosomal aneuploidy is repeated for multiple test specimens from different maternal subjects, and the method comprises determining that in each described sample, there is or does not exist different, part fetal chromosomal aneuploidy. The fetal chromosomal aneuploidy of the part that can be determined according to the method comprises the aneuploidy of the part of any segment of any chromosome. The aneuploidy of these parts can be selected from the duplication of part, the multiplication of part, the insertion of part and the disappearance of part. The example of the part aneuploidy that can be determined according to the method comprises the part monomer of chromosome 1, the part monomer of chromosome 4, the part monomer of chromosome 5, the part monomer of chromosome 7, the part monomer of chromosome 11, the part monomer of chromosome 15, the part monomer of chromosome 17, the part monomer of chromosome 18 and the part monomer of chromosome 22.

In any one of the above-mentioned embodiments, this test sample can be a maternal sample selected from blood, plasma, serum, urine and saliva samples. In any one of these embodiments, this test sample can be a plasma sample. These nucleic acid molecules of the maternal sample are fetal and maternal cell-free DNA molecules. Next generation sequencing (NGS) can be used to order-check these nucleic acids. In some embodiments, order-checking is a large-scale parallel sequencing using synthetic sequencing with reversible dye terminators. In other embodiments, order-checking is ligation sequencing. Still in other embodiments, order-checking is single molecule sequencing. Optionally, an amplification step is performed before order-checking.

In another embodiment, a method is provided for determining the presence or absence of any twenty or more different, complete fetal chromosomal aneuploidies in a maternal plasma test sample comprising a mixture of fetal and maternal cell-free DNA molecules. The method comprises the steps of: (a) sequencing at least a portion of the cell-free DNA molecules to obtain sequence information for the fetal and maternal cell-free DNA molecules in the sample; (b) using the sequence information to identify a number of sequence tags for each of any twenty or more chromosomes of interest selected from chromosomes 1-22, X, and Y and to identify a number of sequence tags for a normalizing chromosome for each of the twenty or more chromosomes of interest; (c) using the number of sequence tags identified for each of the twenty or more chromosomes of interest and the number of sequence tags identified for each of the normalizing chromosomes to calculate a single chromosome dose for each of the twenty or more chromosomes of interest; and (d) comparing each of the single chromosome doses for each of the twenty or more chromosomes of interest to a threshold value for each of the twenty or more chromosomes of interest and thereby determining the presence or absence of any twenty or more different complete fetal chromosomal aneuploidies in the sample.

In another embodiment, the present invention provides a method for identifying copy number variations (CNVs) of a sequence of interest (e.g., a clinically relevant sequence) in a test sample, the method comprising the following steps: (a) obtaining a test sample and a plurality of qualified samples, the test sample comprising a test nucleic acid molecule and the plurality of qualified samples comprising qualified nucleic acid molecules; (b) obtaining sequence information of the fetal and maternal nucleic acids in the sample; (c) calculating a qualified sequence dose for the qualified sequence of interest in each of the plurality of qualified samples based on the sequencing of the qualified nucleic acid molecules, wherein the calculating of the qualified sequence dose comprises determining parameters of the qualified sequence of interest and at least one qualified normalizing sequence; (d) identifying at least one qualified normalizing sequence based on the qualified sequence doses, wherein the at least one qualified normalizing sequence has minimal variability and/or maximum resolvability in the plurality of qualified samples; (e) calculating a test sequence dose for the test sequence of interest based on the sequencing of the nucleic acid molecules in the test sample, wherein the calculating of the test sequence dose comprises determining parameters of the test sequence of interest and at least one normalizing test sequence, the at least one normalizing test sequence corresponding to the at least one qualified normalizing sequence; (f) comparing the test sequence dose to at least one threshold value; and (g) assessing the copy number variation of the sequence of interest in the test sample based on the result of step (f). In one embodiment, the parameters for the qualified sequence of interest and at least one qualified normalizing sequence associate the multiple sequence tags mapped to the qualified sequence of interest with the multiple tags mapped to the qualified normalizing sequence, and wherein the parameters for the test sequence of interest and at least one normalizing test sequence associate the multiple sequence tags mapped to the test sequence of interest with the multiple tags mapped to the normalizing test sequence. In some embodiments, step (b) includes sequencing at least a portion of the qualified and test nucleic acid molecules, wherein sequencing includes providing multiple mapped sequence tags for testing and a qualified sequence of interest, and for at least one test and at least one qualified normalizing sequence; sequencing at least a portion of the nucleic acid molecules of the test sample to obtain sequence information of the fetal and maternal nucleic acid molecules of the test sample. In some embodiments, a next generation sequencing method is used to perform this sequencing step. In some embodiments, the sequencing method can be a massively parallel sequencing method, wherein the sequencing method uses sequencing by synthesis with the help of a reversible dye terminator. In other embodiments, the sequencing method is sequencing by ligation. In some embodiments, sequencing includes a single amplification. In other embodiments, sequencing is single molecule sequencing. The CNV of sequence interested is a kind of aneuploidy, and it can be a chromosomal or a partial aneuploidy.In some embodiments, this chromosomal aneuploidy is selected from trisomy 2, trisomy 8, trisomy 9, trisomy 20, trisomy 16, trisomy 21, trisomy 13, trisomy 18, trisomy 22, Gleifelter's syndrome (klinefelter's syndrome), 47, XXX, 47, XYY and monomer X.In other embodiments, the aneuploidy of this part is a partial chromosome deletion or a partial chromosome insertion.In some embodiments, the CNV identified by the method is a chromosomal or partial aneuploidy relevant to cancer.In some embodiments, these tests and qualified samples are biological fluid samples, for example: derive from the plasma sample of the experimenter (such as the human subject of pregnancy) of pregnancy.In other embodiments, test and qualified biological fluid samples (such as plasma sample) are derive from the experimenter known or suspected of suffering from cancer.

Certain methods for determining the presence or absence of a fetal chromosomal aneuploidy in a maternal test sample may include the following operations: (a) providing sequence reads of fetal and maternal nucleic acids in the maternal test sample, wherein the sequence reads are provided in an electronic format; (b) aligning the sequence reads to one or more chromosome reference sequences using a computing device and thereby providing a plurality of sequence tags corresponding to the sequence reads; (c) computationally identifying the number of the sequence tags from one or more chromosomes of interest or chromosome segments of interest, and computationally identifying the number of the sequence tags from at least one normalizing chromosome sequence or normalizing chromosome segment sequence for each of the one or more chromosomes of interest or chromosome segments of interest; (d) Using the number of sequence tags identified for each of the one or more chromosomes of interest or chromosome segments of interest and the number of sequence tags identified for each of the normalizing chromosome sequences or normalizing chromosome segment sequences, a single chromosome or segment dose for each of the one or more chromosomes of interest or chromosome segments of interest is calculated in a computational manner; and (e) using the computing device, each of the single chromosome doses for each of the one or more chromosomes of interest or chromosome segments of interest is compared to a corresponding threshold for each of the one or more chromosomes of interest or chromosome segments of interest, and thereby determining the presence or absence of at least one fetal aneuploidy in the test sample. In certain implementations, the number of sequence tags identified for each of the one or more chromosomes of interest or chromosome segments of interest is at least about 10,000 or at least about 100,000. The disclosed embodiments also provide a computer program product comprising a non-transitory computer-readable medium on which program instructions for performing the operations and other computational operations described herein are provided.

In certain embodiments, a chromosome reference sequence has a plurality of excluded regions that occur naturally in chromosomes but do not affect the number of sequence tags for any chromosome or chromosome segment. In certain embodiments, a method further comprises: (i) determining whether a read to be considered is aligned to a site on a chromosome reference sequence where another read from a test sample was previously aligned; and (ii) determining whether the read to be considered is included in the number of sequence tags for a chromosome of interest or a chromosome segment of interest. The chromosome reference sequence may be stored on a computer-readable medium.

In certain embodiments, a method further comprises sequencing at least a portion of the nucleic acid molecules of the maternal test sample to obtain the sequence information for the fetal and maternal nucleic acid molecules of the test sample. Sequencing can comprise massively parallel sequencing of the maternal and fetal nucleic acids from the maternal test sample to generate sequence reads.

In certain embodiments, a method further includes using a processor to automatically record the presence or absence of fetal chromosome aneuploidy as determined in (d) in the patient's medical record card of the human subject providing this maternal test sample. The record may include recording chromosome dosage and/or the diagnosis based on the chromosome dosage in a computer-readable medium. In some cases, the patient's medical record card is preserved by a laboratory, doctor's office, hospital, health maintenance organization, insurance company, or personal medical record card website. A method may further include prescribing, starting treatment, and/or changing treatment to the human subject obtaining this maternal test sample. Additionally or alternatively, the method may include making an appointment and/or performing one or more other tests.

Some methods disclosed herein identify a normalizing chromosome sequence or a normalizing chromosome segment sequence for a chromosome or chromosome segment of interest. Some of the methods include the following operations: (a) providing a plurality of qualified samples for a chromosome or chromosome segment of interest; (b) using a plurality of potential normalizing chromosome sequences or normalizing chromosome segment sequences to repeatedly calculate chromosome doses for a chromosome or chromosome segment of interest, wherein this repeated calculation is performed with a computing device; and (c) selecting the normalizing chromosome sequence or normalizing chromosome segment sequence, either individually or in combination, to provide minimal variability and/or large discernibility in the doses calculated for the chromosome or chromosome segment of interest.

The selected normalizing chromosome sequence or normalizing chromosome segment sequence can be part of a combination of normalizing chromosome sequences or normalizing chromosome segment sequences, or can be provided alone rather than in combination with other normalizing chromosome sequences or normalizing chromosome segment sequences.

The disclosed embodiments provide a method for classifying copy number variation in a fetal genome. The operation of the method includes: (a) receiving sequence reads of fetal and maternal nucleic acids from a maternal test sample, wherein the sequence reads are provided in an electronic format; (b) using a computing device to align the sequence reads with one or more chromosome reference sequences, and thereby providing a plurality of sequence tags corresponding to the sequence reads; (c) using the computing device to computationally identify the number of these sequence tags from one or more chromosomes of interest, and determining that a first chromosome of interest in the fetus carries a copy number variation; (d) calculating a first fetal fraction value by a first method that does not use information from the tags of the first chromosome of interest; (e) calculating a second fetal fraction value by a second method that uses information from the tags of the first chromosome; and (f) comparing the first fetal fraction value to the second fetal fraction value and using the comparison to classify the copy number variation of the first chromosome. In certain embodiments, the method further includes sequencing the cell-free DNA from the maternal test sample to provide the sequence reads. In certain embodiments, the method further includes obtaining the maternal test sample from a pregnant organism. In certain embodiments, operation (b) comprises comparing at least about one million reads using a computing device.In certain embodiments, operation (f) may comprise determining whether the two fetal fraction values are approximately equal.

In certain embodiments, operation (f) may further comprise determining that the two fetal fraction values are approximately equal, and thereby determining that a ploidy assumption implicit in the second method is true. In certain embodiments, the ploidy assumption implicit in the second method is that the first chromosome of interest has a complete chromosomal aneuploidy. In certain of these embodiments, the complete chromosomal aneuploidy of the first chromosome of interest is monosomy or trisomy.

In certain embodiments, operation (f) may include determining whether the two fetal fraction values are not approximately equal, and further including analyzing the tag information for the first chromosome of interest to determine (i) whether the first chromosome of interest carries a partial aneuploidy, or (ii) the fetus is a mosaic.

In certain embodiments, this operation can further include binning the sequence of the first chromosome of interest into a plurality of portions; determining whether any of the portions contains significantly more or significantly less nucleic acid than one or more other portions; and if any of the portions contains significantly more or significantly less nucleic acid than one or more other portions, determining that the first chromosome of interest carries a partial aneuploidy. In one embodiment, the operation can further include determining that a portion of the first chromosome of interest that contains significantly more or significantly less nucleic acid than one or more other portions carries a partial aneuploidy.

In one embodiment, operation (f) can also include binning the sequence of the first chromosome of interest into multiple parts; determining whether any of the parts contains significantly more or significantly less nucleic acid than one or more other parts; and if none of the parts contains significantly more or significantly less nucleic acid than one or more other parts, determining that the fetus is a mosaic.

Operation (e) may include: (a) calculating the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and (b) calculating a fetal fraction value from the chromosome dose using a second method. In certain embodiments, this operation further includes calculating a normalized chromosome value (NCV), wherein the second method uses the normalized chromosome value, and wherein the NCV relates the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

Wherein σ and σ _iU are the estimated mean and standard deviation, respectively, for the i-th chromosome dose in the set of qualified samples, and R _iA is the chromosome dose calculated for the chromosome of interest. In another embodiment, operation (d) further comprises a first method calculating a first fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in the fetal and maternal nucleic acids of the maternal test sample.

In various embodiments, if the first fetal fraction value and the second fetal fraction value are not approximately equal, the method further comprises (i) determining whether the copy number variation is caused by partial aneuploidy or mosaicism; and (ii) if the copy number variation is caused by partial aneuploidy, determining the locus of the partial aneuploidy on the first chromosome of interest. In certain embodiments, determining the locus of the partial aneuploidy on the first chromosome of interest comprises dividing the sequence tags for the first chromosome of interest into nucleic acid bins or blocks in the first chromosome of interest; and counting the mapped tags in each bin.

Operation (e) may further include calculating a fetal fraction value by evaluating the following expression:

ff＝2×|NCV _iA CV _iU |

where ff is the second fetal fraction value, NCV _iA is the normalized chromosome value on chromosome i in an affected sample, and CV _iU is the coefficient of variation of the dose of the chromosome of interest determined in the qualified samples.

In any of the above embodiments, the first chromosome of interest is selected from the group consisting of chromosomes 1 to 22, X, and Y. In any of the above embodiments, operation (f) can classify the copy number variation into a category selected from the group consisting of complete chromosome insertion, complete chromosome deletion, partial chromosome duplication, partial chromosome deletion, and mosaicism.

The disclosed embodiments also provide a computer program product comprising a non-transitory computer-readable medium on which program instructions for classifying copy number variation in a fetal genome are provided. The computer program product may include: (a) code for receiving sequence reads of fetal and maternal nucleic acids from a maternal test sample, wherein the sequence reads are provided in an electronic format; (b) code for using a computing device to align the sequence reads with one or more chromosome reference sequences and thereby provide a plurality of sequence tags corresponding to the sequence reads; (c) code for using the computing device to computationally identify the number of sequence tags from one or more chromosomes of interest and determine that a first chromosome of interest in the fetus carries a copy number variation; (d) code for calculating a first fetal fraction value by a first method that does not use information from the tags of the first chromosome of interest; (e) code for calculating a second fetal fraction value by a second method that uses information from the tags of the first chromosome; and (f) code for comparing the first fetal fraction value to the second fetal fraction value and using the comparison to classify the copy number variation of the first chromosome. In certain embodiments, the computer program product includes code for the various operations and methods of any one or more of the disclosed methods.

The disclosed embodiments also provide a system for classifying copy number variation in a fetal genome. The system includes: (a) an interface for receiving at least about 10,000 sequence reads of fetal and maternal nucleic acids from a maternal test sample, wherein the sequence reads are provided in an electronic format; (b) a memory for at least temporarily storing a plurality of the sequence reads; (c) a processor designed or configured with program instructions for: (i) aligning the sequence reads to one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads; (ii) identifying a number of the sequence tags from one or more chromosomes of interest and determining that a first chromosome of interest in the fetus carries a copy number variation; (iii) calculating a first fetal fraction value by a first method that does not use information from the tags of the first chromosome of interest; (iv) calculating a second fetal fraction value by a second method that uses information from the tags of the first chromosome; and (v) comparing the first fetal fraction value to the second fetal fraction value and using the comparison to classify the copy number variation of the first chromosome. According to various embodiments, the first chromosome of interest is selected from the group consisting of chromosomes 1 to 22, X and Y. In certain embodiments, the program instructions for (c) (v) include program instructions for classifying the copy number variation into a category selected from the group consisting of complete chromosome insertion, complete chromosome deletion, partial chromosome duplication, and partial chromosome deletion, and mosaicism. According to various embodiments, the system may include sequencing the cell-free DNA from the maternal test sample to provide program instructions for the sequence reads. According to certain embodiments, the program instructions for operating (c) (i) include program instructions for using a computing device for comparing at least about one million reads.

In certain embodiments, the system further comprises a sequencer configured to sequence fetal and maternal nucleic acids in a maternal test sample and provide sequence reads in an electronic format. In various embodiments, the sequencer and the processor are located in separate facilities, and the sequencer and the processor are connected via a network.

In various embodiments, the system further comprises a device for obtaining a maternal test sample from a pregnant mother. According to certain embodiments, the device for obtaining the maternal test sample and the processor are located in separate facilities. In various embodiments, the system further comprises a device for extracting cell-free DNA from the maternal test sample. In certain embodiments, the device for extracting cell-free DNA is located in the same facility as the sequencer, and the device for obtaining the maternal test sample is located in a remote facility.

According to certain embodiments, the program instructions for comparing the first fetal fraction value to the second fetal fraction value further include program instructions for determining whether the two fetal fraction values are approximately equal.

In some embodiments, the system further comprises program instructions for determining that a ploidy assumption implicit in the second method is true when the two fetal fraction values are approximately equal. In some embodiments, the ploidy assumption implicit in the second method is that the first chromosome of interest has a complete chromosomal aneuploidy. In some embodiments, the complete chromosomal aneuploidy of the first chromosome of interest is monosomy or trisomy.

In certain embodiments, the system further comprises program instructions for analyzing the tag information of the first chromosome of interest to determine (i) whether the first chromosome of interest carries a partial aneuploidy, or (ii) whether the fetus is a mosaic, wherein the program instructions for analyzing are configured to be executed when the program instructions for comparing the first fetal fraction value to the second fetal fraction value indicate that the two fetal fraction values are not approximately equal. In certain embodiments, the program instructions for analyzing the tag information of the first chromosome of interest comprise: program instructions for binning the sequence of the first chromosome of interest into a plurality of portions; program instructions for determining whether any of the portions contains significantly more or significantly less nucleic acid than one or more other portions; and program instructions for determining that the first chromosome of interest carries a partial aneuploidy if any of the portions contains significantly more or significantly less nucleic acid than one or more other portions. In certain embodiments, the system further comprises program instructions for determining that a portion of the first chromosome of interest that contains significantly more or significantly less nucleic acid than one or more other portions carries the partial aneuploidy.

In certain embodiments, program instructions for analyzing the tag information for the first chromosome of interest include: program instructions for binning the sequence of the first chromosome of interest into a plurality of portions; program instructions for determining whether any of the portions contains significantly more or significantly less nucleic acid than one or more other portions; and program instructions for determining that the fetus is a mosaic if none of the portions contains significantly more or significantly less nucleic acid than one or more other portions.

According to various embodiments, the system may include program instructions for a second method for calculating a fetal fraction value, the program instructions comprising: (a) program instructions for counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and (b) program instructions for calculating a fetal fraction value from the chromosome dose using the second method.

In certain embodiments, the system further comprises program instructions for calculating a normalized chromosome value (NCV), wherein the program instructions for the second method comprise program instructions for using the normalized chromosome value, and wherein the program instructions for the NCV relate the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

Wherein σ and σ _iU are the estimated mean and standard deviation, respectively, for the i-th chromosome dose in the set of qualified samples, and R _iA is the calculated chromosome dose for the chromosome of interest. In various embodiments, the program instructions for the first method include program instructions for calculating a first fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids of the maternal test sample.

According to various embodiments, the program instructions for the second method of calculating a fetal fraction value include program instructions for evaluating the following expression:

ff＝2×|NCV _iA CV _iU |

where ff is the second fetal fraction value, NCV _iA is the normalized chromosome value on chromosome i in an affected sample, and CV _iU is the coefficient of variation of the dose of the chromosome of interest determined in the qualified samples.

According to various embodiments, the system further comprises: (i) program instructions for determining whether the copy number variation is caused by a partial aneuploidy or a mosaicism; and (ii) program instructions for determining the locus of the partial aneuploidy on the first chromosome of interest if the copy number variation is caused by a partial aneuploidy, wherein the program instructions in (i) and (ii) are configured to be executed when the program instructions for comparing the first fetal fraction value to the second fetal fraction value determine that the first fetal fraction value and the second fetal fraction value are not approximately equal.

In certain embodiments, program instructions for determining the locus of a partial aneuploidy on a first chromosome of interest include program instructions for dividing sequence tags for the first chromosome of interest into bins or blocks of nucleic acid data in the first chromosome of interest; and program instructions for counting the number of mapped tags in each bin.

In certain embodiments, methods for identifying the presence of cancer and/or an increased risk of cancer in a mammal (e.g., a human) are provided, wherein the methods comprise: (a) providing sequence reads of nucleic acids from a test sample from the mammal, wherein the test sample can comprise genomic nucleic acids from cancerous or precancerous cells and genomic nucleic acids from constitutional (germline) cells, wherein the sequence reads are provided in an electronic format; (b) aligning the sequence reads to one or more chromosome reference sequences using a computing device and thereby providing a plurality of sequence tags corresponding to the sequence reads; (c) computationally identifying the number of sequence tags from fetal and maternal nucleic acids from one or more chromosomes of interest whose amplification or deletion is known to be associated with cancer or chromosome segments of interest whose amplification or deletion is known to be associated with cancer, wherein the chromosomes or chromosome segments are selected from chromosomes 1 to 22, X and Y, and segments thereof, and computationally identifying the number of sequence tags for at least one normalizing chromosome sequence or normalizing chromosome segment sequence for each of the one or more chromosomes of interest or chromosome segments of interest, wherein the number of sequence tags identified for each of the one or more chromosomes of interest or chromosome segments of interest is at least about 2,000, or at least about 5,000, or at least about 10,000; (d) computationally calculating a single chromosome or segment dose for each of the one or more chromosomes of interest or chromosome segments of interest using the number of sequence tags identified for each of the one or more chromosomes of interest or chromosome segments of interest and the number of sequence tags identified for each of the normalizing chromosome sequence or normalizing chromosome segment sequence; and (e) comparing each of the single chromosome doses for each of the one or more chromosomes of interest or chromosome segments of interest to a corresponding threshold value for each of the one or more chromosomes of interest or chromosome segments of interest using the computing device, and thereby determining the presence or absence of an aneuploidy in the sample, wherein the presence of the aneuploidy and/or an increase in the number of sequence tags identified for each of the one or more chromosomes of interest or chromosome segments of interest indicates the presence of cancer and/or an increased risk of cancer. In certain embodiments, the increased risk is compared with the same subject at a different time (e.g., early stage), compared with a reference population (e.g., optionally adjusted for sex and/or race and/or age), compared with a similar subject without a certain risk factor, etc. In certain embodiments, the chromosome of interest or the chromosome segment of interest includes amplification and/or deletion of a whole chromosome known to be associated with cancer (e.g., as described herein). In certain embodiments, the chromosome of interest or the chromosome segment of interest includes amplification or deletion of a chromosome segment known to be associated with one or more cancers. In certain embodiments, the chromosome segment includes substantially a whole chromosome arm (e.g., as described herein). In certain embodiments, the chromosome segment includes a whole chromosome aneuploidy. In certain embodiments, the whole chromosome aneuploidy includes loss, and in certain other embodiments, the whole chromosome aneuploidy includes acquisition (e.g., acquisition or loss as shown in Table 1). In certain embodiments, the chromosome segment of interest is a fragment of substantially arm level, including any one or more short arms or long arms of chromosomes 1 to 22, X and Y. In certain embodiments, aneuploidy includes the amplification of a substantial arm level fragment of a chromosome or the deletion of a substantial arm level fragment of a chromosome. In certain embodiments, the chromosome segment of interest substantially includes one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q and/or 22q. In certain embodiments, the aneuploidy comprises an amplification of one or more arms selected from the group consisting of: 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, 22q. In certain embodiments, the aneuploidy comprises a deletion of one or more arms selected from the group consisting of 1p, 3p, 4p, 4q, 5q, 6q, 8p, 8q, 9p, 9q, 10p, 10q, 11p, 11q, 13q, 14q, 15q, 16q, 17p, 17q, 18p, 18q, 19p, 19q, 22q. In certain embodiments, the chromosome segment of interest is a fragment comprising the regions and/or genes shown in Table 3 and/or Table 5 and/or Table 4 and/or Table 6. In certain embodiments, the aneuploidy comprises an amplification of the regions and/or genes shown in Table 3 and/or Table 5. In certain embodiments, the aneuploidy comprises a deletion of the regions and/or genes shown in Table 4 and/or 6. In certain embodiments, the chromosome segment of interest is a fragment known to contain one or more oncogenes and/or one or more tumor suppressor genes. In certain embodiments, the aneuploidy comprises an amplification of one or more regions selected from the group consisting of 20Q13, 19q12, 1q21-1q23, 8p11-p12, and ErbB2. In certain embodiments, the aneuploidy comprises an amplification of one or more regions comprising genes selected from the group consisting of MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, KRAS, MET, ERBB1, CDK4, MYCB, ERBB2, AKT2, MDM2, and CDK4, etc. In certain embodiments, the cancer is a cancer selected from the group consisting of leukemia, ALL, brain cancer, breast cancer, colorectal cancer, dedifferentiated liposarcoma, esophageal adenocarcinoma, esophageal squamous cell carcinoma, GIST, glioma, HCC, hepatocellular carcinoma, lung cancer, lung NSC, lung SC, medulloblastoma, melanoma, MPD, myeloproliferative disorders, cervical cancer, ovarian cancer, prostate cancer, and renal cancer. In certain embodiments, the biological sample includes a sample selected from the group consisting of whole blood, blood clots, saliva/saliva, urine, tissue biopsy, pleural fluid, pericardial fluid, cerebrospinal fluid, and peritoneal fluid. In certain embodiments, the chromosome reference sequence has a plurality of excluded regions that are naturally present in the chromosome but that do not affect the number of sequence tags for any chromosome or chromosome segment. In certain embodiments, the method further comprises determining whether a read under consideration is aligned to a site on a chromosome reference sequence at which another read was previously aligned; and determining whether the read under consideration is included in the number of sequence tags for a chromosome of interest or a chromosome segment of interest, wherein both determination operations are performed using the computing device. In various embodiments, the method further comprises storing, at least temporarily, sequence information for the nucleic acid in the sample in a computer-readable medium (e.g., a non-transitory medium). In certain embodiments, step (d) comprises calculating a segment dose for a selected one of the segments of interest as the ratio of the number of sequence tags identified for the selected segment of interest to the number of sequence tags identified for at least one normalizing chromosome sequence or normalizing chromosome segment sequence corresponding to the selected segment of interest. In certain embodiments, the one or more chromosome segments of interest include at least 5, or at least 10, or at least 15, or at least 20, or at least 50, or at least 100 different segments of interest. In certain embodiments, at least 5 or at least 10 or at least 15 or at least 20 or at least 50 or at least 100 different aneuploidies are detected. In certain embodiments, at least one normalizing chromosome sequence includes one or more chromosomes selected from the group consisting of chromosomes 1 to 22, X, and Y. In certain embodiments, for each segment, the at least one normalizing chromosome sequence includes a chromosome corresponding to the chromosome in which the segment is located. In certain embodiments, for each segment, the at least one normalizing chromosome sequence includes a chromosome segment corresponding to the chromosome segment being normalized. In certain embodiments, at least one normalizing chromosome sequence or normalizing chromosome segment sequence is a chromosome or segment selected for an associated chromosome of interest or segment, which is performed in the following manner: (i) identifying multiple qualified samples for the segment of interest; (ii) using multiple potential normalizing chromosome sequences or normalizing chromosome segment sequences to repeatedly calculate chromosome doses for the selected chromosome; and (iii) selecting the normalizing chromosome segment sequence alone or in a combination to provide minimal variability and/or maximum distinguishability in the calculated chromosome doses. In certain embodiments, the method further includes calculating a normalized segment value (NSV), wherein as described herein, the NSV associates the segment dose with the mean of the corresponding segment dose in a group of qualified samples. In certain embodiments, the normalizing segment sequence is a single segment of any one or more of chromosomes 1 to 22, X, and Y. In certain embodiments, the normalizing segment sequence is a group of segments of any one or more of chromosomes 1 to 22, X, and Y. In certain embodiments, the normalizing segment sequence includes substantially one arm of any one or more of chromosomes 1 to 22, X and Y. In certain embodiments, the method further includes sequencing at least a portion of the nucleic acid molecules of the test sample to obtain the sequence information. In certain embodiments, sequencing includes sequencing the cell-free DNA from the test sample to provide sequence information. In certain embodiments, sequencing includes sequencing the cell DNA from the test sample to provide sequence information. In certain embodiments, sequencing includes massively parallel sequencing. In certain embodiments, the method (these) further includes automatically recording the presence or absence of a kind of aneuploidy as determined in (d) in the patient medical record card of the human subject providing the test sample, wherein the record is performed using a processor. In certain embodiments, the record includes recording chromosome dosage and/or a diagnosis based on the chromosome dosage in a computer-readable medium. In different embodiments, the patient medical record card is saved by a laboratory, a doctor's office, a hospital, a health maintenance organization, an insurance company, or a personal medical record card website. In certain embodiments, determining the presence or absence of the aneuploidy and/or number includes a factor in a differential diagnosis for cancer. In certain embodiments, the detection of aneuploidy indicates a positive result, and the method further includes prescribing, starting treatment, and/or changing treatment to a human subject taking a test sample. In certain embodiments, prescribing, starting treatment, and/or changing treatment to a human subject taking a test sample includes prescribing and/or performing further diagnosis to determine the presence and/or severity of cancer. In certain embodiments, further diagnosis includes screening samples from the subject for cancer biomarkers, and/or imaging the subject for cancer. In certain embodiments, when the method indicates the presence of neoplastic cells in the mammal, the mammal is treated or the mammal is treated to remove the neoplastic cells and/or inhibit the growth or proliferation of the neoplastic cells. In certain embodiments, treating a mammal includes surgically removing neoplastic (e.g., tumor) cells. In certain embodiments, treating a mammal includes performing radiotherapy on the mammal or causing the mammal to perform radiotherapy to kill the neoplastic cells. In certain embodiments, treating a mammal comprises administering or causing the mammal to be administered an anticancer drug (e.g., matuzumab, erbitux, vectibix, nimotuzumab, matuzumab, panitumumab, floururacil, capecitabine, 5-trifluoromethyl-2'-deoxyuridine, methotrexate, raltitrexed, pemetrexed, cytosine arabinoside, 6-mercaptopurine, azathioprine, 6-thioguanine, pentostatin, fludarabine, cladribine). cladribine, floxuridine, cyclophosphamide, neosar, ifosfamide, thiotepa, 1,3-bis(2-chloroethyl)-1-nitrosourea, 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea, hexamethylmelamine, busulfan, procarbazine, dacarbazine, chlorambucil, melphalan, cisplatin, carboplatin carboplatin, oxaliplatin, bendamustine, carmustine, chloromethine, dacarbazine, fotemustine, lomustine, mannosulfan, nedaplatin, nimustine, prednimustine, ranimustine, satraplatin, semustine, streptozocin, temozolomide, treosulfan, triaziquone, triethylene melamine, thiotepa, triplatin tetranitrate tetranitrate), trofosfamide, uramustine, doxorubicin, daunorubicin, mitoxantrone, etoposide, topotecan, teniposide, irinotecan, camptosar, camptothecin, belotecan, rubitecan, vincristine, vinblastine, vinorelbine (vinorelbine, vindesine, paclitaxel, docetaxel, abraxane, ixabepilone, larotaxel, ortataxel, tesetaxel, vinflunine, imatinib mesylate, sunitinib malate, sorafenib tosylate, nilotinib hydrochloride monohydrate, tasigna, semaxanib, vandetanib, vatalanib, retinoic acid, retinoic acid derivatives, etc.).

In another embodiment, a computer program product for determining the presence of cancer and/or increased risk of cancer in a mammal is provided. The computer program product typically comprises: (a) code for providing sequence readings of nucleic acids in a test sample from the mammal, wherein the test sample may include genomic nucleic acids from cancer cells or precancerous cells and genomic nucleic acids from constitutional (germline) cells, wherein the sequence readings are provided in an electronic format; (b) using a computing device to align the sequence readings with one or more chromosome reference sequences and thereby provide a plurality of sequence tags corresponding to the sequence readings; (c) code for computationally identifying the number of sequence tags from fetal and maternal nucleic acids for one or more chromosomes of interest whose amplification or deletion is known to be associated with cancer or chromosome segments of interest whose amplification or deletion is known to be associated with cancer, wherein the chromosomes or chromosome segments are selected from chromosomes 1 to 22, X and Y, and segments thereof, and computationally identifying the number of sequence tags of at least one normalizing chromosome sequence or normalizing chromosome segment sequence for each of the one or more chromosomes of interest or chromosome segments of interest, wherein the number of sequence tags for the one or more chromosomes of interest or chromosome segments of interest is calculated. The number of sequence tags identified for each of the chromosomes or chromosome segments of interest is at least about 10,000; (d) using the number of sequence tags identified for each of the one or more chromosomes of interest or chromosome segments of interest and the number of sequence tags identified for each of the normalizing chromosome sequence or normalizing chromosome segment sequence, a code for calculating a single chromosome or segment dose for each of the one or more chromosomes of interest or chromosome segments of interest is calculated; and (e) using the computing device to compare each of the single chromosome doses for each of the one or more chromosomes of interest or chromosome segments of interest with a corresponding threshold value for each of the one or more chromosomes of interest or chromosome segments of interest, and thereby determining the presence or absence of aneuploidy in the sample, wherein the presence of aneuploidy and/or the increase in the number of sequence tags identified for each of this or these chromosomes of interest or chromosome segments of interest indicates that cancer exists and/or cancer risk increases. In various embodiments, the code provides instructions for performing the diagnostic method as described above (and below).

Also provided are methods for treating cancer subjects. In certain embodiments, these methods include performing a method for identifying the presence of cancer and/or an increased risk of cancer in a mammal as described herein, the method using a sample from the subject or receiving the results of such a method performed on the sample; and when the method alone or in combination with one or more other indicators from a differential diagnosis for cancer indicates the presence of neoplastic cells in the subject, the subject is treated, or the subject is treated to remove neoplastic cells and/or inhibit the growth or proliferation of neoplastic cells. In certain embodiments, treating the subject includes surgically removing the cells. In certain embodiments, treating the subject includes performing radiotherapy on the subject or causing the subject to perform radiotherapy to kill the neoplastic cells. In certain embodiments, treating a subject comprises administering or causing the subject to be administered an anticancer drug (e.g., matuzumab, erbitux, vectibix, nimotuzumab, matuzumab, panitumumab, fluorouracil, capecitabine, 5-trifluoromethyl-2'-deoxyuridine, methotrexate, raltitrexed, pemetrexed, cytarabine, 6-mercaptopurine, azathioprine, 6-thioguanine, pentostatin, fludastatin, Labine, cladribine, floxuridine, cyclophosphamide, neosarcoma, ifosfamide, thiotepa, 1,3-bis(2-chloroethyl)-1-nitrosourea, 1-(2-chloroethyl)-3-cyclohexyl-1-nitrosourea, hexamethylmelamine, busulfan, procarbazine, dacarbazine, chlorambucil, melphalan, cisplatin, carboplatin, oxaliplatin, bendamustine, carmustine, nitrogen mustard, dacarbazine, and formox tin, lomustine, ganlusuvan, nedaplatin, nimustine, prednimustine, ranimustine, satraplatin, semustine, streptozotocin, temozolomide, treoxazole, triazoline, triethylene melamine, thiotepa, triplatin tetranitrate, clofosfamide, uracil nitrogen mustard, erythromycin, daunorubicin, mitoxantrone, etoposide, topotecan, teniposide, irinotecan, cabotosac, camptothecin, belotecan , rubitecan, vincristine, vinblastine, vinorelbine, vindesine, paclitaxel, docetaxel, abuconazole, ixabepilone, larotaxis, octacil, tesetacil, vinflunine, imatinib mesylate, sunitinib malate, sorafenib tosylate, nilotinib hydrochloride monohydrate/, tasna, semacni, vandetanib, vatalanib, retinoic acid, retinoic acid derivatives, etc.).

Also provided is a method for monitoring the treatment of a cancer subject. In various embodiments, these methods include performing a method as described herein for identifying the presence of cancer and/or an increased risk of cancer in a mammal on a sample from a subject before or during treatment, or receiving the result of such a method performed on the sample; and performing the method again on a second sample from the subject later during treatment or after treatment, or receiving the result of such a method performed on the second sample; wherein the number or severity of aneuploidies in the second measurement (for example, compared with the first measurement) is reduced (for example, the frequency of aneuploidies is reduced and/or some aneuploidies are reduced or absent) indicating a positive course of treatment and the number or severity of aneuploidies in the second measurement (for example, compared with the first measurement) is the same or increases indicating a negative course of treatment, and when the indication is negative, the treatment regimen is adjusted to a more aggressive treatment regimen and/or a palliative treatment regimen.

Also provided are methods for determining the fraction of fetal nucleic acid in a maternal sample comprising a mixture of fetal and maternal nucleic acid. In one embodiment, the method for determining the fetal fraction in a maternal sample comprises: (a) receiving sequence reads of fetal and maternal nucleic acid from the maternal test sample; (b) aligning the sequence reads to one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads; (c) identifying a number of sequence tags from one or more chromosomes of interest or chromosome segments of interest selected from chromosomes 1 to 22, X and Y, and segments thereof, and identifying a number of sequence tags from at least one normalizing chromosome sequence or normalizing chromosome segment sequence for each of the one or more chromosomes of interest or chromosome segments of interest to determine a chromosome dose or chromosome segment dose, wherein the one or more chromosomes of interest or chromosome segments of interest have a copy number variation; and (d) using the chromosome dose or chromosome segment dose corresponding to the copy number variation identified in step (c) to determine the fetal fraction. In some embodiments, the copy number variation is determined by comparing the dose of each of the one or more chromosomes of interest or chromosome segments of interest to a corresponding threshold for each of the one or more chromosomes of interest or chromosome segments of interest. The copy number variation can be selected from the group consisting of: complete chromosome duplication, complete chromosome deletion, partial duplication, partial doubling, partial insertion, and partial deletion.

In certain embodiments, the chromosome or segment dose in step (c) is calculated as the ratio of the number of sequence tags identified for the selected chromosome or segment of interest to the number of sequence tags identified for the corresponding at least one normalizing chromosome sequence or normalizing chromosome segment sequence of the selected chromosome or segment of interest. In some embodiments, the chromosome or segment dose in step (c) is calculated as the ratio of the sequence tag density ratio of the selected chromosome or segment of interest to the sequence tag density ratio of at least one corresponding normalizing chromosome sequence or normalizing chromosome segment sequence of each selected chromosome of interest or segment of interest.

In certain embodiments, the method further comprises calculating a normalized chromosome value (NCV), wherein the NCV is calculated to relate the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

Where σ and _σ are the estimated mean and standard deviation of the dose for the i-th chromosome in the set of qualified samples, respectively, and _R is the calculated chromosome dose for the i-th chromosome in the test sample, where the i-th chromosome is the chromosome of interest. The fetal fraction is then determined according to the following expression:

ff＝2×|NCV _iA CV _iu |

Wherein ff is the fetal fraction value, NCV _iA is the normalized chromosome value on chromosome i in an affected sample, and CV _iU is the coefficient of variation of the dose of chromosome i determined in the qualified samples, where chromosome i is the chromosome of interest.

In certain embodiments, the fetal fraction is determined using a normalized segment value (NSV), wherein the NSV relates the chromosome segment dose to the mean of the corresponding chromosome segment dose in a set of qualified samples as:

Wherein σ and σ _iu are the estimated mean and standard deviation of the dose for the i-th chromosome segment in the set of qualified samples, respectively, and R _iA is the calculated chromosome segment dose for the i-th chromosome segment in the test sample, wherein the i-th chromosome segment is the chromosome segment of interest. The fetal fraction is then determined according to the following expression:

ff＝2×|NSV _iA CV _iU |

Wherein ff is the fetal fraction value, NSV _iA is the normalized chromosome segment value on the i-th chromosome segment in an affected sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome segment determined in the qualified samples, wherein the i-th chromosome segment is the chromosome segment of interest.

In certain embodiments, the chromosome of interest is any one of chromosomes 1-22 or the X chromosome of a male fetus, and the chromosome segment of interest is selected from chromosomes 1-22 or the X chromosome of a male fetus.

In certain embodiments, the at least one normalizing chromosome sequence or normalizing chromosome segment sequence of the various embodiments of the method for determining fetal fraction is a chromosome or segment selected for an associated chromosome or segment of interest by: (i) identifying multiple qualified samples for the chromosome or segment of interest; (ii) repeatedly calculating chromosome doses or chromosome segment doses for the selected chromosome or segment using multiple potential normalizing chromosome sequences or normalizing chromosome segment sequences; and (iii) selecting the normalizing chromosome sequence or normalizing chromosome segment sequence, either alone or in combination, to provide minimal variability or maximum resolvability in the calculated chromosome doses or chromosome segment doses. The normalizing chromosome sequence can be a single chromosome of any one or more of chromosomes 1 to 22, X, and Y. Alternatively, the normalizing chromosome sequence can be a group of chromosomes of any one of chromosomes 1 to 22, X, and Y. Similarly, the normalizing segment sequence can be a single segment of any one or more of chromosomes 1 to 22, X, and Y. Alternatively, the normalizing segment sequence can be a set of segments of any one or more of chromosomes 1 to 22, X, and Y.

In certain embodiments, the method of determining fetal fraction can further comprise comparing the fetal fraction obtained as described with a fetal fraction that can be determined using information from one or more polymorphisms that exhibit allelic imbalance in the fetal and maternal nucleic acids of the maternal test sample. Methods for determining allelic imbalance are described elsewhere in this application and include determining fetal fraction using polymorphic differences between the fetal and maternal genomes, including but not limited to differences detected in SNP or STR sequences.

In certain embodiments, the method further comprises at least temporarily storing the sequence reads.

An additional method for classifying copy number variation in a fetal genome is provided. The additional method includes: (a) obtaining sequence reads of fetal and maternal nucleic acids from a maternal test sample; (b) aligning the sequence reads to one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads; (c) identifying the number of the sequence tags from one or more chromosomes of interest and determining that a first chromosome of interest in the fetus carries a copy number variation; (d) calculating a first fetal fraction value by a first method that does not use information from the tags of the first chromosome of interest; (e) calculating a second fetal fraction value by a second method that uses information from the tags of the first chromosome; and (f) comparing the first fetal fraction value to the second fetal fraction value and using the comparison to classify the copy number variation of the first chromosome.

In certain embodiments, the first method of calculating a fetal fraction value as described in step (d) of the additional method comprises: using information from one or more polymorphisms that exhibit allelic imbalance in fetal and maternal nucleic acids of the maternal test sample to calculate the first fetal fraction value; the second method of calculating a fetal fraction value as described in step (e) of the additional method comprises: (a) counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and (b) calculating the fetal fraction value from the chromosome dose using the second method.

In certain embodiments, the information used in the first method comprises sequence tags obtained by sequencing predetermined polymorphic sequences , each of which comprises the one or more polymorphic sites. In certain embodiments, the information used in the first method is obtained by a non-sequencing method, such as qPCR, digital PCR, mass spectrometry, or capillary gel electrophoresis.

In certain embodiments, the first method includes calculating the first fetal fraction using tags from a chromosome or chromosome segment that does not have a copy number variation. For example, when the first chromosome of interest is chromosome 21, the fetal fraction determined using sequence tags from chromosome 21 can be compared with the fetal fraction determined based on sequence tags from chromosome X in a male fetus. Any chromosome or chromosome segment known to occur in an aneuploid state or determined not to be aneuploid by any of the methods described herein (e.g., by calculating its NCV or NSV) can be used to determine the first fetal fraction.

In certain embodiments, the chromosome or segment dose determined by the second method in step (e) is calculated as the ratio of the number of sequence tags identified for the selected chromosome or segment of interest to the number of sequence tags identified for the corresponding at least one normalizing chromosome sequence or normalizing chromosome segment sequence of the selected chromosome or segment of interest. In certain embodiments, the chromosome dose or segment dose determined in step (e) is calculated as the ratio of the sequence tag density ratio of the selected chromosome or segment of interest to the sequence tag density ratio of at least one corresponding normalizing chromosome sequence or normalizing chromosome segment sequence of each selected chromosome of interest or segment of interest.

Certain embodiments of the additional method further comprise calculating a normalized chromosome value (NCV), wherein the second method uses the normalized chromosome value, and wherein calculating the NCV relates the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

where σ and σ _iU are the estimated mean and standard deviation of the dose for the i-th chromosome in the set of qualified samples, respectively, and R _iA is the chromosome dose calculated for the i-th chromosome in the test sample, where the i-th chromosome is the chromosome of interest.

In certain embodiments, the second method of calculating the fetal fraction value comprises evaluating the following expression:

ff＝2×|NCV _iA CV _iU |

Wherein ff is the fetal fraction value, NSV _iA is the normalized chromosome value on the i-th chromosome in an affected sample or a test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome determined in the qualified samples, wherein the i-th chromosome is the chromosome of interest.

In certain embodiments, the first method for calculating the fetal fraction comprises (a) counting the number of sequence tags from the chromosome other than the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose for the chromosome other than the first chromosome of interest; and (b) calculating the first fetal fraction value from the chromosome dose by the first method; the second method comprises: (a) counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and (b) calculating the second fetal fraction value from the chromosome dose by the second method.

Preferably, the chromosome or segment dose is calculated as the ratio of the number of sequence tags identified for the selected chromosome or segment of interest to the number of sequence tags identified for the corresponding at least one normalizing chromosome sequence or normalizing chromosome segment sequence of the selected chromosome or segment of interest; or, the chromosome dose or segment dose is calculated as the ratio of the sequence tag density ratio of the selected chromosome or segment of interest to the sequence tag density ratio of at least one corresponding normalizing chromosome sequence or normalizing chromosome segment sequence of each of the selected chromosomes of interest or segments.

Preferably, the additional method for classifying copy number variation further comprises calculating a corresponding normalized chromosome value (NCV), and the first method and the second method use the corresponding NCV. Calculating the NCV relates the determined chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

Where σ and σ _iU are the estimated mean and standard deviation of the dose of chromosome i in the set of qualified samples, respectively, and R _iA is the calculated dose of chromosome i in the test sample. The first and second methods can use NCV to calculate fetal fraction, evaluated by the following expression:

ff＝2×|NCV _iA CV _iU |

Wherein ff is the fetal fraction value, NCV _iA is the normalized chromosome value on the i-th chromosome in the test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome in the qualified samples. In the above formula, for the first method, the i-th chromosome is not the first chromosome of interest; for the second method, the i-th chromosome is the first chromosome of interest.

The first chromosome of interest is selected from the group consisting of chromosomes 1 to 22, X, and Y. The chromosome other than the first chromosome of interest may be any one of chromosomes 1 to 22, or an X chromosome when the fetus is male.

In some embodiments, step (f) comprises determining whether the two fetal fraction values are approximately equal. In some embodiments, step (f) further comprises determining that a ploidy assumption implicit in the second method is true when the two fetal fraction values are approximately equal. The ploidy assumption implicit in the second method can be that the first chromosome of interest has a complete chromosomal aneuploidy. For example, the complete chromosomal aneuploidy of the first chromosome of interest is a monosomy or a trisomy.

In certain embodiments, the additional method for classifying copy number variation further comprises a step (g): analyzing the tag information for the first chromosome of interest to determine whether (i) the first chromosome of interest carries a partial aneuploidy, or (ii) the fetus is a mosaic when the two fetal fraction values are not approximately equal.

In certain embodiments, wherein the first method comprises calculating the first fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids from the maternal test sample, the polymorphism being present in a chromosome other than the first chromosome of interest; and the second method comprises calculating the second fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids from the maternal test sample, the polymorphism being present in the first chromosome of interest. The comparing step (f) can comprise: determining that the first chromosome of interest is diploid when the ratio of the second fetal fraction value to the first fetal fraction value is approximately 1; determining that the first chromosome of interest is triploid when the ratio of the second fetal fraction value to the first fetal fraction value is approximately 1.5; and determining that the first chromosome of interest is haploid when the ratio of the second fetal fraction value to the first fetal fraction value is approximately 0.5. Additional methods for classifying copy number variation may further include the step (g) of analyzing the tag information for the first chromosome of interest to determine whether (i) the first chromosome of interest has a partial aneuploidy, or (ii) the fetus is a mosaic, when the ratio of the second fetal fraction value to the first fetal fraction value is not approximately 1, 1.5, or 0.5.

In certain embodiments, the information used by the first and second methods of utilizing polymorphisms includes sequence tags obtained by sequencing predetermined polymorphic sequences, each of which includes the one or more polymorphic sites. Alternatively, the information used by the first and second methods of utilizing polymorphisms is not obtained by sequencing, for example, by non-sequencing methods such as qPCR, digital PCR, mass spectrometry, or capillary gel electrophoresis.

In certain embodiments, step (g) of analyzing the tag information for the first chromosome of interest comprises: (a) binning the sequence of the first chromosome of interest into a plurality of portions; (b) determining whether any of the portions contains significantly more or significantly less nucleic acid than one or more other portions; and, (c) determining that the first chromosome of interest carries a partial aneuploidy if any of the portions contains significantly more or significantly less nucleic acid compared to one or more other portions; or determining that the fetus is a mosaic if none of the portions contains significantly more or significantly less nucleic acid compared to one or more other portions. Thus, the additional method can further comprise determining that a portion of the first chromosome of interest that contains significantly more or significantly less nucleic acid than one or more other portions carries a partial aneuploidy.

Step (f) of the method for classifying the copy number variation comprises classifying the copy number variation into a category selected from the group consisting of complete chromosome duplication or multiplication, complete chromosome deletion, partial chromosome duplication, and partial chromosome deletion, and mosaicism.

In embodiments where step (f) of comparing the first fetal fraction value to the second fetal fraction value determines that the first fetal fraction value is not approximately equal to the second fetal fraction value, the method further comprises:

(i) determining whether the copy number variation is caused by partial aneuploidy or mosaicism; and

(ii) when the copy number variation is caused by partial aneuploidy, determining the locus of the partial aneuploidy on the first chromosome of interest.

In certain embodiments, determining the locus of the partial aneuploidy on the first chromosome of interest comprises dividing the sequence tags of the first chromosome of interest into bins or blocks of nucleic acids in the first chromosome of interest; and counting the mapped tags in each bin.

In certain embodiments, the step of aligning in (b) comprises aligning at least about 1 million reads.

Any method described herein can further include sequencing the fetus and maternal nucleic acid (e.g., cell-free DNA) in the maternal test sample to obtain sequence readings. Sequencing the maternal and fetal nucleic acid from the maternal test sample to produce sequence readings includes massive parallel sequencing. In certain embodiments, massive parallel sequencing is sequencing by synthesis. Sequencing by synthesis can be achieved using reversible dye terminators. In other embodiments, massive parallel sequencing is sequencing by ligation. In other embodiments, massive parallel sequencing is single molecule sequencing.

Maternal samples that can be used to determine fetal fraction according to the methods described herein include blood, plasma, serum, or urine samples. In certain embodiments, the maternal sample is a plasma sample. In other embodiments, the maternal sample is a whole blood sample.

Also provided are a plurality of different apparatuses, including apparatuses for performing medical analysis on samples (e.g., maternal samples), and for performing the various steps of the above methods, e.g., solely for determining copy number variation, for determining fetal fraction, or for classifying copy number variation.

Also provided are kits comprising reagents that can be used alone or in combination with methods for determining the effect of one of two genomes on a mixture of nucleic acids derived from the two genomes (e.g., fetal fraction in a maternal sample) for determining copy number variation. These kits can be used in conjunction with the devices described herein.

Although the examples herein relate to humans and the language is primarily directed to human problems, the concepts described herein are applicable to genomes from any plant or animal.

The present invention provides the following technical solutions:

1. A medical analysis apparatus for determining fetal fraction in a maternal test sample comprising a mixture of fetal and maternal nucleic acids, the apparatus comprising:

(a) a device for receiving a plurality of sequence reads of the fetal and maternal nucleic acids from the maternal test sample;

(b) an apparatus for aligning the plurality of sequence reads to one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads;

(c) a means for identifying a number of sequence tags from one or more chromosomes of interest or chromosome segments of interest, the chromosomes or chromosome segments being selected from chromosomes 1-22, X, and Y, and segments thereof, and for identifying, for each of the one or more chromosomes of interest or chromosome segments of interest, a number of sequence tags from at least one normalizing chromosome sequence or normalizing chromosome segment sequence to determine a chromosome dose or chromosome segment dose,

wherein the chromosome of interest or chromosome segment of interest has a copy number variation, wherein the copy number variation is determined by comparing the chromosome dose of each of the one or more chromosomes of interest or chromosome segments of interest to a corresponding threshold value for each of the one or more chromosomes of interest or chromosome segments of interest;

(d) a means for determining the fetal fraction using the dose of the chromosome of interest or the dose of the chromosome segment of interest; and

(e) a device for calculating a normalized chromosome value or a normalized segment value, wherein calculating the normalized chromosome value relates the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

Wherein NCV _iA is the normalized chromosome value on the i-th chromosome in the test sample, and σ _iU are the estimated mean and standard deviation of the i-th chromosome dose in the set of qualified samples, respectively, and R _iA is the chromosome dose calculated for the i-th chromosome in the test sample, wherein the i-th chromosome is the chromosome of interest;

wherein the normalized segment value is calculated to relate the chromosome segment dose to the mean of the corresponding chromosome segment dose in a set of qualified samples as

wherein NSV _iA is the normalized chromosome segment value on the i-th chromosome segment in the test sample, and σ _iU are the estimated mean and standard deviation of the i-th chromosome segment dose in the set of qualified samples, respectively, and R _iA is the calculated chromosome segment dose for the i-th chromosome segment in the test sample, wherein the i-th chromosome segment is the chromosome segment of interest;

wherein the means (d) determines the fetal fraction according to the following expression:

ff＝2×|NCV _iA CV _iU |

wherein ff is the fetal fraction value, NCV _iA is the normalized chromosome value on the i-th chromosome in the test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome determined in the qualified samples, wherein the i-th chromosome is the chromosome of interest; or

wherein the means (d) determines the fetal fraction according to the following expression;

ff＝2×|NsV _iA CV _iU |

Wherein ff is the fetal fraction value, NSV _iA is the normalized chromosome segment value on the i-th chromosome segment in the test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome segment determined in the qualified samples, wherein the i-th chromosome segment is the chromosome segment of interest.

2. The apparatus of embodiment 1, wherein the chromosome dose or segment dose determined by means (c) is calculated as a ratio of the number of sequence tags identified for the chromosome or segment of interest to the number of sequence tags identified for at least one corresponding normalizing chromosome sequence or normalizing chromosome segment sequence of the chromosome or segment of interest; or wherein the chromosome dose or segment dose determined by means (c) is calculated as a ratio of the sequence tag density ratio of the chromosome or segment of interest to the sequence tag density ratio of the normalizing chromosome sequence or normalizing chromosome segment sequence.

3. The apparatus according to embodiment 1, wherein the chromosome of interest is an autosome or the X chromosome of a male fetus, and the chromosome segment of interest is selected from an autosome or the X chromosome of a male fetus.

4. An apparatus as described in embodiment 1, wherein at least one normalizing chromosome sequence or normalizing chromosome segment sequence is a chromosome or segment selected for an associated chromosome or segment of interest by: (i) identifying multiple qualified samples for the chromosome or segment of interest; (ii) using multiple potential normalizing chromosome sequences or normalizing chromosome segment sequences to repeatedly calculate chromosome doses or chromosome segment doses for the selected chromosome or segment; and (iii) selecting the normalizing chromosome sequence or normalizing chromosome segment sequence, alone or in combination, to give minimal variability and/or maximum distinguishability in the calculated chromosome doses or chromosome segment doses.

5. The apparatus of embodiment 1, wherein the normalizing chromosome sequence is a single chromosome or a group of chromosomes selected from any one or more of chromosomes 1-22, X, and Y.

6. The apparatus of embodiment 1, wherein the normalizing segment sequence is a single segment or a group of segments from any one or more of chromosomes 1-22, X, and Y.

7. The apparatus of embodiment 1, wherein the copy number variation is selected from the group consisting of complete chromosome duplication, complete chromosome deletion, partial duplication, partial doubling, partial insertion, and partial deletion.

8. The apparatus of embodiment 1, further comprising a device for comparing the fetal fraction determined using the chromosome dose or chromosome segment dose with a fetal fraction determined using information on one or more polymorphisms present in a chromosome other than the chromosome of interest that exhibit allelic imbalance in fetal and maternal nucleic acids from a maternal test sample.

9. The apparatus of embodiment 1, wherein the aligning in means (b) comprises aligning at least one million reads.

10. The apparatus of embodiment 1, further comprising a sequencer configured to sequence the fetal and maternal nucleic acids in the maternal test sample to obtain the sequence reads.

11. The apparatus of embodiment 10, wherein the sequencing comprises sequencing cell-free DNA from the maternal test sample to provide the sequence reads.

12. The apparatus of embodiment 10, wherein the sequencing comprises massively parallel sequencing of the maternal and fetal nucleic acids from the maternal test sample to generate the sequence reads.

13. The apparatus of embodiment 12, wherein the massively parallel sequencing is sequencing by synthesis.

14. The apparatus of embodiment 13, wherein the sequencing-by-synthesis uses a reversible dye terminator.

15. The apparatus of embodiment 12, wherein the massively parallel sequencing is sequencing by ligation.

16. The apparatus of embodiment 12, wherein the massively parallel sequencing is single molecule sequencing.

17. The apparatus of embodiment 1, further comprising means for obtaining said maternal test sample from a pregnant organism.

18. The apparatus of embodiment 1, wherein the maternal sample is a blood, plasma, serum, or urine sample.

19. A medical analysis device for classifying copy number variation in a fetal genome, the device comprising:

(1) a device for receiving a plurality of sequence reads from fetal and maternal nucleic acids in a maternal test sample;

(2) a device for aligning the sequence reads with one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads;

(3) a device for identifying a number of sequence tags from one or more chromosomes of interest and determining that a first chromosome of interest in the fetus carries a copy number variation;

(4) a means for calculating a first fetal fraction value by a first method that does not use information from the labels of the first chromosome of interest;

(5) an apparatus for calculating a second fetal fraction value by a second method that uses information from the labels of the first chromosome of interest, wherein the apparatus includes a component for calculating a normalized chromosome value and a component for using the normalized chromosome value, the calculation of the normalized chromosome value relating the chromosome dose for the first chromosome of interest to the average of the corresponding chromosome doses in a set of qualified samples as:

wherein NCV _iA is the normalized chromosome value on the i-th chromosome in the test sample, and σ _iU are the estimated mean and standard deviation of the dose for the i-th chromosome in the set of qualified samples, respectively, and R _iA is the calculated chromosome dose for the i-th chromosome in the test sample, wherein the i-th chromosome is the first chromosome of interest, and

(6) a device for comparing the first fetal fraction value with the second fetal fraction value and classifying the copy number variation of the first chromosome of interest using the comparison;

The component for calculating the first fetal fraction value in the apparatus (4) of the first method is evaluated by the following expression:

ff＝2×|NCV _iA CV _iU |

wherein ff is the fetal fraction value, NCV _iA is the normalized chromosome value on the i-th chromosome in the test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome determined in the qualified samples, wherein the i-th chromosome is the chromosome of interest; or

The component for calculating the first fetal fraction value in the apparatus (4) of the first method is evaluated by the following expression:

ff＝2×|NSV _iA CV _iU |

wherein ff is the fetal fraction value, NSV _iA is the normalized chromosome segment value on the i-th chromosome segment in the test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome segment determined in the qualified samples, wherein the i-th chromosome segment is the chromosome segment of interest;

The component for calculating the second fetal fraction value in the apparatus (5) of the second method is evaluated by the following expression:

ff＝2×|NCV _iA CV _iU |

Wherein ff is the second fetal fraction value, NCV _iA is the normalized chromosome value on the i-th chromosome in the test sample, and CV _iU is the coefficient of variation of the dose of the i-th chromosome determined in the qualified samples, wherein the i-th chromosome is the first chromosome of interest.

20. The apparatus of embodiment 19, wherein the means (4) of the first method comprises a component for calculating the first fetal fraction value using information from one or more polymorphisms that exhibit allelic imbalance in fetal and maternal nucleic acids of the maternal test sample, said polymorphisms being present on a chromosome other than the first chromosome of interest; and

The device (5) of the second method comprises:

(a) a component for counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and

(b) a component for calculating the second fetal fraction value from the chromosome dose by the second method.

21. The apparatus according to embodiment 20, wherein the information used by the apparatus (4) of the first method comprises sequence tags obtained by sequencing predetermined polymorphic sequences, each of the polymorphic sequences comprising the one or more polymorphic sites.

22. The apparatus of embodiment 21, wherein the information used by the means (4) of the first method is obtained by a non-sequencing method.

23. The apparatus of embodiment 22, wherein the method is qPCR, digital PCR, mass spectrometry, or capillary gel electrophoresis.

24. The apparatus according to embodiment 19, wherein the device (4) of the first method comprises:

(a) a component for counting the number of sequence tags from chromosomes other than the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and (b) a component for calculating the first fetal fraction value from the chromosome dose by the first method; and

The device (5) of the second method comprises:

(a) a component for counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and

(b) a component for calculating the second fetal fraction value from the chromosome dose by the second method.

25. The apparatus of embodiment 24, wherein the apparatus (4) of the first method and the apparatus (5) of the second method further comprise a component for calculating a normalized chromosome value and a component for using the normalized chromosome value, respectively, wherein calculating the normalized chromosome value is to relate the calculated chromosome dose to the average value of the corresponding chromosome dose in a group of qualified samples,

As:

where NCV _iA is the normalized chromosome value on chromosome i in the test sample, σ iU and σ _iU are the estimated mean and standard deviation of the dose for chromosome i in the set of qualified samples, respectively, and R _iA is the calculated dose for chromosome i in the test sample,

in

For the device (4) of the first method, the i-th chromosome is a chromosome other than the first chromosome of interest;

For the device (5) of the second method, the i-th chromosome is the first chromosome of interest.

26. The apparatus of embodiment 25, wherein the component for calculating the fetal fraction of the apparatus (4) of the first method and the apparatus (5) of the second method is evaluated by the following expression:

ff＝2×|NCV _iA CV _iU |

Wherein ff is the fetal fraction value, NCV _iA is the normalized chromosome value on chromosome i in the test sample, and CV _iU is the coefficient of variation of the dose of chromosome i in the qualified samples;

in

For the device (4) used in the first method, the i-th chromosome is a chromosome other than the first chromosome of interest;

For the apparatus (5) used in the second method, the i-th chromosome is the first chromosome of interest.

27. The apparatus of embodiment 26, wherein when the fetus is male, the chromosome other than the first chromosome of interest is an X chromosome.

28. The apparatus of embodiment 20 or 24, wherein the means (6) for comparing the first fetal fraction value to the second fetal fraction value comprises a component for determining whether the two fetal fraction values are approximately equal.

29. The apparatus of embodiment 28, wherein the means (6) further comprises a component for determining that a ploidy assumption implicit in the second method is true when the two fetal fraction values are approximately equal.

30. The apparatus of embodiment 29, wherein the ploidy assumption implicit in the second method is that the first chromosome of interest has a complete chromosomal aneuploidy.

31. The apparatus of embodiment 30, wherein the complete chromosomal aneuploidy of the first chromosome of interest is a monosomy or a trisomy.

32. The apparatus of embodiment 31, further comprising a device for analyzing the tag information of the first chromosome of interest to determine whether (i) the first chromosome of interest carries a partial aneuploidy, or (ii) the fetus is a mosaic, wherein the device for analyzing the tag information of the first chromosome of interest is configured to be performed when the device for comparing the first fetal fraction value to the second fetal fraction value indicates that the two fetal fraction values are not approximately equal.

33. The apparatus of embodiment 19, wherein the means (4) of the first method comprises a component for calculating the first fetal fraction value using information from one or more polymorphisms that exhibit allelic imbalance in fetal and maternal nucleic acids of the maternal test sample, the polymorphisms being present on a chromosome other than the first chromosome of interest; and

The apparatus (5) of the second method includes a component for calculating the second fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids of the maternal test sample, said polymorphisms being present on the first chromosome of interest.

34. The apparatus according to embodiment 33, wherein the means for comparing (6) comprises:

a component for determining that the first chromosome of interest is diploid when a ratio of the second fetal fraction value to the first fetal fraction value is approximately 1;

a component for determining that the first chromosome of interest is triploid when a ratio of the second fetal fraction value to the first fetal fraction value is approximately 1.5; and

A component is configured to determine that the first chromosome of interest is haploid when a ratio of the second fetal fraction value to the first fetal fraction value is approximately 0.5.

35. The apparatus of embodiment 34, further comprising a means for analyzing the tag information of the first chromosome of interest to determine whether (i) the first chromosome of interest carries a partial aneuploidy, or (ii) the fetus is a mosaic, wherein the means for analyzing the tag information of the first chromosome of interest is configured to be performed when the means (6) for comparing the first fetal fraction value to the second fetal fraction value indicates that the ratio of the second fetal fraction value to the first fetal fraction value is not approximately 1, 1.5, or 0.5.

36. The apparatus of embodiment 32 or 35, wherein the means for analyzing the tag information of the first chromosome of interest comprises:

(a) a component for packaging the sequence of the first chromosome of interest into a plurality of parts;

(b) a means for determining whether any of the portions comprises significantly more or significantly less nucleic acid than one or more other portions; and

(c) a component for determining that the first chromosome of interest has a partial aneuploidy if any of the portions contains significantly more or significantly less nucleic acid compared to one or more other portions; or determining that the fetus is a mosaic if none of the portions contains significantly more or significantly less nucleic acid compared to one or more other portions.

37. The apparatus of embodiment 36, wherein component (c) further determines that a portion of the first chromosome of interest that comprises significantly more or significantly less nucleic acid than one or more other portions carries the partial aneuploidy.

38. The apparatus of embodiment 19, wherein the first chromosome of interest is selected from the group consisting of chromosomes 1-22, X, and Y.

39. An apparatus as described in embodiment 19, wherein the device (6) includes a component for classifying the copy number variation into a category selected from the group consisting of: complete chromosome insertion, complete chromosome deletion, partial chromosome duplication, and partial chromosome deletion, and mosaicism.

40. The apparatus of embodiment 19, further comprising:

(i) a means for determining whether the copy number variation is caused by a partial aneuploidy or a mosaicism; and

(ii) if the copy number variation is caused by a partial aneuploidy, a means for determining the locus of the partial aneuploidy on the first chromosome of interest,

Wherein the means in (i) and (ii) are configured to be performed when the means for comparing the first fetal fraction value with the second fetal fraction value determines that the first fetal fraction value and the second fetal fraction value are not approximately equal.

41. An apparatus as described in embodiment 40, wherein the means for determining the locus of the partial aneuploidy on the first chromosome of interest comprises a component for sorting the sequence tags of the first chromosome of interest into nucleic acid data bins or blocks in the first chromosome of interest; and a component for counting the mapped tags in each bin.

42. The apparatus of embodiment 1 or 19, wherein the maternal test sample is a blood, plasma, serum, or urine sample.

43. The apparatus of embodiment 1 or 19, wherein the fetal and maternal nucleic acid is cell-free DNA (cfDNA).

44. The apparatus of embodiment 1 or 19, further comprising a sequencer configured to sequence the fetal and maternal nucleic acids in a maternal test sample and obtain the sequence reads.

45. The apparatus of embodiment 44, wherein the sequencer is configured to perform sequencing by synthesis.

46. An apparatus as described in embodiment 45, wherein the sequencer is configured to perform synthesis sequencing using reversible dye terminators.

47. An apparatus as described in embodiment 44, wherein the sequencer is configured to perform ligation sequencing.

48. The apparatus of embodiment 44, wherein the sequencer is configured to perform single molecule sequencing.

49. An apparatus as described in embodiment 44, wherein the sequencer and devices (a)-(d) of the apparatus as described in embodiment 1, or devices (1)-(6) of the apparatus as described in embodiment 19 are located in separate locations and are connected by a network.

50. The apparatus of embodiment 44, further comprising a means for obtaining the maternal test sample from a pregnant mother.

51. An apparatus as described in embodiment 50, wherein the means for obtaining the maternal test sample and means (a)-(d) of the apparatus as described in embodiment 1 or means (1)-(6) of the apparatus as described in embodiment 19 are located in separate locations.

52. The apparatus of embodiment 50, further comprising a device for extracting cell-free DNA from the maternal test sample.

53. The apparatus of embodiment 52, wherein the means for extracting cell-free DNA is located in the same location as the sequencer, and wherein the apparatus for obtaining the maternal test sample is located in a remote location.

54. The apparatus of embodiment 44, wherein the fetal and maternal nucleic acids in the maternal test sample are cell-free DNA.

55. The apparatus of embodiment 1 or 19, wherein the means for aligning (2) aligns at least 1 million reads.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a method 100 for determining the presence or absence of a copy number variation in a test sample comprising a mixture of nucleic acids.

Figure 2 depicts the process flow for preparing sequencing libraries according to the unabridged, abbreviated (ABB), two-step, and one-step protocols described herein. "P" indicates a purification step; and "X" indicates that no purification step and/or DNA repair was included.

3 depicts a process flow of an embodiment of a method for preparing a sequencing library on a solid surface.

FIG4 shows a flow chart of one embodiment 400 of a method for verifying the integrity of a sample undergoing a multi-step, single-plex sequencing bioassay.

FIG5 shows a flow diagram of one embodiment 500 of a method for verifying the integrity of multiple samples undergoing a multi-step multiplex sequencing bioassay.

6 is a flow chart of a method 600 for simultaneously determining the presence or absence of aneuploidy and fetal fraction in a maternal test sample comprising a mixture of fetal and maternal nucleic acids.

7 is a flow chart of a method 700 for determining fetal fraction in a maternal test sample comprising a mixture of fetal and maternal nucleic acids using massively parallel sequencing or size separation of polymorphic nucleic acid sequences.

8 is a flow chart of a method 800 for simultaneously determining the presence or absence of fetal aneuploidy and fetal fraction in a maternal plasma test sample enriched for polymorphic nucleic acids.

9 is a flow chart of a method 900 for simultaneously determining the presence or absence of fetal aneuploidy and fetal fraction in a maternal purified cfDNA test sample enriched for polymorphic nucleic acids.

10 is a flow chart of a method 1000 for simultaneously determining the presence or absence of fetal aneuploidy and fetal fraction in a sequencing library constructed from fetal and maternal nucleic acids derived from a maternal test sample and enriched for polymorphic nucleic acids.

11 is a flow chart outlining an alternative embodiment of a method for determining fetal fraction by massively parallel sequencing as shown in FIG. 7 .

Figure 12 is a bar graph showing the identification of fetal and maternal polymorphic sequences (SNPs) used to determine fetal fraction in a test sample. The total number of sequence reads (Y-axis) mapped to the SNP sequence identified by the rs number (X-axis) is shown, as well as the relative amount of fetal nucleic acid (*).

FIG13 is a block diagram depicting the classification of fetal and maternal typing status for a given genomic location.

FIG14 shows a comparison of results produced using a mixture model with known and estimated fetal fractions.

FIG15 shows error estimates made by sequencing base positions over 30 lanes of the Eland GA2 data aligned to the human genome HG18 using Eland with default parameters.

Figure 16 shows that using the machine error rate as a known parameter can reduce the upward bias by one point.

Figure 17 shows that using the machine error rate as a known parameter, the simulated data for the enhanced error models for Cases 1 and 2 significantly reduced the upward bias for fetal fractions below 0.2 to less than one point.

18 is a flow chart depicting a method for classifying CNVs by comparing fetal score values calculated using two different techniques.

FIG. 19 is a block diagram of a discrete system used to process a test sample and ultimately make a diagnosis.

FIG. 20 schematically illustrates how many different operations can be grouped together by different elements of the system when processing a test sample.

Figures 21A and 21B show electropherograms of cfDNA sequencing libraries prepared according to the abbreviated protocol described in Example 2a (Figure 21A) and the protocol described in Example 2b (Figure 21B).

Figures 22A to 22C provide graphs showing the average (n = 16) of the total percentage of sequence tags mapped to each human chromosome when sequencing libraries were prepared according to the abbreviated protocol (ABB; ◇) and when sequencing libraries were prepared according to the two-step method without repair (INSOL; □) (% ChrN; Figure 22A) and the percentage of sequence tags as a function of chromosome size (Figure 22B). Figure 22C shows the ratio of the percentage of tags mapped when the two-step method was used to prepare the library and the tags obtained when the abbreviated (ABB) method was used to make the library as a function of the GC content of the chromosome.

Figures 23A and 23B show bar graphs providing the mean and standard deviation of the percentage of tags mapped to chromosomes X (Figure 23A; %ChrX) and Y (Figure 23B; %ChrY) obtained from sequencing 10 samples of cfDNA purified from the plasma of 10 pregnant women. Figure 23A shows that the number of tags mapped to chromosome X when using the no-repair method (two-step) is greater than the number of tags obtained using the abbreviated method (ABB). Figure 23B shows that the percentage of tags mapped to chromosome Y when using the no-repair two-step method is no different from the percentage of tags when using the abbreviated method (ABB).

Figure 24 shows the ratio of the number of non-excluded sites (NE sites) on the reference genome (hg18) to the total number of tags mapped to the non-excluded sites for each of the five samples from which cfDNA was prepared and used to construct sequencing libraries according to the abbreviated protocol (ABB) (solid bars), the in-solution no-repair protocol (two-step; open bars), and the solid surface no-repair protocol (one-step; gray bars) described in Example 2.

Figures 25A and 25B are graphs showing the mean (n=5) of the total percentage of sequence tags mapped to each human chromosome when sequencing libraries were prepared according to the abbreviated protocol (ABB; ◇), when sequencing libraries were prepared according to the two-step method without repair (□), and when libraries were prepared according to the one-step method without repair (Δ) (% ChrN; Figure 25A) and the percentage of sequence tags as a function of chromosome size (Figure 25B). Regression coefficients for mapped tags obtained from sequencing libraries prepared according to the abbreviated protocol (ABB; ◇) and the solid surface no repair protocol (two-step; □). Figure 25C shows the percentage ratio of the mapped sequence tags for each chromosome obtained from the sequencing libraries prepared according to the two-step protocol without repair to the tags for each chromosome obtained from the sequencing libraries prepared according to the abbreviated protocol (ABB) as a function of the percentage GC content of each chromosome (◇), and the percentage ratio of the mapped sequence tags for each chromosome obtained from the sequencing libraries prepared according to the one-step protocol without repair to the tags for each chromosome obtained from the sequencing libraries prepared according to the abbreviated protocol (ABB) as a function of the percentage GC content of each chromosome (□).

Figures 26A and 26B show the mean and standard deviation of the percentage of tags mapped to chromosomes X (Figure 26A) and Y (Figure 26B) obtained by sequencing 5 samples of cfDNA purified from the plasma of 5 pregnant women according to the ABB method, the two-step method, and the one-step method. Figure 26A shows that the number of tags mapped to chromosome X when using the no-repair method (two-step and one-step) is larger than the number of tags obtained using the abbreviated method (ABB). Figure 26B shows that the percentage of tags mapped to chromosome Y when using the no-repair two-step method and the one-step method is no different from the percentage of tags when using the abbreviated method.

Figures 27A and 27B show the correlation between the amount of purified cfDNA used to prepare sequencing libraries and the amount of resulting library product for 61 clinical samples prepared in solution using the ABB method (Figure 27A) and 35 research samples prepared using the one-step method using a non-repair solid surface (SS) (Figure 27B).

FIG28 shows the correlation between the amount of cfDNA used to make the library and the amount of library product obtained using the two-step (□), ABB (◇), and one-step (Δ) methods.

Figure 29 shows the percentage of index sequence reads obtained when index libraries were prepared using one-step (open bars) and two-step (solid bars) methods and sequenced as 6-plexes (ie, 6 index samples per flow cell lane).

Figures 30A and 30B are graphs showing the mean (n=42) percentage of the total number of sequence tags that mapped to each human chromosome (%ChrN; Figure 30A) and the percentage of sequence tags obtained as a function of chromosome size (Figure 30B) when indexed sequencing libraries were prepared on a solid surface according to the one-step method and sequenced as 6-plex.

FIG31 shows the percentage of sequence tags that map to chromosome Y (ChrY) relative to the percentage of tags that map to chromosome X (ChrX).

Figures 32A and 32B show the distribution of chromosome doses for chromosome 21 determined from sequencing cfDNA extracted from a set of 48 blood samples obtained from human subjects each carrying a male or female fetus. The doses for qualified (i.e., normal for chromosome 21 (O)) chromosome 21 and trisomy 21 test samples are shown as (Δ) for chromosomes 1-12 and X (Figure 32A) and for chromosomes 1-22 and X (Figure 32B).

FIG33 shows the distribution of chromosome 18 chromosomal doses determined from sequencing cfDNA extracted from a panel of 48 blood samples obtained from human subjects each carrying a male or female fetus. The doses for qualified (i.e., normal for chromosome 18 (O)) chromosome 18 and trisomy 18 (Δ) test samples are shown for chromosomes 1-12 and X ( FIG33A ) and for chromosomes 1-22 and X ( FIG33B ).

Figures 34A and 34B show the distribution of chromosome doses for chromosome 13 determined from sequencing cfDNA extracted from a set of 48 blood samples obtained from human subjects each carrying a male or female fetus. The doses for qualified (i.e., normal for chromosome 13 (O)) chromosome 13 and trisomy 13 (Δ) test samples are shown for chromosomes 1-12 and X (Figure 34A), and for chromosomes 1-22 and X (Figure 34B).

Figures 35A and 35B show the distribution of chromosome doses for chromosome X determined from sequencing cfDNA extracted from a panel of 48 test blood samples obtained from human subjects each carrying a male or female fetus. Chromosome X doses for males (46,XY; (O)), females (46,XX; (Δ)), monosomy X (45,X; (+)), and samples with a complex karyotype (Cplx(X)) are shown for chromosomes 1-12 and X (Figure 35A) and for chromosomes 1-22 and X (Figure 35B).

Figures 36A and 36B show the distribution of chromosome doses for chromosome Y determined from sequencing cfDNA extracted from a set of 48 test blood samples obtained from human subjects each carrying a male or female fetus. Chromosome Y doses for males (46, XY; (Δ)), females (46, XX; (O)), monosomy X (45, X; (+)), and samples with a complex karyotype (Cplx(X)) are shown for chromosomes 1-12 (Figure 36A) and for chromosomes 1-22 (Figure 36B).

FIG. 37 shows the coefficient of variation (CV) for chromosomes 21 (■), 18 (●), and 13 (▲) determined from the doses shown in FIGs. 32A and 32B , 33A and 33B , and 34A and 34B , respectively.

FIG. 38 shows the coefficient of variation (CV) for chromosomes X (■) and Y (●) determined from the doses shown in FIGs. 35A and 35B and 36A and 36B, respectively.

Figure 39 shows the cumulative distribution of the GC fraction of human chromosomes. The vertical axis represents the frequency of chromosomes with a GC content lower than the value shown on the horizontal axis.

Figure 40 shows the sequence dose (Y axis) for a segment of chromosome 11 (81000082-103000103 bp) determined from sequencing cfDNA, which was extracted from a set of 7 qualified samples (O) and 1 test sample (◆) from a pregnant human subject. The sample from one subject was identified, and the subject was pregnant with a fetus with a partial aneuploidy of chromosome 11 (◆).

Figures 41A-41E show the distribution of normalized chromosome doses for chromosome 21 (41A), chromosome 18 (41B), chromosome 13 (41C), chromosome X (41D), and chromosome Y (41E) relative to the standard deviation of the mean (Y-axis) of the corresponding chromosome in unaffected samples.

42 shows the normalized chromosome values for chromosomes 21 (O), 18 (Δ), and 13 (□) determined in samples from Training Set 1 using the normalizing chromosomes as described in Example 12.

43 shows the normalized chromosome values for chromosomes 21 (O), 18 (Δ), and 13 (□) determined in samples from Test Group 1 using the normalizing chromosomes as described in Example 12.

Figure 44 shows the normalized chromosome values for chromosomes 21 (O) and 18 (Δ) determined in samples from test group 1 using the normalization method of Chiu et al. (normalizing the number of sequence tags identified for the chromosome of interest to the number of sequence tags obtained for the remaining chromosomes in the sample, see Example 13 elsewhere in this application).

Figure 45 shows the normalized chromosome values for chromosomes 21 (0), 18 (Δ), and 13 (□) determined in samples from training set 1 using systematically determined normalizing chromosomes (as described in Example 13).

Figure 46 shows the normalized chromosome values for chromosomes X (X-axis) and Y (Y-axis). Arrows point to the 5 (Figure 46A) and 3 (Figure 46B) X monosomy samples identified in the training and test sets, respectively, as described in Example 13.

Figure 47 shows the normalized chromosome values for chromosomes 21 (O), 18 (Δ), and 13 (□) determined in samples from Test Group 1 using systematically determined normalizing chromosomes (as described in Example 13).

Figure 48 shows the normalized chromosome values for chromosome 9 (O) determined in samples from Test Group 1 using the systematically determined normalizing chromosome (as described in Example 13).

FIG49 shows normalized chromosome values for chromosomes 1-22 determined in samples from Test Group 1 using systematically determined normalizing chromosomes (as described in Example 13).

FIG50 shows a flow diagram of the design (A) and random sampling scheme (B) of the study described in Example 16.

Figures 51A to 51F show flow charts for analysis of chromosomes 21, 18, and 13 (Figures 51A to 51C, respectively) and sex analysis for females, males, and monosomy X (Figures 51D to 51F, respectively). The ovals include results obtained from sequencing information from the laboratory, the rectangles include karyotype results, and the rectangles with rounded corners show comparative results used to determine test performance (sensitivity and specificity). The dotted lines in Figures 51A and 51B represent the relationship between mosaic samples for T21 (n=3) and T18 (n=1), which were examined by analysis of chromosomes 21 and 18, respectively, but were correctly determined as described in Example 16.

Figure 52 shows the normalized chromosome value (NCV) versus karyotype classification for chromosomes 21 (●), 18 (■), and 13 (▲) for the test samples from the study described in Example 16. Circle samples represent unclassified samples with a trisomic karyotype.

Figure 53 shows the normalized chromosome value (NCV) for chromosome X versus sex-classified karyotype relationships for the test samples from the study described in Example 16. Samples with a female karyotype (○), samples with a male karyotype (●), samples with 45,X (□), and samples with other karyotypes (i.e., XXX, XXY, and XYY) (■) are shown.

Figure 54 shows a graph of the normalized chromosome value of chromosome Y versus the normalized chromosome value of chromosome X for the test samples of the clinical study described in Example 16. Shows euploid male and female samples (○), XXX samples (●), 45,X samples (X), XYY samples (■) and XXY samples (▲). The dotted line shows the threshold value for classifying the samples as described in Example 16.

Figure 55 schematically illustrates one embodiment of the CNV determination method described herein.

56 shows a graph from Example 17 of the percent "ff" determined using the dose of chromosome 21 (ff ₂₁ ) as a function of the percent "ff" determined using the dose of chromosome X (ff _X ) in a synthetic maternal sample (1) comprising DNA from a child with trisomy 21. FIG.

Figure 57 shows a graph from Example 17 of the percent "ff" determined using the dose for chromosome 7 ( _ff7 ) as a function of the percent "ff" determined using the dose for chromosome X (ffX) in the synthetic maternal sample ( ₂ ) comprising DNA from a euploid mother and her child carrying a partial deletion of chromosome 7.

Figure 58 shows a graph from Example 17 of the percent "ff" determined using the dose of chromosome 15 (ff15) as a function of the percent "ff" determined using the dose of chromosome X ( _ffX ) in the synthetic maternal sample (3) comprising DNA from a euploid mother and her 25% mosaic child with a partial duplication of chromosome ₁₅ .

Figure 59 shows a graph of the "ff" percentage ( _ff22 ) and the NCV obtained therefrom, determined using the doses for chromosome 22 in an artificial sample (4) from Example 17, comprising 0% child DNA (i), and 10% DNA from an unaffected twin son known not to have a partial chromosomal aneuploidy for chromosome 22 (ii), and 10% DNA from an affected twin son known to have a partial chromosomal aneuploidy for chromosome 22 (iii).

Figure 60 shows a graph from Example 18 of the CNffx versus CNff21 relationship determined in samples comprising fetal T21 trisomy.

Figure 61 shows a graph from Example 18 showing the CNffx versus CNff18 relationship determined in samples comprising fetal T18 trisomy.

Figure 62 shows a graph from Example 18 showing the CNffx versus CNff13 relationship determined in samples including fetal T13 trisomy.

Figure 63 shows a graph from Example 19, NCV values for chromosomes 1 to 22 and X in the test samples.

Figure 64 shows the fetal fractions obtained for samples from female fetuses with T21 in Example 18.

Figure 65 shows one embodiment of a medical analysis apparatus for determining fetal fraction as a function of copy number variation present in the fetal genome.

FIG66 illustrates one embodiment of a medical analysis apparatus for determining a fetal fraction to classify copy number variation in a fetal genome.

Figure 67 shows a kit that includes assay control reagents and reagents for tracking and verifying the integrity of maternal cfDNA samples undergoing massively parallel sequencing.

Figure 68 shows a kit that includes a blood collection device, DNA extraction reagents, and control reagents for testing maternal DNA samples.

Figure 69 (A, B, C) shows NCV plots of the internal positive control [□] and maternal samples [◇] tested for copy number variations of chromosomes 13, 18 and 21.

Detailed description

The disclosed embodiments relate to methods, apparatus, and systems for determining copy number variations (CNVs) of sequences of interest in a test sample comprising a mixture of nucleic acids that are known or suspected to differ in the amount of one _or more sequences of interest. Sequences of interest include, for example, genomic segment sequences ranging from kilobases (kb) to megabases (Mb) to entire chromosomes that are known or suspected to be associated with a genetic condition or disease condition. Examples of sequences of interest include chromosomes associated with well-known aneuploidies (e.g., trisomy 21) and segments of chromosomes that are increased in diseases such as cancer, such as partial trisomy 8 in acute myeloid leukemia. CNVs that can be determined according to this method include monosomy and trisomy of any one or more of autosomes 1-22, and sex chromosomes X and Y (e.g., 45,X, 47,XXX, 47,XXY, and 47,XYY), other chromosomal polysomy, i.e., tetrasomy and pentasomy (including but not limited to XXXX , XXXXX , XXXXY , and XYYYY ), and deletions and/or duplications of segments of any one or more of these chromosomes.

The method is a statistical method that is implemented on one or more processors and takes into account the cumulative variability derived from process-related, inter-chromosomal (within round) and inter-sequencing treatment (between rounds) variability. These methods are applicable to determining CNVs for any fetal aneuploidy, as well as CNVs known or suspected to be associated with a variety of medical conditions.

Unless otherwise indicated, the practice of the present invention involves conventional techniques and apparatus commonly used in molecular biology, microbiology, protein purification, protein engineering, protein and DNA sequencing, and recombinant DNA fields, which are within the skill of the art. Such techniques and apparatus are known to those of ordinary skill in the art and are described in numerous documents and reference works (e.g., see Sambrook et al., "Molecular Cloning: A Laboratory Manual," 3rd ed. (Cold Spring Harbor, [2001]); and Ausubel et al., "Current Protocols in Molecular Biology" [1987].

Numerical ranges include the values defining the ranges. It is intended that every maximum numerical limit given throughout this specification include every lower numerical limit, as if such lower numerical limits were expressly set forth herein. Every minimum numerical limit given throughout this specification will include every higher numerical limit, as if such higher numerical limits were expressly set forth herein. Every numerical range given throughout this specification will include every narrower numerical range falling within such broader numerical range, as if such narrower numerical ranges were all expressly set forth herein.

The headings provided herein are not intended to limit this disclosure.

Unless otherwise defined herein, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Various scientific dictionaries encompassing the terms contained herein are well known and available to those skilled in the art. Although any methods and materials similar or equivalent to those described herein find use in practicing or testing the embodiments disclosed herein, only some preferred methods and materials are described.

The terms defined immediately below are more fully described by reference to the specification as a whole. It is to be understood that this disclosure is not limited to the particular methodology, procedures, and reagents described, as these may vary and be used by those skilled in the art according to their circumstances.

definition

As used herein, the singular terms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless otherwise indicated, nucleic acids are written left to right in 5' to 3' orientation and amino acid sequences are written left to right in amino to carboxyl orientation, respectively.

The term "assessment" when used herein in the context of CNV analysis of nucleic acid samples refers to characterizing the state of aneuploidy of a chromosome or segment as one of three types of determinations: "normal" or "unaffected," "affected," and "no determination." Determining normal and affected thresholds is typically set. Parameters related to aneuploidy in the sample are measured, and these measurements are compared with thresholds. For aneuploidy of the duplication type, if the chromosome or segment dose (or other measured values of sequence content) exceeds the defined threshold value set for the affected sample, then it is determined to be affected. For these aneuploidies, if the chromosome or segment dose is lower than the threshold value set for the normal sample, then it is determined to be normal. In contrast, for aneuploidy of the deletion type, if the chromosome or segment dose is lower than the defined threshold value of the affected sample, then it is determined to be affected, and if the chromosome or segment dose exceeds the threshold value set for the normal sample, then it is determined to be normal. For example, in the presence of trisomy, by, for example, the value of a parameter such as a test chromosome dose is lower than a reliability threshold defined by the user, determining that "normal" is determined, and by, for example, a parameter such as a test chromosome dose exceeds a reliability threshold defined by the user, determining that "affected" is determined. A result of "no call" is determined by a parameter such as test chromosome dosage being between the thresholds for a call of "normal" or "affected." The term "no call" is used interchangeably with "unclassified."

The term "copy number variation" refers to the change in the copy number of the nucleic acid sequence present in the test sample compared to the copy number of the nucleic acid sequence present in the qualified sample. In certain embodiments, the nucleic acid sequence is 1kb or larger. In some cases, the nucleic acid sequence is a full chromosome or a significant portion thereof. "Copy number variant" refers to a nucleic acid sequence that is found to have a copy number difference by comparing the expected content of the sequence of interest with the sequence of interest in the test sample. For example, the content of the sequence of interest in the test sample is compared with the content of the sequence of interest present in the qualified sample. Copy number variant/variation includes deletion (including microdeletion), insertion (including microinsertion), duplication, multiplication, inversion, translocation and complex multi-position variation. CNV encompasses chromosome aneuploidy and partial aneuploidy.

The term "aneuploidy" as used herein refers to an imbalance in genetic material caused by the loss or gain of an entire chromosome, or a portion of a chromosome.

The terms "chromosomal aneuploidy" and "complete chromosomal aneuploidy" as used herein refer to an imbalance in genetic material caused by the loss or gain of an entire chromosome and include germline aneuploidy and mosaic aneuploidy.

The terms "partial aneuploidy" and "partial chromosomal aneuploidy" herein refer to an imbalance of genetic material caused by the loss or gain of a portion of a chromosome (e.g., partial monosomy and partial trisomy), and encompass imbalances caused by translocations, deletions, and insertions.

The term "aneuploid sample" herein refers to a sample indicating that the chromosome content of a subject is not euploid, ie, the sample indicates that a subject carries an abnormal copy number of a chromosome or a portion of a chromosome.

The term "aneuploid chromosome" as used herein refers to a chromosome that is known or determined to be present in a sample at an abnormal copy number.

The term "multiple/multiple" refers to more than one at this. For example, the term is used to refer to the number of nucleic acid molecules or sequence tags at this time and is enough to identify the significant difference of copy number variation (such as chromosome dosage) in test specimens and qualified samples under the method disclosed here. In some embodiments, at least about 3x ¹⁰⁶ sequence tags, at least about 5x ¹⁰⁶ sequence tags, at least about 8x ¹⁰⁶ sequence tags, at least about 10x ¹⁰⁶ sequence tags, at least about 15x 106 sequence tags, at least about 20x ¹⁰⁶ sequence tags, at least about 30x ¹⁰⁶ sequence tags, at least about 40x ¹⁰⁶ sequence tags or at least about 50x ¹⁰⁶ sequence tags are ^obtained for each test specimen.

The terms "polynucleotide," "nucleic acid," and "nucleic acid molecule" are used interchangeably and refer to a sequence of covalently linked nucleotides (i.e., ribonucleotides for RNA and deoxyribonucleotides for DNA) in which the 3' position of the pentose sugar of one nucleotide is linked to the 5' position of the pentose sugar of the next nucleotide by a phosphodiester group. This includes sequences of any form of nucleic acid, including but not limited to RNA and DNA molecules, such as cfDNA molecules. The term "polynucleotide" includes but is not limited to single-stranded and double-stranded polynucleotides.

The term "portion" is used herein to refer to an amount of sequence information of fetal and maternal nucleic acid molecules in a biological sample that, in total, is less than the sequence information of a human genome.

The term "test sample" refers to a sample comprising at least one nucleic acid or nucleic acid mixture comprising a nucleic acid sequence that will be screened for copy number variation, typically derived from biological fluids, cells, tissues, organs or organisms. In certain embodiments, the sample includes at least one nucleic acid sequence suspected to have mutated its copy number. These samples include but are not limited to saliva/saliva, amniotic fluid, blood, blood clots or fine needle biopsy samples (such as surgical biopsy, fine needle biopsy, etc.), urine, peritoneal fluid, pleural fluid, etc. Although samples are often taken from human subjects (such as patients), the test can be used for copy number variation (CNV) in samples from any mammals such as dogs, cats, horses, goats, sheep, cattle, pigs, etc. The sample can be used directly when obtained from a biological source, or after pretreatment to change sample characteristics. For example, the pretreatment can include preparing plasma from blood, diluting viscous fluids, etc. The pretreatment method can also include but is not limited to filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, interfering component inactivation, adding reagents, dissolving, etc. If these pretreatment methods are used on a sample, then these pretreatment methods typically result in one or more nucleic acids of interest being retained in the test sample preferably at a concentration proportional to the concentration in an untreated test sample (e.g., a sample that has not been subjected to any such pretreatment methods). For the purposes of the methods described herein, these "treated" or "processed" samples are still considered biological "test" samples.

Term " qualified sample " refers to the sample of the mixture of nucleic acid that comprises the known copy number that compares with the nucleic acid in the test sample at this, and for sequence of interest, this sample is a normal sample, i.e., not an aneuploid sample. In certain embodiments, qualified sample is used to identify one or more normalizing chromosomes or segments of the chromosome being considered. For example, qualified sample can be used to identify the normalizing chromosome of chromosome 21. In this case, qualified sample is a sample that is not a 21 trisomy sample. Qualified sample can also be used to determine the threshold value of the affected sample.

The term "training group" refers to a group of samples at this time, and they can include affected and unaffected samples and are used to develop a kind of model for analyzing test samples. Unaffected samples can be used as qualified samples to identify normalization sequences, such as normalization chromosomes, in the training group, and the chromosome dosage of unaffected samples is used to set a threshold value for each of these sequences of interest (such as chromosomes). These affected samples in a training group can be used to verify that affected test samples can be easily distinguished from unaffected samples.

The term "qualified nucleic acid" is used interchangeably with "qualified sequence," which is a sequence to which a test sequence or test nucleic acid is compared. A qualified sequence is a sequence that is preferably present in a biological sample at known expression (i.e., the amount of the qualified sequence is known). In general, a qualified sequence is a sequence that is present in a "qualified sample." A "qualified sequence of interest" is a qualified sequence whose amount in a qualified sample is known and that is associated with a difference in sequence expression in individuals with a medical condition.

The term "sequence of interest" herein refers to a nucleic acid sequence that is associated with a difference in sequence expression between healthy versus diseased individuals. A sequence of interest can be a sequence on a chromosome that is misexpressed, i.e., overexpressed or underexpressed, in a disease or genetic condition. A sequence of interest can be a portion of a chromosome (i.e., a chromosome segment), or a chromosome. For example, a sequence of interest can be a chromosome that is overexpressed in the case of aneuploidy, or a gene that encodes a tumor suppressor that is underexpressed in cancer. Sequences of interest include sequences that are overexpressed or underexpressed in the total population or subpopulation of cells of a subject. A "qualified sequence of interest" is a sequence of interest in a qualified sample. A "test sequence of interest" is a sequence of interest in a test sample.

The term "normalizing sequence" refers to a sequence that is used to normalize the number of sequence tags mapped to the sequence of interest associated with the normalizing sequence. In certain embodiments, the normalizing sequence shows the variability of the number of sequence tags mapped to the normalizing sequence in sample and sequencing rounds, and this variability is close to the variability of the sequence of interest used as a normalization parameter for the normalizing sequence, and can distinguish affected samples from one or more unaffected samples. In some implementations, compared with other potential normalizing sequences such as other chromosomes, this normalizing sequence is best or effectively distinguishes affected samples from one or more unaffected samples." normalizing chromosome" or "normalizing chromosome sequence" are examples of "normalizing sequence" and "normalizing chromosome sequence" can be made up of a single chromosome or a group of chromosomes. A "normalizing segment" is another example of a "normalizing sequence." A "normalizing segment sequence" can be composed of a single segment of a chromosome, or it can be composed of two or more segments of the same or different chromosomes. In certain embodiments, the normalizing sequence is used to normalize for variability such as process-related variability, variability between chromosomes (within the same round), and variability between sequencing runs (between runs).

The term "resolvability" herein refers to a characteristic of a normalizing chromosome that enables one or more unaffected (ie, normal) samples to be distinguished from one or more affected (ie, aneuploid) samples.

The term "sequence dosage" refers to a parameter associated with the number of sequence tags identified for a sequence of interest and the number of sequence tags identified for a normalizing sequence. In some cases, sequence dosage is the ratio of the number of sequence tags identified for a sequence of interest and the number of sequence tags identified for a normalizing sequence. In some cases, sequence dosage refers to a parameter associated with the sequence tag density of a sequence of interest and the tag density of a normalizing sequence." test sequence dosage " is a parameter that associates the sequence tag density of a sequence of interest (such as chromosome 21) with the sequence tag density of the normalizing sequence (such as chromosome 9) determined in a test sample. Similarly, a " qualified sequence dosage " is a parameter that associates the sequence tag density of a sequence of interest with the tag density of the normalizing sequence determined in a qualified sample.

The term "sequence tag density" herein refers to the number of sequence reads that are mapped to a reference genome sequence. For example, the sequence tag density for chromosome 21 is the number of sequence reads generated by a sequencing method that are mapped to chromosome 21 of the reference genome. The term "sequence tag density ratio" herein refers to the ratio of the number of sequence tags mapped to a chromosome of a reference genome (e.g., chromosome 21) to the length of the reference genome chromosome.

The term "next generation sequencing (NGS)" herein refers to sequencing methods that allow for massively parallel sequencing of clonally amplified molecules and single nucleic acid molecules. Non-limiting examples of NGS include sequencing by synthesis using reversible dye terminators, and sequencing by ligation.

The term "parameter" as used herein refers to a numerical relationship that characterizes a physical property. Often, a parameter numerically characterizes a quantitative data set and/or a numerical relationship between quantitative data sets. For example, the ratio (or a function of the ratio) between the number of sequence tags mapped to a chromosome and the length of the chromosome to which these tags are mapped is a parameter.

The term "threshold value" and "qualified threshold value" refer to any number of samples such as test samples containing nucleic acids from organisms suspected of having a medical condition as a cutoff. The threshold value can be compared with the parameter value to determine whether the sample producing the parameter value indicates that the organism suffers from the medical condition. In certain embodiments, the qualified threshold value is calculated using a qualified data set and serves as the boundary of copy number variations such as aneuploidy in the diagnosis organism. If the result obtained from the method disclosed herein exceeds a threshold value, the subject can be diagnosed with copy number variation, such as trisomy 21. The normalized value (such as chromosome dose, NCV or NSV) calculated by analyzing the sample of a training group can be identified for the appropriate threshold value of the method described here. Qualified (i.e., unaffected) samples in the training group including qualified (i.e., unaffected) samples and affected samples can be used to identify a threshold value. These samples (i.e., affected samples) in the training group known to have chromosomal aneuploidy can be used to confirm that the threshold values of these selections are useful (referring to these examples herein) in distinguishing affected samples from the unaffected samples in the test group. In some embodiments, the training group for identifying suitable threshold value comprises at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100, at least 200, at least 300, at least 400, at least 500, at least 600, at least 700, at least 800, at least 900, at least 1000, at least 2000, at least 3000, at least 4000 or more qualified samples. It may be advantageous to use the qualified sample of larger group to improve the diagnostic utility of threshold value.

The term "normalization value" herein refers to a numerical value that relates the number of sequence tags identified for a sequence of interest (e.g., a chromosome or chromosome segment) to the number of sequence tags identified for a normalizing sequence (e.g., a normalizing chromosome or a normalizing chromosome segment). For example, a "normalization value" can be a chromosome dose as described elsewhere in this application, or it can be an NCV (normalized chromosome value) as described elsewhere in this application, or it can be an NSV (normalized segment value) as described elsewhere in this application.

The term "read" refers to a sequence read from a portion of a nucleic acid sample. Typically, but not necessarily, a read represents a short sequence of adjacent base pairs in a sample. The read can be symbolically represented by a base pair sequence (ATCG) of the sample portion of the sample. The read can be stored in a storage device and processed as appropriate to determine whether the read matches a reference sequence or meets other indicators. The read can be obtained directly from a sequencing device or indirectly from stored sequence information of the relevant sample. In some cases, the term "read" refers to a sufficiently long (e.g., at least 30bp) DNA sequence that can be used to identify a larger sequence or region, such as a chromosome or a genomic region or a gene for comparison and targeted comparison.

The term "sequence tag" is used interchangeably with the term "mapped sequence tag" herein and refers to a sequence read that has been exactly assigned to (i.e., mapped to) a larger sequence (e.g., a reference genome) by comparison. The mapped sequence tags are uniquely mapped to the reference genome, i.e., they are assigned to a single position of the reference genome. The tag can be provided as a data structure or other data set. In certain embodiments, the tag includes relevant information about the read sequence and the read, such as the position of the sequence in the genome, such as the position on the chromosome. In certain embodiments, the position is described in a positive strand direction. The tag can be defined to provide a limited amount of mismatch when compared to the reference genome. Tags that can be mapped to more than one position in the reference genome (i.e., tags that are not uniquely mapped) may not be included in the analysis.

As used herein, the term "aligned, alignment or aligning" refers to comparing a reading or label with a reference sequence and determining thus whether the reference sequence comprises the reading sequence. If the reference sequence comprises the reading, the reading can be mapped to the reference sequence, or in certain embodiments, mapped to a specific position in the reference sequence. In some cases, the comparison simply tells whether the reading is a member of a specific reference sequence (i.e., whether the reading exists or does not exist in the reference sequence). For example, the reading is compared with the reference sequence of human chromosome 13, and it will be told whether the reading exists in the reference sequence of chromosome 13. The tool providing this information can be determined as a set membership tester. In some cases, the comparison indicates in addition the position where the reading or label is mapped in the reference sequence. For example, if the reference sequence is a full human genome sequence, the comparison can indicate that the reading is present on chromosome 13, and can further indicate that the reading is on the specific strand and/or site of chromosome 13.

The reading or label of comparison is identified as one or more sequences matched with the known sequence from the reference genome according to the order of its nucleic acid molecules.Comparison can be carried out manually, but comparison is typically realized by computer algorithm, because for realizing the method disclosed here, it is impossible to compare readings within a reasonable time. An example of an algorithm for comparing sequences is the effective local alignment of nucleotide data (ELAND) computer program, which is assigned as a part of the Illumina Genomics Analysis pipeline. As an alternative, a Bloom filter or similar set membership tester can be used for comparing readings with the reference genome. Referring to U.S. Patent Application No. 61/552,374, filed on October 27, 2011, which is incorporated herein by reference in its entirety. During comparison, the matching of sequence readings can be 100% sequence matching or less than 100% (non-ideal matching).

As used herein, the term "reference genome" or "reference sequence" refers to any specific known genomic sequence (whether partial or complete) of any organism or virus that can be used to compare identified sequences from a subject. For example, reference genomes for human subjects, along with many other organisms, are available at the National Center for Biotechnology Information at www.ncbi.nlm.nih.gov . "Genome" refers to the complete genetic information of an organism or virus, as expressed in nucleic acid sequences.

In various embodiments, the reference sequence is significantly larger than the read to which it is aligned. For example, it can be at least about 100 times larger, or at least about 1000 times larger, or at least about 10,000 times larger, or at least about 10 ⁵ times larger, or at least about 10 ⁶ times larger, or at least about 10 ⁷ times larger.

In one example, the reference sequence is the sequence of the full-length human genome. These sequences can be referred to as genomic reference sequences. In another example, the reference sequence is limited to a specific human chromosome, such as chromosome 13. These sequences can be referred to as chromosome reference sequences. Other examples of reference sequences include genomes of other species, as well as chromosomes, subchromosomal regions (e.g., strands), and the like of any species.

In various embodiments, the reference sequence is a common sequence or other combination derived from multiple individuals. However, in some applications, the reference sequence may be taken from a specific individual.

The term "artificial target sequence genome" herein refers to a known sequence group encompassing alleles of a known polymorphic site. For example, a "SNP reference genome" is an artificial target sequence genome comprising a sequence group encompassing alleles of a known SNP.

The term "clinically relevant sequence" herein refers to a nucleic acid sequence that is known or suspected to be associated with or implicated in a genetic or disease condition. Determining the presence or absence of a clinically relevant sequence can be useful in determining or confirming a diagnosis of a medical condition, or in providing a prediction for the development of a disease.

When the term "derived" is used in the context of a nucleic acid or a mixture of nucleic acids, it refers to the manner in which the nucleic acid(s) are obtained from the source from which the nucleic acid(s) originate. For example, in one embodiment, a mixture of nucleic acids derived from two different genomes means that the nucleic acids (e.g., cfDNA) are naturally released by cells through naturally occurring processes such as necrosis or apoptosis. In another embodiment, a mixture of nucleic acids derived from two different genomes means that the nucleic acids are extracted from two different types of cells from a single subject.

The term "patient sample" herein refers to a biological sample obtained from a patient (i.e., a recipient of medical assistance, care, or treatment). The patient sample can be any sample described herein. In certain embodiments, the patient sample is obtained by a non-invasive procedure, such as a peripheral blood sample or a fecal sample. The methods described herein are not necessarily limited to humans. Therefore, different veterinary applications are encompassed, in which case the patient sample can be a sample from a non-human mammal (e.g., a cat, pig, horse, cow, etc.).

The term "mixed sample" herein refers to a sample containing a mixture of nucleic acids derived from different genomes.

The term "maternal sample" as used herein refers to a biological sample obtained from a pregnant subject (eg, a female).

The term "biological fluid" herein refers to a liquid obtained from a biological source and includes, for example, blood, serum, plasma, saliva, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears, saliva, and the like. As used herein, the terms "blood," "plasma," and "serum" expressly encompass fractions or processed portions thereof. Similarly, in the case where the sample is obtained from a biopsy, swab, smear, or the like, "sample" expressly encompasses the processed fractions or portions derived from the biopsy, swab, smear, or the like.

The terms "maternal nucleic acid" and "fetal nucleic acid" as used herein refer to the nucleic acid of a pregnant female subject and the nucleic acid of a fetus carried by the pregnant female, respectively.

As used herein, the term "corresponding to" sometimes refers to a nucleic acid sequence such as a gene or chromosome that is present in the genomes of different subjects and does not necessarily have the same sequence in all genomes, but is used to provide the identity of the sequence of interest, such as a gene or chromosome, rather than genetic information.

As used herein, the term "substantially cell-free" encompasses a desired sample preparation in which cellular components normally associated with the desired sample are removed. For example, a plasma sample can be rendered substantially cell-free by removing blood cells, such as red blood cells, that are normally associated with plasma. In certain embodiments, the substantially cell-free sample is processed to remove cells that would otherwise interfere with the desired genetic material to be tested for CNVs.

As used herein, the term "fetal fraction" refers to the fraction of fetal nucleic acid present in a sample that includes fetal and maternal nucleic acid. Fetal fraction is often used to characterize cfDNA in maternal blood.

As used herein, the term "chromosome" refers to the genetic carrier of inheritance in living cells, which is derived from chromatin and includes DNA and protein components (especially histones). The internationally recognized conventional individual human genome chromosome numbering system is used herein.

As used herein, the term "polynucleotide length" refers to the absolute number of nucleic acid molecules (nucleotides) in a sequence or region of a reference genome. The term "chromosome length" refers to the known chromosome length in base pairs, such as provided in the NCBI36/hg18 collection of human chromosomes available on the World Wide Web at genome.ucsc.edu/cgi-bin/hgTracks?hgsid=167155613&chromInfoPage=.

The term "subject" herein refers to human subjects as well as non-human subjects, such as mammals, invertebrates, vertebrates, fungi, yeast, bacteria, and viruses. Although the examples herein involve humans and the language is primarily directed to human issues, the concepts disclosed herein are applicable to genomes from any plant or animal and are applicable to fields such as veterinary medicine, animal husbandry, research laboratories, and the like.

The term "condition" is used herein to refer to a "medical condition" as a broad term that includes all diseases and disorders and may also include [injuries] and normal health conditions such as pregnancy, which may affect a person's health, benefit from medical attention or have implications for medical treatment.

The term "complete" is used herein in reference to chromosomal aneuploidy and refers to the gain or loss of an entire chromosome.

The term "partial," when used in reference to a chromosomal aneuploidy, refers herein to the gain or loss of a portion (i.e., a segment) of a chromosome.

The term "chimerism" as used herein refers to the presence of two cell populations with different karyotypes within an individual that develops from a single fertilized egg. Chimerism can arise from a mutation that propagates during development to only a subset of adult cells.

The term "non-chimeric" as used herein refers to an organism comprising cells having one karyotype, such as a human fetus.

The term "using a chromosome" when used in reference to determining a chromosome dose refers herein to using the sequence information obtained for the chromosome, ie, the number of sequence tags obtained for the chromosome.

The term "sensitivity" as used herein is equal to the number of true positives divided by the sum of true positives and false negatives.

As used herein, the term "specificity" is equal to the number of true negatives divided by the sum of true negatives and false positives.

The term "hypodiploid" herein refers to a chromosome number that is one or more less than the normal haploid number of chromosomes characteristic of the species.

A "polymorphic site" is a locus at which nucleotide sequence variation occurs. A locus can be as small as a single base pair. An exemplary marker has at least two alleles, each occurring at a frequency greater than 1% of a selected population, and more typically greater than 10% or 20%. A polymorphic site can be a site of a single nucleotide polymorphism (SNP), a small multi-base deletion or insertion, a multiple nucleotide polymorphism (MNP), or a short tandem repeat (STR). The terms "polymorphic locus" and "polymorphic site" are used interchangeably herein.

"Polymorphic sequence" herein refers to a nucleic acid sequence, such as a DNA sequence, that includes one or more polymorphic sites (e.g., a SNP or a tandem SNP). Polymorphic sequences according to the present technology can be used to specifically distinguish maternal from non-maternal alleles in a maternal sample that includes a mixture of fetal and maternal nucleic acids.

As used herein, " single nucleotide polymorphism " (SNP) occurs on the polymorphic site occupied by a single nucleotide, and this site is the site that mutates between the sequence of allele.This site is usually preceded and followed by a highly conserved sequence of alleles (for example, a sequence that varies in less than 1/100 or 1/1000 members of a population). SNP is usually caused by the replacement of one nucleotide by another nucleotide on the polymorphic site. Conversion is that one purine is replaced by another purine or one pyrimidine is replaced by another pyrimidine. Transversion is that a purine is replaced by a pyrimidine or a pyrimidine is replaced by a purine. SNP can also be caused by nucleotide deletion or nucleotide insertion relative to a reference allele. Single nucleotide polymorphism (SNP) is the situation that two alternative bases occur with considerable frequency (>1%) in the human population, and is the most common type of human genetic variation.

The term "tandem SNPs" herein refers to two or more SNPs present within a polymorphic target nucleic acid sequence.

As used herein, the term "short tandem repeat" or "STR" refers to a class of polymorphisms that occur when a pattern of two or more nucleotides repeats, with the repeated sequences directly adjacent to each other. The pattern can range from 2 to 10 base pairs (bp) in length (e.g., (CATG) _n in genomic regions) and is typically in non-coding intronic regions. By examining several STR loci and counting how many times a particular STR sequence is repeated at a given locus, it is possible to create a unique genetic profile for an individual.

As used herein, the term "miniSTR" refers to four or more base pair tandem repeats that span less than about 300 base pairs, less than about 250 base pairs, less than about 200 base pairs, less than about 150 base pairs, less than about 100 base pairs, less than about 50 base pairs, or less than about 25 base pairs. A "miniSTR" is an STR that can be amplified from a cfDNA template.

The terms "polymorphic target nucleic acid," "polymorphic sequence," "polymorphic target nucleic acid sequence," and "polymorphic nucleic acid" are used interchangeably herein to refer to a nucleic acid sequence (eg, a DNA sequence) that includes one or more polymorphic sites.

The term "multiple polymorphic target nucleic acids" herein refers to a large number of nucleic acid sequences, each comprising at least one polymorphic site (e.g., a SNP), such that 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40 or more different polymorphic sites are amplified from the polymorphic target nucleic acid to identify and/or quantify fetal alleles present in a maternal sample comprising fetal and maternal nucleic acids.

The term "enrichment" herein refers to a process in which a polymorphic target nucleic acid contained in a portion of a maternal sample is amplified and the amplified product is combined with the remainder of the maternal sample from which the portion was removed. For example, the remainder of the maternal sample can be the original maternal sample.

The term "original maternal sample" herein refers to a non-enriched biological sample obtained from a pregnant subject (e.g., a woman) that serves as a source from which a portion is removed to amplify polymorphic target nucleic acids. A "original sample" can be any sample obtained from a pregnant subject and its processed portion, such as a purified cfDNA sample extracted from a maternal plasma sample.

As used herein, the term "primer" refers to an isolated oligonucleotide that can serve as a starting point for synthesis when placed under conditions that induce the synthesis of primer extension products that are complementary to nucleic acid strands (i.e., in the presence of nucleotides and an initiator such as DNA polymerase and at a suitable temperature and pH). For maximum efficiency amplification, the primer is preferably single-stranded, but as an alternative, can be double-stranded. If double-stranded, the primer is first treated to separate its strands before being used to prepare extension products. The primer is preferably an oligodeoxyribonucleotide. The primer must be long enough to induce extension product synthesis in the presence of an initiator. The exact length of the primer will depend on many factors, including temperature, primer source, method use, and parameters for primer design.

The phrase "cause of action" refers to an action taken by a medical professional (e.g., a physician) or a person who controls or directs the medical care of a subject to control and/or authorize the administration of the one or more agents/compounds at issue to the subject. Administration can include diagnosis and/or determination of an appropriate treatment or prevention regimen, and/or prescribing a specific agent/compound for the subject. This prescribing can include, for example, drafting a prescription composition, writing a medical record card, and the like. Similarly, for example, a "cause of action to be performed" of a diagnostic procedure refers to an action taken by a medical professional (e.g., a physician) or a person who controls or directs the medical care of a subject to control and/or authorize the performance of one or more diagnostic protocols on the subject.

introduction

Disclosed herein are methods, devices, systems, and test kits for determining the copy number variation (CNV) of different sequences of interest in a test sample, wherein the test sample comprises a mixture of nucleic acids derived from two different genomes and known or suspected to have different amounts of one or more sequences of interest. Also provided are methods, devices, systems, and test kits for determining the score contributed by two genomes in a nucleic acid mixture. The copy number variation determined by the methods and devices disclosed herein includes a large amount of submicroscopic copy number variations of DNA fragments ranging from kilobases (kb) to megabases (Mb) in size, including acquisition or loss of whole chromosomes, variations in extremely large chromosome segments visible under a microscope, and sizes ranging from kilobases (kb) to megabases (Mb). In various embodiments, these methods include a statistical method implemented by a machine, which illustrates the variability of the natural increase caused by the variability associated with the process, the variability between chromosomes, and the variability between sequences. The method is applicable to determining the CNV of any fetal aneuploidy, and the CNVs known or suspected to be relevant to various medical conditions. CNVs that can be determined using the methods of the present invention include trisomies and monosomies of any one or more of chromosomes 1 to 22, X, and Y, other chromosomal polysomies, and deletions and/or duplications of segments of any one or more chromosomes, which can be detected by sequencing the nucleic acid of the test sample only once. Any aneuploidy can be determined from the sequencing information obtained by sequencing the nucleic acid of the test sample only once.

CNVs in the human genome significantly influence human diversity and susceptibility to disease (Redon et al., Nature 23:444-454 [2006], Shaikh et al. Genome Res (Genome Research) 19: 1682-1690 [2009]. It is known that CNV constitutes genetic diseases through different mechanisms, resulting in gene dosage imbalance or gene destruction in most cases. In addition to being directly related to hereditary disorders, it is also known that CNV mediation can be harmful phenotypic changes. Recently, several studies have reported that, as compared with normal controls, in complex disorders, such as autism, ADHD (attention deficit hyperactivity disorder) and schizophrenia, the increased burden of rare or new CNVs has highlighted the potential pathogenicity of rare or unique CNVs (Sebat et al., 316: 445-449 [2007]; Walsh et al., Science 320: 539-543 [2008]. CNVs from genomic rearrangements rise, mainly because of deletions, duplications, insertions and unbalanced translocation events.

The methods, devices or apparatus described herein can be used to perform large-scale parallel sequencing of the next generation sequencing technology (NGS). In certain embodiments, the clonally amplified DNA template or single DNA molecule is sequenced in a large-scale parallel manner within a flow tank (e.g., as described in Volkerding et al., Clin Chem (Clinical Chemistry) 55: 641-658 [2009]; Metzker (Metzker) M, Nature Rev (Natural Review) 11: 31-46 [2010]). In addition to high-throughput sequence information, NGS provides quantitative information, wherein each sequence read is a calculable "sequence tag" that represents an individual clone DNA template or single DNA molecule. The sequencing technology of NGS includes pyrophosphate sequencing, sequencing by synthesis with reversible dye terminators, sequencing by oligonucleotide probe ligation, and ion semiconductor sequencing. DNA from separate samples can be sequenced individually (i.e., singleplex sequencing), or DNA from multiple samples can be pooled together and sequenced as index genomic molecules in a single sequencing run (i.e., multiplex sequencing) to generate up to hundreds of millions of reads of DNA sequence. The following describes examples of sequencing technologies that can be used to obtain sequence information according to the methods of the present invention.

In some embodiments, the methods and apparatus disclosed herein may employ some or all of the following sequence of operations: obtaining a nucleic acid test sample from a patient (typically by non-invasive procedures); processing the test sample in preparation for sequencing; sequencing the nucleic acid from the test sample to produce a large number of reads (e.g., at least 10,000); aligning the reads to a portion of a reference sequence/genome and determining the amount of DNA (e.g., the number of reads) that maps to a defined portion of the reference sequence (e.g., a defined chromosome or chromosome segment); calculating a dose for one or more defined portions by normalizing the amount of DNA mapped to the defined portion with the amount of DNA mapped to one or more normalizing chromosomes or chromosome segments selected for the defined portion; determining whether the dose indicates that the defined portion is "affected" (e.g., aneuploidy or mosaicism); reporting the determination and optionally converting it into a diagnosis; using the diagnosis or determination to develop a plan to treat, monitor, or further test the patient.

Determine the normalizing sequence in qualified samples: normalizing chromosome sequence and normalizing segment sequence

Normalizing sequences are identified using a set of qualified samples from subjects known to include a normal copy number of any sequence of interest (e.g., a chromosome or segment thereof). The determination of normalizing sequences is outlined in steps 110, 120, 130, 140, and 145 of the embodiment of the method depicted in FIG1 . The sequence information obtained from the qualified samples is used to statistically meaningfully identify chromosomal aneuploidies in the test sample ( FIG1 step 165 and examples).

Fig. 1 provides a flow chart 100 of an embodiment for determining CNV of a sequence of interest such as a chromosome or its segment in a biological sample. In some embodiments, a biological sample is obtained from a subject, and the sample includes a mixture of nucleic acids consisting of different genomes. Different genomes can be formed by samples of two individuals, such as a fetus and a mother carrying the fetus. Alternatively, a genome can be formed by a sample of aneuploid cancer cells and normal euploid cells from the same subject (e.g., a plasma sample from a cancer patient).

In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.In some embodiments, the method for the present invention can be used to determine the normalization chromosome of the chromosome of interest.

Obtain a set of qualified samples to identify qualified normalized sequences, and to provide variation values for determining statistically significant identification of CNVs in test samples. In step 110, multiple biological qualified samples are obtained from multiple subjects, and it is known that these subjects include cells with normal copy numbers of any one sequence of interest. In one embodiment, qualified samples are obtained from a mother pregnant with a fetus, and chromosomes with normal copy numbers have been confirmed using cytogenetic means. Biological qualified samples can be a biological fluid, such as plasma, or any suitable sample as described below. In some embodiments, qualified samples contain a mixture of nucleic acid molecules (such as cfDNA molecules). In some embodiments, qualified samples are maternal plasma samples containing a mixture of fetal and maternal cfDNA molecules. By using any known sequencing method, at least a portion of these nucleic acids (such as fetal and maternal nucleic acids) is sequenced to obtain sequence information of normalized chromosomes and/or a portion thereof. Preferably, any of the next generation sequencing (NGS) methods described elsewhere in this application is used to sequence fetal and maternal nucleic acids as single or clone-amplified molecules. In various embodiments, qualified samples are processed as described below before and during sequencing. These samples can be processed using the apparatus, systems, and kits disclosed herein.

At step 120, at least a portion of each of all qualified nucleic acids contained within the qualified samples is sequenced to generate millions of sequence reads, e.g., 36 bp reads, which are aligned to a reference genome, e.g., hg 18. In some embodiments, the sequence reads comprise about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp, about 130 bp, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, or about 500 bp. It is expected that technological advances will enable single-end reads greater than 500bp, which, when generating paired-end reads, will enable reads greater than about 1000bp. In one embodiment, the mapped sequence reads comprise 36bp. In another embodiment, the mapped sequence reads comprise 25bp. Sequence reads aligned to a reference genome, as well as reads uniquely mapped to a reference genome, are known as sequence tags. In one embodiment, at least about 3 x 10 ⁶ qualified sequence tags, at least about 5 x 10 ⁶ qualified sequence tags, at least about 8 x 10 6 qualified sequence tags, at least about 10 x 10 ⁶ qualified sequence tags, at least about 15 x 10 ⁶ qualified sequence tags, at least about 20 x 10 ⁶ qualified sequence tags, at least about 30 x 10 ⁶ qualified sequence tags, at least about 40 x 10 ⁶ qualified sequence tags, or at least about 50 x 10 ⁶ qualified sequence tags comprising between 20 and 40 bp reads ^are obtained from reads that are uniquely aligned to a reference genome.

In step 130, counting derives from all labels of the nucleic acid in the order-checking qualified sample, to determine qualified sequence label density.In one embodiment, sequence label density is determined as reference corresponding to these multiple qualified sequence labels of the sequence interested on reference genome.In another embodiment, qualified sequence label density is for being determined as these multiple qualified sequence labels mapped to the sequence interested, is normalized to the length of the qualified sequence interested that they map.The sequence label density that is determined as the ratio of label density relative to the length of the sequence interested is referred to as label density ratio at this.Do not need to be normalized to the length of the sequence interested, and can be included as a step, reduce the digit in a number, simplify it and be used for artificial explanation.All qualified sequence labels are mapped and counted to each qualified sample, and the sequence label density of the sequence interested in qualified sample (for example, clinically relevant sequence) is determined, and the sequence label density of sequential identification extra sequence (normalization sequence is from it) is determined simultaneously.

In certain embodiments, sequence of interest is the chromosome associated with complete chromosome aneuploidy, for example chromosome 21, and qualified normalizing sequence is not associated with chromosome aneuploidy and the variation of sequence label density is close to the complete chromosome of sequence of interest (i.e. chromosome) such as for example chromosome 21. Selected normalizing chromosome can be a chromosome or a group of chromosome that the sequence label density changes closest to sequence of interest. Any one or more in chromosome 1-22, X and Y can be sequence of interest, and these one or more chromosomes can be identified as the normalizing sequence of each in any chromosome 1-22, X, Y in qualified samples. Normalizing chromosome can be independent chromosome, or it can be a group of chromosome described in other places of the application.

In another embodiment, the sequence of interest is the chromosome segment associated with partial aneuploidy (such as chromosome deletion or insertion or unbalanced chromosome translocation), and the normalizing sequence is not associated with partial aneuploidy and the variation of sequence tag density is close to a chromosome segment (or one group of segment) of the chromosome segment associated with partial aneuploidy. Selected normalizing chromosome segment can be one or more chromosome segments that the sequence tag density changes closest to the sequence of interest. Any one or more segments of any one or more chromosomes 1-22, X and Y can be a sequence of interest.

In other embodiments, the sequence of interest is a chromosome segment associated with a partial aneuploidy, and the normalizing sequence is a full chromosome or multiple full chromosomes. In yet other embodiments, the sequence of interest is a full chromosome associated with an aneuploidy and the normalizing sequence is a chromosome segment or multiple chromosome segments not associated with the aneuploidy.

No matter single sequence or one group of sequence identification is the normalizing sequence of any one or more sequences interested in qualified samples, it is possible to select the sequence tag density variation to be closest or effectively close to the qualified normalizing sequence of the sequence interested determined in qualified samples.For example, qualified normalizing sequence is when being used to normalize sequence interested, produces the sequence of minimum variability between qualified samples, i.e. the variability of normalizing sequence is closest to the variability of the sequence interested determined in qualified samples. In other words, qualified normalizing sequence is the sequence selected as the minimum variation of sequence dosage (sequence interested) between qualified samples. Therefore, this process selection, when being used as normalizing chromosome, is expected to produce the sequence of minimum variability in the chromosome dosage between different batches of sequence interested.

The normalizing sequence identified for any one or more sequences of interest in qualified samples is maintained to be selected for determining the presence or absence of aneuploidy in the test sample for a normalizing sequence of up to several days, weeks, months, and possibly several years, with the condition that the program needs to produce a sequencing library, and the sequencing performed on the sample is substantially unchanged over time. As described above, the normalizing sequence for determining the presence of aneuploidy is selected because the variability of the number of sequence tags mapped to it between samples (such as different samples) and sequencing rounds (such as sequencing rounds performed on the same day and/or different days) is closest to the variability (and possible other reasons) of the sequence of interest using it as a normalization parameter. The substantial changes of these programs will affect the number of labels mapped to all sequences, thereby again determining which or which group of sequences in identical and/or different sequencing rounds, on the same day or on different days, the variability between samples is closest to the variability of the sequence of interest, and this will need to determine the group of normalizing sequence again. Substantial changes to procedures include changes in laboratory protocols used to prepare sequencing libraries, including changes related to preparing samples for multiplexed rather than singleplex sequencing, and changes in sequencing platforms, including changes in the chemistry used for sequencing.

In some embodiments, the normalizing sequence is a sequence that best distinguishes one or more qualified samples from one or more affected samples, which means that the normalizing sequence is a sequence with maximum solvability, that is, the solvability of the normalizing sequence is such that optimal differentiation is provided to the sequence of interest in the affected test sample for easily distinguishing the affected test sample from other unaffected samples. In other embodiments, the normalizing sequence is a sequence with a combination of minimal variability and maximum solvability.

The level of distinguishability can be determined as the statistical difference between the sequence dose (such as chromosome dose or segment dose) in a group of qualified samples and the one or more chromosome doses in one or more test samples, as described below and shown in these examples. For example, distinguishability can be digitally represented as a T test value, which represents the statistical difference between the chromosome dose in a group of qualified samples and the one or more chromosome doses in one or more test samples. z-score for chromosome doses as long as the distribution for the NCV is normal. _<}0{> Alternatively, distinguishability can be digitally represented as a normalized chromosome value (NCV), as long as the distribution of NCV is normal, it is the z score of chromosome dose. Similarly, distinguishability can be digitally represented as a T test value, which represents the statistical difference between the segment dose in a group of qualified samples and the one or more segment doses in one or more test samples. In the case where a chromosome segment is a sequence of interest, the distinguishability of the segment dose can be digitally represented as a normalized segment value (NSV), which is the z score of the chromosome segment dose, as long as the distribution of NSV is normal. In determining z score, the mean value and standard deviation of the chromosomal or segmental dosage in one group of qualified samples can be used. Alternatively, the mean value and standard deviation of the chromosomal or segmental dosage in a training group comprising qualified samples and affected samples can be used. In other embodiments, the normalizing sequence is the sequence with the best combination of minimum variability and maximum resolvability or small variability and large resolvability.

This method identifies sequences that inherently have similar characteristics and tend to have similar variations between samples and sequencing runs, and it is useful for determining sequence dosage in test samples.

Determine sequence doses (i.e., chromosome doses or segment doses) in qualified samples

At step 140, based on the calculated qualified tag density, a qualified sequence dose (i.e., chromosome dose or segment dose) for the sequence of interest is determined as the ratio of the sequence tag density for the sequence of interest and the qualified sequence tag density for an additional sequence (a normalizing sequence from which is subsequently identified at step 145). The identified normalizing sequence is then used to determine the sequence dose in the test sample.

In one embodiment, the sequence dosage in qualified samples is a chromosome dosage, which is calculated as the ratio of the sequence tag number of the chromosome of interest and the sequence tag number of the normalized chromosome sequence in qualified samples. Normalized chromosome sequence can be a single chromosome, a group of chromosomes, a segment of a chromosome or a group of segments from different chromosomes. Therefore, the chromosome dosage of the chromosome of interest in the sample is determined as: the ratio of these multiple labels of the chromosome of interest and the multiple labels of the normalized chromosome sequence consisting of a single chromosome, (ii) the ratio of the number of labels for the chromosome of interest and the number of labels for the normalized chromosome sequence including two or more chromosomes; (iii) the ratio of the number of labels for the chromosome of interest and the number of labels for the normalized segment sequence including a single segment of a chromosome; (iv) the ratio of the number of labels for the chromosome of interest and the number of labels for the normalized segment sequence including two or more segments from a chromosome; or (v) the ratio of the number of labels for the chromosome of interest and the number of labels for the normalized segment sequence including two or more segments of two or more chromosomes. According to (i)-(v), examples for determining the chromosome dose of a chromosome of interest are as follows: the chromosome dose of a chromosome of interest (e.g., chromosome 21) is determined as the ratio of the sequence tag density of chromosome 21 to the sequence tag density of each of all remaining chromosomes (i.e., chromosomes 1-20, chromosome 22, chromosome X, and chromosome Y); (i) the chromosome dose of a chromosome of interest (e.g., chromosome 21) is determined as the ratio of the sequence tag density of chromosome 21 to the sequence tag density of all possible combinations of two or more remaining chromosomes; (ii) the chromosome dose of a chromosome of interest (e.g., chromosome 21) is determined as the ratio of the sequence tag density of chromosome 21 to the sequence tag density of a segment of another chromosome (e.g., chromosome 9); (iii) the chromosome dose of a chromosome of interest (e.g., chromosome 21) is determined as the ratio of the sequence tag density of chromosome 21 to the sequence tag density of a segment of another chromosome (e.g., chromosome 9); The chromosome dose of is determined as the ratio of the sequence tag density of chromosome 21 to the sequence tag density of two segments of another chromosome (e.g., two segments of chromosome 9); (iv) and the chromosome dose of the chromosome of interest (e.g., chromosome 21) is determined as the ratio of the sequence tag density of chromosome 21 to the sequence tag density of two segments of two different chromosomes (e.g., a segment of chromosome 9 and a segment of chromosome 14).

In another embodiment, the sequence dose in qualified samples is a segment dose, which is calculated as the ratio of the number of sequence tags for the segment of interest of non-whole chromosomes in qualified samples to the number of sequence tags for normalizing segment sequences. Normalizing segment sequences can be, for example, a full chromosome, a group of full chromosomes, a segment of a chromosome, or a group of segments from different chromosomes. For example, in qualified samples, the segment dose of a segment of interest is determined as the ratio of the multiple labels of the multiple labels of the normalizing segment sequence consisting of a single segment of a chromosome of (i) the segment of interest, (ii) the multiple labels of the segment of interest and the multiple labels of the normalizing segment sequence consisting of two or more segments of a chromosome, or (iii) the multiple labels of the segment of interest and the multiple labels of the normalizing segment sequence consisting of two or more segments of two or more chromosomes.

The chromosome doses for one or more chromosomes of interest are determined in all qualified samples, and a normalizing chromosome sequence is identified in step 145. Similarly, the segment doses for one or more segments of interest are determined in all qualified samples, and a normalizing segment sequence is identified in step 145.

Identifying normalized sequences from qualified sequence doses

In step 145, based on the calculated sequence doses, a normalizing sequence for the sequence of interest is identified, e.g., a sequence that minimizes the variability of the sequence dose for the sequence of interest across all qualified samples. This method identifies sequences that inherently have similar characteristics and tend to have similar variations across samples and sequencing runs, and is useful for determining sequence doses in test samples.

In one group of qualified samples, the normalizing sequence of one or more sequences of interest can be identified, and the sequence identified in the qualified samples can be subsequently used to calculate the sequence dosage (step 150) of the one or more sequences of interest in each test sample, to determine the presence or absence of aneuploidy in each test sample. When using different sequencing platforms, and/or when there are differences in the purification of nucleic acid to be sequenced and/or the preparation of sequencing libraries, for chromosome of interest or segment, the normalizing sequence identified can be different. According to the method described herein, using normalizing sequences for the copy number variation of chromosome or its segment provides dedicated and sensitive measurement, no matter how sample preparation and/or the sequencing platform used.

In some embodiments, identification is more than one normalizing sequence, that is, different normalizing sequences can be determined to a sequence interested, and a sequence interested can be determined to multiple sequence dosages.For example, when using the sequence tag density of chromosome 14, the variation (such as coefficient of variation) in the chromosome dosage of chromosome 21 interested is minimum. However, two, three, four, five, six, seven, eight or more normalizing sequences can be identified, for use in determining the sequence dosage of sequence interested in a test sample. As an example, chromosome 7, chromosome 9, chromosome 11 or chromosome 12 can be used as the normalizing chromosome sequence, determine the second dosage of the chromosome 21 in any one test sample, because these chromosomes all have the CV (see example 8 tables 10) close to the CV of chromosome 14. Preferably, when selecting a single chromosome as the normalizing chromosome sequence of a chromosome interested, the normalizing chromosome will be a chromosome, and this chromosome causes the chromosome dosage of a chromosome interested to have the minimum variability across all test samples (such as qualified samples).

Normalized chromosome sequence as the normalized sequence of chromosome

In some embodiments, the normalizing chromosome sequence is a set of chromosomes that are identified as one or more normalizing sequences of chromosome 1-22, X and Y. In other embodiments, the normalizing chromosome sequence is a set of chromosomes that are identified as one or more normalizing sequences of chromosome 1-22, X and Y, for example, one set of chromosome. This set of chromosomes that constitutes the normalizing sequence (i.e. normalizing chromosome sequence) of chromosome interested can be one set of two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one or twenty-two chromosomes, and include or exclude one or the two in chromosome X and Y. > This set of chromosomes that are identified as the normalizing chromosome sequence is such a set of chromosomes, and they cause the chromosome dosage of chromosome interested to have the minimum variability across whole test samples (i.e. qualified samples). Preferably, test together independent or multiple sets of chromosomes, for the ability of their best simulation sequence interested, select them as the normalizing chromosome sequence for this reason.

In one embodiment, the normalizing sequence for chromosome 21 is selected from the group consisting of chromosome 9, chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 10, chromosome 11, chromosome 12, chromosome 13, chromosome 14, chromosome 15, chromosome 16, and chromosome 17. In another embodiment, the normalizing sequence for chromosome 21 is selected from the group consisting of chromosome 9, chromosome 1, chromosome 2, chromosome 11, chromosome 12, and chromosome 14. Alternatively, the normalizing sequence for chromosome 21 is a group of chromosomes selected from the group consisting of chromosome 9, chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 10, chromosome 11, chromosome 12, chromosome 13, chromosome 14, chromosome 15, chromosome 16, and chromosome 17. In another embodiment, the group of chromosomes is a group selected from the group consisting of chromosome 9, chromosome 1, chromosome 2, chromosome 11, chromosome 12, and chromosome 14.

In some embodiments, further improve the method by using normalizing sequence, by individually and in all possible combinations with all remaining chromosomes, use the system calculation of the whole chromosome dosage of each chromosome to determine normalizing sequence (referring to example 13).For example, by using any one in chromosome 1-22, X and Y, and two or more combinations in chromosome 1-22, X and Y to determine which single or grouped chromosome is a normalizing chromosome, this normalizing chromosome causes the minimum variability of the chromosome dosage of the chromosome interested across one group of qualified samples, thus system calculation all possible chromosomes, can determine the normalizing chromosome (referring to example 13) that system determines to each chromosome interested.Therefore, in one embodiment, the normalizing sequence of the system calculation of chromosome 21 is a group of chromosome consisting of chromosome 4, chromosome 14, chromosome 16, chromosome 20, and chromosome 22.To all chromosomes in genome, can determine single or grouped chromosome.

In one embodiment, the normalizing sequence for chromosome 18 is selected from the group consisting of chromosome 8, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 13, and chromosome 14. Preferably, the normalizing sequence for chromosome 18 is selected from the group consisting of chromosome 8, chromosome 2, chromosome 3, chromosome 5, chromosome 6, chromosome 12, and chromosome 14. In one embodiment, the normalizing sequence for chromosome 18 is a group of chromosomes selected from the group consisting of chromosome 8, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 13, and chromosome 14. Preferably, the group of chromosomes is a group selected from the group consisting of chromosome 8, chromosome 2, chromosome 3, chromosome 5, chromosome 6, chromosome 12, and chromosome 14.

In another embodiment, the normalizing sequence for chromosome 18 is determined by systematically calculating all possible chromosome doses using each possible normalizing chromosome individually and in all possible combinations of normalizing chromosomes (as explained elsewhere in this application). Thus, in one embodiment, the normalizing sequence for chromosome 18 is a normalizing sequence consisting of a set of chromosomes consisting of chromosome 2, chromosome 3, chromosome 5, and chromosome 7.

In one embodiment, the normalizing sequence for chromosome X is selected from chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 13, chromosome 14, chromosome 15, and chromosome 16. Preferably, the normalizing sequence for chromosome X is selected from chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, and chromosome 8. In one embodiment, the normalizing sequence for chromosome X is a group of chromosomes selected from chromosome 1, chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 13, chromosome 14, chromosome 15, and chromosome 16. Preferably, the group of chromosomes is a group selected from chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, and chromosome 8.

In another embodiment, the normalizing sequence for chromosome X is determined by systematically calculating all possible chromosome doses using each possible normalizing chromosome individually and in all possible combinations of normalizing chromosomes (as explained elsewhere in this application). Thus, in one embodiment, the normalizing sequence for chromosome X is the normalizing chromosome consisting of the group of chromosomes 4 and chromosome 8.

In one embodiment, the normalizing sequence for chromosome 13 is a chromosome selected from chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 14, chromosome 18, and chromosome 21. Preferably, the normalizing sequence for chromosome 13 is a chromosome selected from chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, and chromosome 8. In another embodiment, the normalizing sequence for chromosome 13 is a group of chromosomes selected from chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, chromosome 7, chromosome 8, chromosome 9, chromosome 10, chromosome 11, chromosome 12, chromosome 14, chromosome 18, and chromosome 21. Preferably, the group of chromosomes is a group selected from chromosome 2, chromosome 3, chromosome 4, chromosome 5, chromosome 6, and chromosome 8.

In another embodiment, the normalizing sequence for chromosome 13 is determined by systematically calculating all possible chromosome doses using each possible normalizing chromosome individually and all possible combinations of normalizing chromosomes (as explained elsewhere in this application). Thus, in one embodiment, the normalizing sequence for chromosome 13 is a normalizing chromosome comprising the group of chromosomes 4 and 5. In another embodiment, the normalizing sequence for chromosome 13 is a normalizing chromosome consisting of the group of chromosomes 4 and 5.

Independent of which normalizing chromosome is used in determining the chromosome Y dose, the variation in chromosome dose for chromosome Y is greater than 30. Thus, a group of two or more chromosomes selected from chromosomes 1-22 and chromosome X can be used as the normalizing sequence for chromosome Y. In one embodiment, at least one normalizing chromosome is a group of chromosomes consisting of chromosomes 1-22 and chromosome X. In another embodiment, the group of chromosomes consists of chromosome 2, chromosome 3, chromosome 4, chromosome 5, and chromosome 6.

In another embodiment, the normalizing sequence for chromosome Y is determined by systematically calculating all possible chromosome doses using each possible normalizing chromosome individually and in all possible combinations of normalizing chromosomes (as explained elsewhere in this application). Thus, in one embodiment, the normalizing sequence for chromosome Y is a normalizing chromosome comprising the set of chromosomes consisting of chromosome 4 and chromosome 6. In another embodiment, the normalizing sequence for chromosome Y is a normalizing chromosome consisting of a set of chromosomes consisting of chromosome 4 and chromosome 6.

The normalizing sequence for calculating the dosage of different chromosomes of interest or different segments of interest can be the same, or it can be different normalizing sequences for different chromosomes or segments, respectively. For example, the normalizing sequence, the normalizing sequence of chromosome A of interest (e.g., normalizing chromosome) (one or a group) can be the same, or it can be different from the normalizing sequence of chromosome B of interest (e.g., normalizing chromosome) (one or a group).

The normalizing sequence for a complete chromosome can be a complete chromosome or a group of complete chromosomes, or it can be a segment of a chromosome, or a group of segments of one or more chromosomes.

Normalizing segment sequence as the normalizing sequence of chromosome

In another embodiment, the normalizing sequence of a chromosome can be a normalizing segment sequence. The normalizing segment sequence can be a single segment, or it can be a group of segments of a chromosome, or they can be multiple segments from two or more different chromosomes. By systematic calculation of all combinations of segment sequences in the genome, the normalizing segment sequence can be determined. For example, the normalizing segment sequence of chromosome 21 can be a single segment that is larger or smaller than the size of chromosome 21 of about 47Mbp (million base pairs), for example, a normalizing segment can be a segment of chromosome 9, which is about 140Mbp. As an alternative, the normalizing sequence of chromosome 21 can be, for example, a combination of segment sequences from two different chromosomes (e.g., from chromosome 1 and from chromosome 12).

In one embodiment, the normalizing sequence for chromosome 21 is a normalizing segment sequence of one segment or one group of two or more segments of chromosomes 1-20, 22, X, and Y. In another embodiment, the normalizing sequence for chromosome 18 is a segment or multiple groups of segments of chromosomes 1-17, 19-22, X', and Y. In another embodiment, the normalizing sequence for chromosome 13 is a segment or multiple groups of segments of chromosomes 1-12, 14-22, X', and Y. In another embodiment, the normalizing sequence for chromosome X is a segment or multiple groups of segments of chromosomes 1-22 and Y. In another embodiment, the normalizing sequence for chromosome Y is a segment or one group of segments of chromosomes 1-22 and X. The normalizing sequence of a single or multiple groups of segments can be determined for all chromosomes in a genome. The two or more segments of the normalizing segment sequence can be segments from one chromosome, or the two or more segments can be segments of two or more different chromosomes. As explained for normalizing chromosome sequences, a normalizing segment sequence can be identical for two or more different chromosomes.

Normalizing segment sequence as the normalizing sequence of chromosome segment

When the sequence of interest is a chromosomal segment, it is possible to determine the presence or absence of the CNV of the sequence of interest. The variation in the copy number of a chromosomal segment allows determination of the presence or absence of a partial chromosomal aneuploidy. The following describes an example of a partial chromosomal aneuploidy associated with different fetal abnormalities and an illness. The chromosomal segment can have any length. For example, it can range from kilobases to hundreds of millions of bases. The human genome only accounts for more than 3 billion DNA bases, and it can be divided into tens, thousands, hundreds of thousands and millions of segments with different sizes, and their copy number can be determined according to the method of the present invention. The normalized sequence of a chromosomal segment is such a normalized segment sequence, which can be a single segment from any one of chromosomes 1-22, X and Y, or it can be a group of segments from any one of chromosomes 1-22, X and Y.

The normalizing sequence for a segment of interest is a sequence having variability across multiple chromosomes and across multiple samples that is closest to the variability of the segment of interest. When the normalizing sequence is a group of segments of any one or more of chromosomes 1-22, X, and Y, the determination of the normalizing sequence can be performed as described to determine the normalizing sequence of the chromosome of interest. By using the segment of interest in each sample of a group of qualified samples (i.e., samples known to be diploid for the segment of interest) as one and all possible combinations of two or more segments of the normalizing sequence to calculate the segment dose, a normalizing segment sequence for one or a group of segments can be identified, and this normalizing sequence is determined to be a normalizing sequence that provides a segment dose, and this segment dose has the lowest variability for this segment of interest across all qualified samples, as described above for the normalizing chromosome sequence.

For example, for a segment of interest, it is 1 Mb (megabase), and the remaining 3 million segments in the approximately 3 Gb human genome (minus the 1 mg segment of interest) can be used individually or in combination with each other to calculate the segment dose of the segment of interest in the sample of the qualified group, thereby determining which one or which group of segments will be used as the normalizing segment sequence for the qualified and tested samples. The segment of interest can vary from about 1000 bases to tens of millions of bases. The normalizing segment sequence can be composed of one or more segments of the same size as the sequence of interest. In other embodiments, the normalizing segment sequence can be composed of segments different from the sequence of interest, and/or different from each other. For example, the normalizing sequence for a sequence of 100,000 bases in length can be 20,000 bases long, and can include, for example, a combination of sequences of different lengths of 7,000+8,000+5,000 bases. As described elsewhere in this application for normalizing chromosome sequences, normalizing segment sequences can be determined by systematically calculating all possible chromosome and/or segment doses using each possible normalizing chromosome segment, both individually and in all possible combinations of normalizing segments (as explained elsewhere in this application). Individual or groups of segments can be determined for all segments and/or chromosomes in the genome.

The normalizing sequence for calculating the dose of different chromosome segments of interest can be the same, or it can be different normalizing sequences for different chromosome segments of interest. For example, the normalizing sequence for chromosome segment A of interest, such as a normalizing segment (one or a group) can be the same, or it can be different from the normalizing sequence for chromosome segment B of interest, such as a normalizing segment (one or a group).

Normalized chromosome sequence as the normalized sequence of chromosome segment

In another embodiment, the copy number variation of chromosome segment can use normalization chromosome to determine, and this normalization chromosome can be single chromosome as above or one group of chromosome.Normalization chromosome sequence can be by systematically determining which or which group of chromosome makes the variability of chromosome dosage in one group of qualified samples minimum, come normalization chromosome or chromosome group for chromosome identification interested in one group of qualified samples.For example, for determining the partial deletion of chromosome 7, be first identified as chromosome or chromosome group that makes the chromosome dosage minimum of whole chromosome 7 in one group of qualified samples for the normalization sequence that is used to analyze the normalization chromosome of partial deletion.As described in this other place for the normalization chromosome sequence of chromosome interested, can systematically calculate all possible chromosome dosages by using each possible normalization chromosome individuation and all possible combinations of normalization chromosome, determine the normalization chromosome sequence (as explained in this other place) of chromosome segment.Can determine single chromosome or chromosome group for all chromosome segments in genome.Illustration uses normalization chromosome to determine that there is partial chromosome deletion and partial chromosome duplication example to be provided as example 17 and 18.

In certain embodiments, CNVs of chromosome segments are determined by first subdividing the chromosome of interest into segments or bins of variable length. The bin length can be at least about 1 kbp, at least about 10 kbp, at least about 100 kbp, at least about 1 mbp, at least about 10 mbp, or at least about 100 mbp. The smaller the bin length, the higher the resolution obtained for locating CNVs in the chromosome segment of interest.

Determining the presence or absence of a CNV for a chromosome segment of interest can be accomplished by comparing the dose for each bin of the chromosome of interest in the test sample with the mean of the corresponding bin doses determined for each bin of the same length in a set of qualified samples. The normalized binary value for each bin can be calculated as a normalized binary value (NBV) as described above for the normalized segment value, which relates the bin dose in the test sample to the mean of the corresponding bin dose in a set of qualified samples. The NBV is calculated as:

where and are the estimated mean and standard deviation, respectively, of the jth bin dose for a set of qualified samples, and _xij is the observed jth bin dose for test sample i.

Determine aneuploidy in test samples

A sequence dose is determined for a sequence of interest in a test sample comprising a mixture of nucleic acids derived from genomes that differ in the one or more sequences of interest based on one or more normalizing sequences identified in qualified samples.

In step 115, a test sample is obtained from a subject suspected or known to carry a clinically relevant CNV of a sequence of interest. This test sample can be a biological fluid (e.g., plasma) or any suitable sample as described below. As described, the sample can be obtained using a non-invasive procedure such as a simple blood draw. In some embodiments, the test sample contains a mixture of nucleic acid molecules (e.g., cfDNA molecules). In some embodiments, the test sample is a maternal plasma sample containing a mixture of fetal and maternal cfDNA molecules.

In step 125, as the situation described for qualified samples, at least a portion of the test nucleic acid in this test sample is checked order, to produce the sequence readings (for example 36bp readings) in millions. As in step 120, the reading produced from the nucleic acid in this test sample is mapped uniquely to a reference genome or compared to produce a label with reference to genome. As described in step 120, from the reading uniquely mapped with reference to genome, obtain at least about 3x106 individual qualified sequence labels, at least about 5x106 individual qualified sequence labels, at least about 8x106 individual qualified sequence labels, at least about 10x106 individual qualified sequence labels, at least about 15x106 individual qualified sequence labels, at least about 20x106 individual qualified sequence labels, at least about 30x106 individual qualified sequence labels, at least about 40x106 individual qualified sequence labels or at least about 50x106 individual qualified sequence labels, these qualified sequence labels comprise the reading between 20 and 40bp. In certain embodiments, the reading produced by sequencing apparatus provides with electronic format. Compare with the computing device discussed below.Individual reading is compared with the reference genome of often very large (millions of base pairs), to identify the site corresponding to reading and reference genome uniqueness.In certain embodiments, alignment program allows reading with limited mispairing between reference genome.In some cases, allow 1,2 or 3 base pairs and corresponding base pair mispairing in reference genome in a reading, yet still produce mapping.

In step 135, use computing device as described below, all or most of the labels obtained from the nucleic acid in the test sample are counted to determine the test sequence label density.In certain embodiments, each reading is compared with a specific region (in most cases a chromosome or segment) with reference to genome, and by attaching site information on the reading, the reading is transformed into a label.When the process was carried out, computing device can keep the number of labels/readings mapped to each region (in most cases a chromosome or segment) with reference to genome being rolled counted.Store the count of each chromosome interested or segment and each corresponding normalization chromosome or segment person.

In certain embodiments, the reference genome has one or more excluded regions that are part of a real biological genome but are not included in the reference genome. Reads that may be aligned with these excluded regions are not counted. Examples of excluded regions include regions of long repetitive sequences, similar regions between chromosomes X and Y, and the like.

In certain embodiments, the method determines whether the tag is counted more than once when multiple reads are compared to the same site on the reference genome or sequence. There may be times when two tags have the same sequence and are therefore compared to the same site on the reference sequence. The method for counting tags can exclude identical tags derived from the same sequencing sample from counting in some cases. If a disproportionate number of tags in a given sample are the same, it indicates that there is a huge deviation or other defect in the program. Therefore, according to certain embodiments, the counting method does not count tags from a given sample that are identical to tags from the sample that were previously counted.

When ignoring the same label from a single sample, different indicators can be set for selection. In certain embodiments, the count labels defining a percentage must be unique. If the labels more than the threshold value are not unique, these labels are ignored. For example, if the definition percentage requires that at least 50% be unique, then until the percentage of the unique labels of the sample exceeds 50%, the same labels are counted. In other embodiments, the critical number of unique labels is at least about 60%. In other embodiments, the critical percentage of unique labels is at least about 75%, or at least about 90%, or at least about 95%, or at least about 98%, or at least about 99%. For chromosome 21, the threshold can be set at 90%. If 30M labels are compared with chromosome 21, at least 27M labels must be unique. If the 3M count labels are not unique and the 30,000,000th label is not unique, then it is not counted.

Appropriate statistical analysis can be used to select a specific threshold or other metric for determining when not to count otherwise identical tags. One factor affecting this threshold or other criterion is the amount of sequencing sample relative to the size of the genome to which the tags can be compared. Other factors include the size of the reads and similar considerations.

In one embodiment, the number of sequence tags mapped to a sequence of interest is normalized to the known length of the sequence of interest they map to above, to provide a test sequence tag density ratio. As described in these qualified samples, it is not necessarily required to be normalized to the known length of a sequence of interest, and this can be included as a step to reduce the number of digits in a number thereby simplifying it for manual interpretation. Along with all the mapped test sequence tags in the test sample being counted, the sequence tag density for the sequence of interest (e.g., clinically relevant sequence) in these test samples is determined, and what is also determined is the sequence tag density for additional sequences, which correspond to at least one normalizing sequence identified in these qualified samples.

At step 150, based on the identification of at least one normalizing sequence in the qualified samples, a test sequence dose is determined for a sequence of interest in the test sample. In various embodiments, the test sequence dose is computationally determined by operating on the sequence tag density of the sequence of interest and the corresponding normalizing sequence as described herein. The computing device responsible for this task electronically accesses the association between the sequence of interest and its associated normalizing sequence, which can be stored in a database, table, chart, or included as code in program instructions.

As described elsewhere in the present application, the at least one normalizing sequence can be a single sequence or a group of sequences. In a test sample, the sequence dosage for a sequence interested is the sequence tag density determined for the sequence interested in the test sample and the ratio of the sequence tag density of at least one normalizing sequence determined in the test sample, wherein the normalizing sequence in the test sample corresponds to the normalizing sequence identified for the specific sequence interested in these qualified samples. For example, if the normalizing sequence identified for the chromosome 21 in these qualified samples is not determined to be a chromosome (such as chromosome 14), then the test sequence dosage for chromosome 21 (sequence interested) is just determined to be the ratio of the sequence tag density for chromosome 21 and the sequence tag density for chromosome 14, and each is determined in a test sample. Similarly, the chromosome dosage for chromosome 13,18,X,Y and other chromosomes associated with multiple chromosome aneuploidy is determined. The normalizing sequence for chromosome interested can be one or a group of chromosomes, or one or a group of chromosome segments. As mentioned above, a sequence interested can be a part for chromosome, such as a chromosome segment. Therefore, the dose for a chromosome segment can be determined as the ratio of the sequence tag density determined for this segment in the test sample to the sequence tag density for the normalized chromosome segment in the test sample, wherein the normalized segment in the test sample corresponds to the normalized segment (single or a group of segments) identified for the specific segment of interest in the qualified samples. Chromosome segments can range in size from kilobases (kb) to megabases (Mb). (For example, about 1 kb to 10 kb, or about 10 kb to 100 kb, or about 100 kb to 1 _Mb ).

In step 155, multiple threshold values are derived from the standard deviation values of the sequence doses determined for the qualified sequence doses determined in the multiple qualified samples and the sequence doses determined for the samples known to be aneuploid of the sequence of interest. Note that this operation is typically performed asynchronously with the analysis of the patient's test sample. It can be performed simultaneously with, for example, selecting a normalized sequence from a qualified sample. Accurate classification depends on the difference between the probability distributions for different categories (i.e., aneuploidy types). In some instances, multiple threshold values are selected from the empirical distribution for each type of aneuploidy (e.g., trisomy 21). As described in the example, possible threshold values are established for classifying trisomy 13, trisomy 18, trisomy 21, and monosomy X aneuploidy, which illustrate the use of a method for determining chromosomal aneuploidy by sequencing cfDNA extracted from a maternal sample, wherein the maternal sample includes a mixture of fetal and maternal nucleic acids. This threshold value that is determined as for distinguishing the aneuploidy for a kind of chromosome and affected sample can be identical or different with the threshold value that is determined as for distinguishing the affected sample for a kind of different aneuploidy.As shown in these examples, the threshold value for each chromosome interested is determined from the variability in the dosage of the chromosome interested across multiple samples and multiple sequencing rounds.The variability for the chromosome dosage of any chromosome interested is less, and the dispersion in the dosage of the chromosome interested across all unaffected samples is more narrow, and these samples are used to setting the threshold value for determining different aneuploidies.

Returning to the process flow associated with classifying a patient test sample, at step 160, the copy number variation of the sequence of interest is determined in the test sample by comparing the test sequence dose for the sequence of interest to at least one threshold established from the qualified sample doses. This operation can be performed by the same computing device used to measure sequence tag density and/or calculate segment doses.

In step 165, the dose calculated for the test sequence of interest is compared to the dose set as the threshold, and these thresholds are selected according to a user-defined reliability threshold to classify the sample as "normal", "affected" or "no call". These "no call" samples are samples for which a reliable, definitive diagnosis cannot be made. Each type of affected sample (e.g., trisomy 21, partial trisomy 21, monosomy X) has its own threshold, one for calling normal (unaffected) samples and another for calling affected samples (although in some cases the two thresholds overlap). As described elsewhere herein, in some cases, if the fetal fraction of the nucleic acid in the test sample is high enough, then a no call can be converted to a call (affected or normal). The classification of the test sequence can be reported by the computing device used for other operations of the process flow. In some cases, the classification is reported in an electronic format and can be displayed, emailed, texted to relevant personnel, etc.

Certain embodiments provide a method for providing a prenatal diagnosis of fetal aneuploidy in a biological sample comprising fetal and maternal nucleic acid molecules. This diagnosis is made based on the following steps: obtaining sequence information for sequencing at least a portion of a mixture of fetal and maternal nucleic acid molecules derived from a biological test sample (e.g., a maternal plasma sample); calculating a normalized chromosome dose for one or more chromosomes of interest and/or a normalized segment dose for one or more segments of interest from the sequencing data; and determining a statistically significant difference between the chromosome dose for the chromosome of interest and/or the segment dose for the segment of interest in the corresponding test sample and a threshold value established in multiple qualified (normal) samples, and providing a prenatal diagnosis based on the statistical difference. As described in step 165 of the method, a normal or affected diagnosis is made. In the case where a normal or affected diagnosis cannot be made with confidence, a "no determination" is provided.

Samples and sample processing

sample

Samples for determining CNVs such as chromosomal aneuploidy, partial aneuploidy, etc. may include samples taken from any cell, tissue, or organ in which copy number variations of one or more sequences of interest are to be determined. It is desirable that these samples contain nucleic acids present in cells and/or "cell-free" nucleic acids (e.g., cfDNA).

In certain embodiments, it is advantageous to obtain cell-free nucleic acids, such as cell-free DNA (cfDNA). Cell-free nucleic acids, including cell-free DNA, can be obtained from biological samples including, but not limited to, plasma, serum, and urine by different methods known in the art (see, for example, Fan et al., Proc Natl Acad Sci 105: 16266-16271 [2008]; Koide et al., Prenatal Diagnosis 25: 604-607 [2005]; Chen et al., Nature Med. 2: 1033-1035 [1996]; Lo et al., Lancet 350: 485-487 [1997]; Botezatu et al., Clin Chem. 46: 1078-1084, 2000; and Su et al., J Molecular Diagnostics 2000). Mol.Diagn.) 6: 101-107 [2004]). To separate cell-free DNA from cells in a sample, different methods can be used, including but not limited to fractionation, centrifugation (e.g., density gradient centrifugation), DNA-specific precipitation, or high-throughput cell sorting and/or other separation methods. Commercially available kits for manual and automatic separation of cfDNA are available (Roche Diagnostics, Indianapolis, IN, Qiagen, Valencia, CA, Macherey-Nagel, Duren, DE). Biological samples containing cfDNA have been used for sequencing tests that can detect chromosomal aneuploidy and/or different polymorphisms, and are used in tests to determine the presence or absence of chromosomal abnormalities such as trisomy 21.

In various embodiments, the cfDNA present in the sample may be specifically enriched or non-specifically enriched prior to use (e.g., prior to preparing a sequencing library). Non-specific enrichment of sample DNA refers to whole-genome amplification of genomic DNA fragments of the sample, which can be used to increase the amount of sample DNA prior to preparing a cfDNA sequencing library. Non-specific enrichment can be selective enrichment of one of two genomes present in a sample comprising more than one genome. For example, non-specific enrichment can be selective for the fetal genome in a maternal sample, which can be achieved by known methods to increase the ratio of fetal DNA to maternal DNA in the sample. Alternatively, non-specific enrichment can be non-selective amplification of two genomes present in the sample. For example, non-specific amplification can be amplification of fetal and maternal DNA in a sample comprising a mixture of DNA from the fetal and maternal genomes. Methods for whole-genome amplification are known in the art. Degenerate oligonucleotide primer PCR (DOP), primer extension PCR (PEP), and multiple displacement amplification (MDA) are examples of whole-genome amplification methods. In certain embodiments, a sample comprising a mixture of cfDNA from different genomes is not enriched for cfDNA from the genomes present in the mixture. In other embodiments, a sample comprising a mixture of cfDNA from different genomes is not specifically enriched for any one genome present in the sample.

Samples comprising nucleic acids to which the methods described herein are applied typically comprise biological samples ("test samples"), such as those described above. In certain embodiments, nucleic acids to be screened for one or more CNVs are purified or isolated by any of a number of well-known methods.

Therefore, in certain embodiments, sample includes or consists of the polynucleotide through purification or separation, or can include samples such as tissue sample, biological fluid sample, cell sample.Suitable biological fluid sample includes but is not limited to blood, blood plasma, serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavage fluid (ravages), bone marrow suspension, vaginal fluid, cervical lavage fluid, brain fluid, ascites, milk, respiratory tract, intestine and genitourinary tract secretion, amniotic fluid, milk and leukocyte infiltration sample.In certain embodiments, sample is the sample that can be obtained easily by noninvasive procedure, such as blood, blood plasma, serum, sweat, tears, sputum, urine, sputum, ear discharge, saliva or feces.In certain embodiments, sample is the plasma and/or serum part of peripheral blood sample or peripheral blood sample.In other embodiments, this biological sample is cotton swab or smear, biopsy specimen or cell culture. In another embodiment, this sample is a mixture of two or more biological samples, for example, a biological sample can include two or more biological fluid samples, tissue samples, and cell culture samples. As used herein, the terms "blood," "plasma," and "serum" clearly encompass their fractionated or processed parts. Similarly, when a sample is taken from a biopsy, cotton swab, smear, etc., the "sample" clearly encompasses a separation or portion derived from the processing of such a biopsy, cotton swab, smear, etc.

In certain embodiments, samples can be obtained from multiple sources, including but not limited to: samples from different individuals, samples from different stages of development of the same or different individuals, samples from different diseased individuals (e.g., individuals with cancer or suspected of having a genetic disorder), normal individuals, samples obtained at different stages of an individual's disease, samples from individuals who have undergone different treatments for the disease, samples from individuals who have been exposed to different environmental factors, samples from individuals who are susceptible to a disease condition, samples from individuals exposed to an infectious agent (e.g., HIV), and the like.

In an illustrative but non-limiting embodiment, this sample is a maternal sample derived from a pregnant female (e.g., a pregnant woman). In this case, the sample can be analyzed using the method described herein to provide a prenatal diagnosis of potential chromosomal abnormalities in the fetus. This maternal sample can be a tissue sample, a biological fluid sample, or a cell sample. Biological fluids include (as non-limiting examples) blood, plasma, serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavage fluid, bone marrow suspension, vaginal discharge, lavage fluid through the cervix, brain fluid, ascites, milk, secretions of the respiratory, intestinal and genitourinary tracts, and leukopheresis samples.

In another illustrative but non-restrictive embodiment, the maternal sample is a mixture of two or more biological samples, for example, the biological sample can include two or more biological fluid samples, tissue samples and cell culture samples. In some embodiments, this sample is a sample easily obtainable by a non-invasive process, for example, blood, plasma, serum, sweat, tears, sputum, urine, milk, sputum, ear discharge, saliva and feces. In some embodiments, this biological sample is a peripheral blood sample and/or its plasma or serum portion. In other embodiments, this biological sample is a sample of a cotton swab or smear, biopsy specimen or cell culture. As disclosed above, the terms "blood", "plasma" and "serum" clearly encompass their separations or processed parts. Similarly, when a sample is taken from a biopsy, cotton swab, smear, etc., this "sample" clearly encompasses a separation or part derived from the processing of a biopsy, cotton swab, smear, etc.

In certain embodiments, the sample can also be a tissue, cell, or other source containing polynucleotides obtained from an in vitro culture. These cultured samples can be taken from a variety of sources, including but not limited to: cultures (e.g., tissues or cells) maintained under different culture media and conditions (e.g., pH, pressure, or temperature), cultures (e.g., tissues or cells) maintained for different lengths of time, cultures (e.g., tissues or cells) treated with different factors or reagents (e.g., drug candidates, or modulators), or cultures of different types of tissues and/or cells.

Methods for isolating nucleic acids from biological sources are well known and will vary depending on the nature of the source. One of ordinary skill in the art can easily isolate one or more nucleic acids as required for the methods described herein from a source. In some cases, it may be advantageous to fragment the nucleic acid molecules in a nucleic acid sample. Fragmentation may be random, or it may be specific, such as that achieved by digestion with a restriction endonuclease. Methods for random fragmentation are well known in the art and include, for example, digestion with restriction endonucleases, alkali treatment, and physical shearing. In one embodiment, the sample nucleic acid is obtained in the form of cfDNA, which has not undergone fragmentation.

In other exemplary embodiments, the sample nucleic acid is obtained in the form of genomic DNA, which is fragmented into fragments of about 300 or more, about 400 or more, or about 500 or more base pairs, and the NGS method can be readily applied thereto.

Sequencing library preparation

In one embodiment, the methods described herein can utilize next generation sequencing technology (NGS), which allows multiple samples to be individually sequenced (i.e., single-channel sequencing) in the form of genomic molecules or sequenced (e.g., multiple sequencing) on a single sequencing batch as a pooled sample of genomic molecules comprising indexes. These methods can produce up to hundreds of millions of readings of DNA sequences. In various embodiments, the sequence of genomic nucleic acids and/or indexed genomic nucleic acids can be determined using next generation sequencing technology (NGS) such as described herein. In various embodiments, one or more processors as described herein can be used to analyze the large amount of sequence data obtained using NGS.

In various embodiments, the use of these sequencing technologies does not involve the preparation of sequencing libraries.

However, in certain embodiments, the sequencing methods encompassed herein relate to the preparation of sequencing libraries. In a schematic method, the preparation of sequencing libraries includes generating a series of random aptamer-modified DNA fragments (e.g., polynucleotides) that are ready for sequencing. Sequencing libraries of polynucleotides can be prepared from DNA or RNA, including equivalents or analogs of DNA or cDNA (e.g., DNA or cDNA that is complementary or copies of DNA produced by an RNA template under the action of a reverse transcriptase). Polynucleotides can start from double-stranded forms (e.g., dsDNA (e.g., genomic DNA fragments), cDNA, PCR amplification products, etc.), or in certain embodiments, polynucleotides can start from single-stranded forms (e.g., ssDNA, RNA, etc.) and have been converted to dsDNA forms. For example, in certain embodiments, single-stranded mRNA molecules can be copied into double-stranded cDNAs suitable for preparing sequencing libraries. The precise sequence of the primary polynucleotide molecules is generally unimportant to the method for library preparation and may be known or unknown. In one embodiment, the polynucleotide molecules are DNA molecules. More specifically, in certain embodiments, the polynucleotide molecules represent the entire genetic complement of an organism or substantially the entire genetic complement of an organism and are genomic DNA molecules (e.g., cellular DNA, cell-free DNA (cfDNA), etc.) that typically include intron sequences and exon sequences (coding sequences) as well as non-coding regulatory sequences (e.g., promoter and enhancer sequences). In certain embodiments, the primary polynucleotide molecules include human genomic DNA molecules, such as cfDNA molecules present in the peripheral blood of a pregnant subject.

The preparation of sequencing libraries for certain NGS sequencing platforms is facilitated by using polynucleotides comprising a specific range of fragment sizes. The preparation of these libraries typically comprises fragmenting large polynucleotides (e.g., cellular genomic DNA) to obtain polynucleotides within the desired size range.

Fragmentation can be achieved by any of a variety of methods known to those of ordinary skill in the art. For example, fragmentation can be achieved by mechanical means including, but not limited to, spraying, sonication, and hydrodynamic shearing. However, mechanical fragmentation typically causes the DNA backbone to cleave at C-O, P-O, and C-C bonds, thereby producing a heterogeneous mixture of blunt ends with disconnected C-O, P-O, and C-C bonds and 3'- and 5'-overhangs (see, e.g., Alnemri and Liwack, J Biol. Chem 265: 17323-17333 [1990]; Richards and Boyer, J Mol Biol 11: 327-240 [1965]), which may require repair because they may lack the 5'-phosphate necessary for subsequent enzymatic reactions (e.g., connection of sequencing adapters) required for preparing DNA for sequencing.

In contrast, cfDNA typically exists in fragments smaller than about 300 base pairs, and thus fragmentation is not typically required for generating sequencing libraries using cfDNA samples.

Typically, whether the polynucleotide is fragmented by force (e.g., in vitro) or exists naturally in fragmented form, it is converted to blunt-ended DNA with a 5'-phosphate and a 3'-hydroxyl group. Standard protocols, such as those for sequencing using the ILuna platform described elsewhere herein, instruct the user to perform end-repair on the sample DNA, to purify the end-repaired products before dA tailing, and to purify the dA tailed products before the adapter ligation step of the library preparation.

The different embodiments of the sequence library preparation methods described herein do not require the execution of one or more steps typically required by standard protocols to obtain modified DNA products that can be sequenced by NGS. The following describes a simplified method (ABB method), a one-step method, and a two-step method. Continuous dA tailing and adapter ligation are referred to as a two-step process herein. Continuous dA tailing, adapter ligation, and amplification are referred to as a one-step method herein. In different embodiments, the ABB method and the two-step method can be performed in solution or on a solid surface. In certain embodiments, the one-step method is performed on a solid surface.

2 illustrates a comparison of standard methods such as ILUNA with an abbreviated method (ABB; Example 2), a two-step method, and a one-step method (Examples 3-6) for preparing DNA molecules for sequencing by NGS according to embodiments of the present invention.

Simple preparation-ABB

In one embodiment, a simplified method for preparing a sequence library (ABB method) is provided, which includes the consecutive steps of end repair, dA tailing, and adaptor ligation (ABB). In embodiments for preparing a sequencing library that do not require a dA tailing step (see, for example, protocols for sequencing using the Roche 454 and SOLID ^™ 3 platforms), the end repair and adaptor ligation steps may not include a step of purifying the end-repaired product before adaptor ligation.

The sequencing library preparation method comprising the consecutive steps of end repair, dA tailing and adapter ligation is referred to herein as the abbreviated method (ABB), and has been shown to produce sequencing libraries with unexpectedly improved quality and accelerated sample analysis (see, e.g., Example 2). According to some embodiments of the method, the ABB method can be performed in solution, as illustrated herein. The ABB method can also be performed on a solid surface by first performing end repair and dA tailing on the DNA in solution, and then binding the DNA to the solid surface as described elsewhere herein for a one-step or two-step preparation on a solid surface. The three enzymatic steps, including the step of ligating the adapter to the DNA with the dA tail, are all performed in the absence of polyethylene glycol. The disclosed protocol for performing the ligation reaction comprising ligating the adapter to the DNA guides the user to perform the ligation in the presence of polyethylene glycol. The applicant has determined that ligating the adapter to the DNA with the dA tail can be performed in the absence of polyethylene glycol.

In another embodiment, the sequencing library is prepared without end-repairing the cfDNA prior to the dA-tailing step. Applicants have determined that cfDNA that does not need to be fragmented does not require end-repair, and according to embodiments of the present invention, the preparation of cfDNA sequencing libraries does not include an end-repair step and a purification step, thereby combining enzymatic reactions and further simplifying the preparation of the DNA to be sequenced. cfDNA exists as a mixture of blunt ends and 3'- and 5'-overhangs, which are generated in vivo by nucleases that cleave cellular genomic DNA into cfDNA fragments terminated with 5'-phosphates and 3'-hydroxyls. Eliminating the end-repair step selects for cfDNA molecules that naturally exist as blunt-ended molecules and for cfDNA molecules that naturally have 5'-overhangs that are filled in by the polymerase activity of an enzyme such as Klenow exopolymerase, which is used to attach one or more deoxynucleotides to the 3'-OH (dA-tailing) as described below. Eliminating the end-repair step for cfDNA does not select for cfDNA molecules with 3'-overhangs (3'-OH). Surprisingly, the exclusion of these 3'-OH cfDNA molecules from the sequencing library did not affect the representation of genomic sequences in the library, suggesting that the end-repair step of cfDNA molecules can be eliminated from the preparation of sequencing libraries (see Examples). In addition to cfDNA, other types of unrepaired polynucleotides that can be used to prepare sequencing libraries include DNA molecules generated by reverse transcription of RNA molecules (e.g., mRNA, siRNA, sRNA) and unrepaired DNA molecules that are DNA amplicons synthesized from phosphorylated primers. When unphosphorylated primers are used, DNA reverse transcribed from RNA and/or DNA amplified from a DNA template (i.e., DNA amplicons) can also be phosphorylated after synthesis by polynucleotide kinases.

In another embodiment, unrepaired DNA is used to prepare a sequencing library according to a two-step method, which does not include the end repair of DNA, and the unrepaired DNA carries out two consecutive steps of dA tailing and adapter connection (see Figure 2). The two-step method can be performed in solution or on a solid surface. When performed in solution, the two-step method includes utilizing the DNA obtained from the biological sample, does not include the step of carrying out end repair on the DNA, and for example, adds a single deoxynucleotide (such as deoxyadenosine (A)) to the 3'-end of the polynucleotide in the unrepaired DNA sample by the activity of certain types of DNA polymerases such as Taq (Taq) polymerase or Klenow exopolymerase. In subsequent consecutive steps, the product of dA tailing is connected to the adapter, and these products are compatible with the ` T ' overhangs present on the 3' end of each double helix region of commercially available adapters. dA tailing prevents the self-connection of two blunt-end polynucleotides, so as to form a sequence through which the adapter is connected. Therefore, in some embodiments, unrepaired cfDNA undergoes sequential dA-tailing and adaptor ligation steps, wherein the dA-tailed DNA is prepared from unrepaired DNA and no purification step is performed after the dA-tailing reaction. Double-stranded adaptors can be ligated to both ends of the dA-tailed DNA. A single set of adaptors with identical sequences or a set of two different adaptors can be utilized. In various embodiments, one or more different sets of the same or different adaptors can also be used. The adaptors can include index sequences to enable multiplexed sequencing of the library DNA. Adapter ligation to the dA-tailed DNA is optionally performed in the absence of polyethylene glycol.

Two-step preparation in solution

In different embodiments, when the two-step method is performed in solution, the product of the adaptor ligation reaction can be purified to remove unconnected adaptors and adaptors that may have been connected to each other. Purification can also select the size range of the template used for cluster generation, and can optionally be amplified before, such as PCR amplification. The ligation product can be purified by any of a variety of methods including but not limited to gel electrophoresis, solid phase reversible immobilization (SPRI), etc. In some embodiments, the purified adaptor-ligated DNA is amplified before sequencing, such as PCR amplification. Certain sequencing platforms require that the library DNA be further amplified. For example, according to the ILuna technology, the ILuna platform requires that cluster amplification of the library DNA should be performed as an integral part of sequencing. In other embodiments, the purified adaptor-ligated DNA is denatured and the single-stranded DNA molecules are attached to the flow cell of the sequencer. Therefore, in certain embodiments, a method for preparing a sequencing library for NGS sequencing from unrepaired DNA in solution includes obtaining DNA molecules from a sample; and performing consecutive steps of dA tailing and adaptor ligation on the unrepaired DNA molecules obtained from the sample.

As indicated above, in various embodiments, these methods of library preparation are incorporated into methods for determining copy number variation (CNV), such as aneuploidy. Thus, in one illustrative embodiment, a method for determining the presence or absence of one or more fetal chromosomal aneuploidies is provided, the method comprising: (a) obtaining a maternal sample comprising a mixture of fetal and maternal cell-free DNA; (b) isolating the mixture of fetal and maternal cfDNA from the sample; (c) preparing a sequencing library from the mixture of fetal and maternal cfDNA; wherein preparing the library comprises the sequential steps of dA tailing and adapter ligation of the cfDNA, and wherein preparing the library does not comprise end repair of the cfDNA, and the preparation is performed in solution; (d) performing massively parallel sequencing on at least a portion of the sequencing library to obtain sequence information for the fetal and maternal cfDNA in the sample; (e) storing the sequence information, at least temporarily, in a computer-readable medium; and (f) using the stored sequence information to computationally identify the number of sequence tags for each of one or more chromosomes of interest and the number of sequence tags for each of any one or more chromosomes of interest. (g) using the number of sequence tags for each of the one or more chromosomes of interest and the number of sequence tags for the normalizing sequence for each of the one or more chromosomes of interest, a chromosome dose is calculated for each of the one or more chromosomes of interest; and (h) each chromosome dose for the one or more chromosomes of interest is compared with a corresponding threshold for each of the one or more chromosomes of interest, and thereby determining the presence or absence of fetal chromosomal aneuploidy in the sample, wherein steps (e)-(h) are performed using one or more processors. This method is exemplified in Examples 3 and 4.

Two-step and one-step solid phase preparation

In certain embodiments, the sequencing library is prepared on a solid surface according to the two-step method described above for preparing the library in solution. Preparing the sequencing library on a solid surface according to the two-step method includes obtaining DNA molecules such as cfDNA from a sample, and performing the continuous steps of dA tailing and adapter ligation, wherein the adapter ligation is performed on a solid surface. Repaired or unrepaired DNA can be used. In certain embodiments, the product of the adapter is separated from the solid surface, purified and amplified before sequencing. In other embodiments, the product of the adapter is separated from the solid surface, purified and not amplified before sequencing. In other embodiments, the product of the adapter is amplified, separated from the solid surface and purified. In certain embodiments, the purified product is amplified. In other embodiments, the purified product is not amplified. The sequencing scheme may include amplification, such as cluster amplification. In different embodiments, the separated product of the adapter is purified before amplification and/or sequencing.

In certain embodiments, the sequencing library is prepared on a solid surface according to a one-step method. In various embodiments, the sequencing library is prepared on a solid surface according to a one-step method, comprising obtaining DNA molecules, such as cfDNA, from a sample and performing the sequential steps of dA tailing, adaptor ligation, and amplification, wherein adaptor ligation is performed on the solid surface. The adaptor-ligated product does not need to be isolated prior to purification.

Fig. 3 depicts a two-step method and a one-step method for preparing a sequencing library on a solid surface. Repaired or unrepaired DNA can be used to prepare a sequencing library on a solid surface. In certain embodiments, unrepaired DNA is used. Examples of unrepaired DNA that can be used to prepare a sequencing library on a solid surface include, but are not limited to, cfDNA, DNA reverse transcribed from RNA using a phosphorylated primer, DNA amplified from a DNA template using a phosphorylated primer (i.e., phosphorylated DNA amplicons). Examples of repaired DNA that can be used to prepare a sequencing library on a solid surface include, but are not limited to, cfDNA and genomic DNA (i.e., phosphorylated DNA of a repair produced by reverse transcription of RNA such as mRNA, sRNA, siRNA, etc.) that has formed blunt ends and phosphorylated fragments. In certain illustrative embodiments, unrepaired cfDNA obtained from a maternal sample is used to prepare a sequencing library.

Preparation of a sequencing library on a solid surface comprises coating the solid surface with a first part of a two-part binder, modifying the first adaptor by attaching the second part of the two-part binder to the adaptor, and immobilizing the adaptor on the solid surface through the binding interaction of the first and second parts of the two-part binder. For example, preparation of a sequencing library on a solid surface can comprise attaching a polypeptide, polynucleotide, or small molecule to one end of the library adaptor that is capable of forming a binding complex with the polypeptide, polynucleotide, or small molecule immobilized on the solid surface. Solid surfaces that can be used to immobilize polypeptides, polynucleotides, or small molecules include, but are not limited to, plastics, paper, films, filter paper, chips, needles or slides, silica or polymer beads (e.g., polypropylene, polystyrene, polycarbonate), 2D or 3D molecular scaffolds, or any support for solid phase synthesis of polypeptides or polynucleotides.

The bonds between polypeptide-polypeptide, polypeptide-polynucleotide, polypeptide-small molecule, and polynucleotide-polynucleotide conjugates can be covalent or non-covalent. Preferably, the binding complex is bound by non-covalent bonds. For example, conjugates that can be used to prepare sequencing libraries on solid surfaces include, but are not limited to, streptavidin-biotin conjugates, antibody-antigen conjugates, and ligand-receptor conjugates. Examples of polypeptide-polynucleotide conjugates that can be used to prepare sequencing libraries on solid surfaces include, but are not limited to, DNA-binding protein-DNA conjugates. Examples of polynucleotide-polynucleotide conjugates that can be used to prepare sequencing libraries on solid surfaces include, but are not limited to, oligodT-oligoA and oligodT-oligodA. Examples of polypeptide-small molecule and polynucleotide-small molecule conjugates include streptavidin-biotin.

According to the embodiments (one-step and two-step) of the solid surface method as shown in FIG3 , the solid surface of a container (e.g., a polypropylene PCR tube or a 96-well plate) for preparing a sequencing library is coated with a polypeptide such as streptavidin. The ends of the first set of adaptors are modified by attaching a small molecule such as a biotin molecule, and the biotinylated adaptors are bound to the streptavidin on the solid surface (1). Subsequently, unrepaired or repaired DNA is attached to the streptavidin-bound biotinylated adaptors, thereby immobilizing them to the solid surface (2). The second set of adaptors is attached to the immobilized DNA (3).

Two-step preparation on solid phase

In one embodiment, the two-step method is performed using unrepaired DNA such as cfDNA for preparing a sequencing library on a solid surface. Unrepaired DNA is dA-tailed by attaching a single nucleotide base such as dA to the 3' end of a strand of unrepaired DNA such as cfDNA. Optionally, multiple nucleotide bases can be attached to the unrepaired DNA. A mixture comprising dA-tailed DNA is added to an adaptor immobilized on a solid surface, and the DNA is connected to the adaptor. The steps of dA-tailing and adaptor-ligation of the DNA are continuous, i.e., purification of the dA-tailed product is not performed (as shown in FIG2 for the two-step method). As described above, the adaptor may have an overhang complementary to the overhang on the unrepaired DNA molecule. Subsequently, a second set of adaptors is added to the DNA-biotinylated adaptor complex to provide a DNA library to which the adaptors are connected. Optionally, the repaired DNA is used to prepare the library. The repaired DNA can be genomic DNA that has been fragmented and subjected to ex vivo enzymatic repair of the 3' and 5' ends. In one embodiment, DNA, such as maternal cfDNA, is end-repaired, dA-tailed, and adaptor-ligated to adaptors immobilized on a solid surface in sequential steps of end-repair, dA-tailing, and adaptor-ligation as described for the simplified method performed in solution.

In certain embodiments utilizing a two-step method, the DNA of the adapter is separated from the solid surface by chemical or physical means (such as heat, ultraviolet light, etc.) (4a in FIG. 2), purified (5 in FIG. 2), and optionally, before starting the sequencing process, it is amplified in the solution. In other embodiments, the DNA of the adapter is not amplified. In the case of not amplifying, the adapter connected to the DNA can be configured to include a sequence (Kozarewa et al., Natural Methods (Nat Methods) 6: 291-295 [2009]) of oligonucleotide hybridization present on the flow cell of the sequencer, and avoids introducing amplification of the sequence for hybridizing the library DNA with the flow cell of the sequencer. As described for the DNA of the adapter generated in the solution, the library of the DNA of the adapter is subjected to large-scale parallel sequencing (6 in FIG. 2). In certain embodiments, sequencing is performed using a large-scale parallel sequencing by synthesis method sequencing with a reversible dye terminator. In other embodiments, sequencing is performed using a connection method sequencing to perform large-scale parallel sequencing. The sequencing process may include solid phase amplification, such as cluster amplification, as described elsewhere herein.

Thus, in various embodiments, a method for preparing a sequencing library for NGS from unrepaired DNA on a solid surface may include obtaining a DNA molecule from a sample; and performing the consecutive steps of dA tailing and adapter ligation on the unrepaired DNA molecule, wherein the adapter ligation is performed on a solid phase. In certain embodiments, the adapter may include an index sequence to allow multiple samples to be sequenced multiplexed within a single reaction vessel (e.g., a channel of a flow cell). As described above, the DNA molecule may be a cfDNA molecule, a DNA molecule transcribed from RNA, an amplicon of a DNA molecule, and the like.

As indicated above, in various embodiments, these library preparation methods are incorporated into methods for determining copy number variations (CNVs) such as aneuploidy. Thus, in certain embodiments, methods for preparing sequencing libraries from unrepaired cfDNA on a solid surface are incorporated into methods for analyzing maternal samples to determine the presence or absence of fetal chromosomal aneuploidy. Thus, in one embodiment, a method for determining the presence or absence of one or more fetal chromosomal aneuploidies is provided, the method comprising: (a) obtaining a maternal sample comprising a mixture of fetal and maternal cell-free DNA; (b) isolating the mixture of fetal and maternal cfDNA from the sample; (c) preparing a sequencing library from the mixture of fetal and maternal cfDNA; wherein preparing the library comprises the sequential steps of dA tailing and adaptor ligation of the cfDNA, wherein preparing the library does not comprise end repair of the cfDNA, and the preparation is performed on a solid surface; (d) performing massively parallel sequencing on at least a portion of the sequencing library to obtain sequence information for the fetal and maternal cfDNA in the sample; (e) storing the sequence information, at least temporarily, in a computer-readable medium; (f) using the stored sequence information to computationally identify the number of sequence tags for each of one or more chromosomes of interest and the number of sequence tags for a normalizing sequence for each of any one or more chromosomes of interest; and (g) The number of sequence tags of each of the one or more chromosomes of interest and the number of sequence tags of the normalized sequence of each of the chromosomes of interest are used, and chromosome dosage is calculated for each of the chromosomes of interest in a computational manner; and (h) each chromosome dosage is compared with a corresponding threshold value for each of the chromosomes of interest and for this or these chromosomes of interest, and thus determine the presence or absence of fetal chromosome aneuploidy in the sample, wherein the use of one or more processors of step (e)-(h) is performed. The sample can be a biological fluid sample, such as blood plasma, serum, urine and saliva. In certain embodiments, the sample is a maternal blood sample or its blood plasma and serum portion. This method is illustrated in example 4.

One-step preparation on solid phase

In another embodiment, the unrepaired DNA is dA-tailed, but the dA-tailed product is not purified before amplification, so that the steps of dA-tailing, adaptor ligation, and amplification are performed continuously or consecutively. The continuous dA-tailing, adaptor ligation, and amplification, followed by purification before sequencing, is referred to herein as a one-step process. The one-step process can be performed on a solid surface (see, for example, FIG3 ). The steps of attaching a first set of adaptors to a solid surface (1), attaching the unrepaired and dA-tailed DNA to surface-bound adaptors (2), and attaching a second set of adaptors to surface-bound DNA (3) can be performed as described above for the two-step process. However, in the one-step process, the adaptor-ligated surface-bound DNA can be amplified while attached to the solid surface ( 4 b in FIG2 ). Subsequently, the resulting library of adaptor-ligated DNA produced on the solid surface is separated and purified ( 5 in FIG2 ), followed by massively parallel sequencing as described for adaptor-ligated DNA produced in solution. In certain embodiments, sequencing is massively parallel sequencing using sequencing-by-synthesis with the aid of reversible dye terminators. In other embodiments, sequencing is massively parallel sequencing using sequencing by ligation.

Thus, in certain embodiments, a method for preparing a sequencing library for NGS sequencing is provided, the method comprising the steps of obtaining DNA molecules from a sample; and performing the sequential steps of dA tailing, adaptor ligation, and amplification on the DNA molecules, wherein the adaptor ligation is performed on a solid surface. As described for the two-step method, in various embodiments, the adaptors may include index sequences to allow multiplex sequencing of multiple samples within a single reaction vessel (e.g., a channel of a flow cell).

In certain embodiments, DNA can be repaired. The DNA molecule can be a cfDNA molecule, which can be a DNA molecule transcribed from RNA, or the DNA molecule can be an amplicon of a DNA molecule. Adapter ligation is performed as described above. Excess unconnected adapters can be washed off from the fixed DNA connected to the adapter; the reagents required for amplification are added to the fixed DNA connected to the adapter, and the DNA is subjected to multiple rounds of amplification, such as PCR amplification, as known in the art. In other embodiments, the DNA connected to the adapter is not amplified. In the case of no amplification, the DNA connected to the adapter can be removed from the solid surface by chemical or physical means (such as heat, ultraviolet light, etc.). In the case of no amplification, the adapter connected to the DNA may include a sequence hybridized with the oligonucleotide present on the flow cell of the sequencer (Kuzhazhiwa et al., Natural Methods (Nat Methods) 6: 291-295 [2009]).

In various embodiments, the sample can be a biological fluid sample (e.g., blood, plasma, serum, urine, cerebrospinal fluid, amniotic fluid, saliva, etc.). In certain embodiments, a method for analyzing a maternal sample to determine the presence or absence of a fetal chromosomal aneuploidy includes as a step a method for preparing a sequencing library from unrepaired cfDNA on a solid surface.

Thus, in one embodiment, a method for determining the presence or absence of one or more fetal chromosomal aneuploidies is provided, the method comprising: (a) obtaining a maternal sample comprising a mixture of fetal and maternal cell-free DNA; (b) isolating the mixture of fetal and maternal cfDNA from the sample; (c) preparing a sequencing library from the mixture of fetal and maternal cfDNA; wherein preparing the library comprises the sequential steps of dA tailing, adaptor ligation, and amplification of the cfDNA, and wherein the preparation is performed on a solid surface; (d) performing massively parallel sequencing on at least a portion of the sequencing library to obtain sequence information for the fetal and maternal cfDNA in the sample; (e) storing the sequence information at least temporarily in a computer-readable medium; and (f) using the stored sequence information to computationally identify the number of sequence tags for each of one or more chromosomes of interest. (g) using the number of sequence tags of each of the one or more chromosomes of interest and the number of sequence tags of the normalizing sequence of each of the one or more chromosomes of interest, a chromosome dose is calculated for each of the one or more chromosomes of interest in a computationally induced manner; and (h) each chromosome dose for the one or more chromosomes of interest is compared with a corresponding threshold for each of the one or more chromosomes of interest, and thereby determining the presence or absence of fetal chromosomal aneuploidy in the sample, wherein steps (e)-(h) are performed using one or more processors. In certain embodiments, DNA is end-repaired. In other embodiments, preparing the library does not include end-repairing cfDNA. This method is exemplified in Examples 5 and 6.

The processes for preparing sequencing libraries as described above are suitable for use in sample analysis methods, including but not limited to methods for determining copy number variation (CNV), and methods for determining the presence or absence of polymorphisms of any sequence of interest in samples comprising a single genome and in samples comprising a mixture of at least two genomes that are known or suspected to differ in one or more sequences of interest.

The amplification of the product of the connection adapter prepared on a solid phase or in a solution may be required to introduce the oligonucleotide sequence required for hybridization with the flow cell or other surfaces present in some NGS platforms into the template molecule of the connection adapter. The content of the amplification reaction is known to those of ordinary skill in the art and includes suitable substrates (such as dNTPs), enzymes (such as DNA polymerase) and the buffer components required for the amplification reaction. Optionally, the amplification of the polynucleotide of the connection adapter can be omitted. In general, the amplification reaction requires at least two amplification primers, such as primer oligonucleotides, which may be identical or different and may include "adapter specific portions" that can be annealed into primer binding sequences in the polynucleotide molecule to be amplified (or if the template is considered as a single strand, its complement, so during the annealing step).

Once formed, the library of the template prepared according to the method described above can be used for the solid phase nucleic acid amplification that may be needed by some NGS platforms. As used herein, the term "solid phase amplification" refers to any nucleic acid amplification reaction carried out on a solid support or in association with a solid support, so that all or a portion of the amplified products are fixed on the solid support when it is formed. In a specific embodiment, the term encompasses solid phase polymerase chain reaction (solid phase PCR) and its solid phase isothermal amplification, which are reactions similar to standard solution phase amplifications, except that one or both of the forward and reverse amplification primers are fixed on a solid support. Solid phase PCR also includes, for example, the following systems: emulsion, in which one primer is anchored to beads and the other primer is in free solution; Colony formation in a solid phase gel matrix, in which one primer is anchored to a surface and a primer is in free solution.

In various embodiments, after amplification, the sequencing library can be analyzed by microfluidic capillary electrophoresis to ensure that the library does not contain adapter dimers or single-stranded DNA. Libraries of template polynucleotide molecules are particularly suitable for solid-phase sequencing methods. In addition to providing templates for solid-phase sequencing and solid-phase PCR, library templates also provide templates for whole genome amplification.

Marker nucleic acids for tracking and verifying sample integrity

In various embodiments, sample integrity can be verified and samples can be tracked by sequencing a mixture of sample genomic nucleic acid (eg, cfDNA) and accompanying marker nucleic acids that have been introduced into the sample, eg, prior to processing.

Marker nucleic acid can be combined with a test sample (e.g., a biological source sample) and subjected to a process comprising, for example, one or more of the following steps: fractionating the biological source sample, for example, obtaining a substantially cell-free plasma fraction from a whole blood sample, purifying nucleic acids from a fractionated biological source sample (e.g., plasma) or a fractionated biological source sample (e.g., tissue sample), and sequencing. In certain embodiments, sequencing includes preparing a sequencing library. The sequence or sequence combination of the marker molecules combined with the source sample is unique to the source sample through selection. In certain embodiments, the unique marker molecules in the sample all have the same sequence. In other embodiments, the unique marker molecules in the sample are a plurality of sequences, for example, a combination of two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty or more different sequences.

In one embodiment, the integrity of sample can use a plurality of marker nucleic acid molecules with identical sequence to verify.As an alternative, the identity of sample can use a plurality of marker nucleic acid molecules with at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 25, at least 30, at least 35, at least 40, at least 50 or more different sequences to verify.Verifying the integrity of multiple biological samples (i.e. two or more biological samples) requires each in these two or more samples to be marked with the marker nucleic acid having a unique sequence for each in the multiple test samples marked.For example, first sample can be marked with the marker nucleic acid labeling with sequence A, and second sample can be marked with the marker nucleic acid labeling with sequence B. Alternatively, the first sample can be labeled with multiple marker nucleic acid molecules all having sequence A, and the second sample can be labeled with a mixture of sequences B and C, where sequences A, B, and C are marker molecules with different sequences.

Marker nucleic acid can be added to the sample at any stage of sample preparation that occurs before library preparation (if a library is to be prepared) and sequencing. In one embodiment, the marker molecule can be combined with an unprocessed source sample. For example, the marker nucleic acid can be provided in a collection tube for collecting a blood sample. As an alternative, the marker nucleic acid can be added to the blood sample after blood is drawn. In one embodiment, the marker nucleic acid is added to a container for collecting a biological fluid sample, for example, the marker nucleic acid is added to a blood collection tube for collecting a blood sample. In another embodiment, the marker nucleic acid is added to a portion of the biological fluid sample. For example, the marker nucleic acid is added to the plasma and/or serum portion (e.g., maternal plasma sample) of the blood sample. In yet another embodiment, the marker molecule is added to a purified sample (e.g., a nucleic acid sample that has been purified from a biological sample). For example, the marker nucleic acid is added to a sample of purified maternal and fetal cfDNA. Similarly, the marker nucleic acid can be added to a biopsy specimen before processing the specimen. In certain embodiments, the marker nucleic acid can be combined with a vector that delivers the marker molecule into cells of a biological sample. Cell delivery vehicles include pH-sensitive liposomes and cationic liposomes.

In various embodiments, the marker molecule has an anti-gene chain sequence that is not present in the genome of the biological source sample. In an exemplary embodiment, the marker molecule used to verify the integrity of the human biological source sample has a sequence that is not present in the human genome. In an alternative embodiment, the marker molecule has a sequence that is not present in the source sample and in any one or more known genomes. For example, the marker molecule used to verify the integrity of the human biological source sample has a sequence that is not present in the human genome and the mouse genome. The alternative allows verification of the integrity of a test sample comprising two or more genomes. For example, the integrity of a human cell-free DNA sample obtained from a subject attacked by a pathogen (e.g., a bacterium) can be verified using a marker molecule with a sequence that is not present in both the human genome and the genome of the attacking bacterium. The sequences of the genomes of many pathogens (e.g., bacteria, viruses, yeasts, fungi, protozoa, etc.) are publicly available on the World Wide Web at ncbi.nlm.nih.gov/genomes. In another embodiment, the marker molecule is a nucleic acid with a sequence that is not present in any known genome. The sequence of the marker molecule can be randomly generated by an algorithm.

In various embodiments, the marker molecule can be a naturally occurring deoxyribonucleic acid (DNA), ribonucleic acid or artificial nucleic acid analog (nucleic acid mimics), which include peptide nucleic acid (PMA), morpholino nucleic acid, locked nucleic acid, glycol nucleic acid and threose nucleic acid (the difference between it and naturally occurring DNA or RNA is that the molecular backbone changes) or a DNA mimics without a phosphodiester backbone. Deoxyribonucleic acid can come from naturally occurring genome or can be produced in the laboratory using an enzyme or by solid phase chemical synthesis. Chemical methods can also be used to produce natural undiscovered DNA mimics. The phosphodiester bond is replaced, but the available DNA derivatives that deoxyribose retains include but are not limited to the DNA mimics with a backbone formed by thiomethylacetal or formamide bonds, and it has been shown that these mimics are excellent structural DNA mimics. Other DNA mimics include morpholino derivatives and peptide nucleic acids (PNAs) containing a pseudopeptide backbone based on N-(2-aminoethyl)glycine (Ann Rev Biophys Biomol Struct 24:167-183 [1995]). PNAs are very good structural mimics of DNA (or ribonucleic acid [RNA]), and PNA oligomers can form very stable duplex structures with Watson-Crick complementary DNA and RNA (or PNA) oligomers, and can also bind to targets in duplex DNA by helix invasion (Mol Biotechnol 26:233-248 [2004]). Another excellent structural mimic/analog of DNA analogs that can be used as marker molecules is phosphorothioate DNA, in which one of the non-bridging oxygens is replaced by sulfur. This modification reduces the action of endonucleases and exonucleases 2 including 5' to 3' and 3' to 5' DNA Pol 1 exonucleases, nucleases S1 and P1, ribonucleases, serum nucleases, and snake venom phosphodiesterases.

The length of the marker molecule can be different from or similar to the length of the sample nucleic acid, that is, the length of the marker molecule can be similar to the length of the sample genome molecule, or it can be greater than or less than the length of the sample genome molecule. The length of the marker molecule is measured by the number of nucleotides or nucleotide analog bases that constitute the marker molecule. Separation methods known in the art can be used to distinguish marker molecules with lengths different from the length of the sample genome molecule from source nucleic acid. For example, the length difference between the marker and the sample nucleic acid molecule can be measured by electrophoretic separation such as capillary electrophoresis. Size differentiation may be conducive to quantifying and evaluating the quality of the marker nucleic acid and the sample nucleic acid. Preferably, the marker nucleic acid is shorter than the genomic nucleic acid, and the length is sufficient to exclude it from being mapped to the sample genome. For example, unique mapping to the human genome requires a human sequence of 30 bases. Therefore, in certain embodiments, the marker molecule used in the sequencing bioassay of human samples should be at least 30bp long.

The selection of marker molecule length is mainly determined by the sequencing technology used to verify the integrity of the source sample. The length of the sample genomic nucleic acid sequenced can also be considered. For example, some sequencing technologies use clonal amplification of polynucleotides, which may require that the genomic polynucleotides to be clonally amplified have a minimum length. For example, sequencing using the Iluna GAII sequence analyzer includes carrying out in vitro clonal amplification by bridge PCR (also known as cluster amplification) of polynucleotides with a minimum length of 110bp, and adapters are attached to these polynucleotides to provide at least 200bp and less than 600bp of nucleic acid clonally amplified and sequenced. In certain embodiments, the length of the marker molecule connected to the adapter is between about 200bp and about 600bp, between about 250bp and 550bp, between about 300bp and 500bp, or between about 350 and 450. In other embodiments, the length of the marker molecule connected to the adapter is about 200bp. For example, when the fetal cfDNA present in the maternal sample is sequenced, the length of the marker molecule can be selected to be similar to the length of the fetal cfDNA molecule. Therefore, in one embodiment, the length of the marker molecule used in the test for the presence or absence of fetal chromosomal aneuploidy including large-scale parallel sequencing of cfDNA in the maternal sample can be about 150bp, about 160bp, 170bp, about 180bp, about 190bp or about 200bp; the marker molecule is preferably about 170bp. Other sequencing methods such as SOLiD sequencing, polymerase clone sequencing (Polony Sequencing) and 454 sequencing use emulsion PCR to clonally amplify DNA molecules for sequencing, and each technology specifies the minimum and maximum length of the molecule to be amplified. The length of the marker molecule to be sequenced in the form of clonally amplified nucleic acid can reach about 600bp. In certain embodiments, the length of the marker molecule to be sequenced can be greater than 600bp.

The single molecule sequencing technology that does not adopt molecular cloning amplification and can order-check the nucleic acid in the extremely wide template length range does not require that the molecule to be sequenced has any specific length in most cases. However, the sequence yield per unit mass depends on the number of 3' terminal hydroxyl groups, so having a relatively short template for order-checking is more effective than having a long template. If starting from a nucleic acid longer than 1000nt, it is generally advisable to shear these nucleic acids to an average length of 100 to 200nt so that more sequence information can be produced from nucleic acids of the same mass. Therefore, the length of the marker molecule can be in the range of tens of bases to several thousand bases. The length of the marker molecule used for single molecule order-checking can reach about 25bp, reach about 50bp, reach about 75bp, reach about 100bp, reach about 200bp, reach about 300bp, reach about 400bp, reach about 500bp, reach about 600bp, reach about 700bp, reach about 800bp, reach about 900bp, reach about 1000bp or more.

The length of the marker molecule selected is also determined by the length of the genomic nucleic acid sequenced. For example, cfDNA circulates in the human bloodstream as a genomic fragment of cellular genomic DNA. Fetal cfDNA molecules found in maternal plasma are generally shorter than maternal cfDNA molecules (Chan et al., Clinical Chemistry (Clin Chem) 50: 8892 [2004]). Size fractionation of circulating fetal DNA has confirmed that the average length of circulating fetal DNA fragments is <300bp, while maternal DNA is estimated to be between about 0.5Kb and 1Kb (Li et al., Clinical Chemistry, 50: 1002-1011 [2004]). These findings are consistent with the findings of Fan et al. (Fan et al., Clinical Chemistry 56: 1279-1286 [2010]) who used NGS to determine that fetal cfDNA rarely exceeds 340bp. DNA isolated from urine using standard silica-based methods consists of two parts: high-molecular-weight DNA derived from exfoliated cells and a low-molecular-weight (150-250 base pairs) fraction of transrenal DNA (Tr-DNA) (Portzato et al., Clinical Chemistry 46:1078-1084, 2000; and Su et al., Journal of Mol. Diagnostics 6:101-107, 2004). Recently developed techniques for isolating cell-free nucleic acids from body fluids have shown that DNA and RNA fragments present in urine are much shorter than 150 base pairs in applications to isolating transrenal nucleic acids (U.S. Patent Application Publication No. 20080139801). In embodiments where cfDNA is the genomic nucleic acid being sequenced, the selected marker molecule can be roughly the length of the cfDNA. For example, the length of the marker molecule used in a maternal cfDNA sample to be sequenced, in the form of a single nucleic acid molecule or in the form of a clonally amplified nucleic acid, can be between about 100 bp and 600. In other embodiments, the sample genomic nucleic acid is a fragment of a larger molecule. For example, the sample genomic nucleic acid being sequenced is fragmented cellular DNA. In embodiments where fragmented cellular DNA is sequenced, the length of the marker molecule can be up to the length of the DNA fragment. In certain embodiments, the length of the marker molecule is at least the minimum length required for uniquely mapping the sequence read to an appropriate reference genome. In other embodiments, the length of the marker molecule is the minimum length required to exclude the marker molecule from being mapped to the sample reference genome.

Furthermore, marker molecules can be used to authenticate samples that have not been tested by nucleic acid sequencing and can be authenticated by common biotechniques other than sequencing (real-time PCR).

Sample controls (e.g., positive controls used in sequencing and/or analysis)

In various embodiments, marker sequences introduced into a sample, such as those described above, can serve as positive controls to verify the accuracy and efficacy of sequencing and subsequent processing and analysis.

Thus, compositions and methods are provided for providing an in-process positive control (IPC) for sequencing DNA in a sample. In certain embodiments, a positive control for sequencing cfDNA in a sample comprising a genomic mixture is provided. The IPC can be used to correlate baseline shifts in sequence information obtained from different sets of samples (e.g., samples sequenced at different times on different sequencing batches). Thus, for example, the IPC can correlate sequence information obtained for a maternal test sample with sequence information obtained from a set of qualified samples sequenced at different times.

Similarly, in the case of fragment analysis, IPC can correlate sequence information obtained from a subject for a specific fragment with sequences (similar sequences) obtained from a set of qualified samples sequenced at different times. In certain embodiments, IPC can correlate sequence information obtained from a subject for a specific cancer-associated locus with sequence information obtained from a set of qualified samples (e.g., from known amplifications/deletions, etc.).

In addition, IPC can be used as a marker to track samples during sequencing. IPC can also provide a qualitative positive sequence dose value (e.g., NCV) for one or more aneuploidies of the chromosome of interest (e.g., trisomy 21, trisomy 13, trisomy 18) to provide a more appropriate interpretation and ensure the reliability and accuracy of the data. In certain embodiments, an IPC comprising nucleic acids from male and female genomes can be established to provide the doses of chromosomes X and Y in the maternal sample, thereby determining whether the fetus is male.

The type and number of controls during the process depend on the type or property of the test required. For example, for the test that needs to be sequenced to determine whether there is a chromosome aneuploidy from the DNA of the sample including the genomic mixture, the control can include the DNA obtained from the test sample known to include the same chromosome aneuploidy during the process. In certain embodiments, the IPC includes the DNA from the sample known to include the chromosome aneuploidy of interest. For example, the IPC for determining the presence or absence of a fetal trisomy (such as trisomy 21) in the maternal sample includes the DNA obtained from the individual with trisomy 21. In certain embodiments, the IPC includes a mixture of DNA obtained from two or more individuals with different aneuploidies. For example, for determining the presence or absence of trisomy 13, trisomy 18, trisomy 21 and X monosomy, the IPC includes a combination of DNA samples obtained from pregnant women each carrying the fetus of one of the test trisomy. Except for complete chromosome aneuploidy, IPCs can be established to provide positive controls for determining the presence or absence of a partial aneuploidy test.

An IPC serving as a control for detecting a single aneuploidy can be established using a mixture of cellular genomic DNA obtained from two subjects, one of which is a donor of the aneuploid genome. For example, an IPC serving as a control for a test to determine fetal trisomy (e.g., trisomy 21) can be established by combining genomic DNA from a male or female subject carrying the trisomy chromosome with genomic DNA from a female subject known not to carry the trisomy chromosome. Genomic DNA can be extracted from cells of two subjects and sheared to provide fragments of between about 100 bp and 400 bp, between about 150 bp and 350 bp, or between about 200 bp and 300 bp to simulate the circulating cfDNA fragments in the maternal sample. The proportion of fragmented DNA from a subject carrying aneuploidy (trisomy 21) is selected to simulate the proportion of circulating fetal cfDNA found in maternal samples, and an IPC comprising a fragmented DNA mixture of about 5%, about 10%, about 15%, about 20%, about 25%, about 30% of the DNA from a subject carrying the aneuploidy is provided. The IPC may include DNA from different subjects each carrying a different aneuploidy. For example, the IPC may include about 80% unaffected female DNA, and the remaining 20% may be DNA from three different subjects each carrying a trisomy chromosome 21, trisomy chromosome 13, and trisomy chromosome 18. A mixture of fragmented DNA is prepared for sequencing. Processing the mixture of fragmented DNA may include preparing a sequencing library, which may be sequenced in a single-channel or multiplex mode using any massively parallel method. The stock solution of the genomic IPC may be stored and used for multiple diagnostic tests.

Alternatively, IPCs can be established using cfDNA obtained from a mother known to carry a fetus with a known chromosomal aneuploidy. For example, cfDNA can be obtained from a pregnant woman carrying a fetus with trisomy 21. cfDNA is extracted from a maternal sample and cloned into a bacterial vector and grown in bacteria to provide an uninterrupted source of IPCs. Restriction enzymes can be used to extract the DNA from the bacterial vector. Alternatively, the cloned cfDNA can be amplified by, for example, PCR. The IPC DNA can be processed to be sequenced in the same batch as the cfDNA from the test sample to be analyzed for the presence or absence of chromosomal aneuploidy.

While the above description describes the creation of IPCs relative to trisomy, it will be appreciated that IPCs can be created that reflect other partial aneuploidies, including, for example, different segment amplifications and/or deletions. Thus, for example, where different cancers are known to be associated with specific amplifications (e.g., breast cancer is associated with 20Q13), IPCs can be created that incorporate those known amplifications.

Sequencing methods

As noted above, as part of the process of identifying copy number variation, the prepared sample (e.g., a sequencing library) is sequenced. Any of a variety of sequencing technologies can be utilized.

Some sequencing technologies are commercially available, such as the hybridization sequencing platform of Affymetrix Inc. (Sunnyvale, CA) and the synthesis sequencing platforms of 454 Life Sciences (Bradford, CT), Illumina/Solexa (Hayward, CA), and Helicos Biosciences (Cambridge, MA), and the ligation sequencing platform of Applied Biosystems (Foster City, CA), as described below. In addition to single molecule sequencing using Helicon Biosciences' sequencing-by-synthesis method, other single molecule sequencing technologies include, but are not limited to, Pacific Biosciences' SMRT ^™ technology, ION TORRENT ^™ technology, and nanopore sequencing methods developed by, for example, Oxford Nanopore Technologies.

Although the automated Sanger method is considered a 'first generation' technology, Sanger sequencing methods including automated Sanger sequencing methods may also be used in the methods described herein. Additional suitable sequencing methods include, but are not limited to, nucleic acid imaging techniques such as atomic force microscopy (AFM) or transmission electron microscopy (TEM). Schematic sequencing techniques are described in more detail below.

In one illustrative but non-limiting embodiment, the methods described herein include using Helicos True Single Molecule Sequencing (tSMS) technology (e.g., as described in Harris TD et al., Science 320:106-109 [2008]) to obtain sequence information for nucleic acids in a test sample, such as cfDNA from a maternal sample, cfDNA from a subject being screened for cancer, or cellular DNA. In tSMS, a DNA sample is split into strands of approximately 100 to 200 nucleotides, and a poly A sequence is added to the 3' end of each DNA strand. Each strand is labeled by adding a fluorescently labeled adenosine nucleotide. The DNA strands are then hybridized to a flow cell containing millions of oligo-T capture sites fixed to the flow cell surface. In certain embodiments, the template density can be approximately 100 million templates/ ^cm2 . The flow cell is then loaded into an instrument, such as a HeliScope ^™ sequencer, and a laser illuminates the flow cell surface to visualize the position of each template. A CCD camera can measure the position of the template on the flow cell surface. The template fluorescent label is then split and washed off. The sequencing reaction begins by introducing DNA polymerase and fluorescently labeled nucleotides. Oligo T nucleic acid serves as a primer. Polymerase binds the labeled nucleotides to the primer in a template-guided manner. Polymerase and unbound nucleotides are removed. The template guiding the binding of the fluorescently labeled nucleotides is distinguished by imaging the flow cell surface. After imaging, the splitting step removes the fluorescent label, and the procedure is repeated for other fluorescently labeled nucleotides until the desired read length is obtained. Sequence information is collected using each nucleotide addition step. Carrying out whole genome sequencing by single molecule sequencing technology can exclude or typically avoid PCR-based amplification when preparing sequencing libraries, and these methods allow direct measurement of samples rather than measuring copies of that sample.

In another illustrative but non-limiting embodiment, the method described herein includes using 454 sequencing (Roche) (e.g., as described in Margulies, M. et al., Nature 437:376-380 [2005]) to obtain sequence information of nucleic acids in a test sample, such as cfDNA in a maternal test sample, cfDNA or cellular DNA from a subject screened for cancer, etc. The 454 sequencing method typically includes two steps. In the first step, the DNA is sheared into fragments having approximately 300 to 800 base pairs, and these fragments are blunt-ended. Oligonucleotide adapters are then attached to the ends of the fragments. The adapters serve as primers for fragment amplification and sequencing. The fragments can be attached to DNA capture beads, such as beads coated with streptavidin, using, for example, adapter B containing a 5'-biotin tag. The fragments attached to the beads are PCR amplified within oil-in-water emulsion droplets. The result is multiple copies of the clonally amplified DNA fragments on each bead. In the second step, the beads are captured in a well (e.g., a picoliter-sized well). Pyrophosphate sequencing is performed in parallel on each DNA fragment. Adding one or more nucleotides generates a light signal, which is recorded by a CCD camera in the sequencing instrument. The signal intensity is proportional to the number of nucleotides bound. Pyrophosphate sequencing utilizes pyrophosphate (PPi) that can be detached when nucleotides are added. PPi is converted into ATP by ATP sulfurylase in the presence of adenosine 5' phosphosulfate. Luciferase uses ATP to convert luciferin into oxyluciferin, and this reaction produces light, which is measured and analyzed.

In another illustrative but non-limiting embodiment, the method described herein includes using SOLiD ^™ technology (Applied Biosystems) to obtain sequence information of nucleic acids in a test sample, such as cfDNA in a maternal test sample, cfDNA or cellular DNA of a subject screened for cancer, and the like. In the SOLiD ^™ ligation sequencing method, genomic DNA is sheared into fragments, and adapters are attached to the 5' and 3' ends of the fragments to produce a fragment library. As an alternative, internal adapters can be introduced as follows: adapters are attached to the 5' and 3' ends of the fragments, the fragments are circularized, the circularized fragments are digested to produce internal adapters, and adapters are attached to the 5' and 3' ends of the resulting fragments to produce a paired library. Next, a clonal bead population is prepared in a microreactor containing beads, primers, templates, and PCR components. Following PCR, the template is denatured and beads are enriched to separate beads with amplified templates. The template on the selected beads is 3' modified to allow binding to a slide. The sequence can be determined by sequential hybridization and ligation of partially random oligonucleotides to a central determined base (or base pair) identified by a specific fluorophore. After the color is recorded, the ligated oligonucleotide is cleaved and removed, and the process is repeated.

In another illustrative but non-limiting embodiment, the method described herein includes using Pacific Biosciences' single molecule real-time (SMRT ^™ ) sequencing technology to obtain sequence information of nucleic acids in test samples, such as cfDNA in maternal test samples, cfDNA or cellular DNA of subjects screened for cancer, etc. In the SMRT sequencing method, during DNA synthesis, the continuous binding of dye-labeled nucleotides is imaged. A single DNA polymerase molecule is attached to the bottom surface of a separate zero-mode wavelength detector (ZMW detector) that has obtained sequence information, and the nucleotides connected by phosphate are just combined into growing primer strands. The ZMW detector includes a closed structure that allows the binding of single nucleotides by DNA polymerase to be observed against the background of fluorescent nucleotides that diffuse rapidly outside the ZMW range (e.g., microseconds). It typically takes several milliseconds for nucleotides to bind to growing strands. During this period, the fluorescent label is excited and produces a fluorescent signal, and the fluorescent label is split. Measuring the corresponding dye fluorescence indicates which base is combined. Repeating the process to obtain a sequence.

In another illustrative but non-limiting embodiment, the method described herein includes using nanopore sequencing (e.g., Sorey GV and Mellor A., Clin Chem 53: 1996-2001 [2007]) to obtain sequence information of nucleic acids in a test sample, such as cfDNA in a maternal test sample, cfDNA or cellular DNA of a subject screened for cancer, etc. Nanopore sequencing DNA analysis technology has been developed by multiple companies, including, for example, Oxford Nanopore Technologies (Oxford, United Kingdom), Sequenom, NABsys, etc. Nanopore sequencing is a single-molecule sequencing technology in which single-molecule DNA is directly sequenced as it passes through a nanopore. A nanopore is a small hole with a diameter typically of about 1 nanometer. The nanopore is immersed in a conductive fluid and a potential (voltage) is applied across it, generating a small current due to ion conduction through the nanopore. The amount of current flowing is sensitive to the size and shape of the nanopore. As a DNA molecule passes through a nanopore, each nucleotide on the DNA molecule blocks the nanopore to varying degrees, causing the magnitude of the current passing through the nanopore to change to varying degrees. Therefore, this change in current as the DNA molecule passes through the nanopore provides a readout of the DNA sequence.

In another illustrative but non-limiting embodiment, the methods described herein include using a chemically sensitive field effect transistor (chemFET) array (e.g., as described in U.S. Patent Application Publication No. 2009/0026082) to obtain sequence information of nucleic acids in a test sample, such as cfDNA in a maternal test sample, cfDNA from a subject being screened for cancer, or cellular DNA, among others. In one example of this technology, a DNA molecule can be placed in a reaction chamber and the template molecule can be hybridized with a sequencing primer bound to a polymerase. One or more triphosphates are incorporated into new nucleic acid strands at the 3' end of the sequencing primer, which can be discerned by a chemFET as a change in current. An array can have multiple chemFET sensors. In another example, a single nucleic acid can be attached to a bead, and the nucleic acid can be amplified on the bead, and the individual beads can be transferred to separate reaction chambers on the chemFET array, each chamber having a chemFET sensor, and the nucleic acid can be sequenced.

In another embodiment, the method of the present invention includes utilizing Halcyon Molecular's technology using transmission electron microscopy (TEM) to obtain sequence information of nucleic acids in a test sample, such as cfDNA in a maternal test sample. The method, called Individual Molecular Placement Rapid Nanotransfer (IMPRNT), includes imaging high molecular weight (150 kb or greater) DNA selectively labeled with heavy atom markers using a single-atom resolution transmission electron microscope, and arranging these molecules on an ultrathin film in a highly dense (3 nm strand to strand) parallel array with consistent base-to-base spacing. Electron microscopy is used to image the molecules on the film to determine the position of the heavy atom markers and extract the base sequence information of the DNA. This method is further described in PCT patent publication WO 2009/046445. This method allows the sequence of a complete human genome to be determined in less than ten minutes.

In another embodiment, the DNA sequencing technology is ion torrent single-molecule sequencing, which combines semiconductor technology with simple sequencing chemistry to convert chemically encoded information (A, C, G, T) directly into digital information (0, 1) on a semiconductor chip. Essentially, when nucleotides are combined into DNA strands by polymerase, hydrogen ions are released as byproducts. Ion torrent uses a high-density array of micromachined wells to perform this biochemical process in a massively parallel manner. Each well accommodates a different DNA molecule. Below the well is an ion-sensitive layer, and below the ion-sensitive layer is an ion sensor. When a nucleotide (e.g., C) is added to the DNA template and then combined into a DNA strand, a hydrogen ion is released. The charge of that ion changes the pH of the solution, which can be detected by the ion sensor of the ion torrent. The sequencer (essentially the world's smallest solid-state pH meter) reads the bases (directly from chemical information to digital information). The Ion Personal Genome Machine (PGM ^™ ) sequencer then continuously bombards the chip with nucleotides one by one. If the next nucleotide to hit the chip does not match, no voltage change will be recorded and the base will not be called. If two identical bases are present on a DNA strand, the voltage doubles and the chip records the presence of two identical bases. Direct detection can record nucleotide incorporation within seconds.

In another embodiment, the method of the present invention includes obtaining sequence information of nucleic acids in a test sample using a hybrid sequencing method, such as cfDNA in a maternal test sample. The hybrid sequencing method includes contacting a plurality of polynucleotide sequences with a plurality of polynucleotide probes, wherein each of the plurality of polynucleotide probes can be optionally tethered to a substrate. The substrate may be a flat surface comprising an array of known nucleotide sequences. The pattern hybridized with the array can be used to determine the polynucleotide sequences present in the sample. In other embodiments, each probe is tethered to a bead, such as a magnetic bead, etc. Hybridization with the bead can be determined and used to identify a plurality of polynucleotide sequences in the sample.

In another embodiment, the method of the present invention comprises using Illumina synthesis sequencing and reversible terminator-based sequencing chemistry (e.g., as described in Bentley et al., Nature 6:53-59 [2009]) to obtain sequence information of nucleic acids in a test sample, such as cfDNA in a maternal test sample, by massively parallel sequencing of millions of DNA fragments. The template DNA can be genomic DNA, such as cfDNA. In certain embodiments, genomic DNA from isolated cells is used as a template and is fragmented into lengths of several hundred base pairs. In other embodiments, cfDNA is used as a template, and because cfDNA exists as short fragments, fragmentation is not required. For example, fetal cfDNA circulates in the bloodstream as fragments of approximately 170 base pairs (bp) in length (Fan et al., Clin Chem 56:1279-1286 [2010]), and prior to sequencing, the DNA does not need to be fragmented. Illumina sequencing technology relies on the attachment of fragmented genomic DNA to an optically transparent flat surface to which oligonucleotide anchors are bound. The template DNA ends are repaired to produce 5'-phosphorylated blunt ends, and the polymerase activity of the Klenow fragment is used to add single A bases to the 3' ends of the blunt-end phosphorylated DNA fragments. This addition prepares DNA fragments for connection to oligonucleotide adapters, which have single T base overhangs at their 3' ends to improve connection efficiency. The adapter oligonucleotides are complementary to the flow cell anchors. Under limiting dilution conditions, single-stranded template DNA modified with adapters is added to the flow cell and fixed to the anchor by hybridization. The attached DNA fragments are extended and bridge-amplified to establish an ultra-high-density sequencing flow cell with hundreds of millions of clusters, each containing about 1,000 copies of the same template. In one embodiment, randomly fragmented genomic DNA (e.g., cfDNA) is amplified using PCR before undergoing cluster amplification. As an alternative, a genomic library preparation without amplification is used, and cluster amplification method (Kozarewa et al., Nature Methods 6:291-295 [2009]) is used alone to enrich for random fragmented genomic DNA, such as cfDNA. Template sequencing is performed using a reliable four-color DNA synthesis sequencing technology with a reversible terminator that can remove fluorescent dyes. High-sensitivity fluorescence detection is obtained using laser excitation and total internal reflection optical devices. Short sequence reads of about 20bp to 40bp (e.g., 36bp) are compared against a reference genome shielded by repeat fragments, and a specially developed data analysis pipeline software is used to identify the unique mapping of short sequence reads to the reference genome. A reference genome shielded by non-repeat fragments can also be used. Whether a reference genome shielded by repeat fragments or a reference genome shielded by non-repeat fragments is used, only the readings uniquely mapped to the reference genome are counted. After the first reading is completed, the template can be regenerated in situ so that a second reading can be performed from the opposite end of the fragment. Therefore, single-end or paired-end sequencing of DNA fragments can be used. The DNA fragments present in the sample are partially sequenced, and the reads comprising a predetermined length (e.g., 36 bp) mapped to the sequence tags of the known reference genome are counted. In one embodiment, the reference genome sequence is the NCBI36/hg18 sequence, which is available on the world wide web at genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105. Alternatively, the reference genome sequence is GRCh37/hg19, which is available on the world wide web at genome.ucsc.edu/cgi-bin/hgGateway. Other public sequence information sources include GenBank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory), and DDBJ (DNA Database of Japan). There are a variety of computer algorithms available for aligning sequences, including but not limited to BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993), FASTA (Person & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology 10: R25.1-R25.10 [2009]), or ELAND (Illumina, Inc., San Diego, CA, USA). In one embodiment, one end of a clonally amplified copy of a plasma cfDNA molecule is sequenced and processed by bioinformatics alignment analysis on an Illumina Genome Analyzer using the Efficiently Aligned Nucleotide Database (ELAND) software.

In certain embodiments of the methods described herein, the mapped sequence tags comprise sequence reads of about 20bp, about 25bp, about 30bp, about 35bp, about 40bp, about 45bp, about 50bp, about 55bp, about 60bp, about 65bp, about 70bp, about 75bp, about 80bp, about 85bp, about 90bp, about 95bp, about 100bp, about 110bp, about 120bp, about 130bp, about 140bp, about 150bp, about 200bp, about 250bp, about 300bp, about 350bp, about 400bp, about 450bp, or about 500bp. It is expected that technological advancements will enable single-end reads greater than 500bp, and when paired-end reads are produced, it is possible to achieve reads greater than about 1000bp. In one embodiment, the mapped sequence tags comprise 36bp sequence reads. Sequence tag mapping is achieved by comparing tag sequences to a reference sequence to determine the chromosomal origin of sequenced nucleic acid (e.g., cfDNA) molecules, without requiring specific genetic sequence information. Small mismatches (0 to 2 mismatches per sequence tag) can account for minor polymorphisms that may exist between the reference genome and the genomes in the mixed sample.

^In certain embodiments, the method for preparing ^the genomic DNA of the ^present invention is ^{to use a plurality of} sequence tags.In ^...

Correctly determine whether there is or lacks the necessary accuracy of CNV (such as aneuploidy) in the sample and be mapped to the variation (inter-chromosome variability) of the number of sequence tags of reference genome between each sample according to the sequencing operation and the variation (inter-sequence variability) of the number of sequence tags mapped to reference genome in different sequencing operations.For example, the variation of the label that is mapped to rich GC or poor GC reference sequence may be particularly significant.Other variations can be caused by using different nucleic acid extraction and purification schemes, preparing sequencing libraries and using different sequencing platforms.The inventive method uses sequence dosage (chromosome dosage or segment dosage) according to the understanding of normalized sequence (normalized chromosome sequence or normalized segment sequence), thereby explaining the variability of the natural increase caused by the variability related to inter-chromosome variability (same batch) and inter-sequence variability (between rounds) and platform in essence.Chromosome dosage is based on the understanding of normalized chromosome sequence, and normalized chromosome sequence can include single chromosome, or include two or more chromosomes selected from chromosome 1 to 22, X and Y. Alternatively, the normalizing chromosome sequence can include a single chromosome segment, or two or more segments of one chromosome or two or more chromosomes. The segment dose is based on knowledge of the normalizing segment sequence, which can include a single segment of any chromosome, or two or more segments of any two or more chromosomes of chromosomes 1 to 22, X, and Y.

Singleplex sequencing

Fig. 4 shows a flow chart of an embodiment of the method, wherein marker nucleic acid is combined with the source sample nucleic acid of a single sample to analyze genetic abnormalities, while determining the integrity of the biological source sample. In step 410, a biological source sample comprising genomic nucleic acid is obtained. In step 420, marker nucleic acid is combined with the biological source sample to obtain a marker sample. In step 430, a sequencing library of a mixture of source sample genomic nucleic acid and marker nucleic acid amplified in a clonal manner is prepared, and in step 440, the library is sequenced to provide sequencing information relevant to sample source genomic nucleic acid and marker nucleic acid in a large-scale parallel manner. Large-scale parallel sequencing methods provide sequencing information about sequence readings, and these sequence readings are mapped to one or more reference genomes to produce sequence tags that can be analyzed. In step 450, all sequencing information is analyzed, and in step 460, according to the sequencing information relevant to the marker molecule, the integrity of the source sample is verified. Verification of the source sample integrity is accomplished by determining the consistency between the sequencing information of the marker molecule obtained in step 450 and the known sequence of the marker molecule added to the original source sample in step 420. The same process can be applied to multiple samples that are sequenced separately, wherein each sample contains molecules with sequences unique to that sample, i.e., a sample is labeled with a unique marker molecule and sequenced separately from other samples in the flow cell or slide of the sequencer. If the sample integrity is tested, the sequencing information associated with the sample genomic nucleic acid can be analyzed to provide information, for example, about the condition of the subject from which the source sample was obtained. For example, if the sample integrity is tested, the sequencing information associated with the genomic nucleic acid is analyzed to determine the presence or absence of chromosomal abnormalities. If the sample integrity is not tested, the sequencing information is not considered.

The method depicted in FIG4 is also applicable to biological assays involving singleplex sequencing of single molecules, such as Helicon's tSMS, Pacific Biosciences' SMRT, Oxford Nanopore's BASE, and other technologies, such as that proposed by IBM, which do not require library preparation.

Multiplex sequencing

The large amount of sequence readings that can be obtained for each batch of sequencing operations allow the sample merged to be analyzed, i.e., multiplex analysis, which maximizes sequencing capabilities and reduces workflow. For example, the large-scale parallel sequencing performed by the eight-lane flow cell of the Illumina genome analyzer to eight libraries can be multiplexed to perform sequencing of two or more samples in each lane, so that 16, 24, 32 or more samples are sequenced in a single operation. Parallel sequencing (i.e., multiplex sequencing) is required to merge sample specificity index sequences (also referred to as barcodes) during the sequencing library preparation. Sequencing index is a unique base sequence of approximately 5, approximately 10, approximately 15, approximately 20, approximately 25 or more bases added to the 3 ' ends of genomic nucleic acid and marker nucleic acid. Multiplex systems can sequence hundreds of biological samples in a single batch of sequencing operations. The sequencing library indexed can be prepared for sequencing the sequence amplified in a clonal manner by incorporating the index sequence into one of the PCR primers for clustered amplification. Alternatively, the index sequence can be incorporated into the adapter and connected to the cfDNA before PCR amplification. The index library for single molecule sequencing can be established by merging the index sequence at the 3' end of the marker and the genomic molecule or adding the sequence required for hybridization with the flow cell anchor (e.g., adding multiple A tails so as to use tSMS for single molecule sequencing). Sequencing the uniquely labeled and indexed nucleic acid provides index sequence information for identifying the sample in the combined sample library, and the sequence information of the marker molecule correlates the sequencing information of the genomic nucleic acid with the sample source. In an embodiment in which multiple samples are sequenced separately (i.e., single sequencing), it is only necessary to modify the marker and genomic nucleic acid molecules of each sample to include the adapter sequence and exclude the index sequence as needed by the sequencing platform.

Fig. 5 provides a flow chart of an embodiment 500 of the method for checking sample integrity, and these samples are subjected to multi-step multiple sequencing biological analysis, that is, the nucleic acid of individual samples is combined and sequenced as a complex mixture. In step 510, multiple biological source samples are obtained, each sample comprising genomic nucleic acid. In step 520, a unique marker nucleic acid is combined with each biological source sample to obtain multiple uniquely labeled samples. In step 530, a sequencing library of sample genomic nucleic acid and marker nucleic acid is prepared for each uniquely labeled sample. The library preparation of the sample scheduled for multiple sequencing includes incorporating a unique index tag into the marker nucleic acid of the sample and each uniquely labeled sample to provide a sample in which its source nucleic acid sequence can be correlated with the corresponding marker nucleic acid sequence and identified in the complex solution. In embodiments of the method comprising a marker molecule (e.g., DNA) that can be enzymatically modified, an index molecule can be incorporated into the 3' end of the sample and the marker molecule by connecting a sequenceable adapter sequence comprising an index sequence. In the embodiment of the method comprising a marker molecule that cannot be enzymatically modified (e.g., a DNA analog without a phosphate backbone), an index sequence is incorporated into the 3' end of the analog marker molecule during synthesis. The sequencing libraries of two or more samples are merged and loaded into the flow cell of a sequencer, and in step 540, they are sequenced in a large-scale parallel manner. In step 550, all sequencing information is analyzed and in step 560, the integrity of the source sample is verified based on the sequencing information relevant to the marker molecule. The integrity of each of the multiple source samples is verified by first grouping the sequence tags relevant to the same index sequence so that these genomic sequences and marker sequences of each library consisting of the genomic molecules of multiple samples are related to the discriminant sequence and completed. The grouped markers and genomic sequences are then analyzed to verify that the sequence obtained for the marker molecule corresponds to the known unique sequence added to the corresponding source sample. If the sample integrity is verified, the sequencing information relevant to the sample genomic nucleic acid can be analyzed to provide the genetic information relevant to the subject derived from the source sample. For example, if the sample integrity is verified, the sequencing information relevant to the genomic nucleic acid is analyzed to determine the presence or absence of chromosomal abnormalities. The lack of concordance between the sequencing information of the marker molecules and the known sequence indicates sample confounding and does not take into account the accompanying sequencing information associated with the genomic cfDNA molecules.

Detection of CNV for prenatal diagnosis

Cell-free fetal DNA and RNA circulating in maternal blood can be used for early non-invasive prenatal diagnosis (NIPD) of a growing number of genetic conditions, both for pregnancy management and to aid reproductive decision-making. The presence of cell-free DNA circulating in the bloodstream has been known for over 50 years. Recently, the presence of small amounts of circulating fetal DNA in the maternal bloodstream during pregnancy was discovered (Lo et al., Lancet 350:485-487 [1997]). Thought to be derived from dying placental cells, cell-free fetal DNA (cfDNA) has been shown to be composed of short fragments typically less than 200 bp in length (Chan et al., Clinical Chemistry, 50:88-92 [2004]), can be identified as early as 4 weeks of gestation (Illanes et al., Early Human Dev, 83:563-566 [2007]), and is known to be cleared from the maternal circulation within hours of delivery (Lo et al., Am J Hum Genet, 64:218-224 [1999]). In addition to cfDNA, fragments of cell-free fetal RNA (cfRNA) can also be identified in the maternal bloodstream, which are derived from genes transcribed in the fetus or placenta. The extraction and subsequent analysis of these fetal genetic elements from maternal blood samples provides new opportunities for NIPD.

This method is a method independent of polymorphism, it is for use in NIPD and it does not require to distinguish fetal cfDNA from maternal cfDNA so that fetal aneuploidy can be determined. In some embodiments, the aneuploidy is a complete chromosome trisomy or monosomy, or a partial trisomy or monosomy. Partial aneuploidy is caused by gaining or losing part of a chromosome, and encompasses chromosomal imbalances, which are generated from unbalanced translocations, unbalanced inversions, deletions, and insertions. So far, the most common known aneuploidy that can coexist with life is trisomy 21, i.e. Down syndrome (DS), which is caused by the presence of part or all of chromosome 21. Rarely, DS can be caused by a hereditary or sporadic defect, whereby an extra copy of all or part of chromosome 21 becomes attached to another chromosome (usually chromosome 14) to form a single aberrant chromosome. DS is associated with intellectual impairment, severe learning difficulties, and excess mortality caused by long-term health problems (such as heart disease). Other aneuploidies with known clinical significance include Edward's syndrome (trisomy 18) and Patau syndrome (trisomy 13), which are often fatal in the first few months of life. Aneuploidies related to the number of sex chromosomes are also known and include monosomy X, such as Turner syndrome (XO) and triple X syndrome (XXX) in female newborns, and Klinefelter syndrome (XXY) and XYY syndrome in male newborns, all of which are associated with different phenotypes including infertility and reduced intellectual skills. Monosomy X [45, X] is a common cause of early pregnancy miscarriage, accounting for approximately 7% of spontaneous miscarriages. Based on a live birth frequency of 45, X (also known as Turner syndrome) of 1-2/10,000, it is estimated that less than 1% of 45, X fetuses survive to the delivery period. Approximately 30% of patients with Turner syndrome are mosaics with a 45,X cell line and a 46,XX cell line, or a cell line containing a rearranged X chromosome (Hook and Warburton, 1983). The phenotype in liveborn infants is relatively mild (given the high embryonic lethality rate), and it has been hypothesized that likely all liveborn females with Turner syndrome carry a cell line containing both sex chromosomes. Monosomy X can occur in females as 45,X or 45,X/46XX, and in males as 45,X/46XY. Autosomal monosomy in humans is generally considered incompatible with life; however, a number of cytogenetic reports have described complete monosomy of one chromosome 21 in live-born children (Vosranova et al., Molecular Cytogen. 1:13 [2008]; Joosten et al., Prenatal Diagn. 17:271-5 [1997]). The methods described herein can be used for prenatal diagnosis of these and other chromosomal abnormalities.

According to some embodiments, the methods disclosed herein can determine the presence or absence of a chromosomal trisomy for any of chromosomes 1 to 22, X, and Y. Examples of chromosomal trisomies that can be detected by the methods of the present invention include, but are not limited to, trisomy 21 (T21; Down syndrome), trisomy 18 (T18; Edwards syndrome), trisomy 16 (T16), trisomy 20 (T20), trisomy 22 (T22; cat eye syndrome), trisomy 15 (T15; Prader-Willi syndrome), trisomy 13 (T13; Patau syndrome), trisomy 8 (T8; Warkany syndrome), trisomy 9, and XXY (Klinefelter syndrome), XYY, or XXX trisomy. Complete trisomies of other autosomes are lethal when present in a non-mosaic state, but can be compatible with life when present in a mosaic state. It will be appreciated that in fetal cfDNA, various complete trisomies (whether present in a mosaic or non-mosaic state) as well as partial trisomies can be determined according to the teachings provided herein.

Non-limiting examples of partial trisomies that can be determined using the methods of the present invention include, but are not limited to, partial trisomy 1q32-44, trisomy 9p, trisomy 4 mosaicism, trisomy 17p, partial trisomy 4q26-qter, partial 2p trisomy, partial trisomy 1q, and/or partial trisomy 6p/monosomy 6q.

The method disclosed herein can also be used to measure chromosome monosomy X, chromosome monosomy 21 and partial monosomy, such as monosomy 13, monosomy 15, monosomy 16, monosomy 21 and monosomy 22, which are known to be relevant to pregnancy and miscarriage. The method described herein can also be utilized to measure the partial monosomy of the chromosome typically relevant to complete aneuploidy. Non-limiting examples of deletion syndromes that can be determined according to the method of the present invention include syndromes caused by partial deletion of chromosomes. Examples of partial deletions that can be measured according to the method described herein include but are not limited to the partial deletion of chromosomes 1, 4, 5, 7, 11, 18, 15, 13, 17, 22 and 10, which are described below.

1q21.1 deletion syndrome or 1q21.1 (recurrent) microdeletion is a rare abnormality of chromosome 1. Following deletion syndrome, there is also 1q21.1 duplication syndrome. While deletion syndromes involve a missing portion of DNA at a specific point, duplication syndromes involve two or three copies of a similar portion of DNA at the same point. Deletions and duplications are referred to in the literature as 1q21.1 copy number variations (CNVs). 1q21.1 deletions can be associated with TAR syndrome (thrombocytopenia with absent radius).

Wolf-Hirschhorn syndrome (WHS) (OMIN #194190) is a contiguous gene deletion syndrome associated with a hemizygous deletion of chromosome 4p16.3. WHS is a congenital malformation syndrome characterized by prenatal and postnatal growth failure, varying degrees of developmental impairment, distinctive craniofacial features (a 'Greek warrior helmet'-like nose, high forehead, prominent cheeks, hypertelorism, high-arched eyebrows, protruding eyes, epicanthus, short philtrum, a well-defined mouth with downturned corners, and a micrognathia), and epilepsy.

Partial deletion of chromosome 5 (also known as 5p- or 5p-minus, and Cris du Chat syndrome (OMIN#123450)) is caused by the deletion of the short arm of chromosome 5 (5p15.3-p15.2). Infants with this condition often have a high-pitched vocalization that sounds like a cat. The condition is characterized by intellectual disability and developmental delays, small head size (microcephaly), low birth weight, weak muscle tone (hypotonia) in infancy, distinctive facial features, and possible heart defects.

Williams-Beuren syndrome (WBS), also known as chromosome 7q11.23 deletion syndrome (OMIN 194050), is a multisystem contiguous gene deletion syndrome caused by a hemizygous deletion of 1.5 to 1.8 Mb on chromosome 7q11.23 encompassing approximately 28 genes.

Jacobsen syndrome, also known as 11q deletion disorder, is a rare congenital disorder caused by a deletion of the terminal region of chromosome 11, including band 11q24.1. It can lead to intellectual disability, distinctive facies, and a variety of practical problems, including heart defects and bleeding disorders.

Partial monosomy of chromosome 18, known as monosomy 18p, is a rare chromosomal disorder in which all or part of the short arm (p) of chromosome 18 is missing (monosomal). This disease is typically characterized by short stature, variable mental retardation, delayed speech development, malformations of the skull and facial (craniofacial) regions, and/or additional physical abnormalities. The associated craniofacial defects can vary greatly in scope and severity from case to case.

Conditions caused by changes in the structure or copy number of chromosome 15 include Angelman syndrome and Prader-Willi syndrome, which involve the loss of gene activity in the same part of chromosome 15 (15q11-q13 region). It should be understood that in parental carriers, some translocations and microdeletions can be asymptomatic, but can still cause major genetic diseases in offspring. For example, a healthy mother carrying a 15q11-q13 microdeletion can give birth to a child suffering from Angelman syndrome (a serious neurodegenerative disease). Therefore, the methods, devices, and systems described herein can be used to identify such partial deletions and other deletions in a fetus.

Partial monosomy 13q is a rare chromosomal disorder that occurs when a segment of the long arm (q) of chromosome 13 is deleted (monosomic). Infants born with partial monosomy 13q have low birth weight, malformations of the head and face (craniofacial region), skeletal abnormalities (especially in the hands and feet), and other physical abnormalities. Mental retardation is a hallmark of this condition. Among individuals born with the disorder, the mortality rate during infancy is high. Almost all cases of partial monosomy 13q occur randomly (sporadic) for no apparent reason.

Smith-Magenis syndrome (SMS-OMIM#182290) is caused by a deletion, or loss of genetic material, on one copy of chromosome 17. This well-known syndrome is associated with developmental delay, mental retardation, intellectual disability, congenital anomalies (such as heart and kidney defects), and neurobehavioral abnormalities (such as severe sleep disturbances and self-injurious behaviors). Smith-Magenis syndrome (SMS) is caused in most cases (90%) by a 3.7-Mb interstitial deletion on chromosome 17p11.2.

22q11.2 deletion syndrome, also known as DiGeorge syndrome, is a syndrome caused by the deletion of a small segment of chromosome 22. This deletion (22q11.2) occurs near the middle of the chromosome on the long arm of one of the pair of chromosomes. The features of the syndrome can vary widely even among members of the same family and affect many parts of the body. Characteristic signs and symptoms can include birth defects such as congenital heart disease, jaw defects that most commonly involve a neuromuscular problem of closing (velopharyngeal insufficiency), learning disabilities, slight differences in facial features, and recurrent infections. Microdeletions in the chromosome region 22q11.2 are associated with a 20- to 30-fold increased risk of schizophrenia.

Deletions on the short arm of chromosome 10 are associated with a DiGeorge syndrome-like phenotype. Partial monosomy of chromosome 10p is rare but has been observed in a subset of individuals with features of DiGeorge syndrome.

In one embodiment, the methods, apparatus, and systems described herein are used to determine partial monosomy, including but not limited to partial monosomy of chromosomes 1, 4, 5, 7, 11, 18, 15, 13, 17, 22, and 10. The methods can also be used to determine, for example, partial monosomy 1q21.11, partial monosomy 4p16.3, partial monosomy 5p15.3-p15.2, partial monosomy 7q11.23, partial monosomy 11q24.1, partial monosomy 18p, partial monosomy of chromosome 15 (15q11-q13), partial monosomy 13q, partial monosomy 17p11.2, partial monosomy of chromosome 22 (22q11.2), and partial monosomy 10p.

Other partial monosomy that can be determined according to the methods described herein include: unbalanced translocation t(8;11)(p23.2;p15.5); 11q23 microdeletion; 17p11.2 deletion; 22q13.3 deletion; Xp22.3 microdeletion; 10p14 deletion; 20p microdeletion [del(22)(q11.2q11.23)], 7q11.23, and 7q36 deletions; 1p36 deletion; 2p microdeletion; neurofibromatosis type 1 (17q11.2 microdeletion), Yq deletion; 4p16.3 microdeletion; 1p36.2 microdeletion; 11q14 deletion; 19q13.2 microdeletion; Rubinstein-Taybi syndrome (16p13.3 microdeletion); 7p21 microdeletion; Miller-Dieker syndrome (19q13.2 microdeletion); syndrome) (17p13.3); and 2q37 microdeletion. A partial deletion can be a small deletion of part of a chromosome, or it can be a microdeletion of a chromosome, in which the deletion of a single gene can occur.

Several duplication syndromes have been identified that result from duplication of a portion of a chromosome arm (see OMIN [Online Mendelian Inheritance in Man, available online at ncbi.nlm.nih.gov/omim]. In one embodiment, the methods of the present invention can be used to determine the presence or absence of duplication and/or amplification of any of chromosome segments 1 to 22, X, and Y. Non-limiting examples of duplication syndromes that can be determined according to the methods of the present invention include duplication of a portion of chromosomes 8, 15, 12, and 17, which are described below.

8p23.1 duplication syndrome is a rare genetic disorder caused by a duplication of a region of human chromosome 8. This duplication syndrome has an estimated incidence of 1 in 64,000 births and is the reciprocal of 8p23.1 deletion syndrome. 8p23.1 duplication is associated with a diverse phenotype, including one or more of delayed speech, developmental delay, mild dysmorphism with forehead bossing and arched eyebrows, and congenital heart disease (CHD).

Chromosome 15q duplication syndrome (Dup15q) is a clinically identifiable syndrome caused by a duplication of chromosome 15q11-13.1. Infants with Dup15q typically present with hypotonia (low muscle tone) and growth retardation; they may be born with cleft lip and/or palate or malformations of the heart, kidneys, or other organs; and they display some degree of cognitive delay/impairment (mental retardation), speech and language delays, and sensory processing disorders.

Pallister-Killian syndrome is the result of an extra chromosome #12. There is usually a mixture of cells (mosaicism), some with the extra #12 material and some normal (46 chromosomes without the extra #12 material). Babies with this syndrome have many problems, including severe mental retardation, low muscle tone, "coarse" facial features, and a protruding forehead. They tend to have a very thin upper lip, a thicker lower lip, and a short nose. Other health problems include seizures, poor feeding, ankylosing of the joints, cataracts in adulthood, hearing loss, and heart defects. People with Pallister-Killian syndrome have a shortened lifespan.

Individuals with a genetic condition designated dup(17)(p11.2p11.2) or dup17p carry extra genetic information (called a duplication) on the short arm of chromosome 17. Duplication of chromosome 17p11.2 causes Potocki-Lupski syndrome (PTLS), a newly identified genetic condition with only a few dozen cases reported in the medical literature. Patients with this duplication often experience low muscle tone, poor feeding, and growth retardation in infancy, and also exhibit delayed development of motor and language milestones. Many individuals with PTLS have difficulty with articulation and language processing. In addition, patients may have behavioral characteristics similar to those seen in patients with autism or autism spectrum disorder. Individuals with PTLS may suffer from heart defects and sleep apnea. Duplication of a large region in chromosome 17p12, including the gene PMP22, is known to cause Charcot-Marie-Tooth disease.

CNVs have been associated with stillbirth. However, due to the inherent limitations of traditional cytogenetics, CNVs are considered to be underrepresented as contributing to stillbirth (Harris et al., Prenatal Diagn 31:932-944 [2011]). As shown in the Examples and described elsewhere herein, the present method can determine the presence of partial aneuploidies, such as deletions and amplifications of chromosome segments, and can be used to identify and determine the presence or absence of CNVs associated with stillbirth.

Determine complete fetal chromosomal aneuploidy

In one embodiment, a method for determining the presence or absence of any one or more different, complete fetal chromosome aneuploidy in a maternal test sample comprising a fetus and maternal nucleic acid molecules is provided. Preferably, the method determines the presence or absence of any four or more different, complete fetal chromosome aneuploidy. The steps of the method include: (a) obtaining sequence information for the fetus and maternal nucleic acid in the maternal test sample; and (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing chromosome sequence for each of the any one or more chromosomes of interest. This normalizing chromosome sequence can be a single chromosome, or it can be a group of chromosomes selected from chromosomes 1-22, X, and Y. The method further uses the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for each of the normalizing chromosome sequences to calculate a single chromosome dose for each of the one or more chromosomes of interest in step (c); and (d) comparing each of the single chromosome doses for each of the one or more chromosomes of interest to a threshold value for each of the one or more chromosomes of interest, thereby determining the presence or absence of any one or more complete, different fetal chromosomal aneuploidies in the maternal test sample.

In some embodiments, step (c) comprises calculating a single chromosome dose for each of the chromosomes of interest as the ratio of the number of sequence tags identified for each of the chromosomes of interest to the number of sequence tags identified for the normalizing chromosome sequence for each of the chromosomes of interest.

In other embodiments, step (c) comprises calculating a single chromosome dosage for each described chromosome interested, as the ratio of the sequence label number identified for each described chromosome interested and the sequence label number identified for the described normalization chromosome of each described chromosome interested.In other embodiments, step (c) comprises by making the sequence label number obtained for chromosome interested be associated with the length of chromosome interested and making the number of labels for the corresponding normalization chromosome sequence of chromosome interested be associated with the length of normalization chromosome sequence to calculate the sequence label ratio of chromosome interested, and calculate a chromosome dosage as the sequence label density of chromosome interested and the ratio of the sequence label density for normalization chromosome sequence for chromosome interested.Repeat this calculation for each of all sequences interested.Can repeat step (a)-(d) for the test sample from different maternal subjects.

By one example of this embodiment, four or more complete fetal chromosomal aneuploidies are determined in a maternal test sample comprising a mixture of fetal and maternal cell-free DNA molecules, the example comprising: (a) sequencing at least a portion of the cell-free DNA molecules to obtain sequence information for the fetal and maternal cell-free DNA molecules in the test sample; (b) using the sequence information to identify a number of sequence tags for each of any twenty or more chromosomes of interest selected from chromosomes 1-22, X, and Y and to identify a number of sequence tags for a normalizing chromosome for each of the twenty or more chromosomes of interest; (c) using the number of sequence tags identified for each of the twenty or more chromosomes of interest and the number of sequence tags identified for each normalizing chromosome to calculate a single chromosome dose for each of the twenty or more chromosomes of interest; and (d) comparing each single chromosome dose for each of the twenty or more chromosomes of interest to a threshold value for each of the twenty or more chromosomes of interest, and thereby determining the presence or absence of any twenty or more different complete fetal chromosomal aneuploidies in the test sample.

In another embodiment, the method for determining the presence or absence of any one or more different, complete fetal chromosomal aneuploidies in a maternal test sample as described above uses a normalizing segment sequence for determining the dosage of the chromosome of interest. In this case, the method comprises: (a) obtaining sequence information for fetal and maternal nucleic acids in the sample; and (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing chromosome sequence for each of any one or more chromosomes of interest. The normalizing segment sequence can be a single segment of a chromosome, or it can be a group of segments from one or more different chromosomes. The method further calculates a single chromosome dose for each of the one or more chromosomes of interest using the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for the normalizing segment sequence in step (c); and (d) comparing each of the single chromosome doses for each of the one or more chromosomes of interest to a threshold value for each of the one or more chromosomes of interest, and thereby determining the presence or absence of one or more different complete fetal chromosomal aneuploidies in the sample.

In some embodiments, step (c) comprises calculating a single chromosome dose for each of the chromosomes of interest as the ratio of the number of sequence tags identified for each of the chromosomes of interest to the number of sequence tags identified for the normalizing segment sequence of each of the chromosomes of interest.

In other embodiments, step (c) includes calculating a sequence tag ratio of a chromosome of interest by associating the number of sequence tags obtained for the chromosome of interest with the length of the chromosome of interest and associating the number of tags for the corresponding normalizing segment sequence of the chromosome of interest with the length of the normalizing segment sequence, and calculating a chromosome dose as the ratio of the sequence tag density of the chromosome of interest to the sequence tag density of the normalizing segment sequence for the chromosome of interest. This calculation is repeated for each of all sequences of interest. Steps (a)-(d) can be repeated for test samples from different maternal subjects.

A means for comparing chromosome doses of different sample groups is provided by determining a normalized chromosome value (NCV), which relates the chromosome dose in a test sample to the mean of the corresponding chromosome dose in a set of qualified samples. This NCV is calculated as:

where and are the estimated mean and standard deviation, respectively, for the jth chromosome dose in a set of qualified samples, and is the observed jth chromosome dose for test sample i.

In some embodiments, the presence or absence of at least one complete fetal chromosomal aneuploidy is determined. In other embodiments, the presence or absence of at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, at least twenty-one, at least twenty-two, at least twenty-three, or twenty-four complete fetal chromosomal aneuploidies is determined in a sample, wherein twenty-two of the complete fetal chromosomal aneuploidies correspond to complete fetal chromosomal aneuploidies of any one or more autosomes; and the twenty-third and twenty-fourth chromosomal aneuploidies correspond to complete fetal chromosomal aneuploidies of chromosomes X and Y. Because sex chromosome aneuploidies can include tetrasomy, pentasomy and other polysomy, the number of different complete chromosomal aneuploidies that can be determined according to the present method can be at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, or at least 30 complete chromosomal aneuploidies. Therefore, the number of different complete chromosomal aneuploidies determined is related to the number of chromosomes of interest selected for analysis.

In one embodiment, determining the presence or absence of any one or more different complete fetal chromosomal aneuploidies in a maternal test sample as described above uses a normalizing segment sequence for a chromosome of interest selected from chromosomes 1-22, X, and Y. In other embodiments, two or more chromosomes of interest are selected from any two or more of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y include at least twenty chromosomes selected from chromosomes 1-22, X, and Y, and wherein the presence or absence of at least twenty different complete fetal chromosomal aneuploidies is determined. In other embodiments, any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y are all chromosomes 1-22, X, and Y, and wherein a complete fetal chromosomal aneuploidy is determined for the presence or absence of all chromosomes 1-22, X, and Y. Different complete fetal chromosomal aneuploidies that can be determined include complete chromosomal trisomies, complete chromosomal monosomies, and complete chromosomal polysomies. Examples of complete fetal chromosomal aneuploidies include, but are not limited to, trisomy of any one or more autosomes, such as trisomy 2, trisomy 8, trisomy 9, trisomy 20, trisomy 21, trisomy 13, trisomy 16, trisomy 18, and trisomy 22; trisomy of sex chromosomes, such as 47,XXY, 47XXX, and 47XYY; tetrasomy of sex chromosomes, such as 48,XXYY, 48,XXXY, 48XXXX, and 48,XYYY; pentasomy of sex chromosomes, such as 49,XXXYY, 49,XXXXY, 49,XXXXX, and 49,XYYYY; and monosomy X. Other complete fetal chromosomal aneuploidies that can be determined according to the present method are described below.

Determine partial fetal chromosomal aneuploidy

In another embodiment, a method for determining the presence or absence of any one or more different, partial fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acid molecules is provided. The steps of the method include: (a) obtaining sequence information for the fetal and maternal nucleic acids in the sample; and (b) using the sequence information to identify a number of sequence tags for each of any one or more segments of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing segment sequence for each of any one or more segments of any one or more chromosomes of interest. The normalizing segment sequence can be a single segment of a chromosome, or it can be a group of segments from one or more different chromosomes. The method further uses the number of sequence tags identified for any one or more segments of any one or more chromosomes of interest and the number of sequence tags identified for each of the normalizing segment sequences to calculate a single segment dose for each of any one or more segments of any one or more chromosomes of interest in step (c); and (d) compares each of the single chromosome doses for each of any one or more segments of any one or more chromosomes of interest to a threshold value for each of any one or more chromosome segments of any one or more chromosomes of interest, and thereby determines the presence or absence of one or more different partial fetal chromosomal aneuploidies in the sample.

In some embodiments, step (c) comprises calculating a single segment dose for each of any one or more segments of any one or more chromosomes of interest as the ratio of the number of sequence tags identified for each of any one or more segments of any one or more chromosomes of interest to the number of sequence tags identified for the normalizing segment sequence for each of any one or more segments of any one or more chromosomes of interest.

In other embodiments, step (c) comprises calculating a sequence tag ratio for a segment of interest by relating the number of sequence tags obtained for the segment of interest to the length of the segment of interest, and relating the number of tags for the corresponding normalizing segment sequence for the segment of interest to the length of the normalizing segment sequence, and calculating a segment dose for the segment of interest as the ratio of the sequence tag density for the segment of interest to the sequence tag density for the normalizing segment sequence. This calculation is repeated for each of all sequences of interest. Steps (a)-(d) can be repeated for test samples from different maternal subjects.

A means for comparing segment doses across different sample groups is provided by determining a normalized segment value (NSV), which relates the segment dose in a test sample to the mean of the corresponding segment doses in a set of qualified samples. The NSV is calculated as:

where and are the estimated mean and standard deviation, respectively, for the jth bin dose in a set of qualified samples, and x _ij is the observed jth bin dose for test sample i.

In some embodiments, the presence or absence of a partial fetal chromosomal aneuploidy is determined. In other embodiments, the presence or absence of two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty-five, or more partial fetal chromosomal aneuploidies are determined in a sample. In one embodiment, a segment of interest selected from any one of chromosomes 1-22, X, and Y is selected from chromosomes 1-22, X, and Y. In another embodiment, two or more segments of interest selected from chromosomes 1-22, X, and Y are selected from chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, or Y. In one embodiment, any one or more segments of interest selected from chromosomes 1-22, X, and Y include at least one, five, ten, 15, 20, 25 or more segments selected from chromosomes 1-22, X, and Y, and wherein the presence or absence of at least one, five, ten, 15, 20, 25 different, partial fetal chromosomal aneuploidies is determined. Different, partial fetal chromosomal aneuploidies that can be determined include partial duplication, partial multiplication, partial insertion, and partial deletion. Examples of partial fetal chromosomal aneuploidies include partial monosomy and partial trisomy of autosomes. Partial monosomy of autosomes includes partial monosomy of chromosome 1, partial monosomy of chromosome 4, partial monosomy of chromosome 5, partial monosomy of chromosome 7, partial monosomy of chromosome 11, partial monosomy of chromosome 15, partial monosomy of chromosome 17, partial monosomy of chromosome 18, and partial monosomy of chromosome 22. Other partial fetal chromosomal aneuploidies that can be determined according to this method will be described below.

In any one of the above embodiments, this test sample is a maternal sample selected from blood, plasma, serum, urine and saliva samples. In some embodiments, the maternal test sample is a plasma sample. The nucleic acid molecules of the maternal sample are a mixture of fetal and maternal cell-free DNA molecules. The sequencing of nucleic acids can be carried out using next generation sequencing (NGS) as described elsewhere in this application. In some embodiments, sequencing is a large-scale parallel sequencing using synthesis sequencing with reversible dye terminators. In other embodiments, sequencing is ligation sequencing. In other embodiments, sequencing is single molecule sequencing. Optionally, an amplification step is performed before sequencing.

Determining CNVs for clinical conditions

In addition to early detection of birth defects, the methods described herein can be used to detect any abnormality in the expression of genetic sequences within the genome. Abnormal expression of genetic sequences within the genome has been associated with various conditions. Such conditions include, but are not limited to, cancer, infectious and autoimmune diseases, neurological diseases, metabolic and/or cardiovascular diseases, and the like.

Accordingly, in various embodiments, the methods described herein are contemplated for use in diagnosing and/or monitoring and/or treating such conditions. For example, the methods can be used to determine the presence or absence of a disease, monitor the progression of a disease and/or the efficacy of a treatment regimen, determine the presence or absence of pathogen (e.g., viral) nucleic acid, determine chromosomal abnormalities associated with graft-versus-host disease (GVHD), and determine the role of an individual in forensic medicine.

CNVs in cancer

It has been shown that plasma and serum DNA from cancer patients contain measurable tumor DNA, which can be recovered and used as a surrogate source of tumor DNA, and the characteristics of the tumor are aneuploidy or an inappropriate number of gene sequences or even complete chromosomes. The difference in the amount of a given sequence (i.e., sequence of interest) determined in a sample from an individual can therefore be used for the prognosis and diagnosis of medical conditions. In some embodiments, this method can be used to determine the presence or absence of chromosomal aneuploidy in a patient suspected or known to suffer from cancer.

In certain embodiments, aneuploidy is a characteristic of the subject's genome and causes an overall increase in cancer susceptibility. In certain embodiments, specific cells (e.g., tumor cells, protumor neoplastic cells, etc.) that are susceptible to tumor formation or have increased susceptibility to tumor formation have aneuploidy characteristics. Specific aneuploidy is associated with specific cancers or specific cancer susceptibility, as described below.

Accordingly, the different embodiments of the methods described herein provide a measure of sequence (e.g., clinically relevant sequence) copy number variation of interest in a test sample from a subject, wherein a certain variation of copy number provides an index of cancer and/or cancer susceptibility. In certain embodiments, the sample comprises a mixture of nucleic acids derived from two or more cells. In one embodiment, the nucleic acid mixture derives from normal cells and cancer cells, and the cancer cell is a subject derived from a medical condition (e.g., cancer).

The development of cancer is often accompanied by changes in the number of whole chromosomes, i.e., complete chromosomal aneuploidy, and/or changes in the number of chromosome segments, i.e., partial aneuploidy, which are caused by a process called chromosomal instability (CIN) (Thoma et al., Swiss Med Weekly 2011:141:w13170). It is believed that many solid tumors (such as breast cancer) develop from initiation to metastasis through the accumulation of several genetic abnormalities. [Sato et al., Cancer Res., 50:7184-7189 [1990]; Jongsma et al., J Clin Pathol: Mol Path 55:305-309 [2002]). When accumulated, such genetic abnormalities may confer a proliferative advantage, genetic instability, and the accompanying ability to rapidly develop drug resistance, as well as enhanced angiogenesis, proteolysis, and metastasis. Genetic abnormalities may affect recessive "tumor suppressor genes" or dominant-acting oncogenes. Deletions and recombination leading to loss of heterozygosity (LOH) are thought to play a major role in tumor progression by unmasking mutant tumor suppressor alleles.

cfDNA has been found in the circulation of patients diagnosed with malignancies including, but not limited to, lung cancer (Pathak et al., Clin Med 52:1833-1842 [2006]), prostate cancer (Schwartzenbach et al., Clin Cancer Res 15:1032-8 [2009]), and breast cancer (Schwartzenbach et al., available online at breast-cancer-research.com/content/11/5/R71 [2009]). Identifying genomic instability associated with cancer (which can be determined from circulating cfDNA in cancer patients) is a potential diagnostic and prognostic tool. In one embodiment, the methods described herein are used to determine CNVs of one or more sequences of interest in a sample (e.g., a sample comprising a mixture of nucleic acids derived from a subject suspected of or known to have a cancer, such as a carcinoma, sarcoma, lymphoma, leukemia, germ cell tumor, and blastoma). In one embodiment, the sample is a plasma sample derived (processed) from peripheral blood, which may contain a mixture of cfDNA derived from normal cells and cancer cells. In another embodiment, the biological sample for determining whether CNV is present is a cell derived from other biological tissues. If cancer is present, the cell includes a mixture of cancer cells and non-cancerous cells. Other biological tissues include, but are not limited to, biological fluids such as serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavage fluid, bone marrow suspension, vaginal fluid, transcervical lavage fluid, brain fluid, ascites, milk, secretions of the respiratory tract, intestinal tract, and genitourinary tract, as well as leukocyte apheresis samples, or in tissue biopsy, cotton swabs, or smears. In other embodiments, the biological sample is a stool (feces) sample.

The methods described herein are not limited to the analysis of cfDNA. It will be appreciated that similar analyses can be performed on cellular DNA samples.

In various embodiments, the sequence of interest includes a nucleic acid sequence that is known or suspected to play a role in cancer development and/or progression. Examples of sequences of interest include nucleic acid sequences that are amplified or deleted in cancer cells as described below, such as complete chromosomes and/or chromosome segments.

Total CNV number and cancer risk.

Common cancer SNPs, and by extension, common cancer CNVs, each confer only a small increase in disease risk. However, taken together, they may lead to a substantial increase in cancer risk. In this regard, it should be noted that germline gains and losses of large DNA fragments have been reported as factors that predispose individuals to neuroblastoma, prostate and colorectal cancer, breast cancer, and BRCA1-associated ovarian cancer (see, e.g., Krepischi et al., Breast Cancer Res., 14:R24 [2012]; Diskin et al., Nature 2009, 459:987-991; Liu et al., Cancer Res 2009, 69:2176-2179; Lucito et al., Cancer Biol Ther 2007, 6:1592-1599; Thean et al., Genes Chromosomes Cancer 2010, 49:99-106; Venkatachalam et al., Int J Cancer 2011, 49:99-106). Cancer) 2011, 129: 1635-1642; and Yoshihara et al., Genes Chromosomes Cancer 2011, 50: 167-177). It should be noted that CNVs frequently found in healthy populations (common CNVs) are thought to play a role in cancer etiology (see, for example, Shlien and Malkin (2009) Genome Medicine, 1(6): 62). In a research test, the following hypothesis was tested: common CNVs are associated with malignant disease (Shlien et al., Proc Natl Acad Sci USA 2008, 105: 11264-11269), a mapping of each known CNV whose locus is consistent with that of a true cancer-associated gene (as classified by Higgins et al., Nucleic Acids Res 2007, 35: D721-726). These CNVs are referred to as "cancer CNVs". In an initial analysis (Shlien et al., Proc Natl Acad Sci USA 2008, 105: 11264-11269), 770 healthy genomes were evaluated using the Affymetrix 500K array set (with an average probe-to-probe distance of 5.8 kb). Since CNVs are generally considered to be excluded in gene regions (Redon et al. (2006), Nature 2006, 444:444-454), it was surprising to find that 49 cancer genes were directly covered or overlapped by CNVs in multiple people from a large reference population. Among the top ten genes, cancer CNVs could be found in four or more people.

It is therefore believed that CNV frequency can be used as a measure of cancer risk (see, for example, US Patent Publication No. 2010/0261183A1). CNV frequency can be determined simply from the constitutive genome of an organism or it can represent the fraction derived from one or more tumors (neoplastic cells), if these exist.

In certain embodiments, the number of CNVs in a test sample (e.g., a sample comprising constitutive (germline) nucleic acids) or in a nucleic acid mixture (e.g., germline nucleic acids and nucleic acids derived from neoplastic cells) is determined using the methods described herein for copy number variation. Identifying an increase in the number of CNVs in a test sample (e.g., compared to a reference value) indicates that the subject is at risk for cancer or has a susceptibility to cancer. It should be understood that the reference value can vary with a given population. It should also be understood that the absolute value of the CNV frequency increase will vary depending on the resolution of the method used to determine the CNV frequency and other parameters. Typically, determining an increase in CNV frequency of at least about 1.2 times the reference value indicates a risk of cancer (see, e.g., U.S. Patent Publication No.: 2010/0261183A1), for example, an increase in CNV frequency of at least 1.5 times or about 1.5 times or greater (such as 2 to 4 times the reference value) is an indicator of an increased risk of cancer (e.g., compared to a normal, healthy reference population).

It is also believed that determining the structural variation of the mammalian genome (compared to a reference value) represents a cancer risk. In this context, in one embodiment, the term "structural variation" can be defined by multiplying the CNV frequency of the mammal by the average CNV size (bp) of the mammal. Therefore, a high structural variation score will be because the CNV frequency increases and/or because of the occurrence of large genomic nucleic acid deletions or duplications. Therefore, in certain embodiments, the number of CNVs in a test sample (for example, a sample comprising a constitutive (germline) nucleic acid) is measured using the methods described herein to measure copy number variation size and number. In certain embodiments, the total score for structural variation in the genomic DNA greater than about 1 megabase, or greater than about 1.1 megabases, or greater than about 1.2 megabases, or greater than about 1.3 megabases, or greater than about 1.4 megabases, or greater than about 1.5 megabases, or greater than about 1.8 megabases, or greater than about 2 megabases of DNA represents a cancer risk.

These methods are believed to provide a measure of risk for any cancer, including but not limited to acute and chronic leukemias, lymphomas, many solid tumors of mesenchymal or epithelial tissue, brain cancer, breast cancer, liver cancer, stomach cancer, colon cancer, B-cell lymphomas, lung cancer, bronchial cancer, colorectal cancer, prostate cancer, breast cancer, pancreatic cancer, stomach cancer, ovarian cancer, bladder cancer, brain cancer or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, melanoma, uterine cancer or endometrial cancer, oral cancer or pharyngeal cancer, liver cancer, kidney cancer, biliary tract cancer, small intestine or appendix cancer, salivary gland cancer, thyroid cancer, adrenal cancer, osteosarcoma, chondrosarcoma, liposarcoma, testicular cancer, and malignant fibrous histiocytoma, among other cancers.

Complete chromosomal aneuploidy.

As noted above, there is a high frequency of aneuploidy in cancer. In some studies examining the prevalence of somatic copy number variation (SCNAs) in cancer, it has been found that full-arm SCNAs or full-chromosome SCNAs of aneuploidy have an impact on a quarter of the genomes of typical cancer cells (see, for example, Beroukhim et al., Nature 463:899-905 [2010]). Full-chromosome variation has been repeatedly observed in several cancer types. For example, gain of chromosome 8 is seen in 10% to 20% of cases of acute myeloid leukemia (AML), as well as in certain solid tumors, including Ewing's Sarcoma and fibroid tumors (see, e.g., Barnard et al., Leukemia 10:5-12 [1996]; Maurici et al., Cancer Genet. Cytogenet. 100:106-110 [1998]; Qi et al., Cancer Genet. Cytogenet. 92:147-149). [1996]; Barnard, D.R. et al., Blood 100:427-434 [2002]; etc. An illustrative but non-limiting list of chromosomal gains and losses in human cancers is shown in Table 1.

Table 1: Schematic representation of specific recurrent chromosomal gains and losses in human cancers (see, e.g., Gordon et al. et al. (2012), Nature Rev. Genetics, 13: 189-203).

In various embodiments, the methods described herein can be used to detect and/or quantify whole chromosome aneuploidies associated with cancer in general and/or associated with specific cancers. Thus, for example, in certain embodiments, detection and/or quantification of whole chromosome aneuploidies characterized by the gains or losses shown in Table 1 are contemplated.

Arm-level chromosome segment copy number variation.

Several studies have reported patterns of arm-level copy number variation across a large number of cancer specimens (Lin et al., Cancer Res 68, 664-673 (2008); George et al., PLoS ONE 2, e255 (2007); Demichelis et al., Genes Chromosomes Cancer 48: 366-380 (2009); Beroukhim et al., Nature. 463(7283): 899-905 [2010]). It has also been observed that the frequency of arm-level copy number variation decreases with chromosome arm length. Adjusted for this tendency, most chromosome arms show strong evidence for preferential gain or loss, but across multiple cancer lineages, both are rare (see, e.g., Beroukhim et al., Nature 463(7283):899-905 [2010]).

Therefore, in one embodiment, the method described herein is used to determine the arm level CNVs (CNVs comprising a chromosome arm or substantially a chromosome arm) in a sample. In the CNVs in a test sample comprising constitutive (germline) nucleic acids, CNVs can be determined, and in these constitutive nucleic acids, arm level CNVs can be identified. In certain embodiments, arm level CNVs (if present) are identified in a sample comprising a mixture of nucleic acids (e.g., nucleic acids derived from normal cells and nucleic acids derived from neoplastic cells). In certain embodiments, the sample is derived from a subject suspected or known to have cancer (e.g., cancer, sarcoma, lymphoma, leukemia, germ cell tumor, blastoma, and similar cancers). In one embodiment, the sample is a plasma sample derived (processed) from peripheral blood, which may comprise a mixture of cfDNA derived from normal cells and cancer cells. In another embodiment, the biological sample used to determine whether the presence of a CNV is derived from cells that, if cancer is present, include a mixture of cancerous and non-cancerous cells from other biological tissues, including but not limited to biological fluids such as serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavages, bone marrow suspensions, vaginal fluid, transcervical lavages, brain fluid, ascites, breast milk, respiratory, intestinal and genitourinary tract secretions, and leukapheresis samples, or in a tissue biopsy, swab or smear. In other embodiments, the biological sample is a fecal (fecal) stool (fecal) sample.

In various embodiments, the CNVs identified as indicative of the presence of a cancer or an increased risk of a cancer include, but are not limited to, the arm-level CNVs listed in Table 2. As illustrated in Table 2, certain CNVs, including substantial arm-level gains, indicate the presence of a cancer or an increased risk of certain cancers. Thus, for example, a gain in 1q indicates the presence of or an increased risk of acute lymphoblastic leukemia (ALL), breast cancer, GIST, HCC, lung NSC, medulloblastoma, melanoma, MPD, ovarian cancer, and/or prostate cancer. A gain in 3q indicates the presence of or an increased risk of esophageal squamous cell carcinoma, lung SC, and/or MPD. A gain in 7q indicates the presence of or an increased risk of colorectal cancer, glioma, HCC, lung NSC, medulloblastoma, melanoma, prostate cancer, and/or renal cancer. A gain in 7p indicates the presence of or an increased risk of breast cancer, colorectal cancer, esophageal adenocarcinoma, glioma, HCC, lung NSC, medulloblastoma, melanoma, and/or renal cancer. A gain of 20q indicates the presence or increased risk of breast cancer, colorectal cancer, dedifferentiated liposarcoma, esophageal adenocarcinoma, esophageal squamous cell carcinoma, glioma, HCC, lung NSC, melanoma, ovarian cancer, and/or renal cancer, among others.

Similarly, as illustrated in Table 2, certain CNVs, including losses at the parenchymal arm level, indicate the presence and/or increased risk of certain cancers. Thus, for example, a loss of 1p indicates the presence or increased risk of gastrointestinal stromal tumors. A loss of 4q indicates the presence or increased risk of colorectal cancer, esophageal adenocarcinoma, lung sc, melanoma, ovarian cancer, and/or renal cancer. A loss of 17p indicates the presence or increased risk of breast cancer, colorectal cancer, esophageal adenocarcinoma, HCC, lung NSC, lung sc, and/or ovarian cancer, among others.

Table 2: 16 cancer subtypes (breast cancer, colorectal cancer, dedifferentiated liposarcoma, esophageal adenocarcinoma, esophageal squamous cell carcinoma, GIST (gastrointestinal stromal tumor), glioma, HCC (hepatocellular carcinoma), lung NSC, lung SC, medulloblastoma, melanoma, MPD (myeloproliferative disorders), ovarian cancer, prostate cancer, acute lymphoblastic leukemia (ALL), and renal cancer) Significant arm-level chromosome segment copy number variation (see, e.g., Beroukhim et al., Nature (2010)463(7283):899-905).

The examples of relationships between arm-level copy number variations are intended to be illustrative and non-limiting. Other arm-level copy number variations and their relationships to cancer are known to those skilled in the art.

Smaller (e.g., focal) copy number variations.

As noted above, in certain embodiments, the methods described herein can be used to determine the presence or absence of chromosomal amplification. In some embodiments, chromosomal amplification is the acquisition of one or more entire chromosomes. In other embodiments, chromosomal amplification is the acquisition of one or more segments in a chromosome. In still other embodiments, chromosomal amplification is the acquisition of two or more segments in two or more chromosomes. In various embodiments, chromosomal amplification can involve the acquisition of one or more oncogenes.

The dominant open genes associated with human solid tumors typically play their role by overexpression or altered expression. Gene amplification is a common mechanism leading to upregulated gene expression. Evidence from cytogenetic studies shows that significant amplification has occurred in more than 50% of human breast cancers. Most notably, the amplification of the proto-oncogene human epidermal growth factor receptor 2 (HER2) located on chromosome 17 (17 (17q21-q22)) causes overexpression of the HER2 receptor on the cell surface, thereby causing excessive and dysregulated signals in breast cancer and other malignancies (Park et al., Clinical Breast Cancer, 8: 392-401 [2008]). Multiple oncogenes have been found to be amplified in other human malignancies. Examples of cellular oncogene amplifications in human tumors include amplification of c-myc in the promyelocytic leukemia cell line HL60 and small cell lung cancer, N-myc in primary neuroblastomas (stages III and IV), neuroblastoma cell lines, retinoblastoma cell lines and primary tumors, and small cell lung cancer cell lines and tumors, L-myc in small cell lung cancer cell lines and tumors, c-myb in acute myeloid leukemia and colon cancer cell lines, c-erbB in epidermoid carcinoma cells and primary gliomas, c-K-ras-2 in primary carcinomas of the lung, colon, bladder, and rectum, N-ras in breast cancer cell lines (Varmus H., Ann Rev Genetics, 18:553-612 (1984), [cited in Watson et al., Molecular Biology of the Gene (4th ed.; Benjamin/Cummings Publishing Co. Company 1987)].

Oncogene duplication is a common cause of many types of cancer, as is the case with p70-S6 kinase 1 amplification and breast cancer. In these cases, the genetic duplication occurs in somatic cells and affects only the genome of the cancer cell itself (not the entire organism), with much less impact on any subsequent progeny. Other examples of oncogenes amplified in human cancers include MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1, and FGFR2 in breast cancer; MYC and ERBB2 in cervical cancer; HRAS, KRAS, and MYB in cervical cancer; MYC, CCND1, and MDM2 in esophageal cancer; CCNE, KRAS, and MET in gastric cancer; ERBB1 and CDK4 in glioblastoma; CCND1, ERBB1, and MYC in head and neck cancer; CCND1 in hepatocellular carcinoma; MYCB in neuroblastoma; MYC: ERBB2 and AKT2 in ovarian cancer; MDM2 and CDK4 in sarcomas; and MYC in small cell lung cancer. In one embodiment, the methods of the present invention can be used to determine the presence or absence of amplification of an oncogene associated with cancer. In certain embodiments, the amplified oncogene is associated with breast cancer, cervical cancer, colorectal cancer, esophageal cancer, gastric cancer, glioblastoma, head and neck cancer, hepatocellular carcinoma, neuroblastoma, ovarian cancer, sarcoma, and small cell lung cancer.

In one embodiment, the present method can be used to determine the presence or absence of a chromosomal deletion. In some embodiments, the chromosomal deletion is the loss of one or more complete chromosomes. In other embodiments, the chromosomal deletion is the loss of one or more segments of a chromosome. In yet other embodiments, the chromosomal deletion is the loss of two or more segments of two or more chromosomes. The chromosomal deletion may involve the loss of one or more tumor suppressor genes.

Chromosomal deletions involving tumor suppressor genes are believed to play an important role in the development and progression of solid tumors. The retinoblastoma tumor suppressor gene (Rb-1) (located on chromosome 13q14) is the most widely characterized tumor suppressor gene. The Rb-1 gene product (a 105 kDa nuclear phosphoprotein) apparently plays an important role in cell cycle regulation (Howe et al., Proc Natl Acad Sci (USA), 87:5883-5887 [1990]). Inactivation of either allele of these two genes, either by a point mutation or chromosomal deletion, results in altered or lost expression of the Rb protein. Rb-1 gene alterations have been found not only in retinoblastoma, but also in other malignancies such as osteosarcoma, small cell lung cancer (Rygaard et al., Cancer Res, 50:5312-5317 [1990]) and breast cancer. Restriction fragment length polymorphism (RFLP) studies have shown that such tumor types often lose heterozygosity at 13q, suggesting that one of the alleles of the Rb-1 gene has been lost due to total chromosomal loss (Bowcock et al., Am J Hum Genet (American Journal of Human Genetics), 46:12 [1990]). Chromosome 1 abnormalities, including duplications, deletions, and unbalanced translocations involving chromosome 6 and other companion chromosomes, suggest that regions of chromosome 1, particularly q21-1q32 and 1p11-13, may house oncogenes or tumor suppressor genes involved in the pathogenesis of chronic and advanced stages of myeloproliferative neoplasms (Caramazza et al., Eur J Hematol (European Journal of Hematology), 84:191-200 [2010]). Myeloproliferative neoplasms are also associated with deletions of chromosome 5. Complete or interstitial deletions of chromosome 5 are the most common karyotypic abnormalities in myelodysplastic syndromes (MDS). Patients with isolated del(5q)/5q-MDS have a more favorable prognosis than those with additional karyotypic defects, and they are prone to developing myeloproliferative neoplasms (MPNs) and acute myeloid leukemia. The frequency of unbalanced chromosome 5 deletions has led to the idea that 5q houses one or more tumor suppressor genes that play a fundamental role in the growth control of hematopoietic stem cells/hematopoietic progenitor cells (HSCs/HPCs). Cytogenetic mapping of commonly deleted regions (CDRs) has focused on candidate tumor suppressor genes identified in 5q31 and 5q32, including the ribosomal subunit RPS14, the transcription factor Egr1/Krox20, and the cytoskeletal remodeling protein, α-catenin (Eisenmann, Oncogene, 28:3429-3441 [2009]). Cytogenetic and allelic studies of fresh tumors and tumor cell lines have demonstrated that loss of alleles from several well-defined regions on chromosome 3p (including 3p25, 3p21–22, 3p21.3, 3p12–13, and 3p14) is the earliest and most common genomic abnormality involved in a broad spectrum of major epithelial cancers, including lung, breast, kidney, head and neck, ovarian, cervical, colon, pancreatic, esophageal, bladder, and other organs. Several tumor suppressor genes have been mapped to the chromosome 3p region, and it is believed that interstitial deletions or promoter hypermethylation precede the loss of 3p or the entire chromosome 3 in the development of cancer (Angeloni D., Briefings Functional Genomics, 6:19-39 [2007]).

The newborn infant and the child who suffers from Down syndrome (DS) usually present congenital transient leukemia and have the risk of increase of acute myeloid leukemia and acute lymphoblastic leukemia.Chromosome 21 (accommodating about 300 genes) can involve multiple structural aberrations, such as translocation, disappearance and amplification in leukemia, lymphoma and solid tumor.In addition, the important role played by the gene on chromosome 21 in tumorigenesis has been identified.The aberration of the entity number of chromosome 21 together with structure is associated with leukemia, and specific genes include RUNX1, TMPRSS2 and TFF, which are located at 21q, and work in tumorigenesis (Fonatsch (Fonatsch) C, Gene Chromosomes Cancer (gene, chromosome and cancer), 49:497-508[2010]).

In view of the above, in various embodiments, the methods described herein can be used to determine segment CNVs that are known to include one or more oncogenes or tumor suppressor genes and/or are known to be associated with cancer or an increased risk of cancer. In certain embodiments, CNVs in a test sample comprising constitutive (germline) nucleic acids can be determined, and segments can be identified in those constitutive nucleic acids. In certain embodiments, segment CNVs (if present) are identified in a sample comprising a mixture of nucleic acids (e.g., nucleic acids derived from normal cells and nucleic acids derived from neoplastic cells). In certain embodiments, the sample is derived from a subject suspected or known to have cancer (e.g., cancer, sarcoma, lymphoma, leukemia, germ cell tumor, blastoma, etc.). In one embodiment, the sample is a plasma sample derived (processed) from peripheral blood, which may contain a mixture of cfDNA derived from normal cells and cancer cells. In another embodiment, the biological sample used to determine whether a CNV is present is derived from a cell that, if cancer is present, comprises a mixture of cancerous and non-cancerous cells from other biological tissues, including but not limited to biological fluids such as serum, sweat, tears, sputum, urine, sputum, ear discharge, lymph, saliva, cerebrospinal fluid, lavages, bone marrow suspensions, vaginal fluid, transcervical lavages, brain fluid, ascites, breast milk, respiratory, intestinal and genitourinary tract secretions, and leukapheresis samples, or in a tissue biopsy, swab, or smear. In other embodiments, the biological sample is a stool (fecal) sample.

CNVs used to determine the presence of cancer and/or increased risk of cancer can include amplifications or deletions.

In various embodiments, the CNVs identified as indicative of the presence of cancer or increased risk of cancer include one or more amplifications shown in Table 3.

Table 3: Illustrative but non-limiting chromosomal segments characterized by amplification associated with cancer. Syndrome types are those identified in Beroukhim, Nature 18:463:899-905.

In certain embodiments, in combination with or separately from the amplifications described above (herein), the CNVs identified as indicative of the presence of cancer or increased risk of cancer include one or more deletions shown in Table 4.

Table 4: Illustrative but non-limiting chromosomal segments characterized by deletions associated with cancer. Syndrome types are those identified in Beroukhim, Nature 18:463:899-905.

Aneuploidies identified as characterizing different cancers (e.g., aneuploidies identified in Tables 3 and 4) may include genes known to be involved in the etiology of cancer (e.g., tumor suppressors, oncogenes, etc.) These aneuploidies can also be probed to identify related, but previously unknown, genes.

For example, the aforementioned Beroukhim et al. utilized GRAIL (gene relationships between involved Loci20), an algorithm that searches for functional relationships between genomic regions, to assess potential oncogenes based on copy number changes. GRAIL evaluates the 'correlation' of each gene in a set of genomic regions with genes in other regions based on the textual similarity of the published abstracts of all papers mentioning the gene in terms of the idea that certain target genes act in common pathways. These methods allow the identification/characterization of controversial genes that were not previously associated with specific cancers. Table 5 illustrates target genes known to be located within the identified amplified segments and predicted genes, and Table 6 illustrates target genes known to be located within the identified deleted segments and predicted genes.

Table 5: Illustrative, but non-limiting, lists of regions known or predicted to be present in regions characterized by amplification in different cancers Regulatory chromosome segments and genes (see, e.g., Beroukhim et al., supra).

[ ] Table 6: Illustrative, but not specific, regions known or predicted to be present in regions characterized by amplification in different cancers Restricted chromosome segments and genes (see, e.g., Beroukhim et al., supra).

In various embodiments, it is contemplated to use the methods identified herein to identify CNVs in segments comprising amplified regions or genes identified in Table 5, and/or to use the methods identified herein to identify CNVs in segments comprising deleted regions or genes identified in Table 6.

In one embodiment, the methods described herein provide a means to assess the correlation between gene amplification and the extent of tumor progression. The correlation between amplification and/or deletion and cancer stage or grade can be important for prognosis, as such information can form the definition of a hereditary tumor grade, which better predicts the future course of more advanced tumors with the worst prognosis. In addition, information about early amplification and/or deletion events can be useful in correlating these events as predictors of subsequent disease progression.

Gene amplification and deletion identified by this method can be associated with other known parameters (such as tumor grade, medical history, Brd/Urd marker index, hormonal status, lymph node metastasis, tumor size, survival time and other tumor characteristics available from epidemiological and biostatistical studies). For example, tumor DNA to be tested by this method can include atypical hyperplasia, ductal carcinoma in situ, stage I-III cancer and metastatic lymph nodes, so as to allow identification of the correlation between amplification and deletion and stage. The association made can make effective therapeutic intervention possible. For example, the region of consistent amplification can contain an overexpressed gene, whose product may be able to accept therapeutic attachment (for example, growth factor receptor tyrosine kinase p185 ^HER2 ).

In various embodiments, the methods described herein can be used to identify amplification and/or deletion events associated with drug resistance by determining the copy number variation of nucleic acid sequences from the primary cancer to cells that have metastasized to other sites. If gene amplification and/or deletion is a manifestation of karyotypic instability that allows rapid development of drug resistance, then more amplification and/or deletion would be expected in primary tumors from chemotherapy-resistant patients compared to tumors from chemotherapy-sensitive patients. For example, if amplification of specific genes contributes to the development of drug resistance, then the regions surrounding those genes would be expected to be consistently amplified in tumor cells from chemotherapy-resistant patients but not in primary tumors. The discovery of a correlation between gene amplification and/or deletion and the development of drug resistance can allow identification of patients who will or will not benefit from adjuvant therapy.

In a manner similar to that described for determining the presence or absence of complete and/or partial fetal chromosomal aneuploidy in a maternal sample, the methods, devices, and systems described herein can be used to determine the presence or absence of complete and/or partial chromosomal aneuploidy in any patient sample (including patient samples that are not maternal samples) comprising nucleic acids (e.g., DNA or cfDNA). Such patient samples can be any biological sample type as described elsewhere in this application. Preferably, such sample is obtained by a non-invasive process. For example, such sample can be a blood sample, or its serum and plasma fractions. Alternatively, such sample can be a urine sample or a fecal sample. In other embodiments, such sample is a tissue biopsy sample. In all cases, such sample includes nucleic acids, such as cfDNA or genomic DNA, which is purified and sequenced using any of the above-mentioned NGS sequencing methods.

Both complete and partial chromosomal aneuploidies associated with the development and progression of cancer can be determined according to the present method.

In various embodiments, when determining the presence and/or increased risk of cancer using the methods described herein, the data can be normalized relative to the chromosome or chromosomes of the CNV being determined. In certain embodiments, the data can be normalized relative to one or more chromosome arms of the CNV being determined. In certain embodiments, the data can be normalized relative to one or more specific segments of the CNV being determined.

In addition to the role of CNVs in cancer, CNVs are also associated with a growing number of common complex diseases, including human immunodeficiency virus (HIV), autoimmune diseases, and a range of neuropsychiatric disorders.

CNVs in infectious and autoimmune diseases

To date, numerous studies have reported associations between CNVs in genes involved in inflammation and immune response and HIV, asthma, Crohn's disease, and other autoimmune disorders (Fanciulli et al., Clin Genet 77:201-213 [2010]). For example, CNVs in CCL3L1 have been implicated in HIV/AIDS susceptibility (CCL3L1, 17q11.2 deletion), rheumatoid arthritis (CCL3L1, 17q11.2 deletion), and Kawasaki disease (CCL3L1, 17q11.2 duplication). CNVs in HBD-2 have been reported to predispose to colonic Crohn's disease (HDB-2, 8p23.1 deletion) and psoriasis (HDB-2, 8p23.1 deletion). CNVs in FCGR3B have been shown to predispose to glomerulonephritis in systemic lupus erythematosus (FCGR3B, 1q23 deletion, 1q23 duplication), anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (FCGR3B, 1q23 deletion), and an increased risk of rheumatoid arthritis. At least two inflammatory or autoimmune diseases have been linked to CNVs at different loci. For example, Crohn's disease is associated not only with low HDB-2 copy numbers but also with common deletion polymorphisms upstream of the IGRM gene, which encodes members of the p47 immunity-related GTPase family. In addition to being associated with FCGR3B copy number, SLE susceptibility has been reported to be significantly increased in subjects with low copy numbers of the complement component C4.

The relationship between genomic deletions of the GSTM1 (GSTM1, 1q23 deletion) and GSTT1 (GSTT1, 22q11.2 deletion) loci and an increased risk of allergic asthma has been reported in a large number of independent studies. In some embodiments, the methods described herein can be used to determine the presence or absence of CNVs associated with inflammatory and/or autoimmune diseases. For example, these methods can be used to determine the presence of CNVs in patients suspected of having HIV, asthma, or Crohn's disease. Examples of CNVs associated with such diseases include, but are not limited to, deletions at 17q11.2, 8p23.1, 1q23, and 22q11.2, and duplications at 17q11.2 and 1q23. In some embodiments, the methods of the present invention can be used to determine the presence of CNVs in genes including, but not limited to, CCL3L1, HBD-2, FCGR3B, GSTM, GSTT1, C4, and IRGM.

CNV diseases of the nervous system

The relationship between newborn CNV and hereditary CNV and some common neurological and psychiatric diseases has been reported in some cases of autism, schizophrenia and epilepsy and neurodegenerative diseases, such as Parkinson's disease, amyotrophic lateral sclerosis (ALS) and autosomal dominant Alzheimer's disease (Fanciulli et al., Clinical Genetics (ClinGenet) 77:201-213[2010]). In patients with autism and autism spectrum disorder (ASD), it has been observed that there is a cytogenetic abnormality of duplication at 15q11-q13. According to the Autism Genome Project Alliance (Autism Genomeproject Consortium), 154 CNVs including some recurrent CNVs are located at chromosome 15q11-q13 or new genomic positions, including chromosome 2p16, 1q21, and 17p12 in the region relevant to Smith-Magenis syndrome, overlapping with ASD. Recurrent microdeletions or microduplications on chromosome 16p11.2 have highlighted the observation that de novo CNVs have been detected at the loci of genes known to regulate synaptic differentiation and regulate the release of glutamatergic neurotransmitters, such as SHANK3 (22q13.3 deletion), presynaptic extrusion protein 1 (NRXN1, 2p16.3 deletion), and neuroligin (NLGN4, Xp22.33 deletion). Schizophrenia is also associated with multiple de novo CNVs. Microdeletions and microduplications associated with schizophrenia include an overrepresentation of genes belonging to neurodevelopmental and glutamatergic pathways, suggesting that multiple CNVs affecting these genes may directly contribute to the pathogenesis of schizophrenia, such as ERBB4, 2q34 deletion; SLC1A3, 5p13.3 deletion; RAPEGF4, 2q31.1 deletion; CIT, 12.24 deletion; and multiple genes with de novo CNVs. CNVs are also associated with other neurological disorders, including epilepsy (CHRNA7, 15q13.3 deletion), Parkinson's disease (SNCA 4q22 duplication), and ALS (SMN1, 5q12.2.-q13.3 deletion; and SMN2 deletion). In some embodiments, the methods described herein can be used to determine the presence or absence of CNVs associated with neurological diseases. For example, these methods can be used to determine the presence of CNVs in patients suspected of having autism, schizophrenia, epilepsy, neurodegenerative diseases (such as Parkinson's disease), amyotrophic lateral sclerosis (ALS), or autosomal dominant Alzheimer's disease. The methods can be used to determine CNVs in genes associated with neurological diseases (including but not limited to any of autism spectrum disorder (ASD), schizophrenia, and epilepsy), as well as CNVs in genes associated with neurodegenerative disorders (such as Parkinson's disease). Examples of CNVs associated with such diseases include, but are not limited to, duplications at 15q11-q13, 2p16, 1q21, 17p12, 16p11.2, and 4q22, and deletions at 22q13.3, 2p16.3, Xp22.33, 2q34, 5p13.3, 2q31.1, 12.24, 15q13.3, and 5q12.2. In some embodiments, these methods can be used to determine the presence of CNVs in genes including, but not limited to, SHANK3, NLGN4, NRXN1, ERBB4, SLC1A3, RAPGEF4, CIT, CHRNA7, SNCA, SMN1, and SMN2.

CNV and metabolic or cardiovascular disease

The relationship between metabolic and cardiovascular disease traits (such as familial hypercholesterolemia (FH), atherosclerosis, and coronary artery disease) and CNVs has been reported in numerous studies (Fanciulli et al., Clin Genet 77:201-213 [2010]). For example, germline rearrangements (primarily deletions) have been observed in the LDLR gene (LDLR, 19p13.2 deletion/duplication) in some FH patients who do not carry other LDLR mutations. Another example is the LPA gene, which encodes apolipoprotein (a) (apo(a)), whose plasma concentration is associated with the risk of coronary artery disease, myocardial infarction (MI), and stroke. Plasma concentrations of apo(a), which comprises lipoprotein Lp(a), vary over 1000-fold between individuals, and 90% of this variability is genetically determined at the LPA locus, where plasma concentrations and Lp(a) isoform sizes are proportional to the highly variable number of 'kringle 4' repeats (range 5 to 50). These data suggest that CNVs in at least two genes can be associated with cardiovascular risk. The methods described herein can be specifically used to search for the relationship between CNVs and cardiovascular conditions in large studies. In some embodiments, the methods of the present invention can be used to determine the presence or absence of CNVs associated with metabolic or cardiovascular diseases. For example, the methods of the present invention can be used to determine the presence of CNVs in patients suspected of having familial hypercholesterolemia. The methods described herein can be used to determine CNVs in genes associated with metabolic or cardiovascular diseases (e.g., hypercholesterolemia). Examples of CNVs associated with such diseases include, but are not limited to, 19p13.2 deletions/duplications in the LDLR gene, and amplifications in the LPA gene.

Determine complete chromosomal aneuploidy in patient samples

In one embodiment, a method is provided for determining the presence or absence of any one or more different, complete chromosomal aneuploidies in a patient test sample comprising a nucleic acid molecule. In some embodiments, the method determines the presence or absence of any one or more different, complete chromosomal aneuploidies. The steps of the method include: (a) obtaining sequence information for the patient's nucleic acid in the patient test sample; and (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing chromosome sequence for each of any one or more chromosomes of interest. This normalizing chromosome sequence can be a single chromosome, or it can be a group of chromosomes selected from chromosomes 1-22, X, and Y. The method further uses the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for each normalizing chromosome sequence to calculate a single chromosome dose for each of the one or more chromosomes of interest in step (c); and (d) comparing each of the single chromosome doses for each of the one or more chromosomes of interest to a threshold value for each of the one or more chromosomes of interest, thereby determining the presence or absence of any one or more different, complete patient chromosomal aneuploidies in the patient test sample.

In some embodiments, step (c) comprises calculating a single chromosome dose for each of the chromosomes of interest as the ratio of the number of sequence tags identified for each of the chromosomes of interest to the number of sequence tags identified for the normalizing chromosome sequence for each of the chromosomes of interest.

In other embodiments, step (c) comprises calculating a single chromosome dosage for each described chromosome interested, as the ratio of the sequence tag number identified for each described chromosome interested and the sequence tag number identified for the described normalizing chromosome of each described chromosome interested. In other embodiments, step (c) comprises: by making the number of sequence tags obtained for chromosome interested be associated with the length of chromosome interested and making the number of tags for the corresponding normalizing chromosome sequence of chromosome interested be associated with the length of normalizing chromosome sequence, calculate a sequence tag ratio for a chromosome interested, and calculate a chromosome dosage for this chromosome interested, as the sequence tag density of chromosome interested and the ratio of the sequence tag density for normalizing chromosome sequence. Repeat this calculation for each of all sequences interested. Steps (a)-(d) can be repeated for test samples from different patients.

By one example of this embodiment, one or more complete chromosomal aneuploidies are determined in a cancer patient test sample comprising cell-free DNA molecules, the example comprising: (a) sequencing at least a portion of the cell-free DNA molecules to obtain sequence information for the patient's cell-free DNA molecules in the test sample; (b) using the sequence information to identify a number of sequence tags for each of any twenty or more chromosomes of interest selected from chromosomes 1-22, X, and Y and to identify a number of sequence tags for a normalizing chromosome for each of the twenty or more chromosomes of interest; (c) using the number of sequence tags identified for each of the twenty or more chromosomes of interest and the number of sequence tags identified for each normalizing chromosome to calculate a single chromosome dose for each of the twenty or more chromosomes of interest; and (d) comparing each single chromosome dose for each of the twenty or more chromosomes of interest to a threshold value for each of the twenty or more chromosomes of interest, and thereby determining the presence or absence of any twenty or more different complete chromosomal aneuploidies in the patient test sample.

In another embodiment, the method for determining the presence or absence of any one or more different, complete chromosomal aneuploidies in a patient test sample as described above uses a normalizing segment sequence to determine the dose of the chromosome of interest. In this example, the method includes: (a) obtaining sequence information for the nucleic acid in the sample; and (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing segment sequence for each of any one or more chromosomes of interest. The normalizing segment sequence can be a single segment of a chromosome, or it can be a group of segments from one or more different chromosomes. The method further uses the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for the normalizing segment sequence to calculate a single chromosome dose for each of the one or more chromosomes of interest in step (c); and (d) comparing each of the single chromosome doses for each of the one or more chromosomes of interest to a threshold value for each of the one or more chromosomes of interest, and thereby determining the presence or absence of one or more different, complete chromosomal aneuploidies in the patient sample.

In some embodiments, step (c) comprises calculating a single chromosome dose for each of the chromosomes of interest as the ratio of the number of sequence tags identified for each of the chromosomes of interest to the number of sequence tags identified for the normalizing segment sequence of each of the chromosomes of interest.

In other embodiments, step (c) comprises: calculating a sequence tag ratio for a chromosome of interest by associating the number of sequence tags obtained for the chromosome of interest with the length of the chromosome of interest and associating the number of tags for the corresponding normalizing segment sequence of the chromosome of interest with the length of the normalizing segment sequence, and calculating a chromosome dose for the chromosome of interest as the ratio of the sequence tag density of the chromosome of interest to the sequence tag density of the normalizing segment sequence. This calculation is repeated for each of all sequences of interest. Steps (a)-(d) can be repeated for test samples from different patients.

A means for comparing chromosome doses in different sample groups is provided by determining a normalized chromosome value (NCV), which relates the chromosome dose in a test sample to the mean of the corresponding chromosome dose in a set of qualified samples. The NCV is calculated as:

where and are the estimated mean and standard deviation of the jth chromosome dose for the qualified sample set, respectively, and _xij is the observed value of the jth chromosome dose for test sample i.

In some embodiments, the presence or absence of a complete chromosomal aneuploidy is determined. In other embodiments, the presence or absence of two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, or twenty-four complete chromosomal aneuploidies is determined in a sample, wherein twenty-two complete chromosomal aneuploidies correspond to the complete chromosomal aneuploidies of any one or more autosomes; the twenty-third and twenty-fourth chromosomal aneuploidies correspond to the complete chromosomal aneuploidies of chromosomes X and Y. Because aneuploidy can include trisomy, tetrasomy, pentasomy and other polysomy, and the number of complete chromosomal aneuploidies varies in different diseases and at different stages of the same disease, the number of complete chromosomal aneuploidies determined according to the present method is at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30complete, at least 40, at least 50, at least 60, at least 70, at least 80, at least 90, at least 100 or more chromosomal aneuploidies. Systematic karyotyping of tumors has revealed that the number of chromosomes in cancer cells is highly variable, ranging from hypodiploid (considerably less than 46 chromosomes) to tetraploid and hypertetraploid (up to 200 chromosomes) (Storchova and Kuffer, J Cell Sci, 121:3859-3866 [2008]). In some embodiments, the method includes determining that in the sample from a patient who suspects or is known to suffer from cancer (such as colon cancer), there is not or does not exist up to 200 kinds or more kinds of chromosomal aneuploidies. These chromosomal aneuploidies include losing one or more complete chromosomes (hypodiploids), and acquisition includes the complete chromosomes of trisomy, tetrasomy, pentasomy and other polysomy. As described elsewhere in the application, it is also possible to determine that the acquisition and/or loss of chromosome segment. The method is applicable to determining that in the sample from the patient who suspects or is known to suffer from cancer as described elsewhere in the application, there is or does not exist different aneuploidies.

In some embodiments, any one of chromosome 1-22, X and Y can be the chromosome of interest in determining the presence or absence of any one or more different, complete chromosome aneuploidy in patient test sample as described above. In other embodiments, two or more chromosomes of interest are selected from any two or more of chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X or Y. In one embodiment, any one or more chromosomes of interest selected from chromosome 1-22, X and Y include at least 20 chromosomes selected from chromosome 1-22, X and Y, and wherein determine the presence or absence of at least 20 different, complete chromosome aneuploidy. In other embodiments, any one or more chromosomes of interest selected from chromosome 1-22, X and Y are whole chromosomes 1-22, X and Y, and wherein determine the presence or absence of the complete chromosome aneuploidy of whole chromosomes 1-22, X and Y. Complete, different chromosomal aneuploidies that can be determined include complete monosomy of any one or more of chromosomes 1-22, X, and Y; complete trisomy of any one or more of chromosomes 1-22, X, and Y; complete tetrasomy of any one or more of chromosomes 1-22, X, and Y; complete pentasomy of any one or more of chromosomes 1-22, X, and Y; and other complete polysomy of any one or more of chromosomes 1-22, X, and Y.

Determine partial chromosomal aneuploidy in patient samples

In another embodiment, a variety of methods are provided for determining the presence or absence of any one or more different, partial chromosomal aneuploidies in a patient test sample comprising nucleic acid molecules. The steps of the method include: (a) obtaining sequence information for the patient nucleic acid in the sample; and (b) using the sequence information to identify a number of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifying a number of sequence tags for a normalizing segment sequence for each of any one or more segments in any one or more chromosomes of interest. The normalizing segment sequence can be a single segment of a chromosome, or it can be a group of segments from one or more different chromosomes. The method further uses the number of sequence tags identified for each of any one or more segments of any one or more chromosomes of interest and the number of sequence tags identified for each of the normalizing segment sequences to calculate a single segment dose for each of any one or more segments of any one or more chromosomes of interest in step (c); and (d) compares each of the single chromosome doses for each of any one or more segments of any one or more chromosomes of interest to a threshold value for each of any one or more chromosome segments of any one or more chromosomes of interest, and thereby determines the presence or absence of one or more different, partial chromosomal aneuploidies in the sample.

In some embodiments, step (c) comprises calculating a single segment dose for each of any one or more segments of any one or more chromosomes of interest as the ratio of the number of sequence tags identified for each of any one or more segments of any one or more chromosomes of interest to the number of sequence tags identified for the normalizing segment sequence for each of any one or more segments of any one or more chromosomes of interest.

In other embodiments, step (c) comprises calculating a sequence tag ratio for a segment of interest by correlating the number of sequence tags obtained for the segment of interest with the length of the segment of interest and correlating the number of tags for the corresponding normalizing segment sequence for the segment of interest with the length of the normalizing segment sequence, and calculating a segment dose for the segment of interest as the ratio of the sequence tag density for the segment of interest to the sequence tag density for the normalizing segment sequence. This calculation is repeated for each of all sequences of interest. Steps (a)-(d) can be repeated for test samples from different patients.

A means for comparing segment doses across different sample groups is provided by determining a normalized segment value (NSV), which relates the segment dose in a test sample to the mean of the corresponding segment doses in a set of qualified samples. The NSV is calculated as:

where and are the estimated mean and standard deviation of the j-th bin dose of the qualified sample set, respectively, and x _ij is the observed j-th bin dose of test sample i.

In some embodiments, it is determined that there is or is not a kind of chromosomal aneuploidy of part.In other embodiments, it is determined that there is or is not two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty-five or more kinds of chromosomal aneuploidy of part in a sample.In one embodiment, a section of interest selected from any one of chromosome 1-22, X and Y is selected from chromosome 1-22, X and Y.In other embodiments, two or more sections of interest selected from chromosome 1-22, X and Y are selected from chromosome 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20, 21, any two or more of 22, X or Y. In one embodiment, any one or more segments of interest selected from chromosomes 1-22, X, and Y include at least one, five, ten, 15, 20, 25, 50, 75, 100 or more segments selected from chromosomes 1-22, X, and Y, and wherein the presence or absence of at least one, five, ten, 15, 20, 25, 50, 75, 100 or more different, partial chromosomal aneuploidies is determined. Different, partial chromosomal aneuploidies that can be determined include partial duplications, partial doublings, partial insertions, and partial deletions.

The sample that can be used to determine the presence or absence of chromosomal aneuploidy (partial or complete) in a patient can be any biological sample described elsewhere in this application. The sample type or sample that can be used to determine the aneuploidy in a patient will depend on the type of disease that the patient is known to or suspected of having. For example, a fecal sample can be selected as a DNA source to determine the presence or absence of aneuploidy associated with colorectal cancer. The method is also applicable to tissue samples described herein. Preferably, the sample is a biological sample obtained in a non-invasive manner, such as a plasma sample. As described elsewhere in this application, next generation sequencing (NGS) described elsewhere in this application can be used to carry out sequencing of nucleic acids in patient samples. In some embodiments, sequencing is a massively parallel sequencing using synthetic sequencing with the aid of reversible dye terminators. In other embodiments, sequencing is ligation sequencing. In other embodiments, sequencing is single molecule sequencing. Optionally, an amplification step is performed before sequencing.

In some embodiments, the presence or absence of aneuploidy is determined in a patient suspected of having a cancer as described elsewhere in this application, such as lung cancer, breast cancer, kidney cancer, head and neck cancer, ovarian cancer, cervical cancer, colon cancer, pancreatic cancer, esophageal cancer, bladder cancer, and cancers of other organs, and blood cancers. Blood cancers include cancers of the bone marrow, blood, and lymphatic system, which includes lymph nodes, lymphatic vessels, tonsils, thymus, spleen, and digestive tract lymphoid tissue. Leukemias and myelomas that begin in the bone marrow, and lymphomas that begin in the lymphatic system are the most common blood cancer types.

The determination of the presence or absence of one or more chromosomal aneuploidies in a patient sample can be made, without limitation, to determine a patient's susceptibility to a particular cancer, to determine the presence or absence of a cancer of interest as part of routine screening in patients known or not to be susceptible to a cancer, to provide a prognosis for a disease, to assess the need for adjuvant therapy, and to determine the progression or regression of a disease.

Genetic counseling

Fetal chromosomal abnormalities are a leading cause of miscarriage, congenital anomalies, and perinatal death (Wellesley et al., Europ. J. Human Genet., 20:521-526 [2012]; Nagaoka et al., Nature Rev. Genetics 13:493-504 [2012]). Since the introduction of amniocentesis and subsequently chorionic villus sampling (CVS), pregnant women have had the right to obtain information about their fetal chromosomal status (ACOG Practice Bulletin No. 77: Obstet Gynecol 109:217-227 [2007]). When sufficient tissue is available, cytogenetic karyotyping of fetal cells or chorionic villi obtained from these procedures results in a high sensitivity and specificity (approximately 99%) in the vast majority of cases (Hahnemann and Vejerslev, Prenat Diagn., 17:801-820 1997; NICHD Amniocentesis National Registry JAMA 236:1471-1476 [1976]). However, these procedures also carry risks to the fetus and the mother (Odibo et al., Obstet Gynecol 112:813-819 [2008]; Odibo et al., Obstet Gynecol 111:589-595 [2008]).

To mitigate these risks, a series of prenatal screening algorithms have been developed to stratify women for their likelihood of developing the most common fetal trisomies - T21 (Down syndrome) and trisomy 18 (T18, Edward's syndrome), and to a lesser extent trisomy 13 (T13, Patau syndrome). Screening typically involves measuring multiple biochemical analytes in maternal serum at different time points, combined with ultrasonographic measurement of the fetal nuchal translucency (NT), and the incorporation of other maternal factors (e.g., age) to generate a risk score. Over the years, and depending on when screening is given (first or second trimester only, continuous or fully integrated) and how it is given (serum only or serum combined with NT), a menu of options has developed with varying detection rates (65% to 90%) and high screen-positive rates (5%) (ACOG Practice Bulletin No. 77: Obstet Gynecol 109:217-227 [2007]).

For patients, the resulting information, or "risk score," following this multi-step process can be confusing and anxiety-provoking, particularly in the absence of comprehensive counseling. Ultimately, women must weigh the outcome against the risk of miscarriage associated with the invasive procedure when making their decision. Better non-invasive methods of obtaining more definitive information about the fetus's chromosomal status could aid in this decision-making process. Such improved non-invasive means of obtaining more definitive information about the fetus's chromosomal status are believed to be provided by the methods described herein.

In various embodiments, genetic counseling is contemplated as part of using the assays described herein, particularly in a clinical setting. Conversely, the aneuploidy detection methods described herein can include an option provided in the context of prenatal care and related genetic counseling.

Thus, in various embodiments, the methods described herein can be provided as a primary screen (e.g., for women with a pre-existing risk for pregnancy) or as a secondary screen for those women who test positive on a "routine" screen. In certain embodiments, it is contemplated that the non-invasive prenatal testing (NIPT) methods described herein additionally include a genetic counseling component and/or that genetic counseling and pregnancy "management" are optionally or explicitly incorporated into the NIPT methods described herein.

For example, in certain embodiments, a woman has one or more pre-existing pregnancy risks. Such risks include, but are not limited to, one or more of the following:

1) The mother is over 35 years old, although it is noted that approximately 80% of children born with Down syndrome are born to women under 35 years old.

2) A previous fetus/child with an autosomal trisomy. The recurrence rate is thought to be approximately 1.6 to 8.2 times the maternal age risk, depending on the type of trisomy, whether the previous pregnancy ended in spontaneous abortion, and the maternal age at the time of the first occurrence and subsequent prenatal diagnosis.

3) Previous fetuses/children with sex chromosome abnormalities - Not all sex chromosome abnormalities are of maternal origin, and not all carry a risk of recurrence. When they occur, the recurrence rate is about 1.6 to about 1.5 times the risk for maternal age.

4) Parental carriers of chromosomal translocation.

5) Parental carriers of chromosomal inversion.

6) Parental aneuploidy or mosaicism.

7) Use of certain assisted reproductive technologies.

In such cases, subject to the various considerations described above, the mother, for example, in consultation with a physician, genetic counselor, etc., can be offered the use of the methods described herein for non-invasively determining the presence or absence of a fetal aneuploidy (e.g., trisomy 21, trisomy 18, trisomy 13, monosomy X, etc.). In this regard, it should be noted that the methods described herein are believed to be effective even in the first three months of pregnancy. Thus, in certain embodiments, the use of the NIPT methods described herein is contemplated as early as 8 weeks, and in various embodiments, at about 10 weeks or later.

In certain embodiments, the methods described herein may be offered as a secondary screening to those women who are positive for a "conventional" screen. For example, in certain embodiments, a pregnant woman may present with a structural abnormality, such as, for example, a fetal cystic hygroma, or an increased nuchal translucency, such as detected using ultrasonography. Typically, ultrasound detection of structural defects is performed at 18 to 22 weeks, and particularly when irregularities are observed, can be coupled to a fetal echocardiogram. It is contemplated that when an abnormality is observed (e.g., a "conventional" screen is positive), the mother, for example, in consultation with a physician, genetic counselor, or the like, may be offered the use of the methods described herein for non-invasive determination of the presence or absence of a fetal aneuploidy (e.g., trisomy 21, trisomy 18, trisomy 13, monosomy X, etc.).

Therefore, in various embodiments, genetic counseling is contemplated in which the (NIPT) analysis described herein is provided as an integral part of prenatal care, pregnancy management, and/or delivery plan development/design. By offering NIPT as a secondary screening test to women who test positive on conventional screening (or other pre-existing risk factors), it is expected that the number of unnecessary amniocentesis and CVS procedures can be reduced. However, because consent is an essential component of NIPT, the need for genetic counseling is heightened.

Because a positive NIPT result (using the method described here) is more similar to a positive result from amniocentesis or CVS, women should be given the opportunity to decide whether they want this level of information during genetic counseling before this testing. Pre-test NIPT genetic counseling should also include discussion/advice to confirm abnormal test results via CVS, amniocentesis, cordocentesis, etc. (depending on gestational age) so that appropriate consideration can be given to the expected timing of the results for post-test planning in accordance with the National Society of Genetic Counselors (NSGC, USA) statements on this topic (see, e.g., Devers et al., Noninvasive Prenatal Testing/Noninvasive Prenatal Diagnosis: the position of the National Society of Genetic Counselors (by NSGC Public Policy Committee). NSGC Position Statements 2012; Benn et al., Prenat Diagn, 31:519-522 [2011]), because NIPT Screening for all chromosomal or genetic conditions is not currently available, so it may not replace standard risk assessment and prenatal diagnosis. Patients who have other factors suggestive of a chromosomal abnormality (eg, certain abnormal ultrasound findings) who are considered here should receive genetic counseling in which they are offered the option of routine confirmatory diagnostic testing, regardless of NIPT results. Women should also be aware during genetic counseling that NIPT results may not be informative for some patients.

NIPT using the methods described herein may be more similar to CVS than amniocentesis in that the detection of aneuploidy typically indicates the chromosomal composition of the fetus, but in some cases may indicate confined placental aneuploidy or confined placental mosaicism (CPM). CPM is present in approximately 1% to 2% of today's CVS results, and some women undergo amniocentesis at a later gestational age after CVS to differentiate between clearly separated placental aneuploidies and fetal aneuploidies. As NIPT is implemented more widely, it is expected that CPM cases may produce a certain number of positive NIPT results that may not be subsequently confirmed by invasive procedures (particularly amniocentesis). Again, in various embodiments, it is contemplated that this information is presented to the patient in the context of genetic counseling (e.g., by a physician, genetic counselor, etc.).

It will be appreciated that, in various embodiments, a component of genetic counseling may be a recommendation for a confirmed diagnosis, information on the timing of risk levels, and timing for different confirmed diagnosis methods, which can be used to provide input on the value of the information provided by such validation methods, particularly in the context of choosing the timing of pregnancy. In various embodiments, genetic counseling can also establish a plan for monitoring the pregnancy (e.g., follow-up ultrasounds, additional physician visits, etc.) and for establishing a series of decision points when appropriate. In addition, genetic counseling can recommend and assist in developing a delivery plan that can include, for example, information on the location of the birth (e.g., home, hospital, specialized facility, etc.), the personnel involved at the location of the birth, third-party care available to the baby, and the like.

Although the above discussion focuses on the methods described herein as a component of prenatal diagnosis (and perhaps a secondary tool), as clinical experience accumulates and if the results from comparative studies to routine screening are successful, the NIPT methods described herein may replace existing screening programs and may be used as the primary tool.

It is also contemplated that the methods described herein will be used for pregnancy discovery in multiple pregnancies.

Typically, it is contemplated that genetic counseling (e.g., as described above) can be provided by a physician (e.g., primary physician, obstetrician, etc.) and/or by a genetic counselor or other qualified medical professional. In certain embodiments, counseling is provided face-to-face, however, it will be appreciated that in certain circumstances, counseling can be provided via remote access (e.g., via text, cell phone, cell phone application, tablet computer application, Internet, etc.).

It should also be appreciated that in certain embodiments, genetic counseling or a component thereof can be delivered by a computer system. For example, a "smart advice" system can be provided that provides genetic counseling information (such as described above) in response to test results, instructions from a healthcare provider, and/or in response to a query (such as a query from a patient). In certain embodiments, the information will be specific clinical information provided by a physician, a healthcare system, and/or a patient. In certain embodiments, the information can be provided in an iterative manner. Thus, for example, a patient can provide a "what if" query and the system can return information such as diagnostic options, risk factors, timelines, and the meaning of different results.

In some embodiments, the information can be provided in a transient manner (e.g., presented on a computer screen). In some embodiments, the information can be provided in a non-transitory manner. Thus, for example, the information can be printed out (e.g., as a menu of options and/or suggestions, optionally with associated schedules, etc.) and/or stored on computer-readable media (e.g., magnetic media, such as a local hard drive, server, etc.; optical media; flash memory, etc.).

It will be appreciated that such systems are typically configured to provide sufficient security to maintain patient privacy, such as in accordance with current standards in the industry.

The above discussion of genetic counseling is intended to be illustrative and not limiting. Genetic counseling is a well-established branch of medical science, and the incorporation of counseling components regarding the analysis described herein is within the skill of the practitioner. Furthermore, it should be recognized that as the field advances, the nature of genetic counseling and related information and recommendations is likely to change.

Determining fetal fraction

Methods for determining fetal fraction are disclosed in U.S. Patent Application Publication No. 2010-0010085 (117.201), U.S. Patent Application Publication No. 2011-0201507 (120.201), U.S. Patent Application No. 13/365,240 (filed February 2, 2012), and U.S. Patent Application No. 13/445,778 (filed April 12, 2012). A comprehensive discussion of techniques for determining fetal fraction can be found in these documents.

The methods described herein enable determination of fetal fraction in a sample comprising a mixture of fetal and maternal nucleic acids, or more generally, a mixture of nucleic acids derived from two different genomes. For the purposes of this discussion, maternal and fetal nucleic acids will be described, but it will be understood that any two genomes may be substituted. In some embodiments, fetal fraction is determined while simultaneously determining the presence or absence of copy number variation (e.g., aneuploidy). As described more fully below, fetal fraction and copy number variation can be determined using a set of labels for a test sample.

The method of quantifying fetal fraction is to rely on the difference between the fetal genome and the maternal genome. In certain embodiments described herein, determining the fetal fraction of sample DNA relies on multiple DNA sequence readings at sequence sites known to accommodate one or more polymorphisms. In some embodiments, polymorphic sites or target nucleic acid sequences are found while sequence tags are compared to each other and/or to reference sequences. In certain embodiments, the fetal fraction of sample DNA is determined by considering the copy number information of a specific chromosome or chromosome sequence, wherein there is a copy number difference between the maternal chromosome and the fetal chromosome. In such embodiments, the fetal fraction of sample DNA is determined by considering the relative number of sample DNA of the mother and fetus, wherein a chromosome or segment is originally determined or known to have a copy number variation. In such embodiments, the fetal fraction can be calculated using the copy number variation between the maternal chromosome and the fetal chromosome. For this purpose, the method and apparatus can calculate a normalized chromosome value (NCV) as described below, or a similar metric.

Some methods are limited by fetal sex, for example, methods for quantifying fetal fraction rely on the presence of sequences specific for the Y chromosome or determine the chromosome dose of the X chromosome in male fetuses. In certain embodiments, fetal DNA is quantified for fetal targets that lack a maternal counterpart, such as Y chromosome sequences (Fan et al., Proc Natl Acad Sci 105:16266-16271 [2008] and U.S. Patent Application Publication No. 2010/0112590, filed November 6, 2009, Lo et al.), or for RhD-negative mothers who lack the RHD1 gene, or for those who differ from the maternal background by multiple DNA base pairs. Other methods are independent of fetal sex and rely on polymorphic differences between the fetal and maternal genomes.

Allelic imbalance in polymorphisms can be detected and quantified by various techniques. In some embodiments, digital PCR is used to determine allelic imbalance in polymorphisms, such as SNPs on mRNA. Alternatively, capillary gel electrophoresis is used to detect differences in the size of polymorphic regions, such as in the case of STRs.

In some embodiments, epigenetic differences can be detected, such as differential methylation of promoter regions, which can be used alone or in combination with digital PCR to determine differences between the fetal and maternal genomes and quantify fetal fraction (Tong et al., Clin Chem 56:90-98 [2010]). Variations of epigenetic methods, such as methylation-based DNA discrimination (Erich et al., AJOG 204: pp. 205.e1-pp. 205.e11 [2011]), are also included. In some embodiments, fetal fraction is estimated using sequencing of one or more preselected sets of polymorphic sequences as described elsewhere in this application.

In addition to methods of sequencing sets of preselected polymorphic sequences as described elsewhere in this application, methods for quantifying fetal DNA in maternal plasma include, but are not limited to, real-time qPCR, mass spectrometry, digital PCR (including microfluidic digital PCR), capillary gel electrophoresis.

This section discusses the fetal fraction as determined from one or more polymorphisms or other information for chromosomes or chromosome segments that do not (or are determined not to) have a copy number variation. The fetal fraction determined by such techniques will be referred to herein as the non-CNV fetal fraction or "NCNFF." Later in this section, various techniques are described for calculating the fetal fraction from chromosomes or chromosome segments that are determined to have a copy number variation. The fetal fraction determined from such techniques will be referred to herein as the CNV fetal fraction or "CNFF."

In some embodiments, the fetal fraction is assessed by determining the relative contribution of a polymorphic allele derived from the fetal genome and the contribution of the corresponding polymorphic allele derived from the maternal genome. In some embodiments, the fetal fraction is assessed by determining the relative contribution of a polymorphic allele derived from the fetal genome compared to the total contribution of the corresponding polymorphic alleles derived from the fetal genome and the maternal genome.

Polymorphisms can be indicative, informative, or both. Indicative polymorphisms indicate the presence of fetal cell-free DNA ("cfDNA") in a maternal sample. Informative polymorphisms (e.g., informative SNPs) generate information about the fetus, e.g., the presence or absence of a disease, a genetic abnormality, or any other biological information, such as gestational stage or gender. In this case, informative polymorphisms are those that identify the difference between the sequences of the mother and the fetus, and are used in the methods disclosed herein. In other words, informative polymorphisms are polymorphisms in nucleic acid samples that have different sequences (i.e., they have different alleles), and these sequences are present in different amounts. In some methods herein, different numbers of sequences/alleles are used to determine fetal fraction, particularly NCNFF.

Polymorphic sites include, but are not limited to, single nucleotide polymorphisms (SNPs), tandem SNPs, small-scale multi-base deletions or insertions (IN-DELS or deletion-insertion polymorphisms (DIPs)), multiple nucleotide polymorphisms (MNPs), short tandem repeats (STRs), restriction fragment length polymorphisms (RFLPs), or any polymorphism with any other allelic sequence variation in a chromosome. In some embodiments, each target nucleic acid comprises two tandem SNPs. Tandem SNPs are analyzed as a single unit (e.g., as a short haplotype) and are provided herein as multiple sets having two SNPs.

In some embodiments, fetal fraction is determined by statistical and approximate techniques that assess the relative contributions of the fetal and maternal genomes using polymorphic sites to determine relative contributions. Fetal fraction can also be determined by electrophoresis, in which certain types of polymorphic sites are electrophoretically separated and used to identify the relative contribution of a polymorphic allele from the fetal genome and the relative contribution of the corresponding polymorphic allele from the maternal genome.

In one embodiment, shown in the process flow diagram of FIG6 , fetal fraction is determined by method 600, which includes first obtaining a test sample comprising a mixture of fetal and maternal nucleic acids in operation 610, enriching the nucleic acid mixture for polymorphic target nucleic acids in operation 620, sequencing the enriched nucleic acid mixture in operation 630, and simultaneously determining fetal fraction and aneuploidy in the sample in operation 640.

Figure 7 shows a process flow diagram for some embodiments. The fetal fraction is determined by (i) obtaining a maternal plasma sample in operation 710, (ii) purifying cfDNA in the sample in operation 720, (iii) amplifying polymorphic nucleic acids in operation 730, (iv) sequencing the mixture using a massively parallel sequencing method in operation 740, and (v) calculating the fetal fraction in operation 760. In another embodiment, the fetal fraction is determined by (i) obtaining a maternal plasma sample in operation 710, (ii) purifying cfDNA in the sample in operation 720, (iii) amplifying polymorphic nucleic acids in operation 730, (iv) separating nucleic acids by size using electrophoresis in operation 750, and (v) calculating the fetal fraction in operation 770.

In one embodiment, as shown in the process flow diagram of FIG8 , fetal fraction is determined by (i) obtaining a sample comprising a mixture of fetal and maternal nucleic acids in operation 810 , (ii) amplifying the sample in operation 820 , (iii) enriching the sample in operation 830 by combining the amplified sample with an unamplified sample of the initial mixture, (iv) purifying the sample in operation 840 , and (v) sequencing the sample using a different method to determine fetal fraction in operation 850 , and simultaneously determining the fetal fraction and the presence or absence of aneuploidy in operation 860 .

In another embodiment, shown in the process flow diagram of FIG9 , fetal fraction is determined by (i) obtaining a sample comprising a mixture of fetal and maternal nucleic acids in operation 910 , (ii) purifying the sample in operation 920 , (iii) amplifying a portion of the sample in operation 930 , (iv) enriching the sample by combining the amplified sample with a purified but non-amplified portion of the initial sample of the initial mixture in operation 940 , and (v) sequencing the sample to determine fetal fraction in operation 950 , and simultaneously determining the fetal fraction and the presence or absence of aneuploidy using different methods in operation 960 .

In another embodiment shown in the process flow diagram of Figure 10, fetal fraction is determined by (i) obtaining a sample comprising a mixture of fetal and maternal nucleic acids in operation 1010, (ii) purifying the sample in operation 1020, (iii) amplifying a first portion of the sample in operation 1040, (iv) preparing a sequencing library of the amplified portion of the sample in operation 1050, (v) preparing a sequencing library of a second purified but unamplified portion of the sample in operation 1030, (vi) enriching the mixture by combining the two sequencing libraries in operation 1060, and (vii) sequencing the mixture in operation 1070, and simultaneously determining the fetal fraction and the presence or absence of aneuploidy using different methods in operation 1080.

In another embodiment, fetal fraction is determined by (i) obtaining a sample comprising a mixture of fetal and maternal nucleic acid, (ii) purifying the sample, (iii) amplifying the sample using labeled primers, and (iv) sequencing the sample using electrophoresis to determine fetal fraction using a different method.

In another embodiment, the fetal fraction is determined by (i) obtaining a sample comprising a mixture of fetal and maternal nucleic acids, (ii) purifying the sample, (iii) optionally enriching the sample by amplifying a portion of the sample, and (iv) sequencing the sample to determine the fetal fraction using a different method.

Purification of the initially obtained sample, the amplified sample, or the amplified and enriched sample, or other nucleic acid samples used in connection with the methods disclosed herein (e.g., in operations 720, 840, 920, and 1020) can be accomplished by any conventional technique. To isolate cfDNA from cells, fractionation, centrifugation (e.g., density gradient centrifugation), DNA-specific precipitation, or high-throughput cell sorting and/or separation methods can be used. Optionally, the resulting sample can be fragmented prior to purification or amplification. If the sample used comprises cfDNA, fragmentation may not be required, as cfDNA is fragmented in nature, with fragment sizes often ranging from approximately 150 bp to 200 bp.

In some of the above procedures, selective amplification and enrichment are used to increase the relative amount of nucleic acid from the region where the polymorphism is located. Similar results can be obtained by deep sequencing of selected regions of the genome, particularly where the polymorphism is located.

Amplification

After obtaining and purifying the sample, a portion of the purified mixture of fetal and maternal nucleic acids (e.g., cfDNA) is used to amplify multiple polymorphic target nucleic acids, each nucleic acid comprising a polymorphic site. Amplifying the target nucleic acid in the mixture of fetal and maternal nucleic acids, in certain implementations, is achieved by using PCR (polymerase chain reaction) or any method of variation of the method (including but not limited to asymmetric PCR, helicase-dependent amplification, hot start PCR, qPCR, solid phase PCR, and touchdown PCR). In some embodiments, the sample can be partially amplified to assist in determining the fetal fraction. In some embodiments, amplification is not performed. The disclosed amplification methods and other amplification techniques can be used in operations 730, 820, 930, and 1040.

Amplification SNP

Numerous nucleic acid primers are available for amplifying DNA fragments containing SNPs, and their sequences are available, for example, from databases known to those skilled in the art. Additional primers can also be designed, for example, using similar methods disclosed in Vieux, E.F., Kwok, P-Y, and Miller, R.D., BioTechniques (June 2002), Vol. 32, Supplement: "SNPs: Discovery of Marker Disease," pp. 28-32.

Sequence-specific primers are selected to amplify target nucleic acid. In one embodiment, the target nucleic acid comprising a polymorphic site is amplified as an amplicon. In another embodiment, the target nucleic acid comprising two or more polymorphic sites (e.g., two tandem SNPs) is amplified as an amplicon. The amplified target nucleic acid amplicon of at least about 100bp comprises a single or tandem SNP. The primers for amplifying a target sequence comprising a tandem SNP can be designed to encompass two SNP sites.

Amplified STR

Nucleic acid primers are available for amplifying DNA fragments containing STRs, and such sequences can be obtained from databases known to one skilled in the art.

In some embodiments, a portion of the fetal and maternal nucleic acid mixture is used as a template for amplifying a target nucleic acid having at least one STR. A comprehensive directory of references, literature, and sequence information on STRs, published PCR primers, common multiplex systems, and related population data is compiled in STRBase, which is accessible via the Internet at cstl.nist.gov/strbase. Sequence information for commonly used STR loci from ncbi.nlm.nih.gov/genbank is also accessible through STRBase.

STR multiplexing systems allow for the simultaneous amplification of multiple, non-overlapping loci in a single reaction, substantially increasing throughput. Because STRs are highly polymorphic, most individuals are heterozygous. STRs can be used in electrophoretic analysis as described further below.

MiniSTRs can also be used for amplification to produce amplicons of reduced size, thereby discerning STR alleles that are shorter in length. Methods of the disclosed embodiments encompass determining the fraction of fetal nucleic acid in a maternal sample that has been enriched for target nucleic acids, each of which comprises a miniSTR, the method comprising quantifying at least one fetal and one maternal allele located at a polymorphic miniSTR that can be amplified to produce amplicons approximately the size of circulating fetal DNA fragments. Any pair of miniSTR primers or a combination of two or more pairs of miniSTR primers can be used to amplify at least one miniSTR.

Enrichment

The samples that are enriched may include: the plasma fraction of a blood sample; a sample of purified cfDNA extracted from plasma; a sequencing library sample prepared from a purified mixture of fetal and maternal nucleic acids; and the like.

In certain embodiments, before full genome sequencing, for full genome non-specific enrichment comprising a sample of a DNA molecule mixture, that is, before sequencing, full genome amplification is performed. Non-specific enrichment nucleic acid mixture refers to that the genomic DNA fragments of a DNA sample are subjected to full genome amplification. This DNA sample can be used to improve the level of sample DNA before identifying polymorphisms by sequencing. Non-specific enrichment can be the selective enrichment of one of the two genomes (fetus and mother) present in the sample.

In other embodiments, the cfDNA in the sample is specifically enriched. Specific enrichment refers to the enrichment of a genomic sample for a specific sequence (e.g., a polymorphic target sequence) by a method that includes specifically amplifying a target nucleic acid sequence that contains a polymorphic site.

In other embodiments, the mixture of nucleic acids present in the sample is enriched for polymorphic target nucleic acids, each of which contains a polymorphic site. Such enrichment can be used in operation 620. Enriching the mixture of fetal and maternal nucleic acids includes amplifying the target sequence from a portion of the nucleic acids contained in the original maternal sample and combining part or all of the amplified product with the remainder of the original maternal sample, for example, in operations 830 and 940.

In yet another embodiment, the enriched sample is a sequencing library sample prepared from a purified mixture of fetal and maternal nucleic acids. The amount of amplification product used to enrich the initial sample is selected to obtain sufficient sequence information for determining fetal fraction. At least about 3%, at least about 5%, at least about 7%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, or more of the total number of sequence tags obtained from sequencing are mapped to determine fetal fraction.

In one embodiment, in FIG10 , enrichment includes amplifying target nucleic acids contained in a portion of an initial sample of a purified mixture of fetal and maternal nucleic acids (e.g., cfDNA purified from a maternal plasma sample) in operation 1040. Similarly, in operation 1050, a primary sequencing library is prepared using a portion of the purified but unamplified cfDNA. In operation 1060, a portion of the target library is combined with a primary library generated from the unamplified nucleic acid mixture, and the mixture of fetal and maternal nucleic acids contained in the two libraries is sequenced in operation 1070. The enriched library may include at least about 5%, at least about 10%, at least about 15%, at least about 20%, or at least about 25% of the target library. In operation 1080, the data from the sequencing rounds are analyzed and the fetal fraction and the presence or absence of aneuploidy are simultaneously determined as described in operation 640 of the embodiment depicted in FIG6 .

Sequencing technology

The enriched fetal and maternal nucleic acid mixture is sequenced. Any known DNA sequencing method can be used to obtain the sequence information necessary for determining the fetal fraction, many of which are described elsewhere in this application. Such sequencing methods include next-generation sequencing (NGS), Sanger sequencing, Helicos True Single Molecule Sequencing (tSMS ^™ ), 454 sequencing (Roche), SOLiD technology (Applied Biosystems), single-molecule real-time (SMRT ^™ ), sequencing technology (Pacific Biosciences), nanopore sequencing, chemically sensitive field-effect transistor (chemFET) arrays, Halcyon Molecular's method using transmission electron microscopy (TEM), ion current single-molecule sequencing, hybridization sequencing, etc. In certain embodiments, a massively parallel sequencing method is used. In one embodiment, Illumina synthesis sequencing and sequencing chemistry based on reversible terminators are used. In certain embodiments, a partial sequencing method is used.

The sequenced DNA is mapped to a reference genome. The reference genome can be an artificial genome or a human reference sequence genome. Such reference genomes include: artificial target sequence genomes containing polymorphic target nucleic acid sequences; artificial SNP reference genomes; artificial STR reference genomes; artificial tandem STR reference genomes; the human reference sequence genome NCBI36/hg18 sequence, available online at genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105; and the human reference sequence genome NCBI36/hg18 sequence containing target polymorphic sequences and artificial target sequence genomes, such as SNP genomes. Some mismatches are tolerated during the mapping process.

In one embodiment, the sequencing information obtained in operation 630 is analyzed and a determination is made simultaneously to determine the fetal fraction and to determine the presence or absence of aneuploidy.

As described above, every kind of sample obtains multiple sequence tags.In certain embodiments, utilize reading to be mapped to reference genome, every kind of sample obtains at least about 3x ¹⁰⁶ sequence tags, at least about 5x ¹⁰⁶ sequence tags, at least about ^8x106 sequence tags, at least about 10x ¹⁰⁶ sequence tags, at least about 15x ¹⁰⁶ sequence tags, at least about 20x ¹⁰⁶ sequence tags, at least about 30x ¹⁰⁶ sequence tags, at least about 40x ¹⁰⁶ sequence tags or at least about 50x ¹⁰⁶ sequence tags, these sequence tags comprise the reading between 20bp and 40bp.In one embodiment, all sequence reads are mapped to all regions of reference genome. In one embodiment, tags comprising reads mapped to all regions of a human reference sequence genome (e.g., all chromosomes) are counted, and fetal aneuploidy, i.e., over-representation or under-representation of a sequence of interest (e.g., a chromosome or a portion thereof), is determined in a mixed DNA sample, and tags comprising reads mapped to an artificial target sequence genome are counted to determine the fetal fraction. The method does not require a distinction to be made between the maternal genome and the fetal genome.

In one embodiment, data from sequencing rounds are analyzed and the fetal fraction, and the presence or absence of aneuploidy are determined simultaneously.

Sequencing library

In some embodiments, a portion or all of the amplified polymorphic sequences are used to prepare a sequencing library for sequencing in the parallel manner. In one embodiment, the library is prepared for sequencing by synthesis using Illumina's reversible terminator-based sequencing chemistry. The library can be prepared from purified cfDNA and include at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, or at least about 50% amplification products.

Sequencing the libraries generated by any of the methods depicted in Figure 11 provides sequence tags derived from the amplified target nucleic acid and tags derived from the original unamplified maternal sample. Fetal fraction is calculated from the number of tags that map to the artificial reference genome.

Calculating fetal fraction

As explained, after sequencing the DNA of interest, computational methods can be used to map or align the sequence to a specific gene, chromosome, allele, or other structure. There are a variety of computer algorithms for aligning sequences, including but not limited to BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993), FASTA (Pearson & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology 10: R25.1-R25.10 [2009]), or ELAND (Illumina, Inc., San Diego, CA, USA). In some embodiments, the data bin sequences are found in nucleic acid databases known to those skilled in the art, including GenBank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory), and DDBJ (DNA Database of Japan). The sequence that can utilize BLAST or similar tool to control sequence database search is identified, and can utilize search hit to classify the sequence that is identified into suitable data box.Alternatively, can adopt Bloom filter (Bloom filter) or similar set member tester (setmembership tester) by reading and reference genome comparison.Referring to U.S. patent application number 61/552,374 that submits on October 27, 2011, this application is combined in this by reference in its full text.

As mentioned, according to some embodiments (particularly NCNFF technology), determining the fetal fraction is based on the total number of tags mapped to the first allele and the total number mapped to the second allele, the second allele being located at an informative polymorphic site (e.g., SNP) contained in the reference genome. Informative polymorphic sites are identified by the difference in allele sequence and the number of each possible allele. Fetal cfDNA is often present at a concentration of <10% of maternal cfDNA. Therefore, relative to the major contribution of the maternal allele, there is a minor contribution of alleles that can be assigned to the fetus, the mixture of fetal and maternal nucleic acids. The alleles derived from the maternal genome are referred to herein as the major alleles, and the alleles derived from the fetal genome are referred to herein as the minor alleles. Alleles represented by similar levels of the mapped sequence tags represent maternal alleles. The results of an exemplary multiplex amplification of target nucleic acids containing SNPs derived from maternal plasma samples are shown in Figure 12.

Herein, the terms "chromosomal aneuploidy" and "complete chromosomal aneuploidy" refer to an imbalance of genetic material caused by the loss or gain of an entire chromosome, and include germline aneuploidy and mosaic aneuploidy. The terms "partial aneuploidy" and "partial chromosomal aneuploidy" refer to an imbalance of genetic material caused by the loss or gain of a portion of a chromosome (e.g., partial monosomy and partial trisomy), and encompass imbalances caused by translocations, deletions, and insertions.

Estimating fetal fraction using allele ratios

For each of the two alleles at a predetermined polymorphic site, the relative abundance of fetal cfDNA in the maternal sample can be determined as a parameter of the total number of unique sequence tags mapped to the target nucleic acid sequence on the reference genome. In one embodiment, the fraction of fetal nucleic acid in the mixture of fetal and maternal nucleic acids is calculated as follows for each informative allele (allele x):

And calculate the fetal fraction for the sample as the fetal fraction average of all informative alleles. Optionally, for each informative allele (allele x), the fraction of fetal nucleic acid in the mixture of fetal and maternal nucleic acid is calculated as follows:

To compensate for the presence of two fetal alleles, one was obscured by the maternal background.

Determine fetal fraction by sequencing predetermined polymorphic sequences

Further details regarding determining fetal fraction by sequencing predetermined polymorphic sequences are provided below.

Referring to Figure 7, operations 720, 730, 740, and 760 illustrate a process flow for determining the fraction of fetal nucleic acid in a maternal biological sample by massively parallel sequencing of polymorphic target nucleic acids amplified by PCR. In step 720, a maternal sample comprising a mixture of fetal and maternal nucleic acids is obtained from a subject. The sample is a maternal sample obtained from a pregnant woman (e.g., a pregnant woman). Other maternal samples can come from mammals, such as cows, horses, dogs, or cats. If the subject is a human, the sample can be obtained during the first or second trimester of pregnancy. Any maternal biological sample can be used as a source of fetal and maternal nucleic acids contained in cells or without cells. In certain embodiments, it is advantageous to obtain a maternal sample comprising cell-free nucleic acid (cfDNA). Preferably, the maternal biological sample is a biological fluid sample. Preferably, the maternal sample is a pregnant sample selected from blood, plasma, serum, urine, and saliva. In certain embodiments, the maternal sample is a plasma sample.

In step 720, the mixture of fetal and maternal nucleic acids is further processed from a sample portion, such as plasma, to obtain a sample comprising a purified mixture of fetal and maternal nucleic acids (e.g., cfDNA). Methods for processing maternal samples are described elsewhere herein.

In step 730, a portion of the purified mixture of fetal and maternal cfDNA is used to amplify multiple polymorphic target nucleic acids, each of which contains a polymorphic site. In certain embodiments, each of these target nucleic acids includes a SNP. In other embodiments, each of these target nucleic acids includes a pair of tandem SNPs. In other embodiments, each target nucleic acid includes an STR. The polymorphic sites contained in the target nucleic acid include, but are not limited to, single nucleotide polymorphisms (SNPs), tandem SNPs, small-scale multi-base deletions or insertions (called IN-DELS, also known as deletion insertion polymorphisms or DIPs), multinucleotide polymorphisms (MNPs), short tandem repeats (STRs), restriction fragment length polymorphisms (RFLPs), or polymorphisms including any other sequence changes in chromosomes. In certain embodiments, the polymorphic sites covered by the method are located on autosomes, thereby enabling the determination of fetal fractions that are unrelated to fetal sex. Polymorphisms associated with chromosomes other than chromosomes 13, 18, 21, and Y can also be used in the methods described herein.

In some embodiments, the present invention provides the method for the present invention.Polymorphism can be indicative, informative, or both.Indicative polymorphism shows that there is fetal cell-free DNA in the maternal sample.For example, concrete genetic sequence (such as SNP) is more many, and a method is more easy to its existence and is converted into concrete color intensity, color density or can detect and measure and show the existence of concrete DNA segment and/or concrete polymorphism (such as embryonic SNP), do not exist and some other properties of amount.About the present invention, these methods are not to use all possible SNPs in a genome to carry out, but to use the polymorphism (i.e. informative polymorphism) that is likely to identify the sequence difference between mother and fetus in advance to carry out.Informative polymorphic sites are identified by the amount of each in the difference of allelic sequence and possible allele.Any polymorphic site contained in the reading produced by sequencing method described herein can be used to determine fetal fraction.

In certain embodiments, the target nucleic acid sequence comprising at least one SNP is amplified using a portion of a mixture of fetus and maternal nucleic acid (e.g., cfDNA) in a sample. In certain embodiments, each target nucleic acid includes a single (i.e., one) SNP. The target nucleic acid sequence comprising SNP can be obtained from publicly accessible databases, including but not limited to the human SNP database at wi.mit.edu, the NCBIdbSNP homepage at ncbi.nlm.nih.gov, the Applied Biosystems (Applied Biosystems) at Life Technologies ^™ (Carlsbad, CA) at appliedbiosystems.com, the Celera human SNP database at celera.com, the SNP database at the Genome Analysis Group (GAN) at gan.iarc.fr. In one embodiment, the SNPs selected for enrichment of fetal and maternal cfDNA are selected from a group of 92 individually identified SNPs (IISNPs) described by Pakstis et al. (Pakstis et al., Hum Genet 127:315-324 [2010]), which have been shown to have very little variation in frequency across populations ( _Fst <0.06) and are highly informative worldwide, with an average heterozygosity of ≥0.4. The SNPs encompassed by the methods of the present invention include both linked and unlinked SNPs. Other available SNPs that may be applied or adapted for use in the methods described herein are disclosed in U.S. Patent Application Nos. 20080070792, 20090280492, 20080113358, 20080026390, 20080050739, 20080220422, and 20080138809, which are incorporated herein by reference in their entirety. Each target nucleic acid comprises at least one polymorphic site, e.g., a single SNP, that is different from a polymorphic site present in another target nucleic acid, thereby generating a set of polymorphic sites, e.g., SNPs, containing a sufficient number of polymorphic sites, e.g., SNPs, of which at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 25, at least 30, at least 40 or more are informative. For example, a set of SNPs can be configured to comprise at least one informative SNP. In one embodiment, the SNP targeted for amplification is selected from rs560681, rs1109037, rs9866013, rs13182883, rs13218440, rs7041158, rs740598, rs10773760, rs4530059, rs7205345, rs8078417, rs576261, rs2567608, rs430046, rs9951171, rs338882, rs10776839, rs9905977, rs1277284, rs258684, rs1347696, rs508485, rs9788670, rs8137254, rs3143, rs2182957, rs3739005, and rs530022. In one embodiment, the set of SNPs includes at least 3, at least 5, at least 10, at least 13, at least 15, at least 20, at least 25, at least 30 or more SNPs. In one embodiment, the set of SNPs includes rs560681, rs1109037, rs9866013, rs13182883, rs13218440, rs7041158, rs740598, rs10773760, rs4530059, rs7205345, rs8078417, rs576261, and rs2567608. Polymorphic nucleic acids comprising SNPs can be amplified using the exemplary primer pairs provided in Example 24 and disclosed as SEQ ID NOs: 63-118.

In other embodiments, each target nucleic acid comprises two or more SNPs, and each target nucleic acid comprises series connection SNPs.Preferably, each target nucleic acid comprises two series connection SNPs. Series connection SNP is analyzed as a single unit (for example, as short haplotype), and is provided as a plurality of sets with two SNPs at this. For identifying applicable series connection SNP sequences, international human genome haplotype graph group (International HapMap Consortium) database (international human genome haplotype graph project (The International HapMap Project), nature (Nature) 426:789-796[2003]) can be searched. This database can be obtained at hapmap.org on the world wide web. In one embodiment, the tandem SNPs targeted for amplification are selected from the following set of tandem SNP pairs: rs7277033-rs2110153; rs2822654-rs1882882; rs368657-rs376635; rs2822731-rs2822732; rs1475881-rs7275487; rs1735976-rs2827016; rs447340-rs2824097; rs418989-rs13047336; rs987980-rs987981; rs414392-rs2822731 rs4143391; rs1691324-rs13050434; rs11909758-rs9980111; rs2826842-rs232414; rs1980969-rs1980970; rs9978999-rs9979175; rs1034346-rs12481852; rs7509629-rs2828358; rs4817013-rs7277036; rs9981121-rs2829696; rs455921-rs2898102; rs2898102-rs458848; rs961301-rs2830208; rs2174536-rs458076; rs11088023-rs11088024; rs1011734-rs1011733; rs2831244-rs9789838; rs8132769-rs2831440; rs8134080-rs2831524; rs4817219-rs4817220; rs2250911-rs2250997; rs2831899-rs2831900; rs2831902-rs2831903; rs11088086-rs2251447; rs2832040-rs11088088; rs2832141-rs2246777; rs2832959-rs9980934; rs2833734-rs2833735; rs933121-rs933122; rs2834140-rs12626953; rs2834485-rs3453; rs9974986-rs2834703; rs2776266-rs2835001; rs1984014-rs1984015; rs7281674-rs2835316; rs13047304-rs13047322; rs2835545-rs4816551; rs2835735-rs2835736; rs13047608-rs2835826; rs2836550-rs2212596; rs2836660-rs2836661; rs465612-rs8131220; rs9980072-rs8130031; rs418359-rs2836926; rs7278447-rs7278858; rs385787-rs367001; rs367001-rs386095; rs2837296-rs2837297; and rs2837381-rs4816672.

In one embodiment, a portion of a mixture of fetal and maternal nucleic acid (e.g., cfDNA) in a sample is used as a template for amplifying a target nucleic acid comprising at least one STR. In certain embodiments, each target nucleic acid comprises a single (i.e., one) SNP. STR loci are found on nearly every chromosome in the genome and can be amplified using a variety of polymerase chain reaction (PCR) primers. Tetranucleotide repeats are preferred by forensic scientists due to their fidelity in PCR amplification, although certain trinucleotide and pentanucleotide repeats are also used. A comprehensive list of references, facts, and sequence information regarding STRs, published PCR primers, commonly used multiplex systems, and related population data is compiled in STRBase, which can be accessed via the World Wide Web at ibm4.carb.nist.gov:8800/dna/home.htm. Sequence information for commonly used STR loci is also available through STRBase (http://www2.ncbi.nlm.nih.gov/cgi-bin/genbank). Commercial kits for analyzing STR loci typically provide all necessary reaction components and controls required for amplification. STR multiplexing systems allow for the simultaneous amplification of multiple, non-overlapping loci in a single reaction, substantially increasing throughput. Using multicolor fluorescence detection, even overlapping loci can be multiplexed. The polymorphism of tandemly repeated DNA sequences, which are widespread throughout the human genome, makes these sequences important genetic markers for gene mapping studies, linkage analysis, and human identification testing. Because STRs are highly polymorphic, most individuals will be heterozygous, meaning they possess two alleles (versions)—one inherited from each parent—each with a different number of repeats. PCR products containing STRs can be separated and detected using manual, semi-automated, or automated methods. Semi-automated systems are gel-based and combine electrophoresis, detection, and analysis into a single unit. On semi-automated systems, gel assembly and sample loading remain manual processes; however, once the sample is loaded on the gel, electrophoresis, detection, and analysis are automated. Data collection occurs in "real time" as fluorescently labeled fragments migrate past detectors at fixed points and can be observed as they are collected. As the name suggests, capillary electrophoresis is performed in microtubes rather than between glass plates. Once the sample, gel polymer, and buffer are loaded onto the instrument, the capillary is filled with gel polymer and the sample is automatically loaded. Therefore, non-maternally inherited fetal STR sequences will differ from the maternal sequences in the number of repeats. Amplification of these STR sequences can produce one or two major amplification products corresponding to the maternal alleles (and maternally inherited fetal alleles) and one minor product corresponding to the non-maternally inherited fetal allele. This technology was first reported in 2000 (Pertl et al., Human Genetics 106:45-49 [2002]) and has subsequently been developed using real-time PCR to simultaneously identify multiple different STR regions (Liu et al., Acta Obset GynScand 86:535-541 [2007]). PCR amplicons of various sizes have been used to discern the corresponding size distributions of circulating fetal and maternal DNA species, and it has been shown that fetal DNA molecules in maternal plasma are generally shorter than maternal DNA molecules (Chan et al., Clin Chem 50:8892 [2004]. Size fractionation of circulating fetal DNA has demonstrated that the average length of circulating fetal DNA fragments is <300 bp, whereas maternal DNA is estimated to be between approximately 0.5 Kb and 1 Kb (Li et al., Clin Chem 50:1002-1011 [2004]). [2004]). The present invention provides a method for determining the fraction of fetal nucleic acid in a maternal sample, the method comprising determining the copy amount of at least one fetal and one maternal allele located at a polymorphic miniSTR locus, the miniSTR being amplified to produce amplicons having a length that is approximately the size of circulating fetal DNA fragments (e.g., less than about 250 base pairs). In one embodiment, the fetal fraction can be determined by a method comprising sequencing at least a portion of an amplified polymorphic target nucleic acid, each target nucleic acid comprising a miniSTR. The fetal and maternal alleles located at an informative STR locus are distinguished by their different lengths, i.e., the number of repeats, and the fetal fraction can be calculated as a percentage ratio of the amount of the fetal to maternal alleles located at the locus. The method can determine the fraction of fetal nucleic acid using one informative miniSTR or a combination of any number of informative miniSTRs. In one embodiment, the method comprises determining the copy number of at least one fetal and at least one maternal allele located at at least one polymorphic miniSTR being amplified to produce amplicons having a length that is less than about In another embodiment, the amplicon generated by amplifying a miniSTR is less than about 300bp. In another embodiment, the amplicon generated by amplifying a miniSTR is less than about 250bp. In another embodiment, the amplicon generated by amplifying a miniSTR is less than about 200bp. Amplification of informative alleles includes the use of miniSTR primers that can amplify the size-reduced amplicons to detect STR alleles of less than about 500bp, less than about 450bp, less than about 400bp, less than about 350bp, less than about 300 base pairs (bp), less than about 250bp, less than about 200bp, less than about 150bp, less than about 100bp, or less than about 50bp. The size-reduced amplicons generated using miniSTR primers are referred to as miniSTRs, which are identified by the marker names corresponding to the loci to which they have been mapped. In one embodiment, MiniSTR primers include primers for all 13 CODIS STR loci found in commercially available STR kits, with the exception of D2S1338, Penta D, and pentaE, miniSTR primers that have been shown to maximize amplicon size reduction (Butler et al., J Forensic Sci 48:1054-1064 [2003]), miniSTR loci not linked to CODIS markers as described by Coble and Butler (Coble and Butler, J Forensic Sci 50:43-53 [2005]), and other miniSTRs characterized at NIST. Information about miniSTRs characterized at NIST is available on the World Wide Web at cstl.nist.gov/biotech/strbase/newSTRs.htm. Any pair of miniSTR primers or a combination of two or more pairs of miniSTR primers can be used to amplify at least one miniSTR.

Amplification of the target nucleic acid in a mixture of fetal and maternal nucleic acid (e.g., cfDNA) is achieved by using PCR or any method of variation as described elsewhere in this application. Amplification of these target sequences is achieved using primer pairs that each amplify a target nucleic acid sequence comprising a polymorphic site (e.g., SNP) in a multiplex PCR reaction. The multiplex PCR reaction comprises combining at least 2, at least three, at least 3, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40 or more primer sets in the same reaction to quantify the amplified target nucleic acid comprising at least two, at least three, at least 5, at least 10, at least 15, at least 20, at least 25, at least 30, at least 40 or more polymorphic sites in the same sequencing reaction. Any group of primer sets can be configured to amplify at least one informative polymorphic sequence.

Primers are designed to hybridize with a sequence close to the SNP site on cfDNA to ensure that the SNP site is included in the length of the reading produced by the sequencer. As provided in the example, at least one of the two primers in the primer set for identifying any polymorphic site hybridizes in a manner close enough to the polymorphic site, so that the polymorphic site is included in the 36bp reading produced by carrying out large-scale parallel sequencing on Illumina analyzer GII, and produces an amplicon of length sufficient to carry out bridge amplification during cluster formation. Therefore, primers are designed to produce an amplicon of at least 110bp, which produces a DNA molecule of at least 200bp when combined with the universal adapter (Illumina Inc., San Diego, CA) for cluster amplification. The SNPs given in Table 33 are used to amplify 13 target sequences simultaneously in a multiplex test. Providing a panel in Table 33 is an exemplary SNP panel. Fewer or more SNPs can be used to enrich fetus and maternal DNA for polymorphic target nucleic acids. Additional SNPs that can be used include those given in Table 34. SNP alleles are shown in bold and underlined. Other SNPs that can be used to determine fetal fraction according to the methods of the present invention include rs315791, rs3780962, rs1410059, rs279844, rs38882, rs9951171, rs214955, rs6444724, rs2503107, rs1019029, rs1413212, rs1031825, rs891700, rs1005533, rs2831700, rs354439, rs1979255, rs1454361, rs8037429, and rs1490413. These SNPs have been analyzed by TaqMan PCR for determining fetal fraction and are disclosed in US Patent Application Publication No. 2010-0010085.

Each forward or reverse primer in the primer set hybridizes to a DNA sequence sufficiently close to the polymorphic site to be included in the sequence read generated by the massively parallel sequencing of the amplified preselected polymorphic nucleic acid. The length of the sequence read is related to the specific sequencing technology. Massively parallel sequencing methods provide sequence reads ranging in size from tens of base pairs to hundreds of base pairs. At least one primer in each primer set is designed to recognize a polymorphic site within a sequence read length of 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp, about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about 80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110 bp, about 120 bp, about 130 bp, about 140 bp, about 150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, or about 500 bp. In certain embodiments, at least one primer in each primer set is designed to recognize a polymorphic site within a sequence read length of about 25 bp, about 40 bp, about 50 bp, or about 100 bp.

Circulating cell-free DNA is approximately <300bp. Therefore, primer sets are designed to hybridize to polymorphic sequences up to an average of about 300bp in length and amplify them, wherein the fetal DNA length is an average of about 170bp. In certain embodiments, primer sets hybridize to DNA to produce an amplicon up to about 300bp. In other embodiments, primer sets hybridize to the DNA sequence to produce an amplicon of at least about 100bp, at least about 150bp, at least about 200bp. Primer sets can hybridize to the DNA sequence present on the same chromosome or to the DNA sequence present on different chromosomes. For example, one or more primer sets can hybridize to the sequence present on the same chromosome. Alternatively, two or more primer sets hybridize to the sequence present on different chromosomes. In one embodiment, a primer pair amplifies the polymorphic sequence present on one or more of chromosomes 1 to 22. In certain embodiments, primer sets do not hybridize to the DNA sequence present on chromosomes 13, 18, 21, X or Y.

In step 740 ( FIG. 7 ), a portion or all of the amplified polymorphic sequences are used to prepare sequencing libraries for sequencing in the parallel manner. In one embodiment, the libraries are prepared for sequencing by synthesis using Illumina's reversible terminator-based sequencing chemistry.

In step 740, the sequence information required to determine the fetal fraction is obtained using any known DNA sequencing method. Preferably, the method described herein employs next-generation sequencing (NGS) to provide countable sequence tags as described elsewhere in this application. Sequencing can be massively parallel sequencing by synthesis. Preferably, massively parallel sequencing by synthesis uses reversible dye terminators. Alternatively, massively parallel sequencing can be sequencing by ligation or single-molecule sequencing.

The amplified target polymorphic nucleic acid is partially sequenced, and sequence tags comprising reads of a predetermined length (e.g., 36 bp) mapped to a known reference genome are counted. Only sequence reads that uniquely align to the reference genome are counted as sequence tags. In one embodiment, the reference genome is an artificial target sequence genome comprising polymorphic target nucleic acid (SNP) sequences. In one embodiment, the reference genome is an artificial SNP reference genome. In another embodiment, the reference genome is an artificial STR reference genome. In yet another embodiment, the reference genome is an artificial tandem STR reference genome. The artificial reference genome can be compiled using the target polymorphic nucleic acid sequence. The artificial reference genome can include polymorphic target sequences that each include one or more different types of polymorphic sequences. For example, the artificial reference genome can include polymorphic sequences comprising SNP alleles and/or STRs. In one embodiment, the reference genome is the human reference sequence genome NCBI36/hg18 sequence, which is available on the World Wide Web at genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105. Other publicly available sources of sequence information include GenBank, dbEST, dbSTS, EMBL (European Molecular Biology Laboratory), and DDBJ (DNA Database of Japan). In another embodiment, the reference genome includes the human reference genome NCBI36/hg18 sequence and an artificial target sequence genome including target polymorphic sequences, such as a SNP genome. Mapping of sequence tags can be achieved by comparing the sequence of the mapped tag with the sequence of the reference genome to determine the chromosomal origin of the sequenced nucleic acid (e.g., cfDNA) molecule, and does not require specific genetic sequence information. A variety of computer algorithms can be used to align sequences, including, but not limited to, BLAST (Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993), FASTA (Pearson & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology 10: R25.1-R25.10 [2009]), or ELAND (Illumina, Inc., San Diego, CA, USA). In one embodiment, one end of a clonally amplified copy of a plasma cfDNA molecule is sequenced and processed by bioinformatics alignment analysis using the Illumina Genome Analyzer, which uses large-scale, efficient alignment of nucleotide databases (ELAND) software. In embodiments comprising methods for determining the presence or absence of aneuploidy and fetal fraction using an NGS sequencing method, analysis of sequencing information for determining aneuploidy may allow for a smaller degree of mismatch (0 to 2 mismatches per sequence tag) to account for minor polymorphisms that may exist between the reference genome and the genome in the mixed sample. Analysis of sequencing information for determining fetal fraction may allow for a smaller degree of mismatch, depending on the polymorphic sequence. For example, if the polymorphic sequence is an STR, a smaller degree of mismatch may be allowed. In the case where the polymorphic sequence is a SNP, all sequences that are precisely matched to any one of the two alleles at the SNP site are first counted and filtered out from the remaining reads, for which a smaller degree of mismatch may be allowed. Can be as described herein, or use adopt and adopt the median of the sequence tag of chromosome of interest relative to the median of the tag of each in other autosomes Normalized (fan (Fan) et al., Proc Natl Acad Sci (Proc Natl Acad Sci) 105:16266-16271[2008]) or compare the number of unique readings compared with each chromosome and the total number of readings compared with all chromosomes to derive the genomic expression percentage of each chromosome alternative analysis, determine the quantification of the number of sequence readings compared with each chromosome to determine chromosome aneuploidy.Produce " z score " to represent the difference between the genomic expression percentage of chromosome of interest and the average expression percentage of the same chromosome between euploid control groups divided by standard deviation (Zhao (Chiu) et al., clinical chemistry (Clin Chem) 56:459-463[2010]). In another embodiment, sequencing information can be determined as described in U.S. Provisional Patent Application No. 32047-768.101, filed January 19, 2010, entitled "Normalized Biological Assays," which is incorporated herein by reference in its entirety.

Analysis of sequencing information to determine fetal fraction can tolerate a small degree of mismatch, depending on the polymorphic sequence. For example, if the polymorphic sequence is an STR, a small degree of mismatch can be tolerated. If the polymorphic sequence is a SNP, all sequences that exactly match either of the two alleles at the SNP site are first counted and filtered out from the remaining reads, for which a small degree of mismatch can be tolerated. The present method for determining fetal fraction by sequencing nucleic acids can be used in combination with other methods.

In step 760 , the fetal fraction is determined based on the total number of tags mapped to the first allele and the total number of tags mapped to the second allele at informative polymorphic sites (eg, SNPs) contained in the reference genome. For example, the reference genome includes SNPs rs560681, rs1109037, rs9866013, rs13182883, rs13218440, rs7041158, rs740598, rs10773760, rs4530059, rs7205345, rs8078417, rs576261, rs2567608, rs430046, rs9951171, rs338882, rs10776839, rs9905977, rs1277284, rs258684, rs1347696, rs508485, An artificial target sequence genome comprising polymorphic sequences of rs9788670, rs8137254, rs3143, rs2182957, rs3739005, and rs530022. In one embodiment, the artificial reference genome comprises polymorphic target sequences of SEQ ID NOs: 7 to 62 (see Example 24).

In another embodiment, the artificial genome is an artificial target sequence genome encompassing polymorphic sequences comprising tandem SNPs. In another embodiment, the artificial target genome encompasses polymorphic sequences comprising STRs. The composition of the artificial target sequence genome will vary depending on the polymorphic sequences used to determine fetal fraction. Therefore, the artificial target sequence genome is not limited to the SNPs, tandem SNPs, or STR sequences exemplified herein.

Informative polymorphic sites (such as SNPs) are identified by the difference in the sequence of the alleles and the amount of each of the possible alleles. Fetal cfDNA is present at a concentration lower than 10% of maternal cfDNA. Therefore, relative to the main contribution of the maternal allele, there is a minor contribution of the alleles to the fetus to the mixture of fetal and maternal nucleic acids that can be assigned to the fetus. The alleles derived from the maternal genome are referred to as major alleles herein, and the alleles derived from the fetal genome are referred to as minor alleles herein. The alleles represented by the similar levels of the mapped sequence tags represent maternal alleles. The result of exemplary multiple amplification of the target nucleic acid comprising SNPs and derived from maternal plasma samples is shown in Figure 12. Informative SNPs are distinguished from single nucleotide changes at the polymorphic sites, and the fetal alleles are distinguished by comparing the main contribution of the maternal nucleic acid to the mixture of fetus and maternal nucleic acids, and their contribution to the mixture in the sample is relatively minor. Thus, for each of the two alleles at a predetermined polymorphic site, the relative abundance of fetal cfDNA in a maternal sample can be determined as a parameter of the total number of unique sequence tags mapped to the target nucleic acid sequence on the reference genome. In one embodiment, for each informative allele (allele _x ), the fraction of fetal nucleic acid in the mixture of fetal and maternal nucleic acids is calculated as described elsewhere in this application.

Estimation of fetal fraction using STR sequencing and capillary electrophoresis

Individuals have different STR lengths due to different repeat numbers. Because STRs are highly polymorphic, most individuals will be heterozygous, that is, most people will have two alleles (versions)—one inherited from each parent—each with a different repeat number. Fetal STR sequences that are not maternally inherited will differ from the maternal sequence in repeat number. Amplification of these STR sequences can produce one or two major amplification products corresponding to the maternal alleles (and maternally inherited fetal alleles) and one minor product corresponding to the non-maternally inherited fetal allele. When sequencing, the collected samples can be associated with the corresponding alleles and counted to determine relative scores using Equation 3.

PCR is performed on the purified sample using fluorescently labeled primers. STR-containing PCR products can be separated and detected using manual, semi-automated, or automated electrophoresis. Semi-automated systems are gel-based and combine electrophoresis, detection, and analysis into a single unit. On semi-automated systems, gel assembly and sample loading remain manual procedures; however, once the sample is loaded onto the gel, electrophoresis, detection, and analysis proceed automatically. As the name suggests, capillary electrophoresis is performed in microtubes rather than between glass plates. Once the sample, gel polymer, and buffer are loaded onto the instrument, the capillary is filled with gel polymer and the sample is loaded automatically. Data collection occurs in "real time" as fluorescently labeled fragments migrate past detectors at fixed points and can be observed as they are collected. Sequences obtained by capillary electrophoresis can be detected using a procedure that measures the wavelength of the fluorescent markers. Fetal fraction is calculated based on the average of all informative markers. Informative markers are identified by the presence of peaks on the electropherogram that fall within preset bin parameters for the STR being analyzed.

The fraction of minor alleles for any given informative marker is calculated by dividing the peak height of the minor component by the sum of the peak heights of the principal components, and the fraction is expressed as a percentage for each informative locus as follows:

The fetal fraction for a sample comprising two or more informative STRs is calculated as the average of the fetal fractions calculated for the two or more informative markers.

Estimating fetal fraction using mixed models

In the embodiments disclosed herein, there are up to four different types of data (typing cases) that constitute the minor allele frequency data for the polymorphism under consideration.

As shown in Figure 13, situation 1 and situation 2 are polymorphism situations, and wherein mother is homozygous at a certain allele place.In situation 1, if baby and mother are all homozygous, then polymorphism is situation 1 polymorphism.This situation is typically not particularly interesting, because the data collected only have a type of allele in the polymorphic site analyzed.In situation 2, if mother is homozygous and baby is heterozygous, so fetal fraction f is obtained in name by 2 times of the ratio of secondary allele count and coverage.Coverage is defined as the reading or label (fetus and mother) sum that are mapped to the polymorphism specific site.The equation that fetal fraction is carried out approximate estimation with the score of fetus and maternal sample in situation 2 is as follows:

In case 3, where the mother is heterozygous and the baby is homozygous, the fetal fraction is nominally 1-2 times the ratio of the minor allele count to coverage. In case 3, the equation that approximates the fetal fraction as a fraction of the total reads in both the fetal and maternal samples is as follows:

Finally, in case 4, where both the mother and fetus are heterozygous, the minor allele fraction should always be 0.5 (excluding error). For polymorphisms falling into case 4, no fetal fraction can be derived.

Table 7 summarizes an example of estimating fetal fraction using Equations 4 and 5 if the number of major allele reads is 300 and the number of minor allele reads is 200. The coverage would be 500.

Table 7: Example of fetal fraction estimation using matching

In certain embodiments, a hybrid model can be used to classify the polymorphism set into two or more proposed matching situations, and simultaneously estimate the fetal DNA fraction from the average allele frequency for each of these situations. In general, the hybrid model assumes that the specific data set is composed of a mixture of different types of data, each of which has its own expected distribution (e.g., normal distribution). The program attempts to find the average value and possible other characteristics of each type of data. In the embodiments disclosed herein, there are up to four different data types (matching situations) that constitute the minor allele frequency data for the polymorphism under consideration.

In certain embodiments employing a mixed model, one or more factorial moments given by equation 1 are calculated for the position being considered as a polymorphism. For example, factorial moments _F (or a set of factorial moments) are calculated using a plurality of SNP positions considered in the dna sequence. As shown in equation 10 below, each different factorial moment _F is the summation of the ratio of minor allele frequency _ai to coverage d _i for a given position over all different polymorphic positions under consideration. As shown in equation 11 below, these factorial moments also relate to parameters α and _pi relevant to each of the above-mentioned four matching situations. Specifically, they relate to the probability _pi for each situation, and the relative amount of each of the four situations given by α, in the concentrated polymorphic situation under consideration. As explained, probability _pi is a function of the fraction of fetal DNA in the cell-free DNA in the mother's blood. As more fully explained below, by calculating a sufficient number of these factorial moments, the method provides a sufficient number of expressions to solve for all unknown quantities. The unknowns in this case would be the relative amounts of each of the four cases and the probabilities associated with each of these four cases (and thus the fetal DNA fraction) in the population of polymorphisms under consideration. Similar results can be obtained using other versions of the admixture model. Some versions utilize only polymorphisms that fall into cases 1 and 2, with polymorphisms in cases 3 and 4 being filtered out using thresholding techniques.

Therefore, factorial moments can be used as part of a mixture model to identify the probability of any combination of the four scenarios of matching. And, as mentioned, these probabilities, or at least those for scenarios 2 and 3, are directly related to the fraction of fetal DNA in the total cell-free DNA in the mother's blood.

It should also be mentioned that the sequencing error given by e can be used to reduce the systematic complexity of the factorial moment equation that must be solved. In this regard, it should be recognized that a sequencing error can actually have any one of four outcomes (corresponding to each of the four possible bases at any given polymorphic position).

Assume that the major allele count at genomic position j is B, a first-order statistic of the count (number of reads) at position j. The major allele, b, is the corresponding arg max. When more than one SNP is considered, a subscript is used. The major allele count is given as follows:

Assuming the minor allele count at position j is A, the second-order statistic for the count at position j (i.e., the next highest allele count) is:

Coverage is defined as the total number of reads (fetal and maternal) mapped to a specific polymorphic site. Suppose the coverage of position j is defined as D:

D≡D _j = {d _i } = A _j + B _j Equation 8

In this embodiment, the minor allele frequency A is the sum of four terms as shown in Equation 9. The four heterozygosity cases suggest the following binomial mixture model for the distribution of _ai minor allele counts at points ( _ai , _dj ), where _dj is the coverage:

A = {a _i } ~ α _{1 data box} (p ₁ , d _i ) + α _{2 data box} (p ₂ , d _i ) + α _{3 data box} (p ₃ , d _i ) + α _{4 data box} (p ₄ , d _i )

in

1＝α ₁ +α ₂ +α ₃ +α ₄

m＝4

Equation 9

Each item corresponds to one of four matching situations. Each item is the product of the binomial distribution of polymorphism score α and minor allele frequency. These α representations fall on the score of the polymorphism in each of the four situations. Each binomial distribution has relevant probability, p, and coverage, d. The minor allele probability of situation 2 is for example given by f/2, where f is fetal fraction. The different models for associating p _i with fetal fraction and sequencing error rate are described below. Parameter α i relates to population-specific parameters and relative to race and offspring such as parental generation, the ability to "float" these values can give these methods extra robustness.

The disclosed embodiments utilize factorial moments for the allele frequency data under consideration. As is well known, the mean of a distribution is a first-order moment. It is the expected value of the minor allele frequency. The variance is a second-order moment. It is calculated from the expected value of the squared allele frequency.

For different heterozygosity conditions, Equation 9 above can be solved for fetal fraction. In certain embodiments, fetal fraction is solved by the factorial moment method, where the mixing parameters can be expressed in terms of moments that can be easily estimated from the observed data.

The allele frequency data across all polymorphisms can be used to calculate the i-th factorial moment F _i (first factorial moment F ₁ , second factorial moment F ₂ , etc.), as shown in Equation 10. (SNPs are used for example purposes only. Other types of polymorphisms can be used as discussed elsewhere in this application.) Given n SNP positions, the factorial moment is defined as follows:

As shown by these equations, the factorial moment is the sum over i terms (individual polymorphisms in the data set), where there are n such polymorphisms in the data set. Each term of the sum is a function of the minor allele count _ai , and the coverage value _dj .

Usefully, the factorial moments are related to the values of _αi and _pi , as illustrated in Equation 11. The factorial moments can be associated with { _αi , _pi } such that

From the probabilities p _, the fetal fraction f can be determined. For example, and therefore, reliable logic can solve a system of equations that relates the unknowns α and p variables to the factorial moment expression for the minor allele fraction across the multiple polymorphisms under consideration. Of course, other techniques for solving the mixture model exist within the scope of the disclosed embodiments.

When n>2*(number of parameters to estimate), a solution can be identified by solving the system of equations for { _αi , _pi } derived from the above relationship, Equation 8. Obviously, the problem becomes much more difficult mathematically, as the higher g, the more { _αi , _pi } need to be estimated.

It is typically not possible to accurately distinguish the data for Case 1 from Case 2 (or Case 3 from Case 4) by a simple threshold at a lower fetal fraction. The data for Cases 1 and 2 can be easily separated from the data for Cases 3 and 4 by distinguishing at the point A, where A is the minor allele count, D is the coverage, and T is the threshold. Using T = 0.5 has been found to perform satisfactorily.

Note that the mixed model approach using Equations 10 and 11 utilizes data from all polymorphisms but does not account for sequencing errors separately. An appropriate method of separating the data from the first and second cases from the data from the third and fourth cases can account for sequencing errors.

In another example, the data set provided to the hybrid model only contains data for the polymorphisms of situation 1 and situation 2. These are polymorphisms that are homozygous for the mother. Thresholding techniques can be used to eliminate the polymorphisms of situation 3 and 4. For example, before adopting the hybrid model, polymorphisms in which the minor allele frequency is greater than a specific threshold value are excluded. Using the data after appropriate filtering and the factorial moment simplified according to equations 13 and 14 below, people can calculate the fetal fraction f, as shown in equation 15. Note that equation 13 is a restatement of equation 9 for this implementation of the hybrid model. Also note that in this specific example, the sequencing error associated with the machine reading is unknown. As a result, the error of the system of equations, e, must be solved separately.

Figure 14 shows the results of using this hybrid model and comparing the known fetal fraction (X-axis) and the estimated fetal fraction (Y-axis). If the hybrid model perfectly predicted the fetal fraction, the results plotted would follow the dashed line. However, the estimated fraction is significantly better, especially considering that most of the data were excluded before applying the hybrid model.

To further elaborate, several other methods can be used to estimate the parameters of the model from Equation 7. In some cases, a tractable solution can be found by setting the derivative of the chi-squared statistic to zero. In cases where a simple solution cannot be found by direct differentiation, a Taylor series expansion of the binomial probability distribution function (PDF) or other approximate polynomial can be effective. The minimum chi-square estimator is well known to be effective. The method of finding the moment solution from Equation 9 can be used as a starting point for an iterative method. The following chi-square estimator can be used:

where _Pi is the number of points counted i. Le Cam's iterative method ["Asymptotic Theory of Estimation and Testing Hypotheses," Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, Berkeley, CA: University of California Press, 1956, pp. 129-156] uses Ralph-Newton iterations in the likelihood function.

According to another application, a method for analyzing mixture models is discussed, which involves an expectation maximization method operating on mixtures of approximately beta-distributions.

Model 1: Cases 1 and 2, sequencing error unknown

Consider a reduced model that accounts for only heterozygosity cases 1 and 2. In this case, the mixture distribution can be written as:

A＝{a _i }～α ₁ Bin(e, d _i )+α ₂ Bin(f/2, d _i )

in

l＝α ₁ +α ₂

m=4 Equation 13.

And the system of equations:

F ₁ =α ₁ e+(1-α ₁ )(f/2)

F ₂ =α ₁ e ² +(1-α ₁ )(f/2) ²

F ₃ = α1e ³ + (1-α1)(f/2) ³ Equation 14,

Solve for e (sequencing error rate), α (proportion of case 1 points), and f (fetal fraction), where _Fi is defined above in Equation 10. The closed-form solution for fetal fraction is chosen to be the real number solution of the following equation:

The solution is between 0 and 1.

To measure the performance of the imputed formula, a simulated data set (a i , d i ) was constructed using fetal fractions designed to be {1%, 3%, 5%, 10%, 15%, 20%, _{and 25} %} and a constant sequencing error rate of 1%. A 1% error rate is currently accepted for the sequencing machines and protocols used and is consistent with the Illumina Genome Analyzer II data shown in Figure 15. Equation 15 was applied to this data and found to be generally consistent with the "known" fetal fraction, except for four points that deviated upward. Interestingly, the sequencing error rate, e, was estimated to be just above 1%.

Model 2: Cases 1 and 2, sequencing error is known

In the next mixture model example, thresholding or another filtering technique is again employed to remove data specific to polymorphisms belonging to Cases 3 and 4. However, in this case, the sequencing error is known. This simplifies the resulting expression for the fetal fraction, f, as shown in Equation 16. Figure 16 shows that this version of the mixture model provides improved results compared to the approach employed in Equation 15. In the subsequent equations, the sequencing machine error rate is set to e.

A similar approach is shown in Equations 17 and 18. This approach recognizes that only some sequencing errors contribute to the minor allele count. However, only one out of every four sequencing errors should increase the minor allele count. Figure 17 shows a very good agreement between the actual and estimated fetal fractions using this technique.

Because the sequencing error rate of the machine used is largely known, the bias and complexity of the calculation can be reduced by eliminating e as a variable to be solved. Therefore, we obtain a system of equations for the fetal fraction f:

F ₁ =α ₁ e+(1-α ₁ )(f/2)

F ₂ =α ₁ e ² +(1-α ₁ )(f/2) ² Equation 16, so as to obtain the solution:

Figure 16 shows that using the machine error rate as a known parameter can reduce the point-up deviation.

Model 3: Cases 1 and 2, sequencing error is known, improved error model

To improve the bias in this model, we expanded the error model of the above equation to account for the fact that not every sequencing error event contributes to the minor allele count A = a _i in heterozygosity case 1. In addition, we allow for the fact that sequencing error events may contribute to the count in heterozygosity case 2. Therefore, we determine the fetal fraction f by solving the following system of factor-moment relations:

F ₁ =α ₁ e/4+(1-α ₁ )(e+f/2)

Then the solution of this system is:

Figure 17 shows simulated data for enhancing the error models for Cases 1 and 2 using the machine error rate as a known parameter, resulting in a significant reduction in the upward bias to a point below for fetal fractions below 0.2.

Classification of affected samples using fetal fraction

In certain embodiments, a fetal fraction estimate is used to further characterize the affected sample. In some cases, the fetal fraction estimate allows the affected sample to be classified as mosaicism, complete aneuploidy, or partial aneuploidy. A computer-implemented method for obtaining this information is described with respect to the flow chart of Figure 18. This and related methods can be performed to simultaneously provide an estimate of fetal fraction, determination of CNVs, and classification of CNVs. In other words, the same label can be used to perform any of these three functions.

To use this method, two models for estimating fetal fraction are employed. One model produces NCNFF values, while the other produces CNFF values. As explained, CNFF values are obtained using a technique that relies on chromosomes or chromosome segments that have been determined to have copy number variations. It is not necessary to rely on polymorphisms to calculate fetal fraction. An example of a non-polymorphic technique for calculating fetal fraction is described in Example 17, which assumes the presence of a whole chromosome duplication or deletion and uses the following expression:

ff _(i) = 2*NCV _jA CV _jU Equation 28,

where j represents the identification of aneuploid chromosomes and CV represents the coefficient of variation obtained from qualified samples to determine the mean and standard deviation in expression for NCV.

The NCNFF value is obtained using a technique that relies on chromosomes or chromosome segments that do not have copy number variation. In other words, the NCN fetal fraction is determined by a technique that reliably determines the fetal fraction, assuming a normal ploidy of the portion of the genome used to calculate the fetal fraction. The CNF fetal fraction is determined by a technique that assumes that the sample under consideration has a form of aneuploidy. The CNV of the affected chromosome or chromosome segment is used to calculate the CNF fetal fraction. The technique used for its calculation is presented below.

By comparing the estimated value of the NCN fetal fraction to the estimated value of the CN fetal fraction, a method can determine the type of aneuploidy that may be present in a sample. Essentially, if the NCN fetal fraction and CN fetal fraction values match, then the ploidy assumption in the technique used to estimate the CN fetal fraction can be considered true. For example, if the method for calculating the CN fetal fraction assumes that the sample has a complete chromosomal aneuploidy, which exhibits a single additional copy of a chromosome or a single deletion of a chromosome, and the NCN fetal fraction value matches the CN fetal fraction value, then the method can conclude that the sample exhibits a complete chromosomal aneuploidy. The basis for making this assumption is described in more detail below.

The NCN fetal fraction can be determined using various techniques. In some embodiments, the NCN fetal fraction is estimated using selected polymorphisms in a reference sequence genome. Examples of these techniques are described above. In other embodiments, the NCN fetal fraction is determined using the relative amounts of chromosomes that are known to be non-aneuploid or have been determined to be non-aneuploid. For example, the chromosome in a sample known to be non-aneuploid may be chromosome X in a male fetus. Therefore, in other embodiments, the NCN fetal fraction is determined using the relative amounts of chromosomes X or Y (e.g., the chromosome doses of such chromosomes) in a sample comprising DNA from a pregnant woman carrying a son. A son's genome should not include a second copy of the X chromosome. Knowing this, the relative amount of X chromosome DNA can be used to provide an NCN value for the fetal fraction. In a sample comprising DNA from a female fetus, the chromosome known to be non-aneuploid may be one known to be incompatible with life. Alternatively, for samples comprising DNA from either a male or female fetus, sequence tags can be used to determine chromosome doses (and NCV or NSV) to confirm that the chromosome is useful for determining the NCN fetal fraction, thereby confirming the presence of normal ploidy for the chromosomes useful for determining the NCN fetal fraction.

18 , the NCN fetal fraction estimate 1802 is compared to the CN fetal fraction estimate 1804. If they match, as indicated at block 1806, the process concludes and determines that the assumptions underlying the technique for estimating the CN fetal fraction are true. In various embodiments, the assumption is that a trisomy or monosomy is present in one of the chromosomes of the fetus.

On the other hand, if the comparison indicates that the two fetal fraction values do not match (condition 1808 ) and that the estimated value of the CN fetal fraction is in fact less than the NCN fetal fraction, then the second stage of the method is performed as indicated at block 1810 .

In this second stage, the method determines whether the sample comprises partial aneuploidy or mosaicism. In addition, if the sample comprises partial aneuploidy, the method determines where the aneuploidy resides on the aneuploid chromosome. In certain embodiments, this is achieved by first boxing the affected chromosome into multiple base blocks. In one example, each base block is about 1 million base pairs in length. Of course, other base block lengths can be used, such as about 1 kilobase, about 10 kilobases, about 100 kilobases, etc. These base blocks do not overlap and span most or all lengths of the chromosome. These base blocks or data boxes are compared with each other, and the comparison provides insights into the condition. In one method, for each base block or data box, the mapped label is counted and optionally converted into a data box dosage. If any one of these data boxes or base blocks is aneuploid, these counts or data box dosages are just pointed out. As a part of the analysis of an independent data box, it can be more appropriate to normalize the information from each data box to illustrate variation between the data boxes, such as G-C content. The resulting normalized data bins can be referred to as NBVs for the normalized data bin values; NBVs are an example of chromosome segments that are normalized to the labels of normalized segments mapped to GC content of segments with similar GC content (as in Example 19 below). In some embodiments, fetal fractions are calculated for each data bin and the individual values of fetal fraction values are compared. This sequence analysis of each data bin is depicted in block 1812 of Figure 18. If any data bin or block is identified as having an aneuploidy (by considering tag density, fetal fraction, or other information), the method determines that the sample contains partial aneuploidy and additionally locates the aneuploidy using the data bins in which the tag counts deviate sufficiently from the expected value. See block 1814.

However, if the method does not identify any chromosomal regions that exhibit aneuploidy when analyzing the ends of the chromosomes under consideration alone, then the method determines that the sample contains mosaicism. See box 1816.

On the chromosome of interest in the affected sample and on a chromosome known not to be aneuploid (e.g., chromosome The true fetal fraction is calculated and compared using polymorphisms, such as SNPs, on chromosome X to determine the presence of a male fetus. Complete or partial aneuploidy is absent

As explained, using informative polymorphic sequences, such as informative SNPs, the determined fetal fraction (FF) can be used to distinguish complete chromosomal aneuploidies from partial aneuploidies.

The presence or absence of an aneuploidy, whether partial or complete, can be determined from the fetal fraction value determined using a polymorphic target sequence present on the chromosome of interest and compared to the fetal fraction value determined using a polymorphic target sequence present on a different chromosome in the sample. In a sample where the fetus is male, the fetal fraction (FF) on the chromosome of interest can be determined and compared to the fetal fraction (FF) determined for chromosome X in the same sample. For example, given a maternal sample from a mother carrying a male fetus with trisomy 21, polymorphic sequences, such as sequences containing at least one informative SNP, are selected so as to be represented on chromosome 21 and chromosome X; the polymorphic target sequences are amplified and sequenced, and the fetal fraction is determined as described elsewhere in this application.

Given that the fetal fraction is proportional to the amount of fetal chromosomes in a sample, the fetal fraction determined using polymorphic sequences present on a trisomic chromosome in a maternal sample will be 1+1/2 times the fetal fraction determined using polymorphic sequences on a chromosome known to be non-aneuploid (e.g., chromosome X) in a male fetus in the same maternal sample. For example, in a normal sample, when the fetal fraction (FF ₂₁ ) is determined using a polymorphism panel on chromosome 21 and the fetal fraction (FF _X ) is determined using a polymorphism panel on chromosome X, it is known that chromosome X is unaffected in male fetuses, then FF ₂₁ = FF _X . However, if the fetus is trisomic for chromosome 21, the fetal fraction for trisomic chromosome 21 (FF ₂₁ ) will be equal to one and a half times the fetal fraction (FF _X ) for chromosome X in the same sample (FF ₂₁ = 1.5 _* FF _X ). Thus, if FF ₂₁ < FF _X , the analysis logic can conclude that there is a partial deletion of chromosome 21 and/or mosaicism. If FF ₂₁ > FF _X , the analysis logic can conclude that there is an increase in a portion of chromosome 21, such as a partial duplication or multiplication of chromosome 21, or a complete duplication, which is not accounted for in the techniques used to calculate fetal fraction from chromosome 21. The difference between the two results can be resolved as a partial duplication, resulting in an FF < 1.5 _* FF _X . Alternatively, the presence of a partial duplication, deletion, or mosaicism can be determined by, for example, increasing the number of polymorphic sequences on chromosome 21 to obtain multiple FF values along the length of the chromosome, such that the local presence of double or multiple FF values indicates an increase in a portion of the chromosome. Alternatively, as will be the case for a mosaic sample, the FF determined from the polymorphic sequences remains constant along the entire length of the chromosome, indicating an overall increase in the amount of the complete chromosome, but this increase is less than the increase for FF _X , as described above. In the case of loss of an entire chromosome, such as chromosome X monosomy, then FF _monosomy = 1/2 FF _X. Fetal fraction values obtained from informative polymorphic sequences can be used in combination with sequence doses and their normalized dose values, such as NCV, NSV, to confirm the presence of a complete aneuploidy.

Calculation of fetal fraction from chromosome dose of aneuploid sequences

Calculate the NCV for the chromosome of interest according to the following equation:

where and are the estimated mean and standard deviation of the jth chromosome dose for the qualified sample set, respectively, and x _ij is the observed jth chromosome dose for test sample i.

In general, the chromosome dose for trisomy will increase proportionally with the fetal fraction (ff). Thus, the ff for the chromosome dose in a sample containing a trisomic chromosome will increase proportionally relative to the fetal fraction:

The chromosome dose for monosomy will be reduced proportionally with the fetal fraction (ff). Thus, the ff for the chromosome dose in a sample containing a monosomy chromosome will be reduced proportionally relative to the fetal fraction:

In equations 20 and 21, R _jA is the chromosome dose (x _ij ) for chromosome j in the affected sample (e.g., the maternal sample to be tested) i; ff is the expected fetal fraction in the unaffected (qualified) sample U; and R _jU is the chromosome dose in the unaffected sample. The factor "2" is included based on the following assumptions: the calculation sign in equation 20 is "plus," i.e., there is an extra copy of the chromosome of interest; the calculation sign in equation 21 is "minus," i.e., there is a complete copy of the chromosome of interest. If a different assumption is made (e.g., this is a partial duplication of the chromosome of interest), then the factor "2" has no practical significance.

Substituting chromosome dose _RA in Equation 19:

where is an equivalent representation of and σ _jU is an equivalent representation of; solve for ff as follows:

or

or

or

Therefore, the percentage "ff _(i) " for any chromosome hypothesized to be trisomic can be determined as:

ff _(i) = 2*NCV _jA CV _jU Equation 26.

The percentage "ff _(i) " for any chromosome hypothesized for a monosomic chromosome can be determined as:

ff _(i) = -2*NCV _jA CV _jU Equation 27.

Equation 27 assumes the deletion of a complete copy of the chromosome. The NCV _jA corresponding to this chromosome is necessarily negative. Therefore, even though Equation 27 contains a negative sign, the calculated fetal fraction is still positive.

Since the fetal fraction cannot be negative, the "ff _(i) " for any chromosome can be calculated using the following equation:

ff _(i) = 2*|NCV _jA CV _jU | Equation 28

Using fetal fraction to resolve no call

The ability to determine significant differences in the expression of one or more sequences present in a mixture of two genomes based on the relative sequence contribution of the first genome relative to the contribution of the second genome. For example, non-invasive prenatal diagnosis using cfDNA in a maternal sample is challenging because only a small portion of the DNA sample is derived from the fetus. For prenatal diagnostic analysis, the background of maternal DNA forms a practical limitation to sensitivity, and therefore, the fraction of fetal DNA present in the maternal sample is an important parameter. The sensitivity of fetal aneuploidy detection by counting DNA molecules depends on the fetal DNA fraction and the number of molecules counted.

Typically, approximately 1% of maternal test samples analyzed for fetal aneuploidy by massively parallel sequencing are "no-call" samples, for which insufficient sequencing information, such as the number of fetal sequence tags, prevents a confident determination of the presence or absence of one or more fetal aneuploidies in the maternal sample. A "no-call" determination may be due to the fetal cfDNA content being too low relative to the maternal contribution to the sample used to provide sequencing information, resulting in the sequencing information determined in a qualified sample being sufficient to discern an aneuploid sample. To determine whether a "no call" sample is or is not an aneuploid sample, a fetal fraction is determined empirically and/or derived, for example, from an NVC value, and used to determine or deny the presence of a chromosomal aneuploidy. As described elsewhere herein, ff can be used to characterize the type of aneuploidy present in a test sample. For example, for a "no call" zone set at a threshold between 2.5 and 4 NCV values, a test sample having an NCV close to 4 times the NCV threshold and exhibiting a low (e.g., less than 3%) fetal fraction may be an affected sample. Conversely, a test sample having an NCV close to the 2.5 NCV threshold and exhibiting a high (e.g., greater than 40%) fetal fraction may be an unaffected sample. Splitting a "no call" sample may rely on one determination of the fetal fraction. Preferably, the fetal fraction is determined according to two or more different methods, or by using NCVs determined from two or more different chromosomes of a sample using the same method. Similarly, the fetal fraction can be used to assess whether a sample having an NCV slightly greater than 4 or slightly less than an NCV 2.5 is likely to be a false positive or false negative call, respectively.

Devices and systems for determining CNVs

The analysis of sequencing data and the diagnosis derived therefrom are typically performed using different computer algorithms and programs. Therefore, certain embodiments employ a process that relates to storing data in one or more computer systems or other processing systems or transferring data therethrough. Multiple embodiments of the present invention are also related to the equipment for performing these operations. The equipment can be specially constructed for the desired purpose, or it can be a general-purpose computer (or a group of computers) selectively activated or reconfigured by a computer program and/or data structure stored in a computer. In some embodiments, a group of processors performs some or all of the analytical operations narrated in a collaborative manner and/or simultaneously (e.g., through a network or cloud computing). A processor or a group of processors for performing the methods described herein may belong to different types, including microcontrollers and microprocessors, such as programmable devices (e.g., CPLDs and FPGAs) and non-programmable devices, such as gate array ASICs or general-purpose microprocessors.

Additionally, certain embodiments relate to tangible and/or non-transitory computer-readable media or computer program products that include program instructions and/or data (including data structures) for performing various computer-implemented operations. Examples of computer-readable media include, but are not limited to, semiconductor memory devices; magnetic media such as disk drives and magnetic tapes; optical media such as CDs; magneto-optical media; and hardware devices specifically configured to store and execute program instructions, such as read-only memory (ROM) and random access memory (RAM). Computer-readable media can be directly controlled by an end user, or the media can be indirectly controlled by the end user. Examples of directly controlled media include media located at a user device and/or media that is not shared with other entities. Examples of indirectly controlled media include media that is indirectly accessible to a user via an external network and/or through a service that provides shared resources (such as the "cloud"). Examples of program instructions include machine code (such as produced by a compiler) and files containing high-level code that can be executed by a computer using an interpreter.

In different embodiments, the data or information adopted in disclosed method and equipment are provided in electronic format.These data or information may include the reading and label derived from nucleic acid sample, the counting or density of these labels compared (such as compared with chromosome or chromosome segment) with the specific region of reference sequence (including only or mainly mentioning polymorphic reference sequence), chromosome and segment dosage, judgment (such as aneuploidy judgment), normalized chromosome and segment value, paired chromosome or segment and corresponding normalization chromosome or segment, consultation, diagnosis etc. As used herein, the data or other information provided in electronic format can be stored on a machine and transmitted between machines.Routinely, the data in electronic format are provided in digital form, and can be stored in different data structures, lists, databases etc. as bit and/or byte form.The data can be embodied in electronic, optical or other ways.

In one embodiment, the present invention provides a computer program product for generating an output indicating the presence or absence of aneuploidy (e.g., fetal aneuploidy) or cancer in a test sample. The computer product may contain instructions for executing any one or more of the above-mentioned methods for determining chromosomal abnormalities. As described, the computer product may include a non-transitory and/or tangible computer-readable medium having computer-executable or compilable logic (e.g., instructions) recorded thereon to start a processor to determine chromosome dosage and, in some cases, the presence or absence of fetal aneuploidy. In one example, the computer product includes a computer-readable medium having computer-executable or compilable logic (e.g., instructions) recorded thereon to start a processor to diagnose fetal aneuploidy, the computer product comprising: a receiving program for receiving sequencing data of at least a portion of nucleic acid molecules from a maternal biological sample, wherein the sequencing data comprises calculated chromosome and/or segment dosages; computer-assisted logic for analyzing fetal aneuploidy based on the received data; and an output program for generating an output indicating the presence, absence, or type of fetal aneuploidy.

The sequencing information from the sample under consideration can be mapped to a chromosome reference sequence to identify a number of sequence tags for each of any one or more chromosomes of interest and to identify a number of sequence tags for normalizing segment sequences for each of the one or more chromosomes of interest. In various embodiments, these reference sequences are stored in a database, such as a relationship curve or target database.

It should be understood that it is impractical or even impossible to allow a person who does not use auxiliary tools to perform the calculation operation of the method disclosed herein in most cases. For example, in the case where there is no computing device auxiliary, a single 30bp reading from a sample is mapped to any human chromosome and may require several years of effort. Of course, this problem is complicated by the fact that reliable aneuploidy judgment generally requires thousands (such as at least about 10,000) or even millions of readings to map one or more chromosomes.

The method disclosed herein can be performed using a computer-readable medium having a computer-readable instruction stored thereon for performing a method for identifying any CNV, such as a chromosome or a partial aneuploidy. Therefore, in one embodiment, the present invention provides a computer-readable medium having a computer-readable instruction stored thereon for performing a method for identifying complete and partial chromosome aneuploidy, such as a fetal aneuploidy. These instructions may include, for example, instructions for performing the following operations: (a) obtaining sequence information for the fetus and maternal nucleic acid in a sample and/or at least temporarily storing this information in a computer-readable medium; (b) using the stored sequence information from a mixture of fetal and maternal nucleic acids, a computer identifies many sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and identifies many sequence tags for at least one normalizing chromosome sequence for each of the one or more chromosomes of interest; and (c) using the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for each normalizing chromosome sequence, a single chromosome dose for each chromosome of interest is calculated by a computer. These instructions can use one or more processors suitably designed or configured to perform.These instructions can additionally comprise comparing each chromosome dosage with a relevant threshold value, and determine thus that in this sample, there are or do not exist any four or more partial or complete different fetal chromosome aneuploidies.As explained above, there are many variations about this technique.All these variations can be implemented when using processing and storage features as described herein.

In some embodiments, these instructions may further be included in the patient medical record for the human subject providing the maternal test sample and automatically record information about the method, such as chromosome dosage and presence or absence of fetal chromosome aneuploidy. The patient medical record can be preserved by, for example, a laboratory, a physician's office, a hospital, a health maintenance organization, an insurance company or a personal medical record website. In addition, based on the result of the analysis implemented by the processor, the method may further relate to prescribing, starting and/or changing the treatment of the human subject obtaining the maternal test sample. This may relate to carrying out one or more additional tests or analysis to the additional sample taken from the experimenter.

The disclosed method can also be performed using a computer processing system that is adapted or configured to perform a method for identifying any CNV, such as a chromosomal or partial aneuploidy. Therefore, in one embodiment, the present invention provides a computer processing system that is adapted or configured to perform the method as described herein. In one embodiment, the device comprises a sequencing device that is adapted or configured to sequence at least a portion of the nucleic acid molecules in the sample to obtain the sequence information type described elsewhere herein. The device can also include a device for processing the sample. These devices are described elsewhere herein.

Sequence or other data can be directly or indirectly input into the computer or be stored on a computer-readable medium. In one embodiment, a computer system is directly connected to a sequencing device that can read and/or analyze the nucleic acid sequence from a sample. The sequence or other information derived from these instruments is provided in the computer system by an interface. As an alternative, a sequence storage source is provided, such as a database or other storage libraries, by a sequence processing system. After using this treatment unit, a storage device or a large-capacity storage device can at least temporarily buffer or store the sequence of nucleic acid. In addition, a storage device can store label counts for different chromosomes or genomes, etc. This memory can also store different subroutines and/or programs for analyzing the sequence or mapping data that exist. These programs/subroutines can comprise programs for performing statistical analysis, etc.

In one example, a user provides a sample to a sequencing device. Collect and/or analyze data by being connected to a sequencing device of a computer. The software on this computer allows data collection and/or analysis. Data can be stored, displayed (by a monitor or other similar device) and/or sent to another location. This computer can be connected to the Internet for transmitting data to the handheld device used by a remote user (such as a physician, scientist or analyst). It should be understood that data can be stored and/or analyzed before transmission. In some embodiments, raw data is collected and sent to a remote user or device that will analyze and/or store this data. Transmission can be performed via the Internet, but it can also be performed by satellite or other connections. As an alternative, data can be stored on a computer-readable medium, and this medium can be sent to an end user (such as by mail). This remote user can be in the same or different geographical location, including but not limited to a building, city, state, country or continent.

In some embodiments, these methods also comprise collecting the data about a plurality of polynucleotide sequences (for example reading, label and/or with reference to chromosome sequence) and sending these data to computer or other computing systems.For example, this computer can be connected to laboratory equipment, for example sample collection device, nucleotide amplification device, nucleotide sequencing device or hybridization device.Then, this computer can collect the appropriate data gathered by laboratory equipment.Can be in any step, for example, when collecting, in real time, before sending, during sending or simultaneously or after sending, these data are stored on computer.These data can be stored on the computer-readable medium that can be pulled out from this computer.The data collected or stored can be transferred to a remote location from this computer, for example, by local area network or wide area network, as the Internet.At this remote location, can carry out different operations to the data transmitted as described below.

The types of electronically formatted data that may be stored, transmitted, analyzed, and/or manipulated in the systems, devices, and methods disclosed herein include the following:

Reads obtained by sequencing nucleic acids in a test sample

Tags obtained by aligning reads to a reference genome or other reference sequence

The reference genome or sequence

Sequence tag density - the count or number of tags for each of two or more regions (typically chromosomes or chromosome segments) of a reference genome or other reference sequence

Normalized chromosome or chromosome segment identity for a specific chromosome or chromosome segment of interest

Doses for chromosomes or chromosome segments (or other regions) obtained from the chromosome or segment of interest and the corresponding normalizing chromosome or segment

Thresholds used to determine chromosome dosage as affected, unaffected, or no determination;

Practical determination of chromosome dosage

Diagnosis (clinical conditions relevant to these determinations)

Recommendations for additional testing arising from these determinations and/or diagnoses

Treatment and/or monitoring plans derived from these determinations and/or diagnoses

In one embodiment, the present invention provides the method for the present invention to prepare the sample of the present invention.These different data types can be obtained, stored, transmitted, analyzed and/or operated using different devices in one or more positions.Processing selection spans a wider range.At one end of the scope, in the position of processing this test sample, for example doctor's office or other clinical environments all or most of these information are stored and used.In another extreme, obtain sample in a position, process it and optionally order-check at different positions, compare reading and judge at one or more different positions, and make diagnosis, suggestion and/or plan in another position again (it can be the position that obtains sample).

In different embodiments, the sequencing device is utilized to produce these readings, which are then transferred to a remote site where they are processed to produce aneuploidy. At the remote location, for example, these readings are compared with a reference sequence to produce a label, which is counted and assigned to a chromosome or segment of interest. Similarly at the remote location, relevant normalization chromosomes or segments are used to convert these counts into dosage. Further still, at the remote location, these dosages are used to produce aneuploidy.

The processing operations that can be applied at different locations are as follows:

Sample collection

Sample processing before sequencing

Sequencing

Analyze sequence data and derive aneuploidy calls

diagnosis

Reporting diagnoses and/or decisions to patients or care providers

Develop a plan for further treatment, testing, and/or monitoring

Execute the plan

consult

Any one or more of these operations can be automated as described in other parts of this paper. Typically, sequencing and analyzing sequence data and deriving aneuploidy determination will be performed on a computer. Other operations can be performed manually or automatically.

The example of the position that can carry out sample collection includes health care personnel's office, clinic, patient's home (wherein sample collection tool or test kit is provided) and mobile nursing vehicle. The example of the position that can carry out sample treatment before sequencing includes health care personnel's office, clinic, patient's home (wherein sample processing device or test kit is provided), mobile nursing vehicle and the facility of aneuploidy analysis supplier. The example of the position that can carry out sequencing includes health care personnel's office, clinic, health care personnel's office, clinic, patient's home (wherein sample sequencing device and/or test kit is provided), mobile nursing vehicle and the facility of aneuploidy analysis supplier. The position that carries out sequencing can provide a dedicated network connection for transmitting sequencing data (typically reading) in electronic format. The connection can be wired or wireless, and has been and may be configured so that before being transferred to the processing point, data is sent to a site that can process and/or summarize data. Data aggregator can be maintained by a healthcare organization, such as a health maintenance organization (HMO).

The analysis and/or derivation operation can be performed at any of the above-mentioned locations, or alternatively, at another remote site dedicated to computing and/or nucleic acid sequence data analysis services. These locations include, for example, clusters, such as general server areas, aneuploidy analysis service facilities, etc. In some embodiments, the computing device used to perform the analysis is rented or leased. Computing resources can be part of a collection of processors accessible on the Internet, such as processing resources commonly known as clouds. In some cases, the calculation is performed by parallel or massively parallel processor groups that are associated with each other or not. Processing can be implemented using distributed processing, such as cluster computing, grid computing, etc. In these embodiments, a cluster or grid of computing resources is concentrated to form a super virtual computer composed of multiple processors or computers that act together to perform analysis and/or derivation as described herein. These technologies and more conventional supercomputers can be used to process sequence data as described herein. Each is a parallel computing form that depends on a processor computer. In the case of grid computing, these processors (often complete computers) are connected by conventional network protocols (such as Ethernet) through a network (private, public or Internet). In contrast, a supercomputer has many processors connected by a local high-speed computer bus.

In certain embodiments, a diagnosis is generated at the same location as the analytical operation (e.g., a fetus suffers from Down syndrome or a patient suffers from a specific type of cancer). In other embodiments, it is performed at different locations. In some instances, reporting a diagnosis is performed at the location where the sample is obtained, but this is not necessarily the case. Examples of locations where a diagnosis and/or a plan can be generated or reported include health care personnel offices, clinics, computer-accessible Internet sites, and handheld devices with a wired or wireless connection to a network, such as mobile phones, tablets, smart phones, etc. Examples of locations for consultation include health care personnel offices, clinics, computer-accessible Internet sites, handheld devices, etc.

In some embodiments, sample collection, sample processing, and sequencing operations are performed at a first location, and derivation operations are performed at a second location. However, in some cases, sample collection is performed at one location (e.g., a health care provider's office or clinic), while sample processing and sequencing are performed at a different location, which may optionally be the same location where analysis and derivation are performed.

In different embodiments, the order of the operations listed above can be triggered by the user or institution that starts sample collection, sample processing and/or sequencing. After having started to perform one or more of these operations, other operations can naturally follow. For example, a sequencing operation can cause readings to be automatically collected and sent to a processing device, which then automatically and possibly performs sequence analysis and derivation of aneuploidy operations without the intervention of other users. In some implementations, the result of the processing operation is then automatically delivered (possibly accompanied by reformatting as a diagnosis) to a system component or institution, which processes the information and reports it to a health expert and/or patient. As described, this information, possibly together with consulting information, can also be automatically processed to produce a treatment, test and/or monitoring plan. Therefore, starting early operations can trigger an end-to-end sequence, wherein providing diagnosis, planning, consultation and/or other information that can be used to act on physical health conditions to health experts, patients or other related groups. This can be achieved even if the various parts of the entire system are physically separated and possibly away from the location of, for example, a sample and a sequencer.

Figure 19 shows an implementation of a decentralized system for generating a judgment or diagnosis from a test sample. Sample collection position 01 is used to obtain a test sample from a patient, such as a pregnant woman or a hypothetical cancer patient. The sample is then provided to processing and sequencing position 03, where the test sample can be processed and sequenced as described above. Position 03 comprises a device for processing the sample and a device for sequencing the processed sample. The sequencing result as described in other parts of this paper is a collection of readings, which typically provide and are provided to a network, such as the Internet, in electronic format, which is indicated by reference number 05 in Figure 19.

This sequence data is provided to remote location 07, analyzes and judges to produce therein.This position can comprise one or more high-efficiency computing devices, as computer or processor.After the computing resource at position 07 has completed their analysis and produced a judgement from the sequence information received, this judgement is passed to network 05 by sub-trip. In some embodiments, not only judgement is produced at position 07, but also relevant diagnosis is produced.Then as shown in Figure 19, this judgement and or diagnosis are transmitted through network and are passed back to sample collection position 01.As illustrated, this is only about how to distribute one of many variations of different operations relevant to generation judgement or diagnosis between different positions.A common variation relates to providing sample collection and processing and order-checking at single position.Another variation relates to providing processing and order-checking at the position identical with analysis and judgement.

Figure 20 details the selection of performing different operations at different locations. In the most comprehensive sense as described in Figure 20, each of the following operations is performed at a separate location: sample collection, sample processing, sequencing, read alignment, determination, diagnosis, and reporting and/or planning.

In one embodiment of some of these operations of gathering together, sample treatment and order-checking are carried out in one position, and read comparison, judgement and diagnosis are carried out in a separated position.Referring to the part by reference letter A mark of Figure 20.In another implementation by the letter B mark in Figure 20, sample collection, sample treatment and order-checking are all carried out in the same position.In this implementation, read comparison and judgement are carried out in second position.Finally, diagnosis and report and/or plan to carry out and be carried out in the third position.In the implementation described by the letter C in Figure 20, sample collection is carried out in first position, and sample treatment, order-checking, read comparison, judgement and diagnosis are all carried out together in second position, and report and/or plan to formulate and carry out in the third position.Finally, in the implementation marked by the letter D in Figure 20, sample collection is carried out in first position, and sample treatment, order-checking, read comparison and judgement are all carried out in second position, and diagnosis and report and/or plan to process and be carried out in the third position.

In one embodiment, the present invention provides a system for determining the presence or absence of any one or more different complete fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids, the system comprising: a sequencer for receiving a nucleic acid sample and providing fetal and maternal nucleic acid sequence information obtained from the sample; a processor; and a machine-readable storage medium comprising instructions for execution on the processor, the instructions comprising:

(a) code for obtaining sequence information of the fetal and maternal nucleic acids in the sample;

(b) code for using the sequence information to identify, by computer, from the fetal and maternal nucleic acids a plurality of sequence tags for each of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y, and to identify a plurality of sequence tags for at least one normalizing chromosome sequence or normalizing chromosome segment sequence for each of the one or more chromosomes of interest;

(c) code for calculating a single chromosome dose for each of the one or more chromosomes of interest using the number of sequence tags identified for each of the one or more chromosomes of interest and the number of sequence tags identified for each normalizing chromosome sequence or normalizing chromosome segment sequence; and

(d) code for comparing each single chromosome dose for each of the one or more chromosomes of interest to a corresponding threshold value for each of the one or more chromosomes of interest, and thereby determining the presence or absence of any one or more complete different fetal chromosomal aneuploidies in the sample.

In some embodiments, the code for calculating a single chromosome dose for each of any one or more chromosomes of interest includes code for calculating the chromosome dose for the selected chromosome of interest as the ratio of the number of sequence tags for the selected chromosome of interest to the number of sequence tags identified for the corresponding at least one normalizing chromosome sequence or normalizing chromosome segment sequence for the selected chromosome of interest.

In some embodiments, the system further comprises code for repeatedly calculating a chromosome dose for each of any remaining chromosome segments of any one or more segments of any one or more chromosomes of interest.

In some embodiments, the one or more chromosomes of interest selected from chromosomes 1-22, X, and Y include at least twenty chromosomes selected from chromosomes 1-22, X, and Y, and wherein the instructions include instructions for determining the presence or absence of at least twenty different complete fetal chromosomal aneuploidies.

In some embodiments, the at least one normalizing chromosome sequence is a group of chromosomes selected from chromosomes 1-22, X, and Y. In other embodiments, the at least one normalizing chromosome sequence is a single chromosome selected from chromosomes 1-22, X, and Y.

In another embodiment, the present invention provides a system for determining the presence or absence of any one or more different partial fetal chromosomal aneuploidies in a maternal test sample comprising fetal and maternal nucleic acids, the system comprising: a sequencer for receiving a nucleic acid sample and providing fetal and maternal nucleic acid sequence information obtained from the sample; a processor; and a machine-readable storage medium comprising instructions for execution on the processor, the instructions comprising:

(a) code for obtaining sequence information of the fetal and maternal nucleic acids in the sample;

(b) code for identifying, by a computer, from the fetal and maternal nucleic acids a plurality of sequence tags for each of any one or more segments of any one or more chromosomes of interest selected from chromosomes 1-22, X, and Y using the sequence information, and identifying a plurality of sequence tags for at least one normalizing segment sequence for each of the one or more segments of any one or more chromosomes of interest;

(c) code for calculating a single chromosome segment dose for each of the any one or more segments of any one or more chromosomes of interest using the number of sequence tags identified for each of the any one or more segments of any one or more chromosomes of interest and the number of sequence tags identified for the normalizing segment sequence; and

(d) code for comparing each of the single chromosome segment doses for each of the any one or more segments of any one or more chromosomes of interest to a corresponding threshold value for each of the any one or more chromosome segments of any one or more chromosomes of interest, and thereby determining the presence or absence of one or more different partial fetal chromosomal aneuploidies in the sample.

In some embodiments, the code for calculating a single chromosome segment dose includes code for calculating the chromosome segment dose for a selected chromosome segment as the ratio of the number of sequence tags identified for the selected chromosome segment to the number of sequence tags identified for a corresponding normalizing segment sequence for the selected chromosome segment.

In some embodiments, the system further comprises code for repeatedly calculating a chromosome segment dose for each of any remaining chromosome segments of any one or more segments of any one or more chromosomes of interest.

In some embodiments, the system further includes (i) code for repeating (a)-(d) for test samples from different maternal subjects, and (ii) code for determining the presence or absence of any one or more different partial fetal chromosomal aneuploidies in each of the samples.

In other embodiments of any of the systems provided herein, the code further comprises code for automatically recording the presence or absence of a fetal chromosomal aneuploidy in a patient medical record for the human subject providing the maternal test sample as determined in (d), wherein the recording is performed using a processor.

In some embodiments of any system provided herein, the sequencer is configured to perform next generation sequencing (NGS). In some embodiments, the sequencer is configured to perform massively parallel sequencing using synthesis sequencing, utilizing reversible dye terminators. In other embodiments, the sequencer is configured to perform ligation sequencing. In yet other embodiments, the sequencer is configured to perform single molecule sequencing.

Devices used to determine fetal fraction

Sequence tags derived from a sequenced sample (e.g., a maternal sample) can be analyzed using an apparatus for performing medical analysis of a sample to provide information about the fraction contributed by one or two genomes to a mixture of nucleic acids. For example, a variety of apparatuses are provided for analyzing sequence tags obtained from sequencing a maternal sample to determine the fraction of fetal nucleic acid in a mixture of fetal and maternal nucleic acids present in the maternal sample. The provided medical apparatus includes a series of devices for performing the steps of the method for determining fetal fraction as described elsewhere in this application.

Figure 65 shows one embodiment of a medical analysis device for determining fetal fraction in a maternal test sample comprising a mixture of fetal and maternal nucleic acids. The device comprises:

a device (a) for receiving a plurality of sequence reads of said fetal and maternal nucleic acids from said maternal test sample;

a means (b) for aligning the plurality of sequence reads to one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads;

a means (c) for identifying a number of sequence tags from one or more chromosomes of interest or chromosome segments of interest, the chromosomes or chromosome segments of interest being selected from chromosomes 1-22, X, and Y, and segments thereof, and for identifying a number of sequence tags from at least one normalizing chromosome sequence or normalizing chromosome segment sequence for each of the one or more chromosomes of interest or chromosome segments of interest to determine a chromosome dose or chromosome segment dose, wherein the chromosome of interest or chromosome segment of interest has a copy number variation; and

a means (d) for determining the fetal fraction using the dose of the chromosome of interest or the dose of the chromosome segment of interest.

Preferably, the signal output end of the device (a) is connected to the device (b), the signal output end of the device (b) is connected to the device (c), and the signal output end of the device (c) is connected to the device (d).

In certain embodiments, the copy number variation is determined by comparing the chromosome dose for each of the one or more chromosomes or chromosome segments of interest to a corresponding threshold value for each of the one or more chromosomes or chromosome segments of interest.

Copy number variations that a fetus may carry include complete chromosome duplication, complete chromosome deletion, partial duplication, partial multiplication, partial insertion, and partial deletion.

In certain embodiments, the chromosome or segment dose determined by means (c) is calculated as the ratio of the number of sequence tags identified for the selected chromosome or segment of interest to the number of sequence tags identified for the corresponding at least one normalizing chromosome sequence or normalizing chromosome segment sequence of the selected chromosome or segment of interest. In certain embodiments, the chromosome dose or segment dose determined by means (c) is calculated as the ratio of the sequence tag density ratio of the selected chromosome or segment of interest to the sequence tag density ratio of at least one corresponding normalizing chromosome sequence or normalizing chromosome segment sequence of each selected chromosome of interest or segment of interest.

In certain embodiments, the apparatus further comprises means (e) for calculating a normalized chromosome value (NCV) or a normalized segment value (NSV), wherein the NCV is calculated to relate the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

where σ and _σ are the estimated mean and standard deviation of the dose for the i-th chromosome in the set of qualified samples, respectively, and _R is the calculated chromosome dose for the i-th chromosome in the test sample, wherein the i-th chromosome is the chromosome of interest; wherein the NSV is calculated to relate the chromosome segment dose to the mean of the corresponding chromosome segment dose in a set of qualified samples as:

Wherein σ and σ _iU are the estimated mean and standard deviation of the dose for the i-th chromosome segment in the set of qualified samples, respectively, and R _iA is the calculated chromosome segment dose for the i-th chromosome segment in the test sample, wherein the i-th chromosome segment is the chromosome segment of interest. Preferably, the signal output terminal of device (c) is connected to device (e).

In certain embodiments, means (d) of the apparatus then determines the fetal fraction according to the following expression:

ff＝2×|NCV _iA CV _iU |

where ff is the fetal fraction value, _NCViA is the normalized chromosome value on chromosome i in an affected sample (e.g., the maternal sample to be tested), and _CViU is the coefficient of variation of the dose of the chromosome of interest determined in the qualified samples; or the fetal fraction is determined according to the following expression:

ff＝2×|NSV _iA CV _iU |

Wherein ff is the fetal fraction value, NSV _iA is the normalized chromosome segment value on the i-th chromosome segment in an affected sample (e.g., the maternal sample to be tested), and CV _iU is the coefficient of variation of the dose of the i-th chromosome determined in the qualified samples, wherein the i-th chromosome is the chromosome of interest. Preferably, the signal output end of device (e) is connected to device (d).

In certain embodiments, the chromosome of interest is an autosome or the X chromosome of a male fetus, and the chromosome segment of interest is selected from an autosome or the X chromosome of a male fetus.

In certain embodiments, the at least one normalizing chromosome sequence or normalizing chromosome segment sequence is a chromosome or segment selected for an associated chromosome or segment of interest by: (i) identifying multiple qualified samples for the chromosome or segment of interest; (ii) repeatedly calculating chromosome doses or chromosome segment doses for the selected chromosome or chromosome segment using multiple potential normalizing chromosome sequences or normalizing chromosome segment sequences; and (iii) selecting the normalizing chromosome sequence or normalizing chromosome segment sequence, either alone or in combination, to provide minimal variability or maximum resolvability in the calculated chromosome doses or chromosome segment doses. In certain embodiments, the normalizing chromosome sequence is a single chromosome of any one or more of chromosomes 1 to 22, X, and Y; alternatively, the normalizing sequence is a group of chromosomes of any chromosome of chromosomes 1 to 22, X, and Y. In certain embodiments, the normalizing segment sequence is a single segment of any one or more of chromosomes 1 to 22, X, and Y; alternatively, the normalizing segment sequence is a group of segments of any one or more of chromosomes 1 to 22, X, and Y.

In certain embodiments, the apparatus for determining a fetal fraction further comprises a means for comparing the fetal fraction determined using chromosome doses or chromosome segment doses with a fetal fraction determined using information on one or more polymorphisms present in a chromosome other than the chromosome of interest that exhibit allelic imbalance in fetal and maternal nucleic acid from a maternal test sample.

In certain embodiments, the apparatus further comprises a sequencing device (10) configured to sequence fetal and maternal nucleic acids in a maternal test sample and obtain sequence reads. Preferably, a signal output of the sequencing device (10) is connected to the device (a).

In certain embodiments, the sequencing apparatus (10) is configured to perform sequencing by synthesis. Sequencing by synthesis can be performed using reversible dye terminators. In other embodiments, the sequencing apparatus (10) is configured to perform sequencing by ligation. In yet other embodiments, the sequencing apparatus (10) is configured to perform single molecule sequencing.

In certain embodiments, the sequencing device (10) and the devices (a)-(d) are located in separate locations, and the signal output of the sequencing device (10) is connected to the device (a) via a network.

In certain embodiments, the apparatus comprising the sequencing apparatus as described further comprises an apparatus (11) for obtaining a maternal test sample from a pregnant mother. The apparatus (11) for obtaining a maternal test sample and the apparatuses (a)-(d) and (10) may be located in separate locations. In addition to the apparatuses (a)-(d) and (10), the apparatus may further comprise an apparatus (12) for extracting cell-free DNA from the maternal test sample. In certain embodiments, the apparatus (12) for extracting cell-free DNA is located in the same location as the sequencing apparatus (10), and the apparatus (11) for obtaining a maternal test sample is located in a remote location.

In certain embodiments, the apparatus for determining fetal fraction further comprises a storage device for at least temporarily storing the sequence reads received by apparatus (a). Preferably, the signal output of apparatus (a) is connected to the storage device, and the signal output of the storage device is connected to apparatus (b).

Additional Instruments for Determining Fetal Fraction - Classifying Copy Number Variants

Also provided is an additional medical analysis device for classifying the copy number variation in the fetal genome in a maternal sample comprising fetus and maternal nucleic acid (e.g., cell-free DNA). The additional device includes a device for determining fetal fraction and a device for comparing fetal fraction values determined by different methods. The additional device uses two calculated fetal fractions to classify the copy number variation in the fetal genome. The maternal sample that can be used for analysis by the device can be selected from blood, plasma, serum, or urine samples. In certain embodiments, the maternal sample is a plasma sample. Figure 66 shows an embodiment of such a medical analysis device.

In one embodiment, a medical analysis device for classifying copy number variation in a fetal genome is provided, the device comprising:

Apparatus (1) for receiving sequence reads of fetal and maternal nucleic acids from a test sample;

Means (2) for aligning the sequence reads with one or more chromosome reference sequences and thereby providing a plurality of sequence tags corresponding to the sequence reads;

Means (3) for identifying the number of sequence tags from one or more chromosomes of interest and determining that a first chromosome of interest in the fetus carries a copy number variation;

Means (4) for calculating a first fetal fraction value by a first method that does not use information from the labels of the first chromosome of interest;

means (5) for calculating a second fetal fraction value by a second method using information from the labels of the first chromosome; and

Means (6) for comparing the first fetal fraction value with the second fetal fraction value and classifying the copy number variation of the first chromosome using the comparison.

Preferably, the signal output end of device (1) is connected to device (2), the signal output end of device (2) is connected to device (3), the signal output ends of devices (2) and (3) are connected to device (4), the signal output ends of devices (2) and (3) are connected to device (5), and the signal output ends of devices (4) and (5) are connected to device (6). The first chromosome of interest can be selected from any one of chromosomes 1 to 2, X, and Y.

In certain embodiments, the additional device further comprises a storage device for at least temporarily storing the sequence reads received by the apparatus (1). Preferably, the signal output of the apparatus (1) is connected to the storage device, and the signal output of the storage device is connected to the apparatus (2).

In certain embodiments, the apparatus (4) for the first method for calculating a first fetal fraction comprises a component for calculating the first fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids of the maternal test sample, the polymorphisms being present on a chromosome other than the first chromosome of interest; and the apparatus (5) for calculating a second fetal fraction value comprises:

(a) component (5-1) for counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and

(b) a component (5-2) for calculating the fetal fraction value from the chromosome dose using the second method. In certain embodiments, the signal outputs of devices (2) and (3) are connected to component (5-1), and the signal output of component (5-1) is connected to component (5-2), and the signal output of component (5-2) is connected to device (6).

In certain embodiments, the information used by the apparatus (4) of the first method includes sequence tags obtained by sequencing predetermined polymorphic sequences, each of which includes the one or more polymorphic sites. The information used by the apparatus (4) of the first method may also be obtained by methods other than sequencing, for example, by non-sequencing methods such as qPCR, digital PCR, mass spectrometry, or capillary gel electrophoresis.

In certain embodiments, the apparatus (4) for the first method includes a component for calculating the first fetal fraction value using tags from a chromosome or chromosome segment that does not have a copy number variation. For example, when the first chromosome of interest is chromosome 21, the fetal fraction determined using sequence tags from chromosome 21 can be compared to the fetal fraction determined based on sequence tags from chromosome X in a male fetus. Any chromosome or chromosome segment that is known not to occur in an aneuploid state, or that is determined not to be aneuploid in a test sample by any of the methods described herein (e.g., by calculating its NCV or NSV), can be used to determine the fetal fraction by apparatus (4).

In certain embodiments, the apparatus (5) for calculating the second method of fetal fraction value further comprises a component (5-3) for calculating a normalized chromosome value (NCV), wherein the component (5-3) for calculating the NCV relates the chromosome dose to the mean of the corresponding chromosome dose in a set of qualified samples as:

where σ and σ _iU are the estimated mean and standard deviation of the dose for the i-th chromosome in the set of qualified samples, respectively, and R _iA is the chromosome dose calculated for the i-th chromosome in the test sample, where the i-th chromosome is the chromosome of interest.

Preferably, the signal output terminal of the component (5-1) is connected to the component (5-3), and the signal output terminal of the component (5-3) is connected to the component (5-2).

In certain embodiments, the component (5-2) for calculating the fetal fraction value from the chromosome dose by the second method uses the normalized chromosome value. The component (5-2) of the apparatus (5) for the second method of calculating the fetal fraction value estimates the fetal fraction according to the following expression:

ff＝2×|NCV _iA CV _iU |

Wherein ff is the second fetal fraction value, NCV _iA is the normalized chromosome value on the i-th chromosome in an affected sample (e.g., the maternal sample to be tested), and CV _iU is the coefficient of variation of the dose of the i-th chromosome determined in the qualified samples, where the i-th chromosome is the chromosome of interest.

In certain embodiments, the apparatus (4) for a first method of calculating a first fetal fraction comprises: (a) a component (4-1) for counting the number of sequence tags from a chromosome other than the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose for the chromosome other than the first chromosome of interest; and (b) a component (4-2) for calculating the first fetal fraction value from the chromosome dose by the first method; and, the apparatus (5) for a second method of calculating a second fetal fraction comprises: (a) a component (5-1) for counting the number of sequence tags from the first chromosome of interest and at least one normalizing chromosome sequence to determine a chromosome dose; and (b) a component (5-2) for calculating the second fetal fraction value from the chromosome dose by the second method.

Preferably, the apparatus (4) of the first method further comprises a component (4-3), and the apparatus (5) of the second method further comprises a component (5-3), wherein the components (4-3) and (5-3) respectively calculate a normalized chromosome value (NCV), and the components (4-3) and (5-3) respectively relate the chromosome doses determined by the components (4-1) and (5-1) to the average value of the corresponding chromosome doses in a group of qualified samples as:

where σiU and _σiU are the estimated mean and standard deviation of the dose for chromosome i in the set of qualified samples, respectively, and R _iA is the calculated dose for chromosome i in the test sample,

Wherein, for the device (4) of the first method, the i-th chromosome is a chromosome that is not the first chromosome of interest; for the device (5) of the second method, the i-th chromosome is the first chromosome of interest.

Preferably, the signal output end of component (4-1) is connected to component (4-3), and the signal output end of component (4-3) is connected to component (4-2), wherein component (4-2) calculates a first fetal fraction value from the corresponding chromosome dose by using the first method of the corresponding normalized chromosome value; the signal output end of component (5-1) is connected to component (5-3), and the signal output end of component (5-3) is connected to component (5-2), wherein component (5-2) calculates a second fetal fraction value from the corresponding chromosome dose by using the second method of the corresponding normalized chromosome value.

In certain embodiments, component (4-2) of apparatus (4) of the first method and component (5-2) of apparatus (5) of the second method are evaluated by the following expressions:

ff＝2×|NCV _iA CV _iU |

Wherein ff is the fetal fraction value, NCV _iA is the normalized chromosome value on chromosome i in an affected sample (e.g., the maternal sample to be tested), and CV _iU is the coefficient of variation of the dose of chromosome i in the qualified samples;

Wherein, for the apparatus (4) used in the first method, the i-th chromosome is a chromosome other than the first chromosome of interest; and for the apparatus (5) used in the second method, the i-th chromosome is the first chromosome of interest. Preferably, when the fetus is male, the chromosome other than the first chromosome of interest is the X chromosome.

In some embodiments, the means (6) for comparing the first fetal fraction value to the second fetal fraction value determines whether the two fetal fraction values are approximately equal. In some embodiments, the means (6) further comprises a component for determining that a ploidy assumption implicit in the second method is true when the two fetal fraction values are approximately equal. The ploidy assumption implicit in the second method can be that the first chromosome of interest has a complete chromosomal aneuploidy, for example, the complete chromosomal aneuploidy of the first chromosome of interest is a monosomy or a trisomy.

In certain embodiments, the additional apparatus further comprises a device (7) for analyzing the tag information of the first chromosome of interest to determine whether (i) the first chromosome of interest carries a partial aneuploidy, or (ii) the fetus is a mosaic, wherein the device (7) for analyzing the tag information of the first chromosome of interest is configured to be executed when the device (6) for comparing the first fetal fraction value with the second fetal fraction value indicates that the two fetal fraction values are not approximately equal. Preferably, the signal outputs of devices (2), (3) and (6) are connected to device (7).

In certain embodiments, in the additional apparatus, the apparatus (4) of the first method includes a component for calculating the first fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids from the maternal test sample, the polymorphisms being present on a chromosome other than the first chromosome of interest; and the apparatus (5) of the second method includes a component for calculating the second fetal fraction value using information from one or more polymorphisms exhibiting allelic imbalance in fetal and maternal nucleic acids from the maternal test sample, the polymorphisms being present on the first chromosome of interest. The information used by the apparatus (4) of the first method may include sequence tags obtained by sequencing predetermined polymorphic sequences, each of the polymorphic sequences including the one or more polymorphic sites. The information used by the apparatus (4) of the first method may also be obtained by methods other than sequencing, for example, by non-sequencing methods such as qPCR, digital PCR, mass spectrometry, or capillary gel electrophoresis.

In certain embodiments, the means for comparing (6) comprises: determining that the first chromosome of interest is a component of a diploid when the ratio of the second fetal fraction value to the first fetal fraction value is approximately 1; determining that the first chromosome of interest is a component of a triploid when the ratio of the second fetal fraction value to the first fetal fraction value is approximately 1.5; and determining that the first chromosome of interest is a component of a haploid when the ratio of the second fetal fraction value to the first fetal fraction value is approximately 0.5.

More preferably, the additional apparatus for classifying copy number variation further comprises a means (7') for analyzing the tag information of the first chromosome of interest to determine whether (i) the first chromosome of interest carries a partial aneuploidy, or (ii) the fetus is a mosaic, wherein the means (7') for analyzing the tag information of the first chromosome of interest is configured to be performed when the means (6) for comparing the first fetal fraction value with the second fetal fraction value indicates that the ratio of the second fetal fraction value to the first fetal fraction value is not approximately 1, 1.5 or 0.5. Preferably, the signal outputs of means (2), (3) and (6) are connected to means (7').

In certain embodiments, the apparatus (7) or (7') for analyzing the tag information for the first chromosome of interest comprises: (a) a component (7-1) for binning the sequence of the first chromosome of interest into a plurality of parts; (b) a component (7-2) for determining whether any of the parts contains significantly more or significantly less nucleic acid than one or more other parts; and (c) a component (7-3) for determining that the first chromosome of interest carries a partial aneuploidy if any of the parts contains significantly more or significantly less nucleic acid compared to one or more other parts, or determining that the fetus is a mosaic if none of the parts contains significantly more or significantly less nucleic acid compared to one or more other parts. Preferably, the signal outputs of apparatuses (2), (3) and (6) are connected to component (7-1), and the signal output of component (7-1) is connected to component (7-2), and the signal output of component (7-2) is connected to component (7-3). In certain embodiments, component (7-3) further determines that a portion of the first chromosome of interest comprises significantly more or significantly less nucleic acid than one or more other portions carries a partial aneuploidy.

In certain embodiments, the first chromosome of interest is selected from the group consisting of chromosomes 1-22, X, and Y.

In certain embodiments, the apparatus (6) comprises a component for classifying the copy number variation into a category selected from the group consisting of: complete chromosome insertion or duplication, complete chromosome deletion, partial chromosome duplication, and partial chromosome deletion, and mosaicism.

In certain embodiments, the additional medical analysis equipment further comprises:

(i) means (8) for determining whether the copy number variation is caused by partial aneuploidy or mosaicism; and

(ii) means (9) for determining the locus of the partial aneuploidy on the first chromosome of interest if the copy number variation is caused by partial aneuploidy.

wherein means (8) and (9) are configured to be executed when means (6) for comparing the first fetal fraction value with the second fetal fraction value determines that the first fetal fraction value is not approximately equal to the second fetal fraction value. Preferably, the signal output of means (6) is connected to means (8), and the signal output of means (8) is connected to means (9). In certain embodiments, means (9) for determining the locus of partial aneuploidy on the first chromosome of interest comprises a component for grouping the sequence tags of the first chromosome of interest into nucleic acid data bins or blocks in the first chromosome of interest; and a component for counting the mapped tags in each data bin.

In certain embodiments, the additional apparatus further comprises a sequencing device (10) configured to sequence fetal and maternal nucleic acids in a maternal test sample (e.g., a blood, plasma, serum, or urine sample) and obtain the sequence reads. Preferably, the fetal and maternal nucleic acids are cell-free DNA (cfDNA). Preferably, a signal output of the sequencing device (10) is connected to the apparatus (1).

In some embodiments, the sequencing apparatus (10) is configured to perform sequencing by synthesis. Sequencing by synthesis can be performed using reversible dye terminators. Alternatively, the sequencing apparatus (10) is configured to perform sequencing by ligation. Alternatively, the sequencing apparatus (10) is configured to perform single molecule sequencing. In some embodiments, the sequencing apparatus (10) and the apparatus (1)-(6) of the additional equipment for sorting are located in separate locations. Preferably, the signal output terminal of the sequencing apparatus (10) is connected to the apparatus (1) via a network.

In certain embodiments, the additional apparatus for sorting further comprises a device (11) for obtaining the maternal test sample from the pregnant mother. The device (11) and the devices (1)-(6) may be located in separate locations. In addition, the additional apparatus may further comprise a device (12) for extracting cell-free DNA from the maternal test sample. The device (12) for extracting cell-free DNA may be located in the same location as the sequencing device (10), and the device (11) for obtaining the maternal test sample is located in a remote location.

In certain embodiments, the device (2) aligns at least about 1 million reads.

Reagent test kit

In various embodiments, a kit is provided for implementing the methods described herein. In certain embodiments, these kits include one or more positive internal controls for complete aneuploidy and/or partial aneuploidy. Typically, but not necessarily, these controls include internal positive controls, which include nucleic acid sequences of the type to be screened. For example, a control for determining the presence or absence of a fetal trisomy (e.g., trisomy 21) in a maternal sample may include DNA characterized by trisomy 21 (e.g., DNA obtained from an individual with trisomy 21). In some embodiments, the control includes a mixture of DNA obtained from two or more individuals with different aneuploidies. For example, for determining the presence or absence of trisomy 13, trisomy 18, trisomy 21, and X monosomy, the control may include a combination of DNA samples obtained from a pregnant woman each harboring a fetus with one of the trisomy tested. In addition to complete chromosomal aneuploidy, an IPC may also be generated to provide a positive control for testing to determine the presence or absence of partial aneuploidy.

In certain embodiments, the positive control(s) include one or more nucleic acids comprising trisomy 21 (T21) and/or trisomy 18 (T18) and/or trisomy 13 (T13). In certain embodiments, nucleic acids comprising T21 for each trisomy present are provided in separate containers. In certain embodiments, nucleic acids comprising two or more trisomies are provided in a single container. Thus, for example, in certain embodiments, a container may contain T21 and T18, T21 and T13, or T18 and T13. In certain embodiments, a container may contain T18, T21, and T13. In these various embodiments, the trisomies may be provided in equal amounts/concentrations. In other embodiments, the trisomies may be provided in specific predetermined ratios. In various embodiments, the controls may be provided as "stock" solutions of known concentrations.

In certain embodiments, the control for detecting aneuploidy comprises a mixture of genomic DNA obtained from cells of two subjects, one of whom is a contributor to the aneuploid genome. For example, as described above, the internal positive control (IPC) generated as a control for determining a test for fetal trisomy (e.g., trisomy 21) may include a combination of genomic DNA from a male or female subject carrying the trisomy chromosome and genomic DNA from a female subject known not to carry the trisomy chromosome. In certain embodiments, the genomic DNA is sheared to provide fragments of about 100-400 bp, about 150-350 bp, or about 200-300 bp to simulate circulating cfDNA fragments in maternal samples.

In certain embodiments, the proportion of fragmented DNA from a subject carrying an aneuploidy (e.g., trisomy 21) in the control is selected to mimic the proportion of circulating fetal cfDNA found in maternal samples, so as to provide an IPC comprising a mixture of fragmented DNA comprising approximately 5%, approximately 10%, approximately 15%, approximately 20%, approximately 25%, or approximately 30% DNA from a subject carrying the aneuploidy. In certain embodiments, the control comprises DNA from different subjects, each carrying a different aneuploidy. For example, an IPC may comprise approximately 80% unaffected female DNA, and the remaining 20% may be DNA from three different subjects, each carrying trisomy 21, trisomy 13, and trisomy 18.

In certain embodiments, the (these) controls include cfDNA obtained from a mother known to be pregnant with a fetus with a known chromosomal aneuploidy. For example, these controls may include cfDNA obtained from a pregnant woman pregnant with a fetus with trisomy 21 and/or trisomy 18 and/or trisomy 13. The cfDNA can be extracted from a maternal sample and cloned into a bacterial vector and grown in bacteria to provide a continuous source of IPCs. As an alternative, the cloned cfDNA can be amplified by, for example, PCR.

Although the control present in the test kit is described above with respect to trisomy, it is not necessary to be limited thereto. It should be understood that the positive control present in the test kit can be produced to embody the aneuploidy of other parts, including, for example, different segment amplifications and/or deletions. Therefore, for example, in the case where it is known that different cancers are associated with the specific amplification or deletion of substantially complete chromosome arms, the (these) positive controls may include any one or more short arms or long arms of chromosomes 1-22, X and Y. In certain embodiments, the control includes the amplification of one or more arms selected from the group consisting of: 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q and/or 22q (see, for example, Table 2).

In certain embodiments, these controls include aneuploidy for any region known to be relevant to a specific amplification or deletion (e.g., breast cancer associated with the amplification at 20Q13). Illustrative regions include, but are not limited to, 17q23 (associated with breast cancer), 19q12 (associated with ovarian cancer), 1q21-1q23 (associated with sarcoma and different solid tumors), 8p11-p12 (associated with breast cancer), ErbB2 amplicon, etc. In certain embodiments, these controls include amplification or deletion of the chromosomal region as shown in any one of Tables 3-6. In certain embodiments, these controls include amplification or deletion of the chromosomal region comprising the gene as shown in any one of Tables 3-6. In certain embodiments, these controls include amplification or deletion of the chromosomal region comprising a gene as shown in any one of Tables 3-6. In certain embodiments, these controls include a plurality of nucleic acid sequences comprising amplification of the nucleic acid comprising one or more oncogenes. In certain embodiments, the controls comprise nucleic acid sequences comprising an amplification of nucleic acids comprising one or more genes selected from the group consisting of: MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, KRAS, MET, ERBB1, CDK4, MYCB, ERBB2, AKT2, MDM2, and CDK4.

The above controls are intended to be illustrative and not limiting. Using the teachings provided herein, one of ordinary skill in the art will be able to identify numerous other controls suitable for incorporation into the kit.

In different embodiments, except these controls or as a substitute for these controls, these test kits include one or more nucleic acids and/or nucleic acid mimics that provide the marker sequence suitable for tracking and determining sample integrity. In certain embodiments, these markers include anti-gene chain sequences. In certain embodiments, the length of these marker sequences is about 30bp to as much as about 600bp length or about 100 bp to about 400bp length range. In certain embodiments, the length of this (these) marker sequence is at least 30bp (or nt). In certain embodiments, this marker is connected to adaptor, and the length of the marker molecule that this adaptor is connected is between about 200bp (or nt) and about 600bp (or nt), between about 250bp (or nt) and 550bp (or nt), between about 300bp (or nt) and 500bp (or nt) or between about 350 and 450. In certain embodiments, the length of the marker molecule that this adaptor is connected is about 200bp (or nt). In certain embodiments, the length of the marker molecule can be about 150bp (or nt), about 160bp (or nt), 170bp (or nt), about 180bp (or nt), about 190bp (or nt) or about 200bp (or nt). In certain embodiments, the length of the marker molecule is in the scope of about 600bp (or nt).

In certain embodiments, this test kit provides at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15, or at least 16, or at least 17, or at least 18, or at least 19, or at least 20, or at least 25, or at least 30, or at least 35, or at least 40, or at least 50 different sequences. Different nucleic acids and/or nucleic acid mimics providing this (these) marker sequence can be stored in separate containers/bottles. Alternatively, different marker molecules can be kept in identical containers/bottles.

In different embodiments, these markers include one or more DNA, or these markers include one or more DNA analogies. Suitable analogies include but are not limited to morpholino derivatives, peptide nucleic acids (PNA) and phosphorothioate DNA. In different embodiments, these markers are incorporated into these controls. In certain embodiments, these markers are incorporated into aptamers and/or provided to be connected to aptamers.

In certain embodiments, the kit further comprises one or more sequencing adapters. These adapters include but are not limited to indexed sequencing adapters. In certain embodiments, these adapters comprise a single arm comprising an index sequence and one or more PCR priming sites.

In certain embodiments, the kit further comprises a sample collection device for collecting a biological sample. In certain embodiments, the sample collection device comprises a device for collecting blood and, optionally, a container for holding the blood. In certain embodiments, the kit comprises a container for holding the blood, and the container comprises an anticoagulant and/or a cell fixative and/or one or more antigenic strand marker sequences.

In certain embodiments, the kit further includes a DNA extraction reagent (e.g., a separation matrix and/or an elution solution). The kit may also include reagents for sequencing the library preparation. These reagents include, but are not limited to, a solution for end-repair DNA and/or a solution for dA-tailed DNA and/or a solution for adaptor-ligated DNA.

In certain embodiments, the kit further comprises a composition comprising one or more primer sets for amplifying at least one preselected polymorphic nucleic acid in a maternal sample, wherein each preselected polymorphic nucleic acid comprises at least one polymorphic site, and wherein the forward or reverse primer in each primer set hybridizes to a DNA sequence sufficiently proximal to the polymorphic site to be included in the sequence read generated by massively parallel sequencing of the amplified preselected polymorphic nucleic acid. Sequencing the amplified preselected polymorphic sequence can be used to determine the fetal fraction in the maternal sample as described elsewhere herein. The preselected polymorphic nucleic acid can comprise a single nucleotide polymorphism (SNP) or a streptavidin (STR). In certain embodiments, at least one primer in each primer set is designed to recognize a polymorphic site within a sequence read of approximately 25 bp, approximately 40 bp, approximately 50 bp, or approximately 100 bp. In certain embodiments, the primer set hybridizes to the DNA sequence to produce an amplicon of at least about 100 bp, at least about 150 bp, or at least about 200 bp. The primer set can hybridize to a DNA sequence present on the same chromosome, or the primer set can hybridize to a DNA sequence present on a different chromosome. In certain embodiments, the primer set does not hybridize to a DNA sequence present on chromosomes 13, 18, 21, X or Y.

Embodiments of kits provided for practicing these methods and for use in combination with various devices as described herein are illustrated in Figures 67 and 68. In one embodiment, a kit is provided for determining fetal fraction. As shown in Figure 67, the kit includes a kit body (1), a clamping groove arranged in the kit body for receiving a bottle, a bottle (2) containing an internal positive control; a bottle (3) containing a marker nucleic acid suitable for tracking and determining sample integrity; and a bottle (4) containing a buffer solution.

The kit can include a plurality of additional bottles, wherein each of the plurality of bottles includes a different internal positive control or a different marker nucleic acid.

In certain embodiments, the bottle (2) includes two or more internal positive controls. The internal positive controls include a trisomy selected from the group consisting of trisomy 21, trisomy 18, trisomy 21, trisomy 13, trisomy 16, trisomy 13, trisomy 9, trisomy 8, trisomy 22, XXX, XXY, and XYY. In certain embodiments, the internal positive controls include a trisomy selected from the group consisting of trisomy 21 (T21), trisomy 18 (T18), and trisomy 13 (T13). In other embodiments, the internal positive controls loaded into the bottle (2) include trisomy 21 (T21), trisomy 18 (T18), and trisomy 13 (T13). Alternatively, the positive controls included in the kit may include amplification or deletion of a portion of one or more of chromosomes 1 to 22, X, and Y. In certain embodiments, the positive control comprises an amplification or deletion of one short arm or one long arm of any one or more of chromosomes 1 to 22, X, and Y. In certain embodiments, bottle (2) comprises an amplification or deletion of one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, and 22q. In other embodiments, bottle (2) comprises an amplification of a region selected from the group consisting of 20Q13, 19q12, 1q21-1q23, 8p11-p12, and ErbB2. Alternatively, the positive control loaded into bottle (2) comprises amplification of a region or a gene presented in Tables 3, 4, 5, and 6. In certain embodiments, the positive control loaded into bottle (2) comprises amplification of a region or a gene selected from the group consisting of MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, KRAS, MET, ERBB1, CDK4, MYCB, ERBB2, AKT2, MDM2, and CDK4.

The marker nucleic acids (also known as marker molecules (MM)) included in the multiple embodiments of the test kit are antigenic strand marker sequences. The length of these marker sequences can be in the range of from about 30bp to about 600bp in length. In other embodiments, the length of these marker sequences is in the range of from about 100bp to about 400bp in length. In certain embodiments, the test kit includes at least 2, or at least 3, or at least 4, or at least 5, or at least 6, or at least 7, or at least 8, or at least 9, or at least 10, or at least 11, or at least 12, or at least 13, or at least 14, or at least 15, or at least 16, or at least 17, or at least 18, or at least 19, or at least 20, or at least 25, or at least 30, or at least 35, or at least 40, or at least 50 bottles for different marker sequences.

In certain embodiments, the labels included in the kit comprise one or more DNAs. In other embodiments, the labels comprise one or more mimetics selected from the group consisting of morpholino derivatives, peptide nucleic acids (PNAs), and phosphorothioate DNAs.

In certain embodiments, the marker is incorporated into the control. In other embodiments, the marker is incorporated into the aptamer. In certain embodiments, the bottle (3) of the kit can be further loaded with one or more sequencing aptamers. The aptamers include indexed sequencing aptamers. These aptamers can further include a single arm comprising an index sequence and one or more PCR priming sites.

Figure 68 shows a schematic diagram of a kit, which may further include a sample collection device for collecting a biological sample. The sample collection device includes a device for collecting blood (5) and a container for holding blood (6). In certain embodiments, the device for collecting blood and the container for holding blood include an anticoagulant and a cell fixative.

In certain embodiments, the kit may further comprise a bottle (7) loaded with DNA extraction reagents. The DNA extraction reagent(s) may comprise a separation matrix and/or an elution solution.

In certain embodiments, the kit further comprises a bottle (8) loaded with reagents for preparing a sequencing library. These reagents for preparing a sequencing library may include a solution for end-repairing DNA, a solution for dA-tailing DNA, and a solution for adaptor ligation of DNA.

In other embodiments, the kit further comprises a bottle (9) comprising a composition of primers for amplifying a predetermined target nucleic acid.

In certain embodiments, the kit further comprises instructional materials that teach the use of the reagents to determine fetal fraction in a biological sample. These instructional materials teach the use of these materials to detect trisomy or monosomy. In certain embodiments, these instructional materials teach the use of these materials to detect cancer or a predisposition to cancer.

In addition, these kits may optionally include labels and/or instructional materials that provide guidance (e.g., protocols) for the use of the reagents and/or devices provided in the kit. For example, these instructional materials can teach the use of these reagents to prepare samples and/or determine copy number variation in biological samples. In certain embodiments, these instructional materials teach the use of these materials to detect trisomy. In certain embodiments, these instructional materials teach the use of these materials to detect cancer or a predisposition to cancer.

While the instructional materials in the various kits typically include handwritten or printed materials, they are not limited thereto. Any medium capable of storing these instructions and communicating them to an end user is contemplated herein. These media include, but are not limited to, electronic storage media (e.g., magnetic disks, tapes, pickups, chips), optical media (e.g., CD ROMs), and the like. These media may include addresses to Internet sites providing these instructional materials.

The various methods, devices, systems, and uses are further described in detail in the following examples, which are in no way intended to limit the scope of the claimed invention. The accompanying drawings are intended to be considered an integral part of this specification and the description of the present invention. The following examples are provided to illustrate, but not to limit, the claimed invention.

experiment

Example 1

Sample processing and cfDNA extraction

Peripheral blood samples were collected from pregnant women in the first or second trimester who were considered at risk for fetal aneuploidy. Written consent was obtained from each participant before blood draw. Blood was collected before amniocentesis or chorionic villus sampling. Karyotyping was performed on the chorionic villus or amniocentesis samples to determine the fetal karyotype.

Peripheral blood was drawn from each subject and collected in an ACD tube. One blood sample (approximately 6 to 9 ml/tube) was transferred to a 15 ml low-speed centrifuge tube. The blood was centrifuged at 2640 rpm and 4°C for 10 minutes using a Beckman Allegra 6R centrifuge and a GA3.8 rotor.

For cell-free plasma extraction, the upper plasma layer was transferred to a 15-ml high-speed centrifuge tube and centrifuged at 16,000 × g at 4°C for 10 minutes using a Beckman Coulter Avanti J-E centrifuge and a JA-14 rotor. Two centrifugation steps were performed within 72 hours of blood collection. Cell-free plasma containing cfDNA was stored at −80°C and thawed only once before plasma cfDNA amplification or cfDNA purification.

Purified cell-free DNA (cfDNA) was extracted from cell-free plasma using the QIAamp Blood DNA Mini kit (Qiagen) essentially according to the manufacturer's instructions. One milliliter of buffer AL and 100 μl of protease solution were added to 1 ml of plasma. The mixture was incubated at 56° C. for 15 minutes. One milliliter of 100% ethanol was added to the plasma digest. The resulting mixture was transferred to a QIAamp mini column combined with the VacValve and VacConnector provided in the QIAvac 24 Plus column assembly (Qiagen). Vacuum was applied to the sample, and the cfDNA trapped on the column filter was washed under vacuum with 750 μl of buffer AW1, followed by a second wash with 750 μl of buffer AW24. The column was centrifuged at 14,000 RPM for 5 minutes to remove any residual buffer from the filter. cfDNA was eluted with Buffer AE by centrifugation at 14,000 RPM, ^and the concentration was determined using the Qubit ^™ Quantitation Platform (Invitrogen).

Example 2

Preparation and sequencing of naive and enriched sequencing libraries

a. Preparation of sequencing library - shortened protocol (ABB)

All sequencing libraries, both initial and enriched, were prepared from approximately 2 ng of purified cfDNA extracted from maternal plasma. The library preparation was performed using the NEBNext ^™ DNA SamplePrep DNA Reagent Set 1 (Article No. E6000L; New England Biolabs, Ipswich, MA) as follows. Because cell- ^free plasma DNA is essentially fragmented, the plasma DNA sample was no longer fragmented by nebulization or sonication. According to the End Repair Module, the overhangs of approximately 2 ng of purified cfDNA fragments contained in 40 ^μl were converted to phosphorylated blunt ends by incubating the cfDNA with 5 μl of 10× phosphorylation buffer, 2 μl of deoxynucleotide solution mixture (10 mM each dNTP), 1 μl of 1:5 DNA polymerase I dilution, 1 μl of T4 DNA polymerase, and 1 μl of T4 polynucleotide kinase in a 1.5 ml microcentrifuge tube at 20°C for 15 minutes. The enzyme was then heat-inactivated by incubating the reaction mixture at 75°C for 5 minutes. The mixture was cooled to 4°C and dA-tailing of the blunt-ended DNA was achieved using 10 μl of dA-tailing master mix containing Klenow fragment (3' to 5' exo minus) (NEBNext ^™ DNA Sample Prep DNA Reagent Set 1) and incubated at 37°C for 15 minutes. Subsequently, the Klenow fragments were heat-inactivated by incubating the reaction mixture at 75°C for 5 minutes. After inactivation of the Klenow fragments, 4 μl of T4 DNA ligase provided in the NEBNext ^™ DNA Sample Preparation DNA Reagent Set 1 were used to ligate the dA-tailed DNA using 1 μl of a 1:5 dilution of Illumina Genomic Adaptor Oligo Mix (Article No. 1000521; Illumina, Hayward, CA) by incubating the reaction mixture at 25°C for 15 minutes. Illumina adaptors (Non-Index Y-Adaptors) were ligated to the dA-tailed DNA using 1 μl of a 1:5 dilution of Illumina Genomic Adaptor Oligo Mix (Article No. 1000521; Illumina, Hayward, CA). The mixture was cooled to 4°C and the magnetic beads provided in the Agencourt AMPure XP PCR Purification System (Article No. A63881; Beckman Coulter Genomics, Danvers, MA) were used to purify the adapter-ligated cfDNA from unligated adapters, adapter dimers, and other reagents. 18 cycles of PCR were performed using High Fidelity Master Mix (25 μl; Finnzymes, Woburn, MA) and Illumina PCR primers (0.5 μM each) (Article Nos. 1000537 and 1000537) to selectively enrich for adapter-ligated cfDNA (25 μl). PCR was performed on adapter-ligated DNA using Illumina genomic PCR primers (Article Nos. 100537 and 1000538) and Phusion HF PCR Master Mix provided in the NEBNext ^™ DNA Sample Preparation DNA Reagent Set 1 according to the manufacturer's instructions (98°C, 30 sec; 98°C, 10 sec, 18 cycles; 65°C, 30 sec; and 72°C, 30 sec; final extension at 72°C for 5 min and hold at 4°C). Use Agencourt AMPure XP PCR purification system (Agencourt Bioscience Corporation, Bilifford, Massachusetts), purify the product through amplification according to the manufacturer's instructions that can obtain at www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf place.In 40 μ l Qiagen EB buffer (Qiagen EB BufferQiagen EB Buffer), elute the amplified product through purification, and use the Agilent DNA 1000 test kit for 2100 bioanalyzers (Agilent technologies Inc., Santa Clara, California) to analyze the concentration and the size distribution of amplified library.

b. Preparation of sequencing library - full-length protocol

The full-length protocol described here is basically the standard protocol provided by Illumina, and is different from the Illumina protocol only in the purification of the amplified library. The Illumina protocol indicates that the amplified library is purified using gel electrophoresis, while the protocol described herein uses magnetic beads to carry out the same purification step. Using NEBNext ^™ DNA sample preparation DNA reagent set 1 (article number E6000L; New England Biolabs, Ipswich, Massachusetts), basically according to the manufacturer's instructions, about 2ng of purified cfDNA extracted from maternal plasma is used to prepare the initial sequencing library. Except that the adapter connection product is finally purified (this step is carried out using Anjinkote magnetic beads and reagents rather than purification columns), all steps are carried out according to the protocol attached to the genomic DNA library sample preparation NEBNext ^™ reagent, and the DNA library is sequenced using GAII. The NEBNext ^™ protocol essentially follows the protocol provided by ILUMINA, which is available at grcf.jhml.edu/hts/protocols/11257047_ChIP_Sample_Prep.pdf.

According to the end repair module, the overhangs of approximately 2 ng of purified ^cfDNA fragments contained in 40 μl were converted to phosphorylated blunt ends by incubating 40 μl of cfDNA with 5 μl of 10× phosphorylation buffer, 2 μl of deoxynucleotide solution mixture (10 mM each dNTP), 1 μl of 1:5 DNA polymerase I dilution, 1 μl of T4 DNA polymerase, and 1 μl of T4 polynucleotide kinase in a 200 μl microcentrifuge tube in a circulating heater at 20°C for 30 minutes. The sample was cooled to 4°C and purified using the QIAQuick column provided in the QIAQuick PCR Purification Kit (Qiagen, Valencia, CA) as follows. 50 μl of the reaction was transferred to a 1.5 ml microcentrifuge tube and 250 μl of Qiagen Buffer PB was added. The gained 300 μ l is transferred to QIAquick column and centrifuged at 13,000RPM for 1 minute in a microcentrifuge. The column is washed with 750 μ l QIAgen buffer PE and centrifuged again. Residual ethanol is removed by additional centrifugation at 13,000RPM for 5 minutes. DNA is eluted by centrifugation in 39 μ l QIAgen buffer EB. 16 μ l dA tailing master mix (NEBNext ^™ DNA sample preparation DNA reagent set 1) comprising Klenow fragment (3' to 5' exo minus) is used and according to the manufacturer's dA tailing module, 34 μ l blunt-end DNA is incubated at 37°C for 30 minutes to achieve dA tailing. The sample is cooled to 4°C and purified as follows using the column provided in the MinElute PCR purification kit (Qiagen, Valencia, California). 50 μ l reactants are transferred to 1.5 ml microcentrifuge tubes and 250 μ l QIAgen buffer PB are added. 300 μl was transferred to a MinElute column and centrifuged at 13,000 RPM for 1 minute in a microcentrifuge. The column was washed with 750 μl of Qiagen Buffer PE and re-centrifuged. Residual ethanol was removed by centrifugation at 13,000 RPM for another 5 minutes. DNA was eluted by centrifugation in 15 μl of Qiagen Buffer EB. According to the rapid ligation module, ten microliters of DNA eluate were incubated with 1 μl of 1:5 Illumina Genomic Adaptor Oligo Mix Diluent (Article No. 1000521), 15 μl of 2X Rapid Ligation Reaction Buffer, and 4 μl of Rapid T4 DNA Ligase at 25°C for 15 minutes. The sample was cooled to 4°C and purified using a MinElute column as follows. One hundred and fifty microliters of Qiagen Buffer PE were added to the 30 μl reaction and the entire volume was transferred to the MinElute column and centrifuged at 13,000 RPM for 1 minute in a microcentrifuge. The column was washed with 750 μl of Qiagen buffer PE and centrifuged again. Residual ethanol was removed by centrifugation at 13,000 RPM for 5 minutes. DNA was eluted by centrifugation in 28 μl of Qiagen buffer EB. Using Illumina genomic PCR primers (item numbers 100537 and 1000538) and the Phusion HF PCR master mix provided in the NEBNext ^™ DNA sample preparation DNA reagent set 1, 23 microliters of the adapter-ligated DNA eluate were subjected to 18 PCR cycles (98°C, 30 seconds; 98°C, 10 seconds, 18 cycles; 65°C, 30 seconds; and 72°C, 30 seconds; a final extension of 5 minutes at 72°C and maintained at 4°C) according to the manufacturer's instructions. The amplified products were purified using the Ankincott AMPure XP PCR purification system (Ankincott Biotech, Biliver, Massachusetts) according to the manufacturer's instructions available at www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf. The Ankincott AMPure XP PCR purification system will remove unbound dNTPs, primers, primer dimers, salts and other contaminants and reclaim an amplicon greater than 100 bp. The amplified products were eluted from the Ankincott beads in 40 μl Kaijie EB buffers and the size distribution of the libraries was analyzed using the Agilent DNA 1000 test kit for 2100 bioanalyzers (Agilent Technologies, Santa Clara, California).

c. Analysis of sequencing libraries prepared according to shortened (a) and full-length (b) protocols

The electropherograms generated by the bioanalyzer are shown in Figures 21A and 21B. Figure 21A shows an electropherogram of library DNA prepared from cfDNA purified from plasma sample M24228 using the full-length protocol described in (a), while Figure 21B shows an electropherogram of library DNA prepared from cfDNA purified from plasma sample M24228 using the full-length protocol described in (b). In both figures, peaks 1 and 4 represent the 15 bp lower and 1,500 bp upper internal standards, respectively; the numbers above the peaks indicate the migration times of the library fragments; and the horizontal line indicates the set threshold for integration. The electropherogram in Figure 21A shows a secondary peak with a 187 bp fragment and a primary peak with a 263 bp fragment, while the electropherogram in Figure 21B shows only a single peak at 265 bp. Integration of the peak areas yielded a calculated DNA concentration of 0.40 ng/μl for the 187 bp peak in Figure 21A, 7.34 ng/μl for the 263 bp peak in Figure 21A, and 14.72 ng/μl for the 265 bp peak in Figure 21B. Given that the Illumina adaptor ligated to cfDNA is 92 bp, subtracting this from 265 bp suggests a cfDNA peak size of 173 bp. The secondary peak at 187 bp likely represents a fragment of two primers ligated end-to-end. When using the shortened protocol, linear double-primed fragments are eliminated from the final library product. The shortened protocol also eliminates other smaller fragments smaller than 187 bp. In this example, the concentration of the purified adaptor-ligated cfDNA was twice that of the adaptor-ligated cfDNA generated using the full-length protocol. As noted, the concentration of these adaptor-ligated cfDNA fragments was consistently greater than that obtained using the full-length protocol (data not shown).

Therefore, one advantage of using the shortened protocol to prepare sequencing libraries is that the resulting libraries consistently contain only a single major peak in the 262-267 bp range, whereas the quality of libraries prepared using the full-length protocol can vary, as reflected by the number and mobility of peaks other than those representing cfDNA. Non-cfDNA products occupy space on the flow cell and reduce the quality of clustered amplification and subsequent imaging of sequencing reactions, which is fundamental to the overall assignment of aneuploidy status. It has been shown that shortening the protocol does not affect library sequencing.

Another advantage of using the shortened protocol to prepare sequencing libraries is that the three enzymatic steps of blunt-end ligation, dA tailing, and adapter ligation take less than an hour to complete, thus supporting the validation and implementation of rapid aneuploidy diagnostic services.

Another advantage is that the three enzyme steps of blunting, dA tailing and adaptor ligation are performed in the same reaction tube, thus avoiding multiple sample transfers that may cause material loss and, more importantly, sample mixing and sample contamination.

Example 3

Preparation of sequencing libraries from unrepaired cfDNA: adaptor ligation in solution

To determine whether the shortening protocol could be further shortened to further expedite sample analysis, sequencing libraries were made from unrepaired cfDNA and sequenced using the ILlumina Genome Analyzer II as previously described.

cfDNA was prepared from peripheral blood samples as described herein. Blunting and phosphorylation of the 5' phosphates required by the published protocol for the Illumina platform were not performed to provide unrepaired cfDNA samples.

It could be determined that omitting DNA repair or DNA repair and phosphorylation did not affect the quality or yield of sequencing libraries (data not shown).

In-solution 2-step method for unindexed, unrepaired DNA

In the first set of experiments, dA tailing and adapter ligation were performed simultaneously on unrepaired cfDNA by combining Klenow Exo- and T4-DNA ligase in the same reaction mixture as follows: Thirty microliters of cfDNA at a concentration between 20-150 pg/μl was dA tailed (5 μl 10X No. 2 NEB buffer, 2 μl 10 nM dNTPs, 1 μl 10 nM ATP, and 1 μl 5000 U/ml Klenow Exo-) and ligated to Illumina Y adapters (1 μl 1:15 dilution of a 3 μM stock solution) in a 50 μl reaction volume using 1 μl 400,000 U/ml T4-DNA ligase. Unindexed Y adapters were obtained from Illumina. The combined reactions were incubated at 25°C for 30 minutes. The enzymes were heat-inactivated at 75°C for 5 minutes, and the reaction products were stored at 10°C.

The product of adapter connection is purified using SPRI beads (Anjin Kete AMPure XP PCR purification system, Beckman Coulter Genomics) and 18 PCR cycles are performed. The library amplified by PCR is purified using SPRI and sequenced using Illumina Genome Analyzer IIx or HiSeq according to the manufacturer's instructions to obtain a single-end read of 36bp. Many 36bp reads are obtained, covering approximately 10% of the genome. After completing sample sequencing, Illumina "Sequencer Control Software/Real-time Analysis" transfers the base determination file to the network connected to the storage device in binary format for data analysis. Sequence data were analyzed using software designed to run on Linux servers that used Illumina "BCLConverter" to convert binary base calls into human-readable text files and then called the open source "Bowtie" program to align sequences to a reference human genome derived from the hg18 genome provided by the National Center for Biotechnology Information (NCBI36/hg18, available on the World Wide Web at http://genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).

The software reads the sequence data that the process that above program produces and the genome from Bowtie output (bowtieout.txt file) are uniquely compared.Allow the sequence alignment with up to 2 base mispairings, and only be included in the comparison count when it and genome are uniquely compared.Exclude the sequence alignment (copy) with identical start and end coordinates.To have 2 or be less than about 500 to 25,000,000 36bp tags of 2 mispairings and be mapped to human genome uniquely.All mapping tags are counted and are included in the chromosome dosage calculation in test and qualified samples.Extend to base 2 × 10 ⁶ , base 10 × 10 ⁶ to base 13 × 10 ⁶ and base 23 × 10 ⁶ to the region of chromosome Y end and exclude from analysis exactly, because the tag that originates from male or female fetus is mapped to these regions of Y chromosome.

Figure 22A shows the mean (n = 16) of the percentage of the total number of sequence tags mapped to each human chromosome when sequencing libraries were prepared according to the shortened protocol (ABB; ◇) and when sequencing libraries were prepared according to the no-repair 2-step method (INSOL; □). These data show that when compared to the percentage of tags mapped to the corresponding chromosomes when using the shortened method, the preparation of sequencing libraries using the no-repair 2-step method produces a greater percentage of tags mapped to chromosomes with lower GC content and a smaller percentage of tags mapped to chromosomes with higher GC content. Figure 22b shows that the percentage of sequence tags varies with chromosome size and that the no-repair method reduces sequence bias. The regression coefficients for mapped tags obtained from sequencing libraries prepared according to the shortened protocol (ABB; Δ) and the no-repair protocol in solution (2 steps; □) are R ² =0.9332 and R ² =0.9806, respectively.

Table 8. Percent GC content/chromosome

Size (Mbp) GC (%) Size (Mbp) GC (%) Chr1 247 41.37 Chr13 114 38.24 Chr2 243 39.44 Chr14 106 40.85 Chr3 199 38.74 Chr15 100 41.80 Chr4 191 38.60 Chr16 89 44.64 Chr5 181 39.35 Chr17 79 45.01 Chr6 171 39.94 Chr18 76 39.66 Chr7 159 39.78 Chr19 63 48.21 Chr8 146 40.30 Chr20 62 42.05 Chr9 140 40.17 Chr21 47 40.68 Chr10 135 40.43 Chr22 50 47.64 Chr11 134 41.37 XOt 155 39.26 Chr12 132 40.59 Y 58 37.74

Shortening method and the comparison of no repair 2-step method are also seen as the ratio of the label percentage that is mapped to individual chromosome when using no repair method and the label percentage that is mapped to individual chromosome when using shortening method and change along with the GC content percentage of each chromosome.The GC content percentage relative to chromosome size is calculated (Constantini et al., Genome Research (Genome Res) 16:536-541[2006]) and is provided in Table 8 based on the public information of chromosome sequence and GC content subregion.Result is provided in Figure 22 C, and this figure shows that the ratio for the chromosome with high GC content significantly reduces, and the ratio for the chromosome with low GC content increases.These data clearly show that no normalization effect for overcoming GC skew that repair method has.

These data show that the repair-free approach corrects to some extent the GC bias that is known to be associated with sequencing of amplified DNA.

To determine whether the no-repair method affects the ratio of fetal to maternal cfDNA sequenced, the percentage of the number of tags mapped to chromosomes x and Y was determined. Figures 23A and 23B show bar graphs that provide the mean and standard deviation of the percentage of tags mapped to chromosomes X (Figure 23A; % chromosome X) and Y (Figure 23B; % chromosome Y), which were obtained by sequencing 10 cfDNA samples purified from the plasma of 10 pregnant women. Figure 23A shows that the number of tags mapped to chromosome X is greater when using the no-repair method relative to the number obtained using the shortening method. Figure 23B shows that the percentage of tags mapped to chromosome Y when using the no-repair method is not different from that when using the shortening method.

These data show that the no-repair approach does not introduce any bias for or against sequencing fetal versus maternal DNA, ie, the proportion of fetal sequences sequenced is unchanged when the no-repair approach is used.

Overall, these data show that the no-repair approach does not adversely affect the quality of the sequencing library, nor does it affect the information obtained from sequencing the library. Eliminating the DNA repair step required by the published protocol will reduce reagent costs and speed up the preparation of sequencing libraries.

In-solution 2-step method for indexing unrepaired DNA

In a second set of experiments, unrepaired cfDNA was dA-tailed, followed by heat inactivation of Klenow Exo- and adapter ligation. Excluding heat inactivation of Klenow Exo- did not affect the yield or quality of the sequencing library when ligation was performed using unindexed Illumina adapters (which carry single-stranded arms with 21 bases).

To determine whether the no-repair approach could be applied to multiplex sequencing, libraries were generated using homemade indexed Y adapters containing a 6-base index sequence, either with or without Klenow heat inactivation. Unlike non-indexed adapters, indexed adapters contained a single 43-base arm that included the index sequence and a PCR priming site.

Twelve different indexed aptamers were made, starting with oligonucleotides obtained from Integrated DNA Technologies (Coralville, IA) that were identical to the ILlumina TruSeq aptamers. Oligonucleotide sequences were obtained from the published ILlumina TruSeq indexed aptamer sequences. Oligonucleotides were dissolved to a final concentration of 300 μM in annealing buffer (10 mM Tris, 1 mM EDTA, 50 mM NaCl, pH 7.5). An equimolar mixture of oligonucleotides, typically 10 μl (300 μM each), containing two cantilevers of any given indexed aptamer was mixed and allowed to anneal (95°C for 6 minutes, followed by slow controlled cooling from 95°C to 10°C). The final 150 μM aptamer was diluted to 7.5 μM in 10 mM Tris, 1 mM EDTA (pH 8) and stored at -20°C until use.

The data showed that when indexed adapters were used, library preparation by the 2-step method was not feasible if active Klenow Exo- was present in the same reaction with the ligase and indexed adapters. However, if Klenow Exo- was first heat-inactivated at 75°C for 5 minutes and then the ligase plus indexed adapters were added, the 2-step method was very feasible. It is possible that when indexed adapters and active Klenow Exo- are present together, the strand displacement activity of Klenow Exo- causes the longer single-stranded DNA arms of the indexed adapters to be digested, thereby eliminating the PCR primer sites. Electropherograms of sequencing libraries were obtained using the same cfDNA and enzymes after Klenow Exo- reactions showed that the 2-step method, including Klenow Exo-heat inactivation before adding the ligase and indexed adapters, could produce a library with the expected characteristic curve (with the main peak at 290 bp) (data not shown). Therefore, since the no-repair method is suitable for multiplex sequencing, all experiments using indexed Y adapters were modified to include heat inactivation of Klenow Exo-.

Example 4

Preparation of sequencing libraries from unrepaired cfDNA: Adapter ligation on solid surface (SS) for unrepaired cfDNA One-step solid surface method for DNA priming

To determine whether the repair-free library process could be further simplified, the repair-free sequencing library preparation method described in Example 3 was configured to be performed on a solid surface. The prepared libraries were sequenced as described in Example 3.

cfDNA was prepared from peripheral blood samples as described in Example 1. Polypropylene tubes were coated with streptavidin, washed, and the first set of biotinylated indexed adapters was bound to the streptavidin-coated tubes as follows. An 8-well PCR tube array (USA Scientific, Ocala, FL) was coated with 50 μl of PBS containing 0.5 nmol streptavidin (ThermoScientific, Rockford, IL) by incubating the tubes with SA overnight at 4°C. The tubes were washed four times with 200 μl of 1XTE. 7.5 pmoles, 3.75 pmoles, 1.8 pmoles, and 0.9 pmoles of biotinylated index 1 adapters, each in 50 μl of TE, were added in duplicate to the SA-coated tubes and incubated at room temperature for 25 minutes. Unbound aptamers were removed and the tubes were washed four times with 200 μl TE. As described in Example 3, biotinylated index 1 aptamers were made using biotinylated universal aptamer oligonucleotides purchased from IDT.

1-step SS method using cfDNA from non-pregnant subjects

In a second row of PCR tubes, a control sample (NTC: no template control) or 30 μl of approximately 120 pg/μl (approximately 32 femtomoles) purified cfDNA from a non-pregnant woman was incubated with 5 units of Klenow Exo- in a 50 μl reaction volume in NEB buffer No. 2 containing 20 nanomoles dNTPs and 10 nanomoles ATP at 37°C for 15 minutes. Subsequently, Klenow enzyme was inactivated by incubating the reaction mixture at 75°C for 5 minutes. The Klenow-DNA mixture was transferred to a corresponding tube containing SA-bound biotinylated adaptors, and the cfDNA was ligated to the immobilized adaptors by incubating the mixture with 400 units of T4 DNA ligase in 10 μl of 1X T4 DNA ligase buffer at 25°C for 15 minutes. Subsequently, 7.5 pmol of non-biotinylated index 1 adapter was ligated to the solid phase-bound cfDNA by incubating it with 200 units of T4-DNA ligase in 10 μl of buffer at 25°C for 15 minutes. The reaction mixture was removed and the tube was washed five times with 200 μl of TE buffer. The adapter-ligated cfDNA was amplified by PCR using 50 μl of Phusion PCR mix [New England Biolabs] containing P5 and P7 primers (IDT; 1 μM each) and cycled as follows: [30 seconds, 98°C; (10 seconds, 98°C; 10 seconds, 50°C; 10 seconds, 60°C; 10 seconds, 72°C) × 18 cycles; 5 minutes, 72°C; 10°C incubation]. The resulting library product was SPRI cleaned [Beckman Coulter Genomics] and the quality of the library was assessed based on the profile curve obtained by analysis using a high-sensitivity bioanalyzer chip [Agilent Technologies, Santa Clara, CA]. These profiles show that solid-phase sequencing library preparation of unrepaired cfDNA provides high yield and high-quality sequencing libraries (data not shown).

1-step SS method using cfDNA from pregnant subjects

The solid surface (SS) method was tested using cfDNA samples obtained from pregnant women.

As described in Example 1, cfDNA was prepared from 8 peripheral blood samples obtained from pregnant women, and sequencing libraries were prepared from the purified cfDNA as described above. The libraries were sequenced and the sequence information was analyzed.

Figure 24 shows the ratio of the number of non-excluded sites (NE sites) on the reference sequence genome (hg18) and the total number of tags mapped to these non-excluded sites for each of the five samples, from which cfDNA was prepared and used to construct sequencing libraries according to the abbreviated protocol (ABB) described in Example 2 (filled bars), the in-solution no-repair protocol described in Example 18 (2 steps; open bars), and the solid surface no-repair protocol described in this example (1 step; gray bars).

The data presented in Figure 24 show that expression of PCR amplified sequences prepared according to the three protocols was comparable, indicating that the solid surface method does not bias the sequence variants represented in the library.

Figure 25A shows that the number of sequence tags uniquely mapped to each chromosome obtained when sequencing the library prepared according to the no-repair solid surface method is comparable to the number obtained when using the no-repair 2-step method in solution. The data show that both no-repair methods reduce the GC bias of the sequencing data.

Figure 25B shows the relationship between the number of mapped tags and the size of the chromosome to which the tags are mapped. The regression coefficients for mapped tags obtained from sequencing libraries prepared according to the abbreviated protocol (ABB), the in-solution no-repair protocol (2 steps), and the solid surface no-repair protocol (1 step) are ^R2 = 0.9332, ^R2 = 0.9802, and ^R2 = 0.9807, respectively.

Figure 25C shows that the ratio of the percentage mapped sequence tags/chromosomes obtained from sequencing libraries prepared according to the no-repair 2-step protocol to the tags/chromosomes obtained from sequencing libraries prepared according to the shortened protocol (ABB) is a function of the percentage GC content of each chromosome (◇), and the ratio of the percentage mapped sequence tags/chromosomes obtained from sequencing libraries prepared according to the no-repair 1-step protocol to the tags/chromosomes obtained from sequencing libraries prepared according to the shortened protocol (ABB) is a function of the percentage GC content of each chromosome (□). In summary, the data in Figures 25B and 25C show that both the 1-step and 2-step methods show similar GC normalization effects because both omit the DNA repair step of the library process.

In order to determine whether the ratio of maternal cfDNA sequenced by the no-repair method affects the fetal contrast, the number percentages of tags mapped to chromosomes x and Y are determined. Figures 26A and 26B show the comparison of the mean and standard deviation of the percentages of tags mapped to chromosomes X (Figure 26A) and Y (Figure 26B), which are obtained from 5 cfDNA samples purified from the plasma of 5 pregnant women using ABB, 2-step, and 1-step methods. Figure 26A shows that the number of tags mapped to chromosome X is larger when no-repair method (2-step and 1-step) is used relative to the number obtained using the shortening method (filled bars). Figure 26B shows that the percentage of tags mapped to chromosome Y is different from that when using the shortening method when using the no-repair 2-step and 1-step methods.

These data show that the no-repair solid surface 1-step method does not introduce any bias for or against sequencing fetal versus maternal DNA, i.e., the proportion of fetal sequences sequenced is unchanged when using the no-repair solid surface method.

In summary, the data show that generating sequencing libraries on solid surfaces is an easy and feasible option for sequencing sample preparations.

Example 5

High throughput compatibility for one-step library preparation on solid surfaces without repair

To determine whether a repair-free, one-step library preparation method for sequencing by NGS technology could be applied to high-throughput sample processing, 96 cfDNA libraries were prepared from 96 peripheral blood samples in a 96-well PCR plate coated with SA-bound indexed adaptors. The prepared libraries were sequenced as described in Example 5.

SA coating of the first PCR plate and ligation of biotinylated indexed adapters were performed as described in Example 4. Each column of a 96-well plate was coated with a biotinylated adapter containing a unique index. Using a second 96-well PCR plate, 37 different cfDNAs in 30 μl were dA-tailed at 37°C for 15 minutes in the presence of 10 μl of Klenow master mix, followed by Klenow enzyme inactivation at 75°C for 5 minutes. Several cfDNAs were used in multiple wells, for a total of 94 wells containing cfDNA; 2 wells served as no-template controls. The dA-tailed cfDNA mixture was transferred to the first PCR plate and ligated to the bound biotinylated adapters in the presence of 10 μl of Rapid Ligase Master Mix 1 at 25°C using a PCT-225 Quadruple Gradient Circulator (BioRad, Hercules, CA). 10 μl of Ligation Master Mix 2, customized for each indexed adaptor, was added and ligated at 5°C for 15 minutes. Unbound DNA was removed, and the bound DNA-biotinylated adaptor complex was washed five times with TE buffer. 50 μl of PCR Master Mix was added to each well, and the adaptor-ligated DNA was amplified and SPRI cleaned as described in Example 4. The library was diluted and analyzed using a HiSens BA chip.

Correlations between the amount of purified cfDNA used to prepare sequencing libraries and the resulting amount of library product were obtained for 61 clinical samples prepared using the ABB method ( FIG27A ) and 35 research samples prepared using the no-repair SS 1-step method ( FIG27B ). These data show that the correlation was significantly greater for libraries prepared using the no-repair SS 1-step method ( R2 = 0.5826; FIG27A ) when compared to the correlation obtained for libraries prepared using the shortened method described in Example 2 ( R2 = 0.1534; FIG27B ). Note: The cfDNA samples in this comparison were not identical, as clinical samples were not available for research and development. However, these results demonstrate that the no-repair SS 1-step method consistently exhibits greater correlation between cfDNA input and library output than the ABB method. Subsequently, the correlations of the three methods—ABB, no-repair 2-step, and no-repair SS 1-step—were compared using serially diluted amounts of the same purified cfDNA for all three methods. As shown in Figure 28, the best correlation was obtained when the library was prepared according to the SS 1-step method ( ^R2 = 0.9457; Δ), followed by the 2-step method ( ^R2 = 0.7666; □) and the ABB method with significantly lower correlation ( ^R2 = 0.0386; ◇). These data show that compared with methods that end-modify [DNA repair and phosphorylation] cfDNA, the no-repair method, whether in solution or on a solid surface, provides consistent and predictable yields, whether including or excluding purification of repaired DNA and dA-tailed products.

The time taken to prepare libraries using the solid surface method described in this example is several times shorter than the time taken to prepare sequencing libraries using the shortened method. For example, 10 to 14 samples can be manually prepared using the ABB method in approximately 4 hours, while 96 or 192 libraries can be manually prepared using the SS 1-step method in 4 and 5 hours, respectively. Furthermore, the SS 1-step method can be easily automated to prepare libraries for multiple 96-plex sequencing runs using NGS technology. Therefore, the SS method is suitable for commercial automated, high-throughput sample analysis.

Analysis of DNA libraries showed that solid-phase sequencing library preparation of unrepaired cfDNA provides high-yield and high-quality sequencing libraries that can be configured for automated processes to further accelerate the analysis of samples requiring massively parallel sequencing using NGS technology. Solid surface methods are suitable for repaired DNA.

Example 6

Multiplex sequencing of libraries prepared using the 1-step SS method

In a multiplexed manner, each Illumina HySeq sequencer flow cell lane was sequenced with six different indexed samples for library samples (Example 20) prepared on a 96-well plate by the SS 1-step method. The prepared library was sequenced as described in Example 2. The data shown in Figure 29 compare the indexing efficiency, as assessed by multiple sequencing between 2 steps (filled bars) and SS 1 step (open bars). These data show that preparing the library on a solid surface does not compromise indexing efficiency. Figures 30A and 30B show the total percentage of sequence tags mapped to each human chromosome when the sequencing library was prepared according to the 1-step solid surface method (% chromosome N; Figure 30A); and Figure 30B (R2=0.9807) shows the percentage of sequence tags as a function of chromosome size. Figures 30A and 30B show that the GC bias of the SS 1-step method is the same as that of the 2-step method because both processes use DNA repair-free sample preparation enzymatics.

Figure 31 shows the percentage of sequence tags mapped to chromosome Y relative to those mapped to chromosome X, obtained from sequencing 42 libraries prepared using the SS 1-step method with indexed adapters and sequenced in multiplex mode using ILlumina sequencing by synthesis with reversible terminator technology. The data clearly distinguish between samples obtained from pregnant women carrying male fetuses and those obtained from pregnant women carrying female fetuses.

Example 7

Sample processing and DNA extraction

Peripheral blood samples were collected from pregnant women in the first or second trimester who were considered at risk for fetal aneuploidy. Written consent was obtained from each participant before blood draw. Blood was collected before amniocentesis or chorionic villus sampling. Karyotyping was performed on the chorionic villus or amniocentesis samples to determine the fetal karyotype.

Peripheral blood was drawn from each subject and collected in an ACD tube. One blood sample (approximately 6 to 9 ml/tube) was transferred to a 15 ml low-speed centrifuge tube. The blood was centrifuged at 2640 rpm and 4°C for 10 minutes using a Beckman Allegra 6R centrifuge and a GA3.8 rotor.

For cell-free plasma extraction, the upper plasma layer was transferred to a 15 ml high-speed centrifuge tube and centrifuged at 16,000 x g at 4°C for 10 minutes using a Beckman Coulter Avanti J-E centrifuge and a JA-14 rotor. Two centrifugation steps were performed within 72 hours of blood collection. Cell-free plasma was stored at -80°C and thawed only once before DNA extraction.

Cell-free DNA was extracted from cell-free plasma using the QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer's instructions. Five milliliters of buffer AL and 500 μl of Qiagen protease were added to 4.5 ml to 5 ml of cell-free plasma. The volume was adjusted to 10 ml with phosphate-buffered saline (PBS), and the mixture was incubated at 56°C for 12 minutes. The precipitated cfDNA was separated from the solution using multiple columns by centrifugation at 8,000 RPM in a Beckman microcentrifuge. The columns were washed with AW1 and AW2 buffers, and the cfDNA was eluted with 55 μl of nuclease-free water. Approximately 3.5 to 7 ng of cfDNA was extracted from the plasma sample.

All sequencing libraries were prepared from approximately 2 ng of purified cfDNA extracted from maternal plasma. The library preparation was performed as follows using the reagent NEBNext ^™ DNA Sample Prep DNA Reagent Set 1 (item number E6000L; New England Biolabs, Ipswich, Massachusetts). Because cell-free plasma DNA is essentially fragmented, the plasma DNA sample was no longer fragmented by nebulization or sonication. The overhangs of approximately 2 ng of purified cfDNA fragments contained in 40 μl were converted to phosphorylated blunt ends according to the End Repair Module by incubating cfDNA in a 1.5 ml microcentrifuge tube with 5 μl 10X phosphorylation buffer provided in NEBNext ^™ DNA Sample Prep DNA Reagent Set 1, 2 μl deoxynucleotide solution mixture (each dNTP has 10 mM), 1 μl of a 1:5 dilution of DNA polymerase I, 1 μl T4 DNA polymerase, and 1 μl T4 polynucleotide kinase at 20°C for 15 minutes. The enzymes were then heat-inactivated by incubating the reaction mixture at 75° C. for 5 minutes. The mixture was cooled to 4° C., and dA-tailing of the blunt-ended DNA was completed using 10 μl of dA-tailing Master Mix containing Klenow Fragment (3' to 5' exo-) (NEBNext ^™ DNA Sample Prep DNA Reagent Set 1) and incubated at 37° C. for 15 minutes. Subsequently, the Klenow Fragments were heat-inactivated by incubating the reaction mixture at 75° C. for 5 minutes. After inactivation of the Klenow fragments, the Illumina adapters (Non- ^Index Y-Adaptors) were ligated to the dA-tailed DNA using 1 μl of a 1:5 dilution of Illumina Genomic Adaptor Oligo Mix (Article No. 1000521; Illumina Inc., Hayward, CA) using 4 μl of T4 DNA ligase provided in NEBNext™ DNA Sample Prep DNA Reagent Set 1 by incubating the mixture at 25°C for 15 minutes. The mixture was cooled to 4°C, and the adapter-ligated cfDNA was purified from unligated adapters, adapter dimers, and other reagents using magnetic beads provided in the Agencourt AMPure XP PCR Purification System (Article No. A63881; Beckman Coulter Genomics, Danvers, MA). Eighteen cycles of PCR were performed to selectively enrich for adaptor-ligated cfDNA using High-Fidelity Master Mix (Finnzymes, Woburn, MA) and Illumina PCR primers complementary to the adaptors (Part Nos. 1000537 and 1000537). Adapter-ligated DNA was subjected to PCR (98° C. for 30 sec; 18 cycles of 98° ^C. for 10 sec, 65° C. for 30 sec, and 72° C. for 30 sec; final extension at 72° C. for 5 min and hold at 4° C.) using Illumina genomic PCR primers (Part Nos. 100537 and 1000538) and Phusion HF PCR Master Mix provided in NEBNext™ DNA Sample Prep DNA Reagent Set 1 according to the manufacturer's instructions. The amplified products were purified using the Agencourt AMPure XP PCR purification system (Agencourt Bioscience Corporation, Beverly, MA) according to the manufacturer's instructions (available at www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf). The purified amplified products were eluted in 40 μl of Qiagen EB buffer, and the amplified libraries were analyzed for concentration and size distribution using the Agilent DNA 1000 Kit of the 2100 Bioanalyzer (Agilent technologies Inc., Santa Clara, CA).

The amplified DNA was sequenced using the Illumina Genome Analyzer II, obtaining 36bp single-end reads. To identify a sequence as belonging to a specific human chromosome, only approximately 30bp of random sequence information is required. Longer sequences can uniquely identify more specific targets. In this case, numerous 36bp reads were obtained, covering approximately 10% of the genome. Once the sample was sequenced, Illumina Sequencer Control Software transferred the image and base call files to a Unix server running Illumina Genome Analyzer Pipeline software version 1.51. The Illumina "Gerald" program was run to align sequences to a reference human genome derived from the hg18 genome provided by the National Center for Biotechnology Information (NCBI36/hg18, available at http://genome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260 105). Sequence data generated from the above program that were uniquely aligned to this genome were read from the Gerald output (export.txt file) by running a program (c2c.pl) on a computer running the Linnux operating system. Sequence alignments with base mismatches were allowed and included in the alignment count only if they were uniquely aligned to this genome. Sequence alignments with identical start and end coordinates (duplicates) were excluded.

The 36bp tags with 2 or less mispairings are mapped uniquely to the human genome. All mapped tags are counted and included in the calculation of the chromosome dosage of test and qualified samples. From the base 0 of chromosome Y to base 2x ¹⁰ , base 10x ¹⁰ to base 13x ¹⁰ and base 23x ¹⁰ to the zone at the end are excluded from the analysis exactly because the tags obtained from male and female fetuses are all mapped to these zones of chromosome Y.

It should be noted that some variation in the total number of sequence tags mapped to individual chromosomes across samples sequenced in the same round (inter-chromosomal variability), but substantially greater variation was noted between different rounds of sequencing (inter-sequencing process variability).

Example 8

Dosages and changes for chromosomes 13, 18, 21, X, and Y

To examine the extent of inter-chromosomal and inter-sequencing variability in the number of sequence tags mapped for all chromosomes, plasma cfDNA obtained from peripheral blood of 48 volunteer pregnant subjects was extracted and sequenced as described in Example 7 and analyzed as follows.

Determine the total number (sequence tag density) that is mapped to each chromosome sequence tag.Alternatively, the number of the sequence tags mapped can be normalized to the length of this chromosome, to produce a sequence tag density ratio.Being normalized to the length of chromosome is not a necessary step, but can carry out separately and reduce the digit of the numeral in a number thereby it is simplified for artificial interpretation.The chromosome length that can be used for these sequence tag count normalizations can be the length that provides at world website genome.ucsc.edu/goldenPath/stats.html#hg18 place.

The sequence label density that obtains for each chromosome is associated with the sequence label density of each remaining chromosome, to obtain a qualified chromosome dosage, this dosage is calculated as the sequence label density for chromosome interested (for example chromosome 21) and the ratio for the sequence label density of remaining chromosome (i.e. chromosome 1-20,22 and X).Table 9 provides an example of the qualified chromosome dosage that calculates for chromosome interested 13,18,21,X and Y, and this dosage is measured in one of a qualified sample.Chromosome dosage has been measured for all chromosomes in all samples, and the average dose for chromosome interested 13,18,21,X and Y in the qualified samples is provided in table 10 and table 11, and is illustrated in Figure 32-36.Figure 32 to 36 also illustrates that the chromosome dosage of each chromosome interested in the chromosome dosage qualified sample of test sample provides a kind of measuring that changes on the total number of the sequence label of (relative to each remaining chromosome) mapping for each chromosome interested. Therefore, a qualified chromosome dose can identify the following chromosome or a group of chromosomes, that is, the normalizing chromosome whose variability between samples is best approximated to the variability of the chromosome of interest, and this normalizing chromosome will serve as the ideal sequence for normalizing the values of further statistical evaluations. Figures 37 and 38 depict the calculated average chromosome doses determined for chromosomes 13, 18, and 21, as well as chromosomes X and Y in a qualified sample population.

In some cases, the best normalizing chromosome may not have the lowest variability, but may have a distribution of qualified doses that best distinguishes one or more test samples from the qualified samples, i.e., the best normalizing chromosome may not have the lowest variability, but may have the greatest distinguishability. Thus, distinguishability takes into account the variation in chromosome doses as well as the distribution of doses in the qualified samples.

Tables 10 and 11 provide the coefficient of variation as a measure of variability and t-test values as a measure of the distinguishability of chromosomes 18, 21, X, and Y, where smaller t-test values indicate greater distinguishability. The distinguishability of chromosome 13 was determined as the ratio of the difference between the mean chromosome dose in qualified samples and the dose of chromosome 13 in the T13 test sample alone to the mean standard deviation of the qualified doses.

Qualified chromosome doses also serve as the basis for determining thresholds when identifying aneuploidy in a test sample as described below.

Table 9. Qualified chromosome doses for chromosomes 13, 18, 21, X, and Y (n=1; sample number 11342, 46XY)

Table 10. Qualified chromosome doses, variations, and resolution for chromosomes 21, 18, and 13

Table 11. Qualified chromosome doses, variations, and resolvability for chromosomes 13, X, and Y

Diagnostic examples for T21, T13, T18, and one case of Turner syndrome obtained using normalized chromosomes, chromosome doses, and resolution for the chromosome of interest are described in Example 9.

Example 9

Diagnosis of fetal aneuploidy using normalized chromosomes

To adapt the use of chromosome dosage to assess aneuploidy in biological test samples, maternal blood test samples were obtained from pregnant volunteers and cfDNA was prepared and sequenced and analyzed as described in Examples 1 and 2.

Trisomy 21

Table 12 provides the dosage calculated for chromosome 21 in an exemplary test specimen (#11403).The threshold value calculated for the positive diagnosis of T21 is set at the standard deviation place of the mean value>2 apart from these qualified (normal) samples.The diagnosis of T21 is based on the chromosome dosage in the test specimen and is given larger than the threshold value of setting.Chromosome 14 and 15 have been used as the normalization chromosome with independent result of calculation, to show that the chromosome with minimum variability (such as chromosome 14) or with maximum resolvability (such as chromosome 15) can be used to identify aneuploidy.The chromosome dosage used to calculate has identified 13 T21 samples, and confirmed that these aneuploidy samples are T21 by karyotype.

Table 12. Chromosome Dosage for T21 Aneuploidy (Sample #11403, 47XY+21)

Trisomy 18

Table 13 provides the dosage calculated for chromosome 18 in a test specimen (#11390).The threshold value calculated for the positive diagnosis of T18 is set as the standard deviation of the meansigma methods＞2 that leave qualified (normal) sample.The diagnosis of T18 is based on the chromosome dosage in the test specimen and is given larger than the threshold value of setting.Chromosome 8 is used as the normalization chromosome.In this example, chromosome 8 has the lowest variability and maximum distinguishability.Chromosome dosage has been identified 18 T18 samples, and is confirmed to be T18 by karyotype.

These data suggest that a normalized chromosome can have the lowest variability and greatest distinguishability.

Table 13. Chromosome Doses for T18 Aneuploidy (Sample #11390, 47XY+18)

Trisomy 13

Table 14 provides the dosage calculated for chromosome 13 in a test sample (#51236).The threshold value calculated for the positive diagnosis of T13 is set as the standard deviation of the mean value>2 of the qualified sample.The diagnosis of T13 is based on the chromosome dosage in the test sample being given larger than the threshold value of setting.Chromosome dosage has been calculated for chromosome 13 using chromosome 5 or 3,4,5 and 6 chromosome groups as normalizing chromosomes.A T13 sample has been identified.

Table 14. Chromosome Dosage for T13 Aneuploidy (Sample #51236, 47XY+13)

The sequence tag density of chromosomes 3 to 6 is the average tag count of chromosomes 3 to 6.

The data indicate that the combination of chromosomes 3, 4, 5, and 6 provides a variability lower than that of chromosome 5, and a maximum resolvability greater than that of any of the other chromosomes.

Therefore, one set of chromosomes can be used as normalizing chromosomes to determine chromosome dosage and identify aneuploidy.

Turner syndrome (monosomy X)

Table 15 provides the calculated doses for chromosomes X and Y in one test sample (#51238). The calculated thresholds for a positive diagnosis of Turner syndrome (monosomy X) were set at <-2 standard deviations from the mean of qualified (normal) samples for the X chromosome and <-2 standard deviations from the mean of qualified (normal) samples for the absence of the Y chromosome.

Table 15. Chromosome Doses for Turner (XO) Aneuploidy (Sample #51238, 45X)

A sample having an X chromosome dose less than a set threshold is identified as having less than one chromosome X. The same sample is determined to have one Y chromosome dose less than a set threshold, indicating that the sample does not have a Y chromosome. Thus, a combination of X and Y doses is used to identify Turner syndrome (monosomy X) samples.

Therefore, the provided method enables determination of chromosomal CNV. Specifically, the method enables determination of over-represented and under-represented chromosomal aneuploidies by performing massively parallel sequencing of maternal plasma cfDNA and identifying normalized chromosomes for statistical analysis of sequencing data. The sensitivity and reliability of the method allow accurate determination of aneuploidies in the first and second trimesters.

Example 10

Determination of partial aneuploidy

The use of sequence dosing was applied to assess partial aneuploidy in a cfDNA biological test sample prepared from plasma and sequenced as described in Example 7. The sample was confirmed by karyotyping to be from a subject with a partial deletion of chromosome 11.

The analysis of the sequencing data for part aneuploidy (chromosome 11, i.e. the partial deletion of q21-q23) is as described for the chromosome aneuploidy in the example before and is carried out.In a test sample, sequence tag is shown to the mapping of chromosome 11 a notable loss (data not shown) of the tag count between base pair 81000082-103000103 in the long arm of chromosome for the tag count obtained relative to the corresponding sequence of the chromosome 11 in qualified samples.Used the sequence tag (810000082-103000103bp) of the sequence interested that is mapped to chromosome 11 in each qualified sample and the sequence tag (i.e. qualified sequence tag density) that is mapped to all 20 megabase fragments in the whole genome of qualified samples to determine the ratio of qualified sequence dosage as the tag density in all qualified samples. Calculate average sequence dosage, standard deviation and coefficient of variation for all 20 megabase fragments in whole genome, and the 20-megabase sequence with minimum variability is identified as the normalization sequence (13000014-33000033bp) on chromosome 5 (referring to Table 16), this normalization sequence is used to calculate the dosage (referring to Table 17) for sequence interested in test specimen.Table 16 provides the sequence dosage of sequence interested (810000082-103000103bp) on chromosome 11 in test specimen, and this sequence dosage is calculated as the sequence tag mapped to sequence interested and the ratio of the sequence tag mapped to the normalization sequence identified. Figure 40 shows the sequence dosage for sequence interested and the test specimen (◇) in 7 qualified samples (○) for corresponding sequence. Mean value is shown by solid line, and the threshold value calculated for the positive diagnosis of part aneuploidy is shown by dotted line, and it is set at apart from mean value 5 standard deviations. The diagnosis of partial aneuploidy was made based on the sequence dosage in the test sample being less than a set threshold. Karyotyping confirmed that the test sample had a deletion q21-q23 on chromosome 11.

Therefore, in addition to identifying chromosomal aneuploidies, the methods of the present invention can also be used to identify partial aneuploidies.

Table 16. Qualified normalizing sequences, dosages, and variations for sequence Chr11:81000082-103000103 (Qualified samples n=7)

Table 17. Sequence doses for the sequence of interest on chromosome 11 (81000082-103000103) (Test Sample 11206)

Example 11

Demonstration of aneuploidy detection

The sequence data obtained for the samples illustrated in Examples 2 and 3 and shown in Figures 32 to 36 were further analyzed to demonstrate the sensitivity of the method in successfully identifying the aneuploidy in maternal samples. The normalized chromosome dosage for chromosomes 21, 18, 13, X, and Y was analyzed as a distribution (Y-axis) relative to the standard deviation from the mean, and illustrated in Figures 41 A-41E. The normalized chromosome used is illustrated as the denominator (X-axis).

Figure 41(A) shows a distribution of chromosome doses relative to standard deviations for chromosome 21 doses in an unaffected sample (o) and a trisomy 21 sample (T21; Δ) when chromosome 14 is used as a normalizing chromosome for chromosome 21. Figure 41(B) shows a distribution of chromosome doses relative to standard deviations for chromosome 18 doses in an unaffected sample (o) and a trisomy 18 sample (T18; Δ) when chromosome 8 is used as a normalizing chromosome for chromosome 18. Figure 41(C) shows a distribution of chromosome doses relative to standard deviations for chromosome 13 doses in an unaffected sample (o) and a trisomy 18 sample (T13; Δ) using the average sequence tag density of a set of chromosomes 3, 4, 5, and 6 as normalizing chromosomes to determine the chromosome dose for chromosome 13. Figure 41 (D) shows a distribution of chromosome doses relative to standard deviations from the mean for chromosome X doses in unaffected female samples (o), unaffected male samples (Δ), and monosomy X samples (XO;+) when chromosome 4 is used as the normalizing chromosome for chromosome X. Figure 41 (E) shows a distribution of chromosome Y doses relative to standard deviations from the mean for unaffected male samples (o), unaffected female samples (Δ), and monosomy X samples (+) when the average sequence tag density of a chromosome group of 1 to 22 and X is used as the normalizing chromosome to determine the chromosome dose of chromosome Y.

The data show that trisomy 21, trisomy 18, and trisomy 13 are clearly distinguishable from unaffected (normal) samples. Monosomy X samples are easily identified when they have a significantly lower chromosome X dose than that of unaffected female samples ( FIG. 41(D) ) and when they have a significantly lower chromosome Y dose than that of unaffected male samples ( FIG. 41(E) ).

Thus, the provided methods are sensitive and specific for determining the presence or absence of chromosomal aneuploidy in a maternal blood sample.

Example 12

Massively parallel DNA sequencing of cell-free fetal DNA from maternal blood to determine fetal chromosomal abnormalities Euploidy: Test set 1 independent of training set 1

This study was conducted by qualified, designated clinical investigators at 13 U.S. clinical sites between April 2009 and October 2010 under a human subjects scientific research plan approved by each institution's institutional review board (IRB). Written consent was obtained from each subject prior to study participation. The scientific research plan was designed to provide blood samples and clinical data to support the development of non-invasive prenatal genetic diagnostic methods. Pregnant women aged 18 years or older were eligible to participate. Blood was collected before the procedure for patients undergoing clinically indicated chorionic villus sampling (CVS) or amniocentesis, and fetal karyotype results were also collected. Peripheral blood samples (two tubes or approximately 20 mL total) were drawn from all subjects and placed in acid citrate dextrose (ACD) tubes (Becton Dickinson). All samples were deidentified and assigned an anonymous patient ID number. Blood samples were shipped overnight to the laboratory in temperature-controlled shipping containers provided by the institute. The time spent between blood draw and sample receipt was recorded as part of sample placement.

Site study coordinators entered clinical data related to the patient's current pregnancy status and history into the study case report form (CRF) using an anonymous patient ID number. Cytogenetic analysis of fetal karyotype was performed on samples from noninvasive prenatal procedures at each laboratory, and the results were also recorded in the study CRF. All data obtained on the CRF were entered into the laboratory's clinical database. Cell-free plasma was obtained from a single blood tube using a two-step centrifugation method 24 to 48 hours after venipuncture sampling. Plasma from a single blood tube was sufficient for sequencing analysis. Cell-free DNA was extracted from the cell-free plasma using the QIAamp DNA Blood Mini kit (Qiagen) according to the manufacturer's instructions. Because these cell-free DNA fragments are known to be approximately 170 base pairs (bp) in length (Fan et al., Clin Chem 56:1279-1286 [2010]), DNA fragmentation was not required before sequencing.

For the samples of this training group, cfDNA was sent to Prognosys Biosciences, Inc. (La Jolla, CA) for sequencing library preparation (cfDNA blunt-ended and ligated to common adapters) and sequenced using an Illumina Genome Analyzer IIx instrument (http://www.illumina.com/) using the standard manufacturer's scientific protocol. Single-end reads of 36 base pairs were obtained. After sequencing was completed, all base call files were collected and analyzed. For the test group samples, sequencing libraries were prepared and sequenced on an Illumina Genome Analyzer IIx instrument. The preparation of the sequencing libraries was performed as follows. The full-length scientific protocol described is primarily the standard protocol provided by Illumina and differs from the Illumina scientific protocol only in the purification of the amplified library. The Illumina scientific protocol indicates that the amplified library is purified using gel electrophoresis, while the scientific protocol described herein uses magnetic beads for the same purification steps. About 2 ng of purified cfDNA extracted from maternal plasma was used to prepare a primary sequencing library, which was mainly performed using the NEBNext ^™ DNA Sample Prep DNA Reagent Set 1 (Part No. E6000L; New England Biolabs, Ipswich, MA) according to the manufacturer's instructions. Except for the use of Agencourt magnetic beads and reagents instead of purification columns for the final purification of the adaptor-ligated products, all steps were performed according to the scientific experimental plan with the NEBNext ^™ reagents for sample preparation of genomic DNA libraries (sequencing was performed using GAII). NEBNext ^™ NEBNext ^™ was mainly performed according to that provided by Illumina, which is available at grcf.jhml.edu/hts/protocols/11257047_ChIP_Sample_Prep.pdf.

The overhangs of approximately 2 ng of purified cfDNA fragments contained in 40 μl were converted to phosphorylated blunt ends by incubating the cfDNA in a 1.5 ml microcentrifuge tube with 5 μl of 10X phosphorylation buffer provided in NEBNext ^™ DNA Sample Prep DNA Reagent Set 1, 2 μl of a deoxynucleotide solution mixture (10 mM each dNTP), 1 μl of a 1:5 dilution of DNA polymerase I, 1 μl of T4 DNA polymerase, and 1 μl of T4 polynucleotide kinase for 15 minutes at 20°C, according to the End Repair Module. The sample was cooled to 4°C and purified using a QIAQuick column provided in the QIAQuick PCR Purification Kit (QIAGEN Inc., Valencia, CA). The 50 μl reaction solution was transferred to a 1.5 ml microcentrifuge tube, and 250 μl of Qiagen Buffer PB was added. The 300 μ l obtained is placed in a QIA quick column and centrifuged at 13,000RPM for 1 minute in a microcentrifuge. The column is washed with 750 μ l of Qiagen Buffer PE and centrifuged again. Residual ethanol is removed by centrifuging again for 5 minutes at 13,000RPM. DNA is eluted by centrifugation in 39 μ l of Qiagen Buffer EB. The dA tailing master mix containing Klenow fragment (3 ' to 5 ' exo-) (NEBNext ^™ DNA Sample Prep DNA Reagent Set 1) of 16 μ l is used to complete the dA tailing of the DNA of 34 μ l blunt ends, and according to the dA- tailing module (dA-Tailing Module) of manufacturers, it was hatched at 37 ℃ for 30 minutes. The sample is cooled to 4 ℃, and the column that provides in MinElute PCR Purification Kit (QIAGEN Inc., Valencia, CA) is used to purify. 50 μ l reaction solution is transferred to 1.5 ml microcentrifuge tube, and the Qiagen buffer PB (QiagenBuffer PB) of 250 μ l is added. 300 μ l is transferred to a MinElute column and centrifuged at 13,000 RPM for 1 minute in a microcentrifuge. The column is washed with 750 μ l of Qiagen buffer (PE Qiagen Buffer PE) and centrifuged again. Residual ethanol is removed by centrifugation at 13,000 RPM for 5 minutes. DNA is eluted by centrifugation in 15 μ l of Qiagen Buffer EB. According to the Quick Ligation Module, ten microlitres of DNA eluate are incubated at 25 ℃ for 15 minutes with 1 μ l of 1:5 Illumina Genomic Adapter Oligo Mix (article number 1000521) diluent, 15 μ l of 2X Quick Ligation Reaction Buffer and 4 μ l of fast T4 DNA ligase. The sample was cooled to 4°C and subjected to a MinElute column as follows. One hundred and fifty microliters of Qiagen Buffer PE was added to the 30 μl reaction solution, and the entire volume was transferred to a MinElute column. The column was centrifuged in a microcentrifuge at 13,000 RPM for 1 minute. The column was washed with 750 μl of Qiagen Buffer PE and centrifuged again. Residual ethanol was removed by centrifugation at 13,000 RPM for another 5 minutes. DNA was eluted by centrifugation in 28 μl of Qiagen Buffer EB. Twenty-three microliters of the adapter-ligated DNA eluate was subjected to 18 cycles of PCR (98° ^C. for 30 seconds; 18 cycles of 98° C. for 10 seconds, 65° C. for 30 seconds, and 72° C. for 30 seconds; final extension at 72° C. for 5 minutes and hold at 4° C.) using Illumina genomic PCR primers (Article Nos. 100537 and 1000538) and Phusion HFPCR Master Mix provided in NEBNext™ DNA Sample Prep DNA Reagent Set 1 (according to the manufacturer's instructions). The amplified product was purified using the Agencourt AMPure XP PCR Purification System (Agencourt Bioscience Corporation, Beverly, MA) according to the manufacturer's instructions (available at www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf). The Agencourt AMPure XP PCR Purification System removed unligated dNTPs, primers, primer dimers, salts, and other contaminants, and recovered amplicons larger than 100 bp. The purified amplified product was eluted from the Agencourt beads in 40 μl of Qiagen EB buffer, and the library size distribution was analyzed using the Agilent DNA 1000 Kit on the 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA). For both the training and test sample sets, single-sided reads of 36 base pairs were sequenced.

Data analysis and sample classification

Sequence reads of 36 bases in length were compared with the human genome assembly hg18 obtained from the UCSC database (http://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/). Comparison was performed using the Bowtie short sequence segment aligner (version 0.12.5) (Langmead et al., Genome Biol 10: R25 [2009]), which allows up to two base mismatches during the comparison process. Only reads that were clearly mapped to a single genomic position were included. The genomic sites mapped by the reads were counted and included in the calculation of chromosome dosage (see below). Regions on the Y chromosome where sequence tags from male and female fetuses were mapped without any distinction were excluded from the analysis (specifically, from base 0 to base 2x 10 ⁶ , base 10x 10 ⁶ to base 13x 10 ⁶ ; and base 23x 10 ⁶ to the end of the Y chromosome.)

The order-checking variation with round and round in the chromosome distribution of sequence reading can make the distribution of fetal aneuploidy to the sequence site mapped not obvious.In order to correct this variation, calculated a chromosome dosage, because the counting for the mapping site of the interested chromosome provided is normalized to the observed counting for the normalized chromosome sequence of pre-set.As explained before, a normalized chromosome sequence can be by a single chromosome or by one group of chromosome composition.In a sample subset in the training group of unaffected (i.e. qualified) sample, the normalized chromosome sequence is first identified as the diploid karyotype with interested chromosome 21,18,13 and X, considers each autosome in the ratio with the counting of our interested chromosome as potential denominator.The denominator chromosome (i.e. normalized chromosome sequence) is selected as the variation minimum of the chromosome dosage between the sequencing batch.Each interested chromosome is determined as having a significant normalized chromosome sequence (denominator) (table 10). Do not have single chromosome to be identified as a normalizing chromosome sequence for chromosome 13, because do not have a chromosome to be determined as the variation that has reduced the dosage of chromosome 13 in the sample, that is, the extension of the NCV value of chromosome 13 is not reduced to be enough to allow T13 aneuploidy to be correctly identified.Chromosome 2 to 6 is randomly selected and has tested the ability of the behavior of their imitation chromosome 13 as a group.Chromosome 2 to 6 group has been found to have substantially reduced the variation on the dosage for chromosome 13 in the training group sample, and therefore has been selected as the normalizing chromosome sequence for chromosome 13.As mentioned above, the variation on the chromosome dosage for chromosome Y is greater than 30, and independently thereof, single chromosome is used as the normalizing chromosome sequence when determining the dosage of chromosome Y.Chromosome 2 to 6 group has been found to have substantially reduced the variation on the dosage for chromosome Y in the training group sample, and therefore has been selected as the normalizing chromosome sequence for chromosome Y.

The chromosome dose for each chromosome of interest in a qualified sample provides a measure of the variation in the total number of sequence tags mapped for each chromosome of interest relative to the total number of sequence tags mapped for each remaining chromosome. Thus, a qualified chromosome dose can identify the chromosome or set of chromosomes, i.e., the normalized chromosome sequence that has a variability that best approximates the variability of the chromosome of interest in the sample and that will serve as the ideal sequence for the normalized value for further statistical evaluation.

The chromosome doses for all samples in the training set (ie, qualified and affected) also served as the basis for determining the thresholds when identifying aneuploidy in the test samples as described below.

Table 18. Normalizing chromosome sequences used to determine chromosome dosage

For each chromosome of interest in each sample of the test group, a normalized value is determined and is used to determine the presence or absence of aneuploidy. The normalized value is calculated as a chromosome dose that can be further calculated to provide a normalized chromosome value (NCV).

Chromosome dosage

For the test set, a chromosome dose was calculated for each chromosome of interest for each sample: 21, 18, 13, X, and Y. As provided in Table 18 above, the chromosome dose for chromosome 21 was calculated as the ratio of the number of tags in the test sample that mapped to chromosome 21 in the test sample to the number of tags in the test sample that mapped to chromosome 9 in the test sample; the chromosome dose for chromosome 18 was calculated as the ratio of the number of tags in the test sample that mapped to chromosome 18 in the test sample to the number of tags in the test sample that mapped to chromosome 8 in the test sample; the chromosome dose for chromosome 13 was calculated as the ratio of the number of tags in the test sample that mapped to chromosome 13 in the test sample to the number of tags in the test sample that mapped to chromosomes 2 to 6 in the test sample; the chromosome dose for chromosome X was calculated as the ratio of the number of tags in the test sample that mapped to chromosome X in the test sample to the number of tags in the test sample that mapped to chromosome 6 in the test sample; and the chromosome dose for chromosome Y was calculated as the ratio of the number of tags in the test sample that mapped to chromosome Y in the test sample to the number of tags in the test sample that mapped to chromosomes 2 to 6 in the test sample.

Normalized chromosome values

Using the chromosome dose for each chromosome of interest in each test sample and the corresponding chromosome dose determined in qualified samples of the training set, the normalized chromosome value (NCV) was calculated using the following equation:

Wherein and be the estimation training group mean value and standard deviation for the jth chromosome dosage accordingly, and be the jth chromosome dosage observed for test sample i.When chromosome dosage is carried out normalized distribution, NCV is equivalent to a statistical z score for these dosages.In the quantile-quantile drawing of the NCV from unaffected sample, do not observe the remarkable departure from linearity.In addition, the standard test for the normalization degree of NCV fails to reject the null hypothesis of normality.

For the test group, an NCV was calculated for each chromosome of interest, 21, 18, 13, X, and Y, for each sample. To ensure a safe and effective classification scheme, conservative boundaries were selected for aneuploidy classification. In order to classify the aneuploidy state of the autosomes, NCV was required to classify the chromosome as affected (i.e., aneuploidy for that chromosome); and NCV<2.5 was required to classify the chromosome as unaffected. Samples with an NCV between 2.5 and 4.0 for the autosomes were classified as "no judgment".

In the test, sex chromosome sorting was performed by applying NCV sequentially for both X and Y as follows:

If NCV Y > -2.0 male sample standard deviation from the mean, the sample was classified as male (XY).

If NCV Y < -2.0 standard deviation from the mean for male samples, and NCV Y > -2.0 standard deviation from the mean for female samples, the sample was classified as female (XX).

If NCV Y < -2.0 standard deviation from the mean for male samples and NCV Y < -3.0 standard deviation from the mean for female samples, the sample was classified as monosomy X, ie, Turner syndrome.

If the NCV did not meet any of the above criteria, the sample cup was classified as "no call" for gender.

result

Study Demographics

A total of 1,014 patients were enrolled between April 2009 and July 2010. The patient demographics, invasive procedure type, and karyotype results are summarized in Table 19. The mean age of the study population was 35.6 years (range 17 to 47 years) and the gestational age range was 6 weeks 1 day to 38 weeks 1 day (average 15 weeks 4 days). The overall incidence of abnormal fetal karyotype was 6.8%, with a T21 incidence of 2.5%. Of the 946 subjects with singleton pregnancies and karyotypes, 906 (96%) presented with at least one clinically recognized risk factor for fetal aneuploidy during the antenatal process. Even excluding those subjects with only high gestational age as their only indication, the data still demonstrated a very high false positive rate for current screening modalities. Ultrasound examinations performed with ultrasound revealed increased nuchal translucency, cystic lymphangioma, or other structural congenital anomalies, which were the most predictive abnormal karyotypes in this age group.

Table 19. Patient Demographics

*Includes fetal outcomes in multiple gestations, **Assessed and reported by clinicians

Abbreviations: AMA = advanced gestational age, NT = nuchal translucency

The distribution of diverse ethnic backgrounds exhibited in this study population is also shown in Table 19. Overall, 63% of the patients in this study were Caucasian, 17% were Hispanic, 6% were Asian, 5% were multiracial, and 4% were African American. It was noted that racial differences varied significantly between sites. For example, one site enrolled 60% Hispanic and 26% Caucasian subjects, while three clinical sites located in the same state enrolled no Hispanic subjects. As expected, no discernible differences were observed in our results by race.

Training Dataset 1

The training set study selected 71 samples from 435 samples collected between April 2009 and December 2009, which were initially accumulated. All subjects with affected fetuses (abnormal karyotype) in this first series of subjects were included for sequencing, as well as a random selection and a random number of unaffected subjects with appropriate samples and data. The clinical characteristics of the training set patients were consistent with the demographics of the overall study shown in Table 19. The gestational age range of the samples in the training set was from 10 weeks 0 days to 23 weeks 1 day. Thirty-eight people underwent CVS, 32 people underwent amniocentesis, and 1 patient did not have the type of invasive procedure specified (unaffected karyotype 46, XY). 70% of the patients were Caucasian, 8.5% were Hispanic, 8.5% were Asian, and 8.5% were multinational. For training purposes, six sequenced samples were removed from this set. Four samples were from subjects with twin pregnancies (discussed in detail below), one sample had T18, which was contaminated during preparation, and one sample had a fetal karyotype of 69,XXX, leaving 65 samples for the training set.

The number of single sequence sites (that is, the label with unique site identification in genome) changes from 2.2M in the early stage of this training group research to 13.7M (due to the improvement in sequencing technology over time) in the later stage. In order to monitor any potential change that chromosome dosage exceeds this 6 times of scope in unique site, different, unaffected samples were moved at the beginning and end of research. For the round of first 15 unaffected samples, the average number of unique sites is 3.8M and is respectively 0.314 and 0.528 for the average chromosome dosage of chromosome 21 and chromosome 18. For the round of rear 15 unaffected samples, the average number of unique sites is 10.7M and is respectively 0.316 and 0.529 for the average chromosome dosage of chromosome 21 and chromosome 18. Between the chromosome dosage of chromosome 21 and chromosome 18, along with the time of training group research, there is no statistical difference.

The training set NCVs for chromosomes 21, 18, and 13 are shown in FIG42 . The results shown in FIG42 are consistent with the assumption of normality that approximately 99% of diploid NCVs will fall within ±2.5 standard deviations of the mean. Of the 65 samples in this set, 8 samples with a clinical karyotype indicating T21 had NCVs ranging from 6 to 20. Four samples with a clinical karyotype indicating fetal T18 had NCVs ranging from 3.3 to 12, and two samples with a clinical karyotype indicating fetal trisomy 13 (T13) had NCVs of 2.6 and 4. The spread of NCVs among the affected samples is due to their dependence on the percentage of fetal cfDNA in the individual samples.

Similar to the autosomes, the mean and standard deviation of the sex chromosomes were determined within the training set.The threshold value of the sex chromosomes allowed 100% discrimination between male and female fetuses within the training set.

Test data set 1

After establishing the mean chromosome dose and the standard deviation from the mean with the training group, a test group of 48 samples was selected from the samples collected from a total of 575 samples between January 2010 and June 2010. One of the samples from a twin pregnancy was removed from the final analysis, leaving 47 samples in the test group. The personnel preparing the samples for sequencing and operating the equipment were blinded to the clinical karyotype information. The gestational age range was similar to that seen in the training group (Table 19). 58% of the invasive procedures were CVS, which is higher than the overall procedural demographics, but also similar to the training group. 50% of the subjects were Caucasians, 27% were Hispanics, 10.4% were Asians and 6.3% were African Americans.

In the test group, the number of unique sequence tags varies from about 13M to 26M. For unaffected samples, the chromosome doses are 0.313 and 0.527 for chromosome 21 and chromosome 18, respectively. For chromosome 21, chromosome 18, and chromosome 13, the test group NCVs are shown in Figure 43 and the classification is given in Table 20.

Table 20. Test group classification data Test group classification data

*MX is monosomy of the X chromosome with no evidence of the Y chromosome

Within the test set, 13/13 subjects with a karyotype indicative of fetal T21 were correctly identified as having NCVs ranging from 5 to 14. Eight/eight subjects with a karyotype indicative of fetal T18 were correctly identified as having NCVs ranging from 8.5 to 22. Within this test set, a single sample with C classified as T13 was classified as no call with an NCV of approximately 3.

For the test data set, all male samples were correctly identified, including samples with a complex karyotype of 46,XY + marker chromosomes (which could not be identified by cytogenetics) (Table 11). Nineteen of the twenty female samples were correctly identified, and one female sample was classified as no call. For the three samples with a karyotype of 45,X in the test set, two of the three were correctly identified as monosomy X, and one was classified as no call (Table 20).

twin

Four of the samples initially selected for the training set and one for the test set were from twin pregnancies. The threshold used here may be hampered by the varying amounts of cfDNA expected in the setting of a twin pregnancy. In the training set, the karyotype from one of the twin samples was monochorionic 47,XY+21. A second twin sample was fraternal, and amniocentesis was performed on each fetus individually. In this twin pregnancy, one fetus had a karyotype of 47,XY+21, while the other had a normal karyotype of 46,XX. In both cases, cell-free classification based on the method discussed above classified the samples as T21. The other two twin pregnancies in the training set were correctly classified as unaffected for T21 (all twins showed a diploid karyotype for chromosome 21). For the twin pregnancies in the test set, a karyotype (46,XX) was established only for twin B, and the algorithm correctly classified it as unaffected for T21.

in conclusion

These data demonstrate that massively parallel sequencing can be used to determine multiple abnormal fetal karyotypes from maternal blood. These data demonstrate that 100% correct classification of samples with trisomy 21 and trisomy 18 can be identified using independent test set data. Even in the case of fetuses with abnormal sex chromosome karyotypes, none of the samples were incorrectly classified using the proposed algorithm. Importantly, the algorithm also performed equally well in determining the presence or absence of T21 in a cohort of twin pregnancies. Furthermore, this study examined numerous consecutive samples from multiple centers, representing not only the range of abnormal karyotypes likely to be seen in a commercial clinical setting but also the importance of accurately classifying pregnancies unaffected by common trisomies, highlighting the unacceptably high false-positive rate in current prenatal screening. These data provide valuable insights into the significant potential for future use of this approach. Analysis of a subset of unique loci demonstrated an increase in the variance-consistent Poisson count statistic.

This data builds on the findings of Fan and Quake, who demonstrated that the sensitivity of noninvasively determining fetal aneuploidy from maternal plasma using massively parallel sequencing is limited only by counting statistics (Fan and Quake, PLoS One 5, e10439 [2010]). Because sequencing information is collected across the entire genome, this approach is able to determine any aneuploidy or other copy number variation, including insertions and deletions. The karyotype from one of the samples had a small deletion in chromosome 11 between q21 and q23, and when the sequencing data were analyzed in 500k base bins, a decrease of approximately 10% in the relative number of tags within a 25Mb region starting at q21 was observed. In addition, within the training set, three of the samples had minute karyotypes due to mosaicism in cytogenetic analysis. These karyotypes were: i) 47,XXX[9]/45,X[6], ii) 45,X[3]/46,XY[17], and iii) 47,XXX[13]/45,X[7]. Sample ii, which exhibited some XY-containing cells, was correctly classified as XY. Samples i (from the CVS procedure) and iii (from amniocentesis), which both exhibited a mixture of XXX and X cells by cytogenetic analysis (consistent with mosaic Turner syndrome), were classified as non-confirmed and monosomy X, respectively.

When testing the algorithm, another interesting data point was observed for chromosome 21 of one sample from the test group (Figure 43) with an NCV between -5 and -6. Although the sample was diploid on chromosome 21 by cytogenetics, the karyotype exhibited mosaicism with partial triploidy for chromosome 9: 47,XX+9[9]/46,XX[6]. Since chromosome 9 was used in the denominator to determine the chromosome dose of chromosome 21 (Table 18), this reduced the overall NCV value. The results provided in Example 13 below demonstrate the ability to determine fetal trisomy 9 in this sample using normalized chromosomes.

The conclusion of the sensitivity of these methods such as Fan is only correct when the algorithm used can take into account any random or systematic deviation brought by the sequencing method. If the sequencing data is not properly normalized, the analysis result obtained will be inferior to counting statistics. Chiu et al. note in their recent paper that the measurement results of chromosomes 18 and 13 obtained by their large-scale parallel sequencing method are inaccurate, and the conclusion is that more research is needed to apply the method to the determination of T18 and T13 (Chiu et al., BMJ 342:c7401[2011]). The method used in the paper of Chiu et al. simply used the number of sequence tags of the chromosome of interest in their case chromosome 21, and the number was normalized by the total number of tags in the sequencing round. The challenge of this approach is that the distribution of tags on each chromosome can be different from sequencing round to sequencing round, and therefore increases the overall change of aneuploidy determination measurement. In order to compare the result of Chiu's algorithm with the dosage of the chromosome used in this example, the test data of chromosomes 21 and 18 are reanalyzed using the method recommended by Chiu et al., as shown in Figure 44. Overall, a compression in the range of NCVs was observed for each of chromosomes 21 and 18, and a reduction in the ascertainment rate was observed, with 10/13 T21 and 5/8 T18 samples correctly identified from our test set using an NCV threshold of 4.0 for aneuploidy classification.

Ehrich et al. also focused only on T21 and used the same algorithm as Chiu et al. (Ehrich et al., AmJ Obstet Gynecol 204:205e1-e11[2011]). In addition, after observing a deviation in their test group z score measurement from the external reference data (i.e., training group), they retrained the test group to establish the classification boundary. Although this approach is feasible in principle, in practice it will be challenging to decide how many samples are required for training and how often retraining is needed to ensure the correctness of these classification data. One way to alleviate this problem is to include controls in each sequencing run that measure the baseline and calibrate the quantitative behavior.

The data obtained using this method show that when the algorithm for chromosome count data being normalized is optimized, large-scale parallel sequencing can determine multiple fetal chromosome abnormalities from the plasma of pregnant women. This method not only minimizes the random and systematic variation between the sequencing rounds for quantitative analysis, but also allows aneuploidy to be classified throughout the entire genome, with the most notable being T21 and T18. requiring larger samples to collect and test the algorithm for T13 determination. For this purpose, a prospective, blind, multi-location clinical study is being conducted to further demonstrate the diagnostic accuracy of this method.

Example 13

Determine the presence or absence of at least five different chromosomal anomalous events in all chromosomes of a single test sample. ploidy

To demonstrate the ability of this method to determine the presence or absence of any chromosomal aneuploidy in each group of maternal test samples (test group 1; Example 12), systematically determined normalized chromosome sequences were identified in unaffected test group samples (training group 1; Example 12), and these normalized chromosome sequences were used to calculate chromosome doses for all chromosomes of each test sample. Determining the presence or absence of any one or more different complete fetal chromosomal aneuploidies in each test and training group sample was achieved by sequencing information obtained from a single sequencing run performed on each individual sample.

Using the chromosome density, i.e., the number of sequence tags identified for each chromosome in each test set of samples described in Example 12, a systematically determined normalizing chromosome sequence consisting of a single chromosome or a group of chromosomes was determined by calculating a single chromosome dose for each of chromosomes 1-22, X, and Y. The systematically determined normalizing chromosome sequence for each of chromosomes 1-22, X, and Y was determined by systematically calculating the chromosome dose for each chromosome using each possible chromosome combination as the denominator. For example, for chromosome 21 as the chromosome of interest, the chromosome dose is calculated as the ratio of (i) the number of sequence tags obtained for chromosome 21 (the chromosome of interest) and (ii) the number of sequence tags obtained for each remaining chromosome to the sum of the number of tags obtained for all possible combinations of the remaining chromosomes (excluding chromosome 21), i.e.: 1, 2, 3, 4, 5, etc. up to 20, 21, 22, X, and Y; 1+2, 1+3, 1+4, 1+5, etc. up to 1+20, 1+22, 1+X, and 1+Y; 1+2+3, 1+2+4, 1+2+5, etc. up to 1+2+20, 1+2+22, 1+2+X, and 1+2+Y; 1+3+4, 1+3+5, 1+3+6, etc. up to 1+3+20, 1+3+22, 1+3+X, and 1+3+Y; 1+2+3+4, 1+2+3+5, 1+2+3+6, etc. until 1+2+3+20, 1+2+3+22, 1+2+3+X, and 1+2+3+Y; and so on, so that all possible combinations of all chromosomes 1-20, 22, X and Y are used as normalizing chromosome sequences (molecules) to determine all possible chromosome doses for each chromosome of interest in each of these qualified (aneuploidy) samples in the training set. Chromosome doses were determined in the same manner for chromosome 21 in all training set samples, and these normalizing chromosome sequences systematically determined for chromosome 21 were determined as a single or a group of chromosomes that resulted in a dose with minimal variability for 21 across all training samples. Repeated identical analysis to determine to use as single chromosome or the chromosome combination of the normalization chromosome sequence that systematically determines for each residue chromosome (comprising chromosome 13,18, X and Y), that is, used all possible chromosome combinations to determine in all training samples for the normalization sequence (single chromosome or one group of chromosome) of all other interested chromosome 1-12,14-17,19-20,22, X and Y.Therefore, all chromosomes are all regarded as interested chromosome, and for each in all chromosome in each unaffected sample in the training group, all have determined a normalization sequence that systematically determines.Table 21 provides single chromosome or the chromosome group that identifies as the normalization sequence that systematically determines for each interested chromosome 1-22, X and Y. As highlighted by table 21, for some interested chromosomes, the normalization chromosome sequence that systematically determines is determined as single chromosome (for example when chromosome 4 is interested chromosome), and for other interested chromosomes, the normalization chromosome sequence that systematically determines is determined as one group of chromosome (for example when chromosome 21 is interested chromosome).

Table 21. Systematically determined normalized chromosome sequences for all chromosomes

The mean, standard deviation (SD) and coefficient of variation (CV) of the systematically determined normalized chromosome sequences determined for each of all chromosomes are given in Table 22.

Table 22. Means, standard deviations (SDs), and variants for systematically determined normalized chromosome sequences. Number(CV)

Chromosome of interest average value SD CV 1 0.36637 0.00266 0.72% 2 0.31580 0.00068 0.22% 3 0.21983 0.00055 0.18% 4 0.98191 0.02509 2.56% 5 0.30109 0.00076 0.25% 6 0.21621 0.00059 0.27% 7 0.21214 0.00044 0.21% 8 0.25562 0.00068 0.27% 9 0.12726 0.00034 0.27% 10 0.24471 0.00098 0.40% 11 0.26907 0.00098 0.36% 12 0.12358 0.00029 0.23% 0.26023 0.00122 0.47% 14 0.09286 0.00028 0.30% 15 0.21568 0.00147 0.68% 16 0.25181 0.00134 0.53% 17 0.46000 0.00248 0.54% 0.10100 0.00038 0.38% 19 1.43709 0.02899 2.02% 20 0.19967 0.00123 0.62% 0.07851 0.00053 0.67% twenty two 0.69613 0.01391 2.00% 0.46865 0.00279 0.68% 0.00028 0.00004 14.97%

^a does not include trisomy

^bFemale fetus

The variation in chromosome dosage across all training samples (as reflected by the CV values) confirms the use of systematically determined normalized chromosome sequences to provide a large signal-to-noise ratio and dynamic range, allowing the determination of aneuploidy with high sensitivity and high specificity, as shown below.

To demonstrate the sensitivity and specificity of this method, chromosome doses for all chromosomes of interest 1-22, X, and Y were determined in each sample within the training set, and the corresponding, systematically determined normalizing chromosome sequences provided in Table 21 above were used for each of all samples within the test set described in Example 11.

Using the normalized chromosome sequence systematically determined for each chromosome of interest, the presence or absence of any fetal aneuploidy was determined in each training set of samples and in each test sample, i.e., it was determined whether each sample contained a complete fetal chromosomal aneuploidy for chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, X, and Y. Sequence information, i.e., the number of sequence tags, was obtained for all chromosomes in each training set of samples and in each test sample, and a single chromosome dose was calculated as described above for each chromosome in each training and test sample using the number of sequence tags obtained using the systematically determined normalized chromosome sequence (Table 21) corresponding to those determined in the test set. The number of sequence tags obtained for the systematically determined normalized chromosome sequence in each training sample was used to determine the chromosome dose for each chromosome in each training sample, and the number of sequence tags obtained for the systematically determined normalized chromosome sequence in each test sample was used to determine the chromosome dose for each chromosome in each test sample. To ensure safe and effective classification of aneuploidy, similarly conservative boundaries were selected as described in Example 12.

Training group results

Figure 45 shows a plot of the chromosome dose for chromosomes 21, 18, and 13 in the samples of the training set using a systematically determined normalizing chromosome sequence. When using a systematically determined normalizing chromosome sequence, i.e., the group of chromosomes 4+14+16+20+22, 8 samples where the clinical karyotype indicated T21 had an NCV between 5.4 and 21.5. When using a systematically determined normalizing chromosome sequence (i.e., the group of chromosomes 4+14+16+20+22), 8 samples where the clinical karyotype indicated T21 had an NCV between 5.4 and 21.5. When using a systematically determined normalizing chromosome sequence (i.e., the group of chromosomes 2+3+5+7), 4 samples where the clinical karyotype indicated T18 had an NCV between 3.3 and 15.3. The T21 sample of the training group is shown as the last 8 samples of the chromosome 21 data (O); the T18 sample of the training group is shown as the last 4 samples of the chromosome 18 data (Δ); and the T13 sample of the training group is shown as the last 2 samples of the chromosome 13 data (□).

These data demonstrate that normalized chromosome sequences can be used with high confidence to determine distinct, complete fetal chromosomal aneuploidies and correctly classify them. Since all samples with affected karyotypes have an NCV greater than 3, there is approximately a 0.1% probability that these samples are part of the unaffected distribution.

Similar to the autosomes, when the systematically determined normalized chromosome sequence (i.e., the chromosome 4+8 group) was used for chromosome X, and when the systematically determined normalized chromosome sequence (i.e., the chromosome 4+6 group) was used for chromosome Y, all female and male fetuses in the training set were correctly identified. In addition, all five monosomy X samples were identified. Figure 46A shows a graph of the NCV (X-axis) determined for the X chromosome and the NCV (Y-axis) determined for the Y chromosome for each sample in the training set. All samples with a monosomy X karyotype had an NCV value of less than -4.83. Those monosomy X samples with a karyotype consistent with a 45,X karyotype (complete or mosaic) had a Y NCV value close to zero, as expected. Female samples clustered around NCV=0 for both X and Y.

Test group results

Provided in Figure 47 is a drawing of the chromosome dosage for chromosomes 21, 18 and 13 in the test sample using the normalizing chromosome sequence determined systematically. When using the normalizing chromosome sequence determined systematically (i.e. the group of chromosome 4+14+16+20+22), 13 of the 13 samples wherein the clinical karyotype indicates T21 were correctly identified to have an NCV between 7.2 and 16.3. When using the normalizing chromosome sequence determined systematically (i.e. the group of chromosome 2+3+5+7), all 8 samples wherein the clinical karyotype indicates T18 were identified to have an NCV between 12.7 and 30.7. When using the normalizing chromosome sequence determined systematically (i.e. the group of chromosome 2+3+5+7), all 8 samples wherein the clinical karyotype indicates T18 were identified to have an NCV between 12.7 and 30.7. The T21 sample of the test group is shown as the last 13 samples of chromosome 21 data (O); the T18 sample of the test group is shown as the last 8 samples of chromosome 18 data (Δ); and the T13 sample of the test group is shown as the last sample of chromosome 13 data (□).

These data demonstrate that systematically determined, normalized chromosome sequences can be used to determine and correctly classify different complete fetal chromosomal aneuploidies with high confidence. Similar to the training set, all samples with affected karyotypes had an NCV greater than 7, indicating a very small likelihood that these samples are part of the unaffected distribution. (Figure 47).

Similar to the autosomes, all female and male fetuses in the test set were correctly identified when the systematically determined normalizing chromosome sequence (i.e., the set of chromosomes 4+8) was used for chromosome X, and when the systematically determined normalizing chromosome sequence (i.e., the set of chromosomes 4+6) was used for chromosome Y. In addition, all three monosomy X samples were identified. Figure 46B shows a plot of the NCV determined for chromosome X (X-axis) and the NCV determined for chromosome Y (Y-axis) for each sample in the test set.

As explained above, this method allows to determine in each sample the presence or absence of a complete or partial chromosomal aneuploidy of each chromosome 1-22, X and Y. Except measuring complete chromosomal aneuploidy T13, T18, T21 monosomy X, this method also measures the existence of trisomy 9 in one of the test specimens. When using the normalization chromosome sequence (i.e. the group of chromosome 3+4+8+10+17+19+20+22) measured by the system, for chromosome 9 interested, identified a sample (Figure 48) with an NCV of 14.4. This sample corresponds to the test specimen in example 12, and this test specimen is suspected to be aneuploidy (wherein chromosome 9 has been used as the normalization chromosome sequence in example 12) for chromosome 9 according to the malformed low dosage for chromosome 21.

The data show that 100% of samples have the sample of the clinical karyotype of indication T21, T13, T18, T9 and monosomy X and are correctly identified. Figure 49 shows the curve diagram of the NCV for each of chromosome 1-22 in each of 47 test samples. The median of NCV is normalized to zero. The data show that method of the present invention (comprising the normalization chromosome sequence determined using systemically) has determined the existence of the chromosomal aneuploidy of all 5 types present in this test group with 100% sensitivity and 100% specificity, and clearly points out that the method can identify any chromosomal aneuploidy for any one of chromosome 1-22, X and Y in any sample.

Example 14

Determining the presence or absence of partial fetal chromosomal aneuploidy: Identifying cat eye syndrome

DiGeorge syndrome (22q11.2 deletion syndrome), a condition caused by a defect in chromosome 22, leads to the poor development of several body systems. Medical problems commonly associated with DiGeorge syndrome include heart defects, poor immune system function, cleft palate, parathyroid gland abnormalities, and behavioral disorders. The number and severity of problems associated with DiGeorge syndrome vary greatly. Almost everyone with DiGeorge syndrome requires treatment from specialists in multiple fields.

To determine the presence or absence of a partial deletion of fetal chromosome 22, a blood sample was obtained by venipuncture of the mother, and cfDNA was prepared as described in the above example. Purified cfDNA was connected to an adaptor and subjected to cluster amplification using an Illumina cBot cluster station. Large-scale parallel sequencing was performed using a reversible dye terminator to generate millions of 36bp reads. These sequence reads were aligned with the human hg19 reference genome, and the reads uniquely mapped to the reference genome were counted as tags.

A group of qualified samples that are all known to be diploid for chromosome 22 (i.e., chromosome 22 or any part thereof is known to exist only in a diploid state) is first sequenced and analyzed to obtain multiple sequence tags for each of the 1000 segments of 3 megabases (Mb) (excluding region 22q11.2). If the human genome includes approximately 3 billion bases (3Gb), the 1000 segments of 3Mb each approximately constitute the remainder of the genome. Each of these 1000 segments can be served individually or as a group of segment sequences that are used to determine the normalized segment sequence of the segment of interest, i.e., the 3Mb region of 22q11.2. The number of sequence tags mapped to each single 1000bp segment is used individually to calculate the segment dose of the 3Mb region of 22q11.2. In addition, all possible combinations of two or more segments are used to determine the segment dose for the segment of interest in all qualified samples. The single 3 Mb segment or combination of two or more 3 Mb segments that resulted in the segment dose with the lowest variability across samples was selected as the normalizing segment sequence.

The number of sequence tags mapped to the segment of interest in each qualified sample is used to determine the segment dose in each qualified sample. The mean value and standard deviation of the segment dose in all qualified samples are calculated and used to determine a threshold value, and the segment dose determined in the test sample can be compared with these threshold values. Preferably, a normalized segment value (NSV) is calculated for all segments of interest in all qualified samples, and these values are used to set the threshold value.

Subsequently, the number of tags that map to the normalizing segment sequence in the corresponding test sample is used to determine the dose of the segment of interest in the test sample. A normalized segment value (NSV) is calculated for the segment in the test sample as described previously and the NCV of the segment of interest in the test sample is compared to a threshold value determined using qualified samples to determine the presence or absence of a 22q11.2 deletion in the test sample.

A test NCV < -3 indicates a loss in the segment of interest, ie, a partial deletion of chromosome 22 (22q11.2) is present in the test sample.

Example 15

Stool DNA test for predictive outcome in patients with stage II colon cancer

Approximately 30% of all stage II colon cancer patients will relapse and die from their disease. Stage II colon cancer patients who have experienced disease recurrence show significantly more losses on chromosomes 4, 5, 15q, 17q, and 18q. Specifically, losses on 4q22.1-4q35.2 have been shown to be associated with worse outcomes in stage II colon cancer patients. Determining the presence or absence of these genomic alterations may aid in selecting patients for adjuvant therapy (Brosens et al., Analytical Cellular Pathology/Cellular Oncology 33:95–104 [2010]).

To determine the presence or absence of one or more chromosome deletions in the 4q22.1 to 4q35.2 region in patients with stage II colon cancer, stool and/or plasma samples were obtained from the patient(s). Fecal DNA was prepared according to the method described in Chen et al., J Natl Cancer Inst 97:1124-1132 [2005]; and plasma DNA was prepared according to the method described in the above examples. DNA was sequenced according to the NGS method described herein, and the sequence information of the patient(s) was used to calculate the segment dose for one or more segments spanning the 4q22.1 to 4q35.2 region. The segment dose was determined using the normalized segment dose previously determined in a qualified stool and/or plasma sample group. The segment dose in the test sample (patient sample) was calculated, and the presence or absence of one or more partial chromosome deletions in the 4q22.1 to 4q35.2 region was determined by comparing each segment of interest with a threshold value set by the NSV in the qualified sample group.

Example 16

Whole-genome fetal aneuploidy detection by sequencing maternal plasma DNA: a prospective, Diagnostic accuracy in a blinded multicenter study

The method for determining the presence or absence of aneuploidy in a maternal test sample is used for prospective studies, and the accuracy of its diagnosis is shown as described below. Prospective studies further demonstrate that the inventive method is used for detecting fetal aneuploidy for multiple chromosomes across genomes. Blind research simulates actual pregnant women populations, in which fetal karyotype is unknown, and selects all samples with any abnormal karyotype for sequencing. The determined results of the classification made according to the inventive method are compared with the fetal karyotype obtained from an invasive procedure to determine the diagnostic capability of the method to multiple chromosome aneuploidies.

Overview of this example

In a prospective, blinded study, blood samples were collected from 2,882 women undergoing prenatal diagnostic procedures at 60 US sites (clinicaltrials.gov NCT01122524).

An independent biostatistician selected all singleton pregnancies with any abnormal karyotype and an equal number of randomly selected pregnancies with euploid karyotypes. Each sample was chromosomally classified according to the method of the present invention and compared with the fetal karyotype.

Within the analysis cohort of 532 samples, 89/89 cases with trisomy 21 (sensitivity 100% (95% CI 95.9-100)), 35/36 cases with trisomy 18 (sensitivity 97.2%, (95% CI 85.5-99.9)), 11/14 cases with trisomy 13 (sensitivity 78.6%, (95% CI 49.2-99.9)), 232/233 females (sensitivity 99.6%, (95% CI 97.6->99.9)), 184/184 males (sensitivity 100%, (95% CI 98.0-100)), and 15/16 cases with monosomy X (sensitivity 93.8%, (95% CI 69.8-99.8)) were classified. In unaffected subjects, there were no false positives for autosomal aneuploidies (100% specificity, (95% CI > 98.5-100)). In addition, fetuses with mosaicism for trisomy 21 (3/3), trisomy 18 (1/1), and monosomy X (2/7), three translocation trisomies, two other autosomal trisomies (20 and 16), and other sex chromosome aneuploidies (XXX, XXY, and XYY) were correctly classified.

These results further demonstrate the efficacy of this method for detecting fetal aneuploidy across multiple chromosomes using maternal plasma DNA. The high sensitivity and specificity for detecting trisomies 21, 18, 13, and monosomy X suggest that this method could be incorporated into existing aneuploidy screening algorithms to reduce unnecessary invasive procedures.

Materials and methods

The MELISSA (Maternal Blood is a Source of Accurate Diagnosis of Fetal Aneuploidy) study was conducted as a prospective, multicenter, observational study with a blinded, nested case:control analysis. Pregnant women aged 18 years and older who underwent an invasive prenatal procedure to determine the fetal karyotype were recruited (Clinicaltrials.gov NCT01122524). Eligibility criteria included pregnant women between 8 weeks and 22 days of gestation who met at least one of the following additional criteria: age ≥38 years; positive screening test results (serum analysis values and/or nuchal translucency (NT) measurements); presence of ultrasound markers associated with an increased risk of fetal aneuploidy; or previous pregnancy with an aneuploid fetus. Written consent was obtained from all women who agreed to participate.

Enrollment was conducted at 60 geographically dispersed medical centers in 25 states under protocols approved by each institution's institutional review board (IRB). Two clinical research organizations (CROs) (Quintiles, Durham, North Carolina; and Emphusion, San Francisco, California) were hired to maintain the study blind and provide clinical data management, data monitoring, biostatistics, and data analysis services.

Before any invasive procedure, peripheral venous blood samples (17mL) were collected in two acid citrate dextrose (ACD) tubes (Bidi), the identification was removed and the blood was marked with a unique study number. The location researcher entered the study number, data and blood draw time into a secure electronic case report form (eCRF). Whole blood samples were transported to the laboratory (Verinata Health, Inc., California) from multiple sites in temperature-controlled containers overnight. After receiving and performing sample inspection, cell-free plasma was prepared according to the previously described method (see Example 13) and frozen in 2 to 4 aliquots and stored at -80°C until sequencing. The date and time that the sample was received was recorded in the laboratory. If the sample was received overnight, was cool to the touch and contained at least 7mL of blood, it was determined that it was suitable for analysis. Qualified samples were reported to the CRO each week and used for the selection of a random sampling list (see below and Figure 50). Clinical data from the woman's current pregnancy and fetal karyotype were entered into the eCRF by site investigators and validated by the CRO.

The determination of sample size is based on the precision of the estimated value of the target range of the performance characteristics (sensitivity and specificity) of the index test. Specifically, the number of cases affected (T21, T18, T13, male, female or monosomy X) and the control of unaffected (non-T21, non-T18, non-T13, non-male, non-female or non-monosomy X) is determined so that sensitivity and specificity are assessed accordingly based on the normal approximation within a smaller margin of error specified in advance (N=(1.96√p(1-p)/margin of error) ² , wherein p=estimate of sensitivity or specificity). Assuming that the true sensitivity is 95% or greater, the sample size between 73 and 114 ensures that the precision of the sensitivity estimate will make the lower bound of the 95% confidence interval (CI) be 90% or greater (margin of error≤5%). For smaller sample sizes, the estimated margin of error of the 95% CI of the planned sensitivity is larger (from 6% to 13.5%). In order to estimate specificity with greater accuracy, a larger number of unaffected controls (approximately a 4:1 ratio for cases) was planned during the sampling phase. This ensured that the accuracy of the estimate of specificity reached at least 3%. Therefore, as sensitivity and/or specificity increased, the accuracy of the confidence partitions also increased.

Determine based on sample size, CRO design random sampling scheme is to produce the list of selected sample so that order-checking (minimum 110 cases affected by T21, T18 or T13 and 400 unaffected with regard to trisomy, thereby allow in these cases up to half to have except 46,XX or 46, the karyotype beyond XY).Be suitable for selecting the experimenter with singleton pregnancy and qualified blood sample.Get rid of the experimenter (Figure 50) with unqualified sample, no karyotype record or multiple pregnancy.Regularly produce list and send to Wei Rui that Thai health laboratory in whole research.

For six kinds of independent classifications, each qualified blood sample is analyzed.These classifications are for chromosome 21, 18 and 13 aneuploidy states, and the sex status of male, female and monosomy X.Although still being blind, for each of six kinds of independent classifications of each plasma DNA sample, one of three classifications (affected, unaffected or not classified) is produced prospectively.When using this scheme, same sample may be classified as affected (such as for the aneuploidy of chromosome 21) in one analysis and be classified as unaffected (such as for the euploidy of chromosome 18) in another analysis.

The conventional metaphase cytogenetic analysis of the cells obtained by chorionic villus sampling (CVS) or amniocentesis is used as a reference standard in this study. In the diagnostic laboratory commonly used by participating sites, fetal karyotype is carried out to determine. If the patient has experienced CVS and amniocentesis after registration, the karyotype produced by amniocentesis is used for research analysis. If metaphase karyotype cannot be obtained, the fluorescence in situ hybridization (FISH) results (table 24) of targeted chromosomes 21, 18, 13, X and Y are allowed. All abnormal karyotype reports (i.e., except 46, XX and 46, XY) are reviewed by a cytogeneticist through committee certification, and are classified as affected or unaffected relative to chromosome 21, 18 and 13 and gender status XX, XY and monosomy X.

The pre-defined protocol stipulates that the following abnormal karyotypes will be designated by the cytogeneticist as the 'checked' state of the karyotype: triploidy, tetraploidy, complex karyotypes (e.g., mosaicism) of chromosomes 21, 18, or 13 involved in addition to trisomy, mosaicism with mixed sex chromosomes, sex chromosome aneuploidy, or a karyotype that cannot be fully translated from the source document (e.g., marker chromosomes of unknown origin). Because cytogenetic diagnosis is unknown to sequencing laboratories, all samples that have undergone cytogenetic examination are independently analyzed and designated as classifications determined using sequencing information according to the method of the present invention (sequencing classifications), but are not included in the statistical analysis. The checked state only belongs to one or more of the six analyses (e.g., mosaicism T18 will be checked from the chromosome 18 analysis, but will be considered 'unaffected' by other analyses, such as chromosomes 21, 13, X, and Y) (Table 25). Other abnormal and rare complex karyotypes that could not be fully anticipated when the protocol was designed were not checked out from the analysis (Table 26).

The data contained in the eCRF and clinical repository are restricted to authorized users (research sites, CROs, and contracted clinical staff). No employee of Verena Health may access them until the time of disclosure.

After receiving the random sample list from CRO, total cell-free DNA (a mixture of maternal and fetal) was extracted from the selected plasma sample through thawing as described in Example 13. Sequencing library was prepared using Illumina TruSeq test kit v2.5. Sequencing was performed on an Illumina HiSeq 2000 instrument at Verona Health Laboratory (6 plexes, i.e., 6 samples/lane). Single-end readings of 36 base pairs were obtained. Readings were mapped across the entire genome, and the sequence tags on each chromosome of interest were counted and used to classify samples for independent categories as described above.

Clinical protocols require evidence of the presence of fetal DNA to report classification results. Classification as male or aneuploid is considered sufficient evidence of fetal DNA. In addition, each sample was tested for the presence of fetal DNA using two allele-specific methods. In the first method, the AmpflSTR Minifiler kit (Life Technologies, San Diego, California) was used to screen for the presence of fetal components in cell-free DNA. Electrophoresis of short tandem repeat (STR) amplicons was performed on an ABI3130 genetic analyzer according to the manufacturer's protocol. All nine STR loci in the kit were analyzed by comparing the intensity of each peak reported as a percentage of the sum of the intensities of all peaks, and the presence of secondary peaks was used to provide evidence of fetal DNA. In the absence of identifiable trace STRs, an aliquot of the sample was examined using an SNP panel of 15 single nucleotide polymorphisms (SNPs), selected from the panel of Kidd et al., with an average heterozygosity ≥ 0.4 (Kidd et al., Forensic Sci Int 164(1):20-32 [2006]). Allele-specific methods that can be used to detect and/or quantify fetal DNA in maternal samples are described in U.S. Patent Publications 20120010085, 20110224087, and 20110201507, which are incorporated herein by reference.

Normalized chromosome value (NCV) is to determine by all possible denominators of calculating all autosomes and sex chromosomes as described in example 13, yet, because the order-checking in this research is to carry out on the instrument different from our previous work of multi-sample/swimming lane, so have to determine new normalized chromosome denominator.The normalized chromosome denominator in current research is based on before analytical research sample to the training group with 110 independent unaffected samples (i.e. not from MELISSA qualified samples) unaffected samples (i.e. qualified sample) being checked order and determined.New normalized chromosome denominator is to determine by all possible denominators of calculating all autosomes and sex chromosomes, thereby for all chromosomes of whole genome with the variation minimization (table 23) of unaffected training group.

The NCV rule applied to provide the autosomal classification of each test sample is that described in Example 12, i.e., for the classification of autosomal aneuploidy, NCV>4.0 requires that the chromosome be classified as affected (i.e., aneuploidy of the chromosome) and NCV<2.5 classifies the chromosome as unaffected. Samples with autosomes having NCVs between 2.5 and 4.0 are referred to as "unclassified."

Sex chromosome classification in this test is performed by applying the NCVs for X and Y in sequence as follows:

1. If NCV X < -4.0 and NCV Y < 2.5, then classify the sample as monosomy X.

2. If NCV X > -2.5 and NCV X < 2.5 and NCV Y < 2.5, then classify the sample as female (XX).

3. If NCV X > 4.0 and NCV Y < 2.5, then classify the sample as XXX.

4. If NCV X > -2.5 and NCV X < 2.5 and NCV Y > 33, then classify the sample as XXY.

5. If NCV X < -4.0 and NCV Y > 4.0, then classify the sample as male (XY).

6. If condition 5 is met, but NCV Y is approximately twice the expected measured value of NCV X, then classify the sample as XYY.

7. If the NCVs for chromosomes X and Y do not meet any of the above criteria, the sample is classified as unclassified with respect to sex.

Because the laboratory was blinded to clinical information, sequencing results were not adjusted for any of the following demographic variables: maternal body mass index, smoking status, presence of diabetes, type of pregnancy (spontaneous or assisted), previous pregnancy, previous aneuploidy, or gestational age. Classification was performed using samples that were neither maternal nor paternal, and classification according to this method does not depend on the measured values of specific loci or alleles.

Sequencing results were returned to an independent contract biostatistician before unblinding and analysis. Research site personnel, CRO (including the biostatistician who generated the randomization list), and contract cytogeneticist were blinded to the sequencing results.

Table 23. Systematically determined normalized chromosome sequences for all chromosomes

Statistical methods are documented in the detailed statistical analysis plan for this study. For each of the six analytical categories, point estimates of sensitivity and specificity, along with exact 95% confidence intervals, were calculated using the Clopper-Pearson method. For all statistical estimation procedures performed, samples for which fetal DNA was not detected, complex karyotypes that were 'checked' (according to protocol-defined conventions), or 'unclassified' by sequencing were excluded.

result

Between June 2010 and August 2011, 2,882 pregnant women were enrolled in the study. The characteristics of the eligible subjects and the selected cohort are provided in Table 24. Subjects who enrolled and provided blood but were subsequently found to have exceeded the inclusion criteria during data monitoring and whose actual gestational age at enrollment was greater than 22 weeks 0 days were allowed to remain in the study (n=22). Three of these samples were in the selected group. Figure 50 shows the flow of samples between enrollment and analysis. There were 2,625 samples suitable for selection.

Table 24. Patient Demographics

^* GA during invasive procedures.

^** The penetration rate of ultrasound abnormalities is higher in fetuses with abnormal karyotypes

Abbreviations: BMI - body mass index; IUGR - intrauterine growth retardation

According to the random sampling scheme, all qualified subjects with abnormal karyotype and the subject group with euploid fetus are selected for analysis (Figure 50B), so that the total sequencing research population produces an approximately 4:1 ratio of unaffected: affected subjects for trisomy 21. By this process, 534 subjects are selected. Subsequently, two samples are removed from the analysis due to sample tracking problems, wherein the entire chain of custody between the sample tube and the data acquisition does not pass quality supervision (Figure 50). Thus, 532 subjects contributed by 53 of the 60 research sites are generated for analysis. The demographics of the selected peer group are similar to those of the total peer group.

Test performance

Figure 51A-51C shows the flow chart of the aneuploidy analysis of chromosomes 21, 18 and 13, and Figure 51D-51F shows the gender analysis process. Table 27 shows the sensitivity, specificity and confidence partition of each of the six analyses, and Figures 52, 53 and 54 show the graphic sample distribution according to the NCV after sequencing. In all 6 analysis categories, 16 samples (3.0%) were removed due to the lack of fetal DNA. After disclosure, there were no discernible clinical features in these samples. The number of karyotypes examined in each category depends on the situation being analyzed (fully described in detail in Figure 52).

The sensitivity and specificity of the method for detecting T21 in the analysis population (n=493) were 100% (95% CI = 95.9, 100.0) and 100% (95% CI = 99.1, 100.0), respectively (Table 27 and Figure 51A). This example includes the correct classification of a complex T21 karyotype, 47,XX,inv(7)(p22q32),+21, and two translocation T21s arising from Robertsonian translocations, one of which was also mosaic for monosomy X (45,X,+21,der(14;21)q10;q10)[4]/46,XY,+21,der(14;21)q10;q10)[17] and 46,XY,+21,der(21;21)q10;q10).

The sensitivity and specificity of detecting T18 in the analysis population (n=496) were 97.2% (85.5, 99.9) and 100% (99.2, 100.0) (Table 27 and Figure 51B). Although examined from the preliminary analysis (according to the protocol), the four samples with mosaic karyotypes for T21 and T18 were correctly classified as 'affected' with respect to aneuploidy by the method of the present invention (Table 25). Because they were correctly detected, they are indicated on the left side of Figures 51A and 51B. All other examined samples were correctly classified as unaffected with respect to chromosomes 21, 18 and trisomy 13 (Table 25). The sensitivity and specificity of detecting T13 in the analysis population were 78.6% (49.2, 99.9) and 100% (99.2, 100.0) (Figure 51C). One detected case of T13 was due to a Robertsonian translocation (46,XY,+13,der(13;13)q10;q10). Seven samples (1.4%) were unclassified in the chromosome 21 analysis, five (1.0%) in the chromosome 18 analysis, and two (0.4%) in the chromosome 13 analysis ( Figures 51A-51C ). Three samples overlapped across all categories, with both a karyotype that was examined (69,XXX) and no fetal DNA detected. One unclassified sample in the chromosome 21 analysis was correctly identified as T13 in the chromosome 13 analysis, and one unclassified sample in the chromosome 18 analysis was correctly identified as T21 in the chromosome 21 analysis.

Table 25. Karyotypes examined

^* Subjects excluded from all analysis categories due to marker chromosomes in one cell line.

^** Subjects in whom karyotype 48,XXY,+18 was not classified in chromosome 18 analysis and no sex chromosome aneuploidy was detected.

Table 26. Abnormal and complex karyotypes that were not examined

^* After disclosure, an increased normalized chromosome value (NCV) of 3.6 was noted from sequencing tags in chromosome 6.

The sex chromosome analysis population (female, male, or monosomy X) used to determine the performance of this method was 433. Our refined algorithm for classifying sex status allows accurate determination of sex chromosome aneuploidy, resulting in a higher number of unclassified results. The sensitivity and specificity for detecting diploid female status (XX) were 99.6% (95% CI = 97.6, >99.9) and 99.5% (95% CI = 97.2, >99.9), respectively; the sensitivity and specificity for detecting male status (XY) were both 100% (95% CI = 98.0, 100.0); and the sensitivity and specificity for detecting monosomy X (45, X) were 93.8% (95% CI = 69.8, 99.8) and 99.8% (95% CI = 98.7, >99.9), respectively. Although checked (according to convention) by analysis, the order-checking classification of mosaic monosomy X karyotype is as follows (table 25): 2/7 are classified as monosomy X, 3/7 are classified as having the Y chromosome component that is classified as XY, and 2/7 with XX chromosome component are classified as female. Two samples that are classified as monosomy X according to the method for the present invention have karyotype 47, XXX and 46, XX. For karyotype 47, XXX, 47, XXY and 47, XYY, 8 out of 10 sex chromosome aneuploidies are correctly classified (table 25). If sex chromosome classification is confined to monosomy X, XY and XX, most of the unclassified samples can be correctly classified as males, but XXY and XYY sex aneuploidies will not be recognized.

In addition to accurately classifying chromosomes 21, 18, trisomy 13, and sex, sequencing results can also correctly classify the aneuploidy for chromosomes 16 and 20 in two samples (47, XX, +16 and 47, XX, +20) (Table 26). Interestingly, a sample with a clinically complex change in the long arm (6q) and two duplications of chromosome 6 (one of which is 37.5 megabases in size) shows that the sequencing tags in chromosome 6 cause an increase in NCV (NCV=3.6). In another sample, the aneuploidy of chromosome 2 was detected according to the method of the present invention, but was not observed in the fetal karyotype at the time of amniocentesis (46, XX). The other complex karyotype variants shown in Tables 25 and 26 include samples from fetuses with chromosome inversion, deletion, translocation, triploidy, and other abnormalities not detected here, but may be classified using the method of the present invention at a higher sequencing density and/or under further algorithm optimization. In these cases, the methods of the invention can correctly classify the sample as unaffected with respect to trisomy 21, 18, or 13, and as male or female.

In this study, 38 of the 532 samples analyzed came from women who had undergone assisted reproduction. Of these, 17 of the 38 samples had chromosomal abnormalities; no false positives or false negatives were detected in this subpopulation.

Table 27. Sensitivity and specificity of the method

discuss

This prospective study of whole chromosome fetal aneuploidy determined by maternal plasma is designed to simulate the situation of sample collection, processing and analysis in the real world. Whole blood samples are obtained at the registration site, do not need to be processed immediately, and are transported to the sequencing laboratory overnight. In contrast to the previous prospective study involving only chromosome 21 (Palomaki et al., Genetics in Medicine 2011:1), in this study, all qualified samples with any abnormal karyotype are sequenced and analyzed. The sequencing laboratory does not know in advance which fetal chromosomes may be affected, nor does it know the ratio of aneuploid to euploid samples. The study design recruited a high-risk research pregnant group to ensure a statistically significant prevalence of aneuploidy, and Tables 25 and 26 indicate the complexity of the karyotypes analyzed. The results show that: i) fetal aneuploidy (including that caused by translocation trisomy, mosaicism and complex variation) can be detected with high sensitivity and specificity; and ii) aneuploidy in one chromosome does not affect the ability of the method of the present invention to correctly identify the euploid state of other chromosomes. The algorithms utilized in previous studies appear to be ineffective in determining other aneuploidies that will inevitably be present in the general clinical population (Erich et al., Am J Obstet Gynecol 2011 Mar;204(3):205e1-11; Zhao et al., BMJ 2011;342:c7401).

About mosaicism, in this study, the analysis of sequencing information can correctly classify samples with mosaic karyotypes for chromosomes 21 and 18 in 4/4 of the affected samples. These results demonstrate the sensitivity of the analysis for detecting the specific characteristics of cell-free DNA in complex mixtures. In one case, the sequencing data for chromosome 2 indicated complete or partial chromosomal aneuploidy, while the amniocentesis karyotype result for chromosome 2 was diploid. In two other examples, one sample had 47,XXX karyotype and the other sample had 46,XX karyotype, and the method of the present invention classified these samples as monosomy X. It is possible that these are mosaic cases, or that the pregnant woman herself is mosaic. (It is important to remember that sequencing is carried out on total DNA, which is a combination of maternal and fetal DNA.) Although it is currently the reference standard for aneuploidy classification to carry out cytogenetic analysis on amniotic cells or chorionic villi by invasive procedures, the karyotype carried out on a limited number of cells cannot rule out low-level mosaicism. The current clinical study design does not include long-term infant follow-up or access to placental tissue at delivery, so we cannot determine whether these are true or false-positive results. We speculate that the specificity of the sequencing process, combined with algorithms optimized according to the present method for whole-genome testing, will ultimately provide more sensitive identification of fetal DNA abnormalities, particularly in cases of mosaicism, compared with standard karyotyping.

The International Society for Prenatal Diagnosis has published a rapid response statement (Benn et al., Prenat Diagn 2012 doi: 10.1002/pd.2919) commenting on the commercial availability of massively parallel sequencing (MPS) for prenatal testing of Down syndrome. They state that before introducing population screening based on conventional massively parallel sequencing for fetal Down syndrome, evidence of testing in some subpopulations, such as in women conceived through in vitro fertilization, is needed. The results reported here show that this method is accurate in this group of pregnant women, many of whom have a higher risk of aneuploidy.

Although these results demonstrate the excellent performance of this method using an optimized algorithm for aneuploidy detection of the entire genome in singleton pregnancies from women at high risk of aneuploidy, more experience is needed to establish confidence in the diagnostic ability of this method when the prevalence is low and the pregnancy is multiple, especially in low-risk groups. In the early stages of clinical implementation, chromosomes 21, 18, and 13 should be classified according to this method using sequencing information after a positive first or second trimester screening result. This will reduce unnecessary invasive procedures caused by false-positive screening results, accompanied by a reduction in procedures associated with adverse events. Invasive procedures may be limited to confirming positive results obtained by sequencing. However, there are clinical situations (such as advanced maternal age and infertility) where pregnant women want to avoid invasive procedures; they may request this test as an alternative to preliminary screening and/or invasive procedures. All patients should receive adequate pre-test counseling to ensure they understand the limitations of the test and the meaning of the results. As experience accumulates with larger samples, this test has the potential to replace current screening protocols and become the primary screening and, ultimately, the noninvasive diagnostic test for fetal aneuploidy.

Example 17

Fetal fraction is determined by NCV to identify the presence of complete or partial fetal chromosomal aneuploidy in the analyzed sample

Assuming that the chromosomal dosage of the relevant fetal chromosome in the maternal sample increases proportionally with the increased fetal fraction, it is expected that for the complete chromosome of interest, the ff value based on the NCV value will determine the presence or absence of complete fetal chromosomal aneuploidy. In order to demonstrate that the ff determined by NCV can be used to distinguish the presence of complete chromosomal aneuploidy from partial chromosomal aneuploidy or the contribution of mosaic samples, genomic DNA from mothers and their children was used to create an artificial sample that simulates the mixture of fetal and maternal cfDNA found in the circulation of pregnant women. The NCV-based value of the fetal fraction is a form of the above-mentioned hypothetical fetal fraction.

DNA from mothers and children was purchased from the Coriell Institute for Medical Research (Camden, NJ). DNA identification and sample karyotypes are provided in Table 27.

Table 27. Example 17

Samples containing complete chromosome or partial chromosome aneuploidies were analyzed as follows.

In all cases, genomic DNA from the mother and genomic DNA from the child were sheared by sonication, with a peak of 200 bp. Artificial samples containing maternal DNA plus 0%, 5% or 10% w/w child DNA were processed to prepare sequencing libraries, which were sequenced using synthesis sequencing in a massively parallel manner as described in Example 12. Each artificial DNA sample was sequenced four times using an independent flow cell on a sequencer to provide four sequence information sets for each sample containing 0%, 5% and 10% child DNA. The 36 bp reads were aligned with the human reference sequence genome hg19, and uniquely mapped tags were counted. For each of the four flow cell lanes used for each sample, approximately 125 x 10 ⁶ sequence tags were obtained.

Normalizing chromosomes (single or chromosome groups) were identified in a qualified sample group comprising 20 male and 20 female gDNA libraries, as described elsewhere herein. The normalizing chromosome for chromosome 21 was identified as chromosome 4+chromosome 16+chromosome 22; the normalizing chromosome for chromosome 7 was identified as chromosome 4+chromosome 6+chromosome 8+chromosome 12+chromosome 19+chromosome 20; the normalizing chromosome for chromosome 15 was identified as chromosome 9+chromosome 12+chromosome 14+chromosome 19+chromosome 20; the normalizing chromosome for chromosome 22 was identified as chromosome 19; and the normalizing chromosome for chromosome X was identified as chromosome 4+chromosome 6+chromosome 7+chromosome 8. The sequence tags of the chromosome of interest and the corresponding normalizing chromosome (single chromosome or chromosome group) obtained by sequencing the artificial sample were counted and used to calculate chromosome dose and calculate NCV.

In this example, ff is determined using the NCV for chromosome 21 in the sample mixture (1), where NCV _21A is the NCV value determined for chromosome 21 in the test sample (1), which contains triploid chromosome 21, and CV _21U is the coefficient of variation of the dose of chromosome 21 determined in qualified samples (containing diploid chromosome 21); and where NCV _XA is the NCV value determined for chromosome X in the test sample (1), which contains triploid chromosome 21, and CV _XU is the coefficient of variation of the dose of chromosome X determined in qualified samples (containing unaffected female fetal chromosomes).

56 shows a graph of the percentage "ff" determined using the dose of chromosome 21 (ff ₂₁ ) as a function of the percentage "ff" determined using the dose of chromosome X (ff _X ) in a synthetic maternal sample ( 1 ) comprising DNA from a child with trisomy 21. FIG.

The data show that chromosome dose and the NCV derived therefrom increase proportionally with increasing ff, and that there is a 1:1 relationship between the percent ff determined using the dose of a triploid chromosome (i.e., chromosome 21) and the percent ff determined using the dose of a chromosome known to exist as a single chromosome (i.e., chromosome X).

Figure 57 shows a graph of the percentage "ff" determined using the dose of chromosome 7 ( _ff7 ) as a function of the percentage "ff" determined using the dose of chromosome X ( _ffX ) in a synthetic maternal sample (2) comprising DNA from a euploid mother and her child carrying a partial deletion in chromosome 7.

As shown for samples (1) and (2), the data show that the chromosome dose and the NCV derived therefrom increase proportionally with increasing ff. However, in cases where the aneuploidy is a partial chromosomal aneuploidy, the percentage ff determined using the chromosome dose of the partially aneuploid chromosome (ff ₇ ) does not correspond to the percentage ff determined using the dose of chromosome X (ff _X ). Therefore, deviations from the 1:1 relationship shown for the complete trisomy samples indicate the presence of partial aneuploidy.

Figure 58 shows a graph of the percentage "ff" determined using the dose of chromosome 15 ( _ff15 ) as a function of the percentage "ff" determined using the dose of chromosome X ( _ffX ) in a synthetic maternal sample (3) comprising DNA from a euploid mother and her child who is 25% mosaic for a partial duplication of chromosome 15.

As shown for samples (1) and (2), the ff determined using the dose and the NCV derived therefrom increase proportionally with increasing ff. As shown in sample (2), sample (3) contains a partial chromosomal aneuploidy, and the percentage ff determined using the chromosome dose for the partially aneuploid chromosome (ff ₁₅ ) does not correspond to the percentage ff determined using the dose for chromosome X (ff _X ). The lack of correspondence between the two ff indicates the presence of a partial aneuploidy rather than a complete chromosomal aneuploidy.

Figure 59 shows a plot of the percentage "ff" determined using the dose of chromosome 22 ( _ff22 ) and the NCVs derived therefrom in an artificial sample (4), the sample comprising 0% child DNA (i); and 10% DNA from the unaffected twin son (ii), who is known not to have a chromosomal aneuploidy of part of chromosome 22; and 10% DNA from the affected twin son (iii), who is known to have a chromosomal aneuploidy of part of chromosome 22. The data show that the "ff" determined for the sample comprising DNA from the unaffected twin and from the four NCVs calculated from the dose of chromosome 22 is close to zero, indicating that aneuploidy of chromosome 22 is not present in the unaffected child; and that the "ff" of the unaffected twin confirms that the "ff" of the unaffected twin sample is approximately 10% when calculated from the dose of chromosome X. The data also showed that the "ff" determined for the sample containing DNA from the affected twin and calculated from the four NCVs based on the dosage of chromosome 22 ( _ff22 ) was approximately 3%, indicating the presence of aneuploidy in chromosome 22; whereas when calculated based on the dosage of chromosome X ( _ffX ), the "ff" confirmed that the "ff" of the unaffected twin sample was approximately 10%. The lack of correspondence between _ff22 and _ffX suggests that the aneuploidy of chromosome 22 in the affected twin is a partial chromosomal aneuploidy.

Therefore, the data show that in maternal samples comprising cfDNA of male fetuses, chromosome doses and NCV values derived therefrom can be used to distinguish between partial aneuploidy and/or complete or partial aneuploidy present in complete trisomy and mosaic samples. Partial aneuploidy can be an increase or decrease in a portion of a chromosome. Optionally, the splitting of partial aneuploidy and/or mosaicism can be obtained by using chromosome doses and estimated fetal fractions as described in Example 12.

The fetal fraction method described above can also be used to determine the likelihood that one or more fetuses in a multiple pregnancy have aneuploidy. For example, in a case of fraternal twins, the fetal fraction determined by the NCV _X value was 8.3%, while the fraction determined by the NCV ₂₁ value was 5.0%. This indicated that only one of the male fetuses in the pair had T21 aneuploidy, a finding confirmed by karyotype. In another example involving maternal twins, the fetal fraction determined by chromosome X was 7.3%, while the fetal fraction determined by chromosome 18 was 8.9%. In this case, both twins were confirmed to be T18 males based on karyotype.

Example 18

Determination of fetal fraction by NCV to identify the presence of complete fetal chromosomal aneuploidy in clinical samples

To demonstrate that ff determined from NCV (CNff) can be used to distinguish the presence of complete from partial chromosomal aneuploidies in clinical samples, cfDNA obtained from maternal blood was used to quantify chromosomes of interest 21, 13, and 18 in clinical samples. The presence of trisomy was confirmed by karyotyping.

cfDNA was obtained from the following samples: 46 maternal samples from pregnant women each carrying a male fetus with trisomy 21 (T21); 13 maternal samples from pregnant women each carrying a fetus with trisomy 18 (T18); and 3 maternal samples from pregnant women carrying a male fetus with trisomy 13 (T13). These clinical samples were from the clinical study described in Example 16. cfDNA was isolated and sequencing libraries were prepared as described in Example 16, but using the new ILlumina v3 chemistry.

The novel ILlumina v3 chemistry was also used to sequence sequencing libraries prepared from cfDNA obtained from qualified samples known to be unaffected for chromosomes 21, 18, and 13. Sequence reads obtained for the qualified samples were mapped to the human reference sequence genome hg19, and sequence reads that uniquely mapped to all chromosomal sequences corresponding to the human reference sequence genome hg19 (unmasked repeat sequences) were counted and used to systematically determine which chromosome or group of chromosomes in the test sample would serve as the normalizing chromosome for each of chromosomes of interest, 21, 18, and 13.

Table 28 below shows the normalizing chromosomes (denominator chromosomes) identified for determining chromosome doses (ratios) for chromosomes 1-22, X, and Y in each test sample.

Table 28. Example 18 - Systematically identified normalizing chromosomes for T21, T18, and T13 test samples

When the normalizing chromosome in the qualified sample has been identified, the test sample is sequenced, and the sequence tags of each chromosome 21, 18, 13 mapped to the test sample and the corresponding normalizing chromosome are counted and used to calculate the chromosome dose (ratio). Then, the NCV value is calculated according to the following equation as previously described:

For each test sample, the fetal fraction for chromosome x and the chromosome of interest was determined according to the following equations described elsewhere in this specification:

ff = 2 × |NCV _iA CV _iU | Equation 28.

Figure 60 shows a graph of CNffx versus CNff21 determined in a sample containing fetal T trisomy 21. As expected for a complete chromosomal aneuploidy, CNffx matches that determined using the NCV for chromosome 21 (CNff21).

Similarly, in the T18 test sample, CNffx matched that determined using the NCV of chromosome 18 (CNff18) (Figure 61), and in the T13 test sample, CNffx matched that determined using the NCV of chromosome 13 (CNff13) (Figure 62).

Figure 60 also shows the fetal fraction obtained for the sample affected by T21 for female fetus.As expected, the CNW21 in these " female " samples can not be verified by comparing with chromosome X.In order to verify the CNW21 of female sample, it is possible to determine the CNW of the chromosome (such as chromosome 1) that is known to be aneuploid of fetus.As an alternative, the CNW21 of " female " sample can be determined by comparing it with NCNW, for example, by counting and determining the label of polymorphic sequence as described in other parts of this paper.

Therefore, the number of sequence tags and the resulting NCV value identifying the copy number variation of the entire chromosome can be used to determine the corresponding fetal fraction in an aneuploid/affected sample. The correspondence of the CNff of the chromosome of interest with the CNff of a chromosome known not to be aneuploid can be used to confirm the presence of a complete chromosomal trisomy.

Example 19

Determine fetal fraction from NCV to identify the presence of partial fetal chromosomal aneuploidy in clinical samples

To demonstrate that ff determined according to NCV (CNff) can be used to identify and locate the presence of partial chromosomal aneuploidies and partial chromosomal aneuploidies in clinical samples, cfDNA from clinical samples identified as having chromosome 17 aneuploidy was sequenced and analyzed as described in Example 18.

The NCV value for each chromosome in the test sample was calculated using sequence tags mapped to chromosome 17 in the test sample and the normalizing chromosomes identified in the qualified sample set (chromosome 16+chromosome 20+chromosome 22) (Table 28 above).

Figure 63 shows a graph of the NCV values for chromosomes 1-22 and X in the test sample. As shown in the figure, the NCV value for chromosome 17 is determined to have NCV>4, which is the threshold value selected for identifying aneuploid chromosomes. The figure also shows the NCV value for chromosome X, and as expected, chromosome X has a negative NCV.

The CNff for chromosome 17 and chromosome X was calculated according to the following equation:

ff _(i) = 2*NCV _jA CV _jU Equation 25,

And it was determined that CNff17=3.9% and CNffX=13.5%.

The discrepancies between CNffs suggest the presence of partial aneuploidy or possibly mosaicism.

In order to distinguish the aneuploidy of part from possible mosaicism, the number of tags is counted for each 100Kbp continuous base block/partition on chromosome 17, and the normalized binary value (NBV) is calculated for each partition. The normalization of the number of tags in the separate partition is performed by determining the ratio of the sum of the number of tags in the 20 data bins with the same size and the GC content closest to the analyzed data bin. Therefore, in this case, normalization is related to GC content. Optionally, data bin normalization may also be related to the variability of data bin dosage, as determined in the qualified samples described for chromosome dosage/ratio. In this example, the GCC Z score is equal to the NBV value determined as follows:

where _Mj and _MADj are the estimated median and median-adjusted deviation of the jth chromosome dose in the qualified sample set, respectively, and _xij is the observed jth chromosome dose for test sample i.

The normalized binary value (NBV) for each 100 Kbp partition along the length of chromosome 17 is shown on the Y-axis of Figure 64 as a form of GCC Z-score indicating GC normalization. The graph shown in Figure 64 clearly shows an increase in copy number for the partition corresponding to the approximately last 200,000 bp of chromosome 17. This finding is consistent with the karyotype provided for the sample illustrating one duplication at the q ter of chromosome 17.

Therefore, CNff can be used to identify and locate partial aneuploidies in chromosomes.

___________________________

Example 20

Verifying sample integrity in multiplexed bioassays of maternal cfDNA

Marker molecules with sequences known not to be contained in any known genome were synthesized and used to verify the integrity of whole blood and plasma maternal-derived samples, which were processed to extract and sequence the mixture of fetal and maternal cfDNA in the maternal samples.

Experimental data at that time and previously showed that the average length of cfDNA is approximately 170 bp. Using a BLAST search across all genomic accessions, a 170 bp antigenome sequence not present in any known genome was identified. Six marker molecules (MM1-MM6) were synthesized based on the sequence of the identified antigenome sequence (SEQ ID NOs: 1-6; Table 29) and used to verify sample integrity as follows.

Table 29

marker molecules

Peripheral blood was collected from a pregnant woman into four blood collection tubes (Cell-Free DNA ^™ BCT from Streck, Inc. Omaha, Nebraska) and shipped overnight to the laboratory for analysis. Two whole blood-derived samples were spiked with marker molecules as follows. One blood-derived sample was spiked with 720 pg marker molecule 1 (MM1), and the second blood-derived sample was spiked with 720 pg marker molecule 2. All four tubes were centrifuged at 1600 g for 10 minutes at 4°C. The plasma supernatant was removed from each of the four tubes and placed in a 5 mL high-speed centrifuge tube and centrifuged at 16,000 g for 10 minutes at 4°C. The plasma portion of the whole blood to which the marker molecules had been spiked was aliquoted into separate tubes and stored at -80°C. The plasma portion from the two remaining blood tubes (not spiked) was then divided into 1.1 mL aliquots. The plasma-derived samples were prepared as follows. One hundred picograms of MM1 were added to one plasma aliquot, 100 pg of MM2 was added to plasma aliquot 2, and so on, to obtain six labeled plasma-derived samples, each containing a different marker molecule (MM1-MM6) stored at -80°C.

One tube of each labeled plasma-derived sample and one tube of each labeled source blood sample were thawed and DNA was extracted using a Qiagen Blood MiniKit according to the method described in Example 1. Thirty microliters of each sample DNA was used to prepare libraries using a TruSeq ^™ DNA Sample Preparation Kit (San Diego, California) including indexes 1-6. Sequencing libraries were prepared such that samples including MM1 were indexed using index molecule 1, samples including MM2 were indexed using index 2, and so on. Sequencing libraries were quantified using an Agilent Bioanalyzer DNA1000 Kit (Agilent Technologies, Santa Clara, California) and diluted to 4 nM with Qiagen Buffer EB. The indexed and labeled samples were pooled and further diluted to 2 nM and then sequenced in four lanes of an Illumina HiSeq flow cell using an Illumina TruSeq SBS Kit v3 according to Table 30.

Table 30

Layout of multiplex sequencing flow cells

Sequence reads were aligned to the human reference genome hg19 and to a synthetic reference genome containing the antigenic marker molecule sequence. Sequence reads that mapped uniquely (i.e., only once) to the hg19 reference genome or the synthetic reference genome with the marker molecule sequence were counted (Table 31).

Table 31

Correspondence between MM sequences and cfDNA sequences of source samples

*I＝index

**L = lane

The data demonstrates that, for each sample, the sequences of the MM spiked into the source sample corresponded exclusively to the sequences of the cfDNA from the source sample spiked with MM. For example, the data for Sample 1 demonstrates that the sequences of reads mapped to MM1 corresponded exclusively to the sequences of cfDNA obtained from the source sample spiked with MM1 (Plasma Sample 1). Furthermore, the absence of distinct sequences in reads obtained from sequenced cfDNA from Source Sample 1 (e.g., MM2) indicates that Source Sample 1 was not cross-contaminated with another sample (e.g., Source Sample 2).

Example 21

Internal positive control

To develop an in-process positive control for massively parallel sequencing of maternal cfDNA to provide qualitative positive chromosome dose and NCV values for trisomy 13, trisomy 18, and trisomy 21.

The fragmented genomic DNA of three male patients with known trisomy of Chr13, Chr18 and Chr21 was added to the fragmented DNA background of female. The fragmented genomic DNA was size-selected by PAGE to include fragments with a length ranging from about 150bp to about 250bp, thereby simulating the size of fetal cfDNA. The size-selected DNA of T13, T18 and T21 controls was purified and end-repaired, and the concentration was measured using Nanodrop (Wilmington, DE, Delaware). The prepared DNA was confirmed on a bioanalyzer high-sensitivity DNA chip (Agilent, Santa Clara, California). These DNAs of trisomy 13, trisomy 18 and trisomy 21 were obtained from Coriell Institute for Medical Research (Camden, NJ, New Jersey). Female genomic DNA was obtained from The Biochain Institute (Hayward, CA, California). A small amount of trisomic DNA was added to the predominantly female DNA background to simulate the fraction of "male fetal" DNA in the female "maternal" DNA background. The composition of this DNA mixture was optimized so that when used in a sequencing assay to determine copy number variation, the mixture consistently reported qualitatively positive for trisomy 13, trisomy 18, and trisomy 21, with NCV values greater than 4 for 13, 18, and 21.

Maternal cfDNA was extracted from plasma samples obtained from pregnant women; sequencing libraries of maternal sample cfDNA and control DNA of T13, T18, and T21 were prepared for multiple sequencing using the Illumina platform. Four positive controls and 56 samples were sequenced in each flow cell of the sequencer. As described elsewhere in this application, 36bp reads were obtained, multiple chromosome labels were identified, and NCV values were calculated.

Figures 69A, B, and C show the NCV values for the maternal test sample (◇) and the internal positive control (□). NCV values greater than 4 were identified as copy number variations for chromosomes 13 (A), 18 (B), and 21 (C), respectively. The figure shows that the NCV of the positive control was correlated with the NCV of the maternal test sample, identifying it as having copy number variations, namely extra copies of chromosomes 13, 18, and 21.

Internal positive controls can be designed to mimic both complete and partial chromosome variations and can be used in prenatal diagnostic tests and related tests such as determining fetal fraction by massively parallel sequencing as described throughout this specification.

Example 22

Determination of fetal fraction using massively parallel sequencing: sample processing and cfDNA extraction

Peripheral blood samples were collected from pregnant women in the first or second trimester who were considered at risk for fetal aneuploidy. Written consent was obtained from each participant before blood draw. Blood was collected before amniocentesis or chorionic villus sampling. Karyotyping was performed on the chorionic villus or amniocentesis samples to determine the fetal karyotype.

Peripheral blood was drawn from each subject and collected in an ACD tube. One blood sample (approximately 6 to 9 ml/tube) was transferred to a 15 ml low-speed centrifuge tube. The blood was centrifuged at 2640 rpm and 4°C for 10 minutes using a Beckman Allegra 6R centrifuge with a GA 3.8 rotor.

For cell-free plasma extraction, the upper plasma layer was transferred to a 15-ml high-speed centrifuge tube and centrifuged at 16,000 × g at 4°C for 10 minutes using a Beckman Coulter Avanti J-E centrifuge and a JA-14 rotor. Two centrifugation steps were performed within 72 hours of blood collection. Cell-free plasma containing cfDNA was stored at −80°C and thawed only once before plasma cfDNA amplification or cfDNA purification.

Purified cell-free DNA (cfDNA) was extracted from cell-free plasma using the QIAamp Blood DNA Mini Kit (Qiagen) essentially according to the manufacturer's instructions. One milliliter of buffer AL and 100 μl of protease solution were added to 1 ml of plasma. The mixture was incubated at 56°C for 15 minutes. One milliliter of 100% ethanol was added to the plasma digest. The resulting mixture was transferred to a QIAamp microcolumn combined with the VacValve and VacConnector provided in the QIAvac 24Plus column assembly (Qiagen). Vacuum was applied to the sample, and the cfDNA trapped on the column filter was washed under vacuum with 750 μl of buffer AW1, followed by a second wash with 750 μl of buffer AW24. The column was centrifuged at 14,000 RPM for 5 minutes to remove any residual buffer from the filter. The cfDNA was eluted with buffer AE by centrifugation at 14,000 RPM, and the concentration was determined using the Qubit ^™ quantification platform (Invitrogen).

Example 23

Determining fetal fraction using massively parallel sequencing: preparing sequencing libraries, sequencing, and analyzing sequencing data

a. Preparation of sequencing library

All sequencing libraries, i.e., target, primary, and enriched libraries, were prepared from approximately 2 ng of purified cfDNA extracted from maternal plasma. The reagents of the NEBNext ^™ DNA Sample Preparation DNA Reagent Set 1 (Article No. E6000L; New England Biolabs, Ipswich, Massachusetts) were used for library preparation as follows. Because cell-free plasma DNA is fragmented in nature, the plasma DNA sample was no longer fragmented by nebulization or sonication. According to the end repair module, the overhangs of approximately 2 ng of purified cfDNA fragments included in 40 μl were converted to phosphorylated blunt ends by incubating ^cfDNA with 5 μl 10X phosphorylation buffer, 2 μl deoxynucleotide solution mixture (10 mM each dNTP), 1 μl 1:5 DNA polymerase I dilution, 1 μl T4 DNA polymerase, and 1 μl T4 polynucleotide kinase provided in a 1.5 ml microcentrifuge tube at 20°C for 15 minutes. The enzyme was then heat-inactivated by incubating the reaction mixture at 75°C for 5 minutes. The mixture was cooled to 4°C and dA-tailing of blunt-ended DNA was achieved using 10 μl of a dA-tailing master mix (NEBNext ^™ DNA Sample Preparation DNA Reagent Set 1) containing Klenow fragment (3' to 5' exo-) and incubated at 37°C for 15 minutes. Subsequently, the Klenow fragment was heat-inactivated by incubating the reaction mixture at 75°C for 5 minutes. After Klenow fragment inactivation, 4 μl of T4 DNA ligase provided in NEBNext ^™ DNA Sample Preparation DNA Reagent Set 1 were used to ligate ILUMINA adaptors (non-indexed Y adaptors) to the dA-tailed DNA using a 1:5 dilution of 1 μl ILUMINA Genomic Adaptor Oligo Mix (Article No. 1000521; ILUMINA, Hayward, CA) by incubating the reaction mixture at 25°C for 15 minutes. The mixture was cooled to 4°C, and the adapter-ligated cfDNA was purified from unligated adapters, adapter dimers, and other reagents using magnetic beads provided in the Agilent AMPure XP PCR Purification System (Cat. No. A63881; Beckman Coulter Genomics, Danvers, MA). 18 cycles of PCR were performed using High Fidelity Master Mix (Fenzimei, Woburn, MA) and ILlumina PCR primers (Cat. Nos. 1000537 and 1000537) that compensate for the adapters to selectively enrich the adapter-ligated cfDNA. PCR was performed on the adapter-ligated DNA using Illumina genomic PCR primers (item numbers 100537 and 1000538) and the Phusion HF PCR Master Mix provided in the NEBNext ^™ DNA Sample Preparation DNA Reagent Set 1 according to the manufacturer's instructions (98° C., 30 sec; 98° C., 10 sec, 18 cycles; 65° C., 30 sec; and 72° C., 30 sec; final extension at 72° C. for 5 min and hold at 4° C.). The amplified product was purified using the Agencot AMPure XP PCR Purification System (Agencot Biotechnologies, Beverly, MA) according to the manufacturer's instructions available at www.beckmangenomics.com/products/AMPureXPProtocol_000387v001.pdf. Purified amplification products were eluted in 40 μl Qiagen EB buffer, and the concentration and size distribution of the amplified libraries were analyzed using the Agilent DNA 1000 kit for the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA).

b. Sequencing

The library DNA was sequenced using a Genome Analyzer II (Illumina, San Diego, CA, USA) according to standard manufacturer's protocols. A copy of the protocol for whole genome sequencing using Illumina/Solaxsa technology can be found in BioTechniques.RTM. Protocol Guide 2007, published December 2006, page 29, and on the World Wide Web at biotechniques.com/default.asp?page=protocol&subsection=article_display&id=112378.

The DNA library is diluted to 1nM and denatured. According to the program described in the Illumina Cluster Station User Guide (Illumina 'sCluster Station User Guide) and the Cluster Station Operations Guide (Cluster Station Operations Guide) that can be obtained on the world wide web illumina.com/systems/genome analyzer/cluster_station.ilmn, library DNA (5pM) is subjected to cluster amplification. Use Illumina Genome Analyzer II to sequence the amplified DNA to obtain a single-end read of 36bp. It only takes about 30bp of random sequence information to identify that a sequence belongs to a specific human chromosome. Longer sequences can uniquely identify more specific targets. In the present case, numerous 36bp reads were obtained, covering approximately 10% of the genome.

c. Analyze sequencing data to determine fetal fraction

Once the sequencing of the sample is completed, the ILUMINA "Sequence Control Software" transfers the image and base call files to a Unix server running the ILUMINA "Genome Analyzer Pipeline" software version 1.51. Using the BOWTIE program, 36bp reads are compared with an artificial reference genome (e.g., a SNP genome). The artificial reference genome is identified as a grouping of polymorphic DNA sequences that encompasses the alleles contained in the polymorphic target sequence. For example, the artificial reference genome is a SNP genome comprising SEQ ID NO: 7-62. Only the reads uniquely mapped to the artificial genome are used to analyze fetal fraction. Reads that fully match the SNP genome are counted as tags and filtered. Of the remaining reads, reads with only one or two mismatches are counted as tags and included in the analysis. The tags mapped to each of the polymorphic alleles are counted, and the fetal fraction is determined as the ratio of the number of tags mapped to the major allele (i.e., maternal allele) to the number of tags mapped to the minor allele (i.e., fetal allele).

Example 24

Selection of autosomal SNPs to determine fetal fraction

A set of 28 autosomal SNPs was selected from a list of 92 SNPs (Parks et al., Human Genetics 127:315-324 [2010]) and from Applied Biosystems of Life Technologies ^™ (Carlsbad, California) at appliedbiosystems.com. Primers were designed to hybridize to a sequence close to the SNP site on cfDNA to ensure that the SNP site was included within the 36 bp read generated by massively parallel sequencing on the Illumina analyzer GII and to generate amplicons of sufficient length to allow bridge amplification during cluster formation. Thus, primers were designed to generate amplicons of at least 110 bp, which, when combined with universal adapters for cluster amplification (Illumina, San Diego, California), generated DNA molecules of at least 200 bp. Primer sequences are identified and primer sets (i.e., forward and reverse primers) are synthesized by Integrated DNA Technologies (San Diego, California) and stored in 1 μM solution form to be used for amplifying polymorphic target sequences as described in Examples 25 to 27. Table 33 provides RefSNP (rs) deposited identity numbers, primers for amplifying target cfDNA sequences, and sequences of amplicons containing possible SNP alleles that will be produced using these primers. The SNPs given in Table 33 are used to simultaneously amplify 13 target sequences in a multiplex test. The panel provided in Table 33 is an exemplary SNP panel. Fewer or more SNPs can be used to enrich fetal and maternal DNA for polymorphic target nucleic acids. Additional SNPs that can be used include those given in Table 34. The SNP alleles are shown in bold and are underlined. Other additional SNPs that can be used to determine fetal fraction according to the methods of the present invention include rs315791, rs3780962, rs1410059, rs279844, rs38882, rs9951171, rs214955, rs6444724, rs2503107, rs1019029, rs1413212, rs1031825, rs891700, rs1005533, rs2831700, rs354439, rs1979255, rs1454361, rs8037429, and rs1490413, which have been analyzed by TaqMan PCR for determining fetal fraction and are disclosed in U.S. Provisional Application Nos. 61/296,358 and 61/360,837.

Table 33

SNP Panel for Determining Fetal Fraction

Table 34

Additional SNPs for determining fetal fraction

Example 25

Determine fetal fraction by massively parallel sequencing of targeted libraries

To determine the fetal cfDNA fraction in a maternal sample, target polymorphic nucleic acid sequences, each containing a SNP, are amplified and used to prepare target libraries that are sequenced in a massively parallel format.

cfDNA was extracted as described above. The target sequencing library was prepared as follows. The cfDNA contained in 5 μl of purified cfDNA was amplified in a 50 μl reaction volume containing 7.5 μl of 1 μM primer mix (Table 1), 10 μl of NEB 5X master mix, and 27 μl of water. Thermal cycling was performed with a Gene Amp9700 (Applied Biosystems) using the following cycling conditions: incubation at 95°C for 1 minute, followed by 20 seconds at 95°C, 1 minute at 68°C, and 30 seconds at 68°C, for 20 to 30 cycles, followed by a final incubation at 68°C for 5 minutes. The sample was finally kept at 4°C until it was removed for combination with the unamplified portion of the purified cfDNA sample. The amplified product was purified using the Ankincot AMPure XP PCR Purification System (Article No. A63881; Beckman Coulter Genomics, Danvers, MA). The sample was finally kept at 4°C until it was removed for preparation of the target library. The amplified products were analyzed by 2100 bioanalyzer (Agilent Technologies, Sunnyvale, CA) and the concentration of the amplified products was determined. The sequencing library of the amplified target nucleic acid was prepared as described in Example 23, and sequencing by synthesis with a reversible dye terminator was used and sequenced according to the Illumina protocol (BioTechniques.RTM. Protocol Guide 2007, page 29, disclosed in December 2006, and on the world wide web biotechniques.com/default.asp?page=protocol&subsection=article_display&id=112 378) in a massively parallel mode. As described, the tags mapped to the reference genome consisting of 26 sequences (13 pairs, each pair representing two alleles) (i.e., SEQ ID NO:7-32) comprising SNPs were analyzed and counted.

Table 35 provides the tag counts obtained from sequencing the target library and the calculated fetal fraction obtained from the sequencing data.

Table 35

Determining fetal fraction by massively parallel sequencing of polymorphic nucleic acid libraries

The results showed that each polymorphic nucleic acid sequence containing at least one SNP can be amplified from cfDNA derived from a maternal plasma sample to construct a library that can be sequenced in a massively parallel format to determine the fraction of fetal nucleic acid in the maternal sample.

Example 26

Fetal fraction was determined after enrichment of fetal and maternal nucleic acids in cfDNA sequencing library samples.

In order to enrich the fetal and maternal cfDNA contained in the primary sequencing library constructed using purified fetal and maternal cfDNA, a portion of the purified cfDNA sample is used to amplify polymorphic target nucleic acid sequences, and a sequencing library of the amplified polymorphic target nucleic acids is prepared, which is used to enrich the fetal and maternal nucleic acid sequences contained in the primary library.

This method corresponds to the workflow illustrated in FIG10 . A target sequencing library was prepared from a portion of the purified cfDNA, as described in Example 23. A primary sequencing library was prepared using the remaining portion of the purified cfDNA, as described in Example 23. The primary library was enriched for amplified polymorphic nucleic acids contained in the target library by diluting the primary and target sequencing libraries to 10 nM and combining the target library with the primary library at a 1:9 ratio to provide an enriched sequencing library. The enriched library was sequenced and the sequencing data analyzed, as described in Example 23.

Table 36 provides the number of sequence tags mapped to the SNP genome for informative SNPs identified by sequencing enriched libraries from plasma samples from pregnant women carrying fetuses with T21, T13, T18, and monosomy X, respectively. Fetal fraction was calculated as follows:

Allele _x fetal fraction % = ((∑ fetal sequence tag for allele _x )/(∑ maternal sequence tag for allele _x )) × 100

Table 36 also provides the number of sequence tags mapped to the human reference genome.Use the plasma sample identical with for determining corresponding fetal fraction, use the label that is mapped to the human reference genome to determine the presence or absence of aneuploidy.Use sequence tag counting to determine the method for aneuploidy to be described in U.S. Provisional Application 61/407,017 and 61/455,849778, these applications are incorporated herein by reference in their entirety.

Table 36 Determination of fetal fraction by massively parallel sequencing of enriched libraries of polymorphic nucleic acids

Example 27

Determining fetal fraction by massively parallel sequencing:

Enrichment of fetal and maternal nucleic acids for polymorphic nucleic acids in purified cfDNA samples.

In order to enrich fetal and maternal cfDNA contained in a purified sample of cfDNA extracted from a maternal plasma sample, a portion of the purified cfDNA is used to amplify polymorphic target nucleic acid sequences, each polymorphic target nucleic acid sequence comprising a SNP selected from the SNP group given in Table 33.

This method corresponds to the workflow illustrated in Figure 9. As described in Example 22, cell-free plasma was obtained from a maternal blood sample, and cfDNA was purified from the plasma sample. The final concentration was determined to be 92.8 pg/μl. The cfDNA contained in 5 μl of purified cfDNA was amplified in a 50 μl reaction volume containing 7.5 μl of 1 μM primer mixture (Table 1), 10 μl of NEB 5X master mix, and 27 μl of water. Thermal cycling was performed using Gene Amp9700 (Applied Biosystems). The following cycling conditions were used: incubation at 95°C for 1 minute, followed by 20 seconds at 95°C, 1 minute at 68°C, and 30 seconds at 68°C, 30 cycles, followed by a final incubation at 68°C for 5 minutes. The sample was finally kept at 4°C until it was removed for combination with the unamplified portion of the purified cfDNA sample. Amplified products were purified using the Agencot AMPure XP PCR Purification System (Article No. A63881; Beckman Coulter Genomics, Danvers, MA), and concentrations were quantified using a Nanodrop 2000 (Thermo Scientific, Wilmington, DE). The purified amplified product was diluted 1:10 in water, and 0.9 μl (371 pg) was added to 40 μl of purified cfDNA sample to obtain a 10% spike-in. The enriched fetal and maternal cfDNA present in the purified cfDNA sample was used to prepare sequencing libraries and sequenced as described in Example 22.

Table 37 provides the tag counts obtained for each of chromosomes 21, 18, 13, X and Y, i.e., sequence tag density, and the tag counts obtained for the informative polymorphic sequences contained in the SNP reference genome, i.e., SNP tag density. The data show that sequencing information can be obtained by sequencing a single library constructed from purified maternal cfDNA samples, which has been enriched with sequences containing SNPs to simultaneously determine the presence or absence of aneuploidy and fetal fraction. As described in U.S. provisional applications 61/407,017 and 61/455,849, the number of tags mapped to chromosomes is used to determine the presence or absence of aneuploidy. In the example given, data show that the score of fetal DNA in plasma sample AFR105 can be quantified and determined to be 3.84% from five informative SNP sequencing results. For chromosomes 21, 13, 18, X and Y, sequence tag density is provided.

This example demonstrates that the enrichment protocol provides the necessary tag counts for determining aneuploidy and fetal fraction in a single sequencing process.

Table 37

Determining fetal fraction by massively parallel sequencing:

Enrichment of fetal and maternal nucleic acids for polymorphic nucleic acids in purified cfDNA samples

Example 28

Determination of fetal fraction by capillary electrophoresis of polymorphic sequences containing STRs

To determine the fetal fraction in a maternal sample containing fetal and maternal cfDNA, peripheral blood samples were collected from volunteer pregnant women carrying either a male or female fetus. Peripheral blood samples were obtained and processed as described in Example 22 to provide purified cfDNA.

Ten microliters of cfDNA samples were analyzed using the MiniFiler ^™ PCR Amplification Kit (Applied Biosystems, Foster City, CA) according to the manufacturer's instructions. Briefly, cfDNA contained in 10 μl was amplified in a 25 μl reaction volume containing 5 μl of fluorescently labeled primers (MiniFiler ^™ Primer Set) and MiniFiler ^™ Master Mix, which contains AmpliTaq DNA polymerase and associated buffers, salts ( ^1.5 mM _MgCl2 ), and 200 μM deoxynucleoside triphosphates (dNTPs: dATP, dCTP, dGTP, and dTTP). Fluorescently labeled primers were forward primers labeled with 6FAM ^™ , VIC ^™ , NED ^™ , and PET ^™ dyes. The following cycling conditions were used to perform thermal cycling on a Gene Amp9700 (Applied Biosystems): incubation at 95°C for 10 minutes, followed by 20 seconds at 94°C, 2 minutes at 59°C, and 1 minute at 72°C for 30 cycles, followed by a final incubation at 60°C for 45 minutes. The samples were then kept at 4°C until analysis was performed. The amplified products were prepared by diluting 1 μl of the amplified products in 8.7 μl Hi-Di™ formamide (Applied Biosystems) and 0.3 μl GeneScan™-500LIZ internal size standards (Applied Biosystems), and analyzed using an ABI PRISM 3130xl genetic analyzer (Applied Biosystems) using data collection HID_G5_POP4 (Applied Biosystems) and a 36 cm capillary array. All genotyping was performed with GeneMapper_ID v3.2 software (Applied Biosystems) using allelic ladders and data boxes and panels provided by the manufacturer.

All genotyping measurements are performed on an Applied Biosystems 3130xl genetic analyzer using a size ± 0.5-nt "window" obtained for each allele to allow detection and correction of allelic comparisons. Any sample allele of size outside the ± 0.5-nt window is defined as OL, i.e., "off ladder." The OL allele is an allele whose size is not represented in the MiniFiler ^™ allelic ladder, or is not corresponding to the allelic ladder, but whose size is just outside the window due to measurement error. A minimum peak height threshold value of >50 RFU is set based on a validation experiment, which is performed to avoid typing when random effects may interfere with the accurate interpretation of a mixture. The calculation of fetal fraction is based on averaging all informative markers. Informative markers are identified by having a peak value that falls within the parameters of the preset data box for the analyzed STR on the electrophoretogram.

Fetal fraction was calculated using the mean peak height of the major and minor alleles at each STR locus determined from triplicate injections. The rule applicable to this calculation was:

1. Allele out of ladder (OL) data for alleles not included in the calculation; and

2. Only peak heights obtained with >50 RFU (relative fluorescence units) were included in the calculation.

3. If only one data bin exists, the marker is considered non-informative; and

4. If a second bin is identified, but the peaks of the first and second bins are within 50% to 70% of their relative fluorescence units (RFUs) in peak height, the minority score is not measured and the marker is not considered informative.

The fraction of minor alleles for any given informative marker was calculated by dividing the peak height of the minor component by the sum of the peak heights of the major components and expressed as a percentage, first calculated for each informative locus as

Fetal fraction = (∑ peak height of minor allele/∑ peak height of major allele) × 100,

The fetal fraction for a sample containing two or more informative STRs will be calculated as the average of the fetal fractions calculated for the two or more informative markers.

Table 38 provides data obtained from analysis of cfDNA from subjects carrying a male fetus.

Table 38

Fetal fraction determined by analysis of STRs in cfDNA of pregnant subjects

The results show that cfDNA can be used to determine the presence or absence of fetal DNA, as indicated by the detection of a minor component at one or more STR alleles, to determine the fetal fraction percentage, and to determine the sex of the fetus, as indicated by the presence or absence of the Amelogenin allele.

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agaaccatct ccggcgcaag tctacgcgag ttggccttag ctcataccta cggatgtgga 60

ggataagtcc ttagctcgta ccatcgtaac ctagtggcgt catgcgccta cgtgagaagg 120

attctttact gagcgcagag ttgtccgtct actgccacgg gccataacgc 170

<210> 6

<211> 170

<212> DNA

<213> Artificial sequence

<400> 6

cctaaggcct acttcaatat cgtgatgcac ccgaatgact aaaggggtat atggagtatg 60

tccatggcgt cattgagccc gcttaggatc tactgtaatc cgagggatac atgcctcacg 120

cgagtctttc ctaccgctac tagacattat ggtgcgcgcc ttgagtacgt 170

<210> 7

<211> 111

<212> DNA

<213> Homo sapiens

<400> 7

cacatgcaca gccagcaacc ctgtcagcag gagttcccac cagtttcttt ctgagaacat 60

ctgttcaggt ttctctccat ctctatttac tcaggtcaca ggaccttggg g 111

<210> 8

<211> 111

<212> DNA

<213> Homo sapiens

<400> 8

cacatgcaca gccagcaacc ctgtcagcag gagttcccac cagtttcttt ctgagaacat 60

ctgttcaggt ttctctccat ctctgtttac tcaggtcaca ggaccttggg g 111

<210> 9

<211> 126

<212> DNA

<213> Homo sapiens

<400> 9

tgaggaagtg aggctcagag ggtaagaaac tttgtcacag agctggtggt gagggtggag 60

attttacact ccctgcctcc cacaccagtt tctccagagt ggaaagactt tcatctcgca 120

ctggca 126

<210> 10

<211> 126

<212> DNA

<213> Homo sapiens

<400> 10

tgaggaagtg aggctcagag ggtaagaaac tttgtcacag agctggtggt gagggtggag 60

attttacact ccctgcctcc cacaccagtt tctccggagt ggaaagactt tcatctcgca 120

ctggca 126

<210> 11

<211> 121

<212> DNA

<213> Homo sapiens

<400> 11

gtgccttcag aacctttgag atctgattct atttttaaag cttcttagaa gagagattgc 60

aaagtgggtt gtttctctag ccagacaggg caggcaaata ggggtggctg gtgggatggg 120

a 121

<210> 12

<211> 121

<212> DNA

<213> Homo sapiens

<400> 12

gtgccttcag aacctttgag atctgattct atttttaaag cttcttagaa gagagattgc 60

aaagtgggtt gtttctctag ccagacaggg caggtaaata ggggtggctg gtgggatggg 120

a 121

<210> 13

<211> 111

<212> DNA

<213> Homo sapiens

<400> 13

aggtgtgtct ctcttttgtg aggggagggg tcccttctgg cctagtagag ggcctggcct 60

gcagtgagca ttcaaatcct caaggaacag ggtggggagg tgggacaaag g 111

<210> 14

<211> 111

<212> DNA

<213> Homo sapiens

<400> 14

aggtgtgtct ctcttttgtg aggggagggg tcccttctgg cctagtagag ggcctggcct 60

gcagtgagca ttcaaatcct cgaggaacag ggtggggagg tgggacaaag g 111

<210> 15

<211> 139

<212> DNA

<213> Homo sapiens

<400> 15

cctcgcctac tgtgctgttt ctaaccatca tgcttttccc tgaatctctt gagtcttttt 60

ctgctgtgga ctgaaacttg atcctgagat tcacctctag tccctctgag cagcctcctg 120

gaatactcag ctgggatgg 139

<210> 16

<211> 139

<212> DNA

<213> Homo sapiens

<400> 16

cctcgcctac tgtgctgttt ctaaccatca tgcttttccc tgaatctctt gagtcttttt 60

ctgctgtgga ctgaaacttg atcctgagat tcacctctag tccctctggg cagcctcctg 120

gaatactcag ctgggatgg 139

<210> 17

<211> 117

<212> DNA

<213> Homo sapiens

<400> 17

aattgcaatg gtgagaggtt gatggtaaaa tcaaacggaa cttgttattt tgtcattctg 60

atggactgga actgaggatt ttcaatttcc tctccaaccc aagacacttc tcactgg 117

<210> 18

<211> 117

<212> DNA

<213> Homo sapiens

<400> 18

aattgcaatg gtgagaggtt gatggtaaaa tcaaacggaa cttgttattt tgtcattctg 60

atggactgga actgaggatt ttcaatttcc tttccaaccc aagacacttc tcactgg 117

<210> 19

<211> 114

<212> DNA

<213> Homo sapiens

<400> 19

gaaatgcctt ctcaggtaat ggaaggttat ccaaatattt ttcgtaagta tttcaaatag 60

caatggctcg tctatggtta gtctcacagc cacattctca gaactgctca aacc 114

<210> 20

<211> 114

<212> DNA

<213> Homo sapiens

<400> 20

gaaatgcctt ctcaggtaat ggaaggttat ccaaatattt ttcgtaagta tttcaaatag 60

caatggctcg tctatggtta gtctcgcagc cacattctca gaactgctca aacc 114

<210> 21

<211> 128

<212> DNA

<213> Homo sapiens

<400> 21

acccaaaaca ctggaggggc ctcttctcat tttcggtaga ctgcaagtgt tagccgtcgg 60

gaccagcttc tgtctggaag ttcgtcaaat tgcagttaag tccaagtatg ccacatagca 120

gataaggg 128

<210> 22

<211> 128

<212> DNA

<213> Homo sapiens

<400> 22

acccaaaaca ctggaggggc ctcttctcat tttcggtaga ctgcaagtgt tagccgtcgg 60

gaccagcttc tgtctggaag ttcgtcaaat tgcagttagg tccaagtatg ccacatagca 120

gataaggg 128

<210> 23

<211> 110

<212> DNA

<213> Homo sapiens

<400> 23

gcaccagaat ttaaacaacg ctgacaataa atatgcagtc gatgatgact tcccagagct 60

ccagaagcaa ctccagcaca cagagaggcg ctgatgtgcc tgtcaggtgc 110

<210> 24

<211> 110

<212> DNA

<213> Homo sapiens

<400> 24

gcaccagaat ttaaacaacg ctgacaataa atatgcagtc gatgatgact tcccagagct 60

ccagaagcaa ctccagcaca cggagaggcg ctgatgtgcc tgtcaggtgc 110

<210> 25

<211> 116

<212> DNA

<213> Homo sapiens

<400> 25

tgactgtata ccccaggtgc acccttgggt catctctatc atagaactta tctcacagag 60

tataagagct gatttctgtg tctgcctctc acactagact tccacatcct tagtgc 116

<210> 26

<211> 116

<212> DNA

<213> Homo sapiens

<400> 26

tgactgtata ccccaggtgc acccttgggt catctctatc atagaactta tctcacagag 60

tataagagct gatttctgtg tctgcctgtc acactagact tccacatcct tagtgc 116

<210> 27

<211> 110

<212> DNA

<213> Homo sapiens

<400> 27

tgtacgtggt caccagggga cgcctggcgc tgcgaggggag gccccgagcc tcgtgccccc 60

gtgaagcttc agctcccctc cccggctgtc cttgaggctc ttctcacact 110

<210> 28

<211> 110

<212> DNA

<213> Homo sapiens

<400> 28

tgtacgtggt caccagggga cgcctggcgc tgcgaggggag gccccgagcc tcgtgccccc 60

gtgaagcttc agctcccctc cctggctgtc cttgaggctc ttctcacact 110

<210> 29

<211> 114

<212> DNA

<213> Homo sapiens

<400> 29

cagtggaccc tgctgcacct ttcctcccct cccatcaacc tcttttgtgc ctccccctcc 60

gtgtaccacc ttctctgtca ccaaccctgg cctcacaact ctctcctttg ccac 114

<210> 30

<211> 114

<212> DNA

<213> Homo sapiens

<400> 30

cagtggaccc tgctgcacct ttcctcccct cccatcaacc tcttttgtgc ctccccctcc 60

gtgtaccacc ttctctgtca ccacccctgg cctcacaact ctctcctttg ccac 114

<210> 31

<211> 110

<212> DNA

<213> Homo sapiens

<400> 31

cagtggcata gtagtccagg ggctcctcct cagcacctcc agcaccttcc aggaggcagc 60

agcgcaggca gagaacccgc tggaagaatc ggcggaagtt gtcggagagg 110

<210> 32

<211> 110

<212> DNA

<213> Homo sapiens

<400> 32

cagtggcata gtagtccagg ggctcctcct cagcacctcc agcaccttcc aggaggcagc 60

agcgcaggca gagaacccgc tggaaggatc ggcggaagtt gtcggagagg 110

<210> 33

<211> 129

<212> DNA

<213> Homo sapiens

<400> 33

aggtctgggg gccgctgaat gccaagctgg gaatcttaaa tgttaaggaa caaggtcata 60

caatgaatgg tgtgatgtaa aagcttggga ggtgatttct gagggtaggt gctgggttta 120

atgggagga 129

<210> 34

<211> 129

<212> DNA

<213> Homo sapiens

<400> 34

aggtctgggg gccgctgaat gccaagctgg gaatcttaaa tgttaaggaa caaggtcata 60

caatgaatgg tgtgatgtaa aagcttggga ggtgattttt gagggtaggt gctgggttta 120

atgggagga 129

<210> 35

<211> 107

<212> DNA

<213> Homo sapiens

<400> 35

acggttctgt cctgtagggg agaaaagtcc tcgttgttcc tctggggatgc aacatgagag 60

agcagcacac tgaggcttta tggattgccc tgccacaagt gaacagg 107

<210> 36

<211> 107

<212> DNA

<213> Homo sapiens

<400> 36

acggttctgt cctgtagggg agaaaagtcc tcgttgttcc tctggggatgc aacatgagag 60

agcagcacac tgaggcttta tgggttgccc tgccacaagt gaacagg 107

<210> 37

<211> 127

<212> DNA

<213> Homo sapiens

<400> 37

gcgcagtcag atgggcgtgc tggcgtctgt cttctctctc tcctgctctc tggcttcatt 60

tttctctcct tctgtctcac cttctttcgt gtgcctgtgc acacacacgt ttgggacaag 120

ggctgga 127

<210> 38

<211> 127

<212> DNA

<213> Homo sapiens

<400> 38

gcgcagtcag atgggcgtgc tggcgtctgt cttctctctc tcctgctctc tggcttcatt 60

tttctctcct tctgtctcac cttctttcgt gtgcctgtgc atacacacgt ttgggacaag 120

ggctgga 127

<210> 39

<211> 130

<212> DNA

<213> Homo sapiens

<400> 39

gccggacctg cgaaatccca aaatgccaaa cattcccgcc tcacatgatc ccagagagag 60

gggacccagt gttcccagct tgcagctgag gagcccgagg ttgccgtcag atcagagccc 120

cagttgcccg 130

<210> 40

<211> 130

<212> DNA

<213> Homo sapiens

<400> 40

gccggacctg cgaaatccca aaatgccaaa cattcccgcc tcacatgatc ccagagagag 60

gggacccagt gttcccagct tgcagctgag gagcccgagt ttgccgtcag atcagagccc 120

cagttgcccg 130

<210> 41

<211> 121

<212> DNA

<213> Homo sapiens

<400> 41

agcagcctcc ctcgactagc tcacactacg ataaggaaaa ttcatgagct ggtgtccaag 60

gagggctggg tgactcgtgg ctcagtcagc atcaagattc ctttcgtctt tcccctctgc 120

c 121

<210> 42

<211> 121

<212> DNA

<213> Homo sapiens

<400> 42

agcagcctcc ctcgactagc tcacactacg ataaggaaaa ttcatgagct ggtgtccaag 60

gagggctggg tgactcgtgg ctcagtcagc gtcaagattc ctttcgtctt tcccctctgc 120

c 121

<210> 43

<211> 138

<212> DNA

<213> Homo sapiens

<400> 43

tggcattgcc tgtaatatac atagccatgg ttttttatag gcaatttaag atgaatagct 60

tctaaactat agataagttt cattacccca ggaagctgaa ctatagctac tttacccaaa 120

atcattagaa tggtgctt 138

<210> 44

<211> 138

<212> DNA

<213> Homo sapiens

<400> 44

tggcattgcc tgtaatatac atagccatgg ttttttatag gcaatttaag atgaatagct 60

tctaaactat agataagttt cattacccca ggaagctgaa ctatagctac tttccccaaa 120

atcattagaa tggtgctt 138

<210> 45

<211> 136

<212> DNA

<213> Homo sapiens

<400> 45

atgaagcctt ccaccaactg cctgtatgac tcatctgggg acttctgctc tatactcaaa 60

gtggcttagt cactgccaat gtatttccat atgagggacg atgattacta aggaaatata 120

gaaacaacaa ctgatc 136

<210> 46

<211> 136

<212> DNA

<213> Homo sapiens

<400> 46

atgaagcctt ccaccaactg cctgtatgac tcatctgggg acttctgctc tatactcaaa 60

gtggcttagt cactgccaat gtatttccat atgagggacg gtgattacta aggaaatata 120

gaaacaacaa ctgatc 136

<210> 47

<211> 118

<212> DNA

<213> Homo sapiens

<400> 47

acaacagaat caggtgattg gagaaaagat cacaggccta ggcacccaag gcttgaagga 60

tgaaagaatg aaagatggac ggaacaaaat taggacctta attctttgtt cagttcag 118

<210> 48

<211> 118

<212> DNA

<213> Homo sapiens

<400> 48

acaacagaat caggtgattg gagaaaagat cacaggccta ggcacccaag gcttgaagga 60

tgaaagaatg aaagatggac ggaagaaaat taggacctta attctttgtt cagttcag 118

<210> 49

<211> 150

<212> DNA

<213> Homo sapiens

<400> 49

ttggggtaaa ttttcattgt catatgtgga atttaaatat accatcatct acaaagaatt 60

ccacagagtt aaatatctta agttaaacac ttaaaataag tgtttgcgtg atattttgat 120

gacagataaa cagagtctaa ttcccacccc 150

<210> 50

<211> 150

<212> DNA

<213> Homo sapiens

<400> 50

ttggggtaaa ttttcattgt catatgtgga atttaaatat accatcatct acaaagaatt 60

ccacagagtt aaatatctta agttaaacac ttaaaataag tgtttgcgtg atattttgat 120

gatagataaa cagagtctaa ttcccacccc 150

<210> 51

<211> 145

<212> DNA

<213> Homo sapiens

<400> 51

tgcaattcaa atcaggaagt atgaccaaaa gacagagatc ttttttggat gatccctagc 60

ctagcaatgc ctggcagcca tgcaggtgca atgtcaacct taaataatgt attgcaaact 120

cagagctgac aaacctcgat gttgc 145

<210> 52

<211> 145

<212> DNA

<213> Homo sapiens

<400> 52

tgcaattcaa atcaggaagt atgaccaaaa gacagagatc ttttttggat gatccctagc 60

ctagcaatgc ctggcagcca tgcaggtgca atgtcaacct taaataatgt attgcaaatt 120

cagagctgac aaacctcgat gttgc 145

<210> 53

<211> 124

<212> DNA

<213> Homo sapiens

<400> 53

ctgtgctctg cgaatagctg cagaagtaac ttggggaccc aaaataaagc agaatgctaa 60

tgtcaagtcc tgagaaccaa gccctgggac tctggtgcca tttcggattc tccatgagca 120

tggt 124

<210> 54

<211> 124

<212> DNA

<213> Homo sapiens

<400> 54

ctgtgctctg cgaatagctg cagaagtaac ttggggaccc aaaataaagc agaatgctaa 60

tgtcaagtcc tgagaaccaa gccctgggac tctggtgcca ttttggattc tccatgagca 120

tggt 124

<210> 55

<211> 118

<212> DNA

<213> Homo sapiens

<400> 55

tttttccagc caactcaagg ccaaaaaaaa tttcttaata tagttattat gcgaggggag 60

gggaagcaaa ggagcacagg tagtccacag aataagacac aagaaacctc aagctgtg 118

<210> 56

<211> 118

<212> DNA

<213> Homo sapiens

<400> 56

tttttccagc caactcaagg ccaaaaaaaa tttcttaata tagttattat gcgaggggag 60

gggaagcaaa ggagcacagg tagtccacag aataggacac aagaaacctc aagctgtg 118

<210> 57

<211> 110

<212> DNA

<213> Homo sapiens

<400> 57

tcttctcgtc ccctaagcaa acaacatccg cttgcttctg tctgtgtaac cacagtgaat 60

gggtgtgcac gcttgatggg cctctgagcc cctgttgcac aaaccagaaa 110

<210> 58

<211> 110

<212> DNA

<213> Homo sapiens

<400> 58

tcttctcgtc ccctaagcaa acaacatccg cttgcttctg tctgtgtaac cacagtgaat 60

gggtgtgcac gcttggtggg cctctgagcc cctgttgcac aaaccagaaa 110

<210> 59

<211> 110

<212> DNA

<213> Homo sapiens

<400> 59

cacatggggg cattaagaat cgcccaggga ggaggaggga gaacgcgtgc ttttcacatt 60

tgcatttgaa ttttcgagtt cccaggatgt gtttttgtgc tcatcgatgt 110

<210> 60

<211> 110

<212> DNA

<213> Homo sapiens

<400> 60

cacatggggg cattaagaat cgcccaggga ggaggaggga gaacgcgtgc ttttcacatt 60

tgcatttgaa tttttgagtt cccaggatgt gtttttgtgc tcatcgatgt 110

<210> 61

<211> 128

<212> DNA

<213> Homo sapiens

<400> 61

gggctctgag gtgtgtgaaa taaaaacaaa tgtccatgtc tgtcctttta tggcattttg 60

ggactttaca tttcaaacat ttcagacatg tatcacaaca cgaaggaata acagttccag 120

ggatatct 128

<210> 62

<211> 128

<212> DNA

<213> Homo sapiens

<400> 62

gggctctgag gtgtgtgaaa taaaaacaaa tgtccatgtc tgtcctttta tggcattttg 60

ggactttaca tttcaaacat ttcagacatg tatcacaaca cgagggaata acagttccag 120

ggatatct 128

<210> 63

<211> 21

<212> DNA

<213> Artificial sequence

<400> 63

cacatgcaca gccagcaacc c 21

<210> 64

<211> 23

<212> DNA

<213> Artificial sequence

<400> 64

ccccaaggtc ctgtgacctg agt 23

<210> 65

<211> 23

<212> DNA

<213> Artificial sequence

<400> 65

tgaggaagtg aggctcagag ggt 23

<210> 66

<211> 24

<212> DNA

<213> Artificial sequence

<400> 66

tgccagtgcg agatgaaagt cttt 24

<210> 67

<211> 27

<212> DNA

<213> Artificial sequence

<400> 67

gtgccttcag aacctttgag atctgat 27

<210> 68

<211> 20

<212> DNA

<213> Artificial sequence

<400> 68

tcccatccca ccagccaccc 20

<210> 69

<211> 25

<212> DNA

<213> Artificial sequence

<400> 69

aggtgtgtct ctcttttgtg agggg 25

<210> 70

<211> 21

<212> DNA

<213> Artificial sequence

<400> 70

cctttgtccc acctccccac c 21

<210> 71

<211> 26

<212> DNA

<213> Artificial sequence

<400> 71

cctcgcctac tgtgctgttt ctaacc 26

<210> 72

<211> 25

<212> DNA

<213> Artificial sequence

<400> 72

ccatcccagc tgagtattcc aggag 25

<210> 73

<211> 26

<212> DNA

<213> Artificial sequence

<400> 73

aattgcaatg gtgagaggtt gatggt 26

<210> 74

<211> 24

<212> DNA

<213> Artificial sequence

<400> 74

ccagtgagaa gtgtcttggg ttgg 24

<210> 75

<211> 27

<212> DNA

<213> Artificial sequence

<400> 75

gaaatgcctt ctcaggtaat ggaaggt 27

<210> 76

<211> 27

<212> DNA

<213> Artificial sequence

<400> 76

ggtttgagca gttctgagaa tgtggct 27

<210> 77

<211> 22

<212> DNA

<213> Artificial sequence

<400> 77

acccaaaaca ctggaggggc ct 22

<210> 78

<211> 27

<212> DNA

<213> Artificial sequence

<400> 78

cccttatctg ctatgtggca tacttgg 27

<210> 79

<211> 27

<212> DNA

<213> Artificial sequence

<400> 79

gcaccagaatttaaacaacgctgacaa 27

<210> 80

<211> 22

<212> DNA

<213> Artificial sequence

<400> 80

gcacctgaca ggcacatcag cg 22

<210> 81

<211> 24

<212> DNA

<213> Artificial sequence

<400> 81

tgactgtata ccccaggtgc accc 24

<210> 82

<211> 27

<212> DNA

<213> Artificial sequence

<400> 82

gcactaagga tgtggaagtc tagtgtg 27

<210> 83

<211> 22

<212> DNA

<213> Artificial sequence

<400> 83

tgtacgtggt caccagggga cg 22

<210> 84

<211> 26

<212> DNA

<213> Artificial sequence

<400> 84

agtgtgagaa gagcctcaag gacagc 26

<210> 85

<211> 21

<212> DNA

<213> Artificial sequence

<400> 85

cagtggaccc tgctgcacct t 21

<210> 86

<211> 24

<212> DNA

<213> Artificial sequence

<400> 86

gtggcaaagg agagagttgt gagg 24

<210> 87

<211> 24

<212> DNA

<213> Artificial sequence

<400> 87

cagtggcata gtagtccagg ggct 24

<210> 88

<211> 21

<212> DNA

<213> Artificial sequence

<400> 88

cctctccgac aacttccgcc g 21

<210> 89

<211> 20

<212> DNA

<213> Artificial sequence

<400> 89

aggtctgggg gccgctgaat 20

<210> 90

<211> 23

<212> DNA

<213> Artificial sequence

<400> 90

tcctcccatt aaacccagca cct 23

<210> 91

<211> 23

<212> DNA

<213> Artificial sequence

<400> 91

acggttctgt cctgtagggg aga 23

<210> 92

<211> 22

<212> DNA

<213> Artificial sequence

<400> 92

cctgttcact tgtggcaggg ca 22

<210> 93

<211> 20

<212> DNA

<213> Artificial sequence

<400> 93

gcgcagtcag atgggcgtgc 20

<210> 94

<211> 23

<212> DNA

<213> Artificial sequence

<400> 94

tccagccctt gtcccaaacg tgt 23

<210> 95

<211> 21

<212> DNA

<213> Artificial sequence

<400> 95

gccggacctg cgaaatccca a 21

<210> 96

<211> 21

<212> DNA

<213> Artificial sequence

<400> 96

cgggcaactg gggctctgat c 21

<210> 97

<211> 21

<212> DNA

<213> Artificial sequence

<400> 97

agcagcctcc ctcgactagc t 21

<210> 98

<211> 23

<212> DNA

<213> Artificial sequence

<400> 98

ggcagagggg aaagacgaaa gga 23

<210> 99

<211> 24

<212> DNA

<213> Artificial sequence

<400> 99

tggcattgcc tgtaatatac atag 24

<210> 100

<211> 23

<212> DNA

<213> Artificial sequence

<400> 100

aagcaccatt ctaatgattt tgg 23

<210> 101

<211> 20

<212> DNA

<213> Artificial sequence

<400> 101

atgaagcctt ccaccaactg 20

<210> 102

<211> 27

<212> DNA

<213> Artificial sequence

<400> 102

gatcagttgt tgtttctata tttcctt 27

<210> 103

<211> 22

<212> DNA

<213> Artificial sequence

<400> 103

acaacagaat caggtgattg ga 22

<210> 104

<211> 25

<212> DNA

<213> Artificial sequence

<400> 104

ctgaactgaa caaagaatta aggtc 25

<210> 105

<211> 22

<212> DNA

<213> Artificial sequence

<400> 105

ttggggtaaa ttttcattgt ca 22

<210> 106

<211> 20

<212> DNA

<213> Artificial sequence

<400> 106

ggggtgggaa ttagactctg 20

<210> 107

<211> 23

<212> DNA

<213> Artificial sequence

<400> 107

tgcaattcaa atcaggaagt atg 23

<210> 108

<211> 20

<212> DNA

<213> Artificial sequence

<400> 108

gcaacatcga ggtttgtcag 20

<210> 109

<211> 20

<212> DNA

<213> Artificial sequence

<400> 109

ctgtgctctg cgaatagctg 20

<210> 110

<211> 20

<212> DNA

<213> Artificial sequence

<400> 110

accatgctca tggagaatcc 20

<210> 111

<211> 20

<212> DNA

<213> Artificial sequence

<400> 111

tttttccagc caactcaagg 20

<210> 112

<211> 21

<212> DNA

<213> Artificial sequence

<400> 112

cacagcttga ggtttcttgt g 21

<210> 113

<211> 20

<212> DNA

<213> Artificial sequence

<400> 113

tcttctcgtc ccctaagcaa 20

<210> 114

<211> 20

<212> DNA

<213> Artificial sequence

<400> 114

tttctggttt gtgcaacagg 20

<210> 115

<211> 20

<212> DNA

<213> Artificial sequence

<400> 115

cacatggggg cattaagaat 20

<210> 116

<211> 22

<212> DNA

<213> Artificial sequence

<400> 116

acatcgatga gcacaaaaac ac 22

<210> 117

<211> 20

<212> DNA

<213> Artificial sequence

<400> 117

gggctctgag gtgtgtgaaa 20

<210> 118

<211> 24

<212> DNA

<213> Artificial sequence

<400> 118

agatatccct ggaactgtta ttcc 24

Claims (31)

1. A method for determining the fetal fraction in a maternal test sample comprising a mixture of fetal and maternal nucleic acids, the method being for non-diagnostic or therapeutic purposes, the method comprising: (a) Receiving multiple sequence reads from fetal and maternal nucleic acids in maternal test samples; (b) The sequence readings are aligned with one or more chromosome reference sequences, thereby providing multiple sequence tags corresponding to these sequence readings; (c) Identify a number of sequence tags from one or more chromosomes of interest or chromosomal regions of interest selected from chromosomes 1-22, X, and Y and their segments, and for each of these chromosomes of interest or chromosomal regions of interest, identify a number of sequence tags from at least one normalized chromosome sequence or normalized chromosomal region sequence to determine a chromosome dose or chromosomal region dose. Wherein, the one or more chromosomes of interest or chromosome segments of interest have copy number variations, wherein the copy number variations are determined by comparing a dose of each of the one or more chromosomes of interest or chromosome segments of interest with a corresponding threshold for each of the one or more chromosomes of interest or chromosome segments of interest; (d) Using the said chromosome dose or chromosome segment dose, determine the fetal fraction; and (e) Calculate a normalized chromosome value, wherein calculating the normalized chromosome value correlates the chromosome dose with the average of the corresponding chromosome doses in a set of qualified samples, as follows: Where and are the estimated mean and standard deviation of the dose of the i- th chromosome in the combined grid sample, respectively, _{and RiA} is the chromosome dose calculated for the i- th chromosome in the test sample, wherein the i- th chromosome is the chromosome of interest. 2. The method of claim 1, wherein the chromosome dose or the segment dose determined in step (c) is calculated as a ratio of the number of sequence tags identified for the chromosome or segment of interest to the number of sequence tags identified for at least one corresponding normalized chromosome sequence or normalized chromosome segment sequence corresponding to the chromosome or segment of interest; or The chromosome dose or segment dose determined by step (c) is calculated as the ratio of the sequence tag density ratio of the chromosome or segment of interest to the sequence tag density ratio of the normalized chromosome sequence or normalized chromosome segment sequence. 3. The method of claim 1, wherein the fetal fraction is determined according to the following expression: Where ff is the fetal fraction value, NCV _iA is the normalized chromosome value on the i- th chromosome in the test sample, and CV _iU is the coefficient of variation of the dose of the i- th chromosome determined in these qualified samples, wherein the i- th chromosome is the chromosome of interest. 4. The method of claim 3, wherein the chromosome of interest is an autosome or the X chromosome of a male fetus, and the chromosome segment of interest is selected from an autosome or the X chromosome of a male fetus. 5. The method of claim 1, further comprising calculating a normalized segment value, wherein calculating the normalized segment value compares the chromosomal segment dose with that in a set of qualified samples. The average dose of the corresponding chromosomal segment is associated with, as: Where and are the estimated mean and standard deviation of the dose for the i- th chromosome segment in the combined grid sample, _{respectively, and RiA} is the chromosome segment dose calculated for the i-th chromosome segment in the test sample, wherein the i- th chromosome is the chromosome of interest. 6. The method of claim 5, wherein the fetal fraction is determined according to the following expression: Where ff is the fetal fraction value, NSV _iA is the normalized chromosome segment value on the i- th chromosome segment in the test sample, and CV _iU is the coefficient of variation of the dose of the i- th chromosome segment determined in these qualified samples, wherein the i- th chromosome is the chromosome of interest. 7. The method of claim 1, wherein at least one normalized chromosome sequence or normalized chromosome segment sequence is a chromosome or segment selected for an associated chromosome or segment of interest, which is done by: (i) identifying a plurality of eligible samples for the chromosome or segment of interest; (ii) repeatedly calculating the chromosome dose or chromosome segment dose for the selected chromosome or segment using a plurality of potential normalized chromosome sequences or normalized chromosome segment sequences; and (iii) selecting the normalized chromosome sequence or normalized chromosome segment sequence individually or in a combination to give minimal variability and/or maximum distinguishability in the calculated chromosome dose or chromosome segment dose. 8. The method of claim 1, wherein the normalized chromosome sequence is selected from any one or more of chromosomes 1-22, X, and Y, or a group of chromosomes. 9. The method of claim 1, wherein the normalized segment sequence is derived from a single segment or a set of segments from any one or more of chromosomes 1-22, X, and Y. 10. The method of claim 1, wherein the copy number variation is selected from the group consisting of: complete chromosome duplication, complete chromosome deletion, partial duplication, partial duplication, partial insertion, and partial deletion. 11. The method of claim 1, further comprising comparing the fetal fraction determined using a chromosome dose or chromosome segment dose with a fetal fraction determined using information on one or more polymorphisms in chromosomes present in a non-chromosome of interest that exhibit allelic imbalance in fetal and maternal nucleic acids from a maternal test sample. 12. The method of claim 1, wherein the comparison in step (b) comprises comparing at least one million readings. 13. The method of claim 1, further comprising sequencing fetal and maternal nucleic acids in the maternal test sample to obtain these nucleic acid reads. 14. The method of claim 13, wherein the sequencing comprises sequencing cell-free DNA from the maternal test sample to provide those sequence reads. 15. The method of claim 13, wherein the sequencing comprises performing massively parallel sequencing of the maternal and fetal nucleic acids from the maternal test sample to generate the sequence reads. 16. The method of claim 15, wherein the massively parallel sequencing is synthetic sequencing. 17. The method of claim 16, wherein the synthetic sequencing uses a reversible dye terminator. 18. The method of claim 15, wherein the massively parallel sequencing is ligation sequencing. 19. The method of claim 15, wherein the massively parallel sequencing is single-molecule sequencing. 20. The method of claim 1, further comprising obtaining the maternal test sample from a pregnant organism. 21. The method of claim 1, wherein the maternal test sample is a blood, plasma, serum, or urine sample. 22. The method of claim 13, wherein the sequencing comprises the following steps: (a) Provide cfDNA from the maternal sample; (b) A series of steps for end repair, dA tailing and aptamer ligation of the cfDNA, wherein the series of steps do not include purifying the end repair product before dA tailing and do not include purifying the dA tailing product before aptamer ligation. The sequential steps of end repair and dA tailing of the cfDNA are carried out in solution, followed by binding of the cfDNA on a solid surface; The adapter connection is performed on a solid surface. 23. The method of claim 22, wherein step (b) comprises thermally inactivating the enzyme between the successive steps of end repair and dA tailing. 24. The method of claim 22, wherein step (b) comprises thermally inactivating the enzyme between the consecutive steps of dA tailing and aptamer ligation. 25. The method of claim 22, wherein the maternal sample is a biological fluid selected from blood, plasma, serum, urine, and saliva. 26. The method of claim 22, further comprising sequencing at least a portion of the nucleic acid molecules from the sequencing library. 27. The method of claim 22, wherein the sequencing is synthetic sequencing using a reversible dye terminator, or massively parallel sequencing. 28. The method of claim 22, wherein the sequencing comprises amplification. 29. The method of claim 22, wherein the successive steps of end repair, dA tailing, and aptamer ligation of the cfDNA are performed in solution. 30. The method of claim 22, wherein the successive steps of end repair, dA tailing, and aptamer ligation of the cfDNA are performed without polyethylene glycol. 31. The method of claim 13, wherein the sequencing comprises the following steps: (a) Provide cfDNA from the maternal sample; (b) A series of steps involving dA tailing and aptamer ligation of the cfDNA, wherein the series of steps does not include purifying the dA tailing product prior to aptamer ligation; The dA tailing step of the cfDNA is carried out in solution, followed by binding of the cfDNA on a solid surface; The adapter connection is performed on a solid surface.