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WO2011104730A1 - Genetic variants predictive of lung cancer risk - Google Patents

Genetic variants predictive of lung cancer risk Download PDF

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Publication number
WO2011104730A1
WO2011104730A1 PCT/IS2011/050004 IS2011050004W WO2011104730A1 WO 2011104730 A1 WO2011104730 A1 WO 2011104730A1 IS 2011050004 W IS2011050004 W IS 2011050004W WO 2011104730 A1 WO2011104730 A1 WO 2011104730A1
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lung cancer
allele
risk
markers
marker
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Daniel Gudbjartsson
Thorunn Rafnar
Thorgeir Thorgeirsson
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Decode Genetics ehf
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Decode Genetics ehf
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/172—Haplotypes

Definitions

  • SNPs single nucleotide polymorphisms
  • SNPs are located on average every 1000 base pairs in the human genome. Accordingly, a typical human gene containing 250,000 base pairs may contain 250 different SNPs. Only a minor number of SNPs are located in exons and alter the amino acid sequence of the protein encoded by the gene. Most SNPs may have little or no effect on gene function, while others may alter transcription, splicing, translation, or stability of the mRNA encoded by the gene.
  • Genetic polymorphisms in the human genome are caused by insertions, deletions, translocations or inversion of either short or long stretches of DNA. Genetic polymorphisms conferring disease risk may directly alter the amino acid sequence of proteins, may increase the amount of protein produced from the gene, or may decrease the amount of protein produced by the gene.
  • apolipoprotein E testing to identify genetic carriers of the apoE4 polymorphism in dementia patients for the differential diagnosis of Alzheimer's disease, and of Factor V Leiden testing for predisposition to deep venous thrombosis. More importantly, in the treatment of cancer, diagnosis of genetic variants in tumor cells is used for the selection of the most appropriate treatment regime for the individual patient.
  • lung cancer is the primary cause of cancer death among both men and women . In 2007, the death rate from lung cancer was an estimated 160,390 deaths, exceeding the combined total for breast, prostate and colon cancer (America Cancer Society, www.cancer.org) . Lung cancer is also the leading cause of cancer death in all European countries and is rapidly increasing in developing countries. While environmental factors, such as lifestyle factors (e.g., smoking) and dietary factors, play an important role in lung cancer, genetic factors also contribute to the disease. For example, a family of enzymes responsible for carcinogen activation, degradation and subsequent DNA repair have been implicated in susceptibility to lung cancer.
  • Smoking of tobacco products, and in particular cigarettes, is the largest known risk factor lung cancer with a global attributable proportion estimated to be approximately 90% in men and 80% in women.
  • the risk of lung cancer associated with tobacco smoking is strongly related to duration of smoking, and declines with increasing time from cessation, the estimated lifetime risk of lung cancer among former smokers remains high, ranging from approximately 6% in smokers who give up at the age of 50, to 10% for smokers who give up at age 60, compared to 15% for lifelong smokers and less than 1% in never-smokers (Peto et al. 2000 BMJ, 321, 323- 32, Brennan, et al. 2006 Am J Epidemiol 164, 1233-1241) .
  • the present invention is based on the finding that genetic variants in certain genetic regions contain variants that are correlated with risk of developing lung cancer in humans. Markers in the genomic regions 8pll (e.g., rs6474412), 7pl4 (e.g., rs215614) and 19ql3 (e.g., rs4105144) have been found to be indicative of lung cancer risk.
  • the invention provides a method of determining a susceptibility to lung cancer, the method comprising (a) obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and (b) determining a susceptibility to lung cancer from the sequence data, wherein the at least one polymorphic marker is a marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith .
  • Another aspect relates to a method of determining a susceptibility to lung cancer, the method comprising analyzing nucleic acid sequence data from a human individual for at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, wherein the marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and determining a susceptibility to lung cancer from the nucleic acid sequence data.
  • a second aspect of the invention relates to a method of assessing a susceptibility to lung cancer in a human individual, comprising (i) obtaining sequence data about the individual for at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans; (ii) identifying the presence or absence of at least one allele in the at least one polymorphic marker that correlates with increased occurrence of lung cancer in humans.
  • determination of the presence of the at least one allele identifies the individual as having elevated susceptibility to lung cancer, and determination of the absence of the at least one allele identifies the individual as not having the elevated susceptibility.
  • a method of identification of a marker for use in assessing susceptibility to lung cancer in human individuals comprising (a) identifying at least one polymorphic marker in linkage disequilibrium with rs215614, rs6474412 or rs4105144; (b) obtaining sequence information about the at least one polymorphic marker in a group of individuals diagnosed with lung cancer; and (c) obtaining sequence information about the at least one polymorphic marker in a group of control individuals; wherein determination of a significant difference in frequency of at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer as compared with the frequency of the at least one allele in the control group is indicative of the at least one polymorphism is useful for assessing susceptibility to lung cancer.
  • an increase in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group is indicative of the at least one polymorphism being useful for assessing increased susceptibility to lung cancer
  • a decrease in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group is indicative of the at least one polymorphism being useful for assessing decreased susceptibility to, or protection against, lung cancer.
  • the invention also relates to methods of prognosis and response to therapy.
  • One such aspect provides a method of predicting prognosis of an individual diagnosed with lung cancer, the method comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs215614, rs6474412 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and predicting prognosis of lung cancer from the sequence data .
  • Another aspect provides a method of assessing probability of response of a human individual to a therapeutic agent for preventing, treating and/or ameliorating symptoms associated with lung cancer, comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different probabilities of response to the therapeutic agent in humans, and determining the probability of a positive response to the therapeutic agent from the sequence data.
  • kits for assessing susceptibility to lung cancer comprising reagents for selectively detecting at least one allele of at least one polymorphic marker in the genome of the individual, wherein the polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and a collection of data comprising correlation data between the at least one polymorphism and susceptibility to lung cancer.
  • an oligonucleotide probe in the manufacture of a diagnostic reagent for diagnosing and/or assessing a susceptibility to lung cancer, wherein the probe is capable of hybridizing to a segment of a nucleic acid whose nucleotide sequence is given by any one of SEQ ID NO: 1-737, and wherein the segment is 15-400 nucleotides in length.
  • One such aspect relates to a computer- readable medium having computer executable instructions for determining susceptibility to lung cancer, the computer readable medium comprising data indicative of at least one polymorphic marker, and a routine stored on the computer readable medium and adapted to be executed by a processor to determine risk of developing lung cancer for the at least one polymorphic marker, wherein the at least one polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith.
  • Another computer-implemented aspect relates to an apparatus for determining a genetic indicator for lung cancer in a human individual, comprising (a) a processor, and (b) a computer readable memory having computer executable instructions adapted to be executed on the processor to analyze marker information for at least one human individual with respect to at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and generate an output based on the marker information, wherein the output comprises a measure of susceptibility of the at least one marker or haplotype as a genetic indicator of the condition for the human individual.
  • FIG 1 shows the genomic regions of association on chromosomes 15q25 (A), 19q l3 (B), and 8pl l (C) and 7pl4 (D) associated with smoking quantity (CPD) and lung cancer. Shown are the -logio association P values of SNPs in the region with CPD from the ENGAGE meta analysis (circles), the in silico replication studies (plus-signs), and joint analysis of ENGAGE, TAG, and OX-GSK GWA data (crosses), the SNP build 36 coordinates, the genes in the region and their exons and recombination rates in centimorgans (cM) per megabase (Mb) (histogram) .
  • cM centimorgans
  • Mb megabase
  • FIG 2 provides a diagram illustrating a computer-implemented system utilizing risk variants as described herein.
  • nucleic acid sequences are written left to right in a 5' to 3' orientation .
  • Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer or any non-integer fraction within the defined range.
  • all technical and scientific terms used herein have the same meaning as commonly understood by the ordinary person skilled in the art to which the invention pertains.
  • the marker can comprise any allele of any variant type found in the genome, including SNPs, mini- or microsatellites, translocations and copy number variations (insertions, deletions, duplications) .
  • Polymorphic markers can be of any measurable frequency in the population . For mapping of disease genes, polymorphic markers with population frequency higher than 5-10% are in general most useful . However, polymorphic markers may also have lower population frequencies, such as 1-5% frequency, or even lower frequency, in particular copy number variations (CNVs) . The term shall, in the present context, be taken to include polymorphic markers with any population frequency.
  • an “allele” refers to the nucleotide sequence of a given locus (position) on a chromosome.
  • a polymorphic marker allele thus refers to the composition (i.e., sequence) of the marker on a chromosome.
  • CEPH sample (Centre d'Etudes du Polymorphisme Humain, genomics repository, CEPH sample 1347-02) is used as a reference, the shorter allele of each microsatellite in this sample is set as 0 and all other alleles in other samples are numbered in relation to this reference.
  • allele 1 is 1 bp longer than the shorter allele in the CEPH sample
  • allele 2 is 2 bp longer than the shorter allele in the CEPH sample
  • allele 3 is 3 bp longer than the lower allele in the CEPH sample
  • allele -1 is 1 bp shorter than the shorter allele in the CEPH sample
  • allele -2 is 2 bp shorter than the shorter allele in the CEPH sample, etc.
  • Sequence conucleotide ambiguity as described herein is as proposed by IUPAC-IUB. These codes are compatible with the codes used by the EMBL, GenBank, and PIR databases.
  • a nucleotide position at which more than one sequence is possible in a population is referred to herein as a "polymorphic site”.
  • a "Single Nucleotide Polymorphism” or "SNP” is a DNA sequence variation occurring when a single nucleotide at a specific location in the genome differs between members of a species or between paired chromosomes in an individual. Most SNP polymorphisms have two alleles. Each individual is in this instance either homozygous for one allele of the polymorphism (i.e. both chromosomal copies of the individual have the same nucleotide at the SNP location), or the individual is heterozygous (i .e. the two sister chromosomes of the individual contain different nucleotides).
  • the SNP nomenclature as reported herein refers to the official Reference SNP (rs) ID identification tag as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI) .
  • a “variant”, as described herein, refers to a segment of DNA that differs from the reference DNA.
  • a “marker” or a “polymorphic marker”, as defined herein, is a variant. Alleles that differ from the reference are referred to as “variant” alleles.
  • a "microsatellite” is a polymorphic marker that has multiple small repeats of bases that are 2-8 nucleotides in length (such as CA repeats) at a particular site, in which the number of repeat lengths varies in the general population .
  • An “indel” is a common form of polymorphism comprising a small insertion or deletion that is typically only a few nucleotides long.
  • haplotype refers to a segment of genomic DNA that is characterized by a specific combination of alleles arranged along the segment.
  • a haplotype comprises one member of the pair of alleles for each polymorphic marker or locus along the segment.
  • the haplotype can comprise two or more alleles, three or more alleles, four or more alleles, or five or more alleles.
  • Haplotypes are described herein in the context of the marker name and the allele of the marker in that haplotype, e.g., "4 rs6474412” refers to the 4 allele of marker rs6474412 being in the haplotype, and is equivalent to "rs6474412 allele 4".
  • susceptibility refers to the proneness of an individual towards the development of a certain state (e.g., a certain trait, phenotype or disease, e.g. lung cancer), or towards being less able to resist a particular state than the average individual.
  • the term encompasses both increased susceptibility and decreased susceptibility.
  • particular alleles at polymorphic markers of the invention as described herein are characteristic of increased susceptibility (i .e., increased risk) of lung cancer, as characterized by a relative risk (RR) or odds ratio (OR) of greater than one for the particular allele or haplotype.
  • Other particular alleles at the markers described herein are characteristic of decreased susceptibility (i.e., decreased risk) of lung cancer, as characterized by a relative risk of less than one.
  • look-up table is a table that correlates one form of data to another form, or one or more forms of data to a predicted outcome to which the data is relevant, such as phenotype or trait.
  • a look-up table can comprise a correlation between allelic data for at least one polymorphic marker and a particular trait or phenotype, such as a particular disease diagnosis, that an individual who comprises the particular allelic data is likely to display, or is more likely to display than individuals who do not comprise the particular allelic data.
  • Look-up tables can be multidimensional, i.e. they can contain information about multiple alleles for single markers simultaneously, or they can contain information about multiple markers, and they may also comprise other factors, such as particulars about diseases diagnoses, racial information, biomarkers, biochemical measurements, therapeutic methods or drugs, etc.
  • a "computer-readable medium” is an information storage medium that can be accessed by a computer using a commercially available or custom-made interface.
  • Exemplary computer- readable media include memory (e.g., RAM, ROM, flash memory, etc.), optical storage media (e.g., CD-ROM), magnetic storage media (e.g., computer hard drives, floppy disks, etc.), punch cards, or other commercially available media.
  • Information may be transferred between a system of interest and a medium, between computers, or between computers and the computer- readable medium for storage or access of stored information. Such transmission can be electrical, or by other available methods, such as IR links, wireless connections, etc.
  • nucleic acid sample refers to a sample obtained from an individual that contains nucleic acid (DNA or RNA).
  • the nucleic acid sample comprises genomic DNA.
  • a nucleic acid sample can be obtained from any source that contains genomic DNA, including a blood sample, sample of amniotic fluid, sample of cerebrospinal fluid, or tissue sample from skin, muscle, buccal or conjunctival mucosa, placenta, gastrointestinal tract or other organs.
  • lung cancer therapeutic agent refers to an agent that can be used to ameliorate or prevent symptoms associated with lung cancer.
  • lung cancer-associated nucleic acid refers to a nucleic acid that has been found to be associated to lung cancer. This includes, but is not limited to, the markers and haplotypes described herein and markers and haplotypes in strong linkage disequilibrium (LD) therewith.
  • LD linkage disequilibrium
  • antisense agent or “antisense oligonucleotide” refers, as described herein, to molecules, or compositions comprising molecules, which include a sequence of purine an pyrimidine heterocyclic bases, supported by a backbone, which are effective to hydrogen bond to a corresponding contiguous bases in a target nucleic acid sequence.
  • the backbone is composed of subunit backbone moieties supporting the purine and pyrimidine hetercyclic bases at positions which allow such hydrogen bonding. These backbone moieties are cyclic moieties of 5 to 7 atoms in size, linked together by phosphorous-containing linkage units of one to three atoms in length.
  • the antisense agent comprises an oligonucleotide molecule.
  • LD block C07 refers to the Linkage Disequilibrium (LD) block on Chromosome 7 between markers rs55661693 and rs215749, corresponding to position
  • LD block C08 refers to the Linkage Disequilibrium (LD) block on Chromosome 8 between markers s.42329845 (SEQ ID NO :431) and s.43167001 (SEQ ID NO: 616), corresponding to position 42,329,845 - 43,167,001 of NCBI, Build 36.
  • LD block C19 refers to the Linkage Disequilibrium (LD) block on Chromosome 19 between markers s.45831417 (SEQ ID NO: 617) and rsl0416968,
  • Variants associated with risk of lung cancer in humans The present inventors have for the first time shown that certain genetic variants are associated with risk of lung cancer in humans.
  • Certain polymorphic markers on chromosome 8pl l, 7pl4 and 19ql3 have been found to associate with risk of lung cancer.
  • Particular alleles at markers in these genomic regions e.g., rs215614 on chromosome 7pl4, rs6474412 on chromosome 8pll and rs4105144 on chromosome 19ql3
  • a first aspect relates to a method of determining a susceptibility to lung cancer, the method comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and determining a susceptibility to lung cancer from the sequence data, wherein the at least one polymorphic marker is a marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith.
  • the sequence data is nucleic acid sequence data.
  • Nucleic acid sequence data identifying particular alleles of polymorphic markers is sometimes also referred to as genotype data.
  • Nucleic acid sequence data can be obtained for example by analyzing sequence of the at least one polymorphic marker in a biological sample from the individual.
  • nucleic acid sequence data can be obtained in a genotype dataset from the human individual and analyzing sequence of the at least one polymorphic marker in the dataset.
  • Such analysis in certain embodiments comprises determining the presence or absence of a particular allele of specific polymorphic markers. Identification of particular alleles in general terms should be taken to mean that determination of the presence or absence of the allele(s) is made.
  • determination of both allelic copies in the genome of an individual is performed, by determining the occurrence of all possible alleles of the particular polymorphism in a particular individual (for SNPs, each of the two possible nucleotides possible for the allelic site) . It is also possible to determine whether only particular alleles are present or not. For example, in certain
  • determination of the presence or absence of certain alleles that have been shown to associate with risk of kidney cancer is made, but not necessarily other alleles of the particular marker, and a determination of susceptibility is made based on such determination.
  • sequence data about at least two polymorphic markers is obtained.
  • the allele that is detected can be the allele of the complementary strand of DNA, such that the nucleic acid sequence data includes the identification of at least one allele which is complementary to any of the alleles of the polymorphic markers referenced above.
  • the allele that is detected may be the complementary C allele of the at-risk G allele of rsl058396, the complementary G allele of the at-risk C allele of rsl l877062, the complementary C allele of the at-risk G allele of rs2298720, or the complementary T allele to the at-risk A allele of rs2298720.
  • the nucleic acid sequence data is obtained from a biological sample containing nucleic acid from the human individual .
  • the nucleic acids sequence may suitably be obtained using a method that comprises at least one procedure selected from (i) amplification of nucleic acid from the biological sample; (ii) hybridization assay using a nucleic acid probe and nucleic acid from the biological sample; (iii) hybridization assay using a nucleic acid probe and nucleic acid obtained by amplification of the biological sample, and (iv) sequencing, in particular high-throughput sequencing.
  • the nucleic acid sequence data may also be obtained from a preexisting record .
  • the preexisting record may comprise a genotype dataset for at least one polymorphic marker.
  • the determining comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to lung cancer.
  • certain embodiments of the methods of the invention comprise a further step of preparing a report containing results from the
  • report is written in a computer readable medium, printed on paper, or displayed on a visual display.
  • it may be convenient to report results of susceptibility to at least one entity selected from the group consisting of the individual, a guardian of the individual, a genetic service provider, a physician, a medical organization, and a medical insurer.
  • markers on chromosome 8pll that are predictive of lung cancer risk are markers associated with a gene selected from the group consisting of CHRNB3 and CHRNA6.
  • markers on chromosome 7pl4 that are predictive of lung cancer risk are markers associated with a gene selected from the group consisting of PDE1C
  • markers on chromosome 19ql3 that are predictive of lung cancer risk are markers associated with a gene selected from the group consisting of CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, and RAB4B.
  • the marker associated with risk of lung cancer is a marker located within LD block C07, LD block C08, or LD block C19, as defined herein .
  • markers in linkage disequilibrium with rs215614 are selected from the group consisting of the markers rs55661693, rs2392052, s.32208500, rsl860222, rsl017085, rsl0951323, rsl0951324, rs2240676, rs6462343, rs7779445, rs7796264, rsl6875791, rsl860224, s.32222361, rsl2672267, rs719585, rs6945244, rs719586, rsl2531292, rsl2533732, rsl l771370, rsl3241693, rsl3228936, rsl6875793, rsl7161043, rsl7161045, rsl7426873, rsl2669911, s.32229303,
  • markers in linkage disequilibrium with rs6474412 are selected from the group consisting of the markers s.42329845, s.42601955, s.42618302, rs7013926, rsl2156092, rsl868860, rsl530850, rs6989472, s.42626122, rsll785591, rsl947295, rsl376442, rs4737060, s.42631425, s.42632599, rs34842664, rsl868859, rsl868858, rsl0958724, rs28441235, rs7006469, rsl0097384, rs4305884, s.42639491, s.42639835, rs6990603, rs34456987, rsl0958725, rs36057318,
  • markers in linkage disequilibrium with rs4105144 are selected from the group consisting of the markers s.45831417, s.45859717, rsl l083565, rs2561537, rs2604885, rs7260405, rs2607420, rs2369302, rs2254343, rsl457141, rs2604874, s.45950500, s.45950502, rs2607415, rs2607414, rs2279011, rs2249835, rs2607424, rs2604869, rsl2973666, rs2604893, rs2644898, rs7252227, rs7937, rs2644916, rs3733828, rs4803372, s.46014685, s.46014686, rs4803373, s.46019153
  • Surrogate markers in linkage disequilibrium with particular key markers can in general be selected based on any particular numerical values of the linkage disequilibrium measures D' and r 2 , as described further herein.
  • markers that are in linkage disequilibrium with rs215614 are exemplified by the markers listed in Table 1 herein, but the skilled person will appreciate that other markers in linkage disequilibrium with these markers may also be used in the diagnostic applications described herein.
  • exemplary surrogate markers in linkage disequilibrium with rs6474412 are listed in Table 2 herein
  • exemplary surrogate markers in linkage disequilibrium with rs4105144 are listed in Table 3 herein .
  • linkage disequilibrium is a continuous measure
  • certain values of the LD measures D' and r 2 may be suitably chosen to define markers that are useful as surrogate markers in LD with the markers described herein.
  • Numeric values of D' and r 2 may thus in certain embodiments be used to define marker subsets that fulfill certain numerical cutoff values of D' and/or r 2 .
  • markers in linkage disequilibrium with a particular anchor marker are in LD with the anchor marker characterized by numerical values of D' greater than 0.8 and/or numerical values of r 2 of greater than 0.2.
  • markers in linkage disequilibrium with a particular anchor marker are in LD with the anchor marker characterized by numerical values of r 2 greater than 0.2.
  • the markers provided in Tables 1 to 3 provide exemplary markers that fulfill this criterion.
  • markers in linkage disequilibrium with a particular anchor marker are in LD with the anchor marker characterized by numerical values of r 2 of greater than 0.3, greater than 0.4, greater than 0.5, greater than 0.6, greater than 0.7, greater than 0.8, greater than 0.9, and greater than 0.95.
  • such markers are selected from the markers listed in Tables 1 to 3, using the information on LD measures provided in the tables.
  • other suitable numerical values of r 2 and/or D' may be used to select markers that are in LD with the anchor marker. The stronger the LD, the more similar the association signal and/or the predictive risk by the surrogate marker will be to that of the anchor marker.
  • suitable surrogate markers are those markers that have values of r 2 to an anchor marker of greater than 0.8.
  • LD may be determined in samples from any particular population.
  • LD is determined in Caucasian samples.
  • LD is determined in European samples.
  • LD is determined in African American samples, in Asian samples, or the LD may be suitably determined in samples of any other population.
  • Surrogate markers of anchor marker rs6474412 (SEQ ID NO:497) on Chromosome 8pl l. Markers were selected using data from Caucasian HapMap dataset or the publically available 1000 Genomes project (http://www.1000genomes.org). Markers that have not been assigned rs names are identified by their position in NCBI Build 36 of the human genome assembly. Shown are predicted risk alleles for the surrogate markers, i.e. alleles that are correlated with the risk allele of the anchor marker, rs6474412 allele T. Linkage disequilibrium measures D' and R 2 , and corresponding p-value, are also shown. The last column refers to the sequence listing number, identifying the particular SNP.
  • rs7822100 is a mixed SNP with possible alleles -/C/T/TTC where - means deletion
  • ** rs34727690 is an indel marker with possible alleles -/ CTATAT Table 3.
  • Surrogate markers of anchor marker rs4105144 (SEQ ID NO : 668) on Chromosome 19q l3. Markers were selected using data from Caucasian HapMap dataset or the publically available 1000 Genomes project (http ://www.1000genomes.org). Markers that have not been assigned rs names are identified by their position in NCBI Build 36 of the human genome assembly. Shown are predicted risk alleles for the surrogate markers, i.e. alleles that are correlated with the risk allele of the anchor marker, rs4105144 allele C. Linkage disequilibrium measures D' and R 2 , and corresponding p-value, are also shown. The last column refers to the sequence listing number, identifying the particular SNP.
  • sequence data that is obtained may in certain embodiments, be amino acid sequence data .
  • Polymorphic markers can result in alterations in the amino acid sequence of encoded polypeptide or protein sequence.
  • the analysis of amino acid sequence data comprises determining the presence or absence of an amino acid substitution in the amino acid encoded by the at least one polymorphic marker.
  • Sequence data can in certain embodiments be obtained by analyzing the amino acid sequence encoded by the at least one polymorphic marker in a biological sample obtained from the individual.
  • the at least one polymorphic marker that is assessed is an amino acid substitution in a polypeptide encoded by a gene selected from the group consisting of the human CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B genes.
  • the marker may be an amino acid substitution in a polypeptide encoded by any one of those genes.
  • determination of the presence of particular marker alleles or particular haplotypes is predictive of an increased susceptibility of lung cancer in humans.
  • determination of the presence of a marker allele selected from the group consisting of the T allele of rs6474412, the G allele of rs215614, the T allele of rs7937, the C allele of rs4105144 and the G allele of rs7260329 is indicative of increased risk of lung cancer in the individual.
  • These marker alleles confer increased risk of lung cancer with relative risk or odds ratio of greater than unity, and are sometimes also referred to as at-risk alleles or at-risk variants.
  • risk alleles as presented in the surrogate markers Tables 1 to 3 are at-risk alleles indicative of increased risk of lung cancer. Individuals who are homozygous for at-risk alleles are at particularly high risk of developing lung cancer, since their genome includes two copies of the at-risk variant.
  • Measures of susceptibility or risk include measures such as relative risk (RR), odds ratio (OR), and absolute risk (AR), as described in more detail herein .
  • increased susceptibility refers to a risk with values of RR or OR of at least 1.05, at least 1.06, at least 1.07, at least 1.08, at least 1.09, at least 1.10, at least 1.11, at least 1.12, at least 1.13, at least 1.14 or at least 1.15.
  • Other numerical non-integer values greater than unity are also possible to characterize the risk, and such numerical values are also contemplated.
  • Certain embodiments relate to homozygous individuals for a particular marker, i.e. individuals who carry two copies of the same allele in their genome.
  • One preferred embodiment relates to individuals who are homozygous carriers of an allele selected from the group consisting of the T allele of rs6474412, the G allele of rs215614, the T allele of rs7937, the C allele of rs4105144 and the G allele of rs7260329, or a marker allele in linkage disequilibrium therewith .
  • determination of the presence of particular marker alleles or particular haplotypes is predictive of a decreased susceptibility of lung cancer in humans.
  • the alternate allele to an at-risk allele will be in decreased frequency in patients compared with controls.
  • determination of the presence of the non-risk (alternate) allele is indicative of a decreased susceptibility of lung cancer.
  • Individuals who are homozygous for the alternate (protective) allele are at particularly decreased susceptibility or risk.
  • the control individuals may be a random sample from the general population, i.e. a population cohort.
  • the control individuals may also be a sample from individuals that are disease-free, e.g. individuals who have been confirmed not to have lung cancer, or individuals who have not been diagnosed with lung cancer.
  • an increase in frequency of at least one allele in at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group is indicative of the at least one allele being useful for assessing increased susceptibility to lung cancer.
  • a decrease in frequency of at least one allele in at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control sample is indicative of the at least one allele being useful for assessing decreased susceptibility to, or protection against, lung cancer.
  • sequence data can be obtained by analyzing a sample from an individual, or by analyzing information about specific markers in a database or other data collection, for example a genotype database or a sequence database.
  • the sample is in certain embodiments a nucleic acid sample, or a sample that contains nucleic acid material.
  • Analyzing a sample from an individual may in certain embodiments include steps of isolating genomic nucleic acid from the sample, amplifying a segment of the genomic nucleic acid that contains at least one polymorphic marker, and determine sequence information about the at least one polymorphic marker.
  • sequence data can be obtained through nucleic acid sequence information or amino acid sequence information from a preexisting record.
  • a preexisting record can be any documentation, database or other form of data storage containing such information.
  • Determination of a susceptibility or risk of a particular individual in general comprises comparison of the genotype information (sequence information) of the individual to a record or database providing a correlation about particular polymorphic marker(s) and susceptibility to disease, e.g. lung cancer.
  • determining a susceptibility comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to lung cancer.
  • the database comprises at least one measure of susceptibility to lung cancer for the at least one polymorphic marker.
  • the database comprises a look-up table comprising at least one measure of susceptibility to kidney cancer for the at least one polymorphic marker.
  • the measure of susceptibility may in the form of relative risk (RR), absolute risk (AR), percentage (%) .
  • RR relative risk
  • AR absolute risk
  • percentage percentage
  • Certain embodiments of the invention relate to markers associated with particular genes, e.g. the human CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B genes. Markers associated with one or more of these genes are in certain embodiments useful susceptibility markers of lung cancer. Markers that are associated with any one of these genes are in certain embodiments markers that are in linkage
  • markers are located within the genomic segments LD block C07, LD block C08 or LD block C19, as defined herein .
  • markers associated with a particular gene are selected from the markers within the gene, i.e. within the genomic region that contain exons, introns and promoter sequences of the gene.
  • Certain embodiments of the invention relate to markers located within the LD block C07, LD block C08 or LD block C19 as defined herein . It is however also contemplated that surrogate markers may be located outside the physical boundaries of these LD blocks as defined by their genomic locations. This is because, recombination events may have led to certain risk surrogates having been "separated” from the main cluster of surrogates, although these surrogates are detecting the same variant. Thus, certain embodiments of the invention are contemplated to also encompass surrogate markers in linkage disequilibrium with rs215614, rs6474412 or rs4105144 that are located outside the physical boundaries of LD block C07, LD block C08 or LD block C19 as defined.
  • more than one polymorphic marker is analyzed to determine lung cancer risk.
  • at least two polymorphic markers are analyzed.
  • nucleic acid data about at least two polymorphic markers is obtained.
  • a further step of analyzing at least one haplotype comprising two or more polymorphic markers is included. Any convenient method for haplotype analysis known to the skilled person, such as those described in more detail herein, may be employed in such embodiments.
  • One aspect of the invention relates to a method for determining a susceptibility to lung cancer in a human individual, comprising determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, or in a genotype dataset from the individual, wherein the at least one polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and wherein determination of the presence of the at least one allele is indicative of a susceptibility to lung cancer.
  • Determination of the presence of an allele that correlates with lung cancer is indicative of an increased susceptibility to lung cancer.
  • Individuals who are homozygous for such alleles are particularly susceptible to lung cancer.
  • individuals who do not carry such at-risk alleles are at a decreased susceptibility of developing lung cancer, as compared with a randomly selected individual from the general population. For SNPs, such individuals will be homozygous for the alternate (protective) allele of the polymorphism.
  • Determination of susceptibility is in some embodiments reported by a comparison with non- carriers of the at-risk allele(s) of polymorphic markers. In certain embodiments, susceptibility is reported based on a comparison with the general population, e.g. compared with a random selection of individuals from the population .
  • polymorphic markers are detected by sequencing technologies.
  • sequence information about an individual identifies particular nucleotides in the context of a nucleic acid sequence.
  • sequence information about a single unique sequence site is sufficient to identify alleles at that particular SNP.
  • sequence information about the genomic region of the individual that contains the polymorphic site identifies the alleles of the individual for the particular site.
  • the sequence information can be obtained from a sample from the individual .
  • the sample is a nucleic acid sample. In certain other embodiments, the sample is a protein sample.
  • nucleic acid sequence Various methods for obtaining nucleic acid sequence are known to the skilled person, and all such methods are useful for practicing the invention.
  • Sanger sequencing is a well-known method for generating nucleic acid sequence information.
  • Recent methods for obtaining large amounts of sequence data have been developed, and such methods are also contemplated to be useful for obtaining sequence information .
  • These include pyrosequencing technology (Ronaghi, M . et al. Anal Biochem 267: 65-71 (1999); Ronaghi, et al. Biotechniques 25: 876-878 (1998)), e.g. 454 pyrosequencing (Nyren, P., et al.
  • genomic sequence within populations is not identical when individuals are compared .
  • the genome exhibits sequence variability between individuals at many locations in the genome.
  • Such variations in sequence are commonly referred to as polymorphisms, and there are many such sites within each genome.
  • the human genome exhibits sequence variations which occur on average every 500 base pairs.
  • the most common sequence variant consists of base variations at a single base position in the genome, and such sequence variants, or polymorphisms, are commonly called Single Nucleotide Polymorphisms ("SNPs”) .
  • SNPs Single Nucleotide Polymorphisms
  • a polymorphic microsatellite has multiple small repeats of bases (such as CA repeats, TG on the complimentary strand) at a particular site in which the number of repeat lengths varies in the general population .
  • each version of the sequence with respect to the polymorphic site represents a specific allele of the polymorphic site.
  • sequence variants can all be referred to as polymorphisms, occurring at specific polymorphic sites characteristic of the sequence variant in question .
  • polymorphisms can comprise any number of specific alleles within the population, although each human individual has two alleles at each polymorphic site - one maternal and one paternal allele.
  • the polymorphism is characterized by the presence of two or more alleles in the population . In another embodiment, the polymorphism is characterized by the presence of three or more alleles in the population . In other embodiments, the polymorphism is characterized by four or more alleles, five or more alleles, six or more alleles, seven or more alleles, nine or more alleles, or ten or more alleles. All such polymorphisms can be utilized in the methods and kits of the present invention, and are thus within the scope of the invention .
  • SNPs Due to their abundance, SNPs account for a majority of sequence variation in the human genome. Over 6 million human SNPs have been validated to date
  • CNVs are receiving increased attention .
  • These large-scale polymorphisms (typically lkb or larger) account for polymorphic variation affecting a substantial proportion of the assembled human genome; known CNVs covery over 15% of the human genome sequence (Estivill, X Armengol; L, PloS Genetics 3 : 1787-99 (2007); http://projects.tcag.ca/variation/) .
  • Most of these polymorphisms are however very rare, and on average affect only a fraction of the genomic sequence of each individual.
  • CNVs are known to affect gene expression, phenotypic variation and adaptation by disrupting gene dosage, and are also known to cause disease (microdeletion and
  • CNVs include comparative genomic hybridization (CGH) and genotyping, including use of genotyping arrays, as described by Carter (Nature Genetics 39:S16-S21 (2007)) .
  • the Database of Genomic Variants http://projects.tcag.ca/variation/) contains updated information about the location, type and size of described CNVs. The database currently contains data for over 21,000 CNVs.
  • reference is made to different alleles at a polymorphic site without choosing a reference allele.
  • a reference sequence can be referred to for a particular polymorphic site.
  • the reference allele is sometimes referred to as the "wild-type” allele and it usually is chosen as either the first sequenced allele or as the allele from a "non-affected" individual (e.g., an individual that does not display a trait or disease phenotype) .
  • Alleles for SNP markers as referred to herein refer to the bases A, C, G or T as they occur at the polymorphic site.
  • the methodology employed to detect the marker may be designed to specifically detect the presence of one or both of the two bases possible, i.e. A and G.
  • the methodology employed to detect the marker may be designed to specifically detect the presence of one or both of the two bases possible, i.e. A and G.
  • an assay that is designed to detect the complimentary strand on the DNA template the presence of the complementary bases T and C can be measured . Quantitatively (for example, in terms of risk estimates), identical results would be obtained from measurement of either DNA strand (+ strand or - strand) .
  • variant sequence refers to a sequence that differs from the reference sequence but is otherwise substantially similar. Alleles at the polymorphic genetic markers described herein are variants. Variants can include changes that affect a polypeptide.
  • Sequence differences when compared to a reference nucleotide sequence, can include the insertion or deletion of a single nucleotide, or of more than one nucleotide, resulting in a frame shift; the change of at least one nucleotide, resulting in a change in the encoded amino acid; the change of at least one nucleotide, resulting in the generation of a premature stop codon; the deletion of several nucleotides, resulting in a deletion of one or more amino acids encoded by the nucleotides; the insertion of one or several nucleotides, such as by unequal recombination or gene conversion, resulting in an interruption of the coding sequence of a reading frame; duplication of all or a part of a sequence; transposition; or a rearrangement of a nucleotide sequence,.
  • sequence changes can alter the polypeptide encoded by the nucleic acid .
  • the change in the nucleic acid sequence causes a frame shift
  • the frame shift can result in a change in the encoded amino acids, and/or can result in the generation of a premature stop codon, causing generation of a truncated polypeptide.
  • a polymorphism can be a synonymous change in one or more nucleotides (i.e., a change that does not result in a change in the amino acid sequence) .
  • Such a polymorphism can, for example, alter splice sites, affect the stability or transport of mRNA, or otherwise affect the transcription or translation of an encoded polypeptide. It can also alter DNA stability so as to increase the possibility that structural changes, such as amplifications or deletions, occur at the somatic level.
  • a haplotype refers to a single-stranded segment of DNA that is characterized by a specific combination of alleles arranged along the segment.
  • a haplotype comprises one member of the pair of alleles for each polymorphic marker or locus .
  • the haplotype can comprise two or more alleles, three or more alleles, four or more alleles, or five or more alleles, each allele corresponding to a specific polymorphic marker along the segment.
  • Haplotypes can comprise a combination of various polymorphic markers, e.g., SNPs and microsatellites, having particular alleles at the polymorphic sites. The haplotypes thus comprise a combination of alleles at various genetic markers.
  • Detecting specific polymorphic markers and/or haplotypes can be accomplished by methods known in the art for detecting sequences at polymorphic sites.
  • standard techniques for genotyping for the presence of SNPs and/or microsatellite markers can be used, such as fluorescence-based techniques (e.g. , Chen, X. et a/., Genome Res. 9(5) : 492-98 (1999); Kutyavin et al. , Nucleic Acid Res. 34:el28 (2006)), utilizing PCR, LCR, Nested PCR and other techniques for nucleic acid amplification.
  • SNP genotyping include, but are not limited to, TaqMan genotyping assays and SNPlex platforms (Applied Biosystems), gel electrophoresis (Applied Biosystems), mass spectrometry (e.g. , MassARRAY system from Sequenom), minisequencing methods, real-time PCR, Bio-Plex system (BioRad), CEQ and SNPstream systems (Beckman), array hybridization technology(e.g., Affymetrix GeneChip; Perlegen), BeadArray Technologies (e.g., Illumina GoldenGate and Infinium assays), array tag technology (e.g., Parallele), and endonuclease-based fluorescence hybridization technology (Invader; Third Wave) .
  • Some of the available array platforms including Affymetrix SNP Array 6.0 and Illumina CNV370-Duo and 1M BeadChips, include SNPs that tag certain CNVs. This allows detection of CNVs via surrogate SNPs included in these platforms.
  • one or more alleles at polymorphic markers including microsatellites, SNPs or other types of polymorphic markers, can be identified.
  • polymorphic markers are detected by sequencing technologies.
  • sequence information about an individual identifies particular nucleotides in the context of a sequence.
  • sequence information about a single unique sequence site is sufficient to identify alleles at that particular SNP.
  • sequence information about the nucleotides of the individual that contain the polymorphic site identifies the alleles of the individual for the particular site.
  • the sequence information can be obtained from a sample from the individual.
  • the sample is a nucleic acid sample.
  • the sample is a protein sample.
  • Various methods for obtaining nucleic acid sequence are known to the skilled person, and all such methods are useful for practicing the invention. Sanger sequencing is a well-known method for generating nucleic acid sequence information.
  • genotypes of un-genotyped relatives For every un-genotyped case, it is possible to calculate the probability of the genotypes of its relatives given its four possible phased genotypes. In practice it may be preferable to include only the genotypes of the case's parents, children, siblings, half-siblings (and the half-sibling's parents), grand-parents, grand-children (and the grand-children's parents) and spouses. It will be assumed that the individuals in the small sub-pedigrees created around each case are not related through any path not included in the pedigree. It is also assumed that alleles that are not transmitted to the case have the same frequency - the population allele frequency. Let us consider a SNP marker with the alleles A and G. The probability of the genotypes of the case's relatives can then be computed by:
  • Pr(genotypes of relatives; ⁇ ) ⁇ Pr( z; ⁇ ) Pr(genotypes of relatives
  • the likelihood function in (*) may be thought of as a pseudolikelihood approximation of the full likelihood function for ⁇ which properly accounts for all dependencies.
  • genotyped cases and controls in a case-control association study are not independent and applying the case-control method to related cases and controls is an analogous approximation .
  • the method of genomic control (Devlin, B. et al ., Nat Genet 36, 1129-30; author reply 1131 (2004)) has proven to be successful at adjusting case-control test statistics for relatedness. We therefore apply the method of genomic control to account for the dependence between the terms in our
  • an individual who is at an increased susceptibility (i.e., increased risk) for a disease is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring increased susceptibility (increased risk) for the disease is identified (i.e., at-risk marker alleles or haplotypes).
  • the at-risk marker or haplotype is one that confers an increased risk (increased susceptibility) of the disease.
  • significance associated with a marker or haplotype is measured by a relative risk (RR).
  • significance associated with a marker or haplotye is measured by an odds ratio (OR).
  • the significance is measured by a percentage.
  • a significant increased risk is measured as a risk (relative risk and/or odds ratio) of at least 1.05, including but not limited to: at least 1.06, at least 1.07, at least 1.08, at least 1.09, at least 1.10, at least 1.11, at least 1.12, at least 1.13, at least 1.14, at least 1.15, at least 1.16, at least 1.17, at least 1.18, at least 1.19, at least 1.20, at least 1.25, at least 1.30, at least 1.35, at least 1.40, at least 1.45 and at least 1.50.
  • a risk (relative risk and/or odds ratio) of at least 1.05 is significant.
  • a risk of at least 1.09 is significant.
  • a risk of at least 1.10 is significant.
  • a relative risk of at least 1.12 is significant.
  • another further further risk of at least 1.05 including but not limited to: at least 1.06, at least 1.07, at least 1.08, at least 1.09, at least 1.10, at least 1.11, at least 1.12
  • a significant increase in risk is at least 1.15 is significant.
  • other cutoffs are also contemplated, e.g., at least 1.20, 1.25, 1.35, and so on, and such cutoffs are also within scope of the present invention.
  • a significant increase in risk is at least about 5%, including but not limited to about 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, and 20%.
  • a significant increase in risk is at least 9%.
  • a significant increase in risk is at least 10%, at least 12%, and at least 15%.
  • a significant increase in risk is characterized by a p-value, such as a p-value of less than 0.05, less than 0.01, less than 0.001, less than 0.0001, less than 0.00001, less than 0.000001, less than 0.0000001, less than 0.00000001, or less than 0.000000001.
  • An at-risk polymorphic marker or haplotype as described herein is one where at least one allele of at least one marker or haplotype is more frequently present in an individual at risk for the disease (e.g., lung cancer) (affected), or diagnosed with the disease, compared to the frequency of its presence in a comparison group (control), such that the presence of the marker or haplotype is indicative of susceptibility to the disease.
  • the control group may in one
  • control group is represented by a group of individuals who are disease-free, e.g. those that have not been diagnosed with lung cancer.
  • disease-free control group is characterized by the absence of one or more disease-specific risk factors.
  • risk factors are in one embodiment at least one environmental risk factor, such as smoking .
  • the control group comprises individuals who have never smoked and have never been diagnosed with lung cancer.
  • the control group comprises individuals who do have a history of smoking but have not been diagnosed with lung cancer.
  • a simple test for correlation would be a Fisher-exact test on a two by two table.
  • the two by two table is constructed out of the number of chromosomes that include both of the markers or haplotypes, one of the markers or haplotypes but not the other and neither of the markers or haplotypes.
  • Other statistical tests of association known to the skilled person are also contemplated and are also within scope of the invention.
  • an individual who is at a decreased susceptibility (i.e., at a decreased risk) for a disease is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring decreased susceptibility for the disease is identified.
  • the marker alleles and/or haplotypes conferring decreased risk are also said to be protective.
  • the protective marker or haplotype is one that confers a significant decreased risk (or susceptibility) of the disease or trait.
  • significant decreased risk is measured as a relative risk (or odds ratio) of less than 0.95, including but not limited to less than 0.90, less than 0.85, less than 0.80, less and less than 0.75.
  • the decrease in risk is at least 5%, including but not limited to 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 20%, at least 25%, and at least 30%.
  • a significant decrease in risk is at least about 10%.
  • a significant decrease in risk is at least about 12%.
  • the decrease in risk is at least about 15%.
  • Other cutoffs or ranges as deemed suitable by the person skilled in the art to characterize the invention are however also contemplated, and those are also within scope of the present invention.
  • a genetic variant associated with a disease can be used alone to predict the risk of the disease for a given genotype.
  • a biallelic marker such as a SNP
  • the combined risk is the product of the locus specific risk values and also corresponds to an overall risk estimate compared with the
  • the combined risk corresponds to an estimate that compares the person with a given combination of genotypes at all loci to a group of individuals who do not carry risk variants at any of those loci.
  • the group of non-carriers of any at risk variant has the lowest estimated risk and has a combined risk compared with itself ⁇ i.e., non-carriers) of 1.0, but has an overall risk, compare with the population, of less than 1.0. It should be noted that the group of non-carriers can potentially be very small, especially for large number of loci, and in that case, its relevance is correspondingly small .
  • the multiplicative model is a parsimonious model that usually fits the data of complex traits reasonably well. Deviations from multiplicity have been rarely described in the context of common variants for common diseases, and if reported are usually only suggestive since very large sample sizes are usually required to be able to demonstrate statistical interactions between loci.
  • multiplicative model applied in the case of multiple genetic variant will also be valid in conjugation with non-genetic risk variants assuming that the genetic variant does not clearly correlate with the "environmental" factor.
  • genetic and non-genetic at- risk variants can be assessed under the multiplicative model to estimate combined risk, assuming that the non-genetic and genetic risk factors do not interact.
  • the combined or overall risk associated with any plurality of variants associated with lung cancer may be assessed.
  • the combined risk of any plurality of the variants described herein ⁇ e.g., rs6474412, rs215614 and rs4105144, and their surrogates
  • other markers described to be associated with risk of lung cancer may be assessed in combination of any one of the markers described herein, e.g. markers in the CHRNA5/CHRNA3/CHRNB4 gene cluster on chromosome 15 ⁇ e.g, rsl051730, or its surrogates) .
  • Linkage Disequilibrium refers to a non-random assortment of two genetic elements. For example, if a particular genetic element (e.g. , an allele of a polymorphic marker, or a haplotype) occurs in a population at a frequency of 0.50 (50%) and another element occurs at a frequency of 0.50 (50%), then the predicted occurrance of a person's having both elements is 0.25 (25%), assuming a random distribution of the elements.
  • a particular genetic element e.g. , an allele of a polymorphic marker, or a haplotype
  • Allele or haplotype frequencies can be determined in a population by genotyping individuals in a population and determining the frequency of the occurence of each allele or haplotype in the population .
  • populations of diploids e.g. , human populations, individuals will typically have two alleles or allelic combinations for each genetic element (e.g. , a marker, haplotype or gene) .
  • that is ⁇ 1 indicates that historical recombination may have occurred between two sites (recurrent mutation can also cause
  • the measure r 2 represents the statistical correlation between two sites, and takes the value of 1 if only two haplotypes are present.
  • the r 2 measure is arguably the most relevant measure for association mapping, because there is a simple inverse relationship between r 2 and the sample size required to detect association between susceptibility loci and SNPs. These measures are defined for pairs of sites, but for some applications a determination of how strong LD is across an entire region that contains many polymorphic sites might be desirable (e.g. , testing whether the strength of LD differs significantly among loci or across populations, or whether there is more or less LD in a region than predicted under a particular model) . Roughly speaking, r measures how much recombination would be required under a particular population model to generate the LD that is seen in the data .
  • a significant r 2 value between markers indicative of the markers being in linkage disequilibrium can be at least 0.1, such as at least 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or at least 0.99.
  • the significant r 2 value can be at least 0.2. In another embodiment, the significant r 2 value is at least 0.3.
  • the significant r 2 value is at least 0.4.
  • Other r 2 values are also contemplated, and are also within the scope of the invention, including but not limited to at least 0.5, 0.6, 0.7, 0.8 and 0.9.
  • the values of r 2 given in the surrogate Tables 1 to 3 may be used to select markers fulfilling any suitable criteria of r 2 values.
  • Linkage disequilibrium can be determined in a single human population, as defined herein, or it can be determined in a collection of samples comprising individuals from more than one human population .
  • LD is determined in a sample from one or more of the HapMap populations
  • LD is determined in the CEU population of the HapMap samples (Utah residents with ancestry from northern and western Europe). In another embodiment, LD is determined in the YRI population of the HapMap samples (Yuroba in Ibadan, Nigeria) . In another embodiment, LD is determined in the CHB population of the HapMap samples (Han Chinese from Beijing, China) . In another embodiment, LD is determined in the JPT population of the HapMap samples (Japanese from Tokyo, Japan) . In yet another embodiment, LD is determined in samples from the Icelandic population .
  • polymorphisms that need to be investigated in an association study to observe a significant association.
  • Another consequence of LD is that many polymorphisms may give an association signal due to the fact that these polymorphisms are strongly correlated .
  • Genomic LD maps have been generated across the genome, and such LD maps have been proposed to serve as framework for mapping disease-genes (Risch, N . & Merkiangas, K, Science 273 : 1516-1517 (1996); Maniatis, N ., et ai., Proc Natl Acad Sci USA 99 : 2228-2233 (2002); Reich, DE et ai, Nature 411 : 199-204 (2001)) .
  • blocks can be defined as regions of DNA that have limited haplotype diversity (see, e.g., Daly, M . et al., Nature Genet. 29: 229-232 (2001); Patil, N . et a ⁇ ., Science 294: 1719-1723 (2001); Dawson, E. et al., Nature 4.28: 544-548 (2002); Zhang, K. et al., Proc. Natl. Acad. Sci. USA 99: 7335-7339 (2002)), or as regions between transition zones having extensive historical recombination, identified using linkage disequilibrium (see, e.g., Gabriel, S.B.
  • the map reveals the enormous variation in recombination across the genome, with recombination rates as high as 10-60 cM/Mb in hotspots, while closer to 0 in intervening regions, which thus represent regions of limited haplotype diversity and high LD.
  • the map can therefore be used to define haplotype blocks/LD blocks as regions flanked by recombination hotspots.
  • haplotype block or "LD block” includes blocks defined by any of the above described characteristics, or other alternative methods used by the person skilled in the art to define such regions.
  • Haplotype blocks can be used to map associations between phenotype and haplotype status, using single markers or haplotypes comprising a plurality of markers.
  • the main haplotypes can be identified in each haplotype block, and then a set of "tagging" SNPs or markers (the smallest set of SNPs or markers needed to distinguish among the haplotypes) can then be identified .
  • These tagging SNPs or markers can then be used in assessment of samples from groups of individuals, in order to identify association between phenotype and haplotype. For example, markers shown herein to be associated with lung cancer are such tagging markers.
  • markers used to detect association thus in a sense represent "tags" for a genomic region (i.e., a haplotype block or LD block) that is associating with a given disease or trait, and as such are useful for use in the methods and kits of the present invention.
  • One or more causative (functional) variants or mutations may reside within the region found to be associating to the disease or trait.
  • the functional variant may be another SNP, a tandem repeat polymorphism (such as a minisatellite or a microsatellite), a transposable element, or a copy number variation, such as an inversion, deletion or insertion.
  • Such variants in LD with the variants described herein may confer a higher relative risk (RR) or odds ratio (OR) than observed for the tagging markers used to detect the association.
  • the present invention thus refers to the markers used for detecting association to the disease, as described herein, as well as markers in linkage disequilibrium with the markers.
  • markers that are in LD with the markers originally used to detect an association may be used as surrogate markers.
  • the surrogate markers have in one embodiment relative risk (RR) and/or odds ratio (OR) values smaller than originally detected.
  • the surrogate markers have RR or OR values greater than those initially determined for the markers initially found to be associating with the disease.
  • An example of such an embodiment would be a rare, or relatively rare (such as ⁇ 10% allelic population frequency) variant in LD with a more common variant (> 10% population frequency) initially found to be associating with the disease. Identifying and using such surrogate markers for detecting the association can be performed by routine methods well known to the person skilled in the art, and are therefore within the scope of the present invention .
  • the frequencies of haplotypes in patient and control groups can be estimated using an expectation-maximization algorithm (Dempster A. et al., J. R. Stat. Soc. B, 39 : 1-38 (1977)) .
  • An implementation of this algorithm that can handle missing genotypes and uncertainty with the phase can be used .
  • the patients and the controls are assumed to have identical frequencies.
  • a likelihood approach an alternative hypothesis is tested, where a candidate at-risk-haplotype, which can include the markers described herein, is allowed to have a higher frequency in patients than controls, while the ratios of the frequencies of other haplotypes are assumed to be the same in both groups.
  • Likelihoods are maximized separately under both hypotheses and a corresponding 1-df likelihood ratio statistic is used to evaluate the statistical significance.
  • a susceptibility region for example within an LD block
  • association of all possible combinations of genotyped markers within the region is studied.
  • the combined patient and control groups can be randomly divided into two sets, equal in size to the original group of patients and controls.
  • the marker and haplotype analysis is then repeated and the most significant p-value registered is determined.
  • This randomization scheme can be repeated, for example, over 100 times to construct an empirical distribution of p-values.
  • a p-value of ⁇ 0.05 is indicative of a significant marker and/or haplotype association .
  • haplotype analysis involves using likelihood-based inference applied to NEsted MOdels (Gretarsdottir S., et al., Nat. Genet. 35: 131-38 (2003)) .
  • the method is implemented in the program NEMO, which allows for many polymorphic markers, SNPs and microsatellites.
  • the method and software are specifically designed for case-control studies where the purpose is to identify haplotype groups that confer different risks. It is also a tool for studying LD structures.
  • maximum likelihood estimates, likelihood ratios and p-values are calculated directly, with the aid of the EM algorithm, for the observed data treating it as a missing-data problem .
  • the Fisher exact test can be used to calculate two- sided p-values for each individual allele. Correcting for relatedness among patients can be done by extending a variance adjustment procedure previously described (Risch, N. &Teng, J.
  • the method of genomic controls (Devlin, B. & Roeder, K. Biometrics 55:997 (1999)) can also be used to adjust for the relatedness of the individuals and possible stratification.
  • relative risk and the population attributable risk (PAR) can be calculated assuming a multiplicative model (haplotype relative risk model) (Terwilliger, J.D. & Ott, J., Hum. Hered.42:337-46 (1992) and Falk, C.T. & Rubinstein, P, Ann. Hum. Genet. 51 (Pt 3):227-33 (1987)), i.e., that the risks of the two alleles/haplotypes a person carries multiply.
  • a multiplicative model haplotype relative risk model
  • haplotypes are independent, i.e., in Hardy-Weinberg equilibrium, within the affected population as well as within the control population.
  • fand p denote, respectively, frequencies in the affected population and in the control population. While there is some power loss if the true model is not multiplicative, the loss tends to be mild except for extreme cases. Most importantly, p-values are always valid since they are computed with respect to null hypothesis.
  • An association signal detected in one association study may be replicated in a second cohort, ideally from a different population ⁇ e.g., different region of same country, or a different country) of the same or different ethnicity.
  • the advantage of replication studies is that the number of tests performed in the replication study is usually quite small, and hence the less stringent the statistical measure that needs to be applied. For example, for a genome-wide search for susceptibility variants for a particular disease or trait using 300,000 SNPs, a correction for the 300,000 tests performed (one for each SNP) can be performed. Since many SNPs on the arrays typically used are correlated ⁇ i.e., in LD), they are not independent. Thus, the correction is conservative.
  • the appropriate statistical test for significance is that for a single statistical test, i.e., P-value less than 0.05.
  • Replication studies in one or even several additional case-control cohorts have the added advantage of providing assessment of the association signal in additional populations, thus simultaneously confirming the initial finding and providing an assessment of the overall significance of the genetic variant(s) being tested in human populations in general.
  • the results from several case-control cohorts can also be combined to provide an overall assessment of the underlying effect.
  • the methodology commonly used to combine results from multiple genetic association studies is the Mantel-Haenszel model (Mantel and Haenszel, J Natl Cancer Inst 22 : 719-48 (1959)).
  • the model is designed to deal with the situation where association results from different populations, with each possibly having a different population frequency of the genetic variant, are combined.
  • the model combines the results assuming that the effect of the variant on the risk of the disease, a measured by the OR or RR, is the same in all populations, while the frequency of the variant may differ between the populations.
  • an absolute risk of developing a disease or trait defined as the chance of a person developing the specific disease or trait over a specified time-period .
  • a woman's lifetime absolute risk of breast cancer is one in nine. That is to say, one woman in every nine will develop breast cancer at some point in their lives.
  • Risk is typically measured by looking at very large numbers of people, rather than at a particular individual. Risk is often presented in terms of Absolute Risk (AR) and Relative Risk (RR) .
  • AR Absolute Risk
  • RR Relative Risk
  • Relative Risk is used to compare risks associating with two variants or the risks of two different groups of people. For example, it can be used to compare a group of people with a certain genotype with another group having a different genotype.
  • a relative risk of 2 means that one group has twice the chance of developing a disease as the other group.
  • the creation of a model to calculate the overall genetic risk involves two steps: i) conversion of odds-ratios for a single genetic variant into relative risk and ii) combination of risk from multiple variants in different genetic loci into a single relative risk value.
  • allelic odds ratio equals the risk factor:
  • RR(aa) Pr(A
  • aa)/Pr(A) (Pr(A
  • allele T of marker rs6474412 has an allelic OR of 1.12 and a frequency (p) around 0.8 in white populations.
  • the genotype relative risk compared to genotype CC are estimated based on the multiplicative model.
  • Population frequency of each of the three possible genotypes at this marker is:
  • the at-risk allele T is common in the population, which means that a large proportion of the population is at-risk. Therefore, the risk compared with the general population is relatively small than the risk compared with non-carriers of the at-risk T allele.
  • RR(gl,g2) RR(g l)RR(g2)
  • gl,g2) Pr(A
  • g2)/Pr(A) and Pr(gl,g2) Pr(gl)Pr(g2)
  • Obvious violations to this assumption are markers that are closely spaced on the genome, i .e. in linkage disequilibrium, such that the concurrence of two or more risk alleles is correlated.
  • the model applied is not expected to be exactly true since it is not based on an underlying bio-physical model.
  • the multiplicative model has so far been found to fit the data adequately, i.e. no significant deviations are detected for many common diseases for which many risk variants have been discovered.
  • certain polymorphic markers are found to be useful for risk assessment of lung cancer.
  • Risk assessment can involve the use of such markers for determining a susceptibility to lung cancer.
  • Tagging markers in linkage disequilibrium with at-risk variants (or protective variants) can be used as surrogates for these markers.
  • Such surrogate markers can be located within a particular haplotype block or LD block.
  • Such surrogate markers can also sometimes be located outside the physical boundaries of such a haplotype block or LD block, either in close vicinity of the LD block/haplotype block, but possibly also located in a more distant genomic location.
  • Long-distance LD can for example arise if particular genomic regions (e.g., genes) are in a functional relationship. For example, if two genes encode proteins that play a role in a shared metabolic pathway, then particular variants in one gene may have a direct impact on observed variants for the other gene. Let us consider the case where a variant in one gene leads to increased expression of the gene product. To counteract this effect and preserve overall flux of the particular pathway, this variant may have led to selection of one (or more) variants at a second gene that confers decreased expression levels of that gene.
  • genomic regions e.g., genes
  • Markers with values of r 2 equal to 1 are perfect surrogates for the at-risk variants (anchor variants), i .e. genotypes for one marker perfectly predicts genotypes for the other. Markers with smaller values of r 2 than 1 can also be surrogates for the at-risk variant, or alternatively represent variants with relative risk values as high as or possibly even higher than the at-risk variant. In certain preferred embodiments, markers with values of r 2 to the at-risk anchor variant are useful surrogate markers.
  • the at-risk variant identified may not be the functional variant itself, but is in this instance in linkage disequilibrium with the true functional variant.
  • the functional variant may be a SNP, but may also for example be a tandem repeat, such as a minisatellite or a microsatellite, a transposable element ⁇ e.g., an Alu element), or a structural alteration, such as a deletion, insertion or inversion (sometimes also called copy number variations, or CNVs) .
  • the present invention encompasses the assessment of such surrogate markers for the markers as disclosed herein.
  • markers are annotated, mapped and listed in public databases, as well known to the skilled person, or can alternatively be readily identified by sequencing the region or a part of the region identified by the markers of the present invention in a group of individuals, and identify polymorphisms in the resulting group of sequences.
  • the person skilled in the art can readily and without undue experimentation identify and genotype surrogate markers in linkage disequilibrium with the markers and/or haplotypes as described herein.
  • the present invention can in certain embodiments be practiced by assessing a sample comprising genomic DNA from an individual for the presence variants described herein to be associated with lung cancer.
  • Such assessment typically includes steps that detect the presence or absence of at least one allele of at least one polymorphic marker, using methods well known to the skilled person and further described herein, and based on the outcome of such assessment, determine whether the individual from whom the sample is derived is at increased or decreased risk (i.e., increased or decreased susceptibility) of lung cancer.
  • Detecting particular alleles of polymorphic markers can in certain embodiments be done by obtaining nucleic acid sequence data about a particular human individual that identifies at least one allele of at least one polymorphic marker.
  • nucleic acid sequence data can comprise nucleic acid sequence at a single nucleotide position, which is sufficient to identify alleles at SNPs.
  • the nucleic acid sequence data can also comprise sequence at any other number of nucleotide positions, in particular for genetic markers that comprise multiple nucleotide positions, and can be anywhere from two to hundreds of thousands, possibly even millions, of nucleotides (in particular, in the case of copy number variations (CNVs)) .
  • CNVs copy number variations
  • the invention can be practiced utilizing a dataset comprising information about the genotype status of at least one polymorphic marker.
  • a dataset containing information about such genetic status for example in the form of genotype counts at a certain polymorphic marker, or a plurality of markers (e.g., an indication of the presence or absence of certain at-risk alleles), or actual genotypes for one or more markers (for example in the form of sequence information), can be queried for the presence or absence of certain at-risk alleles at certain polymorphic markers shown by the present inventors to be associated with lung cancer.
  • a positive result for a variant (e.g., marker allele) associated with lung cancer is indicative of the individual from which the dataset is derived is at increased susceptibility (increased risk) of lung cancer.
  • a polymorphic marker is correlated to lung cancer by referencing genotype data for the polymorphic marker to a database, such as a look-up table, that comprises correlation data between at least one allele of the polymorphism and lung cancer.
  • the table comprises a correlation for one polymorphism.
  • the table comprises a correlation for a plurality of polymorphisms. In both scenarios, by referencing to a look-up table that gives an indication of a correlation between a marker and lung cancer, a risk or susceptibility of lung cancer can be identified in the individual from whom the sample is derived.
  • the correlation is reported as a statistical measure.
  • the statistical measure may be reported as a risk measure, such as a relative risk (RR), an absolute risk (AR) or an odds ratio (OR) .
  • Risk markers may be useful for risk assessment and diagnostic purposes, either alone or in combination .
  • Results of disease risk assessment can also be combined with data for other genetic markers or risk factors for the disease, to establish overall risk.
  • the association may have significant implications when combined with other risk markers.
  • relatively common variants may have significant contribution to the overall risk (Population Attributable Risk is high), or combination of markers can be used to define groups of individual who, based on the combined risk of the markers, is at significant combined risk of developing the disease.
  • a significant risk may be captured using the combination of variants, even though each variant may, on its own, capture a relatively small proportion of the overall genetic risk.
  • a plurality of variants are used for overall risk assessment. These variants are in one embodiment selected from the variants as disclosed herein . Other embodiments include the use of the variants of the present invention in combination with other variants known to be useful for diagnosing a susceptibility to lung cancer. In such embodiments, the genotype status of a plurality of markers (or haplotypes) is determined in an individual, and the status of the individual compared with the population frequency of the associated variants, or the frequency of the variants in clinically healthy subjects, such as age-matched and sex-matched subjects.
  • Methods known in the art such as multivariate analyses or joint risk analyses, such as those described herein, or other methods known to the skilled person, may subsequently be used to determine the overall risk conferred based on the genotype status at the multiple loci. Assessment of risk based on such analysis may subsequently be used in the methods, uses and kits of the invention, as described herein.
  • the methods and kits described herein can be utilized from samples containing nucleic acid material (DNA or RNA) for protein material rom any source and from any individual, or from genotype or sequence data derived from such samples.
  • the individual is a human individual.
  • the individual can be an adult, child, or fetus.
  • the nucleic acid or protein source may be any sample comprising nucleic acid or protein material, including biological samples, or a sample comprising nucleic acid or protein material derived therefrom.
  • the present invention also provides for assessing markers in individuals who are members of a target population .
  • a target population is in one embodiment a population or group of individuals at particular risk, for example smokers.
  • the invention provides for embodiments that include individuals with age of onset or age at diagnosis of lung cancer in certain age subgroups, such as those over the age of 40, over age of 45, or over age of 50, 55, 60, 65, 70, 75, 80, or 85.
  • Other embodiments of the invention pertain to other age groups, such as individuals aged less than 85, such as less than age 80, less than age 75, or less than age 70, 65, 60, 55, 50, 45, 40, 35, or age 30.
  • Other embodiments relate to individuals with age at onset or age at diagnosis of lung cancer in any of the age ranges described in the above. It is also contemplated that a range of ages may be relevant in certain embodiments, such as age at onset at more than age 45 but less than age 60. Other age ranges are however also contemplated, including all age ranges bracketed by the age values listed in the above.
  • the invention furthermore relates to individuals of either gender, males or females.
  • the Icelandic population is a Caucasian population of Northern European ancestry.
  • a large number of studies reporting results of genetic linkage and association in the Icelandic population have been published in the last few years. Many of those studies show replication of variants, originally identified in the Icelandic population as being associating with a particular disease, in other populations (Sulem, P., et al. Nat Genet May 17 2009 (Epub ahead of print); Rafnar, T., et al. Nat Genet 41 : 221-7 (2009); Greta rsdottir, S., et al. Ann Neurol 64:402-9 (2008); Stacey, S.N ., et al.
  • Eurasian populations Asian populations, Central/South Asian populations, East Asian populations, Middle Eastern populations, African populations, Hispanic populations, and Oceanian populations.
  • European populations include, but are not limited to, Swedish,
  • the racial contribution in individual subjects may also be determined by genetic analysis.
  • Genetic analysis of ancestry may be carried out using unlinked microsatellite markers such as those set out in Smith et al. ⁇ Am J Hum Genet 74, 1001-13 (2004)) .
  • the invention relates to markers and/or haplotypes identified in specific populations, as described in the above.
  • measures of linkage disequilibrium (LD) may give different results when applied to different populations. This is due to different population history of different human populations as well as differential selective pressures that may have led to differences in LD in specific genomic regions.
  • certain markers e.g. SNP markers, have different population frequency in different populations, or are polymorphic in one population but not in another. The person skilled in the art will however apply the methods available and as described herein to practice the present invention in any given human population.
  • the at-risk variants of the present invention may reside on different haplotype background and in different frequencies in various human populations. However, utilizing methods known in the art and the markers of the present invention, the invention can be practiced in any given human population.
  • the variants described herein in general do not, by themselves, provide an absolute identification of individuals who will develop lung cancer.
  • the variants described herein do however indicate increased and/or decreased likelihood that individuals carrying the at-risk variants disclosed herein will develop lung cancer.
  • This information is however extremely valuable in itself, as outlined in more detail in the following, as it can be used, for example, to initiate preventive measures at an early stage, perform regular physical exams to monitor the development, progress and/or appearance of symptoms, or to schedule exams at a regular interval to identify lung cancer in its early stages, so as to be able to apply treatment at an early stage which is often critical for successful lung cancer therapy.
  • the knowledge about a genetic variant that confers a risk of developing lung cancer also offers the opportunity to apply a genetic test to distinguish between individuals with increased risk of developing lung cancer (i.e. carriers of at-risk variants) and those with decreased risk of developing lung cancer (i.e. carriers of protective variants, and/or non-carriers of at-risk variants) .
  • the core value of genetic testing is the possibility of being able to identify a predisposition to disease at an early stage of disease, or before appearance of disease, so as to allow the clinician to apply the most appropriate treatment and/or preventive measure.
  • Individuals with a family history of lung cancer and carriers of at-risk variants may also benefit from genetic testing since the knowledge of the presence of at-risk genetic risk factors may provide incentive for implementing a healthier lifestyle, by avoiding or minimizing known environmental risk factors for lung cancer.
  • an individual who is a current smoker and is identified as a carrier of one or more at-risk variants of lung cancer may, due to his/her increased risk of developing the disease, choose to quit smoking.
  • the polymorphic markers shown herein to be associated with risk of lung cancer are useful in diagnostic methods. Although methods of diagnosing lung cancer are known, genetic risk markers such as those described herein provide added value to such diagnostic methods. Thus, by obtaining sequence data about particular markers, e.g., nucleic acid sequence data identifying at least one allele of at least one polymorphic marker, a diagnostic measure of lung cancer risk is obtained that may be utilized in various diagnostic methods as described herein.
  • the present invention pertains in some embodiments to methods of clinical applications of diagnosis, e.g., diagnosis performed by a medical professional . In other embodiments, the invention pertains to methods of diagnosis or methods of determination of a susceptibility performed by a layman. The layman can be the customer of a genotyping service.
  • the layman may also be a genotype service provider, who performs genotype analysis on a DNA sample from an individual, in order to provide service related to genetic risk factors for particular traits or diseases, based on the genotype status of the individual ⁇ i.e., the customer) .
  • genotyping technologies including high-throughput genotyping of SNP markers, such as Molecular Inversion Probe array technology ⁇ e.g., Affymetrix GeneChip), and BeadArray Technologies ⁇ e.g., Illumina GoldenGate and Infinium assays) have made it possible for individuals to have their own genome assessed for up to one million SNPs simultaneously, at relatively little cost.
  • the resulting genotype information which can be made available to the individual, can be compared to information about disease or trait risk associated with various SNPs, including information from public literature and scientific publications.
  • the diagnostic application of disease-associated alleles as described herein can thus for example be performed by the individual, through analysis of his/her genotype data, by a health professional based on results of a clinical test, or by a third party, including the genotype service provider.
  • the third party may also be service provider who interprets genotype information from the customer to provide service related to specific genetic risk factors, including the genetic markers described herein .
  • the diagnosis or determination of a susceptibility of genetic risk can be made by health professionals, genetic counselors, third parties providing genotyping service, third parties providing risk assessment service or by the layman ⁇ e.g. , the individual), based on information about the genotype status of an individual and knowledge about the risk conferred by particular genetic risk factors ⁇ e.g., particular SNPs) .
  • diagnosis can be made by health professionals, genetic counselors, third parties providing genotyping service, third parties providing risk assessment service or by the layman ⁇ e.g. , the individual), based on information about the genotype status of an individual and knowledge about the risk conferred by particular genetic risk factors ⁇ e.g., particular SNPs) .
  • diagnosis or determination of a susceptibility of genetic risk can be made by health professionals, genetic counselors, third parties providing genotyping service, third parties providing risk assessment service or by the layman ⁇ e.g. , the individual), based on information about the genotype status of an individual and knowledge about the risk conferred by particular genetic risk factors ⁇ e.
  • a sample containing genomic DNA from an individual is collected .
  • sample can for example be a buccal swab, a saliva sample, a blood sample, or other suitable samples containing genomic DNA, as described further herein .
  • the genomic DNA is then analyzed using any common technique available to the skilled person, such as high-throughput array technologies. Results from such genotyping are stored in a convenient data storage unit, such as a data carrier, including computer databases, data storage disks, or by other convenient data storage means.
  • the computer database is an object database, a relational database or a post-relational database.
  • Genotype data is subsequently analyzed for the presence of certain variants known to be susceptibility variants for a particular human conditions, such as the genetic variants described herein .
  • Genotype data can be retrieved from the data storage unit using any convenient data query method.
  • Calculating risk conferred by a particular genotype for the individual can be based on comparing the genotype of the individual to previously determined risk (expressed as a relative risk (RR) or and odds ratio (OR), for example) for the genotype, for example for an heterozygous carrier of an at-risk variant for a particular disease or trait.
  • the calculated risk for the individual can be the relative risk for a person, or for a specific genotype of a person, compared to the average population with matched gender and ethnicity.
  • the average population risk can be expressed as a weighted average of the risks of different genotypes, using results from a reference population, and the appropriate calculations to calculate the risk of a genotype group relative to the population can then be performed.
  • the risk for an individual is based on a comparison of particular genotypes, for example heterozygous carriers of an at-risk allele of a marker compared with non-carriers of the at-risk allele.
  • Using the population average may in certain embodiments be more convenient, since it provides a measure which is easy to interpret for the user, i.e. a measure that gives the risk for the individual, based on his/her genotype, compared with the average in the population.
  • the calculated risk estimated can be made available to the customer via a website, preferably a secure website.
  • a service provider will include in the provided service all of the steps of isolating genomic DNA from a sample provided by the customer, performing genotyping of the isolated DNA, calculating genetic risk based on the genotype data, and report the risk to the customer.
  • the service provider will include in the service the interpretation of genotype data for the individual, i.e. , risk estimates for particular genetic variants based on the genotype data for the individual .
  • the service provider may include service that includes genotyping service and interpretation of the genotype data, starting from a sample of isolated DNA from the individual (the customer) .
  • determination of a susceptibility to lung cancer can be accomplished using hybridization methods, (see Current Protocols in Molecular Biology, Ausubel, F. et ai. , eds., John Wiley & Sons, including all supplements) .
  • the presence of a specific marker allele can be indicated by sequence-specific hybridization of a nucleic acid probe specific for the particular allele.
  • the presence of more than one specific marker allele or a specific haplotype can be indicated by using several sequence-specific nucleic acid probes, each being specific for a particular allele.
  • a sequence-specific probe can be directed to hybridize to genomic DNA, RNA, or cDNA.
  • a “nucleic acid probe”, as used herein, can be a DNA probe or an RNA probe that hybridizes to a complementary sequence.
  • RNA probe that hybridizes to a complementary sequence.
  • One of skill in the art would know how to design such a probe so that sequence specific hybridization will occur only if a particular allele is present in a genomic sequence from a test sample.
  • a hybridization sample can be formed by contacting the test sample, such as a genomic DNA sample, with at least one nucleic acid probe.
  • a probe for detecting mRNA or genomic DNA is a labeled nucleic acid probe that is capable of hybridizing to mRNA or genomic DNA sequences described herein.
  • the nucleic acid probe can be, for example, a full-length nucleic acid molecule, or a portion thereof, such as an oligonucleotide of at least 15, 30, 50, 100, 250 or 500 nucleotides in length that is sufficient to specifically hybridize under stringent conditions to appropriate mRNA or genomic DNA.
  • the oligonucleotide is from about 15 to about 100 nucleotides in length. In certain other embodiments, the oligonucleotide is from about 20 to about 50 nucleotides in length .
  • the nucleic acid probe can comprise all or a portion of the nucleotide sequence of LD block C07, LD block C08 or LD block C19, as described herein, optionally comprising at least one marker described herein, or the probe can be the complementary sequence of such a sequence.
  • the nucleic acid probe can also comprise all or a portion of the nucleotide sequence of a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B, or the probe can be the
  • hybridization can be performed by methods well known to the person skilled in the art (see, e.g., Current Protocols in Molecular Biology, Ausubel, F. et al., eds., John Wiley & Sons, including all supplements).
  • hybridization refers to specific hybridization, i.e., hybridization with no mismatches (exact hybridization).
  • the hybridization conditions for specific hybridization are high stringency.
  • Specific hybridization if present, is detected using standard methods. If specific hybridization occurs between the nucleic acid probe and the nucleic acid in the test sample, then the sample contains the allele that is complementary to the nucleotide that is present in the nucleic acid probe. The process can be repeated for any markers of the present invention, or markers that make up a haplotype of the present invention, or multiple probes can be used concurrently to detect more than one marker alleles at a time.
  • a test sample containing genomic DNA obtained from the subject is collected and the polymerase chain reaction (PCR) is used to amplify a fragment comprising one ore more markers of the present invention .
  • PCR polymerase chain reaction
  • identification of a particular marker allele or haplotype can be accomplished using a variety of methods (e.g. , sequence analysis, analysis by restriction digestion, specific hybridization, single stranded conformation polymorphism assays (SSCP), electrophoretic analysis, etc.) .
  • a method utilizing a detection oligonucleotide probe comprising a fluorescent moiety or group at its 3' terminus and a quencher at its 5' terminus, and an enhancer oligonucleotide is employed, as described by Kutyavin et al. (Nucleic Acid Res. 34: el28 (2006)) .
  • diagnosis is accomplished by expression analysis, for example by using quantitative PCR (kinetic thermal cycling) .
  • This technique can, for example, utilize commercially available technologies, such as TaqMan ® (Applied Biosystems, Foster City, CA) .
  • the technique can assess the presence of an alteration in the expression or composition of a polypeptide or splicing variant(s) . Further, the expression of the variant(s) can be quantified as physically or functionally different.
  • restriction digestion in another embodiment, analysis by restriction digestion can be used to detect a particular allele if the allele results in the creation or elimination of a restriction site relative to a reference sequence.
  • Restriction fragment length polymorphism (RFLP) analysis can be conducted, e.g., as described in Current Protocols in Molecular Biology, supra. The digestion pattern of the relevant DNA fragment indicates the presence or absence of the particular allele in the sample.
  • Sequence analysis can also be used to detect specific alleles or haplotypes. Therefore, in one embodiment, determination of the presence or absence of a particular marker alleles or haplotypes comprises sequence analysis of a test sample of DNA or RNA obtained from a subject or individual . PCR or other appropriate methods can be used to amplify a portion of a nucleic acid that contains a polymorphic marker or haplotype, and the presence of specific alleles can then be detected directly by sequencing the polymorphic site (or multiple polymorphic sites in a haplotype) of the genomic DNA in the sample. The direct sequence analysis can be of the nucleic acid of a biological sample obtained from the human individual for which a susceptibility is being determined.
  • the biological sample can be any sample containing nucleic acid (e.g., genomic DNA) obtained from the human individual.
  • obtaining nucleic acid sequence data comprises obtaining nucleic acid sequence information from a preexisting record, e.g., a preexisting medical record comprising genotype information of the human individual.
  • a preexisting record e.g., a preexisting medical record comprising genotype information of the human individual.
  • direct sequence analysis of the allele of the polymorphic marker can be accomplished by mining a pre-existing genotype dataset for the sequence of the allele of the polymorphic marker.
  • arrays of oligonucleotide probes that are complementary to target nucleic acid sequence segments from a subject can be used to identify particular alleles at polymorphic sites.
  • an oligonucleotide array can be used.
  • Oligonucleotide arrays typically comprise a plurality of different oligonucleotide probes that are coupled to a surface of a substrate in different known locations. These arrays can generally be produced using mechanical synthesis methods or light directed synthesis methods that incorporate a combination of photolithographic methods and solid phase oligonucleotide synthesis methods, or by other methods known to the person skilled in the art (see, e.g. , Bier, F.F., et al.
  • nucleic acid analysis can be used to detect a particular allele at a polymorphic site.
  • Representative methods include, for example, direct manual sequencing (Church and Gilbert, Proc. Natl. Acad. Sci. USA, 81 : 1991-1995 (1988); Sanger, F., et al. , Proc. Natl. Acad. Sci. USA, 74: 5463-5467 (1977); Beavis, et al., U .S. Patent No.
  • CMC chemical mismatch cleavage
  • RNase protection assays Myers, R., et al., Science, 230: 1242-1246 (1985); use of polypeptides that recognize nucleotide mismatches, such as E. coli mutS protein; and allele-specific PCR.
  • the nucleic acid sequence data may be obtained through indirect analysis of the nucleic acid sequence of the allele of the polymorphic marker.
  • the allele could be one which leads to the expression of a variant protein comprising an altered amino acid sequence, as compared to the non-variant (e.g., wild-type) protein, due to one or more amino acid substitutions, deletions, or insertions, or truncation (due to, e.g., splice variation) .
  • Other possible effects include alterations in relative amounts of alternative splice forms of mRNA, effects on RNA stability, effects on transport from the nucleus to cytoplasm, and effects on the efficiency and accuracy of translation .
  • ELISA enzyme linked immunosorbent assays
  • Western blots Western blots
  • a test sample from a subject can be assessed for the presence of an alteration in the expression and/or an alteration in polypeptide composition. Both quantitative and qualitative alterations can be present.
  • An "alteration" in the polypeptide expression or composition refers to an alteration in expression or composition in a test sample, as compared to the expression or composition of the polypeptide in a control sample.
  • a control sample is suitably a sample that corresponds to the test sample (e.g. , is from the same type of cells), and is from a subject who is not affected by, and/or who does not have a susceptibility to, lung cancer.
  • control sample is from a subject who does not possess an at-risk marker allele for lung cancer, as described herein.
  • Various means of examining expression or composition of a polypeptide encoded by a nucleic acid are known to the person skilled in the art and can be used, including spectroscopy, colorimetry, electrophoresis, isoelectric focusing, and immunoassays (e.g. , David et al. , U.S. Pat. No. 4,376,110) such as immunoblotting (see, e.g. , Current Protocols in Molecular Biology, particularly chapter 10, supra) .
  • an antibody e.g. , an antibody with a detectable label
  • Antibodies can be polyclonal or monoclonal. An intact antibody, or a fragment thereof (e.g. , Fv, Fab, Fab', F(ab') 2 ) can be used.
  • the term "labeled", with regard to the probe or antibody is intended to encompass direct labeling of the probe or antibody by coupling (i .e., physically linking) a detectable substance to the probe or antibody, as well as indirect labeling of the probe or antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a labeled secondary antibody (e.g. , a fluorescently-labeled secondary antibody) and end-labeling of a DNA probe with biotin such that it can be detected with fluorescently-labeled streptavidin .
  • a labeled secondary antibody e.g. , a fluorescently-labeled secondary antibody
  • a level or amount of the polypeptide in the test sample that is higher or lower than the level or amount of the polypeptide in the control sample, such that the difference is statistically significant, is indicative of an alteration in the expression of the polypeptide, and is a diagnostic for a particular allele or haplotype associated with the difference in expression .
  • the composition of the polypeptide in a test sample is compared with the composition of the polypeptide in a control sample.
  • both the level or amount and the composition of the polypeptide can be assessed in the test sample and in the control sample.
  • risk assessment of lung cancer may be made by detecting at least one marker of the present invention in combination with an additional protein-based, RNA-based or DNA-based assay.
  • Kits useful in the methods of the invention comprise components useful in any of the methods described herein, including for example, primers for nucleic acid amplification, hybridization probes, restriction enzymes (e.g. , for RFLP analysis), allele-specific oligonucleotides, antibodies that bind to an altered polypeptide encoded by a nucleic acid of the invention as described herein (e.g.
  • kits can for example include necessary buffers, nucleic acid primers for amplifying nucleic acids of the invention (e.g.
  • kits can provide reagents for assays to be used in combination with the methods of the present invention, e.g. , reagents for use with other diagnostic assays for lung cancer.
  • the invention pertains to a kit for assaying a sample from a subject to detect a susceptibility to lung cancer in a subject, wherein the kit comprises reagents necessary for selectively detecting at least one allele of at least one polymorphism of the present invention in the genome of the individual.
  • the reagents comprise at least one contiguous oligonucleotide that hybridizes to a fragment of the genome of the individual comprising at least one polymorphism of the present invention .
  • the reagents comprise at least one pair of oligonucleotides that hybridize to opposite strands of a genomic segment obtained from a subject, wherein each oligonucleotide primer pair is designed to selectively amplify a fragment of the genome of the individual that includes at least one polymorphism associated with lung cancer risk.
  • the polymorphism is selected from the group consisting of the polymorphisms rs6474412, rs215614 and rs4105144, and polymorphic markers in linkage disequilibrium therewith.
  • the polymorphism is selected from the group consisting of the markers listed in Table 1, Table 2 and/or Table 3 herein.
  • the fragment is at least 20 base pairs in size.
  • oligonucleotides or nucleic acids e.g., oligonucleotide primers
  • the kit comprises one or more labeled nucleic acids capable of allele-specific detection of one or more specific polymorphic markers or haplotypes, and reagents for detection of the label.
  • Suitable labels include, e.g. , a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label .
  • the polymorphic marker or haplotype to be detected by the reagents of the kit comprises one or more markers, two or more markers, three or more markers, four or more markers or five or more markers selected from the group consisting of the markers set forth in any one of Table 1, Table 2 and Table 3.
  • the marker or haplotype to be detected comprises at least one marker from the group of markers in strong linkage disequilibrium, as defined by values of r 2 greater than 0.2, to a marker selected from the group consisting of rs6474412, rs215614 and rs4105144.
  • the marker or haplotype to be detected is selected from the group consisting of rs6474412, rs215614 and rs4105144.
  • the DNA template containing the SNP polymorphism is amplified by Polymerase Chain Reaction (PCR) prior to detection, and primers for such amplification are included in the reagent kit.
  • PCR Polymerase Chain Reaction
  • the amplified DNA serves as the template for the detection probe and the enhancer probe.
  • the DNA template is amplified by means of Whole Genome Amplification (WGA) methods, prior to assessment for the presence of specific polymorphic markers as described herein.
  • WGA Whole Genome Amplification
  • Standard methods well known to the skilled person for performing WGA may be utilized, and are within scope of the invention .
  • reagents for performing WGA are included in the reagent kit.
  • a pharmaceutical pack comprising a therapeutic agent and a set of instructions for administration of the therapeutic agent to humans diagnostically tested for one or more variants of the present invention, as disclosed herein .
  • the therapeutic agent can be a small molecule drug, an antibody, a peptide, an antisense or rnai molecule, or other therapeutic molecules.
  • an individual identified as a carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent.
  • an individual identified as a homozygous carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent.
  • an individual identified as a non-carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent.
  • the kit further comprises a set of instructions for using the reagents comprising the kit.
  • the kit further comprises a collection of data comprising correlation data between the polymorphic markers assessed by the kit and susceptibility to lung cancer.
  • genes containing, or in linkage disequilibrium with, one or more of these variants, or their products ⁇ e.g., CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B), as well as genes or their products that are directly or indirectly regulated by or interact with such variant genes or their products, can be targeted for the development of novel therapeutic agents for lung cancer.
  • Therapeutic agents may comprise one or more of, for example, small non-protein and non-nucleic acid molecules, proteins, peptides, protein fragments, nucleic acids (DNA, RNA), PNA (peptide nucleic acids), or their derivatives or mimetics which can modulate the function and/or levels of the target genes or their gene products.
  • small non-protein and non-nucleic acid molecules proteins, peptides, protein fragments, nucleic acids (DNA, RNA), PNA (peptide nucleic acids), or their derivatives or mimetics which can modulate the function and/or levels of the target genes or their gene products.
  • antisense agents are comprised of single stranded oligonucleotides (RNA or DNA) that are capable of binding to a complimentary nucleotide segment.
  • RNA or DNA single stranded oligonucleotides
  • the antisense oligonucleotides are complementary to the sense or coding strand of a gene. It is also possible to form a triple helix, where the antisense oligonucleotide binds to duplex DNA.
  • antisense oligonucleotide binds to target RNA sites, activate intracellular nucleases (e.g., RnaseH or Rnase L), that cleave the target RNA.
  • Blockers bind to target RNA, inhibit protein translation by steric hindrance of the ribosomes. Examples of blockers include nucleic acids, morpholino compounds, locked nucleic acids and methylphosphonates (Thompson, Drug
  • Antisense oligonucleotides are useful directly as therapeutic agents, and are also useful for determining and validating gene function, for example by gene knock-out or gene knock-down experiments. Antisense technology is further described in Lavery et al. , Curr. Opin. Drug Discov. Devel. 6: 561-569 (2003), Stephens et al., Curr. Opin. Mol. Ther. 5 : 118-122 (2003), Kurreck, Eur. J. Biochem. 270: 1628-44 (2003), Dias et al., Mol. Cancer Ter. 1 : 347-55 (2002), Chen, Methods Mol. Med. 75: 621-636 (2003), Wang et al., Curr. Cancer Drug Targets 1 : 177-96 (2001), and Bennett, Antisense Nucleic Acid Drug. Dev. 12 : 215- 24 (2002) .
  • the antisense agent is an oligonucleotide that is capable of binding to a particular nucleotide segment.
  • Antisense nucleotides can be from 5-500 nucleotides in length, including 5-200 nucleotides, 5-100 nucleotides, 10-50 nucleotides, and 10-30 nucleotides.
  • the antisense nucleotides is from 14-50 nucleotides in length, includign 14-40 nucleotides and 14-30 nucleotides. All integer lengths from 5-500 are specifically contemplated for the present invention, as are all subranges of lengths.
  • the antisense nucleotides is from 14-50 nucleotides in length, includign 14-40 nucleotides and 14-30 nucleotides.
  • the antisense nucleotide is capable of binding to a nucleotide segment of the a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, and RAB4B.
  • the variants described herein can also be used for the selection and design of antisense reagents that are specific for particular variants. Using information about the variants described herein, antisense oligonucleotides or other antisense molecules that specifically target mRNA molecules that contain one or more variants of the invention can be designed. In this manner, expression of mRNA molecules that contain one or more variant of the present invention (i.e. certain marker alleles and/or haplotypes) can be inhibited or blocked.
  • the antisense molecules are designed to specifically bind a particular allelic form (i.e., one or several variants (alleles and/or haplotypes)) of the target nucleic acid, thereby inhibiting translation of a product originating from this specific allele or haplotype, but which do not bind other or alternate variants at the specific polymorphic sites of the target nucleic acid molecule.
  • allelic form i.e., one or several variants (alleles and/or haplotypes)
  • the molecules can be used for disease treatment.
  • the methodology can involve cleavage by means of ribozymes containing nucleotide sequences complementary to one or more regions in the mRNA that attenuate the ability of the mRNA to be translated .
  • Such mRNA regions include, for example, protein-coding regions, in particular protein-coding regions corresponding to catalytic activity, substrate and/or ligand binding sites, or other functional domains of a protein .
  • RNA interference also called gene silencing, is based on using double-stranded RNA molecules (dsRNA) to turn off specific genes.
  • dsRNA double-stranded RNA molecules
  • siRNA small interfering RNA
  • the siRNA guide the targeting of a protein-RNA complex to specific sites on a target mRNA, leading to cleavage of the mRNA (Thompson, Drug Discovery Today, 7 : 912-917 (2002)) .
  • the siRNA molecules are typically about 20, 21, 22 or 23 nucleotides in length .
  • one aspect of the invention relates to isolated nucleic acid molecules, and the use of those molecules for RNA interference, i.e. as small interfering RNA molecules (siRNA) .
  • the isolated nucleic acid molecules are 18-26 nucleotides in length, preferably 19-25 nucleotides in length, more preferably 20-24 nucleotides in length, and more preferably 21, 22 or 23 nucleotides in length.
  • RNAi-mediated gene silencing originates in endogenously encoded primary microRNA (pri-miRNA) transcripts, which are processed in the cell to generate precursor miRNA (pre-miRNA) .
  • pri-miRNA primary microRNA
  • pre-miRNA precursor miRNA
  • miRNA molecules are exported from the nucleus to the cytoplasm, where they undergo processing to generate mature miRNA molecules (miRNA), which direct translational inhibition by recognizing target sites in the 3' untranslated regions of mRNAs, and subsequent mRNA degradation by processing P-bodies (reviewed in Kim & Rossi, Nature Rev. Genet 8: 173-204 (2007)) .
  • RNAi Clinical applications of RNAi include the incorporation of synthetic siRNA duplexes, which preferably are approximately 20-23 nucleotides in size, and preferably have 3' overlaps of 2 nucleotides. Knockdown of gene expression is established by sequence-specific design for the target mRNA. Several commercial sites for optimal design and synthesis of such molecules are known to those skilled in the art.
  • siRNA molecules typically 25-30 nucleotides in length, preferably about 27 nucleotides
  • shRNAs small hairpin RNAs
  • siRNAs and shRNAs are substrates for in vivo processing, and in some cases provide more potent gene-silencing than shorter designs (Kim et al., Nature Biotechnol. 23: 222-226 (2005); Siolas et al., Nature Biotechnol. 23: 227-231 (2005)) .
  • siRNAs provide for transient silencing of gene expression, because their intracellular concentration is diluted by subsequent cell divisions.
  • expressed shRNAs mediate long-term, stable knockdown of target transcripts, for as long as transcription of the shRNA takes place (Marques et al., Nature Biotechnol. 23 : 559-565 (2006); Brummelkamp et al., Science 296: 550-553 (2002)) .
  • RNAi molecules including siRNA, miRNA and shRNA
  • the variants presented herein can be used to design RNAi reagents that recognize specific nucleic acid molecules comprising specific alleles and/or haplotypes (e.g., the alleles and/or haplotypes of the present invention), while not recognizing nucleic acid molecules comprising other alleles or haplotypes.
  • RNAi reagents can thus recognize and destroy the target nucleic acid molecules.
  • RNAi reagents can be useful as therapeutic agents (i.e., for turning off disease-associated genes or disease-associated gene variants), but may also be useful for characterizing and validating gene function (e.g., by gene knock-out or gene knockdown experiments) .
  • RNAi may be performed by a range of methodologies known to those skilled in the art. Methods utilizing non-viral delivery include cholesterol, stable nucleic acid-lipid particle (SNALP), heavy-chain antibody fragment (Fab), aptamers and nanoparticles. Viral delivery methods include use of lentivirus, adenovirus and adeno-associated virus.
  • the siRNA molecules are in some embodiments chemically modified to increase their stability. This can include modifications at the 2' position of the ribose, including 2'-0-methylpurines and 2'- fluoropyrimidines, which provide resistance to Rnase activity. Other chemical modifications are possible and known to those skilled in the art.
  • a genetic defect leading to increased predisposition or risk for development of a disease, such as lung cancer, or a defect causing the disease may be corrected permanently by administering to a subject carrying the defect a nucleic acid fragment that incorporates a repair sequence that supplies the normal/wild-type nucleotide(s) at the site of the genetic defect.
  • site-specific repair sequence may concompass an RNA/DNA oligonucleotide that operates to promote endogenous repair of a subject's genomic DNA.
  • the administration of the repair sequence may be performed by an appropriate vehicle, such as a complex with polyethelenimine, encapsulated in anionic liposomes, a viral vector such as an adenovirus vector, or other pharmaceutical compositions suitable for promoting intracellular uptake of the adminstered nucleic acid.
  • an appropriate vehicle such as a complex with polyethelenimine, encapsulated in anionic liposomes, a viral vector such as an adenovirus vector, or other pharmaceutical compositions suitable for promoting intracellular uptake of the adminstered nucleic acid.
  • the genetic defect may then be overcome, since the chimeric oligonucleotides induce the
  • a particular therapy e.g., a therapeutic agent or therapeutic method
  • Pharmacogenomics addresses the issue of how genetic variations (e.g., the variants (markers and/or haplotypes) of the present invention) affect drug response, due to altered drug disposition and/or abnormal or altered action of the drug.
  • the basis of the differential response may be genetically determined in part.
  • Clinical outcomes due to genetic variations affecting drug response may result in toxicity of the drug in certain individuals (e.g., carriers or non-carriers of the genetic variants of the present invention), or therapeutic failure of the drug.
  • the variants of the present invention may determine the manner in which a therapeutic agent and/or method acts on the body, or the way in which the body metabolizes the therapeutic agent. Accordingly, in one embodiment, the presence of a particular allele at a polymorphic site or haplotype is indicative of a different response, e.g. a different response rate, to a particular treatment modality.
  • a patient diagnosed with lung cancer, and carrying a certain variant of the present invention e.g. , the at-risk alleles of the invention
  • the presence of a variant may be assessed (e.g., through testing DNA derived from a blood sample, as described herein) . If the patient is positive for the variant, then the physician recommends one particular therapy, while if the patient is negative for the variant, then a different course of therapy may be recommended (which may include recommending that no immediate therapy, other than serial monitoring for progression of the disease, be performed) . Thus, the patient's carrier status could be used to help determine whether a particular treatment modality should be administered.
  • the value lies within the possibilities of being able to diagnose disease, or susceptibility to disease, at an early stage, to select the most appropriate treatment, and provide information to the clinician about prognosis/aggressiveness of the disease in order to be able to apply the most appropriate treatment.
  • the treatment for lung cancer can in certain embodiments be selected from surgical treatment (surgical removal of tumor), radiation therapy and chemotherapy. It is contemplated that the markers described herein to be associated with lung cancer can be used to predict the efficacy of any of these particular treatment modules. In certain embodiments, the markers of the inventions, as described herein may be used to determine an appropriate combination of therapy, which can include any one, two or three of these treatment modules. In certain embodiments, the radiation therapy is brachytherapy.
  • the agent useful for chemotherapy may be any chemical agent commonly used, or in development, as a chemotherapy agent, including, but not limited to, cisplatin, carboplatin, gemcitabine (4-amino-l-[3,3-difluoro-4-hydroxy-5- (hydroxymethyl) a tetrahydrofuran-2-yl]- lH-pyrimidin- 2-one), paclitaxel
  • Chemotherapy agents may be used alone or in combination .
  • the agent targets an epidermal growth factor receptor.
  • the agent is gefitinib (Iressa; /V-(3-chloro-4-fluoro-phenyl)-7- methoxy-6-(3-morpholin-4-ylpropoxy)quinazolin-4-amine) or erlotinib (Tarceva; ⁇ /-(3- ethynylphenyl)-6,7-bis(2-methoxyethoxy)quinazolin-4-amine) .
  • the agent is angiogenesis inhibitor.
  • Such inhibitors can for example be antibodies that inhibit the vascular endotheliar growth factor, such as Bevacizumab (Avastin) .
  • the present invention also relates to methods of monitoring progress or effectiveness of a treatment for lung cancer. This can be done by assessing for the absence or presence of at least one variant associated with lung cancer, as disclosed herein, or by monitoring expression of genes that are associated with the variants (markers and haplotypes) of the present invention .
  • Another aspect of the invention relates to methods of selecting individuals suitable for a particular treatment modality, based on their likelihood of developing particular complications or side effects of the particular treatment. It is well known that most therapeutic agents can lead to certain unwanted complications or side effects. Likewise, certain therapeutic procedures or operations may have complications associated with them. Complications or side effects of these particular treatments or associated with specific therapeutic agents may have a genetic component. It is therefore contemplated that selection of the appropriate treatment or therapeutic agent can in part be performed by determining the genotype of an individual, and using the genotype status of the individual to decide on a suitable therapeutic procedure or on a suitable therapeutic agent to treat the particular disease. It is therefore contemplated that the polymorphic markers of the invention can be used in this manner.
  • the polymorphic markers of the invention can be used to determine whether administration of a particular therapeutic agent or treatment modality or method is suitable for the individual, based on estimating the likelihood that the individual will benefit from the administration of the particular therapeutic agent or treatment modality or method . Indiscriminate use of such a therapeutic agents or treatment modalities may lead to unnecessary and needless adverse complications.
  • the invention provides a method of assessing an individual for probability of response to a therapeutic agent for preventing, treating, and/or ameliorating symptoms associated with lung cancer.
  • the method comprises: determining the identity of at least one allele of at least one polymorphic marker in a sample, e.g., a nucleic acid sample, obtained from the individual, wherein the at least one polymorphic marker is selected from polymorphic markers selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein the identity of the at least one allele of the at least one marker is indicative of a probability of a positive response to the therapeutic agent
  • the markers of the present invention can be used to increase power and effectiveness of clinical trials.
  • individuals who are carriers of at least one at-risk variant of the present invention may be more likely to respond favorably to a particular treatment.
  • individuals who carry at-risk variants for gene(s), or their gene product which a particular treatment (e.g., small molecule drug) is targeting are more likely to be responders to the treatment.
  • the treatment is targeting a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, and RAB4B.
  • individuals who carry at-risk variants associated with a gene which expression and/or function is altered by the at-risk variant are more likely to be responders to a treatment modality targeting that gene, its expression or its gene product.
  • This application can improve the safety of clinical trials, but can also enhance the chance that a clinical trial will demonstrate statistically significant efficacy, which may be limited to a certain sub-group of the population .
  • one possible outcome of such a trial is that carriers of certain genetic variants, e.g., the markers and haplotypes of the present invention, are statistically significantly likely to show positive response to the therapeutic agent, i.e. experience alleviation of symptoms associated with lung cancer when taking the therapeutic agent or drug as prescribed.
  • the methods and information described herein may be implemented, in all or in part, as computer executable instructions on known computer readable media.
  • the methods described herein may be implemented in hardware.
  • the method may be implemented in software stored in, for example, one or more memories or other computer readable medium and implemented on one or more processors.
  • the processors may be associated with one or more controllers, calculation units and/or other units of a computer system, or implanted in firmware as desired.
  • the routines may be stored in any computer readable memory such as in RAM, ROM, flash memory, a magnetic disk, a laser disk, or other storage medium, as is also known .
  • this software may be delivered to a computing device via any known delivery method including, for example, over a communication channel such as a telephone line, the Internet, a wireless connection, etc., or via a transportable medium, such as a computer readable disk, flash drive, etc.
  • a communication channel such as a telephone line, the Internet, a wireless connection, etc.
  • a transportable medium such as a computer readable disk, flash drive, etc.
  • the various steps described above may be implemented as various blocks, operations, tools, modules and techniques which, in turn, may be implemented in hardware, firmware, software, or any combination of hardware, firmware, and/or software.
  • some or all of the blocks, operations, techniques, etc. may be implemented in, for example, a custom integrated circuit (IC), an application specific integrated circuit (ASIC), a field programmable logic array (FPGA), a programmable logic array (PLA), etc.
  • the software When implemented in software, the software may be stored in any known computer readable medium such as on a magnetic disk, an optical disk, or other storage medium, in a RAM or ROM or flash memory of a computer, processor, hard disk drive, optical disk drive, tape drive, etc. Likewise, the software may be delivered to a user or a computing system via any known delivery method including, for example, on a computer readable disk or other transportable computer storage mechanism.
  • FIG. 2 illustrates an example of a suitable computing system environment 100 on which a system for the steps of the claimed method and apparatus may be implemented.
  • the computing system environment 100 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the method or apparatus of the claims. Neither should the computing environment 100 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 100.
  • the steps of the claimed method and system are operational with numerous other general purpose or special purpose computing system environments or configurations.
  • Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the methods or system of the claims include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • the methods and apparatus may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer storage media including memory storage devices.
  • an exemplary system for implementing the steps of the claimed method and system includes a general purpose computing device in the form of a computer 110.
  • Components of computer 110 may include, but are not limited to, a processing unit 120, a system memory 130, and a system bus 121 that couples various system components including the system memory to the processing unit 120.
  • the system bus 121 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures.
  • such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.
  • ISA Industry Standard Architecture
  • MCA Micro Channel Architecture
  • EISA Enhanced ISA
  • VESA Video Electronics Standards Association
  • PCI Peripheral Component Interconnect
  • Computer 110 typically includes a variety of computer readable media .
  • Computer readable media can be any available media that can be accessed by computer 110 and includes both volatile and nonvolatile media, removable and non-removable media .
  • Computer readable media may comprise computer storage media and
  • Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computer 110.
  • Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media .
  • modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
  • communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
  • the system memory 130 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 131 and random access memory (RAM) 132.
  • ROM read only memory
  • RAM random access memory
  • BIOS basic input/output system
  • RAM 132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 120.
  • Figure 2 illustrates operating system 134, application programs 135, other program modules 136, and program data 137.
  • the computer 110 may also include other removable/non-removable, volatile/nonvolatile computer storage media .
  • Figure 2 illustrates a hard disk drive 140 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 151 that reads from or writes to a removable, nonvolatile magnetic disk 152, and an optical disk drive 155 that reads from or writes to a removable, nonvolatile optical disk 156 such as a CD ROM or other optical media .
  • removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like.
  • the hard disk drive 141 is typically connected to the system bus 121 through a non-removable memory interface such as interface 140, and magnetic disk drive 151 and optical disk drive 155 are typically connected to the system bus 121 by a removable memory interface, such as interface 150.
  • hard disk drive 141 is illustrated as storing operating system 144, application programs 145, other program modules 146, and program data 147. Note that these components can either be the same as or different from operating system 134, application programs 135, other program modules 136, and program data 137. Operating system 144, application programs 145, other program modules 146, and program data 147 are given different numbers here to illustrate that, at a minimum, they are different copies.
  • a user may enter commands and information into the computer 20 through input devices such as a keyboard 162 and pointing device 161, commonly referred to as a mouse, trackball or touch pad .
  • Other input devices may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 120 through a user input interface 160 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB) .
  • a monitor 191 or other type of display device is also connected to the system bus 121 via an interface, such as a video interface 190.
  • computers may also include other peripheral output devices such as speakers 197 and printer 196, which may be connected through an output peripheral interface 190.
  • the computer 110 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 180.
  • the remote computer 180 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 110, although only a memory storage device 181 has been illustrated in Figure 2.
  • the logical connections depicted in Figure 2 include a local area network (LAN) 171 and a wide area network (WAN) 173, but may also include other networks.
  • LAN local area network
  • WAN wide area network
  • Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.
  • the computer 110 When used in a LAN networking environment, the computer 110 is connected to the LAN 171 through a network interface or adapter 170. When used in a WAN networking environment, the computer 110 typically includes a modem 172 or other means for establishing communications over the WAN 173, such as the Internet.
  • the modem 172 which may be internal or external, may be connected to the system bus 121 via the user input interface 160, or other appropriate mechanism.
  • program modules depicted relative to the computer 110, or portions thereof may be stored in the remote memory storage device.
  • Figure 2 illustrates remote application programs 185 as residing on memory device 181. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
  • the risk evaluation system and method, and other elements have been described as preferably being implemented in software, they may be implemented in hardware, firmware, etc., and may be implemented by any other processor.
  • the elements described herein may be implemented in a standard multi-purpose CPU or on specifically designed hardware or firmware such as an application-specific integrated circuit (ASIC) or other hard-wired device as desired, including, but not limited to, the computer 110 of Figure 2.
  • ASIC application-specific integrated circuit
  • the software routine may be stored in any computer readable memory such as on a magnetic disk, a laser disk, or other storage medium, in a RAM or ROM of a computer or processor, in any database, etc.
  • this software may be delivered to a user or a diagnostic system via any known or desired delivery method including, for example, on a computer readable disk or other transportable computer storage mechanism or over a communication channel such as a telephone line, the internet, wireless communication, etc. (which are viewed as being the same as or interchangeable with providing such software via a transportable storage medium) .
  • the invention relates to computer-implemented applications using the polymorphic markers and haplotypes described herein, and genotype and/or disease-association data derived therefrom.
  • Such applications can be useful for storing, manipulating or otherwise analyzing genotype data that is useful in the methods of the invention.
  • One example pertains to storing genotype information derived from an individual on readable media, so as to be able to provide the genotype information to a third party (e.g., the individual, a guardian of the individual, a health care provider or genetic analysis service provider), or for deriving information from the genotype data, e.g. , by comparing the genotype data to information about genetic risk factors contributing to increased susceptibility to the disease, and reporting results based on such comparison.
  • a third party e.g., the individual, a guardian of the individual, a health care provider or genetic analysis service provider
  • computer-readable media suitably comprise capabilities of storing (i) identifier information for at least one polymorphic marker or a haplotype, as described herein; (ii) an indicator of the identity (e.g., presence or absence) of at least one allele of said at least one marker, or a haplotype, in individuals with the disease; and (iii) an indicator of the risk associated with the marker allele or haplotype.
  • the markers and haplotypes described herein to be associated with increased susceptibility (increased risk) of lung cancer are in certain embodiments useful for interpretation and/or analysis of genotype data.
  • determination of the presence of an at- risk allele for lung cancer, as shown herein, or determination of the presence of an allele at a polymorphic marker in LD with any such risk allele is indicative of the individual from whom the genotype data originates is at increased risk of lung cancer.
  • genotype data is generated for at least one polymorphic marker shown herein to be associated with lung cancer, or a marker in linkage disequilibrium therewith.
  • the genotype data is subsequently made available to a third party, such as the individual from whom the data originates, his/her guardian or representative, a physician or health care worker, genetic counsellor, or insurance agent, for example via a user interface accessible over the internet, together with an interpretation of the genotype data, e.g., in the form of a risk measure (such as an absolute risk (AR), risk ratio (RR) or odds ratio (OR)) for the disease.
  • a risk measure such as an absolute risk (AR), risk ratio (RR) or odds ratio (OR)
  • at-risk markers identified in a genotype dataset derived from an individual are assessed and results from the assessment of the risk conferred by the presence of such at-risk variants in the dataset are made available to the third party, for example via a secure web interface, or by other communication means.
  • results of such risk assessment can be reported in numeric form (e.g. , by risk values, such as absolute risk, relative risk, and/or an odds ratio, or by a percentage increase in risk compared with a reference), by graphical means, or by other means suitable to illustrate the risk to the individual from whom the genotype data is derived.
  • nucleic acids and polypeptides described herein can be used in methods and kits of the present invention.
  • An "isolated" nucleic acid molecule is one that is separated from nucleic acids that normally flank the gene or nucleotide sequence (as in genomic sequences) and/or has been completely or partially purified from other transcribed sequences (e.g., as in an RNA library) .
  • an isolated nucleic acid of the invention can be substantially isolated with respect to the complex cellular milieu in which it naturally occurs, or culture medium when produced by recombinant techniques, or chemical precursors or other chemicals when chemically synthesized.
  • the isolated material will form part of a composition (for example, a crude extract containing other substances), buffer system or reagent mix.
  • the material can be purified to essential homogeneity, for example as determined by polyacrylamide gel electrophoresis (PAGE) or column chromatography (e.g., HPLC) .
  • An isolated nucleic acid molecule of the invention can comprise at least about 50%, at least about 80% or at least about 90% (on a molar basis) of all macromolecular species present.
  • genomic DNA the term "isolated" also can refer to nucleic acid molecules that are separated from the chromosome with which the genomic DNA is naturally associated .
  • the isolated nucleic acid molecule can contain less than about 250 kb, 200 kb, 150 kb, 100 kb, 75 kb, 50 kb, 25 kb, 10 kb, 5 kb, 4 kb, 3 kb, 2 kb, 1 kb, 0.5 kb or 0.1 kb of the nucleotides that flank the nucleic acid molecule in the genomic DNA of the cell from which the nucleic acid molecule is derived .
  • nucleic acid molecule can be fused to other coding or regulatory sequences and still be considered isolated.
  • recombinant DNA contained in a vector is included in the definition of "isolated” as used herein.
  • isolated nucleic acid molecules include recombinant DNA molecules in heterologous host cells or heterologous organisms, as well as partially or substantially purified DNA molecules in solution .
  • isolated nucleic acid molecules also encompass in vivo and in vitro RNA transcripts of the DNA molecules of the present invention .
  • An isolated nucleic acid molecule or nucleotide sequence can include a nucleic acid molecule or nucleotide sequence that is synthesized chemically or by recombinant means.
  • Such isolated nucleotide sequences are useful, for example, in the manufacture of the encoded polypeptide, as probes for isolating homologous sequences (e.g., from other mammalian species), for gene mapping (e.g., by in situ hybridization with chromosomes), or for detecting expression of the gene in tissue (e.g., human tissue), such as by Northern blot analysis or other hybridization techniques.
  • homologous sequences e.g., from other mammalian species
  • gene mapping e.g., by in situ hybridization with chromosomes
  • tissue e.g., human tissue
  • the invention also pertains to nucleic acid molecules that hybridize under high stringency hybridization conditions, such as for selective hybridization, to a nucleotide sequence described herein (e.g., nucleic acid molecules that specifically hybridize to a nucleotide sequence containing a polymorphic site associated with a marker or haplotype described herein) .
  • nucleic acid molecules can be detected and/or isolated by allele- or sequence-specific hybridization (e.g., under high stringency conditions) .
  • Stringency conditions and methods for nucleic acid hybridizations are well known to the skilled person (see, e.g., Current Protocols in Molecular Biology, Ausubel, F. et al, John Wiley & Sons, (1998), and Kraus, M. and Aaronson, S., Methods Enzymol., 200 : 546-556 (1991), the entire teachings of which are incorporated by reference herein .
  • the percent identity of two nucleotide or amino acid sequences can be determined by aligning the sequences for optimal comparison purposes (e.g., gaps can be introduced in the sequence of a first sequence) .
  • the length of a sequence aligned for comparison purposes is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%, of the length of the reference sequence.
  • Another example of an algorithm is BLAT (Kent, W.J. Genome Res. 12 : 656-64 (2002)) .
  • the percent identity between two amino acid sequences can be accomplished using the GAP program in the GCG software package (Accelrys, Cambridge, UK) .
  • the present invention also provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid that comprises, or consists of, the nucleotide sequence of LD block C07, LD block C08 or LD block C19, or a nucleotide sequence comprising, or consisting of, the complement of the nucleotide sequence of LD block C07, LD block C08 or LD block C19, wherein the nucleotide sequence optionally comprises at least one polymorphic marker described herein.
  • the invention also provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid that comprises, or consists of, the nucleotide sequence of a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B, or a nucleotide sequence comprising, or consisting of, the complement of the nucleotide sequence of the gene, wherein the nucleotide sequence optionally comprises at least one polymorphic marker described herein.
  • the nucleic acid fragments are at least about 15, at least about 18, 20, 23 or 25 nucleotides, and can be 30, 40, 50, 100, 200, 500, 1000, 10,000 or more nucleotides in length. In a specific embodiment, the nucleic acid fragments are 15-400 nucleotides in length.
  • the invention further provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid comprising a nucleotide sequence of any of SEQ ID NOs: 1-737, each of which sequences comprise one of the polymorphic markers associated with lung cancer, as described herein .
  • nucleic acid molecules e.g., oligonucleotide probes, can be used in the manufacture of a diagnostic reagent for diagnosing and/or assessing susceptibility to lung cancer.
  • probes or primers are oligonucleotides that hybridize in a base- specific manner to a complementary strand of a nucleic acid molecule.
  • probes and primers include polypeptide nucleic acids (PNA), as described in Nielsen, P. et al., Science 254: 1497-1500 (1991) .
  • PNA polypeptide nucleic acids
  • a probe or primer comprises a region of nucleotide sequence that hybridizes to at least about 15, typically about 20-25, and in certain embodiments about 40, 50 or 75, consecutive nucleotides of a nucleic acid molecule.
  • the probe or primer comprises at least one allele of at least one polymorphic marker or at least one haplotype described herein, or the complement thereof.
  • a probe or primer can comprise 100 or fewer nucleotides; for example, in certain embodiments from 6 to 50 nucleotides, or, for example, from 12 to 30 nucleotides.
  • the probe or primer is at least 70% identical, at least 80% identical, at least 85% identical, at least 90% identical, or at least 95% identical, to the contiguous nucleotide sequence or to the complement of the contiguous nucleotide sequence.
  • the probe or primer is capable of selectively hybridizing to the contiguous nucleotide sequence or to the complement of the contiguous nucleotide sequence.
  • the probe or primer further comprises a label, e.g ., a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label .
  • the nucleic acid molecules of the invention can be identified and isolated using standard molecular biology techniques well known to the skilled person.
  • the amplified DNA can be labeled (e.g., radiolabeled, fluorescently labeled) and used as a probe for screening a cDNA library derived from human cells.
  • the cDNA can be derived from mRNA and contained in a suitable vector.
  • Corresponding clones can be isolated, DNA obtained following in vivo excision, and the cloned insert can be sequenced in either or both orientations by art- recognized methods to identify the correct reading frame encoding a polypeptide of the appropriate molecular weight. Using these or similar methods, the polypeptide and the DNA encoding the polypeptide can be isolated, sequenced and further characterized .
  • the invention also provides antibodies which bind to an epitope comprising either a variant amino acid sequence (e.g., comprising an amino acid substitution) encoded by a variant allele or the reference amino acid sequence encoded by the corresponding non-variant or wild-type allele. For example, if a variant allele encodes an amino acid sequence comprising the epitope
  • the antibody of the invention specifically binds to either the epitope CYSTWFEH or CYSAWFEH.
  • the term "antibody” as used herein refers to immunoglobulin molecules and immunologically active portions of immunoglobulin molecules, i.e. , molecules that contain antigen-binding sites that specifically bind an antigen .
  • a molecule that specifically binds to an epitope is a molecule that binds to that epitope, but does not substantially bind other epitopes in a sample, e.g., a biological sample, which naturally contains the epitope.
  • immunoglobulin molecules examples include F(ab) and F(ab fragments which can be generated by treating the antibody with an enzyme such as pepsin.
  • the antibody can be polyclonal or monoclonal.
  • composition refers to a population of antibody molecules that contain only one species of an antigen binding site capable of immunoreacting with a particular epitope of a polypeptide of the invention.
  • a monoclonal antibody composition thus typically displays a single binding affinity for a particular polypeptide of the invention with which it immunoreacts.
  • Polyclonal antibodies can be prepared as described above by immunizing a suitable subject with a desired immunogen, e.g. , polypeptide of the invention or a fragment thereof.
  • a desired immunogen e.g. , polypeptide of the invention or a fragment thereof.
  • the antibody titer in the immunized subject can be monitored over time by standard techniques, such as with an enzyme linked immunosorbent assay (ELISA) using immobilized polypeptide.
  • ELISA enzyme linked immunosorbent assay
  • the antibody molecules directed against the polypeptide can be isolated from the mammal (e.g., from the blood) and further purified by well-known techniques, such as protein A
  • antibody-producing cells can be obtained from the subject and used to prepare monoclonal antibodies by standard techniques, such as the hybridoma technique originally described by Kohler and Milstein, Nature 256:495-497 (1975), the human B cell hybridoma technique (Kozbor et al., Immunol. Today 4: 72 (1983)), the EBV-hybridoma technique (Cole et al., Monoclonal Antibodies and Cancer Therapy, Alan R. Liss,1985, Inc., pp. 77-96) or trioma techniques.
  • hybridomas The technology for producing hybridomas is well known (see generally Current Protocols in Immunology (1994) Coligan et al., (eds.) John Wiley & Sons, Inc., New York, NY) .
  • an immortal cell line typically a myeloma
  • lymphocytes typically splenocytes
  • the culture supernatants of the resulting hybridoma cells are screened to identify a hybridoma producing a monoclonal antibody that binds a polypeptide of the invention .
  • a monoclonal antibody to a polypeptide of the invention can be identified and isolated by screening a recombinant combinatorial immunoglobulin library (e.g., an antibody phage display library) with the polypeptide to thereby isolate immunoglobulin library members that bind the polypeptide.
  • Kits for generating and screening phage display libraries are commercially available (e.g., the Pharmacia Recombinant Phage Antibody System, Catalog No. 27-9400-01; and the Stratagene SurfZAPTM Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S.
  • recombinant antibodies such as chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, which can be made using standard recombinant DNA techniques, are within the scope of the invention .
  • chimeric and humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art.
  • antibodies of the invention can be used to isolate a polypeptide of the invention by standard techniques, such as affinity chromatography or immunoprecipitation .
  • a polypeptide-specific antibody can facilitate the purification of natural polypeptide from cells and of recombinantly produced polypeptide expressed in host cells.
  • an antibody specific for a polypeptide of the invention can be used to detect the polypeptide (e.g. , in a cellular lysate, cell supernatant, or tissue sample) in order to evaluate the abundance and pattern of expression of the polypeptide.
  • Antibodies can be used diagnostically to monitor protein levels in tissue as part of a clinical testing procedure, e.g. , to, for example, determine the efficacy of a given treatment regimen.
  • the antibody can be coupled to a detectable substance to facilitate its detection . Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials,
  • bioluminescent materials examples include horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125 I, 131 I, 35 S or 3 H.
  • Antibodies may also be useful in pharmacogenomic analysis.
  • antibodies against variant proteins encoded by nucleic acids according to the invention such as variant proteins that are encoded by nucleic acids that contain at least one polymorpic marker of the invention, can be used to identify individuals that require modified treatment modalities.
  • Antibodies can furthermore be useful for assessing expression of variant proteins in disease states, such as in active stages of lung cancer, or in an individual with a predisposition to a disease related to the function of the protein, in particular lung cancer.
  • Antibodies specific for a variant protein of the present invention that is encoded by a nucleic acid that comprises at least one polymorphic marker as described herein can be used to screen for the presence of the variant protein, for example to screen for a predisposition to lung cancer as indicated by the presence of the variant protein.
  • Antibodies can be used in other methods. Thus, antibodies are useful as diagnostic tools for evaluating proteins, such as variant proteins of the invention, in conjunction with analysis by electrophoretic mobility, isoelectric point, tryptic or other protease digest, or for use in other physical assays known to those skilled in the art. Antibodies may also be used in tissue typing . In one such embodiment, a specific variant protein has been correlated with expression in a specific tissue type, and antibodies specific for the variant protein can then be used to identify the specific tissue type.
  • Subcellular localization of proteins can also be determined using antibodies, and can be applied to assess aberrant subcellular localization of the protein in cells in various tissues. Such use can be applied in genetic testing, but also in monitoring a particular treatment modality. In the case where treatment is aimed at correcting the expression level or presence of the variant protein or aberrant tissue distribution or developmental expression of the variant protein, antibodies specific for the variant protein or fragments thereof can be used to monitor therapeutic efficacy.
  • kits for using antibodies in the methods described herein includes, but is not limited to, kits for detecting the presence of a variant protein in a test sample.
  • kits for detecting the presence of a variant protein in a test sample comprises antibodies such as a labelled or labelable antibody and a compound or agent for detecting variant proteins in a biological sample, means for determining the amount or the presence and/or absence of variant protein in the sample, and means for comparing the amount of variant protein in the sample with a standard, as well as instructions for use of the kit.
  • kits for detecting the presence of a variant protein in a test sample comprises antibodies such as a labelled or labelable antibody and a compound or agent for detecting variant proteins in a biological sample, means for determining the amount or the presence and/or absence of variant protein in the sample, and means for comparing the amount of variant protein in the sample with a standard, as well as instructions for use of the kit.
  • CPD CPD we selected 14 regions in the same manner, and included a region on chromosome 8pl l based on large number of SNPs exhibiting suggestive associations with CPD, strong candidacy of region genes (encoding nicotine acetylcholine receptor subunits a6 and ⁇ 3 (CHRNA6 and CHRNB3)), and prior suggestive evidence for association between SNPs within this region and ND 3"4 .
  • the CHRNB3 gene is implicated by the location of the associating SNPs, these markers could be tagging variation elsewhere within the LD block that also contains the a6 nicotinic acetylcholine receptor subunit gene (CHRNA6) ( Figure 1) .
  • CHRNA6 a6 nicotinic acetylcholine receptor subunit gene
  • nicotinic cholinergic receptor subunits ⁇ 2- ⁇ 7, ⁇ 2- ⁇ 4 are expressed in the human brain, and they combine with each other in diverse patterns to form various types of functional pentameric receptors.
  • the different receptor subtypes are distinguished by subunit composition and sensitivity to nicotine 12 .
  • Involvement of CHRNA6 and CHRNB3 receptor subunits in nicotine- induced dopamine-release is indicated in rodent studies 13 .
  • Neither CHRNA6 nor CHRNB3 are expressed in lung tissue 14 .
  • the CPD associated markers on chromosome 19ql3 are located in a region harboring CYP2A6, which encodes CYP2A6, an enzyme that plays a major role in the oxidation of nicotine in human liver microsomes, as well as several other genes and pseudogenes belonging to the CYP gene family ( Figure 1) .
  • CYP2A6 encodes CYP2A6, an enzyme that plays a major role in the oxidation of nicotine in human liver microsomes, as well as several other genes and pseudogenes belonging to the CYP gene family
  • Figure 1 A number of sequence variants in or near CYP2A6 that reduce CYP2A6's enzymatic activity have beeln identified 15 . For some of these variants, effects on smoking behavior have been suggested 15 . In the present study, the most significant association in the region was observed with rs4105144.
  • Rs7260329 is an intronic SNP in CYP2B6, but its product converts nicotine to cotinine with about 10% of the catalytic activity of the CYP2A6 enzyme, and also metabolizes several drugs of abuse, and buproprion, an atypical antidepressant also used as a smoking cessation aid 15 .
  • the CYP2B6 levels in the human brain are higher than those of CYP2A6 and are altered in smokers and alcoholics 15"16 .
  • ND Nicotine Dependence
  • FTND Fagerstrom Test for Nicotine Dependence
  • DSM-IV Diagnostic and Statistical Manual of Mental Disorders 4 th edition
  • Allele frequencies for 1,979 Icelandic (deCODE) and 835 Dutch (NTR-NESDA) ND cases were compared to 36,202 Icelandic and 611 Dutch population controls.
  • SNPs on chromosome 8pl l, and chromosome 7pl4 associated nominally with ND, but none of the SNPs on chromosome 19q l3 (Table 10) .
  • Computer Assisted Personal interview is filled during 1-2 hours at doctors office including personal data (place of birth, place(s) of living, nationality etc.), genealogical data (family history, four generations), educational and occupational history, lifestyle data (physical activity, dietary habits, smoking, alcohol consumption, women ' s health, quality of life), also anthropometric and physiological measurements are taken .
  • personal data place of birth, place(s) of living, nationality etc.
  • genealogical data family history, four generations
  • educational and occupational history educational and occupational history
  • lifestyle data physical activity, dietary habits, smoking, alcohol consumption, women ' s health, quality of life
  • anthropometric and physiological measurements are taken .
  • GWAS was performed on 1,019 selected randomly from all over the country.
  • the smoking quantity was determined from the following questions: "If you have ever smoked how old were you when you started to smoke regularly?"; "How often and how much have you smoked in last 12 months?”; “How many years have you smoked?”; “If you have changed your smoking habits then how?"; "How long have you smoked so?” and "How many hours per day do you spend in a smoking area?”.
  • Smoking quantity was available for 506 individuals (325 current smokers and 181 former smokers) .
  • the cohort mean age was 42.7 (SD 14.9) years and included 327 (64.6%) males and 179 (35.4%) females.
  • Decode The Icelandic cigarette smoking data were described in detail previously 7 , and additional subjects were characterized in the same way.
  • ERF This is a family-based cohort study that is embedded in the Genetic Research in Isolated Populations (GRIP) program in the South West of the Netherlands 26 . The aim of this program was to identify genetic risk factors in the development of complex disorders. For the ERF study, 22 families that had at least five children baptized in the community church between 1850-1900 were identified with the help of genealogical records. All living descendants of these couples and their spouses were invited to take part in the study (N ⁇ 4,700) . Data collection started in June 2002 and was finished in February 2005. 2,923 successfully completed the questionnaire.
  • Genmets/FTC The Finnish Twin Cohort includes nationwide samples of twins follow-up longitudinally, and forms a part of the GenomEUtwin project, in which female monozygotic pairs were genotyped. DNA samples from one member of each monozygotic twin pair were used for genotyping 27 .
  • the Finnish twins were unselected with respect to disease status, and had participated in several waves of data collection in which smoking behaviors have been asked as a part of larger surveys of health, health habits and other health-related factors. Details of the data collection are available elsewhere 28"29 .
  • the female twins came from the older Finnish Twin Cohort (questionnaire assessments in 1975, 1981 and 1990) and from the Finntwin l6 sample (surveys as young adults was used for smoking assessments) .
  • Health 2000 is a large Finnish cross-sectional health examination survey. It includes a total of 8,028 subjects aged 30 or over and is a nationally representative sample of adult Finnish population .
  • GenMetS selected for GWA study on metabolic syndrome. Cases were selected according to the IDF Worldwide Definition of the Metabolic Syndrome
  • NTR-NESDA The sample comes from two large-scale longitudinal studies: the Netherlands Study of Depression and Anxiety (NESDA) 26 and the Netherlands Twin Registry (NTR) 27 . NESDA and NTR studies were approved by the Central Ethics Committee of the VU University Medical Center Amsterdam. The GWA sample consisted of 1,777 participants from the NTR and 1,763 participants from NESDA 31 . The mean age of the participants was 43.8 years (SD 13.4) and 65.7% of the sample was female. For participants of the NTR data longitudinal survey data from 7 waves of data collection (1991-2004) were used to determine smoking behavior. For participants from NESDA, data on smoking behavior were collected during a clinical interview between 2004 and 2007 26 . The total sample consisted of 1,207 never smokers and 2,236 ever smokers.
  • Rotterdam The Rotterdam Study was planned and designed in the early 1990s as a longitudinal study investigating the incidence and progression of diseases in the elderly. From 1991 to 1995 all inhabitants of Ommoord, a district of Rotterdam in the Netherlands, who were 55 years or older, were invited to participate in this study. Of 10,275 eligible individuals, 7,983 agreed to participate (78%) . In 1999, 3,011 participants (out of 4,472 invitees) who had become 55 years of age or moved into the study district since the start of the study were added to the cohort 32 . The Rotterdam Study has been approved by the institutional review board (Medical Ethics Committee) of the Erasmus Medical Center and by the review board of the Netherlands Ministry of Health, Welfare and Sports. All participants provided written informed consent.
  • the current analysis included 6,234 participants for whom genotyping was successful and information on smoking behavior was available. 3,610 participants reported to smoke or have smoked in the past while 2,624 participants were never smokers. The mean age was 67.9 years (SD - 8.81) and 60% were female.
  • SORBS All subjects are part of a sample from an extensively phenotyped isolated population from Eastern Germany, the Sorbs.
  • the Sorbs are of Slavonic origin, and have lived in ethnic isolation among the Germanic majority during the past 1, 100 years.
  • Smoking habits were assessed in a standardized interview. Subjects were asked "Do you smoke or have you ever smoked?, If yes, how many cigarettes per day do/did you smoke on average (on most days) and for how many years ?” At present, more than 1,000 Sorbian individuals are enrolled in the study.
  • TWINS UK The cohort (www.twinsuk.ac.uk) is an adult twin British registry shown to be representative of singleton populations and the United Kingdom population 33 . A total of 924 females with smoking phenotype were included in the analysis. The mean age of the TwinsUK cohort was 53.73 (22-80). Ethics approval was obtained from the Guy's and St. Thomas' Hospital Ethics Committee. Written informed consent was obtained from every participant to the study. The study design and genotyping methodology is described in detail elsewhere 34 .
  • WTCCC-CAD Detailed descriptions of the Wellcome Trust Case Controls Consortium Study data have already been provided elsewhere, and the CAD cases are European Caucasians who had a validated history of either myocardial infarction (MI) or coronary revascularisation (coronary artery bypass surgery or percutaneous coronary angioplasty) before their 66th birthday . The were recruited from April 1998 to November 2003 on a national basis 35 .
  • MI myocardial infarction
  • coronary revascularisation coronary artery bypass surgery or percutaneous coronary angioplasty
  • AUS The Australian sample took part in the single SNP assay replication. Data obtained from 3264 Australian subjects (49% women), 18-88 years of age (mean : 45; SD: 11 years) were used as one of the replication samples. Subjects were participants in either the Australian Nicotine Addiction Genetics (NAG) or a community-based (BigSib) family study. Families chosen for both studies were identified from two cohorts of the Australian Twin Panel, which included spouses of the older of these two cohorts. The NAG families were identified through heavy cigarette smoking index cases, and the BigSib families were comprised of families ascertained through the Australian Twin Panel selected for five or more offspring sharing both biological parents. The ancestry of the Australian samples is predominantly Anglo-Celtic or northern European (>90%) . The same assessment protocol was used for both the NAG and BigSib studies 36 . Clinical data were collected using a computer-assisted telephone diagnostic interview (CATI), and adaptation of the Semi-Structured Assessment for the Genetics of Alcoholism
  • SSAGA 37"38 for telephone administration .
  • the tobacco section of the CATI was derived from the Composite International Diagnostic Interview (CIDI) 39 and incorporated standard FTND, DSM- IIR, and DSM-IV assessments of nicotine dependence. It also included a detailed history of cigarette and other tobacco use, including quantity and frequency of use for current, most recent, and heaviest period of use. The measure examined for the purposes of this study was the number of cigarettes smoked per day, during heaviest period of use.
  • DCLST The Danish Lung Cancer Screening Trial (DCLST) 40 participated in the single-SNP assay replications for CPD.
  • DLCST is a randomised 5-year trial comparing the effect of annual screening with low dose CT on the mortality of lung cancer, with no genetic screening in the control arm. Lung function tests are performed annually and information on smoking exposure recorded in all participants. Individuals volunteered for the study in response to advertisements in local and regional free newspapers and weeklies. Participants were current or former smokers of both sexes at an age between 50-70 years at inclusion and with a smoking history of more than 20 pack years. Participants had to be able to climb 2 flights of stairs (around 36 steps) without pausing . FEV1 was at least 30% of predicted normal .
  • Ineligible were those applicants with body weight above 130 kg or previous treatment for lung cancer, breast cancer, malignant melanoma or hypernephroma . Individuals with a history of any other cancer within 5 years or tuberculosis within 2 years or any serious illness that would shorten life expectancy to less than 10 years were also excluded.
  • GER Unrelated community-based volunteers of German descent (i.e., both parents German) were randomly selected from the general population of Munich, Germany, and contacted by mail. To exclude subjects with central neurological diseases and psychotic disorders or subjects who had first-degree relatives with psychotic disorders, several screenings were conducted before the volunteers were enrolled in the study. First, subjects who responded were initially screened by phone for the absence of neuropsychiatric disorders. Second, detailed medical and psychiatric histories were assessed for both themselves and their first-degree relatives by using a semi- structured interview. Third, if no exclusion criteria were fulfilled, they were invited to a comprehensive interview including the Structured Clinical Interview for DSM-IV (SCID I and SCID II) to validate the absence of any lifetime psychotic disorder.
  • DSM-IV Structured Clinical Interview for DSM-IV
  • the Family History Assessment Module was conducted to exclude psychotic disorders among their first- degree relatives. Furthermore, a neurological examination was conducted to exclude subjects with subjects with current CNS impairment. In the case that the volunteers were older than 60 years, the Mini Mental Status Test was performed to exclude subjects with possible cognitive impairment.
  • NLBLC The Nijmegen Lung and Bladder Cancer study
  • This sample set is comprised of samples, previously described in studies of lung and bladder cancer.
  • the Dutch series consists of 3 groups: population controls, patients with urinary bladder cancer, and patients with lung cancer.
  • the lung cancer cases are Population controls: the 1,832 population controls (46% males) were recruited within a project entitled "Nijmegen Biomedical Study” (NBS) . The details of this study were reported previously 40 .
  • the study protocols of the NBS were approved by the Institutional Review Board of the RUNMC and all study subjects signed a written informed consent form .
  • the Dutch bladder cancer population has been described in a previous publication 41 . Briefly, patients were recruited for the Nijmegen Bladder Cancer Study (NBCS) (see http://dceg.cancer.gov/icbc/membership.html) .
  • the NBCS identified patients through the population-based regional cancer registry held by the
  • Lung cancer Pain. Patients were recruited at the Oncology Department of Zaragoza Hospital. Clinical information including age at onset and histology were collected from medical records. All lung cancer cases and 865 of the 1507 control individuals answered a lifestyle questionnaire, including questions on smoking status (never, former, current), and the amount of smoking. Study protocols were approved by the Institutional Review Board of Zaragoza University Hospital .
  • Lung cancer (Denver). DNA samples from blood samples and clinical data were provided from the University of Colorado Cancer Center under COMIRB protocol 08-0380. Blood samples were collected from 1217 patients enrolled in any of 20 clinical research trials carried out at Colorado SPORE protocols between 1993 and 2008. Of these 1217 patients, 246 were lung cancer cases and 971 had never had lung cancer at the time of sample shipment. Lung cancer cases were identified either from data matches with the Colorado Central Cancer Registry or by having malignant lung tissue collected via enrollment in a surgical protocol.
  • PAD sample (Austria): patients and controls were recruited through the Linz Peripheral Arterial Disease (LIPAD) study during 2000 to 2002, at the Department of Surgery, St John of God Hospital. All patients with chronic atherosclerotic occlusive disease of the lower extremities with typical symptoms, eg claudication or leg pain on exertion, rest pain, or minor or major tissue loss, were included on the basis of the final clinical diagnosis established by attending vascular surgeons. The diagnosis was verified by interview, physical examination, noninvasive techniques, and angiography 33 .
  • LIPAD Linz Peripheral Arterial Disease
  • PAD sample (Denmark). The sample consist of five hundred and seven patients were consecutively included during November 1999 to January 2004. All patients had PAD. The diagnosis was established from typical findings in clinical investigation (intermittent claudication, rest pain, ulcer or gangrene, and ankle-brachial-index ⁇ 0.9) . The samples were taken at baseline in a randomized, double-blind trial of roxithromycin versus placebo 43 . All patients were enrolled at Vascular Surgery Department, Viborg Hospital, Denmark. Exclusion criteria were allergy to macrolides and liver insufficiency.
  • PAD Iceland. Patients have been recruited over the past eleven years, as part of a genetic study at deCODE, from a registry of individuals diagnosed with PAD at the major hospital in Reykjavik, the Landspitali University Hospital, during the years 1983-2006. Diagnosis was confirmed by vascular imaging or segmental pressure measurements.
  • PAD New Zealand
  • Patients were recruited from the Otago-Southland region, and PAD was confirmed by an ankle brachial index of less than 0.7, pulse volume recordings and
  • the control group consisted of elderly individuals with no history of vascular disease from the same geographical region . Controls were asymptomatic for PAD and had ankle brachial indexes of more than 1. An abdominal ultrasound scan excluded concurrent abdominal aortic aneurysm from both the PAD and control groups, and Anglo- European ancestry was required for inclusion.
  • SNPs approximately 2.5 million SNPs. SNPs were excluded if they had (a) yield lower than 95%, (b) minor allele frequency less than 1% in the population, or (c) showed significant deviation from Hardy-Weinberg equilibrium in the controls (P ⁇ 0.001) . Any samples with a call rate below 98% were excluded from the analysis.
  • Heterogeneity is tested by comparing the null hypothesis of the effect being the same in all populations to the alternative hypothesis of each population having a different effect using a likelihood ratio test.
  • I 2 lies between 0% and 100% and describes the proportion of total variation in study estimates that is due to heterogeneity 49 .
  • the TAG and OX-GSK consortia provided results for the selected SNPs, using the same methods (i.e. categorical CPD corrected for age and sex) as described above, and provided results from each of the participating populations.
  • Data from samples also present in the ENGAGE analysis were excluded from the in-silico replication stage, and data derived from samples participating in both the TAG and the OX-GSK consortia were entered only once into the analysis.
  • CPD Association of markers within the regions selected by ENGAGE. Results are given for the ENGAGE discovery sample, the in-silico replication studies using data from the TAG and OX/GSK consortia (see accompanying papers). Shown are the number of smokers (N), the effect allele (Al) and the other allele (A2), the allele frequencies (Freq), the chromosome number and position, the estimated allelic effects on CPD and their standard errors in CPD (Effect and SE), the P value for the test of association (P), the P value for the test for heterogeneity in effect size (P he t), and an estimate of the proportion of total variation in study estimates that is due to heterogeneity (I 2 )
  • rsl2634857 A G 3 128566720 0 30 ⁇ 0 09 0.00063 -0.05 ⁇ 0.07 0 46 76 984 0 08 ⁇ 0 05 0 13 0 22 15 rs2121851 T C 3 128641324 0 35 ⁇ 0 07 1.6e-06 -0.07 ⁇ 0.06 0 29 76 808 0 10 ⁇ 0 05 0 028 0 045 31 rsl3065243 T C 3 128642649 0 37 ⁇ 0 08 le-06 -0.08 ⁇ 0.06 0 18 76 517 0 09 ⁇ 0 05 0 046 0 014 38 rsl2486396 G A 3 128643205 0 36 ⁇ 0 07 l.
  • rs4869056 G A 5 166924656 0. ,04 ⁇ 0. ,05 0, , 35 0, , 18 ⁇ 0.11 0, , 1 0, ,07 ⁇ 0, ,04 0.12 0, .017 35 rs4869058 T A 5 166924825 0. ,58 ⁇ 0. 15 7, . le-05 0, , 17 ⁇ 0. 11 0, , 13 0, ,24 ⁇ 0, ,09 0.0055 0, .021 34 rsl l747772 c T 5 166925286 0. ,56 ⁇ 0. 14 0, ,0001 0, , 16 ⁇ 0.
  • Netherlands 502 1,709 0 366 0 367 0 99 (0 86, 1 15) 0 92
  • Netherlands 513 1,665 0.638 0 640 0 99 (0 86, 1 15) 0 93
  • Pillai, S.G. et al A genome-wide association study in chronic obstructive pulmonary disease (COPD) : identification of two major susceptibility loci.
  • COPD chronic obstructive pulmonary disease
  • Nicotinic receptor gene variants influence susceptibility to heavy smoking .
  • acetylcholine receptor genes (CHRNA3/CH RNA5/CHRNB4) on chromosome 15. Hum Mol Genet 18, 4007- 12 (2009).

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Abstract

The present invention discloses certain genetic variants that are susceptibility variants for lung cancer. The invention relates to risk assessment and diagnostic methods using the variants. The invention further relates to kits for use in risk assessment of lung cancer.

Description

GENETIC VARIANTS PREDICTIVE OF LUNG CANCER RISK
BACKGROUND OF THE INVENTION
Genetic risk is conferred by subtle differences in the genome among individuals in a population. Variations in the human genome are most frequently due to single nucleotide polymorphisms (SNPs), although other variations are also important. SNPs are located on average every 1000 base pairs in the human genome. Accordingly, a typical human gene containing 250,000 base pairs may contain 250 different SNPs. Only a minor number of SNPs are located in exons and alter the amino acid sequence of the protein encoded by the gene. Most SNPs may have little or no effect on gene function, while others may alter transcription, splicing, translation, or stability of the mRNA encoded by the gene. Additional genetic polymorphisms in the human genome are caused by insertions, deletions, translocations or inversion of either short or long stretches of DNA. Genetic polymorphisms conferring disease risk may directly alter the amino acid sequence of proteins, may increase the amount of protein produced from the gene, or may decrease the amount of protein produced by the gene.
As genetic polymorphisms conferring risk of common diseases are uncovered, genetic testing for such risk factors is becoming increasingly important for clinical medicine. Examples are apolipoprotein E testing to identify genetic carriers of the apoE4 polymorphism in dementia patients for the differential diagnosis of Alzheimer's disease, and of Factor V Leiden testing for predisposition to deep venous thrombosis. More importantly, in the treatment of cancer, diagnosis of genetic variants in tumor cells is used for the selection of the most appropriate treatment regime for the individual patient. In breast cancer, genetic variation in estrogen receptor expression or heregulin type 2 (Her2) receptor tyrosine kinase expression determine if anti-estrogenic drugs (tamoxifen) or anti-Her2 antibody (Herceptin) will be incorporated into the treatment plan. In chronic myeloid leukemia (CML) diagnosis of the Philadelphia chromosome genetic translocation fusing the genes encoding the Bcr and Abl receptor tyrosine kinases indicates that Gleevec (STI571), a specific inhibitor of the Bcr-Abl kinase should be used for treatment of the cancer. For CML patients with such a genetic alteration, inhibition of the Bcr- Abl kinase leads to rapid elimination of the tumor cells and remission from leukemia.
Furthermore, genetic testing services are now available, providing individuals with information about their disease risk based on the discovery that certain SNPs have been associated with risk of many of the common diseases.
Lung cancer causes more deaths from cancer worldwide than any other form of cancer
(Goodman, G.E., Thorax 57:994-999 (2002)) . In the United States, lung cancer is the primary cause of cancer death among both men and women . In 2007, the death rate from lung cancer was an estimated 160,390 deaths, exceeding the combined total for breast, prostate and colon cancer (America Cancer Society, www.cancer.org) . Lung cancer is also the leading cause of cancer death in all European countries and is rapidly increasing in developing countries. While environmental factors, such as lifestyle factors (e.g., smoking) and dietary factors, play an important role in lung cancer, genetic factors also contribute to the disease. For example, a family of enzymes responsible for carcinogen activation, degradation and subsequent DNA repair have been implicated in susceptibility to lung cancer. In addition, an increased risk to familial members outside of the nuclear family has been shown by deCODE geneticists by analysing all lung cancer cases diagnosed in Iceland over 48 years. This increased risk could not be entirely accounted for by smoking indicating that genetic variants may predispose certain individuals to lung cancer (Jonsson et.al., JAMA 292(24) : 2977-83 (2004); Amundadottir et.al., PLoS Med. I (3) :e65 (2004)) .
The five-year survival rate among all lung cancer patients, regardless of the stage of disease at diagnosis, is only 13%. This contrasts with a five-year survival rate of 46% among cases detected while the disease is still localized . However, only 16% of lung cancers are discovered before the disease has spread. Early detection is difficult as clinical symptoms are often not observed until the disease has reached an advanced stage. Currently, diagnosis is aided by the use of chest x-rays, analysis of the type of cells contained in sputum and fiberoptic examination of the bronchial passages. Treatment regimens are determined by the type and stage of the cancer, and include surgery, radiation therapy and/or chemotherapy. In spite of considerable research into therapies for this and other cancers, lung cancer remains difficult to diagnose and treat effectively. Accordingly, there is a great need in the art for improved methods for detecting and treating such cancers.
Smoking of tobacco products, and in particular cigarettes, is the largest known risk factor lung cancer with a global attributable proportion estimated to be approximately 90% in men and 80% in women. Although the risk of lung cancer associated with tobacco smoking is strongly related to duration of smoking, and declines with increasing time from cessation, the estimated lifetime risk of lung cancer among former smokers remains high, ranging from approximately 6% in smokers who give up at the age of 50, to 10% for smokers who give up at age 60, compared to 15% for lifelong smokers and less than 1% in never-smokers (Peto et al. 2000 BMJ, 321, 323- 32, Brennan, et al. 2006 Am J Epidemiol 164, 1233-1241) . In populations where the large majority of smokers have quit smoking, such as men in the US and UK, the majority of lung cancer cases now occurs among ex-smokers (Doll et al. 1994 BMJ 309, 901-911, Zhu et al. 2001 Cancer Res, 61, 7825-7829) . This emphasizes the importance of developing alternative prevention measures for lung cancer including the identification of high risk subgroups.
Notably, only about 15% of lifelong smokers will develop lung cancer by the age of 75, and approximately 5 to 10% of lifetime smokers will develop another tobacco-related cancer (Kjaerheim et al. 1998 Cancer Causes Control 9, 99-108) . A possible explanation for this large differences in risk for individuals with similar level of tobacco exposures could be that genetic factors play a determining role in lung cancer susceptibility (Spitz et al. 2005 J Clin Oncol 23, 267-275) . Identifying genes, which influence the risk of lung cancer, could be important for several aspects of management of the disease. Segregation analyses predict that the majority of genetic risk for lung cancer is most likely to be polygenic in nature, with multiple risk alleles that confer low to moderate risk and which may interact with each other and with environmental risk factors. Many studies have investigated lung cancer susceptibility based on the presence of low-penetrance, high-frequency single nucleotide polymorphisms in candidate genes belonging to specific metabolic pathways. Genetic polymorphisms of xenobiotic metabolism, DNA repair, cell-cycle control, immunity, addiction and nutritional status have been described as promising candidates but have in many cases proven difficult to confirm (Hung et al . 2005 J Natl Cancer Inst 97, 567-576, Hung et al. 2006 Cancer Res 66, 8280-8286, Landi et al. 2006 Carcinogenesis, in press, Brennan et al.2005 Lancet 366, 1558-60, Hung et al. 2007 Carcinogenesis 28, 1334-40, Campa et al. 2007 Cancer Causes Control 18, 449-455, Gemignani et al. 2007 Carcinogenesis 28(6), 1287-93, Hall et al. 2007 Carcinogenesis 28, 665-671, Campa et al. 2005 Cancer Epidemiol Biomarkers Prev 14, 2457- 2458, Campa et al. 2005 Cancer Epidemiol Biomarkers Prev 14, 538-539, Hashibe et al. 2006 Cancer Epidemiol Biomarkers Prev 15, 696-703) .
For cancers that show a familial risk of around two-fold such as lung cancer (Jonsson et al . 2004 JAMA 292, 2977-2983, Li and Hemminki 2005 Lung Cancer 47, 301-307, Goldgar et al. 1994 J Natl Cancer Inst 86, 1600-1608), the majority of cases will arise from approximately 10%-15% of the population at greatest risk (Pharoah et al. 2002 NatGenet 31, 33-36). The identification of common genetic variants that affect the risk of lung cancer may enable the identification of individuals who are at a very high risk because of their increased genetic susceptibility or, in the case of genes related to nicotine metabolism, because of their inability to quit smoking. Such findings could potentially lead to chemoprevention programs for high risk individuals, and are especially of importance given the high residual risk that remains among ex-smokers, among whom the majority of lung cancers in the US and Europe now occur. Common variants on chromosome 15 that confer risk of lung cancer have been described (Thorgeirsson, T.E., et al. Nature 452: 638-42 (2008); Hung R.J., et al. Nature 452 : 633-7 (2008); Amos, C.I., et al. Nat Genet 40 : 616-22 (2008)) . The present invention relates to further genetic variants predictive of lung cancer risk in humans.
SUMMARY OF THE INVENTION
The present invention is based on the finding that genetic variants in certain genetic regions contain variants that are correlated with risk of developing lung cancer in humans. Markers in the genomic regions 8pll (e.g., rs6474412), 7pl4 (e.g., rs215614) and 19ql3 (e.g., rs4105144) have been found to be indicative of lung cancer risk.
In a first aspect, the invention provides a method of determining a susceptibility to lung cancer, the method comprising (a) obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and (b) determining a susceptibility to lung cancer from the sequence data, wherein the at least one polymorphic marker is a marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith .
Another aspect relates to a method of determining a susceptibility to lung cancer, the method comprising analyzing nucleic acid sequence data from a human individual for at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, wherein the marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and determining a susceptibility to lung cancer from the nucleic acid sequence data.
Identification of risk may be based on a determination of the presence or absence of certain alleles indicative of lung cancer risk. Thus, a second aspect of the invention relates to a method of assessing a susceptibility to lung cancer in a human individual, comprising (i) obtaining sequence data about the individual for at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans; (ii) identifying the presence or absence of at least one allele in the at least one polymorphic marker that correlates with increased occurrence of lung cancer in humans. In a preferred embodiment, determination of the presence of the at least one allele identifies the individual as having elevated susceptibility to lung cancer, and determination of the absence of the at least one allele identifies the individual as not having the elevated susceptibility.
Further provided is a method of identification of a marker for use in assessing susceptibility to lung cancer in human individuals, the method comprising (a) identifying at least one polymorphic marker in linkage disequilibrium with rs215614, rs6474412 or rs4105144; (b) obtaining sequence information about the at least one polymorphic marker in a group of individuals diagnosed with lung cancer; and (c) obtaining sequence information about the at least one polymorphic marker in a group of control individuals; wherein determination of a significant difference in frequency of at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer as compared with the frequency of the at least one allele in the control group is indicative of the at least one polymorphism is useful for assessing susceptibility to lung cancer. In a preferred embodiment, an increase in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group, is indicative of the at least one polymorphism being useful for assessing increased susceptibility to lung cancer, and a decrease in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group, is indicative of the at least one polymorphism being useful for assessing decreased susceptibility to, or protection against, lung cancer. The invention also relates to methods of prognosis and response to therapy. One such aspect provides a method of predicting prognosis of an individual diagnosed with lung cancer, the method comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs215614, rs6474412 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and predicting prognosis of lung cancer from the sequence data .
Another aspect provides a method of assessing probability of response of a human individual to a therapeutic agent for preventing, treating and/or ameliorating symptoms associated with lung cancer, comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different probabilities of response to the therapeutic agent in humans, and determining the probability of a positive response to the therapeutic agent from the sequence data.
Further provided is a kit for assessing susceptibility to lung cancer, the kit comprising reagents for selectively detecting at least one allele of at least one polymorphic marker in the genome of the individual, wherein the polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and a collection of data comprising correlation data between the at least one polymorphism and susceptibility to lung cancer.
Also provided is use of an oligonucleotide probe in the manufacture of a diagnostic reagent for diagnosing and/or assessing a susceptibility to lung cancer, wherein the probe is capable of hybridizing to a segment of a nucleic acid whose nucleotide sequence is given by any one of SEQ ID NO: 1-737, and wherein the segment is 15-400 nucleotides in length.
Computer-implemented aspects are also provided. One such aspect relates to a computer- readable medium having computer executable instructions for determining susceptibility to lung cancer, the computer readable medium comprising data indicative of at least one polymorphic marker, and a routine stored on the computer readable medium and adapted to be executed by a processor to determine risk of developing lung cancer for the at least one polymorphic marker, wherein the at least one polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith.
Another computer-implemented aspect relates to an apparatus for determining a genetic indicator for lung cancer in a human individual, comprising (a) a processor, and (b) a computer readable memory having computer executable instructions adapted to be executed on the processor to analyze marker information for at least one human individual with respect to at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and generate an output based on the marker information, wherein the output comprises a measure of susceptibility of the at least one marker or haplotype as a genetic indicator of the condition for the human individual.
It should be understood that all combinations of features described herein are contemplated, even if the combination of feature is not specifically found in the same sentence or paragraph herein . This includes in particular the use of all markers disclosed herein, alone or in combination, for analysis individually or in haplotypes, in all aspects of the invention as described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention .
FIG 1 shows the genomic regions of association on chromosomes 15q25 (A), 19q l3 (B), and 8pl l (C) and 7pl4 (D) associated with smoking quantity (CPD) and lung cancer. Shown are the -logio association P values of SNPs in the region with CPD from the ENGAGE meta analysis (circles), the in silico replication studies (plus-signs), and joint analysis of ENGAGE, TAG, and OX-GSK GWA data (crosses), the SNP build 36 coordinates, the genes in the region and their exons and recombination rates in centimorgans (cM) per megabase (Mb) (histogram) .
FIG 2 provides a diagram illustrating a computer-implemented system utilizing risk variants as described herein.
DETAILED DESCRIPTION
Definitions
Unless otherwise indicated, nucleic acid sequences are written left to right in a 5' to 3' orientation . Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer or any non-integer fraction within the defined range. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by the ordinary person skilled in the art to which the invention pertains.
The following terms shall, in the present context, have the meaning as indicated :
A "polymorphic marker", sometime referred to as a "marker", as described herein, refers to a genomic polymorphic site. Each polymorphic marker has at least two sequence variations characteristic of particular alleles at the polymorphic site. Thus, genetic association to a polymorphic marker implies that there is association to at least one specific allele of that particular polymorphic marker. The marker can comprise any allele of any variant type found in the genome, including SNPs, mini- or microsatellites, translocations and copy number variations (insertions, deletions, duplications) . Polymorphic markers can be of any measurable frequency in the population . For mapping of disease genes, polymorphic markers with population frequency higher than 5-10% are in general most useful . However, polymorphic markers may also have lower population frequencies, such as 1-5% frequency, or even lower frequency, in particular copy number variations (CNVs) . The term shall, in the present context, be taken to include polymorphic markers with any population frequency.
An "allele" refers to the nucleotide sequence of a given locus (position) on a chromosome. A polymorphic marker allele thus refers to the composition (i.e., sequence) of the marker on a chromosome. Genomic DNA from an individual contains two alleles (e.g. , allele-specific sequences) for any given polymorphic marker, representative of each copy of the marker on each chromosome. Sequence codes for nucleotides used herein are : A = 1, C = 2, G = 3, T = 4. For microsatellite alleles, the CEPH sample (Centre d'Etudes du Polymorphisme Humain, genomics repository, CEPH sample 1347-02) is used as a reference, the shorter allele of each microsatellite in this sample is set as 0 and all other alleles in other samples are numbered in relation to this reference. Thus, e.g., allele 1 is 1 bp longer than the shorter allele in the CEPH sample, allele 2 is 2 bp longer than the shorter allele in the CEPH sample, allele 3 is 3 bp longer than the lower allele in the CEPH sample, etc., and allele -1 is 1 bp shorter than the shorter allele in the CEPH sample, allele -2 is 2 bp shorter than the shorter allele in the CEPH sample, etc.
Sequence conucleotide ambiguity as described herein is as proposed by IUPAC-IUB. These codes are compatible with the codes used by the EMBL, GenBank, and PIR databases.
Figure imgf000008_0001
A nucleotide position at which more than one sequence is possible in a population (either a natural population or a synthetic population, e.g. , a library of synthetic molecules) is referred to herein as a "polymorphic site".
A "Single Nucleotide Polymorphism" or "SNP" is a DNA sequence variation occurring when a single nucleotide at a specific location in the genome differs between members of a species or between paired chromosomes in an individual. Most SNP polymorphisms have two alleles. Each individual is in this instance either homozygous for one allele of the polymorphism (i.e. both chromosomal copies of the individual have the same nucleotide at the SNP location), or the individual is heterozygous (i .e. the two sister chromosomes of the individual contain different nucleotides). The SNP nomenclature as reported herein refers to the official Reference SNP (rs) ID identification tag as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI) .
A "variant", as described herein, refers to a segment of DNA that differs from the reference DNA. A "marker" or a "polymorphic marker", as defined herein, is a variant. Alleles that differ from the reference are referred to as "variant" alleles.
A "microsatellite" is a polymorphic marker that has multiple small repeats of bases that are 2-8 nucleotides in length (such as CA repeats) at a particular site, in which the number of repeat lengths varies in the general population . An "indel" is a common form of polymorphism comprising a small insertion or deletion that is typically only a few nucleotides long.
A "haplotype," as described herein, refers to a segment of genomic DNA that is characterized by a specific combination of alleles arranged along the segment. For diploid organisms such as humans, a haplotype comprises one member of the pair of alleles for each polymorphic marker or locus along the segment. In a certain embodiment, the haplotype can comprise two or more alleles, three or more alleles, four or more alleles, or five or more alleles. Haplotypes are described herein in the context of the marker name and the allele of the marker in that haplotype, e.g., "4 rs6474412" refers to the 4 allele of marker rs6474412 being in the haplotype, and is equivalent to "rs6474412 allele 4". Furthermore, allelic codes in haplotypes are as for individual markers, i.e. 1 = A, 2 = C, 3 = G and 4 = T.
The term "susceptibility", as described herein, refers to the proneness of an individual towards the development of a certain state (e.g., a certain trait, phenotype or disease, e.g. lung cancer), or towards being less able to resist a particular state than the average individual. The term encompasses both increased susceptibility and decreased susceptibility. Thus, particular alleles at polymorphic markers of the invention as described herein (or haplotypes comprising such markers) are characteristic of increased susceptibility (i .e., increased risk) of lung cancer, as characterized by a relative risk (RR) or odds ratio (OR) of greater than one for the particular allele or haplotype. Other particular alleles at the markers described herein are characteristic of decreased susceptibility (i.e., decreased risk) of lung cancer, as characterized by a relative risk of less than one.
The term "and/or" shall in the present context be understood to indicate that either or both of the items connected by it are involved . In other words, the term herein shall be taken to mean "one or the other or both".
The term "look-up table", as described herein, is a table that correlates one form of data to another form, or one or more forms of data to a predicted outcome to which the data is relevant, such as phenotype or trait. For example, a look-up table can comprise a correlation between allelic data for at least one polymorphic marker and a particular trait or phenotype, such as a particular disease diagnosis, that an individual who comprises the particular allelic data is likely to display, or is more likely to display than individuals who do not comprise the particular allelic data. Look-up tables can be multidimensional, i.e. they can contain information about multiple alleles for single markers simultaneously, or they can contain information about multiple markers, and they may also comprise other factors, such as particulars about diseases diagnoses, racial information, biomarkers, biochemical measurements, therapeutic methods or drugs, etc.
A "computer-readable medium", is an information storage medium that can be accessed by a computer using a commercially available or custom-made interface. Exemplary computer- readable media include memory (e.g., RAM, ROM, flash memory, etc.), optical storage media (e.g., CD-ROM), magnetic storage media (e.g., computer hard drives, floppy disks, etc.), punch cards, or other commercially available media. Information may be transferred between a system of interest and a medium, between computers, or between computers and the computer- readable medium for storage or access of stored information. Such transmission can be electrical, or by other available methods, such as IR links, wireless connections, etc.
A "nucleic acid sample" as described herein, refers to a sample obtained from an individual that contains nucleic acid (DNA or RNA). In certain embodiments, i.e. the detection of specific polymorphic markers and/or haplotypes, the nucleic acid sample comprises genomic DNA. Such a nucleic acid sample can be obtained from any source that contains genomic DNA, including a blood sample, sample of amniotic fluid, sample of cerebrospinal fluid, or tissue sample from skin, muscle, buccal or conjunctival mucosa, placenta, gastrointestinal tract or other organs.
The term "lung cancer therapeutic agent" refers to an agent that can be used to ameliorate or prevent symptoms associated with lung cancer.
The term "lung cancer-associated nucleic acid", as described herein, refers to a nucleic acid that has been found to be associated to lung cancer. This includes, but is not limited to, the markers and haplotypes described herein and markers and haplotypes in strong linkage disequilibrium (LD) therewith.
The term "antisense agent" or "antisense oligonucleotide" refers, as described herein, to molecules, or compositions comprising molecules, which include a sequence of purine an pyrimidine heterocyclic bases, supported by a backbone, which are effective to hydrogen bond to a corresponding contiguous bases in a target nucleic acid sequence. The backbone is composed of subunit backbone moieties supporting the purine and pyrimidine hetercyclic bases at positions which allow such hydrogen bonding. These backbone moieties are cyclic moieties of 5 to 7 atoms in size, linked together by phosphorous-containing linkage units of one to three atoms in length. In certain preferred embodiments, the antisense agent comprises an oligonucleotide molecule.
The term "LD block C07", as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 7 between markers rs55661693 and rs215749, corresponding to position
32,198,199 - 32,424,097 of NCBI (National Center for Biotechnology Information) Build 36. The term "LD block C08", as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 8 between markers s.42329845 (SEQ ID NO :431) and s.43167001 (SEQ ID NO: 616), corresponding to position 42,329,845 - 43,167,001 of NCBI, Build 36.
The term "LD block C19", as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 19 between markers s.45831417 (SEQ ID NO: 617) and rsl0416968,
corresponding to position 45,831,417 - 46,099,477 of NCBI, Build 36.
Variants associated with risk of lung cancer in humans The present inventors have for the first time shown that certain genetic variants are associated with risk of lung cancer in humans. Certain polymorphic markers on chromosome 8pl l, 7pl4 and 19ql3 have been found to associate with risk of lung cancer. Particular alleles at markers in these genomic regions (e.g., rs215614 on chromosome 7pl4, rs6474412 on chromosome 8pll and rs4105144 on chromosome 19ql3) are found more frequently in individuals with lung cancer than in the general population . These markers are therefore predictive of risk of lung cancer, i.e. individuals carrying the particular alleles (at-risk alleles) are at increased risk of developing lung cancer compared with the general population . Markers that are in linkage disequilibrium with these markers are also predictive of risk of lung cancer, as described in more detail herein .
Methods of determining susceptibility to lung cancer Accordingly, the present invention provides methods of determining a susceptibility to lung cancer in a human individual. A first aspect relates to a method of determining a susceptibility to lung cancer, the method comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and determining a susceptibility to lung cancer from the sequence data, wherein the at least one polymorphic marker is a marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith.
In certain embodiments, the sequence data is nucleic acid sequence data. Nucleic acid sequence data identifying particular alleles of polymorphic markers is sometimes also referred to as genotype data. Nucleic acid sequence data can be obtained for example by analyzing sequence of the at least one polymorphic marker in a biological sample from the individual. Alternatively, nucleic acid sequence data can be obtained in a genotype dataset from the human individual and analyzing sequence of the at least one polymorphic marker in the dataset. Such analysis in certain embodiments comprises determining the presence or absence of a particular allele of specific polymorphic markers. Identification of particular alleles in general terms should be taken to mean that determination of the presence or absence of the allele(s) is made. Usually, determination of both allelic copies in the genome of an individual is performed, by determining the occurrence of all possible alleles of the particular polymorphism in a particular individual (for SNPs, each of the two possible nucleotides possible for the allelic site) . It is also possible to determine whether only particular alleles are present or not. For example, in certain
embodiments, determination of the presence or absence of certain alleles that have been shown to associate with risk of kidney cancer is made, but not necessarily other alleles of the particular marker, and a determination of susceptibility is made based on such determination. In certain embodiments, sequence data about at least two polymorphic markers is obtained.
Alternatively, the allele that is detected can be the allele of the complementary strand of DNA, such that the nucleic acid sequence data includes the identification of at least one allele which is complementary to any of the alleles of the polymorphic markers referenced above. For example, the allele that is detected may be the complementary C allele of the at-risk G allele of rsl058396, the complementary G allele of the at-risk C allele of rsl l877062, the complementary C allele of the at-risk G allele of rs2298720, or the complementary T allele to the at-risk A allele of rs2298720.
In certain embodiments, the nucleic acid sequence data is obtained from a biological sample containing nucleic acid from the human individual . The nucleic acids sequence may suitably be obtained using a method that comprises at least one procedure selected from (i) amplification of nucleic acid from the biological sample; (ii) hybridization assay using a nucleic acid probe and nucleic acid from the biological sample; (iii) hybridization assay using a nucleic acid probe and nucleic acid obtained by amplification of the biological sample, and (iv) sequencing, in particular high-throughput sequencing. The nucleic acid sequence data may also be obtained from a preexisting record . For example, the preexisting record may comprise a genotype dataset for at least one polymorphic marker. In certain embodiments, the determining comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to lung cancer.
It is contemplated that in certain embodiments of the invention, it may be convenient to prepare a report of results of risk assessment. Thus, certain embodiments of the methods of the invention comprise a further step of preparing a report containing results from the
determination, wherein said report is written in a computer readable medium, printed on paper, or displayed on a visual display. In certain embodiments, it may be convenient to report results of susceptibility to at least one entity selected from the group consisting of the individual, a guardian of the individual, a genetic service provider, a physician, a medical organization, and a medical insurer.
In certain embodiments, markers on chromosome 8pll that are predictive of lung cancer risk are markers associated with a gene selected from the group consisting of CHRNB3 and CHRNA6.
In certain embodiments, markers on chromosome 7pl4 that are predictive of lung cancer risk are markers associated with a gene selected from the group consisting of PDE1C
(phosphodiesterase 1C), LSM5 and AVL9 (KIAA0241) .
In certain embodiments, markers on chromosome 19ql3 that are predictive of lung cancer risk are markers associated with a gene selected from the group consisting of CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, and RAB4B. In certain embodiment, the marker associated with risk of lung cancer is a marker located within LD block C07, LD block C08, or LD block C19, as defined herein .
In certain embodiments, markers in linkage disequilibrium with rs215614 are selected from the group consisting of the markers rs55661693, rs2392052, s.32208500, rsl860222, rsl017085, rsl0951323, rsl0951324, rs2240676, rs6462343, rs7779445, rs7796264, rsl6875791, rsl860224, s.32222361, rsl2672267, rs719585, rs6945244, rs719586, rsl2531292, rsl2533732, rsl l771370, rsl3241693, rsl3228936, rsl6875793, rsl7161043, rsl7161045, rsl7426873, rsl2669911, s.32229303, rsl7161049, s.32229594, rsl2701192, rsl3225493, rsl860225, s.32230966, rsl0233045, rsl0233473, rsl l762455, rsll769301, s.32231924, s.32232040, rsl0951325, s.32232190, s.32232206, rsl0237329, rs7791872, s.32233149, s.32233418, s.32233449, rs4141108, s.32233694, rsl2531396, rs7803347, rsl2537174, rsl7161066, rsl3246764, s.32237653, s.32237781, s.32237796, rsl7161068, rsl0269368, s.32238062, s.32238131, s.32238187, s.32238385, s.32238637, s.32238720, s.32238770, rsl014242, s.32238891, s.32238954, rs7786576, rs7786797, rs7806224, s.32239995, s.32240628, s.32240965, s.32241373, rsl0216007, s.32241650, rsl3221037, s.32242123, s.32242180, s.32242305, s.32243452, rsl0215287, s.32243761, s.32243957, s.32244134, s.32244142, s.32244149, s.32244315, s.32244333, rs6977493, s.32246045, rs9639646, rsl2701200, rs73306623, rsl0259431, rs9639648, rsl0263751, rsl0263673, rs9638875, s.32249522, s.32249615, s.32250320, rsl2538475, rsl2538504, rsl2539063, rsl3238880, rsl3242197, rsl l772510, s.32252191, rsl2701202, s.32252891, rsl0447633, rs7804687, s.32254102, rsl2701203, rsl2701204, rsl2701205, s.32254884, s.32254907, s.32256403, rsl0241729, s.32256486, rsl7161076, s.32257219, rsll975968, rs6955339, s.32257405, rs6955990, s.32257970, rsl2701206, s.32258116, rs6960114, rsl0236197, s.32258299, rsll773343, rsl0951326, rsl0951327, rs7798739, rsl3221985, rs929456, rs6977000, rs6977468, s.32261763, rsl7161087, rsl7161090, rsl2701209, rs6959931, s.32263762, rsl3224417, rs58894937, rs6975208, rsl l762194, s.32266791, s.32266901, s.32267023, s.32267024, rs4723139, rs6947159, s.32268168, rs6948856, rs975122, rs7806397, rsl2537591, s.32271190, rs7796692, rs7780515, rs7780674, rs7801559, s.32273407, s.32273416, rs4368879, s.32274498, rs4370439, rsl7161127, rs7806417, s.32276650, rsl450869, rsl450870, rs7780377, rs6947060, s.32279209, rsl0951328, s.32280165, rs6977490, s.32280356, s.32280614, s.32280995, rs7778162, rs7778443, s.32281443, rsl0226228, s.32286305, rs2159237, rsl476765, s.32286984, rsl l770877, s.32288268, s.32288404, s.32288470, s.32288482, s.32288491, s.32288538, s.32288631, rs9771228, rsl2540204, rsl2540232, s.32289625, rsl7161134, rs215596, s.32293784, rsl l768207, s.32294392, s.32295805, s.32296223, rs215599, rsl0271037, s.32298436, rs215600, rs215601, rs215603, s.32303110, rs215605, rsl349399, s.32303968, s.32304331, s.32304468, rs215607, rs215608, rs215610, rsl2531858, rs59238577, rs215611, rs7780009, rs7780609, s.32309135, rs6952052, rs6952609, s.32310279, rsl2538119, rs7779181, rs6967626, rsl2536117, s.32312803, s.32313220, rs7779130, rs7778788, rs7779180, rs215614, s.32314071, s.32314173, s.32314645, s.32314726, s.32316109, s.32316473, s.32316498, s.32316632, s.32318702, s.32318703, s.32318774, s.32319085, s.32319226, rsl0951330, s.32320457, rs6462351, rsll981007, rs6462352, rsl0951331, rsll981809, rs6955946, s.32323398, s.32323503, rsl7161177, rs215622, s.32324242, s.32324582, rs215623, rs215624, rs215625, rs6943670, s.32325802, s.32325803, rsl3235908, rsl2531102, s.32326579, s.32326621, rs215629, rsl653876, rsl7161184, s.32328426, s.32328795, rs215630, rs6462353, rsl376281, rs215631, rs6462354, s.32333151, s.32333776, s.32333955, rslll5318, rs215632, s.32335243, rs215634, rs6955346, rs215635, rsll520787, rsl0264177, rsll514764, rs215636, rs215637, rs215638, rs215639, rs6977196, s.32340738, s.32340852, s.32343121, rsl0238006, s.32344007, rsl0447642, s.32344169, s.32344734, s.32345369, rs215669, rs215670, rs717757, s.32347343, s.32347366, s.32347375, s.32347376, s.32347462, rsl86229, s.32348187, rs215672, s.32348546, s.32348867, rsl653884, rs215674, s.32349420, rs215675, rs215676, rs387575, rs215677, s.32350287, s.32350891, rsl70011, rs215678, rs215679, rs215680, rs215681, rsl653887, rsl653888, rsl668386, rsl653889, rsl668387, rsl668388, rsl668389, s.32353491, rsl668390, rs690247, rs690250, rsl83347, rsl77362, rsl77363, rsl653890, rs215682, rs215683, rs215684, rs215685, rsl77364, s.32355034, rs215686, rs215687, rs215690, rsl376284, s.32355855, s.32355892, rs35554640, s.32356066, rsl376286, s.32356229, rsl376287, rsl013772, rsl013771, s.32357132, s.32357194, s.32357656, s.32358191, rsl668393, rsl3227922, rs215692, rs6979697, s.32362045, rs412876, rs7777166, rs7808851, rs215694, rs4723146, rs215695, rs215696, rs215697, rs215698, rs4723147, rsl653891, s.32364994, s.32365034, s.32365036, rsl668394, rsl77365, rs215699, rs215700, rsl653892, rs215702, rsl0486507, s.32380353, rs2099306, rsl70016, rsl0236370, s.32413299, rsll23893, rs384711, rs430356, rs730725, rs435584, s.32414636, rsl72558, rs7457071 and rs215749, which are the markers set forth in Table 1.
In certain embodiments, markers in linkage disequilibrium with rs6474412 are selected from the group consisting of the markers s.42329845, s.42601955, s.42618302, rs7013926, rsl2156092, rsl868860, rsl530850, rs6989472, s.42626122, rsll785591, rsl947295, rsl376442, rs4737060, s.42631425, s.42632599, rs34842664, rsl868859, rsl868858, rsl0958724, rs28441235, rs7006469, rsl0097384, rs4305884, s.42639491, s.42639835, rs6990603, rs34456987, rsl0958725, rs36057318, s.42644785, rs7837296, s.42646284, s.42646543, rs5005909, rsl979140, rsl3277840, s.42651452, s.42652114, rsll783507, rs7816726, rsl0958726, rs7842601, s.42656893, rs4295650, s.42659223, rs6474411, s.42661513, s.42661651, rsl3273442, s.42664453, s.42664514, s.42664708, s.42664984, rsl451239, rsl451240, s.42666045, rs4736835, s.42666333, s.42666490, rs6987704, rsl530847, s.42668648, rsl955185, rsl3277254, rsl3280301, rsl3277524, rs6474412, rs6474413, rs7004381, rs6985052, rs4950, rsl530848, rs9643891, rsl3280604, rs6997909, rs6474414, s.42679689, rs6474415, rs4951, rsl3263434, s.42692248, rsll783289, rs4236926, rsl3261190, s.42698182, rsl6891561, rs7459838, rs55828312, rs7017612, rs6984031, s.42718611, s.42721825, rs7822100, rs7825907, rs6982753, rs9298628, rs9298629, rs7824155, s.42726264, rs7824614, s.42726938, rs2304297, rs7845663, rs7812298, rs7004108, s.42728158, s.42728590, s.42729587, s.42729589, s.42731443, s.42731535, s.42731657, s.42731840, s.42732056, rsl0110332, s.42732673, rs892413, rsl0087172, rs4398905, rs2196128, rs2196129, rs2217732, rsl072003, s.42741317, s.42744231, rsl0109040, rsl6891620, s.42745575, s.42745581, rs4737069, rs2117225, rs2164024, rs7828365, rsl0107450, s.42750441, s.42752754, s.42753787, s.42756840, s.42757418, rsl0092346, s.42759608, s.42760352, s.42761317, s.42761892, s.42761909, s.42761965, rsl960346, s.42763404, s.42763808, s.42764045, s.42765003, rs4567031, s.42765421, s.42769020, rs4737071, rs6474420, s.42770829, s.42771871, rsl l986893, s.42772405, s.42779341, s.42780448, rs6985527, s.42782778, s.42782938, s.42783806, s.42784397, s.42786368, s.42787290, s.42787506, s.42789990, s.42806166, rsl0087388, s.42837205, s.42844765, s.42859016, s.42886979, s.42936582, rsl0106661, rs34727690, s.43009750, s.43018519, s.43113569, s.43150703, s.43166986, s.43167001, which are the markers set forth in Table 2.
In certain embodiments, markers in linkage disequilibrium with rs4105144 are selected from the group consisting of the markers s.45831417, s.45859717, rsl l083565, rs2561537, rs2604885, rs7260405, rs2607420, rs2369302, rs2254343, rsl457141, rs2604874, s.45950500, s.45950502, rs2607415, rs2607414, rs2279011, rs2249835, rs2607424, rs2604869, rsl2973666, rs2604893, rs2644898, rs7252227, rs7937, rs2644916, rs3733828, rs4803372, s.46014685, s.46014686, rs4803373, s.46019153, s.46019626, s.46020036, s.46024011, s.46024197, s.46024672, rsll670760, rsl2459249, rs7251418, rs7251418, rs7251570, rs4343391, rs4343391, rs7245507, s.46037829, rsl l083571, rs2302989, rs2316213, rs8192725, rs7250713, rsl l37115, rs4105144, rsl0404667, rs4001921, rs8102683, rs8102683, rs8105704, rsl496402, rsl496402, rsl2610432, s.46062016, rsl2461383, rsl2461383, rs4570984, s.46067487, s.46067573, s.46067911, rsll882981, rs7247469, rs7251315, rs6508951, s.46075055, rs3869579, s.46075829, s.46075942, s.46077574, rsl2973598, s.46077976, s.46078049, s.46078122, s.46078260, s.46078326, s.46078327, s.46078334, s.46078367, s.46078384, s.46078387, s.46078424, s.46079081, s.46079140, s.46080547, s.46080617, s.46082249, s.46083888, rs3875159, rs28503746, rs3909341, rs4105142, rs4105141, rs5007415, s.46085777, rsl0411264, rs67421541, s.46086876, s.46087595, rsll083582, rs3909342, rs4803397, rs4803397, s.46088755, rs3852870, s.46089339, rs8103444, rs3865457, rs4803398, rs6508953, rsl0419393, rs7343061, rs4803400, rs7254188, rsl0416968, which are the markers set forth in Table 3.
Surrogate markers in linkage disequilibrium with particular key markers can in general be selected based on any particular numerical values of the linkage disequilibrium measures D' and r2, as described further herein. For example, markers that are in linkage disequilibrium with rs215614 are exemplified by the markers listed in Table 1 herein, but the skilled person will appreciate that other markers in linkage disequilibrium with these markers may also be used in the diagnostic applications described herein. Likewise, exemplary surrogate markers in linkage disequilibrium with rs6474412 are listed in Table 2 herein, and exemplary surrogate markers in linkage disequilibrium with rs4105144 are listed in Table 3 herein . Further, as also described in more detail herein, the skilled person will appreciate that since linkage disequilibrium is a continuous measure, certain values of the LD measures D' and r2 may be suitably chosen to define markers that are useful as surrogate markers in LD with the markers described herein. Numeric values of D' and r2 may thus in certain embodiments be used to define marker subsets that fulfill certain numerical cutoff values of D' and/or r2. In one embodiment, markers in linkage disequilibrium with a particular anchor marker (e.g., rs215614, rs6474412 and/or rs4105144) are in LD with the anchor marker characterized by numerical values of D' greater than 0.8 and/or numerical values of r2 of greater than 0.2. In one embodiment, markers in linkage disequilibrium with a particular anchor marker are in LD with the anchor marker characterized by numerical values of r2 greater than 0.2. The markers provided in Tables 1 to 3 provide exemplary markers that fulfill this criterion. In other embodiments, markers in linkage disequilibrium with a particular anchor marker are in LD with the anchor marker characterized by numerical values of r2 of greater than 0.3, greater than 0.4, greater than 0.5, greater than 0.6, greater than 0.7, greater than 0.8, greater than 0.9, and greater than 0.95. In certain embodiments, such markers are selected from the markers listed in Tables 1 to 3, using the information on LD measures provided in the tables. In other words, other suitable numerical values of r2 and/or D' may be used to select markers that are in LD with the anchor marker. The stronger the LD, the more similar the association signal and/or the predictive risk by the surrogate marker will be to that of the anchor marker. Markers with values of r2 = 1 to the anchor marker are perfect surrogates of the anchor marker and will provide identical association and risk prediction data. In one preferred embodiment, suitable surrogate markers are those markers that have values of r2 to an anchor marker of greater than 0.8.
Further, as described in more detail in the following, LD may be determined in samples from any particular population. In one embodiment, LD is determined in Caucasian samples. In another embodiment, LD is determined in European samples. In other embodiments, LD is determined in African American samples, in Asian samples, or the LD may be suitably determined in samples of any other population.
Table 1. Surrogate markers of anchor marker rs215614 (SEQ ID NO:252) on Chromosome 7pl4. Markers were selected using data from Caucasian HapMap dataset or the publically available 1000 Genomes project (http://www.1000genomes.org). Markers that have not been assigned rs names are identified by their position in NCBI Build 36 of the human genome assembly. Shown are predicted risk alleles for the surrogate markers, i.e. alleles that are correlated with the risk allele of the anchor marker, rs215614 allele G. Linkage disequilibrium measures D'and R2, and corresponding p-value, are also shown. The last column refers to the sequence listing number, identifying the particular SNP.
Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rs55661693 T 32198199 0.88 0.23 0.00062 1 rs2392052 G 32199038 0.88 0.23 0.00062 2 s.32208500 A 32208500 0.87 0.2 0.00081 3 rsl860222 C 32208505 0.87 0.2 0.00081 4 rsl017085 C 32213984 0.69 0.29 0.00012 5 rsl0951323 G 32214715 0.680907 0.201028 0.00007894 6 rsl0951324 G 32215509 0.76 0.24 0.00016 7 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rs2240676 A 32215741 0.76 0.24 0.00016 8 rs6462343 G 32216583 0.76 0.24 0.00016 9 rs7779445 T 32216842 1 0.306594 1.57E- 12 10 rs7796264 A 32216898 0.76 0.24 0.00016 11 rsl6875791 G 32222133 0.782372 0.431935 1.27E-09 12 rsl860224 A 32222167 0.803317 0.427077 1.18E- 11 13 s.32222361 A 32222361 1 0.25 5.80E-09 14 rsl2672267 G 32225259 1 0.255268 1.06E- 10 15 rs719585 C 32225610 1 0.22366 8.58E- 10 16 rs6945244 T 32225798 0.921077 0.684761 5.74E-20 17 rs719586 c 32226042 1 0.23 2.00E-08 18 rsl2531292 c 32226358 1 0.23 2.00E-08 19 rsl2533732 G 32226529 1 0.23 2.00E-08 20 rsl l771370 T 32226690 0.92 0.4 7.50E-07 21 rsl3241693 T 32227618 1 0.23 2.00E-08 22 rsl3228936 c 32227634 1 0.2 2.20E-07 23 rsl6875793 A 32227755 1 0.23 2.00E-08 24 rsl7161043 A 32227806 1 0.330959 1.85E- 11 25 rsl7161045 C 32227983 0.96 0.82 6.90E- 17 26 rsl7426873 G 32228073 1 0.2 2.20E-07 27 rsl2669911 A 32228902 0.887848 0.762515 6.27E-22 28 s.32229303 C 32229303 1 0.23 2.00E-08 29 rsl7161049 G 32229427 1 0.21 6.80E-08 30 s.32229594 T 32229594 0.96 0.82 6.90E- 17 31 rsl2701192 G 32229886 1 0.23 2.00E-08 32 rsl3225493 T 32230126 1 0.2 2.20E-07 33 rsl860225 G 32230236 0.96 0.82 6.90E- 17 34 s.32230966 C 32230966 1 0.25 5.80E-09 35 rsl0233045 A 32231017 0.923436 0.795023 3.78E-23 36 rsl0233473 T 32231532 0.934931 0.458387 7.21 E- 12 37 rsl l762455 G 32231657 1 0.23 2.00E-08 38 rsl l769301 G 32231725 1 0.23 2.00E-08 39 s.32231924 T 32231924 1 0.2 2.20E-07 40 s.32232040 G 32232040 1 0.23 2.00E-08 41 rsl0951325 C 32232070 1 0.2 1.20E-08 42 s.32232190 G 32232190 1 0.2 2.20E-07 43 s.32232206 G 32232206 1 0.2 2.20E-07 44 rsl0237329 C 32232250 0.923436 0.795023 3.78E-23 45 rs7791872 A 32233048 1 0.2 2.20E-07 46 s.32233149 T 32233149 1 0.25 5.80E-09 47 s.32233418 A 32233418 1 0.23 2.00E-08 48 s.32233449 G 32233449 1 0.2 2.20E-07 49 rs4141108 T 32233484 0.934918 0.458374 1.46E- 11 50 s.32233694 G 32233694 1 0.23 2.00E-08 51 rsl2531396 T 32235732 1 0.23 2.00E-08 52 rs7803347 c 32235840 1 0.23 2.00E-08 53 rsl2537174 G 32236562 1 0.2 2.20E-07 54 rsl7161066 A 32237383 1 0.2 2.20E-07 55 rsl3246764 C 32237506 1 0.2 2.20E-07 56 s.32237653 A 32237653 1 0.2 2.20E-07 57 s.32237781 T 32237781 1 0.2 2.20E-07 58 s.32237796 A 32237796 1 0.2 2.20E-07 59 rsl7161068 G 32237849 1 0.2 2.20E-07 60 rsl0269368 G 32238039 0.943973 0.495664 4.45E- 14 61 s.32238062 C 32238062 1 0.2 2.20E-07 62 s.32238131 A 32238131 1 0.2 2.20E-07 63 s.32238187 T 32238187 0.96 0.82 6.90E- 17 64 s.32238385 A 32238385 1 0.2 2.20E-07 65 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
s.32238637 G 32238637 0.93 0.45 1.40E-07 66 s.32238720 G 32238720 1 0.2 2.20E-07 67 s.32238770 G 32238770 1 0.2 2.20E-07 68 rsl014242 C 32238830 0.916847 0.68787 7.32E- 19 69 s.32238891 C 32238891 1 0.2 2.20E-07 70 s.32238954 A 32238954 0.96 0.82 6.90E- 17 71 rs7786576 C 32239239 0.874374 0.639319 2.09E- 16 72 rs7786797 C 32239480 1 0.2 2.20E-07 73 rs7806224 C 32239632 0.95707 0.735702 1.37E- 19 74 s.32239995 A 32239995 1 0.2 2.20E-07 75 s.32240628 C 32240628 1 0.2 2.20E-07 76 s.32240965 T 32240965 1 0.2 2.20E-07 77 s.32241373 A 32241373 1 0.2 2.20E-07 78 rsl0216007 T 32241504 0.9 0.26 2.90E-05 79 s.32241650 A 32241650 1 0.2 2.20E-07 80 rsl3221037 G 32241842 1 0.2 2.20E-07 81 s.32242123 C 32242123 1 0.2 2.20E-07 82 s.32242180 C 32242180 1 0.29 4.50E- 10 83 s.32242305 C 32242305 1 0.2 2.20E-07 84 s.32243452 G 32243452 1 0.29 4.50E- 10 85 rsl0215287 C 32243475 1 0.2 2.20E-07 86 s.32243761 C 32243761 1 0.29 4.50E- 10 87 s.32243957 G 32243957 1 0.23 1.20E-09 88 s.32244134 T 32244134 0.9 0.26 2.90E-05 89 s.32244142 G 32244142 0.96 0.86 2.90E- 18 90 s.32244149 C 32244149 1 0.2 1.20E-08 91 s.32244315 T 32244315 1 0.2 2.20E-07 92 s.32244333 T 32244333 1 0.2 2.20E-07 93 rs6977493 c 32245491 1 0.2 2.20E-07 94 s.32246045 G 32246045 1 0.2 2.20E-07 95 rs9639646 G 32246768 0.931593 0.387448 2.61 E- 10 96 rsl2701200 C 32247097 1 0.2 2.20E-07 97 rs73306623 A 32247340 0.93 0.45 1.40E-07 98 rsl0259431 C 32247922 0.912032 0.669887 2.89E- 17 99 rs9639648 A 32248667 0.929026 0.360581 5.74E- 10 100 rsl0263751 G 32248982 1 0.2 2.20E-07 101 rsl0263673 T 32249044 0.927154 0.367918 1.38E-09 102 rs9638875 A 32249439 0.92333 0.794842 7.59E-23 103 s.32249522 T 32249522 1 0.2 2.20E-07 104 s.32249615 G 32249615 1 0.2 2.20E-07 105 s.32250320 G 32250320 1 0.2 2.20E-07 106 rsl2538475 A 32250709 1 0.2 2.20E-07 107 rsl2538504 A 32250763 1 0.2 2.20E-07 108 rsl2539063 T 32251008 1 0.2 2.20E-07 109 rsl3238880 A 32251424 1 0.2 2.20E-07 110 rsl3242197 A 32252028 1 0.2 2.20E-07 111 rsl l772510 A 32252162 0.96 0.82 6.90E- 17 112 s.32252191 A 32252191 1 0.25 5.80E-09 113 rsl2701202 A 32252269 1 0.2 2.20E-07 114 s.32252891 C 32252891 1 0.29 4.50E- 10 115 rsl0447633 A 32253083 1 0.342466 6.62E- 12 116 rs7804687 A 32253844 1 0.2 2.20E-07 117 s.32254102 C 32254102 1 0.2 2.20E-07 118 rsl2701203 A 32254285 1 0.2 2.20E-07 119 rsl2701204 A 32254300 1 0.2 2.20E-07 120 rsl2701205 C 32254406 1 0.2 2.20E-07 121 s.32254884 G 32254884 1 0.2 2.20E-07 122 s.32254907 T 32254907 1 0.2 2.20E-07 123 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
s.32256403 C 32256403 1 0.2 2.20E-07 124 rsl0241729 G 32256404 0.91 0.34 1.70E-06 125 s.32256486 G 32256486 1 0.29 4.50E- 10 126 rsl7161076 G 32257090 0.849569 0.286191 5.75E-08 127 s.32257219 C 32257219 0.91 0.34 1.70E-06 128 rsl l975968 G 32257255 1 0.342466 6.62E- 12 129 rs6955339 C 32257367 0.96 0.82 6.90E- 17 130 s.32257405 C 32257405 1 0.25 5.80E-09 131 rs6955990 G 32257826 1 0.2 2.20E-07 132 s.32257970 A 32257970 1 0.27 1.60E-09 133 rsl2701206 C 32257988 0.96 0.82 6.90E- 17 134 s.32258116 C 32258116 0.96 0.82 6.90E- 17 135 rs6960114 G 32258124 0.96 0.82 6.90E- 17 136 rsl0236197 C 32258286 0.919969 0.791593 5.47E-22 137 s.32258299 A 32258299 0.96 0.82 6.90E- 17 138 rsl l773343 T 32258841 0.950079 0.581454 6.27E- 16 139 rsl0951326 c 32259097 1 0.342466 6.62E- 12 140 rsl0951327 T 32259283 1 0.57 1.20E- 18 141 rs7798739 A 32259486 0.919363 0.782219 2.01 E-20 142 rsl3221985 A 32259510 0.950816 0.589346 1.16E- 16 143 rs929456 G 32260169 0.923436 0.795023 3.78E-23 144 rs6977000 T 32261546 1 0.2 2.20E-07 145 rs6977468 A 32261700 1 0.2 2.20E-07 146 s.32261763 A 32261763 1 0.29 4.50E- 10 147 rsl7161087 C 32262294 1 0.342466 6.62E- 12 148 rsl7161090 G 32262316 1 0.2 2.20E-07 149 rsl2701209 A 32262583 1 0.2 2.20E-07 150 rs6959931 G 32263757 0.96 0.82 6.90E- 17 151 s.32263762 T 32263762 0.96 0.82 6.90E- 17 152 rsl3224417 A 32265118 0.952005 0.60945 4.22E- 17 153 rs58894937 T 32265423 1 0.2 2.20E-07 154 rs6975208 A 32266228 1 0.57 1.20E- 18 155 rsl l762194 A 32266694 0.950348 0.581788 3.12E- 16 156 s.32266791 C 32266791 1 0.25 5.80E-09 157 s.32266901 A 32266901 1 0.27 1.60E-09 158 s.32267023 C 32267023 0.83 0.62 4.50E- 11 159 s.32267024 C 32267024 0.96 0.82 6.90E- 17 160 rs4723139 G 32267242 0.96 0.82 6.90E- 17 161 rs6947159 T 32267968 1 0.2 2.20E-07 162 s.32268168 A 32268168 1 0.25 5.80E-09 163 rs6948856 A 32268872 0.902096 0.666863 1.51 E- 15 164 rs975122 A 32269319 0.947836 0.581536 3.64E- 15 165 rs7806397 T 32269864 0.919255 0.777792 1.43E-20 166 rsl2537591 c 32270244 1 0.2 2.20E-07 167 s.32271190 A 32271190 1 0.25 5.80E-09 168 rs7796692 G 32271390 0.94922 0.591575 5.40E- 16 169 rs7780515 T 32271799 0.92333 0.794842 7.59E-23 170 rs7780674 T 32271916 1 0.236678 4.22E- 10 171 rs7801559 G 32271936 0.96 0.82 6.90E- 17 172 s.32273407 A 32273407 0.96 0.86 3.30E- 18 173 s.32273416 G 32273416 0.96 0.86 3.30E- 18 174 rs4368879 C 32274450 0.920996 0.817498 5.51 E-23 175 s.32274498 C 32274498 1 0.9 3.20E-30 176 rs4370439 C 32274626 0.952005 0.60945 4.22E- 17 177 rsl7161127 C 32275242 1 0.314815 2.77E- 10 178 rs7806417 C 32276626 1 0.6 2.00E- 19 179 s.32276650 G 32276650 1 0.23 1.20E-09 180 rsl450869 G 32278197 0.924212 0.824382 6.97E-24 181 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rsl450870 T 32278251 0.890378 0.792774 7.66E-23 182 rs7780377 A 32278384 1 0.330959 1.85E- 11 183 rs6947060 C 32278671 1 0.6 2.00E- 19 184 s.32279209 G 32279209 1 0.27 1.60E-09 185 rsl0951328 C 32279366 1 0.6 2.00E- 19 186 s.32280165 G 32280165 0.96 0.86 3.30E- 18 187 rs6977490 A 32280351 1 0.6 2.00E- 19 188 s.32280356 A 32280356 1 0.6 2.00E- 19 189 s.32280614 G 32280614 1 0.6 2.00E- 19 190 s.32280995 T 32280995 1 0.6 2.00E- 19 191 rs7778162 c 32281009 0.952407 0.616547 1.57E- 17 192 rs7778443 T 32281215 0.892731 0.795031 3.03E-23 193 s.32281443 G 32281443 1 0.6 2.00E- 19 194 rsl0226228 G 32282138 0.925327 0.82756 1.76E-24 195 s.32286305 A 32286305 0.96 0.86 3.30E- 18 196 rs2159237 A 32286594 1 0.6 2.00E- 19 197 rsl476765 G 32286983 0.924185 0.826363 5.46E-24 198 s.32286984 T 32286984 0.96 0.86 3.30E- 18 199 rsl l770877 c 32286987 0.96 0.86 3.30E- 18 200 s.32288268 A 32288268 0.96 0.86 3.30E- 18 201 s.32288404 G 32288404 1 0.22 2.70E-09 202 s.32288470 G 32288470 1 0.22 2.70E-09 203 s.32288482 A 32288482 1 0.22 2.70E-09 204 s.32288491 C 32288491 1 0.93 1.60E-31 205 s.32288538 A 32288538 1 0.22 2.70E-09 206 s.32288631 A 32288631 1 0.22 2.70E-09 207 rs9771228 C 32289021 0.921447 0.763313 6.37E-22 208 rsl2540204 C 32289359 0.96 0.86 3.30E- 18 209 rsl2540232 C 32289486 0.926118 0.85712 9.89E-25 210 s.32289625 C 32289625 1 0.27 1.60E-09 211 rsl7161134 T 32290104 1 0.363322 1.54E- 12 212 rs215596 A 32292898 0.892731 0.795031 3.03E-23 213 s.32293784 T 32293784 1 0.27 1.60E-09 214 rsl l768207 c 32293832 0.952407 0.616547 1.57E- 17 215 s.32294392 c 32294392 1 0.28 4.70E- 11 216 s.32295805 T 32295805 1 0.27 1.60E-09 217 s.32296223 G 32296223 0.96 0.86 3.30E- 18 218 rs215599 C 32296654 0.887325 0.756523 4.05E-21 219 rsl0271037 T 32296861 0.952407 0.616547 1.57E- 17 220 s.32298436 c 32298436 1 0.2 2.20E-07 221 rs215600 G 32300167 0.961738 0.859477 8.13E-26 222 rs215601 A 32300446 0.925402 0.827693 8.77E-25 223 rs215603 C 32301405 1 0.93 1.60E-31 224 s.32303110 T 32303110 1 0.2 2.20E-07 225 rs215605 G 32303490 1 1 2.10E-36 226 rsl349399 A 32303864 1 0.363322 1.54E- 12 227 s.32303968 C 32303968 1 1 3.90E-35 228 s.32304331 T 32304331 1 0.2 2.20E-07 229 s.32304468 c 32304468 1 0.31 1.20E- 10 230 rs215607 G 32304862 1 0.407286 1.17E- 13 231 rs215608 C 32305081 1 0.25 5.80E-09 232 rs215610 G 32306119 1 0.416667 2.74E- 14 233 rsl2531858 A 32306624 1 0.409956 5.83E- 14 234 rs59238577 A 32306870 1 0.27 1.60E-09 235 rs215611 C 32307963 1 0.963729 1.49E-33 236 rs7780009 A 32308068 1 0.399358 1.62E- 13 237 rs7780609 A 32308441 1 0.224793 3.98E-08 238 s.32309135 C 32309135 1 0.2 2.20E-07 239 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rs6952052 G 32309418 1 0.2 2.20E-07 240 rs6952609 G 32309860 1 0.388199 2.71 E- 13 241 s.32310279 T 32310279 1 0.25 5.80E-09 242 rsl2538119 c 32311169 1 0.2443 6.45E-09 243 rs7779181 c 32311808 1 0.416667 2.74E- 14 244 rs6967626 c 32312312 1 0.45 5.30E- 15 245 rsl2536117 T 32312397 1 0.2 2.20E-07 246 s.32312803 T 32312803 1 0.27 1.60E-09 247 s.32313220 c 32313220 1 0.2 2.20E-07 248 rs7779130 T 32313483 1 0.384615 5.57E- 13 249 rs7778788 c 32313499 1 0.416667 2.74E- 14 250 rs7779180 G 32313727 1 0.428571 1.56E- 14 251 rs215614 G 32313860 1 1 0 252 s.32314071 C 32314071 1 0.2 2.20E-07 253 s.32314173 G 32314173 1 0.25 5.80E-09 254 s.32314645 T 32314645 1 0.25 5.80E-09 255 s.32314726 c 32314726 1 0.27 1.60E-09 256 s.32316109 T 32316109 1 1 3.90E-35 257 s.32316473 G 32316473 1 0.34 5.60E- 13 258 s.32316498 G 32316498 1 1 3.90E-35 259 s.32316632 G 32316632 1 1 3.90E-35 260 s.32318702 C 32318702 1 0.25 5.80E-09 261 s.32318703 A 32318703 1 0.25 5.80E-09 262 s.32318774 C 32318774 1 0.25 5.80E-09 263 s.32319085 A 32319085 1 0.25 5.80E-09 264 s.32319226 T 32319226 1 0.25 5.80E-09 265 rsl0951330 A 32320302 1 0.2 2.20E-07 266 s.32320457 G 32320457 1 0.25 5.80E-09 267 rs6462351 C 32320751 1 0.310296 7.74E- 11 268 rsl l981007 C 32320832 1 0.205368 1.51 E-07 269 rs6462352 T 32320870 1 0.363322 1.54E- 12 270 rsl0951331 G 32320899 1 0.418182 4.33E- 14 271 rsl l981809 T 32321312 1 0.2 2.20E-07 272 rs6955946 A 32322564 1 0.363322 1.54E- 12 273 s.32323398 G 32323398 1 0.2 2.20E-07 274 s.32323503 T 32323503 1 0.2 2.20E-07 275 rsl7161177 A 32323543 1 0.322034 2.77E- 11 276 rs215622 C 32324184 1 0.892857 5.65E-30 277 s.32324242 T 32324242 1 0.2 2.20E-07 278 s.32324582 T 32324582 1 0.31 1.20E- 10 279 rs215623 A 32324690 1 0.346507 2.84E- 11 280 rs215624 G 32324698 1 0.363322 1.54E- 12 281 rs215625 G 32324838 1 0.864979 1.75E-29 282 rs6943670 C 32325702 1 0.2 2.20E-07 283 s.32325802 T 32325802 1 0.52 4.10E- 17 284 s.32325803 G 32325803 1 0.89 1.10E-29 285 rsl3235908 T 32326014 1 0.2 2.20E-07 286 rsl2531102 c 32326553 1 0.2 2.20E-07 287 s.32326579 A 32326579 1 0.2 2.20E-07 288 s.32326621 C 32326621 1 1 3.90E-35 289 rs215629 G 32326989 1 0.862069 4.74E-29 290 rsl653876 T 32327144 1 0.652174 1.42E-21 291 rsl7161184 c 32327864 1 0.322034 2.77E- 11 292 s.32328426 G 32328426 1 0.2 2.20E-07 293 s.32328795 A 32328795 1 0.2 2.20E-07 294 rs215630 G 32330037 1 0.363322 1.54E- 12 295 rs6462353 C 32330668 1 0.363322 1.54E- 12 296 rsl376281 T 32330805 1 0.341985 1.81 E- 11 297 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rs215631 C 32331499 1 0.89 1.10E-29 298 rs6462354 G 32333008 1 0.600465 7.80E-20 299 s.32333151 A 32333151 1 0.55 7.40E- 18 300 s.32333776 A 32333776 1 0.27 1.60E-09 301 s.32333955 T 32333955 1 0.29 2.00E- 11 302 rsl l l5318 A 32334175 1 0.604886 1.77E- 19 303 rs215632 A 32335049 1 0.963293 4.28E-33 304 s.32335243 C 32335243 1 0.86 2.40E-28 305 rs215634 A 32335673 0.964393 0.929302 9.57E-30 306 rs6955346 C 32336078 1 0.864979 1.75E-29 307 rs215635 C 32336745 1 0.964519 8.27E-34 308 rsl l520787 T 32337284 1 0.27 1.60E-09 309 rsl0264177 G 32337387 0.911909 0.736185 1.31 E- 18 310 rsl l514764 T 32337477 1 0.259022 5.56E-09 311 rs215636 c 32338444 1 0.6451 3.92E-21 312 rs215637 G 32339923 1 0.55 7.40E- 18 313 rs215638 G 32340066 0.96 0.93 4.90E-21 314 rs215639 C 32340164 1 0.852753 3.35E-27 315 rs6977196 C 32340403 1 0.63334 1.52E-20 316 s.32340738 C 32340738 0.94 0.58 1.20E- 11 317 s.32340852 A 32340852 1 0.27 1.60E-09 318 s.32343121 T 32343121 1 0.57 1.20E- 18 319 rsl0238006 A 32343478 1 0.625 1.09E-20 320 s.32344007 G 32344007 0.9 0.57 6.60E- 11 321 rsl0447642 T 32344090 0.949465 0.573797 5.70E- 16 322 s.32344169 G 32344169 1 0.33 3.20E- 11 323 s.32344734 C 32344734 1 0.57 1.20E- 18 324 s.32345369 G 32345369 0.9 0.32 6.90E-06 325 rs215669 G 32345504 0.964379 0.928341 1.50E-29 326 rs215670 G 32345743 1 0.863471 3.31 E-29 327 rs717757 C 32346675 1 0.414279 5.37E- 14 328 s.32347343 C 32347343 1 0.21 5.80E-09 329 s.32347366 A 32347366 1 0.25 5.80E-09 330 s.32347375 T 32347375 1 0.25 5.80E-09 331 s.32347376 G 32347376 1 0.57 1.20E- 18 332 s.32347462 A 32347462 0.9 0.32 6.90E-06 333 rsl86229 C 32348082 0.962689 0.893533 1.31 E-27 334 s.32348187 T 32348187 0.94 0.58 1.20E- 11 335 rs215672 T 32348235 0.932344 0.372398 3.26E- 11 336 s.32348546 A 32348546 1 0.2 1.20E-08 337 s.32348867 A 32348867 1 0.33 3.20E- 11 338 rsl653884 T 32348916 0.933025 0.383656 2.38E- 11 339 rs215674 A 32349397 0.933111 0.380095 2.61 E- 11 340 s.32349420 A 32349420 0.96 0.89 2.30E- 19 341 rs215675 G 32349522 0.932344 0.372398 3.26E- 11 342 rs215676 A 32349676 0.87004 0.341326 4.37E- 10 343 rs387575 G 32349712 0.87004 0.341326 4.37E- 10 344 rs215677 C 32350076 0.932344 0.372398 3.26E- 11 345 s.32350287 T 32350287 0.83 0.31 1.90E-05 346 s.32350891 A 32350891 1 0.21 6.80E-08 347 rsl70011 C 32351269 0.931455 0.371953 4.05E- 11 348 rs215678 T 32351518 0.81 0.27 6.80E-05 349 rs215679 A 32351884 0.9 0.32 6.90E-06 350 rs215680 A 32352036 0.9 0.32 6.90E-06 351 rs215681 C 32352243 0.930845 0.361693 8.72E- 11 352 rsl653887 G 32352768 0.932344 0.372398 3.26E- 11 353 rsl653888 G 32353017 0.932344 0.372398 3.26E- 11 354 rsl668386 C 32353115 0.929915 0.380428 5.03E- 10 355 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rsl653889 G 32353178 0.960334 0.849991 9.46E-25 356 rsl668387 C 32353293 0.933894 0.384101 1.92E- 11 357 rsl668388 G 32353349 0.81 0.27 6.80E-05 358 rsl668389 G 32353440 0.961345 0.884803 8.80E-26 359 s.32353491 G 32353491 0.9 0.32 6.90E-06 360 rsl668390 C 32353707 0.931482 0.371708 6.54E- 11 361 rs690247 C 32353796 0.932344 0.372398 3.26E- 11 362 rs690250 A 32353847 0.932344 0.372398 3.26E- 11 363 rsl83347 G 32354018 0.932344 0.372398 3.26E- 11 364 rsl77362 C 32354268 0.933894 0.384101 1.92E- 11 365 rsl77363 G 32354318 0.81 0.27 6.80E-05 366 rsl653890 A 32354480 0.929951 0.391891 4.64E- 11 367 rs215682 C 32354577 0.96 0.8 4.30E- 16 368 rs215683 C 32354591 1 0.33 3.20E- 11 369 rs215684 G 32354704 1 0.65 2.70E-22 370 rs215685 C 32354928 0.9 0.32 6.90E-06 371 rsl77364 G 32355007 0.9 0.32 6.90E-06 372 s.32355034 T 32355034 1 0.33 3.20E- 11 373 rs215686 G 32355190 1 0.33 3.20E- 11 374 rs215687 C 32355242 0.930845 0.361693 8.72E- 11 375 rs215690 C 32355344 0.853488 0.295775 2.85E-08 376 rsl376284 G 32355622 0.932344 0.372398 3.26E- 11 377 s.32355855 T 32355855 0.83 0.47 1.10E-07 378 s.32355892 G 32355892 0.73 0.23 0.00041 379 rs35554640 C 32356058 0.81 0.27 6.80E-05 380 s.32356066 A 32356066 0.81 0.27 6.80E-05 381 rsl376286 G 32356079 0.933025 0.383656 2.38E- 11 382 s.32356229 G 32356229 0.92 0.86 7.00E- 18 383 rsl376287 A 32356324 0.932344 0.372398 3.26E- 11 384 rsl013772 T 32356476 0.938582 0.426198 1.15E- 12 385 rsl013771 G 32356613 0.938644 0.429861 1.64E- 12 386 s.32357132 C 32357132 1 0.31 3.50E- 12 387 s.32357194 T 32357194 0.92 0.33 6.40E-07 388 s.32357656 A 32357656 0.92 0.86 7.00E- 18 389 s.32358191 T 32358191 1 0.28 4.70E- 11 390 rsl668393 c 32360136 0.960244 0.849828 3.82E-24 391 rsl3227922 A 32361067 0.932344 0.372398 3.26E- 11 392 rs215692 T 32361383 0.962689 0.893533 1.31 E-27 393 rs6979697 T 32361995 0.933025 0.383656 2.38E- 11 394 s.32362045 G 32362045 0.81 0.27 6.80E-05 395 rs412876 G 32362185 0.962158 0.893002 6.46E-27 396 rs7777166 T 32362822 0.956881 0.702553 5.73E-21 397 rs7808851 c 32362945 0.936165 0.409231 7.64E- 12 398 rs215694 T 32363551 0.959834 0.850463 8.11 E-24 399 rs4723146 c 32363973 1 0.294118 1.62E- 10 400 rs215695 c 32364433 0.947652 0.569003 1.92E- 16 401 rs215696 G 32364553 1 0.564202 1.44E- 18 402 rs215697 C 32364566 1 0.555556 1.22E- 18 403 rs215698 C 32364619 0.947652 0.569003 1.92E- 16 404 rs4723147 A 32364681 1 0.503952 4.63E- 16 405 rsl653891 C 32364947 0.94 0.55 6.10E- 10 406 s.32364994 A 32364994 1 0.31 3.50E- 12 407 s.32365034 C 32365034 0.83 0.47 1.10E-07 408 s.32365036 G 32365036 1 0.25 2.50E- 10 409 rsl668394 A 32365210 0.947119 0.561362 5.05E- 16 410 rsl77365 G 32365359 0.88 0.51 1.10E-08 411 rs215699 C 32365499 0.945811 0.543414 1.23E- 15 412 rs215700 C 32365691 0.915915 0.203676 3.51 E-07 413 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rsl653892 T 32365994 0.88 0.51 1.10E-08 414 rs215702 G 32366183 0.957584 0.736531 5.29E-22 415 rsl0486507 T 32366358 0.89732 0.221997 8.91E-07 416 s.32380353 A 32380353 0.65 0.35 2.30E-05 417 rs2099306 G 32397104 0.678859 0.275093 2.31E-07 418 rsl70016 G 32412842 0.768362 0.575708 2.93E-13 419 rsl0236370 C 32412984 0.695048 0.383604 4.90E-10 420 s.32413299 C 32413299 0.94 0.58 1.90E-10 421 rsll23893 A 32413455 0.916113 0.212125 7.00E-07 422 rs384711 T 32413608 0.710852 0.364773 2.45E-09 423 rs430356 A 32413751 0.720613 0.325 8.34E-08 424 rs730725 T 32413787 0.526692 0.229073 5.31E-06 425 rs435584 A 32414209 0.721713 0.331452 1.72E-08 426 s.32414636 C 32414636 0.55 0.29 0.00024 427 rsl72558 T 32414646 0.662696 0.209565 0.00001892 428 rs7457071 G 32416597 0.69 0.36 1.20E-05 429 rs215749 G 32424097 0.79 0.24 0.00096 430
Table 2. Surrogate markers of anchor marker rs6474412 (SEQ ID NO:497) on Chromosome 8pl l. Markers were selected using data from Caucasian HapMap dataset or the publically available 1000 Genomes project (http://www.1000genomes.org). Markers that have not been assigned rs names are identified by their position in NCBI Build 36 of the human genome assembly. Shown are predicted risk alleles for the surrogate markers, i.e. alleles that are correlated with the risk allele of the anchor marker, rs6474412 allele T. Linkage disequilibrium measures D' and R2, and corresponding p-value, are also shown. The last column refers to the sequence listing number, identifying the particular SNP.
Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
s.42329845 A 42329845 0.74 0.22 0.0008 431 s.42601955 A 42601955 0.76 0.25 0.00055 432 s.42618302 C 42618302 1 0.26 3.70E-07 433 rs7013926 G 42620874 1 0.254665 1.92E-06 434 rsl2156092 T 42622389 0.68 0.46 1.40E-07 435 rsl868860 G 42622950 1 0.51 3.30E-16 436 rsl530850 C 42624110 0.756221 0.516684 1.30E-12 437 rs6989472 C 42624623 0.768726 0.585241 6.87E-14 438 s.42626122 C 42626122 1 0.36 1.20E-09 439 rsll785591 A 42626897 1 0.418502 4.09E-10 440 rsl947295 A 42628094 0.94 0.8 1.80E-14 441 rsl376442 G 42628754 1 0.770883 2.54E-18 442 rs4737060 T 42629500 0.95 0.86 6.10E-16 443 s.42631425 A 42631425 1 0.29 6.60E-11 444 s.42632599 T 42632599 1 0.47 2.50E-12 445 rs34842664 T 42632832 0.95 0.81 3.40E-15 446 rsl868859 G 42634958 0.88 0.34 2.50E-05 447 rsl868858 C 42635887 1 0.29 5.80E-08 448 rsl0958724 C 42636284 0.865042 0.61527 3.10E-14 449 rs28441235 T 42636892 1 0.95 1.40E-26 450 rs7006469 G 42637051 0.84 0.23 0.0008 451 rsl0097384 G 42637768 0.84 0.23 0.0008 452 rs4305884 G 42637880 0.84 0.23 0.0008 453 s.42639491 T 42639491 1 1 1.90E-28 454 s.42639835 A 42639835 1 0.59 2.70E-15 455 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rs6990603 G 42642196 1 0.882641 2.11E-21 456 rs34456987 A 42642486 1 1 1.90E-28 457 rsl0958725 G 42643741 1 1 5.10E-26 458 rs36057318 G 42644441 1 0.81 3.00E-21 459 s.42644785 G 42644785 1 0.44 2.10E- 11 460 rs7837296 C 42646051 1 0.882641 2.11E-21 461 s.42646284 G 42646284 1 0.29 5.80E-08 462 s.42646543 A 42646543 1 0.59 2.70E- 15 463 rs5005909 A 42647824 1 0.876701 1.25E-20 464 rsl979140 C 42649993 1 1 5.10E-26 465 rsl3277840 G 42650156 1 0.542384 1.55E- 12 466 s.42651452 G 42651452 1 0.4 1.70E- 10 467 s.42652114 C 42652114 1 0.81 3.00E-21 468 rsl l783507 A 42653552 1 0.95 1.40E-26 469 rs7816726 G 42654594 1 1 1.90E-28 470 rsl0958726 T 42655066 1 1 5.10E-26 471 rs7842601 T 42656212 1 1 5.10E-26 472 s.42656893 G 42656893 1 0.59 2.70E- 15 473 rs4295650 A 42656968 1 1 1.90E-28 474 s.42659223 T 42659223 1 0.76 1.10E-21 475 rs6474411 G 42660603 1 1 1.90E-28 476 s.42661513 C 42661513 1 0.91 2.70E-25 477 s.42661651 G 42661651 1 0.26 3.70E-07 478 rsl3273442 G 42663174 1 1 5.10E-26 479 s.42664453 C 42664453 1 0.64 4.00E- 19 480 s.42664514 T 42664514 1 1 1.90E-28 481 s.42664708 T 42664708 1 0.44 2.10E- 11 482 s.42664984 A 42664984 1 1 1.90E-28 483 rsl451239 A 42665699 1 0.94382 4.58E-24 484 rsl451240 G 42665868 1 1 5.10E-26 485 s.42666045 A 42666045 1 1 1.90E-28 486 rs4736835 C 42666190 1 1 1.90E-28 487 s.42666333 T 42666333 1 0.83 2.70E-23 488 s.42666490 c 42666490 1 0.76 6.50E-20 489 rs6987704 c 42666780 1 0.76 6.50E-20 490 rsl530847 T 42667396 1 0.826087 8.63E-20 491 s.42668648 c 42668648 1 1 1.90E-28 492 rsl955185 T 42668804 1 1 5.10E-26 493 rsl3277254 A 42669139 1 1 1.90E-28 494 rsl3280301 G 42669174 1 0.59 2.70E- 15 495 rsl3277524 T 42669214 1 1 1.90E-28 496 rs6474412 T 42669655 1 1 0 497 rs6474413 T 42670221 1 1 1.90E-28 498 rs7004381 G 42670318 1 1 5.10E-26 499 rs6985052 T 42670476 1 0.79 1.90E-22 500 rs4950 A 42671790 1 1 5.10E-26 501 rsl530848 T 42672065 1 1 5.10E-26 502 rs9643891 T 42675754 1 1 1.90E-28 503 rsl3280604 A 42678743 1 1 5.10E-26 504 rs6997909 G 42679406 1 1 5.10E-26 505 rs6474414 C 42679493 1 1 5.10E-26 506 s.42679689 C 42679689 1 0.26 3.70E-07 507 rs6474415 A 42682095 1 1 5.10E-26 508 rs4951 T 42682714 1 0.95 1.40E-26 509 rsl3263434 G 42692210 1 0.59 2.70E- 15 510 s.42692248 T 42692248 1 0.47 2.50E- 12 511 rsl l783289 T 42693753 0.934844 0.762283 6.90E- 17 512 rs4236926 G 42697216 0.939315 0.873078 6.70E- 18 513 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rsl3261190 A 42697466 1 0.594716 1.08E- 13 514 s.42698182 A 42698182 1 0.91 2.70E-25 515 rsl6891561 C 42698896 0.942255 0.885692 1.77E-21 516 rs7459838 A 42703436 1 0.87 3.10E-24 517 rs55828312 A 42708759 0.95 0.85 5.00E- 16 518 rs7017612 A 42718402 0.87891 0.733511 1.40E- 17 519 rs6984031 T 42718609 0.94 0.74 1.90E- 13 520 s.42718611 T 42718611 1 0.81 3.00E-21 521 s.42721825 A 42721825 0.89 0.38 1.70E-06 522 rs7822100* C 42722508 0.94 0.7 1.00E- 12 523 rs7825907 G 42723438 0.78 0.44 3.50E-07 524 rs6982753 A 42723948 0.814692 0.5656 7.35E- 14 525 rs9298628 C 42725148 0.814815 0.567575 6.24E- 14 526 rs9298629 G 42725343 0.814815 0.567575 6.24E- 14 527 rs7824155 A 42726008 0.814815 0.567575 6.24E- 14 528 s.42726264 T 42726264 0.84 0.67 3.30E- 11 529 rs7824614 A 42726280 0.84 0.67 3.30E- 11 530 s.42726938 T 42726938 0.92 0.54 4.60E-09 531 rs2304297 G 42727356 0.935932 0.641678 1.76E- 16 532 rs7845663 G 42727720 0.757761 0.517408 1.30E- 12 533 rs7812298 C 42727736 0.814567 0.563595 8.68E- 14 534 rs7004108 A 42727867 0.84 0.67 3.30E- 11 535 s.42728158 C 42728158 1 0.22 2.30E-06 536 s.42728590 C 42728590 0.7 0.25 0.00021 537 s.42729587 T 42729587 0.88 0.34 6.00E-06 538 s.42729589 A 42729589 0.88 0.34 6.00E-06 539 s.42731443 G 42731443 0.82 0.57 2.20E-09 540 s.42731535 C 42731535 0.77 0.4 1.50E-06 541 s.42731657 A 42731657 0.79 0.29 4.40E-05 542 s.42731840 C 42731840 0.7 0.25 0.00021 543 s.42732056 C 42732056 0.82 0.57 2.20E-09 544 rsl0110332 C 42732343 0.84 0.67 3.30E- 11 545 s.42732673 G 42732673 0.82 0.57 2.20E-09 546 rs892413 C 42733535 0.756221 0.516684 1.30E- 12 547 rsl0087172 T 42736025 0.756221 0.516684 1.30E- 12 548 rs4398905 c 42737067 0.642179 0.314258 7.98E-08 549 rs2196128 T 42737443 0.750908 0.459129 1.48E- 11 550 rs2196129 G 42737563 0.642179 0.314258 7.98E-08 551 rs2217732 A 42737603 0.814815 0.567575 6.24E- 14 552 rsl072003 C 42739158 0.756221 0.516684 1.30E- 12 553 s.42741317 T 42741317 0.82 0.57 2.20E-09 554 s.42744231 c 42744231 1 0.33 8.70E-09 555 rsl0109040 c 42744470 0.756221 0.516684 1.30E- 12 556 rsl6891620 c 42744820 0.642179 0.314258 7.98E-08 557 s.42745575 A 42745575 0.82 0.57 2.20E-09 558 s.42745581 C 42745581 0.82 0.57 2.20E-09 559 rs4737069 A 42745717 0.82 0.57 2.20E-09 560 rs2117225 G 42746270 0.82 0.57 2.20E-09 561 rs2164024 A 42746361 0.82 0.57 2.20E-09 562 rs7828365 C 42748471 0.642179 0.314258 7.98E-08 563 rsl0107450 C 42749052 0.873551 0.58822 8.33E- 15 564 s.42750441 G 42750441 0.74 0.54 5.10E-09 565 s.42752754 C 42752754 0.76 0.36 5.80E-06 566 s.42753787 A 42753787 1 0.22 2.30E-06 567 s.42756840 A 42756840 0.73 0.41 8.80E-07 568 s.42757418 A 42757418 0.73 0.41 8.80E-07 569 rsl0092346 T 42758450 0.73 0.41 8.80E-07 570 s.42759608 T 42759608 0.67 0.22 0.00059 571 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
s.42760352 G 42760352 1 0.33 8.70E-09 572 s.42761317 A 42761317 0.65 0.25 7.60E-05 573 s.42761892 A 42761892 0.65 0.38 1.60E-06 574 s.42761909 T 42761909 0.7 0.25 0.00021 575 s.42761965 T 42761965 0.67 0.22 0.00059 576 rsl960346 c 42762202 0.65 0.38 1.60E-06 577 s.42763404 T 42763404 0.78 0.44 3.50E-07 578 s.42763808 c 42763808 0.69 0.41 8.60E-07 579 s.42764045 A 42764045 0.7 0.25 0.00021 580 s.42765003 C 42765003 0.69 0.41 8.60E-07 581 rs4567031 G 42765212 0.78 0.44 3.50E-07 582 s.42765421 C 42765421 0.7 0.25 0.00021 583 s.42769020 G 42769020 0.7 0.25 0.00021 584 rs4737071 C 42769593 0.812793 0.536568 2.33E- 13 585 rs6474420 A 42770089 0.69 0.41 8.60E-07 586 s.42770829 C 42770829 0.69 0.41 8.60E-07 587 s.42771871 G 42771871 0.69 0.41 8.60E-07 588 rsl l986893 A 42772016 0.812793 0.536568 2.33E- 13 589 s.42772405 C 42772405 0.7 0.25 0.00021 590 s.42779341 T 42779341 0.77 0.26 0.00014 591 s.42780448 c 42780448 0.77 0.26 0.00014 592 rs6985527 G 42781004 0.710785 0.499137 5.51E- 12 593 s.42782778 G 42782778 0.62 0.21 0.00073 594 s.42782938 C 42782938 0.54 0.28 0.00011 595 s.42783806 A 42783806 0.77 0.26 0.00014 596 s.42784397 C 42784397 0.67 0.37 7.20E-06 597 s.42786368 C 42786368 0.79 0.29 4.40E-05 598 s.42787290 T 42787290 0.79 0.29 4.40E-05 599 s.42787506 A 42787506 0.62 0.21 0.00073 600 s.42789990 C 42789990 1 0.22 2.30E-06 601 s.42806166 C 42806166 1 0.26 3.70E-07 602 rsl0087388 T 42824082 0.853593 0.339812 1.91E-09 603 s.42837205 c 42837205 1 0.26 3.70E-07 604 s.42844765 G 42844765 1 0.22 2.30E-06 605 s.42859016 C 42859016 1 0.26 3.70E-07 606 s.42886979 A 42886979 0.47 0.2 0.0014 607 s.42936582 G 42936582 0.75 0.22 0.0004 608 rsl0106661 G 42972871 0.588753 0.291428 4.66E-07 609 rs34727690** A 42979501 1 0.26 3.70E-07 610 s.43009750 C 43009750 0.84 0.23 0.00039 611 s.43018519 A 43018519 0.86 0.27 0.00013 612 s.43113569 A 43113569 1 0.22 2.30E-06 613 s.43150703 T 43150703 0.86 0.27 0.00013 614 s.43166986 G 43166986 1 0.26 3.70E-07 615 s.43167001 T 43167001 1 0.26 3.70E-07 616
* rs7822100 is a mixed SNP with possible alleles -/C/T/TTC where - means deletion
** rs34727690 is an indel marker with possible alleles -/ CTATAT Table 3. Surrogate markers of anchor marker rs4105144 (SEQ ID NO : 668) on Chromosome 19q l3. Markers were selected using data from Caucasian HapMap dataset or the publically available 1000 Genomes project (http ://www.1000genomes.org). Markers that have not been assigned rs names are identified by their position in NCBI Build 36 of the human genome assembly. Shown are predicted risk alleles for the surrogate markers, i.e. alleles that are correlated with the risk allele of the anchor marker, rs4105144 allele C. Linkage disequilibrium measures D' and R2, and corresponding p-value, are also shown. The last column refers to the sequence listing number, identifying the particular SNP.
Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
s.45831417 T 45831417 0.89 0.26 7.50E-05 617 s.45859717 G 45859717 1 0.24 5.20E-09 618 rsl l083565 T 45866989 0.519214 0.255528 4.06E-06 619 rs2561537 c 45885420 0.923106 0.252897 5.54E-08 620 rs2604885 A 45898595 0.688941 0.250967 9.13E-07 621 rs7260405 G 45921359 0.59 0.21 0.0021 622 rs2607420 T 45936727 0.568423 0.27559 7.12E-07 623 rs2369302 G 45943020 0.674108 0.264619 1.01 E-07 624 rs2254343 C 45947340 0.645593 0.305039 2.48E-08 625 rsl457141 G 45949016 0.553248 0.240493 1.15E-06 626 rs2604874 A 45949656 0.645593 0.305039 2.48E-08 627 s.45950500 A 45950500 0.78 0.22 0.0017 628 s.45950502 G 45950502 0.78 0.22 0.0017 629 rs2607415 C 45954527 0.525153 0.252423 2.95E-07 630 rs2607414 G 45958969 0.525153 0.252423 2.95E-07 631 rs2279011 A 45961128 0.750071 0.230092 1.88E-07 632 rs2249835 T 45961606 0.525153 0.252423 2.95E-07 633 rs2607424 A 45966987 0.525153 0.252423 2.95E-07 634 rs2604869 G 45975533 0.507237 0.240842 9.91 E-07 635 rsl2973666 C 45981237 0.808936 0.27006 2.57E-08 636 rs2604893 C 45985383 0.603341 0.323045 8.20E-09 637 rs2644898 A 45990855 0.573022 0.300496 2.05E-08 638 rs7252227 T 45992955 0.7488 0.306518 4.16E-08 639 rs7937 T 45994546 0.822109 0.317805 1.20E-09 640 rs2644916 G 46001051 0.578519 0.320014 7.71 E-09 641 rs3733828 C 46002449 0.596605 0.275581 8.70E-08 642 rs4803372 T 46013212 0.65 0.21 0.00028 643 s.46014685 A 46014685 0.8 0.33 2.70E-06 644 s.46014686 A 46014686 0.8 0.33 2.70E-06 645 rs4803373 G 46018266 0.59 0.29 3.10E-05 646 s.46019153 A 46019153 1 0.2 2.50E-08 647 s.46019626 C 46019626 0.55 0.25 0.00026 648 s.46020036 T 46020036 0.55 0.25 0.00026 649 s.46024011 T 46024011 0.9 0.28 1.20E-05 650 s.46024197 A 46024197 0.74 0.29 2.30E-05 651 s.46024672 G 46024672 0.58 0.21 0.00067 652 rsl l670760 C 46028635 0.6 0.24 0.00022 653 rsl2459249 C 46031736 1 0.25 3.50E- 10 654 rs7251418 G 46033429 0.905271 0.786167 2.18E-20 655 rs7251418 G 46033429 1 0.25 3.50E- 10 656 rs7251570 G 46033590 0.858289 0.615742 8.30E- 16 657 rs4343391 C 46036208 1 0.871875 1.06E-25 658 rs4343391 C 46036208 1 0.25 3.50E- 10 659 rs7245507 G 46037064 1 0.25 3.50E- 10 660 s.46037829 G 46037829 1 0.25 3.50E- 10 661 rsl l083571 T 46037939 1 0.25 3.50E- 10 662 rs2302989 A 46043322 0.686783 0.33864 8.65E-08 663 rs2316213 G 46043445 0.568588 0.208213 0.00002672 664 rs8192725 G 46046552 1 0.22 4.70E-09 665 rs7250713 C 46047035 0.68 0.22 0.0014 666 rsl l37115 C 46048121 1 0.23 2.00E-09 667 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
rs4105144 C 46050464 1 1 0 668 rsl0404667 C 46054738 1 0.23 2.00E-09 669 rs4001921 T 46055141 1 0.23 2.00E-09 670 rs8102683 c 46055605 1 0.865314 1.67E-24 671 rs8102683 c 46055605 1 0.23 2.00E-09 672 rs8105704 c 46055738 1 0.23 2.00E-09 673 rsl496402 A 46057974 1 0.955563 1.77E-28 674 rsl496402 A 46057974 0.86 0.2 0.0011 675 rsl2610432 C 46059609 0.79 0.21 0.00065 676 s.46062016 C 46062016 0.79 0.21 0.00065 677 rsl2461383 G 46062178 1 0.541149 7.86E- 18 678 rsl2461383 G 46062178 0.79 0.21 0.00065 679 rs4570984 C 46066942 0.617212 0.330101 5.73E-07 680 s.46067487 G 46067487 1 0.32 3.30E- 12 681 s.46067573 C 46067573 0.61 0.2 0.00075 682 s.46067911 G 46067911 0.79 0.21 0.00065 683 rsl l882981 G 46069846 0.61 0.2 0.00075 684 rs7247469 C 46071161 0.61 0.2 0.00075 685 rs7251315 A 46071683 0.93 0.38 8.70E-08 686 rs6508951 C 46071748 0.54 0.29 0.00011 687 s.46075055 C 46075055 0.58 0.22 0.00036 688 rs3869579 A 46075639 0.78 0.26 2.30E-05 689 s.46075829 T 46075829 0.78 0.26 2.30E-05 690 s.46075942 T 46075942 0.78 0.26 2.30E-05 691 s.46077574 T 46077574 0.77 0.25 3.00E-05 692 rsl2973598 G 46077674 0.78 0.26 2.30E-05 693 s.46077976 C 46077976 0.71 0.23 7.30E-05 694 s.46078049 G 46078049 0.78 0.26 2.30E-05 695 s.46078122 C 46078122 0.71 0.23 7.30E-05 696 s.46078260 C 46078260 0.77 0.25 3.00E-05 697 s.46078326 A 46078326 0.71 0.23 7.30E-05 698 s.46078327 G 46078327 0.71 0.23 7.30E-05 699 s.46078334 C 46078334 0.71 0.23 7.30E-05 700 s.46078367 A 46078367 0.71 0.23 7.30E-05 701 s.46078384 G 46078384 0.71 0.23 7.30E-05 702 s.46078387 C 46078387 0.78 0.26 2.30E-05 703 s.46078424 T 46078424 0.78 0.26 2.30E-05 704 s.46079081 A 46079081 0.78 0.26 2.30E-05 705 s.46079140 A 46079140 0.78 0.26 2.30E-05 706 s.46080547 C 46080547 0.78 0.26 2.30E-05 707 s.46080617 G 46080617 0.78 0.26 2.30E-05 708 s.46082249 C 46082249 0.67 0.36 2.50E-06 709 s.46083888 T 46083888 0.78 0.26 2.30E-05 710 rs3875159 c 46083994 0.77 0.25 3.00E-05 711 rs28503746 G 46084975 0.78 0.26 2.30E-05 712 rs3909341 C 46085166 0.56 0.2 0.00044 713 rs4105142 T 46085410 0.71 0.23 7.30E-05 714 rs4105141 A 46085440 0.56 0.2 0.00044 715 rs5007415 A 46085600 0.56 0.2 0.00044 716 s.46085777 A 46085777 0.78 0.26 2.30E-05 717 rsl0411264 T 46086176 0.56 0.2 0.00044 718 rs67421541 T 46086260 0.78 0.26 2.30E-05 719 s.46086876 A 46086876 0.78 0.26 2.30E-05 720 s.46087595 A 46087595 0.56 0.2 0.00044 721 rsl l083582 C 46087691 0.78 0.26 2.30E-05 722 rs3909342 A 46088530 0.78 0.26 2.30E-05 723 rs4803397 A 46088705 0.661873 0.351138 1.68E-08 724 rs4803397 A 46088705 0.71 0.23 7.30E-05 725 Pos in
Marker Risk Allele NCBI Build D' R2 P-value Seq ID NO:
36
s.46088755 G 46088755 0.56 0.2 0.00044 726 rs3852870 C 46089210 0.78 0.26 2.30E-05 727 s.46089339 G 46089339 0.56 0.2 0.00044 728 rs8103444 A 46089501 0.661873 0.351138 1.68E-08 729 rs3865457 T 46090809 0.56 0.2 0.00044 730 rs4803398 c 46093790 0.81 0.24 0.00011 731 rs6508953 G 46094419 0.81 0.24 0.00011 732 rsl0419393 G 46096036 0.56 0.2 0.00044 733 rs7343061 A 46097188 0.78 0.26 2.30E-05 734 rs4803400 G 46097802 0.78 0.26 2.30E-05 735 rs7254188 A 46099183 0.56 0.2 0.00044 736 rsl0416968 A 46099477 0.68 0.2 0.0004 737
The sequence data that is obtained may in certain embodiments, be amino acid sequence data . Polymorphic markers can result in alterations in the amino acid sequence of encoded polypeptide or protein sequence. In certain embodiments, the analysis of amino acid sequence data comprises determining the presence or absence of an amino acid substitution in the amino acid encoded by the at least one polymorphic marker. Sequence data can in certain embodiments be obtained by analyzing the amino acid sequence encoded by the at least one polymorphic marker in a biological sample obtained from the individual. In certain embodiments, the at least one polymorphic marker that is assessed is an amino acid substitution in a polypeptide encoded by a gene selected from the group consisting of the human CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B genes. In other words, the marker may be an amino acid substitution in a polypeptide encoded by any one of those genes.
In certain embodiments of the invention, determination of the presence of particular marker alleles or particular haplotypes is predictive of an increased susceptibility of lung cancer in humans. In certain embodiments, determination of the presence of a marker allele selected from the group consisting of the T allele of rs6474412, the G allele of rs215614, the T allele of rs7937, the C allele of rs4105144 and the G allele of rs7260329 is indicative of increased risk of lung cancer in the individual. These marker alleles confer increased risk of lung cancer with relative risk or odds ratio of greater than unity, and are sometimes also referred to as at-risk alleles or at-risk variants. In certain other embodiments, risk alleles as presented in the surrogate markers Tables 1 to 3 are at-risk alleles indicative of increased risk of lung cancer. Individuals who are homozygous for at-risk alleles are at particularly high risk of developing lung cancer, since their genome includes two copies of the at-risk variant.
Measures of susceptibility or risk include measures such as relative risk (RR), odds ratio (OR), and absolute risk (AR), as described in more detail herein .
In certain embodiments, increased susceptibility refers to a risk with values of RR or OR of at least 1.05, at least 1.06, at least 1.07, at least 1.08, at least 1.09, at least 1.10, at least 1.11, at least 1.12, at least 1.13, at least 1.14 or at least 1.15. Other numerical non-integer values greater than unity are also possible to characterize the risk, and such numerical values are also contemplated. Certain embodiments relate to homozygous individuals for a particular marker, i.e. individuals who carry two copies of the same allele in their genome. One preferred embodiment relates to individuals who are homozygous carriers of an allele selected from the group consisting of the T allele of rs6474412, the G allele of rs215614, the T allele of rs7937, the C allele of rs4105144 and the G allele of rs7260329, or a marker allele in linkage disequilibrium therewith .
In certain other embodiments, determination of the presence of particular marker alleles or particular haplotypes is predictive of a decreased susceptibility of lung cancer in humans. For SNP markers with two alleles, the alternate allele to an at-risk allele will be in decreased frequency in patients compared with controls. Thus, in the determination of SNPs, determination of the presence of the non-risk (alternate) allele is indicative of a decreased susceptibility of lung cancer. Individuals who are homozygous for the alternate (protective) allele are at particularly decreased susceptibility or risk.
To identify further markers that are useful for assessing susceptibility to lung cancer, it may be useful to compare the frequency of markers alleles in individuals with lung cancer to control individuals. The control individuals may be a random sample from the general population, i.e. a population cohort. The control individuals may also be a sample from individuals that are disease-free, e.g. individuals who have been confirmed not to have lung cancer, or individuals who have not been diagnosed with lung cancer. In one embodiment, an increase in frequency of at least one allele in at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group is indicative of the at least one allele being useful for assessing increased susceptibility to lung cancer. In another embodiment, a decrease in frequency of at least one allele in at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control sample is indicative of the at least one allele being useful for assessing decreased susceptibility to, or protection against, lung cancer.
In general, sequence data can be obtained by analyzing a sample from an individual, or by analyzing information about specific markers in a database or other data collection, for example a genotype database or a sequence database. The sample is in certain embodiments a nucleic acid sample, or a sample that contains nucleic acid material. Analyzing a sample from an individual may in certain embodiments include steps of isolating genomic nucleic acid from the sample, amplifying a segment of the genomic nucleic acid that contains at least one polymorphic marker, and determine sequence information about the at least one polymorphic marker.
Amplification is preferably performed by Polymerase Chain Reaction (PCR) techniques. In certain embodiments, sequence data can be obtained through nucleic acid sequence information or amino acid sequence information from a preexisting record. Such a preexisting record can be any documentation, database or other form of data storage containing such information. Determination of a susceptibility or risk of a particular individual in general comprises comparison of the genotype information (sequence information) of the individual to a record or database providing a correlation about particular polymorphic marker(s) and susceptibility to disease, e.g. lung cancer. Thus, in specific embodiments, determining a susceptibility comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to lung cancer. In certain embodiments, the database comprises at least one measure of susceptibility to lung cancer for the at least one polymorphic marker. In certain embodiments, the database comprises a look-up table comprising at least one measure of susceptibility to kidney cancer for the at least one polymorphic marker. The measure of susceptibility may in the form of relative risk (RR), absolute risk (AR), percentage (%) . Other convenient measures for describing genetic susceptibility of individuals are also possible, and within scope of the invention.
Certain embodiments of the invention relate to markers associated with particular genes, e.g. the human CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B genes. Markers associated with one or more of these genes are in certain embodiments useful susceptibility markers of lung cancer. Markers that are associated with any one of these genes are in certain embodiments markers that are in linkage
disequilibrium (LD) with at least one genetic marker within the genes. In certain embodiments, the markers are located within the genomic segments LD block C07, LD block C08 or LD block C19, as defined herein . In certain embodiments, markers associated with a particular gene are selected from the markers within the gene, i.e. within the genomic region that contain exons, introns and promoter sequences of the gene.
Certain embodiments of the invention relate to markers located within the LD block C07, LD block C08 or LD block C19 as defined herein . It is however also contemplated that surrogate markers may be located outside the physical boundaries of these LD blocks as defined by their genomic locations. This is because, recombination events may have led to certain risk surrogates having been "separated" from the main cluster of surrogates, although these surrogates are detecting the same variant. Thus, certain embodiments of the invention are contemplated to also encompass surrogate markers in linkage disequilibrium with rs215614, rs6474412 or rs4105144 that are located outside the physical boundaries of LD block C07, LD block C08 or LD block C19 as defined.
In certain embodiments of the invention, more than one polymorphic marker is analyzed to determine lung cancer risk. In certain embodiments, at least two polymorphic markers are analyzed. Thus, in certain embodiments, nucleic acid data about at least two polymorphic markers is obtained.
In certain embodiments, a further step of analyzing at least one haplotype comprising two or more polymorphic markers is included. Any convenient method for haplotype analysis known to the skilled person, such as those described in more detail herein, may be employed in such embodiments. One aspect of the invention relates to a method for determining a susceptibility to lung cancer in a human individual, comprising determining the presence or absence of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, or in a genotype dataset from the individual, wherein the at least one polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and wherein determination of the presence of the at least one allele is indicative of a susceptibility to lung cancer. Determination of the presence of an allele that correlates with lung cancer is indicative of an increased susceptibility to lung cancer. Individuals who are homozygous for such alleles are particularly susceptible to lung cancer. On the other hand, individuals who do not carry such at-risk alleles are at a decreased susceptibility of developing lung cancer, as compared with a randomly selected individual from the general population. For SNPs, such individuals will be homozygous for the alternate (protective) allele of the polymorphism.
Determination of susceptibility is in some embodiments reported by a comparison with non- carriers of the at-risk allele(s) of polymorphic markers. In certain embodiments, susceptibility is reported based on a comparison with the general population, e.g. compared with a random selection of individuals from the population .
In certain embodiments, polymorphic markers are detected by sequencing technologies.
Obtaining sequence information about an individual identifies particular nucleotides in the context of a nucleic acid sequence. For SNPs, sequence information about a single unique sequence site is sufficient to identify alleles at that particular SNP. For markers comprising more than one nucleotide, sequence information about the genomic region of the individual that contains the polymorphic site identifies the alleles of the individual for the particular site. The sequence information can be obtained from a sample from the individual . In certain
embodiments, the sample is a nucleic acid sample. In certain other embodiments, the sample is a protein sample.
Various methods for obtaining nucleic acid sequence are known to the skilled person, and all such methods are useful for practicing the invention. Sanger sequencing is a well-known method for generating nucleic acid sequence information. Recent methods for obtaining large amounts of sequence data have been developed, and such methods are also contemplated to be useful for obtaining sequence information . These include pyrosequencing technology (Ronaghi, M . et al. Anal Biochem 267: 65-71 (1999); Ronaghi, et al. Biotechniques 25: 876-878 (1998)), e.g. 454 pyrosequencing (Nyren, P., et al. Anal Biochem 208: 171-175 (1993)), Illumina/Solexa sequencing technology (http ://www.illumina.com; see also Strausberg, RL, et al Drug Disc Today 13 : 569-577 (2008)), and Supported Oligonucleotide Ligation and Detection Platform (SOLiD) technology (Applied Biosystems, http://www.appliedbiosystems.com); Strausberg, RL, et al Drug Disc Today 13 : 569-577 (2008) . Assessment for markers and haplotypes
The genomic sequence within populations is not identical when individuals are compared .
Rather, the genome exhibits sequence variability between individuals at many locations in the genome. Such variations in sequence are commonly referred to as polymorphisms, and there are many such sites within each genome. For example, the human genome exhibits sequence variations which occur on average every 500 base pairs. The most common sequence variant consists of base variations at a single base position in the genome, and such sequence variants, or polymorphisms, are commonly called Single Nucleotide Polymorphisms ("SNPs") . These SNPs are believed to have occurred in a single mutational event, and therefore there are usually two possible alleles possible at each SNPsite; the original allele and the alternate (mutated) allele. Due to natural genetic drift and possibly also selective pressure, the original mutation has resulted in a polymorphism characterized by a particular frequency of its alleles in any given population. Many other types of sequence variants are found in the human genome, including mini- and microsatellites, and insertions, deletions and inversions (also called copy number variations (CNVs)) . A polymorphic microsatellite has multiple small repeats of bases (such as CA repeats, TG on the complimentary strand) at a particular site in which the number of repeat lengths varies in the general population . In general terms, each version of the sequence with respect to the polymorphic site represents a specific allele of the polymorphic site. These sequence variants can all be referred to as polymorphisms, occurring at specific polymorphic sites characteristic of the sequence variant in question . In general, polymorphisms can comprise any number of specific alleles within the population, although each human individual has two alleles at each polymorphic site - one maternal and one paternal allele. Thus in one
embodiment of the invention, the polymorphism is characterized by the presence of two or more alleles in the population . In another embodiment, the polymorphism is characterized by the presence of three or more alleles in the population . In other embodiments, the polymorphism is characterized by four or more alleles, five or more alleles, six or more alleles, seven or more alleles, nine or more alleles, or ten or more alleles. All such polymorphisms can be utilized in the methods and kits of the present invention, and are thus within the scope of the invention .
Due to their abundance, SNPs account for a majority of sequence variation in the human genome. Over 6 million human SNPs have been validated to date
(http://www. ncbi. nlm .nih .gov/projects/SNP/snp_summary.cgi) . However, CNVs are receiving increased attention . These large-scale polymorphisms (typically lkb or larger) account for polymorphic variation affecting a substantial proportion of the assembled human genome; known CNVs covery over 15% of the human genome sequence (Estivill, X Armengol; L, PloS Genetics 3 : 1787-99 (2007); http://projects.tcag.ca/variation/) . Most of these polymorphisms are however very rare, and on average affect only a fraction of the genomic sequence of each individual. CNVs are known to affect gene expression, phenotypic variation and adaptation by disrupting gene dosage, and are also known to cause disease (microdeletion and
microduplication disorders) and confer risk of common complex diseases, including HIV-1 infection and glomerulonephritis (Redon, R., et al. Nature 23 :444-454 (2006)) . It is thus possible that either previously described or unknown CNVs represent causative variants in linkage disequilibrium with the disease-associated markers described herein. Methods for detecting CNVs include comparative genomic hybridization (CGH) and genotyping, including use of genotyping arrays, as described by Carter (Nature Genetics 39:S16-S21 (2007)) . The Database of Genomic Variants (http://projects.tcag.ca/variation/) contains updated information about the location, type and size of described CNVs. The database currently contains data for over 21,000 CNVs.
In some instances, reference is made to different alleles at a polymorphic site without choosing a reference allele. Alternatively, a reference sequence can be referred to for a particular polymorphic site. The reference allele is sometimes referred to as the "wild-type" allele and it usually is chosen as either the first sequenced allele or as the allele from a "non-affected" individual (e.g., an individual that does not display a trait or disease phenotype) .
Alleles for SNP markers as referred to herein refer to the bases A, C, G or T as they occur at the polymorphic site. The allele codes for SNPs used herein are as follows: 1= A, 2=C, 3=G, 4=T. Since human DNA is double-stranded, the person skilled in the art will realise that by assaying or reading the opposite DNA strand, the complementary allele can in each case be measured.
Thus, for a polymorphic site (polymorphic marker) characterized by an A/G polymorphism, the methodology employed to detect the marker may be designed to specifically detect the presence of one or both of the two bases possible, i.e. A and G. Alternatively, by designing an assay that is designed to detect the complimentary strand on the DNA template, the presence of the complementary bases T and C can be measured . Quantitatively (for example, in terms of risk estimates), identical results would be obtained from measurement of either DNA strand (+ strand or - strand) .
Typically, a reference sequence is referred to for a particular sequence. Alleles that differ from the reference are sometimes referred to as "variant" alleles. A variant sequence, as used herein, refers to a sequence that differs from the reference sequence but is otherwise substantially similar. Alleles at the polymorphic genetic markers described herein are variants. Variants can include changes that affect a polypeptide. Sequence differences, when compared to a reference nucleotide sequence, can include the insertion or deletion of a single nucleotide, or of more than one nucleotide, resulting in a frame shift; the change of at least one nucleotide, resulting in a change in the encoded amino acid; the change of at least one nucleotide, resulting in the generation of a premature stop codon; the deletion of several nucleotides, resulting in a deletion of one or more amino acids encoded by the nucleotides; the insertion of one or several nucleotides, such as by unequal recombination or gene conversion, resulting in an interruption of the coding sequence of a reading frame; duplication of all or a part of a sequence; transposition; or a rearrangement of a nucleotide sequence,. Such sequence changes can alter the polypeptide encoded by the nucleic acid . For example, if the change in the nucleic acid sequence causes a frame shift, the frame shift can result in a change in the encoded amino acids, and/or can result in the generation of a premature stop codon, causing generation of a truncated polypeptide. Alternatively, a polymorphism can be a synonymous change in one or more nucleotides (i.e., a change that does not result in a change in the amino acid sequence) . Such a polymorphism can, for example, alter splice sites, affect the stability or transport of mRNA, or otherwise affect the transcription or translation of an encoded polypeptide. It can also alter DNA stability so as to increase the possibility that structural changes, such as amplifications or deletions, occur at the somatic level.
A haplotype refers to a single-stranded segment of DNA that is characterized by a specific combination of alleles arranged along the segment. For diploid organisms such as humans, a haplotype comprises one member of the pair of alleles for each polymorphic marker or locus . In a certain embodiment, the haplotype can comprise two or more alleles, three or more alleles, four or more alleles, or five or more alleles, each allele corresponding to a specific polymorphic marker along the segment. Haplotypes can comprise a combination of various polymorphic markers, e.g., SNPs and microsatellites, having particular alleles at the polymorphic sites. The haplotypes thus comprise a combination of alleles at various genetic markers.
Detecting specific polymorphic markers and/or haplotypes can be accomplished by methods known in the art for detecting sequences at polymorphic sites. For example, standard techniques for genotyping for the presence of SNPs and/or microsatellite markers can be used, such as fluorescence-based techniques (e.g. , Chen, X. et a/., Genome Res. 9(5) : 492-98 (1999); Kutyavin et al. , Nucleic Acid Res. 34:el28 (2006)), utilizing PCR, LCR, Nested PCR and other techniques for nucleic acid amplification. Specific commercial methodologies available for SNP genotyping include, but are not limited to, TaqMan genotyping assays and SNPlex platforms (Applied Biosystems), gel electrophoresis (Applied Biosystems), mass spectrometry (e.g. , MassARRAY system from Sequenom), minisequencing methods, real-time PCR, Bio-Plex system (BioRad), CEQ and SNPstream systems (Beckman), array hybridization technology(e.g., Affymetrix GeneChip; Perlegen), BeadArray Technologies (e.g., Illumina GoldenGate and Infinium assays), array tag technology (e.g., Parallele), and endonuclease-based fluorescence hybridization technology (Invader; Third Wave) . Some of the available array platforms, including Affymetrix SNP Array 6.0 and Illumina CNV370-Duo and 1M BeadChips, include SNPs that tag certain CNVs. This allows detection of CNVs via surrogate SNPs included in these platforms. Thus, by use of these or other methods available to the person skilled in the art, one or more alleles at polymorphic markers, including microsatellites, SNPs or other types of polymorphic markers, can be identified.
In certain embodiments, polymorphic markers are detected by sequencing technologies.
Obtaining sequence information about an individual identifies particular nucleotides in the context of a sequence. For SNPs, sequence information about a single unique sequence site is sufficient to identify alleles at that particular SNP. For markers comprising more than one nucleotide, sequence information about the nucleotides of the individual that contain the polymorphic site identifies the alleles of the individual for the particular site. The sequence information can be obtained from a sample from the individual. In certain embodiments, the sample is a nucleic acid sample. In certain other embodiments, the sample is a protein sample. Various methods for obtaining nucleic acid sequence are known to the skilled person, and all such methods are useful for practicing the invention. Sanger sequencing is a well-known method for generating nucleic acid sequence information. Recent methods for obtaining large amounts of sequence data have been developed, and such methods are also contemplated to be useful for obtaining sequence information . These include pyrosequencing technology (Ronaghi, M . et al. Anal Biochem 267: 65-71 (1999); Ronaghi, et al. Biotechniques 25: 876-878 (1998)), e.g. 454 pyrosequencing (Nyren, P., et al. Anal Biochem 208: 171-175 (1993)), Illumina/Solexa sequencing technology (http ://www.illumina.com; see also Strausberg, RL, et al Drug Disc Today 13 : 569-577 (2008)), and Supported Oligonucleotide Ligation and Detection Platform (SOLiD) technology (Applied Biosystems, http://www.appliedbiosystems.com); Strausberg, RL, et al Drug Disc Today 13 : 569-577 (2008) .
It is possible to impute or predict genotypes for un-genotyped relatives of genotyped individuals. For every un-genotyped case, it is possible to calculate the probability of the genotypes of its relatives given its four possible phased genotypes. In practice it may be preferable to include only the genotypes of the case's parents, children, siblings, half-siblings (and the half-sibling's parents), grand-parents, grand-children (and the grand-children's parents) and spouses. It will be assumed that the individuals in the small sub-pedigrees created around each case are not related through any path not included in the pedigree. It is also assumed that alleles that are not transmitted to the case have the same frequency - the population allele frequency. Let us consider a SNP marker with the alleles A and G. The probability of the genotypes of the case's relatives can then be computed by:
Pr(genotypes of relatives; Θ) = ^ Pr( z; Θ) Pr(genotypes of relatives | h) , where Θ denotes the A allele's frequency in the cases. Assuming the genotypes of each set of relatives are independent, this allows us to write down a likelihood function for Θ:
L{9) = ]^[ Pr(genotypesof relativesof case z'; #) . (*)
This assumption of independence is usually not correct. Accounting for the dependence between individuals is a difficult and potentially prohibitively expensive computational task. The likelihood function in (*) may be thought of as a pseudolikelihood approximation of the full likelihood function for Θ which properly accounts for all dependencies. In general, the genotyped cases and controls in a case-control association study are not independent and applying the case-control method to related cases and controls is an analogous approximation . The method of genomic control (Devlin, B. et al ., Nat Genet 36, 1129-30; author reply 1131 (2004)) has proven to be successful at adjusting case-control test statistics for relatedness. We therefore apply the method of genomic control to account for the dependence between the terms in our
pseudolikelihood and produce a valid test statistic.
Fisher's information can be used to estimate the effective sample size of the part of the pseudolikelihood due to un-genotyped cases. Breaking the total Fisher information, J, into the part due to genotyped cases, Ig, and the part due to ungenotyped cases, Iu, I = Ig + Iu, and denoting the number of genotyped cases with N, the effective sample size due to the ungenotyped cases is estimated by — N .
In the present context, an individual who is at an increased susceptibility (i.e., increased risk) for a disease, is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring increased susceptibility (increased risk) for the disease is identified (i.e., at-risk marker alleles or haplotypes). The at-risk marker or haplotype is one that confers an increased risk (increased susceptibility) of the disease. In one embodiment, significance associated with a marker or haplotype is measured by a relative risk (RR). In another embodiment, significance associated with a marker or haplotye is measured by an odds ratio (OR). In a further embodiment, the significance is measured by a percentage. In one embodiment, a significant increased risk is measured as a risk (relative risk and/or odds ratio) of at least 1.05, including but not limited to: at least 1.06, at least 1.07, at least 1.08, at least 1.09, at least 1.10, at least 1.11, at least 1.12, at least 1.13, at least 1.14, at least 1.15, at least 1.16, at least 1.17, at least 1.18, at least 1.19, at least 1.20, at least 1.25, at least 1.30, at least 1.35, at least 1.40, at least 1.45 and at least 1.50. In a particular embodiment, a risk (relative risk and/or odds ratio) of at least 1.05 is significant. In another particular embodiment, a risk of at least 1.09 is significant. In yet another embodiment, a risk of at least 1.10 is significant. In a further embodiment, a relative risk of at least 1.12 is significant. In another further
embodiment, a significant increase in risk is at least 1.15 is significant. However, other cutoffs are also contemplated, e.g., at least 1.20, 1.25, 1.35, and so on, and such cutoffs are also within scope of the present invention. In other embodiments, a significant increase in risk is at least about 5%, including but not limited to about 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, and 20%. In one particular embodiment, a significant increase in risk is at least 9%. In other embodiments, a significant increase in risk is at least 10%, at least 12%, and at least 15%. Other cutoffs or ranges as deemed suitable by the person skilled in the art to characterize the invention are however also contemplated, and those are also within scope of the present invention. In certain embodiments, a significant increase in risk is characterized by a p-value, such as a p-value of less than 0.05, less than 0.01, less than 0.001, less than 0.0001, less than 0.00001, less than 0.000001, less than 0.0000001, less than 0.00000001, or less than 0.000000001.
An at-risk polymorphic marker or haplotype as described herein is one where at least one allele of at least one marker or haplotype is more frequently present in an individual at risk for the disease (e.g., lung cancer) (affected), or diagnosed with the disease, compared to the frequency of its presence in a comparison group (control), such that the presence of the marker or haplotype is indicative of susceptibility to the disease. The control group may in one
embodiment be a population sample, i.e. a random sample from the general population. In another embodiment, the control group is represented by a group of individuals who are disease- free, e.g. those that have not been diagnosed with lung cancer. In another embodiment, the disease-free control group is characterized by the absence of one or more disease-specific risk factors. Such risk factors are in one embodiment at least one environmental risk factor, such as smoking . In certain embodiments, the control group comprises individuals who have never smoked and have never been diagnosed with lung cancer. In certain other embodiments, the control group comprises individuals who do have a history of smoking but have not been diagnosed with lung cancer.
As an example of a simple test for correlation would be a Fisher-exact test on a two by two table. Given a cohort of chromosomes, the two by two table is constructed out of the number of chromosomes that include both of the markers or haplotypes, one of the markers or haplotypes but not the other and neither of the markers or haplotypes. Other statistical tests of association known to the skilled person are also contemplated and are also within scope of the invention.
In certain embodiments of the invention, an individual who is at a decreased susceptibility (i.e., at a decreased risk) for a disease is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring decreased susceptibility for the disease is identified. The marker alleles and/or haplotypes conferring decreased risk are also said to be protective. In one aspect, the protective marker or haplotype is one that confers a significant decreased risk (or susceptibility) of the disease or trait. In one embodiment, significant decreased risk is measured as a relative risk (or odds ratio) of less than 0.95, including but not limited to less than 0.90, less than 0.85, less than 0.80, less and less than 0.75. In another embodiment, the decrease in risk (or susceptibility) is at least 5%, including but not limited to 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 20%, at least 25%, and at least 30%. In one particular embodiment, a significant decrease in risk is at least about 10%. In another embodiment, a significant decrease in risk is at least about 12%. In another embodiment, the decrease in risk is at least about 15%. Other cutoffs or ranges as deemed suitable by the person skilled in the art to characterize the invention are however also contemplated, and those are also within scope of the present invention.
A genetic variant associated with a disease can be used alone to predict the risk of the disease for a given genotype. For a biallelic marker, such as a SNP, there are 3 possible genotypes:
homozygote for the at risk variant, heterozygote, and non carrier of the at risk variant. Risk associated with variants at multiple loci can be used to estimate overall risk. For multiple SNP variants, there are k possible genotypes k = 3" x 2P; where n is the number autosomal loci and p the number of gonosomal (sex chromosomal) loci. Overall risk assessment calculations for a plurality of risk variants usually assume that the relative risks of different genetic variants multiply, i.e. the overall risk (e.g., RR or OR) associated with a particular genotype combination is the product of the risk values for the genotype at each locus. If the risk presented is the relative risk for a person, or a specific genotype for a person, compared to a reference
population with matched gender and ethnicity, then the combined risk is the product of the locus specific risk values and also corresponds to an overall risk estimate compared with the
population. If the risk for a person is based on a comparison to non-carriers of the at risk allele, then the combined risk corresponds to an estimate that compares the person with a given combination of genotypes at all loci to a group of individuals who do not carry risk variants at any of those loci. The group of non-carriers of any at risk variant has the lowest estimated risk and has a combined risk compared with itself {i.e., non-carriers) of 1.0, but has an overall risk, compare with the population, of less than 1.0. It should be noted that the group of non-carriers can potentially be very small, especially for large number of loci, and in that case, its relevance is correspondingly small .
The multiplicative model is a parsimonious model that usually fits the data of complex traits reasonably well. Deviations from multiplicity have been rarely described in the context of common variants for common diseases, and if reported are usually only suggestive since very large sample sizes are usually required to be able to demonstrate statistical interactions between loci.
By way of an example, let us consider the case where a total of eight variants that have been associated with a disease. One such example is provided by prostate cancer (Gudmundsson, J., et al., Nat Genet 39:631-7 (2007), Gudmundsson, J., et al., Nat Genet 39:977-83 (2007); Yeager, M., et al, Nat Genet 39:645-49 (2007), Amundadottir, L, el al., Nat Genet 38:652-8 (2006); Haiman, C.A., et al., Nat Genet 39:638-44 (2007)). Seven of these loci are on autosomes, and the remaining locus is on chromosome X. The total number of theoretical genotypic combinations is then 37 x 21 = 4374. Some of those genotypic classes are very rare, but are still possible, and should be considered for overall risk assessment.
It is likely that the multiplicative model applied in the case of multiple genetic variant will also be valid in conjugation with non-genetic risk variants assuming that the genetic variant does not clearly correlate with the "environmental" factor. In other words, genetic and non-genetic at- risk variants can be assessed under the multiplicative model to estimate combined risk, assuming that the non-genetic and genetic risk factors do not interact.
Using the same quantitative approach, the combined or overall risk associated with any plurality of variants associated with lung cancer may be assessed. For example, the combined risk of any plurality of the variants described herein {e.g., rs6474412, rs215614 and rs4105144, and their surrogates) may be assessed. Further, other markers described to be associated with risk of lung cancer may be assessed in combination of any one of the markers described herein, e.g. markers in the CHRNA5/CHRNA3/CHRNB4 gene cluster on chromosome 15 {e.g, rsl051730, or its surrogates) .
Linkage Disequilibrium
The natural phenomenon of recombination, which occurs on average once for each chromosomal pair during each meiotic event, represents one way in which nature provides variations in sequence (and biological function by consequence) . It has been discovered that recombination does not occur randomly in the genome; rather, there are large variations in the frequency of recombination rates, resulting in small regions of high recombination frequency (also called recombination hotspots) and larger regions of low recombination frequency, which are commonly referred to as Linkage Disequilibrium (LD) blocks (Myers, S. et al., Biochem Soc Trans 34: 526- 530 (2006); Jeffreys, A.J., et al., Nature Genet 29 : 217-222 (2001); May, C.A., et al., Nature Genet 31 : 272-275(2002)) .
Linkage Disequilibrium (LD) refers to a non-random assortment of two genetic elements. For example, if a particular genetic element (e.g. , an allele of a polymorphic marker, or a haplotype) occurs in a population at a frequency of 0.50 (50%) and another element occurs at a frequency of 0.50 (50%), then the predicted occurrance of a person's having both elements is 0.25 (25%), assuming a random distribution of the elements. However, if it is discovered that the two elements occur together at a frequency higher than 0.25, then the elements are said to be in linkage disequilibrium, since they tend to be inherited together at a higher rate than what their independent frequencies of occurrence (e.g., allele or haplotype frequencies) would predict. Roughly speaking, LD is generally correlated with the frequency of recombination events between the two elements. Allele or haplotype frequencies can be determined in a population by genotyping individuals in a population and determining the frequency of the occurence of each allele or haplotype in the population . For populations of diploids, e.g. , human populations, individuals will typically have two alleles or allelic combinations for each genetic element (e.g. , a marker, haplotype or gene) .
Many different measures have been proposed for assessing the strength of linkage disequilibrium (LD; reviewed in Devlin, B. & Risch, N ., Genomics 29 : 311-22 (1995)) . Most capture the strength of association between pairs of biallelic sites. Two important pairwise measures of LD are r2 (sometimes denoted Δ2) and | D'| (Lewontin, R., Genetics 49:49-67 (1964); Hill, W.G . &
Robertson, A. Theor. Appl. Genet. 22: 226-231 (1968)) . Both measures range from 0 (no disequilibrium) to 1 ('complete' disequilibrium), but their interpretation is slightly different. | D'| is defined in such a way that it is equal to 1 if just two or three of the possible haplotypes for two markers are present, and it is < 1 if all four possible haplotypes are present. Therefore, a value of | D'| that is < 1 indicates that historical recombination may have occurred between two sites (recurrent mutation can also cause | D'| to be < 1, but for single nucleotide polymorphisms (SNPs) this is usually regarded as being less likely than recombination) . The measure r2 represents the statistical correlation between two sites, and takes the value of 1 if only two haplotypes are present.
The r2 measure is arguably the most relevant measure for association mapping, because there is a simple inverse relationship between r2 and the sample size required to detect association between susceptibility loci and SNPs. These measures are defined for pairs of sites, but for some applications a determination of how strong LD is across an entire region that contains many polymorphic sites might be desirable (e.g. , testing whether the strength of LD differs significantly among loci or across populations, or whether there is more or less LD in a region than predicted under a particular model) . Roughly speaking, r measures how much recombination would be required under a particular population model to generate the LD that is seen in the data . This type of method can potentially also provide a statistically rigorous approach to the problem of determining whether LD data provide evidence for the presence of recombination hotspots. For the methods described herein, a significant r2 value between markers indicative of the markers being in linkage disequilibrium can be at least 0.1, such as at least 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, or at least 0.99. In one preferred embodiment, the significant r2 value can be at least 0.2. In another embodiment, the significant r2 value is at least 0.3. In another embodiment, the significant r2 value is at least 0.4. Other r2 values are also contemplated, and are also within the scope of the invention, including but not limited to at least 0.5, 0.6, 0.7, 0.8 and 0.9. The values of r2 given in the surrogate Tables 1 to 3 may be used to select markers fulfilling any suitable criteria of r2 values. Linkage disequilibrium can be determined in a single human population, as defined herein, or it can be determined in a collection of samples comprising individuals from more than one human population . In one embodiment of the invention, LD is determined in a sample from one or more of the HapMap populations
(Caucasian, African (Yuroban), Japanese, Chinese), as defined (http://www.hapmap.org) . In one such embodiment, LD is determined in the CEU population of the HapMap samples (Utah residents with ancestry from northern and western Europe). In another embodiment, LD is determined in the YRI population of the HapMap samples (Yuroba in Ibadan, Nigeria) . In another embodiment, LD is determined in the CHB population of the HapMap samples (Han Chinese from Beijing, China) . In another embodiment, LD is determined in the JPT population of the HapMap samples (Japanese from Tokyo, Japan) . In yet another embodiment, LD is determined in samples from the Icelandic population .
If all polymorphisms in the genome were independent at the population level (i.e., no LD), then every single one of them would need to be investigated in association studies, to assess all the different polymorphic states. However, due to linkage disequilibrium between polymorphisms, tightly linked polymorphisms are strongly correlated, which reduces the number of
polymorphisms that need to be investigated in an association study to observe a significant association. Another consequence of LD is that many polymorphisms may give an association signal due to the fact that these polymorphisms are strongly correlated .
Genomic LD maps have been generated across the genome, and such LD maps have been proposed to serve as framework for mapping disease-genes (Risch, N . & Merkiangas, K, Science 273 : 1516-1517 (1996); Maniatis, N ., et ai., Proc Natl Acad Sci USA 99 : 2228-2233 (2002); Reich, DE et ai, Nature 411 : 199-204 (2001)) .
It is now established that many portions of the human genome can be broken into series of discrete haplotype blocks containing a few common haplotypes; for these blocks, linkage disequilibrium data provides little evidence indicating recombination (see, e.g., Wall., J.D. and Pritchard, J.K., Nature Reviews Genetics 4: 587-597 (2003); Daly, M . et ai., Nature Genet.
29: 229-232 (2001); Gabriel, S.B. et ai., Science 296: 2225-2229 (2002); Patil, N . et ai., Science 294: 1719-1723 (2001); Dawson, E. et ai., Nature 4.28: 544-548 (2002); Phillips, M.S. et ai., Nature Genet. 33: 382-387 (2003)) .
There are two main methods for defining these haplotype blocks: blocks can be defined as regions of DNA that have limited haplotype diversity (see, e.g., Daly, M . et al., Nature Genet. 29: 229-232 (2001); Patil, N . et a\., Science 294: 1719-1723 (2001); Dawson, E. et al., Nature 4.28: 544-548 (2002); Zhang, K. et al., Proc. Natl. Acad. Sci. USA 99: 7335-7339 (2002)), or as regions between transition zones having extensive historical recombination, identified using linkage disequilibrium (see, e.g., Gabriel, S.B. et al., Science 296: 2225-2229 (2002); Phillips, M .S. et al., Nature Genet. 33: 382-387 (2003); Wang, N . et al., Am. J. Hum. Genet. 71 : 1221- 1234 (2002); Stumpf, M .P., and Goldstein, D.B., Curr. Biol. 13: 1-8 (2003)) . More recently, a fine-scale map of recombination rates and corresponding hotspots across the human genome has been generated (Myers, S., et al., Science 310: 321-32324 (2005); Myers, S. et al., Biochem Soc Trans 34: 526530 (2006)). The map reveals the enormous variation in recombination across the genome, with recombination rates as high as 10-60 cM/Mb in hotspots, while closer to 0 in intervening regions, which thus represent regions of limited haplotype diversity and high LD. The map can therefore be used to define haplotype blocks/LD blocks as regions flanked by recombination hotspots. As used herein, the terms "haplotype block" or "LD block" includes blocks defined by any of the above described characteristics, or other alternative methods used by the person skilled in the art to define such regions.
Haplotype blocks (LD blocks) can be used to map associations between phenotype and haplotype status, using single markers or haplotypes comprising a plurality of markers. The main haplotypes can be identified in each haplotype block, and then a set of "tagging" SNPs or markers (the smallest set of SNPs or markers needed to distinguish among the haplotypes) can then be identified . These tagging SNPs or markers can then be used in assessment of samples from groups of individuals, in order to identify association between phenotype and haplotype. For example, markers shown herein to be associated with lung cancer are such tagging markers.
It has thus become apparent that for any given observed association to a polymorphic marker in the genome, additional markers in the genome also show association. This is a natural consequence of the uneven distribution of LD across the genome, as observed by the large variation in recombination rates. The markers used to detect association thus in a sense represent "tags" for a genomic region (i.e., a haplotype block or LD block) that is associating with a given disease or trait, and as such are useful for use in the methods and kits of the present invention. One or more causative (functional) variants or mutations may reside within the region found to be associating to the disease or trait. The functional variant may be another SNP, a tandem repeat polymorphism (such as a minisatellite or a microsatellite), a transposable element, or a copy number variation, such as an inversion, deletion or insertion. Such variants in LD with the variants described herein may confer a higher relative risk (RR) or odds ratio (OR) than observed for the tagging markers used to detect the association. The present invention thus refers to the markers used for detecting association to the disease, as described herein, as well as markers in linkage disequilibrium with the markers. Thus, in certain embodiments of the invention, markers that are in LD with the markers originally used to detect an association may be used as surrogate markers. The surrogate markers have in one embodiment relative risk (RR) and/or odds ratio (OR) values smaller than originally detected. In other embodiments, the surrogate markers have RR or OR values greater than those initially determined for the markers initially found to be associating with the disease. An example of such an embodiment would be a rare, or relatively rare (such as < 10% allelic population frequency) variant in LD with a more common variant (> 10% population frequency) initially found to be associating with the disease. Identifying and using such surrogate markers for detecting the association can be performed by routine methods well known to the person skilled in the art, and are therefore within the scope of the present invention .
Determination of haplotype frequency
The frequencies of haplotypes in patient and control groups can be estimated using an expectation-maximization algorithm (Dempster A. et al., J. R. Stat. Soc. B, 39 : 1-38 (1977)) . An implementation of this algorithm that can handle missing genotypes and uncertainty with the phase can be used . Under the null hypothesis, the patients and the controls are assumed to have identical frequencies. Using a likelihood approach, an alternative hypothesis is tested, where a candidate at-risk-haplotype, which can include the markers described herein, is allowed to have a higher frequency in patients than controls, while the ratios of the frequencies of other haplotypes are assumed to be the same in both groups. Likelihoods are maximized separately under both hypotheses and a corresponding 1-df likelihood ratio statistic is used to evaluate the statistical significance.
To look for at-risk and protective markers and haplotypes within a susceptibility region, for example within an LD block, association of all possible combinations of genotyped markers within the region is studied. The combined patient and control groups can be randomly divided into two sets, equal in size to the original group of patients and controls. The marker and haplotype analysis is then repeated and the most significant p-value registered is determined. This randomization scheme can be repeated, for example, over 100 times to construct an empirical distribution of p-values. In a preferred embodiment, a p-value of <0.05 is indicative of a significant marker and/or haplotype association .
Haplotype Analysis
One general approach to haplotype analysis involves using likelihood-based inference applied to NEsted MOdels (Gretarsdottir S., et al., Nat. Genet. 35: 131-38 (2003)) . The method is implemented in the program NEMO, which allows for many polymorphic markers, SNPs and microsatellites. The method and software are specifically designed for case-control studies where the purpose is to identify haplotype groups that confer different risks. It is also a tool for studying LD structures. In NEMO, maximum likelihood estimates, likelihood ratios and p-values are calculated directly, with the aid of the EM algorithm, for the observed data treating it as a missing-data problem .
Even though likelihood ratio tests based on likelihoods computed directly for the observed data, which have captured the information loss due to uncertainty in phase and missing genotypes, can be relied on to give valid p-values, it would still be of interest to know how much information had been lost due to the information being incomplete. The information measure for haplotype analysis is described in Nicolae and Kong (Technical Report 537, Department of Statistics, University of Statistics, University of Chicago; Biometrics, 60(2): 368-75 (2004)) as a natural extension of information measures defined for linkage analysis, and is implemented in NEMO.
Association analysis
For single marker association to a disease, the Fisher exact test can be used to calculate two- sided p-values for each individual allele. Correcting for relatedness among patients can be done by extending a variance adjustment procedure previously described (Risch, N. &Teng, J.
Genome Res., 8: 1273-1288 (1998)) for sibships so that it can be applied to general familial relationships. The method of genomic controls (Devlin, B. & Roeder, K. Biometrics 55:997 (1999)) can also be used to adjust for the relatedness of the individuals and possible stratification.
For both single-marker and haplotype analyses, relative risk (RR) and the population attributable risk (PAR) can be calculated assuming a multiplicative model (haplotype relative risk model) (Terwilliger, J.D. & Ott, J., Hum. Hered.42:337-46 (1992) and Falk, C.T. & Rubinstein, P, Ann. Hum. Genet. 51 (Pt 3):227-33 (1987)), i.e., that the risks of the two alleles/haplotypes a person carries multiply. For example, if RR is the risk of A relative to a, then the risk of a person homozygote AA will be RR times that of a heterozygote Aa and RR2 times that of a homozygote aa. The multiplicative model has a nice property that simplifies analysis and computations— haplotypes are independent, i.e., in Hardy-Weinberg equilibrium, within the affected population as well as within the control population. As a consequence, haplotype counts of the affecteds and controls each have multinomial distributions, but with different haplotype frequencies under the alternative hypothesis. Specifically, for two haplotypes, Λ, and h}, risk(ft)/risk(ftj) =
(fi/Pi)/(fj/Pj), where fand p denote, respectively, frequencies in the affected population and in the control population. While there is some power loss if the true model is not multiplicative, the loss tends to be mild except for extreme cases. Most importantly, p-values are always valid since they are computed with respect to null hypothesis.
An association signal detected in one association study may be replicated in a second cohort, ideally from a different population {e.g., different region of same country, or a different country) of the same or different ethnicity. The advantage of replication studies is that the number of tests performed in the replication study is usually quite small, and hence the less stringent the statistical measure that needs to be applied. For example, for a genome-wide search for susceptibility variants for a particular disease or trait using 300,000 SNPs, a correction for the 300,000 tests performed (one for each SNP) can be performed. Since many SNPs on the arrays typically used are correlated {i.e., in LD), they are not independent. Thus, the correction is conservative. Nevertheless, applying this correction factor requires an observed P-value of less than 0.05/300,000 = 1.7 x 10"7 for the signal to be considered significant applying this conservative test on results from a single study cohort. Obviously, signals found in a genome- wide association study with P-values less than this conservative threshold {i.e., more significant) are a measure of a true genetic effect, and replication in additional cohorts is not necessary from a statistical point of view. Importantly, however, signals with P-values that are greater than this threshold may also be due to a true genetic effect. The sample size in the first study may not have been sufficiently large to provide an observed P-value that meets the conservative threshold for genome-wide significance, or the first study may not have reached genome-wide significance due to inherent fluctuations due to sampling. Since the correction factor depends on the number of statistical tests performed, if one signal (one SNP) from an initial study is replicated in a second case-control cohort, the appropriate statistical test for significance is that for a single statistical test, i.e., P-value less than 0.05. Replication studies in one or even several additional case-control cohorts have the added advantage of providing assessment of the association signal in additional populations, thus simultaneously confirming the initial finding and providing an assessment of the overall significance of the genetic variant(s) being tested in human populations in general.
The results from several case-control cohorts can also be combined to provide an overall assessment of the underlying effect. The methodology commonly used to combine results from multiple genetic association studies is the Mantel-Haenszel model (Mantel and Haenszel, J Natl Cancer Inst 22 : 719-48 (1959)). The model is designed to deal with the situation where association results from different populations, with each possibly having a different population frequency of the genetic variant, are combined. The model combines the results assuming that the effect of the variant on the risk of the disease, a measured by the OR or RR, is the same in all populations, while the frequency of the variant may differ between the populations.
Combining the results from several populations has the added advantage that the overall power to detect a real underlying association signal is increased, due to the increased statistical power provided by the combined cohorts. Furthermore, any deficiencies in individual studies, for example due to unequal matching of cases and controls or population stratification will tend to balance out when results from multiple cohorts are combined, again providing a better estimate of the true underlying genetic effect.
Risk assessment and Diagnostics
Within any given population, there is an absolute risk of developing a disease or trait, defined as the chance of a person developing the specific disease or trait over a specified time-period . For example, a woman's lifetime absolute risk of breast cancer is one in nine. That is to say, one woman in every nine will develop breast cancer at some point in their lives. Risk is typically measured by looking at very large numbers of people, rather than at a particular individual. Risk is often presented in terms of Absolute Risk (AR) and Relative Risk (RR) . Relative Risk is used to compare risks associating with two variants or the risks of two different groups of people. For example, it can be used to compare a group of people with a certain genotype with another group having a different genotype. For a disease, a relative risk of 2 means that one group has twice the chance of developing a disease as the other group. The risk presented is usually the relative risk for a person, or a specific genotype of a person, compared to the population with matched gender and ethnicity. Risks of two individuals of the same gender and ethnicity could be compared in a simple manner. For example, if, compared to the population, the first individual has relative risk 1.5 and the second has relative risk 0.5, then the risk of the first individual compared to the second individual is 1.5/0.5 = 3.
Risk Calculations
The creation of a model to calculate the overall genetic risk involves two steps: i) conversion of odds-ratios for a single genetic variant into relative risk and ii) combination of risk from multiple variants in different genetic loci into a single relative risk value.
Deriving risk from odds-ratios
Most gene discovery studies for complex diseases that have been published to date in authoritative journals have employed a case-control design because of their retrospective setup. These studies sample and genotype a selected set of cases (people who have the specified disease condition) and control individuals. The interest is in genetic variants (alleles) which frequency in cases and controls differ significantly.
The results are typically reported in odds ratios, that is the ratio between the fraction
(probability) with the risk variant (carriers) versus the non-risk variant (non-carriers) in the groups of affected versus the controls, i.e. expressed in terms of probabilities conditional on the affection status:
OR = (Pr(c|A)/Pr(nc|A)) / (Pr(c| C)/Pr(nc| C))
Sometimes it is however the absolute risk for the disease that we are interested in, i.e. the fraction of those individuals carrying the risk variant who get the disease or in other words the probability of getting the disease. This number cannot be directly measured in case-control studies, in part, because the ratio of cases versus controls is typically not the same as that in the general population. However, under certain assumption, we can estimate the risk from the odds ratio.
It is well known that under the rare disease assumption, the relative risk of a disease can be approximated by the odds ratio. This assumption may however not hold for many common diseases. Still, it turns out that the risk of one genotype variant relative to another can be estimated from the odds ratio expressed above. The calculation is particularly simple under the assumption of random population controls where the controls are random samples from the same population as the cases, including affected people rather than being strictly unaffected individuals. To increase sample size and power, many of the large genome-wide association and replication studies use controls that were neither age-matched with the cases, nor were they carefully scrutinized to ensure that they did not have the disease at the time of the study.
Hence, while not exactly, they often approximate a random sample from the general population . It is noted that this assumption is rarely expected to be satisfied exactly, but the risk estimates are usually robust to moderate deviations from this assumption.
Calculations show that for the dominant and the recessive models, where we have a risk variant carrier, "c", and a non-carrier, "nc", the odds ratio of individuals is the same as the risk ratio between these variants: OR = Pr(A| c)/Pr(A| nc) = r
And likewise for the multiplicative model, where the risk is the product of the risk associated with the two allele copies, the allelic odds ratio equals the risk factor:
OR = Pr(A| aa)/Pr(A| ab) = Pr(A| ab)/Pr(A| bb) = r
Here "a" denotes the risk allele and "b" the non-risk allele. The factor "r" is therefore the relative risk between the allele types.
For many of the studies published in the last few years, reporting common variants associated with complex diseases, the multiplicative model has been found to summarize the effect adequately and most often provide a fit to the data superior to alternative models such as the dominant and recessive models.
The risk relative to the average population risk
It is most convenient to represent the risk of a genetic variant relative to the average population since it makes it easier to communicate the lifetime risk for developing the disease compared with the baseline population risk. For example, in the multiplicative model we can calculate the relative population risk for variant "aa" as:
RR(aa) = Pr(A| aa)/Pr(A) = (Pr(A| aa)/Pr(A| bb))/(Pr(A)/Pr(A| bb)) = r2/(Pr(aa) r2 + Pr(ab) r + Pr(bb)) = r2/(p2 r2 + 2pq r + q2) = r2/R
Here "p" and "q" are the allele frequencies of "a" and "b" respectively. Likewise, we get that RR(ab) = r/R and RR(bb) = 1/R. The allele frequency estimates may be obtained from the publications that report the odds-ratios and from the HapMap database. Note that in the case where we do not know the genotypes of an individual, the relative genetic risk for that test or marker is simply equal to one.
As an example, for lung cancer risk, allele T of marker rs6474412 has an allelic OR of 1.12 and a frequency (p) around 0.8 in white populations. The genotype relative risk compared to genotype CC are estimated based on the multiplicative model.
For TT it is 1.12 x 1.12 = 1.25; for CT it is simply the OR 1.12, and for CC it is 1.0 by definition .
The frequency of allele C is q = l - p = l - 0.8 = 0.2. Population frequency of each of the three possible genotypes at this marker is:
Pr(TT) = p2 = 0.64, Pr(CT) = 2pq = 0.32, and Pr(CC) = q2 = 0.04
The average population risk relative to genotype CC (which is defined to have a risk of one) is: R = 0.64x 1.25 + 0.32 x 1.12 + 0.04x 1 = 1.20
Therefore, the risk relative to the general population (RR) for individuals who have one of the following genotypes at this marker is:
RR(TT) = 1.25/1.20 = 1.04, RR(CT) = 1.12/1.20 = 0.93, RR(CC) = 1/1.20 = 0.83. Of course, using non-carriers of the T allele of rs6474412 as reference, the risk will be considerably greater. The at-risk allele T is common in the population, which means that a large proportion of the population is at-risk. Therefore, the risk compared with the general population is relatively small than the risk compared with non-carriers of the at-risk T allele.
Combining the risk from multiple markers
When genotypes of many SNP variants are used to estimate the risk for an individual a multiplicative model for risk can generally be assumed. This means that the combined genetic risk relative to the population is calculated as the product of the corresponding estimates for individual markers, e.g. for two markers gl and g2:
RR(gl,g2) = RR(g l)RR(g2)
The underlying assumption is that the risk factors occur and behave independently, i .e. that the joint conditional probabilities can be represented as products:
Pr(A| gl,g2) = Pr(A| gl)Pr(A| g2)/Pr(A) and Pr(gl,g2) = Pr(gl)Pr(g2)
Obvious violations to this assumption are markers that are closely spaced on the genome, i .e. in linkage disequilibrium, such that the concurrence of two or more risk alleles is correlated. In such cases, we can use so called haplotype modeling where the odds-ratios are defined for all allele combinations of the correlated SNPs.
As is in most situations where a statistical model is utilized, the model applied is not expected to be exactly true since it is not based on an underlying bio-physical model. However, the multiplicative model has so far been found to fit the data adequately, i.e. no significant deviations are detected for many common diseases for which many risk variants have been discovered.
As an example, an individual who has the following genotypes at 4 hypothetical markers associated with a particular disease along with the risk relative to the population at each marker:
Figure imgf000049_0001
Combined, the overall risk relative to the population for this individual is: 1.03 x 1.30 x0.88x 1.54 = 1.81.
Adjusted life-time risk
The lifetime risk of an individual is derived by multiplying the overall genetic risk relative to the population with the average life-time risk of the disease in the general population of the same ethnicity and gender and in the region of the individual's geographical origin. As there are usually several epidemiologic studies to choose from when defining the general population risk, we will pick studies that are well-powered for the disease definition that has been used for the genetic variants. For example, if the overall genetic risk relative to the population is 1.8 for a disease for an individual, and if the average life-time risk of the disease demographic group of the individual is 20%, then the adjusted lifetime risk for the individual is 20% x 1.8 = 36%.
Note that since the average RR for a population is one, this multiplication model provides the same average adjusted life-time risk of the disease. Furthermore, since the actual life-time risk cannot exceed 100%, there must be an upper limit to the genetic RR.
Risk assessment for lung cancer
As described herein, certain polymorphic markers are found to be useful for risk assessment of lung cancer. Risk assessment can involve the use of such markers for determining a susceptibility to lung cancer. Tagging markers in linkage disequilibrium with at-risk variants (or protective variants) can be used as surrogates for these markers. Such surrogate markers can be located within a particular haplotype block or LD block. Such surrogate markers can also sometimes be located outside the physical boundaries of such a haplotype block or LD block, either in close vicinity of the LD block/haplotype block, but possibly also located in a more distant genomic location.
Long-distance LD can for example arise if particular genomic regions (e.g., genes) are in a functional relationship. For example, if two genes encode proteins that play a role in a shared metabolic pathway, then particular variants in one gene may have a direct impact on observed variants for the other gene. Let us consider the case where a variant in one gene leads to increased expression of the gene product. To counteract this effect and preserve overall flux of the particular pathway, this variant may have led to selection of one (or more) variants at a second gene that confers decreased expression levels of that gene. These two genes may be located in different genomic locations, possibly on different chromosomes, but variants within the genes are in apparent LD, not because of their shared physical location within a region of high LD, but rather due to evolutionary forces. Such LD is also contemplated and within scope of the present invention. The skilled person will appreciate that many other scenarios of functional gene-gene interaction are possible, and the particular example discussed here represents only one such possible scenario.
Markers with values of r2 equal to 1 are perfect surrogates for the at-risk variants (anchor variants), i .e. genotypes for one marker perfectly predicts genotypes for the other. Markers with smaller values of r2 than 1 can also be surrogates for the at-risk variant, or alternatively represent variants with relative risk values as high as or possibly even higher than the at-risk variant. In certain preferred embodiments, markers with values of r2 to the at-risk anchor variant are useful surrogate markers. The at-risk variant identified may not be the functional variant itself, but is in this instance in linkage disequilibrium with the true functional variant. The functional variant may be a SNP, but may also for example be a tandem repeat, such as a minisatellite or a microsatellite, a transposable element {e.g., an Alu element), or a structural alteration, such as a deletion, insertion or inversion (sometimes also called copy number variations, or CNVs) . The present invention encompasses the assessment of such surrogate markers for the markers as disclosed herein. Such markers are annotated, mapped and listed in public databases, as well known to the skilled person, or can alternatively be readily identified by sequencing the region or a part of the region identified by the markers of the present invention in a group of individuals, and identify polymorphisms in the resulting group of sequences. As a consequence, the person skilled in the art can readily and without undue experimentation identify and genotype surrogate markers in linkage disequilibrium with the markers and/or haplotypes as described herein.
The present invention can in certain embodiments be practiced by assessing a sample comprising genomic DNA from an individual for the presence variants described herein to be associated with lung cancer. Such assessment typically includes steps that detect the presence or absence of at least one allele of at least one polymorphic marker, using methods well known to the skilled person and further described herein, and based on the outcome of such assessment, determine whether the individual from whom the sample is derived is at increased or decreased risk (i.e., increased or decreased susceptibility) of lung cancer. Detecting particular alleles of polymorphic markers can in certain embodiments be done by obtaining nucleic acid sequence data about a particular human individual that identifies at least one allele of at least one polymorphic marker. Different alleles of the at least one marker are associated with different susceptibility to the disease in humans. Obtaining nucleic acid sequence data can comprise nucleic acid sequence at a single nucleotide position, which is sufficient to identify alleles at SNPs. The nucleic acid sequence data can also comprise sequence at any other number of nucleotide positions, in particular for genetic markers that comprise multiple nucleotide positions, and can be anywhere from two to hundreds of thousands, possibly even millions, of nucleotides (in particular, in the case of copy number variations (CNVs)) .
In certain embodiments, the invention can be practiced utilizing a dataset comprising information about the genotype status of at least one polymorphic marker. In other words, a dataset containing information about such genetic status, for example in the form of genotype counts at a certain polymorphic marker, or a plurality of markers (e.g., an indication of the presence or absence of certain at-risk alleles), or actual genotypes for one or more markers (for example in the form of sequence information), can be queried for the presence or absence of certain at-risk alleles at certain polymorphic markers shown by the present inventors to be associated with lung cancer. A positive result for a variant (e.g., marker allele) associated with lung cancer, is indicative of the individual from which the dataset is derived is at increased susceptibility (increased risk) of lung cancer.
In certain embodiments of the invention, a polymorphic marker is correlated to lung cancer by referencing genotype data for the polymorphic marker to a database, such as a look-up table, that comprises correlation data between at least one allele of the polymorphism and lung cancer. In some embodiments, the table comprises a correlation for one polymorphism. In other embodiments, the table comprises a correlation for a plurality of polymorphisms. In both scenarios, by referencing to a look-up table that gives an indication of a correlation between a marker and lung cancer, a risk or susceptibility of lung cancer can be identified in the individual from whom the sample is derived. In some embodiments, the correlation is reported as a statistical measure. The statistical measure may be reported as a risk measure, such as a relative risk (RR), an absolute risk (AR) or an odds ratio (OR) .
Risk markers may be useful for risk assessment and diagnostic purposes, either alone or in combination . Results of disease risk assessment can also be combined with data for other genetic markers or risk factors for the disease, to establish overall risk. Thus, even in cases where the increase in risk by individual markers is relatively modest, e.g. on the order of 10- 30%, the association may have significant implications when combined with other risk markers. Thus, relatively common variants may have significant contribution to the overall risk (Population Attributable Risk is high), or combination of markers can be used to define groups of individual who, based on the combined risk of the markers, is at significant combined risk of developing the disease. Thus, by assaying for multiple genetic markers associated with lung cancer risk, a significant risk may be captured using the combination of variants, even though each variant may, on its own, capture a relatively small proportion of the overall genetic risk.
As a consequence, in certain embodiments of the invention, a plurality of variants are used for overall risk assessment. These variants are in one embodiment selected from the variants as disclosed herein . Other embodiments include the use of the variants of the present invention in combination with other variants known to be useful for diagnosing a susceptibility to lung cancer. In such embodiments, the genotype status of a plurality of markers (or haplotypes) is determined in an individual, and the status of the individual compared with the population frequency of the associated variants, or the frequency of the variants in clinically healthy subjects, such as age-matched and sex-matched subjects. Methods known in the art, such as multivariate analyses or joint risk analyses, such as those described herein, or other methods known to the skilled person, may subsequently be used to determine the overall risk conferred based on the genotype status at the multiple loci. Assessment of risk based on such analysis may subsequently be used in the methods, uses and kits of the invention, as described herein.
Study population
In a general sense, the methods and kits described herein can be utilized from samples containing nucleic acid material (DNA or RNA) for protein material rom any source and from any individual, or from genotype or sequence data derived from such samples. In preferred embodiments, the individual is a human individual. The individual can be an adult, child, or fetus. The nucleic acid or protein source may be any sample comprising nucleic acid or protein material, including biological samples, or a sample comprising nucleic acid or protein material derived therefrom. The present invention also provides for assessing markers in individuals who are members of a target population . Such a target population is in one embodiment a population or group of individuals at particular risk, for example smokers.
The invention provides for embodiments that include individuals with age of onset or age at diagnosis of lung cancer in certain age subgroups, such as those over the age of 40, over age of 45, or over age of 50, 55, 60, 65, 70, 75, 80, or 85. Other embodiments of the invention pertain to other age groups, such as individuals aged less than 85, such as less than age 80, less than age 75, or less than age 70, 65, 60, 55, 50, 45, 40, 35, or age 30. Other embodiments relate to individuals with age at onset or age at diagnosis of lung cancer in any of the age ranges described in the above. It is also contemplated that a range of ages may be relevant in certain embodiments, such as age at onset at more than age 45 but less than age 60. Other age ranges are however also contemplated, including all age ranges bracketed by the age values listed in the above. The invention furthermore relates to individuals of either gender, males or females.
The Icelandic population is a Caucasian population of Northern European ancestry. A large number of studies reporting results of genetic linkage and association in the Icelandic population have been published in the last few years. Many of those studies show replication of variants, originally identified in the Icelandic population as being associating with a particular disease, in other populations (Sulem, P., et al. Nat Genet May 17 2009 (Epub ahead of print); Rafnar, T., et al. Nat Genet 41 : 221-7 (2009); Greta rsdottir, S., et al. Ann Neurol 64:402-9 (2008); Stacey, S.N ., et al. Nat Genet 40 : 1313-18 (2008); Gudbjartsson, D.F., et al. Nat Genet 40: 886-91 (2008); Sty rka rsdottir, U ., et al. N Engl J Med 358: 2355-65 (2008); Thorgeirsson, T., et al.
Nature 452: 638-42 (2008); Gudmundsson, J., et al. Nat Genet. 40 : 281-3 (2008); Stacey, S.N ., et al., Nat Genet. 39 : 865-69 (2007); Helgadottir, A., et al., Science 316: 1491-93 (2007);
Steinthorsdottir, V., et al., Nat Genet. 39 : 770-75 (2007); Gudmundsson, J., et al., Nat Genet. 39 : 631-37 (2007); Frayling, TM, Nature Reviews Genet 8:657-662 (2007); Amundadottir, L.T., et al., Nat Genet. 38: 652-58 (2006); Grant, S.F., et al., Nat Genet. 38: 320-23 (2006)) . Thus, genetic findings in the Icelandic population have in general been replicated in other populations, including populations from Africa and Asia .
It is thus believed that the markers described herein to be associated with risk of lung cancer will show similar association in other human populations. Particular embodiments comprising individual human populations are thus also contemplated and within the scope of the invention . Such embodiments relate to human subjects that are from one or more human population including, but not limited to, Caucasian populations, European populations, American
populations, Eurasian populations, Asian populations, Central/South Asian populations, East Asian populations, Middle Eastern populations, African populations, Hispanic populations, and Oceanian populations. European populations include, but are not limited to, Swedish,
Norwegian, Finnish, Russian, Danish, Icelandic, Irish, Kelt, English, Scottish, Dutch, Belgian, French, German, Spanish, Portuguese, Italian, Polish, Bulgarian, Slavic, Serbian, Bosnian, Czech, Greek and Turkish populations.
The racial contribution in individual subjects may also be determined by genetic analysis.
Genetic analysis of ancestry may be carried out using unlinked microsatellite markers such as those set out in Smith et al. {Am J Hum Genet 74, 1001-13 (2004)) .
In certain embodiments, the invention relates to markers and/or haplotypes identified in specific populations, as described in the above. The person skilled in the art will appreciate that measures of linkage disequilibrium (LD) may give different results when applied to different populations. This is due to different population history of different human populations as well as differential selective pressures that may have led to differences in LD in specific genomic regions. It is also well known to the person skilled in the art that certain markers, e.g. SNP markers, have different population frequency in different populations, or are polymorphic in one population but not in another. The person skilled in the art will however apply the methods available and as described herein to practice the present invention in any given human population. This may include assessment of polymorphic markers in particular LD regions, so as to identify those markers that give strongest association within the specific population. Thus, the at-risk variants of the present invention may reside on different haplotype background and in different frequencies in various human populations. However, utilizing methods known in the art and the markers of the present invention, the invention can be practiced in any given human population.
Utility of Genetic Testing
The person skilled in the art will appreciate and understand that the variants described herein in general do not, by themselves, provide an absolute identification of individuals who will develop lung cancer. The variants described herein do however indicate increased and/or decreased likelihood that individuals carrying the at-risk variants disclosed herein will develop lung cancer. This information is however extremely valuable in itself, as outlined in more detail in the following, as it can be used, for example, to initiate preventive measures at an early stage, perform regular physical exams to monitor the development, progress and/or appearance of symptoms, or to schedule exams at a regular interval to identify lung cancer in its early stages, so as to be able to apply treatment at an early stage which is often critical for successful lung cancer therapy.
The knowledge about a genetic variant that confers a risk of developing lung cancer also offers the opportunity to apply a genetic test to distinguish between individuals with increased risk of developing lung cancer (i.e. carriers of at-risk variants) and those with decreased risk of developing lung cancer (i.e. carriers of protective variants, and/or non-carriers of at-risk variants) . The core value of genetic testing is the possibility of being able to identify a predisposition to disease at an early stage of disease, or before appearance of disease, so as to allow the clinician to apply the most appropriate treatment and/or preventive measure.
Individuals with a family history of lung cancer and carriers of at-risk variants may also benefit from genetic testing since the knowledge of the presence of at-risk genetic risk factors may provide incentive for implementing a healthier lifestyle, by avoiding or minimizing known environmental risk factors for lung cancer. For example, an individual who is a current smoker and is identified as a carrier of one or more at-risk variants of lung cancer may, due to his/her increased risk of developing the disease, choose to quit smoking.
Integration of Genetic Risk Models into Clinical Management of Lung Cancer:
Management of lung cancer currently relies on a combination of primary prevention (most importantly abstinence from smoking), early diagnosis and appropriate treatment. There are clear clinical imperatives for integrating genetic testing into several aspects of these management areas. Identification of cancer susceptibility genes may also reveal key molecular pathways that may be manipulated (e.g., using small or large molecular weight drugs) and may lead to more effective treatments.
Primary prevention
Primary prevention options currently focus on avoiding exposure to tobacco smoke or other environmental toxins that have been associated with the development of lung cancer.
Early Diagnosis
Patients who are identified as being at high risk for lung cancer may be referred to have chest X- rays or sputum cytology examination. In addition, a spiral CT scan is a newly-developed procedure for lung cancer screening. Numerous lung cancer screening trials are currently taking place but presently, the U.S. Preventive Services Task Force (USPSTF) concludes that evidence is insufficient to recommend for or against screening asymptomatic persons for lung cancer with either low dose computerized tomography (LDCT), chest x-ray, sputum cytology, or a combination of these tests.
Many of the screening protocols being evaluated involve some form of radiation or invasive procedure such as bronchoscopy. These protocols carry certain risks and may prove hard to implement due to the considerable costs involved. In light of the fact that only about 15% of lifetime smokers develop lung cancer, it is clear that the great majority of individuals at risk would be needlessly subjected to repeated screening tests with the associated costs and negative side-effects. The identification of genetic biomarkers that affect the risk of developing lung cancer could be used to help identify individuals should be offered extreme help in risk reduction programs such as smoking termination . In the case of failure to stop smoking, or in the case of ex-smokers, such genetic biomarkers could help in defining the subpopulation of individuals that would benefit the most from screening .
Less than 10% of lung cancer cases arise in individuals that have never smoked. Genetic biomarkers that predict the risk of lung cancer would be particularly useful in this group. The genetic component of this form of the disease is likely to be even stronger than in tobacco- related lung cancer. If genetic variants that affect the risk of non-smoking lung cancer were known, it might be possible to identify individuals at high risk for this disease and subject them to regular screening tests.
Diagnostic and screening methods
The polymorphic markers shown herein to be associated with risk of lung cancer are useful in diagnostic methods. Although methods of diagnosing lung cancer are known, genetic risk markers such as those described herein provide added value to such diagnostic methods. Thus, by obtaining sequence data about particular markers, e.g., nucleic acid sequence data identifying at least one allele of at least one polymorphic marker, a diagnostic measure of lung cancer risk is obtained that may be utilized in various diagnostic methods as described herein. The present invention pertains in some embodiments to methods of clinical applications of diagnosis, e.g., diagnosis performed by a medical professional . In other embodiments, the invention pertains to methods of diagnosis or methods of determination of a susceptibility performed by a layman. The layman can be the customer of a genotyping service. The layman may also be a genotype service provider, who performs genotype analysis on a DNA sample from an individual, in order to provide service related to genetic risk factors for particular traits or diseases, based on the genotype status of the individual {i.e., the customer) . Recent technological advances in genotyping technologies, including high-throughput genotyping of SNP markers, such as Molecular Inversion Probe array technology {e.g., Affymetrix GeneChip), and BeadArray Technologies {e.g., Illumina GoldenGate and Infinium assays) have made it possible for individuals to have their own genome assessed for up to one million SNPs simultaneously, at relatively little cost. The resulting genotype information, which can be made available to the individual, can be compared to information about disease or trait risk associated with various SNPs, including information from public literature and scientific publications. The diagnostic application of disease-associated alleles as described herein, can thus for example be performed by the individual, through analysis of his/her genotype data, by a health professional based on results of a clinical test, or by a third party, including the genotype service provider. The third party may also be service provider who interprets genotype information from the customer to provide service related to specific genetic risk factors, including the genetic markers described herein . In other words, the diagnosis or determination of a susceptibility of genetic risk can be made by health professionals, genetic counselors, third parties providing genotyping service, third parties providing risk assessment service or by the layman {e.g. , the individual), based on information about the genotype status of an individual and knowledge about the risk conferred by particular genetic risk factors {e.g., particular SNPs) . In the present context, the term "diagnosing", "diagnose a susceptibility" and "determine a susceptibility" is meant to refer to any available diagnostic method, including those mentioned above.
In certain embodiments, a sample containing genomic DNA from an individual is collected . Such sample can for example be a buccal swab, a saliva sample, a blood sample, or other suitable samples containing genomic DNA, as described further herein . The genomic DNA is then analyzed using any common technique available to the skilled person, such as high-throughput array technologies. Results from such genotyping are stored in a convenient data storage unit, such as a data carrier, including computer databases, data storage disks, or by other convenient data storage means. In certain embodiments, the computer database is an object database, a relational database or a post-relational database. The genotype data is subsequently analyzed for the presence of certain variants known to be susceptibility variants for a particular human conditions, such as the genetic variants described herein . Genotype data can be retrieved from the data storage unit using any convenient data query method. Calculating risk conferred by a particular genotype for the individual can be based on comparing the genotype of the individual to previously determined risk (expressed as a relative risk (RR) or and odds ratio (OR), for example) for the genotype, for example for an heterozygous carrier of an at-risk variant for a particular disease or trait. The calculated risk for the individual can be the relative risk for a person, or for a specific genotype of a person, compared to the average population with matched gender and ethnicity. The average population risk can be expressed as a weighted average of the risks of different genotypes, using results from a reference population, and the appropriate calculations to calculate the risk of a genotype group relative to the population can then be performed. Alternatively, the risk for an individual is based on a comparison of particular genotypes, for example heterozygous carriers of an at-risk allele of a marker compared with non-carriers of the at-risk allele. Using the population average may in certain embodiments be more convenient, since it provides a measure which is easy to interpret for the user, i.e. a measure that gives the risk for the individual, based on his/her genotype, compared with the average in the population. The calculated risk estimated can be made available to the customer via a website, preferably a secure website.
In certain embodiments, a service provider will include in the provided service all of the steps of isolating genomic DNA from a sample provided by the customer, performing genotyping of the isolated DNA, calculating genetic risk based on the genotype data, and report the risk to the customer. In some other embodiments, the service provider will include in the service the interpretation of genotype data for the individual, i.e. , risk estimates for particular genetic variants based on the genotype data for the individual . In some other embodiments, the service provider may include service that includes genotyping service and interpretation of the genotype data, starting from a sample of isolated DNA from the individual (the customer) .
Overall risk for multiple risk variants can be performed using standard methodology. For example, assuming a multiplicative model, i.e. assuming that the risk of individual risk variants multiply to establish the overall effect, allows for a straight-forward calculation of the overall risk for multiple markers.
In one embodiment, determination of a susceptibility to lung cancer can be accomplished using hybridization methods, (see Current Protocols in Molecular Biology, Ausubel, F. et ai. , eds., John Wiley & Sons, including all supplements) . The presence of a specific marker allele can be indicated by sequence-specific hybridization of a nucleic acid probe specific for the particular allele. The presence of more than one specific marker allele or a specific haplotype can be indicated by using several sequence-specific nucleic acid probes, each being specific for a particular allele. A sequence-specific probe can be directed to hybridize to genomic DNA, RNA, or cDNA. A "nucleic acid probe", as used herein, can be a DNA probe or an RNA probe that hybridizes to a complementary sequence. One of skill in the art would know how to design such a probe so that sequence specific hybridization will occur only if a particular allele is present in a genomic sequence from a test sample.
To determine a susceptibility to lung cancer, a hybridization sample can be formed by contacting the test sample, such as a genomic DNA sample, with at least one nucleic acid probe. A non- limiting example of a probe for detecting mRNA or genomic DNA is a labeled nucleic acid probe that is capable of hybridizing to mRNA or genomic DNA sequences described herein. The nucleic acid probe can be, for example, a full-length nucleic acid molecule, or a portion thereof, such as an oligonucleotide of at least 15, 30, 50, 100, 250 or 500 nucleotides in length that is sufficient to specifically hybridize under stringent conditions to appropriate mRNA or genomic DNA. In certain embodiments, the oligonucleotide is from about 15 to about 100 nucleotides in length. In certain other embodiments, the oligonucleotide is from about 20 to about 50 nucleotides in length . The nucleic acid probe can comprise all or a portion of the nucleotide sequence of LD block C07, LD block C08 or LD block C19, as described herein, optionally comprising at least one marker described herein, or the probe can be the complementary sequence of such a sequence. The nucleic acid probe can also comprise all or a portion of the nucleotide sequence of a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B, or the probe can be the
complementary sequence of such a sequence. Other suitable probes for use in the diagnostic assays of the invention are described herein . Hybridization can be performed by methods well known to the person skilled in the art (see, e.g., Current Protocols in Molecular Biology, Ausubel, F. et al., eds., John Wiley & Sons, including all supplements). In one embodiment, hybridization refers to specific hybridization, i.e., hybridization with no mismatches (exact hybridization). In one embodiment, the hybridization conditions for specific hybridization are high stringency.
Specific hybridization, if present, is detected using standard methods. If specific hybridization occurs between the nucleic acid probe and the nucleic acid in the test sample, then the sample contains the allele that is complementary to the nucleotide that is present in the nucleic acid probe. The process can be repeated for any markers of the present invention, or markers that make up a haplotype of the present invention, or multiple probes can be used concurrently to detect more than one marker alleles at a time.
In one embodiment of the invention, a test sample containing genomic DNA obtained from the subject is collected and the polymerase chain reaction (PCR) is used to amplify a fragment comprising one ore more markers of the present invention . As described herein, identification of a particular marker allele or haplotype can be accomplished using a variety of methods (e.g. , sequence analysis, analysis by restriction digestion, specific hybridization, single stranded conformation polymorphism assays (SSCP), electrophoretic analysis, etc.) . In one embodiment, a method utilizing a detection oligonucleotide probe comprising a fluorescent moiety or group at its 3' terminus and a quencher at its 5' terminus, and an enhancer oligonucleotide, is employed, as described by Kutyavin et al. (Nucleic Acid Res. 34: el28 (2006)) . In another embodiment, diagnosis is accomplished by expression analysis, for example by using quantitative PCR (kinetic thermal cycling) . This technique can, for example, utilize commercially available technologies, such as TaqMan® (Applied Biosystems, Foster City, CA) . The technique can assess the presence of an alteration in the expression or composition of a polypeptide or splicing variant(s) . Further, the expression of the variant(s) can be quantified as physically or functionally different.
In another embodiment of the methods of the invention, analysis by restriction digestion can be used to detect a particular allele if the allele results in the creation or elimination of a restriction site relative to a reference sequence. Restriction fragment length polymorphism (RFLP) analysis can be conducted, e.g., as described in Current Protocols in Molecular Biology, supra. The digestion pattern of the relevant DNA fragment indicates the presence or absence of the particular allele in the sample.
Sequence analysis can also be used to detect specific alleles or haplotypes. Therefore, in one embodiment, determination of the presence or absence of a particular marker alleles or haplotypes comprises sequence analysis of a test sample of DNA or RNA obtained from a subject or individual . PCR or other appropriate methods can be used to amplify a portion of a nucleic acid that contains a polymorphic marker or haplotype, and the presence of specific alleles can then be detected directly by sequencing the polymorphic site (or multiple polymorphic sites in a haplotype) of the genomic DNA in the sample. The direct sequence analysis can be of the nucleic acid of a biological sample obtained from the human individual for which a susceptibility is being determined. The biological sample can be any sample containing nucleic acid (e.g., genomic DNA) obtained from the human individual. In a specific aspect of the invention, obtaining nucleic acid sequence data comprises obtaining nucleic acid sequence information from a preexisting record, e.g., a preexisting medical record comprising genotype information of the human individual. For example, direct sequence analysis of the allele of the polymorphic marker can be accomplished by mining a pre-existing genotype dataset for the sequence of the allele of the polymorphic marker.
In another embodiment, arrays of oligonucleotide probes that are complementary to target nucleic acid sequence segments from a subject, can be used to identify particular alleles at polymorphic sites. For example, an oligonucleotide array can be used. Oligonucleotide arrays typically comprise a plurality of different oligonucleotide probes that are coupled to a surface of a substrate in different known locations. These arrays can generally be produced using mechanical synthesis methods or light directed synthesis methods that incorporate a combination of photolithographic methods and solid phase oligonucleotide synthesis methods, or by other methods known to the person skilled in the art (see, e.g. , Bier, F.F., et al. Adv Biochem Eng Blotechnol 109:433-53 (2008); Hoheisel, J.D., Nat Rev Genet 7 : 200-10 (2006); Fan, J .B., et al. Methods Enzymol 410 : 57-73 (2006); Raqoussis, J. & Elvidge, G., Expert Rev Mol Diagn 6: 145-52 (2006); Mockler, T.C., et al Genomics 85 : 1-15 (2005), and references cited therein, the entire teachings of each of which are incorporated by reference herein). Many additional descriptions of the preparation and use of oligonucleotide arrays for detection of polymorphisms can be found, for example, in US 6,858,394, US 6,429,027, US 5,445,934, US 5,700,637, US
5,744,305, US 5,945,334, US 6,054,270, US 6,300,063, US 6,733,977, US 7,364,858, EP 619 321, and EP 373 203, the entire teachings of which are incorporated by reference herein.
Other methods of nucleic acid analysis that are available to those skilled in the art can be used to detect a particular allele at a polymorphic site. Representative methods include, for example, direct manual sequencing (Church and Gilbert, Proc. Natl. Acad. Sci. USA, 81 : 1991-1995 (1988); Sanger, F., et al. , Proc. Natl. Acad. Sci. USA, 74: 5463-5467 (1977); Beavis, et al., U .S. Patent No. 5,288,644); automated fluorescent sequencing; single-stranded conformation polymorphism assays (SSCP); clamped denaturing gel electrophoresis (CDGE); denaturing gradient gel electrophoresis (DGGE) (Sheffield, V., et al., Proc. Natl. Acad. Sci. USA, 86: 232-236 (1989)), mobility shift analysis (Orita, M ., et al., Proc. Natl. Acad. Sci. USA, 86: 2766-2770 (1989)), restriction enzyme analysis (Flavell, R., et al., Cell, 15 : 25-41 (1978); Geever, R., et al., Proc. Natl. Acad. Sci. USA, 78: 5081-5085 (1981)); heteroduplex analysis; chemical mismatch cleavage (CMC) (Cotton, R., et al., Proc. Natl. Acad. Sci. USA, 85:4397-4401 (1985)); RNase protection assays (Myers, R., et al., Science, 230: 1242-1246 (1985); use of polypeptides that recognize nucleotide mismatches, such as E. coli mutS protein; and allele-specific PCR.
Indirect analyses
Alternatively, the nucleic acid sequence data may be obtained through indirect analysis of the nucleic acid sequence of the allele of the polymorphic marker. For example, the allele could be one which leads to the expression of a variant protein comprising an altered amino acid sequence, as compared to the non-variant (e.g., wild-type) protein, due to one or more amino acid substitutions, deletions, or insertions, or truncation (due to, e.g., splice variation) . Other possible effects include alterations in relative amounts of alternative splice forms of mRNA, effects on RNA stability, effects on transport from the nucleus to cytoplasm, and effects on the efficiency and accuracy of translation .
A variety of methods are known in the art and can be used for detecting protein expression levels, including enzyme linked immunosorbent assays (ELISA), Western blots,
immunoprecipitations and immunofluorescence. For example, a test sample from a subject can be assessed for the presence of an alteration in the expression and/or an alteration in polypeptide composition. Both quantitative and qualitative alterations can be present. An "alteration" in the polypeptide expression or composition, as used herein, refers to an alteration in expression or composition in a test sample, as compared to the expression or composition of the polypeptide in a control sample. A control sample is suitably a sample that corresponds to the test sample (e.g. , is from the same type of cells), and is from a subject who is not affected by, and/or who does not have a susceptibility to, lung cancer. In one embodiment, the control sample is from a subject who does not possess an at-risk marker allele for lung cancer, as described herein. Various means of examining expression or composition of a polypeptide encoded by a nucleic acid are known to the person skilled in the art and can be used, including spectroscopy, colorimetry, electrophoresis, isoelectric focusing, and immunoassays (e.g. , David et al. , U.S. Pat. No. 4,376,110) such as immunoblotting (see, e.g. , Current Protocols in Molecular Biology, particularly chapter 10, supra) .
For example, in one embodiment, an antibody (e.g. , an antibody with a detectable label) can be used. Antibodies can be polyclonal or monoclonal. An intact antibody, or a fragment thereof (e.g. , Fv, Fab, Fab', F(ab')2) can be used. The term "labeled", with regard to the probe or antibody, is intended to encompass direct labeling of the probe or antibody by coupling (i .e., physically linking) a detectable substance to the probe or antibody, as well as indirect labeling of the probe or antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a labeled secondary antibody (e.g. , a fluorescently-labeled secondary antibody) and end-labeling of a DNA probe with biotin such that it can be detected with fluorescently-labeled streptavidin .
A level or amount of the polypeptide in the test sample that is higher or lower than the level or amount of the polypeptide in the control sample, such that the difference is statistically significant, is indicative of an alteration in the expression of the polypeptide, and is a diagnostic for a particular allele or haplotype associated with the difference in expression . Alternatively, the composition of the polypeptide in a test sample is compared with the composition of the polypeptide in a control sample. In another embodiment, both the level or amount and the composition of the polypeptide can be assessed in the test sample and in the control sample. Further, risk assessment of lung cancer may be made by detecting at least one marker of the present invention in combination with an additional protein-based, RNA-based or DNA-based assay.
Kits
Kits useful in the methods of the invention comprise components useful in any of the methods described herein, including for example, primers for nucleic acid amplification, hybridization probes, restriction enzymes (e.g. , for RFLP analysis), allele-specific oligonucleotides, antibodies that bind to an altered polypeptide encoded by a nucleic acid of the invention as described herein (e.g. , a genomic segment comprising at least one polymorphic marker and/or haplotype of the present invention) or to a non-altered (native) polypeptide encoded by a nucleic acid of the invention as described herein, means for amplification of a nucleic acids associated with lung cancer, means for analyzing the nucleic acid sequence of a nucleic acid associated with lung cancer, means for analyzing the amino acid sequence of a polypeptide encoded by a nucleic acid associated with lung cancer, etc. The kits can for example include necessary buffers, nucleic acid primers for amplifying nucleic acids of the invention (e.g. , a nucleic acid segment comprising one or more of the polymorphic markers as described herein), and reagents for allele-specific detection of the fragments amplified using such primers and necessary enzymes (e.g. , dna polymerase) . Additionally, kits can provide reagents for assays to be used in combination with the methods of the present invention, e.g. , reagents for use with other diagnostic assays for lung cancer.
In one embodiment, the invention pertains to a kit for assaying a sample from a subject to detect a susceptibility to lung cancer in a subject, wherein the kit comprises reagents necessary for selectively detecting at least one allele of at least one polymorphism of the present invention in the genome of the individual. In a particular embodiment, the reagents comprise at least one contiguous oligonucleotide that hybridizes to a fragment of the genome of the individual comprising at least one polymorphism of the present invention . In another embodiment, the reagents comprise at least one pair of oligonucleotides that hybridize to opposite strands of a genomic segment obtained from a subject, wherein each oligonucleotide primer pair is designed to selectively amplify a fragment of the genome of the individual that includes at least one polymorphism associated with lung cancer risk. In one such embodiment, the polymorphism is selected from the group consisting of the polymorphisms rs6474412, rs215614 and rs4105144, and polymorphic markers in linkage disequilibrium therewith. In one embodiment, the polymorphism is selected from the group consisting of the markers listed in Table 1, Table 2 and/or Table 3 herein. In yet another embodiment the fragment is at least 20 base pairs in size. Such oligonucleotides or nucleic acids (e.g., oligonucleotide primers) can be designed using portions of the nucleic acid sequence flanking polymorphisms (e.g., SNPs or microsatellites) that are associated with risk of lung cancer. In another embodiment, the kit comprises one or more labeled nucleic acids capable of allele-specific detection of one or more specific polymorphic markers or haplotypes, and reagents for detection of the label. Suitable labels include, e.g. , a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label .
In particular embodiments, the polymorphic marker or haplotype to be detected by the reagents of the kit comprises one or more markers, two or more markers, three or more markers, four or more markers or five or more markers selected from the group consisting of the markers set forth in any one of Table 1, Table 2 and Table 3. In another embodiment, the marker or haplotype to be detected comprises at least one marker from the group of markers in strong linkage disequilibrium, as defined by values of r2 greater than 0.2, to a marker selected from the group consisting of rs6474412, rs215614 and rs4105144. In another embodiment, the marker or haplotype to be detected is selected from the group consisting of rs6474412, rs215614 and rs4105144.
In a preferred embodiment, the DNA template containing the SNP polymorphism is amplified by Polymerase Chain Reaction (PCR) prior to detection, and primers for such amplification are included in the reagent kit. In such an embodiment, the amplified DNA serves as the template for the detection probe and the enhancer probe.
In one embodiment, the DNA template is amplified by means of Whole Genome Amplification (WGA) methods, prior to assessment for the presence of specific polymorphic markers as described herein. Standard methods well known to the skilled person for performing WGA may be utilized, and are within scope of the invention . In one such embodiment, reagents for performing WGA are included in the reagent kit.
In a further aspect of the present invention, a pharmaceutical pack (kit) is provided, the pack comprising a therapeutic agent and a set of instructions for administration of the therapeutic agent to humans diagnostically tested for one or more variants of the present invention, as disclosed herein . The therapeutic agent can be a small molecule drug, an antibody, a peptide, an antisense or rnai molecule, or other therapeutic molecules. In one embodiment, an individual identified as a carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent. In one such embodiment, an individual identified as a homozygous carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent. In another embodiment, an individual identified as a non-carrier of at least one variant of the present invention is instructed to take a prescribed dose of the therapeutic agent.
In certain embodiments, the kit further comprises a set of instructions for using the reagents comprising the kit. In certain embodiments, the kit further comprises a collection of data comprising correlation data between the polymorphic markers assessed by the kit and susceptibility to lung cancer.
Therapeutic agents
The variants (markers and/or haplotypes) disclosed herein to confer increased risk of lung cancer can be useful for the identification of novel therapeutic targets for lung cancer. For example, genes containing, or in linkage disequilibrium with, one or more of these variants, or their products {e.g., CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B), as well as genes or their products that are directly or indirectly regulated by or interact with such variant genes or their products, can be targeted for the development of novel therapeutic agents for lung cancer. Therapeutic agents may comprise one or more of, for example, small non-protein and non-nucleic acid molecules, proteins, peptides, protein fragments, nucleic acids (DNA, RNA), PNA (peptide nucleic acids), or their derivatives or mimetics which can modulate the function and/or levels of the target genes or their gene products.
The nucleic acids and/or variants described herein, or nucleic acids comprising their
complementary sequence, may be used as antisense constructs to control gene expression in cells, tissues or organs. The methodology associated with antisense techniques is well known to the skilled artisan, and is for example described and reviewed in AntisenseDrug Technology: Principles, Strategies, and Applications, Crooke, ed ., Marcel Dekker Inc., New York (2001) . In general, antisense agents (antisense oligonucleotides) are comprised of single stranded oligonucleotides (RNA or DNA) that are capable of binding to a complimentary nucleotide segment. By binding the appropriate target sequence, an RNA-RNA, DNA-DNA or RNA-DNA duplex is formed. The antisense oligonucleotides are complementary to the sense or coding strand of a gene. It is also possible to form a triple helix, where the antisense oligonucleotide binds to duplex DNA.
Several classes of antisense oligonucleotide are known to those skilled in the art, including cleavers and blockers. The former bind to target RNA sites, activate intracellular nucleases (e.g., RnaseH or Rnase L), that cleave the target RNA. Blockers bind to target RNA, inhibit protein translation by steric hindrance of the ribosomes. Examples of blockers include nucleic acids, morpholino compounds, locked nucleic acids and methylphosphonates (Thompson, Drug
Discovery Today, 7 : 912-917 (2002)) . Antisense oligonucleotides are useful directly as therapeutic agents, and are also useful for determining and validating gene function, for example by gene knock-out or gene knock-down experiments. Antisense technology is further described in Lavery et al. , Curr. Opin. Drug Discov. Devel. 6: 561-569 (2003), Stephens et al., Curr. Opin. Mol. Ther. 5 : 118-122 (2003), Kurreck, Eur. J. Biochem. 270: 1628-44 (2003), Dias et al., Mol. Cancer Ter. 1 : 347-55 (2002), Chen, Methods Mol. Med. 75: 621-636 (2003), Wang et al., Curr. Cancer Drug Targets 1 : 177-96 (2001), and Bennett, Antisense Nucleic Acid Drug. Dev. 12 : 215- 24 (2002) .
In certain embodiments, the antisense agent is an oligonucleotide that is capable of binding to a particular nucleotide segment. Antisense nucleotides can be from 5-500 nucleotides in length, including 5-200 nucleotides, 5-100 nucleotides, 10-50 nucleotides, and 10-30 nucleotides. In certain preferred embodiments, the antisense nucleotides is from 14-50 nucleotides in length, includign 14-40 nucleotides and 14-30 nucleotides. All integer lengths from 5-500 are specifically contemplated for the present invention, as are all subranges of lengths. In certain preferred embodiments, the antisense nucleotides is from 14-50 nucleotides in length, includign 14-40 nucleotides and 14-30 nucleotides. In certain such embodiments, the antisense nucleotide is capable of binding to a nucleotide segment of the a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, and RAB4B.
The variants described herein can also be used for the selection and design of antisense reagents that are specific for particular variants. Using information about the variants described herein, antisense oligonucleotides or other antisense molecules that specifically target mRNA molecules that contain one or more variants of the invention can be designed. In this manner, expression of mRNA molecules that contain one or more variant of the present invention (i.e. certain marker alleles and/or haplotypes) can be inhibited or blocked. In one embodiment, the antisense molecules are designed to specifically bind a particular allelic form (i.e., one or several variants (alleles and/or haplotypes)) of the target nucleic acid, thereby inhibiting translation of a product originating from this specific allele or haplotype, but which do not bind other or alternate variants at the specific polymorphic sites of the target nucleic acid molecule. As antisense molecules can be used to inactivate mRNA so as to inhibit gene expression, and thus protein expression, the molecules can be used for disease treatment. The methodology can involve cleavage by means of ribozymes containing nucleotide sequences complementary to one or more regions in the mRNA that attenuate the ability of the mRNA to be translated . Such mRNA regions include, for example, protein-coding regions, in particular protein-coding regions corresponding to catalytic activity, substrate and/or ligand binding sites, or other functional domains of a protein .
The phenomenon of RNA interference (RNAi) has been actively studied for the last decade, since its original discovery in C. elegans (Fire et al., Nature 391 : 806-11 (1998)), and in recent years its potential use in treatment of human disease has been actively pursued (reviewed in Kim & Rossi, Nature Rev. Genet. 8: 173-204 (2007)) . RNA interference (RNAi), also called gene silencing, is based on using double-stranded RNA molecules (dsRNA) to turn off specific genes. In the cell, cytoplasmic double-stranded RNA molecules (dsRNA) are processed by cellular complexes into small interfering RNA (siRNA) . The siRNA guide the targeting of a protein-RNA complex to specific sites on a target mRNA, leading to cleavage of the mRNA (Thompson, Drug Discovery Today, 7 : 912-917 (2002)) . The siRNA molecules are typically about 20, 21, 22 or 23 nucleotides in length . Thus, one aspect of the invention relates to isolated nucleic acid molecules, and the use of those molecules for RNA interference, i.e. as small interfering RNA molecules (siRNA) . In one embodiment, the isolated nucleic acid molecules are 18-26 nucleotides in length, preferably 19-25 nucleotides in length, more preferably 20-24 nucleotides in length, and more preferably 21, 22 or 23 nucleotides in length.
Another pathway for RNAi-mediated gene silencing originates in endogenously encoded primary microRNA (pri-miRNA) transcripts, which are processed in the cell to generate precursor miRNA (pre-miRNA) . These miRNA molecules are exported from the nucleus to the cytoplasm, where they undergo processing to generate mature miRNA molecules (miRNA), which direct translational inhibition by recognizing target sites in the 3' untranslated regions of mRNAs, and subsequent mRNA degradation by processing P-bodies (reviewed in Kim & Rossi, Nature Rev. Genet 8: 173-204 (2007)) .
Clinical applications of RNAi include the incorporation of synthetic siRNA duplexes, which preferably are approximately 20-23 nucleotides in size, and preferably have 3' overlaps of 2 nucleotides. Knockdown of gene expression is established by sequence-specific design for the target mRNA. Several commercial sites for optimal design and synthesis of such molecules are known to those skilled in the art.
Other applications provide longer siRNA molecules (typically 25-30 nucleotides in length, preferably about 27 nucleotides), as well as small hairpin RNAs (shRNAs; typically about 29 nucleotides in length) . The latter are naturally expressed, as described in Amarzguioui et al.
{FEBS Lett. 579 : 5974-81 (2005)) . Chemically synthetic siRNAs and shRNAs are substrates for in vivo processing, and in some cases provide more potent gene-silencing than shorter designs (Kim et al., Nature Biotechnol. 23: 222-226 (2005); Siolas et al., Nature Biotechnol. 23: 227-231 (2005)) . In general siRNAs provide for transient silencing of gene expression, because their intracellular concentration is diluted by subsequent cell divisions. By contrast, expressed shRNAs mediate long-term, stable knockdown of target transcripts, for as long as transcription of the shRNA takes place (Marques et al., Nature Biotechnol. 23 : 559-565 (2006); Brummelkamp et al., Science 296: 550-553 (2002)) .
Since RNAi molecules, including siRNA, miRNA and shRNA, act in a sequence-dependent manner, the variants presented herein can be used to design RNAi reagents that recognize specific nucleic acid molecules comprising specific alleles and/or haplotypes (e.g., the alleles and/or haplotypes of the present invention), while not recognizing nucleic acid molecules comprising other alleles or haplotypes. These RNAi reagents can thus recognize and destroy the target nucleic acid molecules. As with antisense reagents, RNAi reagents can be useful as therapeutic agents (i.e., for turning off disease-associated genes or disease-associated gene variants), but may also be useful for characterizing and validating gene function (e.g., by gene knock-out or gene knockdown experiments) .
Delivery of RNAi may be performed by a range of methodologies known to those skilled in the art. Methods utilizing non-viral delivery include cholesterol, stable nucleic acid-lipid particle (SNALP), heavy-chain antibody fragment (Fab), aptamers and nanoparticles. Viral delivery methods include use of lentivirus, adenovirus and adeno-associated virus. The siRNA molecules are in some embodiments chemically modified to increase their stability. This can include modifications at the 2' position of the ribose, including 2'-0-methylpurines and 2'- fluoropyrimidines, which provide resistance to Rnase activity. Other chemical modifications are possible and known to those skilled in the art.
The following references provide a further summary of RNAi, and possibilities for targeting specific genes using RNAi : Kim & Rossi, Nat. Rev. Genet. 8: 173-184 (2007), Chen & Rajewsky, Nat. Rev. Genet. 8: 93-103 (2007), Reynolds, et al., Nat. Biotechnol. 22: 326-330 (2004), Chi et al., Proc. Natl. Acad. Sci. USA 100 : 6343-6346 (2003), Vickers et al., J. Biol. Chem. 278: 7108- 7118 (2003), Agami, Curr. Opin. Chem. Biol. 6: 829-834 (2002), Lavery, et al., Curr. Opin. Drug Discov. Devel. 6: 561-569 (2003), Shi, Trends Genet. 19: 9-12 (2003), Shuey et al., Drug Discov. Today 7: 1040-46 (2002), McManus et al., Nat. Rev. Genet. 3 : 737-747 (2002), Xia et al., Nat. Biotechnol. 20 : 1006-10 (2002), Plasterk et al., curr. Opin. Genet. Dev. 10: 562-7 (2000), Bosher et al., Nat. Cell Biol. 2: E31-6 (2000), and Hunter, Curr. Biol. 9 : R440-442 (1999) .
A genetic defect leading to increased predisposition or risk for development of a disease, such as lung cancer, or a defect causing the disease, may be corrected permanently by administering to a subject carrying the defect a nucleic acid fragment that incorporates a repair sequence that supplies the normal/wild-type nucleotide(s) at the site of the genetic defect. Such site-specific repair sequence may concompass an RNA/DNA oligonucleotide that operates to promote endogenous repair of a subject's genomic DNA. The administration of the repair sequence may be performed by an appropriate vehicle, such as a complex with polyethelenimine, encapsulated in anionic liposomes, a viral vector such as an adenovirus vector, or other pharmaceutical compositions suitable for promoting intracellular uptake of the adminstered nucleic acid. The genetic defect may then be overcome, since the chimeric oligonucleotides induce the
incorporation of the normal sequence into the genome of the subject, leading to expression of the normal/wild-type gene product. The replacement is propagated, thus rendering a permanent repair and alleviation of the symptoms associated with the disease or condition.
Methods of assessing probability of response to therapeutic agents, methods of monitoring progress of treatment and methods of treatment
As is known in the art, individuals can have differential responses to a particular therapy (e.g., a therapeutic agent or therapeutic method) . Pharmacogenomics addresses the issue of how genetic variations (e.g., the variants (markers and/or haplotypes) of the present invention) affect drug response, due to altered drug disposition and/or abnormal or altered action of the drug. Thus, the basis of the differential response may be genetically determined in part. Clinical outcomes due to genetic variations affecting drug response may result in toxicity of the drug in certain individuals (e.g., carriers or non-carriers of the genetic variants of the present invention), or therapeutic failure of the drug. Therefore, the variants of the present invention may determine the manner in which a therapeutic agent and/or method acts on the body, or the way in which the body metabolizes the therapeutic agent. Accordingly, in one embodiment, the presence of a particular allele at a polymorphic site or haplotype is indicative of a different response, e.g. a different response rate, to a particular treatment modality. This means that a patient diagnosed with lung cancer, and carrying a certain variant of the present invention (e.g. , the at-risk alleles of the invention) would respond better to, or worse to, a specific therapeutic, drug and/or other therapy used to treat the disease. Therefore, the presence or absence of the variant could aid in deciding what treatment should be used for the patient. For example, for a newly diagnosed patient, the presence of a variant may be assessed (e.g., through testing DNA derived from a blood sample, as described herein) . If the patient is positive for the variant, then the physician recommends one particular therapy, while if the patient is negative for the variant, then a different course of therapy may be recommended (which may include recommending that no immediate therapy, other than serial monitoring for progression of the disease, be performed) . Thus, the patient's carrier status could be used to help determine whether a particular treatment modality should be administered. The value lies within the possibilities of being able to diagnose disease, or susceptibility to disease, at an early stage, to select the most appropriate treatment, and provide information to the clinician about prognosis/aggressiveness of the disease in order to be able to apply the most appropriate treatment.
The treatment for lung cancer can in certain embodiments be selected from surgical treatment (surgical removal of tumor), radiation therapy and chemotherapy. It is contemplated that the markers described herein to be associated with lung cancer can be used to predict the efficacy of any of these particular treatment modules. In certain embodiments, the markers of the inventions, as described herein may be used to determine an appropriate combination of therapy, which can include any one, two or three of these treatment modules. In certain embodiments, the radiation therapy is brachytherapy. The agent useful for chemotherapy may be any chemical agent commonly used, or in development, as a chemotherapy agent, including, but not limited to, cisplatin, carboplatin, gemcitabine (4-amino-l-[3,3-difluoro-4-hydroxy-5- (hydroxymethyl) a tetrahydrofuran-2-yl]- lH-pyrimidin- 2-one), paclitaxel
((2a,4a,5 ,7 ,10 ,13a)-4, 10-bis(acetyloxy)-13-{[(2R,3S)-3-(benzoylamino)-2-hydroxy-3- phenylpropanoyl]oxy>-l,7-dihydroxy-9-oxo-5,20-epoxytax-ll-en-2-yl benzoate), docetaxel ((2R,3S)-/V-carboxy-3-phenylisoserine, /V-tert-butyl ester, 13-ester with 5, 20-epoxy-l, 2, 4, 7, 10, 13-hexahydroxytax-l l-en-9-one 4-acetate 2-benzoate), etoposide (4'-demethyl- epipodophyllotoxin 9-[4,6-0-(R)-ethylidene-beta-D-glucopyranoside], 4' -(dihydrogen phosphate)), vinorelbine (4-(acetyloxy)-6,7-didehydro-15- ((2R,6R,8S)-4-ethyl-l,3,6,7,8,9- hexahydro- 8-(methoxycarbonyl)-2,6-methano- 2H-azecino(4,3-0)indol-8-yl)-3-hydroxy- 16- methoxy-lmethyl,methylester, (2beta,3beta,4beta,5alpha,12R,19alpha) - aspidospermidine-3- carboxylic acid), and etoposide (4'-demethyl-epipodophyllotoxin 9-[4,6-0-(R)-ethylidene-beta-D- glucopyranoside] 4' -(dihydrogen phosphate)) . Chemotherapy agents may be used alone or in combination . In one embodiment, the agent targets an epidermal growth factor receptor. In certain such embodiments, the agent is gefitinib (Iressa; /V-(3-chloro-4-fluoro-phenyl)-7- methoxy-6-(3-morpholin-4-ylpropoxy)quinazolin-4-amine) or erlotinib (Tarceva; Λ/-(3- ethynylphenyl)-6,7-bis(2-methoxyethoxy)quinazolin-4-amine) . In certain other embodiments, the agent is angiogenesis inhibitor. Such inhibitors can for example be antibodies that inhibit the vascular endotheliar growth factor, such as Bevacizumab (Avastin) .
The present invention also relates to methods of monitoring progress or effectiveness of a treatment for lung cancer. This can be done by assessing for the absence or presence of at least one variant associated with lung cancer, as disclosed herein, or by monitoring expression of genes that are associated with the variants (markers and haplotypes) of the present invention .
Another aspect of the invention relates to methods of selecting individuals suitable for a particular treatment modality, based on their likelihood of developing particular complications or side effects of the particular treatment. It is well known that most therapeutic agents can lead to certain unwanted complications or side effects. Likewise, certain therapeutic procedures or operations may have complications associated with them. Complications or side effects of these particular treatments or associated with specific therapeutic agents may have a genetic component. It is therefore contemplated that selection of the appropriate treatment or therapeutic agent can in part be performed by determining the genotype of an individual, and using the genotype status of the individual to decide on a suitable therapeutic procedure or on a suitable therapeutic agent to treat the particular disease. It is therefore contemplated that the polymorphic markers of the invention can be used in this manner. In particular, the polymorphic markers of the invention can be used to determine whether administration of a particular therapeutic agent or treatment modality or method is suitable for the individual, based on estimating the likelihood that the individual will benefit from the administration of the particular therapeutic agent or treatment modality or method . Indiscriminate use of such a therapeutic agents or treatment modalities may lead to unnecessary and needless adverse complications.
In view of the foregoing, the invention provides a method of assessing an individual for probability of response to a therapeutic agent for preventing, treating, and/or ameliorating symptoms associated with lung cancer. In one embodiment, the method comprises: determining the identity of at least one allele of at least one polymorphic marker in a sample, e.g., a nucleic acid sample, obtained from the individual, wherein the at least one polymorphic marker is selected from polymorphic markers selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein the identity of the at least one allele of the at least one marker is indicative of a probability of a positive response to the therapeutic agent
In a further aspect, the markers of the present invention can be used to increase power and effectiveness of clinical trials. Thus, individuals who are carriers of at least one at-risk variant of the present invention may be more likely to respond favorably to a particular treatment. In one embodiment, individuals who carry at-risk variants for gene(s), or their gene product which a particular treatment (e.g., small molecule drug) is targeting, are more likely to be responders to the treatment. In certain embodiments, the treatment is targeting a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, and RAB4B. In another embodiment, individuals who carry at-risk variants associated with a gene which expression and/or function is altered by the at-risk variant, are more likely to be responders to a treatment modality targeting that gene, its expression or its gene product. This application can improve the safety of clinical trials, but can also enhance the chance that a clinical trial will demonstrate statistically significant efficacy, which may be limited to a certain sub-group of the population . Thus, one possible outcome of such a trial is that carriers of certain genetic variants, e.g., the markers and haplotypes of the present invention, are statistically significantly likely to show positive response to the therapeutic agent, i.e. experience alleviation of symptoms associated with lung cancer when taking the therapeutic agent or drug as prescribed.
Computer-implemented aspects
As understood by those of ordinary skill in the art, the methods and information described herein may be implemented, in all or in part, as computer executable instructions on known computer readable media. For example, the methods described herein may be implemented in hardware. Alternatively, the method may be implemented in software stored in, for example, one or more memories or other computer readable medium and implemented on one or more processors. As is known, the processors may be associated with one or more controllers, calculation units and/or other units of a computer system, or implanted in firmware as desired. If implemented in software, the routines may be stored in any computer readable memory such as in RAM, ROM, flash memory, a magnetic disk, a laser disk, or other storage medium, as is also known .
Likewise, this software may be delivered to a computing device via any known delivery method including, for example, over a communication channel such as a telephone line, the Internet, a wireless connection, etc., or via a transportable medium, such as a computer readable disk, flash drive, etc.
More generally, and as understood by those of ordinary skill in the art, the various steps described above may be implemented as various blocks, operations, tools, modules and techniques which, in turn, may be implemented in hardware, firmware, software, or any combination of hardware, firmware, and/or software. When implemented in hardware, some or all of the blocks, operations, techniques, etc. may be implemented in, for example, a custom integrated circuit (IC), an application specific integrated circuit (ASIC), a field programmable logic array (FPGA), a programmable logic array (PLA), etc.
When implemented in software, the software may be stored in any known computer readable medium such as on a magnetic disk, an optical disk, or other storage medium, in a RAM or ROM or flash memory of a computer, processor, hard disk drive, optical disk drive, tape drive, etc. Likewise, the software may be delivered to a user or a computing system via any known delivery method including, for example, on a computer readable disk or other transportable computer storage mechanism.
Figure 2 illustrates an example of a suitable computing system environment 100 on which a system for the steps of the claimed method and apparatus may be implemented. The computing system environment 100 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the method or apparatus of the claims. Neither should the computing environment 100 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment 100.
The steps of the claimed method and system are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with the methods or system of the claims include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The steps of the claimed method and system may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The methods and apparatus may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In both integrated and distributed computing environments, program modules may be located in both local and remote computer storage media including memory storage devices.
With reference to Figure 2, an exemplary system for implementing the steps of the claimed method and system includes a general purpose computing device in the form of a computer 110. Components of computer 110 may include, but are not limited to, a processing unit 120, a system memory 130, and a system bus 121 that couples various system components including the system memory to the processing unit 120. The system bus 121 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.
Computer 110 typically includes a variety of computer readable media . Computer readable media can be any available media that can be accessed by computer 110 and includes both volatile and nonvolatile media, removable and non-removable media . By way of example, and not limitation, computer readable media may comprise computer storage media and
communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by computer 110. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media . The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
Combinations of the any of the above should also be included within the scope of computer readable media.
The system memory 130 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 131 and random access memory (RAM) 132. A basic input/output system 133 (BIOS), containing the basic routines that help to transfer information between elements within computer 110, such as during start-up, is typically stored in ROM 131. RAM 132 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 120. By way of example, and not limitation, Figure 2 illustrates operating system 134, application programs 135, other program modules 136, and program data 137.
The computer 110 may also include other removable/non-removable, volatile/nonvolatile computer storage media . By way of example only, Figure 2 illustrates a hard disk drive 140 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 151 that reads from or writes to a removable, nonvolatile magnetic disk 152, and an optical disk drive 155 that reads from or writes to a removable, nonvolatile optical disk 156 such as a CD ROM or other optical media . Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive 141 is typically connected to the system bus 121 through a non-removable memory interface such as interface 140, and magnetic disk drive 151 and optical disk drive 155 are typically connected to the system bus 121 by a removable memory interface, such as interface 150.
The drives and their associated computer storage media discussed above and illustrated in Figure 2, provide storage of computer readable instructions, data structures, program modules and other data for the computer 110. In Figure 2, for example, hard disk drive 141 is illustrated as storing operating system 144, application programs 145, other program modules 146, and program data 147. Note that these components can either be the same as or different from operating system 134, application programs 135, other program modules 136, and program data 137. Operating system 144, application programs 145, other program modules 146, and program data 147 are given different numbers here to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer 20 through input devices such as a keyboard 162 and pointing device 161, commonly referred to as a mouse, trackball or touch pad . Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 120 through a user input interface 160 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB) . A monitor 191 or other type of display device is also connected to the system bus 121 via an interface, such as a video interface 190. In addition to the monitor, computers may also include other peripheral output devices such as speakers 197 and printer 196, which may be connected through an output peripheral interface 190.
The computer 110 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 180. The remote computer 180 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 110, although only a memory storage device 181 has been illustrated in Figure 2. The logical connections depicted in Figure 2 include a local area network (LAN) 171 and a wide area network (WAN) 173, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.
When used in a LAN networking environment, the computer 110 is connected to the LAN 171 through a network interface or adapter 170. When used in a WAN networking environment, the computer 110 typically includes a modem 172 or other means for establishing communications over the WAN 173, such as the Internet. The modem 172, which may be internal or external, may be connected to the system bus 121 via the user input interface 160, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 110, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation, Figure 2 illustrates remote application programs 185 as residing on memory device 181. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
Although the forgoing text sets forth a detailed description of numerous different embodiments of the invention, it should be understood that the scope of the invention is defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possibly embodiment of the invention because describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims defining the invention .
While the risk evaluation system and method, and other elements, have been described as preferably being implemented in software, they may be implemented in hardware, firmware, etc., and may be implemented by any other processor. Thus, the elements described herein may be implemented in a standard multi-purpose CPU or on specifically designed hardware or firmware such as an application-specific integrated circuit (ASIC) or other hard-wired device as desired, including, but not limited to, the computer 110 of Figure 2. When implemented in software, the software routine may be stored in any computer readable memory such as on a magnetic disk, a laser disk, or other storage medium, in a RAM or ROM of a computer or processor, in any database, etc. Likewise, this software may be delivered to a user or a diagnostic system via any known or desired delivery method including, for example, on a computer readable disk or other transportable computer storage mechanism or over a communication channel such as a telephone line, the internet, wireless communication, etc. (which are viewed as being the same as or interchangeable with providing such software via a transportable storage medium) .
Thus, many modifications and variations may be made in the techniques and structures described and illustrated herein without departing from the spirit and scope of the present invention . Thus, it should be understood that the methods and apparatus described herein are illustrative only and are not limiting upon the scope of the invention.
Accordingly, the invention relates to computer-implemented applications using the polymorphic markers and haplotypes described herein, and genotype and/or disease-association data derived therefrom. Such applications can be useful for storing, manipulating or otherwise analyzing genotype data that is useful in the methods of the invention. One example pertains to storing genotype information derived from an individual on readable media, so as to be able to provide the genotype information to a third party (e.g., the individual, a guardian of the individual, a health care provider or genetic analysis service provider), or for deriving information from the genotype data, e.g. , by comparing the genotype data to information about genetic risk factors contributing to increased susceptibility to the disease, and reporting results based on such comparison.
In certain embodiments, computer-readable media suitably comprise capabilities of storing (i) identifier information for at least one polymorphic marker or a haplotype, as described herein; (ii) an indicator of the identity (e.g., presence or absence) of at least one allele of said at least one marker, or a haplotype, in individuals with the disease; and (iii) an indicator of the risk associated with the marker allele or haplotype.
The markers and haplotypes described herein to be associated with increased susceptibility (increased risk) of lung cancer, are in certain embodiments useful for interpretation and/or analysis of genotype data. Thus in certain embodiments, determination of the presence of an at- risk allele for lung cancer, as shown herein, or determination of the presence of an allele at a polymorphic marker in LD with any such risk allele, is indicative of the individual from whom the genotype data originates is at increased risk of lung cancer. In one such embodiment, genotype data is generated for at least one polymorphic marker shown herein to be associated with lung cancer, or a marker in linkage disequilibrium therewith. The genotype data is subsequently made available to a third party, such as the individual from whom the data originates, his/her guardian or representative, a physician or health care worker, genetic counsellor, or insurance agent, for example via a user interface accessible over the internet, together with an interpretation of the genotype data, e.g., in the form of a risk measure (such as an absolute risk (AR), risk ratio (RR) or odds ratio (OR)) for the disease. In another embodiment, at-risk markers identified in a genotype dataset derived from an individual are assessed and results from the assessment of the risk conferred by the presence of such at-risk variants in the dataset are made available to the third party, for example via a secure web interface, or by other communication means. The results of such risk assessment can be reported in numeric form (e.g. , by risk values, such as absolute risk, relative risk, and/or an odds ratio, or by a percentage increase in risk compared with a reference), by graphical means, or by other means suitable to illustrate the risk to the individual from whom the genotype data is derived.
Nucleic acids and polypeptides
The nucleic acids and polypeptides described herein can be used in methods and kits of the present invention. An "isolated" nucleic acid molecule, as used herein, is one that is separated from nucleic acids that normally flank the gene or nucleotide sequence (as in genomic sequences) and/or has been completely or partially purified from other transcribed sequences (e.g., as in an RNA library) . For example, an isolated nucleic acid of the invention can be substantially isolated with respect to the complex cellular milieu in which it naturally occurs, or culture medium when produced by recombinant techniques, or chemical precursors or other chemicals when chemically synthesized. In some instances, the isolated material will form part of a composition (for example, a crude extract containing other substances), buffer system or reagent mix. In other circumstances, the material can be purified to essential homogeneity, for example as determined by polyacrylamide gel electrophoresis (PAGE) or column chromatography (e.g., HPLC) . An isolated nucleic acid molecule of the invention can comprise at least about 50%, at least about 80% or at least about 90% (on a molar basis) of all macromolecular species present. With regard to genomic DNA, the term "isolated" also can refer to nucleic acid molecules that are separated from the chromosome with which the genomic DNA is naturally associated . For example, the isolated nucleic acid molecule can contain less than about 250 kb, 200 kb, 150 kb, 100 kb, 75 kb, 50 kb, 25 kb, 10 kb, 5 kb, 4 kb, 3 kb, 2 kb, 1 kb, 0.5 kb or 0.1 kb of the nucleotides that flank the nucleic acid molecule in the genomic DNA of the cell from which the nucleic acid molecule is derived .
The nucleic acid molecule can be fused to other coding or regulatory sequences and still be considered isolated. Thus, recombinant DNA contained in a vector is included in the definition of "isolated" as used herein. Also, isolated nucleic acid molecules include recombinant DNA molecules in heterologous host cells or heterologous organisms, as well as partially or substantially purified DNA molecules in solution . "Isolated" nucleic acid molecules also encompass in vivo and in vitro RNA transcripts of the DNA molecules of the present invention . An isolated nucleic acid molecule or nucleotide sequence can include a nucleic acid molecule or nucleotide sequence that is synthesized chemically or by recombinant means. Such isolated nucleotide sequences are useful, for example, in the manufacture of the encoded polypeptide, as probes for isolating homologous sequences (e.g., from other mammalian species), for gene mapping (e.g., by in situ hybridization with chromosomes), or for detecting expression of the gene in tissue (e.g., human tissue), such as by Northern blot analysis or other hybridization techniques.
The invention also pertains to nucleic acid molecules that hybridize under high stringency hybridization conditions, such as for selective hybridization, to a nucleotide sequence described herein (e.g., nucleic acid molecules that specifically hybridize to a nucleotide sequence containing a polymorphic site associated with a marker or haplotype described herein) . Such nucleic acid molecules can be detected and/or isolated by allele- or sequence-specific hybridization (e.g., under high stringency conditions) . Stringency conditions and methods for nucleic acid hybridizations are well known to the skilled person (see, e.g., Current Protocols in Molecular Biology, Ausubel, F. et al, John Wiley & Sons, (1998), and Kraus, M. and Aaronson, S., Methods Enzymol., 200 : 546-556 (1991), the entire teachings of which are incorporated by reference herein .
The percent identity of two nucleotide or amino acid sequences can be determined by aligning the sequences for optimal comparison purposes (e.g., gaps can be introduced in the sequence of a first sequence) . The nucleotides or amino acids at corresponding positions are then compared, and the percent identity between the two sequences is a function of the number of identical positions shared by the sequences (i.e., % identity = # of identical positions/total # of positions x 100) . In certain embodiments, the length of a sequence aligned for comparison purposes is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, or at least 95%, of the length of the reference sequence. The actual comparison of the two sequences can be accomplished by well-known methods, for example, using a mathematical algorithm. A non-limiting example of such a mathematical algorithm is described in Karlin, S. and Altschul, S., Proc. Natl . Acad. Sci. USA, 90 : 5873-5877 (1993) . Such an algorithm is incorporated into the NBLAST and XBLAST programs (version 2.0), as described in Altschul, S. et al., Nucleic Acids Res., 25 : 3389-3402 (1997) . When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g ., NBLAST) can be used. See the website on the world wide web at ncbi. nlm .nih .gov. In one embodiment, parameters for sequence comparison can be set at score= 100, wordlength = 12, or can be varied (e.g., W=5 or W=20) . Another example of an algorithm is BLAT (Kent, W.J. Genome Res. 12 : 656-64 (2002)) .
Other examples include the algorithm of Myers and Miller, CABIOS (1989), ADVANCE and ADAM as described in Torellis, A. and Robotti, C, Comput. Appl. Biosci. 10 : 3-5 (1994); and FASTA described in Pearson, W. and Lipman, D., Proc. Natl. Acad. Sci. USA, 85 : 2444-48 (1988).
In another embodiment, the percent identity between two amino acid sequences can be accomplished using the GAP program in the GCG software package (Accelrys, Cambridge, UK) .
The present invention also provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid that comprises, or consists of, the nucleotide sequence of LD block C07, LD block C08 or LD block C19, or a nucleotide sequence comprising, or consisting of, the complement of the nucleotide sequence of LD block C07, LD block C08 or LD block C19, wherein the nucleotide sequence optionally comprises at least one polymorphic marker described herein.
The invention also provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid that comprises, or consists of, the nucleotide sequence of a gene selected from the group consisting of CHRNB3, CHRNA6, PDE1C, LSM5 AVL9 (KIAA0241), CYP2A6, CYP2A7, CYP2B7P1, CYP2A13, CYP2B6, or RAB4B, or a nucleotide sequence comprising, or consisting of, the complement of the nucleotide sequence of the gene, wherein the nucleotide sequence optionally comprises at least one polymorphic marker described herein.
The nucleic acid fragments are at least about 15, at least about 18, 20, 23 or 25 nucleotides, and can be 30, 40, 50, 100, 200, 500, 1000, 10,000 or more nucleotides in length. In a specific embodiment, the nucleic acid fragments are 15-400 nucleotides in length.
The invention further provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid comprising a nucleotide sequence of any of SEQ ID NOs: 1-737, each of which sequences comprise one of the polymorphic markers associated with lung cancer, as described herein . Such nucleic acid molecules, e.g., oligonucleotide probes, can be used in the manufacture of a diagnostic reagent for diagnosing and/or assessing susceptibility to lung cancer.
The nucleic acid fragments of the invention are used as probes or primers in assays such as those described herein . "Probes" or "primers" are oligonucleotides that hybridize in a base- specific manner to a complementary strand of a nucleic acid molecule. In addition to DNA and RNA, such probes and primers include polypeptide nucleic acids (PNA), as described in Nielsen, P. et al., Science 254: 1497-1500 (1991) . A probe or primer comprises a region of nucleotide sequence that hybridizes to at least about 15, typically about 20-25, and in certain embodiments about 40, 50 or 75, consecutive nucleotides of a nucleic acid molecule. In one embodiment, the probe or primer comprises at least one allele of at least one polymorphic marker or at least one haplotype described herein, or the complement thereof. In particular embodiments, a probe or primer can comprise 100 or fewer nucleotides; for example, in certain embodiments from 6 to 50 nucleotides, or, for example, from 12 to 30 nucleotides. In other embodiments, the probe or primer is at least 70% identical, at least 80% identical, at least 85% identical, at least 90% identical, or at least 95% identical, to the contiguous nucleotide sequence or to the complement of the contiguous nucleotide sequence. In another embodiment, the probe or primer is capable of selectively hybridizing to the contiguous nucleotide sequence or to the complement of the contiguous nucleotide sequence. Often, the probe or primer further comprises a label, e.g ., a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label .
The nucleic acid molecules of the invention, such as those described above, can be identified and isolated using standard molecular biology techniques well known to the skilled person. The amplified DNA can be labeled (e.g., radiolabeled, fluorescently labeled) and used as a probe for screening a cDNA library derived from human cells. The cDNA can be derived from mRNA and contained in a suitable vector. Corresponding clones can be isolated, DNA obtained following in vivo excision, and the cloned insert can be sequenced in either or both orientations by art- recognized methods to identify the correct reading frame encoding a polypeptide of the appropriate molecular weight. Using these or similar methods, the polypeptide and the DNA encoding the polypeptide can be isolated, sequenced and further characterized .
Antibodies
The invention also provides antibodies which bind to an epitope comprising either a variant amino acid sequence (e.g., comprising an amino acid substitution) encoded by a variant allele or the reference amino acid sequence encoded by the corresponding non-variant or wild-type allele. For example, if a variant allele encodes an amino acid sequence comprising the epitope
CYSTWFEH, wherein the T is an amino acid substitution from the native or wild-type A, the antibody of the invention specifically binds to either the epitope CYSTWFEH or CYSAWFEH. The term "antibody" as used herein refers to immunoglobulin molecules and immunologically active portions of immunoglobulin molecules, i.e. , molecules that contain antigen-binding sites that specifically bind an antigen . A molecule that specifically binds to an epitope is a molecule that binds to that epitope, but does not substantially bind other epitopes in a sample, e.g., a biological sample, which naturally contains the epitope. Examples of immunologically active portions of immunoglobulin molecules include F(ab) and F(ab fragments which can be generated by treating the antibody with an enzyme such as pepsin. The antibody can be polyclonal or monoclonal. The term "monoclonal antibody" or "monoclonal antibody
composition", as used herein, refers to a population of antibody molecules that contain only one species of an antigen binding site capable of immunoreacting with a particular epitope of a polypeptide of the invention. A monoclonal antibody composition thus typically displays a single binding affinity for a particular polypeptide of the invention with which it immunoreacts.
Polyclonal antibodies can be prepared as described above by immunizing a suitable subject with a desired immunogen, e.g. , polypeptide of the invention or a fragment thereof. The antibody titer in the immunized subject can be monitored over time by standard techniques, such as with an enzyme linked immunosorbent assay (ELISA) using immobilized polypeptide. If desired, the antibody molecules directed against the polypeptide can be isolated from the mammal (e.g., from the blood) and further purified by well-known techniques, such as protein A
chromatography to obtain the IgG fraction. At an appropriate time after immunization, e.g. , when the antibody titers are highest, antibody-producing cells can be obtained from the subject and used to prepare monoclonal antibodies by standard techniques, such as the hybridoma technique originally described by Kohler and Milstein, Nature 256:495-497 (1975), the human B cell hybridoma technique (Kozbor et al., Immunol. Today 4: 72 (1983)), the EBV-hybridoma technique (Cole et al., Monoclonal Antibodies and Cancer Therapy, Alan R. Liss,1985, Inc., pp. 77-96) or trioma techniques. The technology for producing hybridomas is well known (see generally Current Protocols in Immunology (1994) Coligan et al., (eds.) John Wiley & Sons, Inc., New York, NY) . Briefly, an immortal cell line (typically a myeloma) is fused to lymphocytes (typically splenocytes) from a mammal immunized with an immunogen as described above, and the culture supernatants of the resulting hybridoma cells are screened to identify a hybridoma producing a monoclonal antibody that binds a polypeptide of the invention .
Any of the many well known protocols used for fusing lymphocytes and immortalized cell lines can be applied for the purpose of generating a monoclonal antibody to a polypeptide of the invention (see, e.g. , Current Protocols in Immunology, supra; Galfre et al. , Nature 266: 55052 (1977); R.H. Kenneth, in Monoclonal Antibodies: A New Dimension In Biological Analyses, Plenum Publishing Corp., New York, New York (1980); and Lerner, Yale J. Biol. Med. 54: 387-402 (1981)) . Moreover, the ordinarily skilled worker will appreciate that there are many variations of such methods that also would be useful.
Alternative to preparing monoclonal antibody-secreting hybridomas, a monoclonal antibody to a polypeptide of the invention can be identified and isolated by screening a recombinant combinatorial immunoglobulin library (e.g., an antibody phage display library) with the polypeptide to thereby isolate immunoglobulin library members that bind the polypeptide. Kits for generating and screening phage display libraries are commercially available (e.g., the Pharmacia Recombinant Phage Antibody System, Catalog No. 27-9400-01; and the Stratagene SurfZAP™ Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S. Patent No. 5,223,409; PCT Publication No. WO 92/18619; PCT Publication No. WO 91/17271; PCT Publication No. WO 92/20791; PCT Publication No. WO 92/15679; PCT Publication No. WO 93/01288; PCT Publication No. WO 92/01047; PCT
Publication No. WO 92/09690; PCT Publication No. WO 90/02809; Fuchs et al., Bio/Technology 9 : 1370-1372 (1991); Hay et al. , Hum. Antibod. Hybridomas 3 : 81-85 (1992); Huse et al. , Science 246: 1275-1281 (1989); and Griffiths et al., EMBO J. 12 : 725-734 (1993).
Additionally, recombinant antibodies, such as chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, which can be made using standard recombinant DNA techniques, are within the scope of the invention . Such chimeric and humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art.
In general, antibodies of the invention (e.g. , a monoclonal antibody) can be used to isolate a polypeptide of the invention by standard techniques, such as affinity chromatography or immunoprecipitation . A polypeptide-specific antibody can facilitate the purification of natural polypeptide from cells and of recombinantly produced polypeptide expressed in host cells.
Moreover, an antibody specific for a polypeptide of the invention can be used to detect the polypeptide (e.g. , in a cellular lysate, cell supernatant, or tissue sample) in order to evaluate the abundance and pattern of expression of the polypeptide. Antibodies can be used diagnostically to monitor protein levels in tissue as part of a clinical testing procedure, e.g. , to, for example, determine the efficacy of a given treatment regimen. The antibody can be coupled to a detectable substance to facilitate its detection . Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials,
bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125I, 131I, 35S or 3H.
Antibodies may also be useful in pharmacogenomic analysis. In such embodiments, antibodies against variant proteins encoded by nucleic acids according to the invention, such as variant proteins that are encoded by nucleic acids that contain at least one polymorpic marker of the invention, can be used to identify individuals that require modified treatment modalities.
Antibodies can furthermore be useful for assessing expression of variant proteins in disease states, such as in active stages of lung cancer, or in an individual with a predisposition to a disease related to the function of the protein, in particular lung cancer. Antibodies specific for a variant protein of the present invention that is encoded by a nucleic acid that comprises at least one polymorphic marker as described herein can be used to screen for the presence of the variant protein, for example to screen for a predisposition to lung cancer as indicated by the presence of the variant protein.
Antibodies can be used in other methods. Thus, antibodies are useful as diagnostic tools for evaluating proteins, such as variant proteins of the invention, in conjunction with analysis by electrophoretic mobility, isoelectric point, tryptic or other protease digest, or for use in other physical assays known to those skilled in the art. Antibodies may also be used in tissue typing . In one such embodiment, a specific variant protein has been correlated with expression in a specific tissue type, and antibodies specific for the variant protein can then be used to identify the specific tissue type.
Subcellular localization of proteins, including variant proteins, can also be determined using antibodies, and can be applied to assess aberrant subcellular localization of the protein in cells in various tissues. Such use can be applied in genetic testing, but also in monitoring a particular treatment modality. In the case where treatment is aimed at correcting the expression level or presence of the variant protein or aberrant tissue distribution or developmental expression of the variant protein, antibodies specific for the variant protein or fragments thereof can be used to monitor therapeutic efficacy.
The present invention further relates to kits for using antibodies in the methods described herein . This includes, but is not limited to, kits for detecting the presence of a variant protein in a test sample. One preferred embodiment comprises antibodies such as a labelled or labelable antibody and a compound or agent for detecting variant proteins in a biological sample, means for determining the amount or the presence and/or absence of variant protein in the sample, and means for comparing the amount of variant protein in the sample with a standard, as well as instructions for use of the kit. The present invention will now be exemplified by the following non-limiting examples.
EXAMPLE 1
To search for common variants associating with smoking behavior we performed meta-analyses of GWA studies, mainly using samples of European ancestry from the ENGAGE consortium (www.euengage.org), focusing on two smoking phenotypes: CPD and smoking initiation. The smoking initiation analysis was performed with a total of 30,431 ever-smokers and 16,050 never-smokers, using data from 12 GWA studies, Corogene, deCODE, EGPUT, ERF, NFBC, KORA, NTR-NESDA, Rotterdam, SUSOD, TwinUK and WTCCC-CAD. For CPD we combined data from the same studies and the NL-BLC with a total of 31,266 subjects. Information on the meta-analysis studies for CPD and smoking initiation is provided in Table 4, and in the Methods. After genomic control correction of each component study, we combined association data for ~2, 500, 000 imputed and genotyped autosomal SNPs with a fixed-effects additive meta-analysis using the inverse-variance method for CPD and smoking initiation . QQ-plots for CPD, excluding markers in the 15q25 region, displayed only modest inflation of the x2-test statistic (( GC = 1.02). In addition to the 15q25 locus previously described, SNPs at two loci, 19ql3 and 7pl4, reached genome-wide significance for CPD (P<5- 10~8) in the meta-analysis data. The QQ-plot for smoking initiation displayed weak inflation of the x2-test statistic ( GC= 1.03) and no locus reached GWS.
We selected 15 regions for smoking initiation totaling 277 SNPs (Table 5) and 14 regions totaling 443 SNPs for CPD (Table 6), for in silico replication in samples from the Tobacco and Genetics (TAG) and the Oxford-Glaxo Smith Kline (Ox-GSK) consortia . Sample descriptions and their study results are described elsewhere (see OX-GSK and TAG Consortium papers) . In the case of smoking initiation, the regions were identified by sequential selection among SNPs with P<0.001 that had been ordered by increasing p-value, skipping over markers in already selected regions, as determined by visual inspection of the LD displayed in the UCSC Genome Browser. For CPD we selected 14 regions in the same manner, and included a region on chromosome 8pl l based on large number of SNPs exhibiting suggestive associations with CPD, strong candidacy of region genes (encoding nicotine acetylcholine receptor subunits a6 and β3 (CHRNA6 and CHRNB3)), and prior suggestive evidence for association between SNPs within this region and ND3"4.
In addition to the 15q25 locus, three novel loci, 7pl4, 8pll and 19ql3, were GWS for CPD after combining the results from the ENGAGE meta-analysis set with those of TAG and OX-GSK (Table 7, Figure 1, and Table 8) . No GWS associations for the selected smoking initiation regions were observed in the combined analysis of the meta-analysis and the in silico data (Table 9), although many markers gave association signal that is close to GWS. For further confirmation of the CPD association signals at the 7pl4, 8pl l and 19ql3 loci, selected markers from these regions were genotyped in additional samples (n=9,040) from Iceland, Australia, Denmark, Germany and Spain (Table 7) . Markers at 8pl l and the 19ql3 loci had effects in the same directions, but not the marker on 7pl4 (Table 7) . After combining these data with ENGAGE results and the in silico replication the 8pll and the 19q l3 loci remained GWS but not the 7pl4 locus (Table 7) .
Rs6474412 on chromosome 8pllis located about 2.1 kb from the 5' end of the β3 nicotinic acetylcholine receptor subunit gene (CHRNB3), and belongs to a group of highly correlated SNPs, that includes two SNPs in exons of CHRNB3: a synonymous SNP (rs4593), and the only known non-synonymous SNP in CHRNB3 (rs4952)3. Although the CHRNB3 gene is implicated by the location of the associating SNPs, these markers could be tagging variation elsewhere within the LD block that also contains the a6 nicotinic acetylcholine receptor subunit gene (CHRNA6) (Figure 1) .
Nine different nicotinic cholinergic receptor subunits (α2-α7, β2-β4) are expressed in the human brain, and they combine with each other in diverse patterns to form various types of functional pentameric receptors. The different receptor subtypes are distinguished by subunit composition and sensitivity to nicotine12. Involvement of CHRNA6 and CHRNB3 receptor subunits in nicotine- induced dopamine-release is indicated in rodent studies13. Neither CHRNA6 nor CHRNB3 are expressed in lung tissue14.
The CPD associated markers on chromosome 19ql3 are located in a region harboring CYP2A6, which encodes CYP2A6, an enzyme that plays a major role in the oxidation of nicotine in human liver microsomes, as well as several other genes and pseudogenes belonging to the CYP gene family (Figure 1) . A number of sequence variants in or near CYP2A6 that reduce CYP2A6's enzymatic activity have beeln identified15. For some of these variants, effects on smoking behavior have been suggested15. In the present study, the most significant association in the region was observed with rs4105144. This SNP is in LD with CYP2A6*2 (rsl801272) ( ^=0.13 and D'= 1.0 in the HapMap CEU samples) and the CYP2A6*2 reduced function allele is only found on the background of rs4105144-C which associates with reduced smoking quantity. Although the effect of rs4105144 (0.41±0.06) is smaller than that of rsl801272 (0.68±0.18)(Table 7), its association is more significant (lower P value) because of higher minor allele frequency. This suggests that rs4105144-C may be tagging many reduced function variants. The second most significant association in the region was with rs7937, in the untranslated 3' end of the RAB4B gene, which is in LD with rs4105144 ( ^=0.32, D'=0.82 in the HapMap CEU samples) . The third most significant association in the region was with rs7260329 that is almost independent of rs4105144 ( ^=0.0064, D'=0.091 in the HapMap CEU samples) . Rs7260329 is an intronic SNP in CYP2B6, but its product converts nicotine to cotinine with about 10% of the catalytic activity of the CYP2A6 enzyme, and also metabolizes several drugs of abuse, and buproprion, an atypical antidepressant also used as a smoking cessation aid15. The CYP2B6 levels in the human brain are higher than those of CYP2A6 and are altered in smokers and alcoholics15"16. We next assessed the SNPs from the novel regions associating with CPD for association with Nicotine Dependence (ND), defined as a score of four or higher on the Fagerstrom Test for Nicotine Dependence (FTND), or endorsement of at least three of the seven Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) criteria7 (See also Methods) . Allele frequencies for 1,979 Icelandic (deCODE) and 835 Dutch (NTR-NESDA) ND cases were compared to 36,202 Icelandic and 611 Dutch population controls. SNPs on chromosome 8pl l, and chromosome 7pl4 associated nominally with ND, but none of the SNPs on chromosome 19q l3 (Table 10) .
We directly genotyped selected markers from the 7pl4, 8pl l and 19ql3 regions for association with LC (2,019 cases and 40,509 controls) in samples of European ancestry. The LC data were also combined with summary-level data from the publicly available GWA dataset on lung cancer (2,518 case and 1,921 controls) from the International Agency for Research on Cancer (IARC) (Table 11) . Nominally significant associations with LC were observed for rs6474412-T on 8pl l (OR = 1.12, 95%CI : 1.05-1.20, P=0.00060), rs215614-G on 7pl4 (OR = 1.07, 95%CI : 1.02- 1.13, P=0.011), and rs7260329-G and rs4105144-C on 19q l3 (OR = 1.06, 95%CI : 1.00-1.12, P=0.041 and (OR = 1.09, 95%CI : 1.00-1.18, P=0.040) (Table 11) . As for the effect on CPD (Table 7) the effects of these variants on LC is substantially weaker than that of the 15q25 variants (OR = 1.31, P= 1.5 10"8)7_9(Table 11), warranting further analysis in additional sample sets. The potential effect of rs7260329-G and rs4105144-C on LC, is interesting in light of the fact that CYP2A6 gene product activates procarcinogenic nitrosamines15.
Methods
Written informed consent was obtained from all subjects in the populations from 11 countries (Australia, Austria, Denmark, Estonia, Finland, Germany, Iceland, the Netherlands, New Zealand, Spain, and United Kingdom) . Inclusion in the study required the availability of genotypes from either GWA studies or follow-up genotyping of selected SNPs in additional subjects. All subjects are of European descent. The sample sizes are listed in Table 4, for each of the samples used in the study. For the ENGAGE meta-analysis of CPD data for 31,266 smokers were utilized, and for the meta-analysis of SI the number of cases and controls were 30,431 and 16,050, respectively. A brief description of each sample follows (the NLBLC sample that participated in the CPD meta analysis is described with the lung cancer and peripheral arterial disease samples) .
Description of individual ENGAGE CPD and smoking initiation samples COROGENE: Corogene controls are selected as population controls for CAD cases from the National Finrisk 1997, 2002, and 2007 surveys (More information at
http://www.ktl.fi/portal/english/research people programs/health_promotion_and_chronic_ disease_prevention/units/chronic_disease_epidemiology_unit/the_national_finrisk_study ) . The number of individuals that smoked regularly was 554 and 610 had never smoked. The number of cigarettes smoked per day was available for 353 individuals with mean of 16.3 (9.6) .The mean age in the Corogene controls was 57.5 (11.0) and 56 % of the individuals were males. EGPUT : The Estonian cohort is from the population-based biobank of the Estonian Genome Project of University of Tartu (EGPUT) . The project is conducted according to Estonian Gene Research Act and all participants have signed the broad informed consent (www.geenivaramu .ee ref23). Cohort size is 37,000, from 18 years of age and up which reflects closely the age distribution in the Estonian population, 33% male, 67% female, 83% Estonians, 14% Russians, 3% other. Subjects are recruited by the general practitioners (GP) and physicians in the hospitals were randomly selected from individuals visiting GP offices or hospitals24. Computer Assisted Personal interview (CAPI) is filled during 1-2 hours at doctors office including personal data (place of birth, place(s) of living, nationality etc.), genealogical data (family history, four generations), educational and occupational history, lifestyle data (physical activity, dietary habits, smoking, alcohol consumption, women ' s health, quality of life), also anthropometric and physiological measurements are taken . For the current study GWAS was performed on 1,019 selected randomly from all over the country. The smoking quantity was determined from the following questions: "If you have ever smoked how old were you when you started to smoke regularly?"; "How often and how much have you smoked in last 12 months?"; "How many years have you smoked?"; "If you have changed your smoking habits then how?"; "How long have you smoked so?" and "How many hours per day do you spend in a smoking area?". Smoking quantity was available for 506 individuals (325 current smokers and 181 former smokers) . The cohort mean age was 42.7 (SD 14.9) years and included 327 (64.6%) males and 179 (35.4%) females. Decode: The Icelandic cigarette smoking data were described in detail previously7, and additional subjects were characterized in the same way. Altogether we included data for 15,310 smokers and 6,077 never smokers who have been genotyped on a chip containing the Illumina 317K set of SNPs in one of several GWA studies conducted by deCODE Genetics. Of these 10,995 smokers were included in our previous study of CPD7. These studies were approved by the Data Protection Commission of Iceland and the National Bioethics Committee of Iceland . Personal identifiers associated with phenotypic information and blood samples were encrypted using a third-party encryption system as previously described25. In addition 4,859 Icelandic subjects, were genotyped for replication using a single-track assay (Nanogen - Centaurus) .
ERF: This is a family-based cohort study that is embedded in the Genetic Research in Isolated Populations (GRIP) program in the South West of the Netherlands26. The aim of this program was to identify genetic risk factors in the development of complex disorders. For the ERF study, 22 families that had at least five children baptized in the community church between 1850-1900 were identified with the help of genealogical records. All living descendants of these couples and their spouses were invited to take part in the study (N~4,700) . Data collection started in June 2002 and was finished in February 2005. 2,923 successfully completed the questionnaire.
Females constituted 55% of this sample and average age was 50 years.
Genmets/FTC: The Finnish Twin Cohort includes nationwide samples of twins follow-up longitudinally, and forms a part of the GenomEUtwin project, in which female monozygotic pairs were genotyped. DNA samples from one member of each monozygotic twin pair were used for genotyping27. The Finnish twins were unselected with respect to disease status, and had participated in several waves of data collection in which smoking behaviors have been asked as a part of larger surveys of health, health habits and other health-related factors. Details of the data collection are available elsewhere28"29. The female twins came from the older Finnish Twin Cohort (questionnaire assessments in 1975, 1981 and 1990) and from the Finntwin l6 sample (surveys as young adults was used for smoking assessments) . Because of the low number of subjects in the FTC cohort we pooled the data with Health 2000 dataset. Health 2000 is a large Finnish cross-sectional health examination survey. It includes a total of 8,028 subjects aged 30 or over and is a nationally representative sample of adult Finnish population . Here, we studied a subcohort of 2,124 individuals, GenMetS, selected for GWA study on metabolic syndrome. Cases were selected according to the IDF Worldwide Definition of the Metabolic Syndrome
(http://www. idf.org/home/index. cfm?node= 1429) . Controls were selected for not carrying the trait. For the cigarettes per day measure for the twins, mean of the two measures was used if both had the information . We had smoking information for 1996 individuals of which 488 had smoked regularly and 1,508 had never smoked. The continuous smoking information as cigarettes per day (CPD) was available for 502 individuals with mean of 15.4 (sd = 9.5) . The mean age in the pooled dataset was 51.4 (11.7) and 51.3 % of the dataset were females. For a subset of the GenMetS subjects (N=485) serum cotinine levels were available. The cotinine concentration (ng/ml) was determined from the serum using liquid-phase radioimmunoassay methodology (Nicotine Metabolite DOUBLE ANTIBODY kit, Diagnostic Products Corporation, Los Angeles, USA) .
KORA: All participants from KORA study are of white European ancestry. Briefly, KORA S4 and KORA F3 epidemiological cohorts represent independent samples of unrelated subjects from the general population from the Augsburg Area (Southern Germany) . KORA F3 was a follow-up examination in 2004/05 of KORA S3 individuals recruited in 1994-1995, where as individuals in KORA S4 were recruited in 1999-2001. From KORA F3 survey (full cohort n = 3,006), 1,644 individuals between 35 to 79 years were selected for Genotyping on Affymetrix 500K30. From KORA S4 survey (full cohort n = 4261), 1,814 individuals between 25 to 74 years were selected for Genotyping on Affymetrix 1000K.
NFBC (Northern Finnish Birth Cohort of 1966): Mothers expected to give birth in the two northern provinces of Oulu and Lapland in 1966 were enrolled in NFBC1966 (n = 12,058 live births)34. At the 31-year clinical examination (n = 5,654) and DNA was also extracted from the blood samples provided at this time. Of the genotyped individuals 3,299 had smoked regularly and 1,896 had never smoked. The continuous smoking measured as cigarettes per day (CPD) was available for 2,233 individuals having mean CPD of 12.4 (7.9) . The sex distribution in the NFBC1966 was 47.8 % males and 52.2 % females.
NTR-NESDA: The sample comes from two large-scale longitudinal studies: the Netherlands Study of Depression and Anxiety (NESDA)26 and the Netherlands Twin Registry (NTR)27. NESDA and NTR studies were approved by the Central Ethics Committee of the VU University Medical Center Amsterdam. The GWA sample consisted of 1,777 participants from the NTR and 1,763 participants from NESDA31. The mean age of the participants was 43.8 years (SD 13.4) and 65.7% of the sample was female. For participants of the NTR data longitudinal survey data from 7 waves of data collection (1991-2004) were used to determine smoking behavior. For participants from NESDA, data on smoking behavior were collected during a clinical interview between 2004 and 200726. The total sample consisted of 1,207 never smokers and 2,236 ever smokers.
Rotterdam: The Rotterdam Study was planned and designed in the early 1990s as a longitudinal study investigating the incidence and progression of diseases in the elderly. From 1991 to 1995 all inhabitants of Ommoord, a district of Rotterdam in the Netherlands, who were 55 years or older, were invited to participate in this study. Of 10,275 eligible individuals, 7,983 agreed to participate (78%) . In 1999, 3,011 participants (out of 4,472 invitees) who had become 55 years of age or moved into the study district since the start of the study were added to the cohort32. The Rotterdam Study has been approved by the institutional review board (Medical Ethics Committee) of the Erasmus Medical Center and by the review board of the Netherlands Ministry of Health, Welfare and Sports. All participants provided written informed consent. The current analysis included 6,234 participants for whom genotyping was successful and information on smoking behavior was available. 3,610 participants reported to smoke or have smoked in the past while 2,624 participants were never smokers. The mean age was 67.9 years (SD - 8.81) and 60% were female.
SORBS: All subjects are part of a sample from an extensively phenotyped isolated population from Eastern Germany, the Sorbs. The Sorbs are of Slavonic origin, and have lived in ethnic isolation among the Germanic majority during the past 1, 100 years. Today, the Sorbian speaking, Catholic minority comprises approximately 15,000 full-blooded Sorbs resident in about 10 villages in rural Upper Lusatia (Oberlausitz), Eastern Saxony. Smoking habits were assessed in a standardized interview. Subjects were asked "Do you smoke or have you ever smoked?, If yes, how many cigarettes per day do/did you smoke on average (on most days) and for how many years ?" At present, more than 1,000 Sorbian individuals are enrolled in the study. 913 subjects (321 smokers and 592 never-smokers) were available for the present study. The smokers (208 males, 113 women) had a mean age of 42.77 (± 18.2) years, and the never- smokers (162 males, 430 females) had a mean age of 47.97 (± 18.75) years.
TWINS UK: The cohort (www.twinsuk.ac.uk) is an adult twin British registry shown to be representative of singleton populations and the United Kingdom population 33. A total of 924 females with smoking phenotype were included in the analysis. The mean age of the TwinsUK cohort was 53.73 (22-80). Ethics approval was obtained from the Guy's and St. Thomas' Hospital Ethics Committee. Written informed consent was obtained from every participant to the study. The study design and genotyping methodology is described in detail elsewhere34.
WTCCC-CAD: Detailed descriptions of the Wellcome Trust Case Controls Consortium Study data have already been provided elsewhere, and the CAD cases are European Caucasians who had a validated history of either myocardial infarction (MI) or coronary revascularisation (coronary artery bypass surgery or percutaneous coronary angioplasty) before their 66th birthday . The were recruited from April 1998 to November 2003 on a national basis35.
Samples genotyped for individual markers
AUS: The Australian sample took part in the single SNP assay replication. Data obtained from 3264 Australian subjects (49% women), 18-88 years of age (mean : 45; SD: 11 years) were used as one of the replication samples. Subjects were participants in either the Australian Nicotine Addiction Genetics (NAG) or a community-based (BigSib) family study. Families chosen for both studies were identified from two cohorts of the Australian Twin Panel, which included spouses of the older of these two cohorts. The NAG families were identified through heavy cigarette smoking index cases, and the BigSib families were comprised of families ascertained through the Australian Twin Panel selected for five or more offspring sharing both biological parents. The ancestry of the Australian samples is predominantly Anglo-Celtic or northern European (>90%) . The same assessment protocol was used for both the NAG and BigSib studies36. Clinical data were collected using a computer-assisted telephone diagnostic interview (CATI), and adaptation of the Semi-Structured Assessment for the Genetics of Alcoholism
(SSAGA)37"38 for telephone administration . The tobacco section of the CATI was derived from the Composite International Diagnostic Interview (CIDI)39 and incorporated standard FTND, DSM- IIR, and DSM-IV assessments of nicotine dependence. It also included a detailed history of cigarette and other tobacco use, including quantity and frequency of use for current, most recent, and heaviest period of use. The measure examined for the purposes of this study was the number of cigarettes smoked per day, during heaviest period of use.
All data-collection procedures were approved by institutional review boards at Washington University in the United States, and the Queensland Institute of Medical Research in Australia.
DCLST: The Danish Lung Cancer Screening Trial (DCLST)40 participated in the single-SNP assay replications for CPD. DLCST is a randomised 5-year trial comparing the effect of annual screening with low dose CT on the mortality of lung cancer, with no genetic screening in the control arm. Lung function tests are performed annually and information on smoking exposure recorded in all participants. Individuals volunteered for the study in response to advertisements in local and regional free newspapers and weeklies. Participants were current or former smokers of both sexes at an age between 50-70 years at inclusion and with a smoking history of more than 20 pack years. Participants had to be able to climb 2 flights of stairs (around 36 steps) without pausing . FEV1 was at least 30% of predicted normal . Ineligible were those applicants with body weight above 130 kg or previous treatment for lung cancer, breast cancer, malignant melanoma or hypernephroma . Individuals with a history of any other cancer within 5 years or tuberculosis within 2 years or any serious illness that would shorten life expectancy to less than 10 years were also excluded.
GER: Unrelated community-based volunteers of German descent (i.e., both parents German) were randomly selected from the general population of Munich, Germany, and contacted by mail. To exclude subjects with central neurological diseases and psychotic disorders or subjects who had first-degree relatives with psychotic disorders, several screenings were conducted before the volunteers were enrolled in the study. First, subjects who responded were initially screened by phone for the absence of neuropsychiatric disorders. Second, detailed medical and psychiatric histories were assessed for both themselves and their first-degree relatives by using a semi- structured interview. Third, if no exclusion criteria were fulfilled, they were invited to a comprehensive interview including the Structured Clinical Interview for DSM-IV (SCID I and SCID II) to validate the absence of any lifetime psychotic disorder. Additionally, the Family History Assessment Module was conducted to exclude psychotic disorders among their first- degree relatives. Furthermore, a neurological examination was conducted to exclude subjects with subjects with current CNS impairment. In the case that the volunteers were older than 60 years, the Mini Mental Status Test was performed to exclude subjects with possible cognitive impairment.
Lung Cancer and Peripheral Arterial Disease
The case-control samples utilized for testing for association with the smoking-related diseases LC and PAD were included in our prior study7, with the addition of LC samples from Denver and a the Danish PAD sample. The NLBLC sample was also included in the GWA study of CPD. All samples included in the present study are described below.
NLBLC (The Nijmegen Lung and Bladder Cancer study): This sample set is comprised of samples, previously described in studies of lung and bladder cancer. The Dutch series consists of 3 groups: population controls, patients with urinary bladder cancer, and patients with lung cancer. The lung cancer cases are Population controls: the 1,832 population controls (46% males) were recruited within a project entitled "Nijmegen Biomedical Study" (NBS) . The details of this study were reported previously40. Briefly, this is a population-based survey conducted by the Department of Epidemiology and Biostatistics and the Department of Clinical Chemistry of the Radboud University Nijmegen Medical Centre (RUNMC), in which 9,371 individuals participated from a total of 22,500 age- and sex stratified, randomly selected inhabitants of Nijmegen . Control individuals from the NBS were invited to participate in a study on gene- environment interactions in multifactorial diseases, such as cancer. The 1,832 controls is a subsample of all the participants to the NBS, frequency-age-matched to a series of breast cancer and a series of prostate cancer patients. All the 1,832 participants are of self- re ported European descent and were fully informed about the goals and the procedures of the study. The study protocols of the NBS were approved by the Institutional Review Board of the RUNMC and all study subjects signed a written informed consent form . The Dutch bladder cancer population has been described in a previous publication41. Briefly, patients were recruited for the Nijmegen Bladder Cancer Study (NBCS) (see http://dceg.cancer.gov/icbc/membership.html) . The NBCS identified patients through the population-based regional cancer registry held by the
Comprehensive Cancer Centre East, Nijmegen . Patients diagnosed between 1995 and 2006 under the age of 75 years were selected and their vital status and current addresses updated through the hospital information systems of the 7 community hospitals and one university hospital (RUNMC) that are covered by the cancer registry. All patients still alive on August 1, 2007 were invited to the study by the Comprehensive Cancer Center on behalf of the patients' treating physicians. In case of consent, patients were sent a lifestyle questionnaire to fill out and blood samples were collected by Thrombosis Service centers which hold offices in all the communities in the region . 1,651 patients were invited to participate. Of all the invitees, 1,082 gave informed consent (66%) : 992 filled out the questionnaire (60%) and 1016 (62%) provided a blood sample. The number of participating patients was increased with a non-overlapping series of 376 bladder cancer patients who were recruited previously for a study on gene- environment interactions in three hospitals (RUNMC, Canisius Wilhelmina Hospital, Nijmegen, and Streekziekenhuis Midden-Twente, Hengelo, the Netherlands) . Ultimately, completed questionnaires that included questions on smoking and blood samples were available for 1,276 and 1,392 patients, respectively. All the patients that were selected for the analyses (N= l,277) were of self-reported European descent. The median age at diagnosis was 62 (range 25-93) years and 82% of the participants were males. The study protocols of the NBCS were approved by the Institutional Review Board of the RUNMC and all study subjects gave written informed consent. The series of patients with lung cancer has been described before42. Briefly, Patients with lung cancer were identified through the population-based cancer registry of the
Comprehensive Cancer Center IKO, Nijmegen, the Netherlands. Patients who were diagnosed in one of three hospitals (Radboud University Nijmegen Medical Center and Canisius Wilhelmina Hospital in Nijmegen and Rijnstate Hospital in Arnhem) and who were alive at April 15th, 2008 were recruited for a study on gene-environment interactions in lung cancer. 458 patients gave informed consent and donated a blood sample. This case series was increased with 94 patients to a total of 552 by linking three other studies to the population-based cancer registry in order to identify new occurrences of lung cancer among the participants of these other studies.
Information on histology, stage of disease, and age at diagnoses was obtained through the cancer registry.
Lung cancer (Iceland). Recruitment began in the year 1998 with a nationwide list from the Icelandic Cancer Registry (ICR) . About 1,265 LC patients were alive during the period of recruitment, and 665 participated in the project. Information in the ICR includes year and age at diagnosis, year of death, SNOMED (Systematized Nomenclature of Medicine) code and ICD-10 (International Statistical Classification of Diseases and Related Health Problems, 10th revision) classification. Histological and cytological verification was available for 647 cases; the remaining 18 cases were diagnosed clinically.
Lung cancer (Spain). Patients were recruited at the Oncology Department of Zaragoza Hospital. Clinical information including age at onset and histology were collected from medical records. All lung cancer cases and 865 of the 1507 control individuals answered a lifestyle questionnaire, including questions on smoking status (never, former, current), and the amount of smoking. Study protocols were approved by the Institutional Review Board of Zaragoza University Hospital .
Lung cancer (Denver). DNA samples from blood samples and clinical data were provided from the University of Colorado Cancer Center under COMIRB protocol 08-0380. Blood samples were collected from 1217 patients enrolled in any of 20 clinical research trials carried out at Colorado SPORE protocols between 1993 and 2008. Of these 1217 patients, 246 were lung cancer cases and 971 had never had lung cancer at the time of sample shipment. Lung cancer cases were identified either from data matches with the Colorado Central Cancer Registry or by having malignant lung tissue collected via enrollment in a surgical protocol.
PAD sample (Austria): patients and controls were recruited through the Linz Peripheral Arterial Disease (LIPAD) study during 2000 to 2002, at the Department of Surgery, St John of God Hospital. All patients with chronic atherosclerotic occlusive disease of the lower extremities with typical symptoms, eg claudication or leg pain on exertion, rest pain, or minor or major tissue loss, were included on the basis of the final clinical diagnosis established by attending vascular surgeons. The diagnosis was verified by interview, physical examination, noninvasive techniques, and angiography33. All control subjects were patients at the St John of God Hospital and fulfilled the following criteria : no clinical indication of PAD by history and physical examination, and systolic brachial blood pressure equal to or less than the blood pressure in each of the right and left anterior tibial and posterior tibial arteries (that is, ankle brachial index≥ 1.0)33. Smoking status was assessed as described in ref. 34.
PAD sample (Denmark). The sample consist of five hundred and seven patients were consecutively included during November 1999 to January 2004. All patients had PAD. The diagnosis was established from typical findings in clinical investigation (intermittent claudication, rest pain, ulcer or gangrene, and ankle-brachial-index<0.9) . The samples were taken at baseline in a randomized, double-blind trial of roxithromycin versus placebo43. All patients were enrolled at Vascular Surgery Department, Viborg Hospital, Denmark. Exclusion criteria were allergy to macrolides and liver insufficiency.
PAD (Iceland). Patients have been recruited over the past eleven years, as part of a genetic study at deCODE, from a registry of individuals diagnosed with PAD at the major hospital in Reykjavik, the Landspitali University Hospital, during the years 1983-2006. Diagnosis was confirmed by vascular imaging or segmental pressure measurements.
PAD (New Zealand). Patients were recruited from the Otago-Southland region, and PAD was confirmed by an ankle brachial index of less than 0.7, pulse volume recordings and
angiography/ultrasound imaging . The control group consisted of elderly individuals with no history of vascular disease from the same geographical region . Controls were asymptomatic for PAD and had ankle brachial indexes of more than 1. An abdominal ultrasound scan excluded concurrent abdominal aortic aneurysm from both the PAD and control groups, and Anglo- European ancestry was required for inclusion.
Genome-Wide Genotyping
Samples had been genotyped on various platforms. Most of the ENGAGE projects utilized the Illumina platform, either HumanHap300-HH370 (DCGN, NLBLC), HumanHap550 (Rotterdam study, ERF, TwinUK), or the 610-quad (Corogene, GenMets/FTC, and NFBC), but others used Affymetrix 500k (KORA, Sorbs, WTCCC-CAD) and Perlegen 600k (NTR-NESDA) . SNP imputation was based on the Phase II CEU HapMap samples , and was done mostly using IMPUTE , but some studies used MACH46 (Corogene, GenMets/FTC, and NFBC), yielding a total of
approximately 2.5 million SNPs. SNPs were excluded if they had (a) yield lower than 95%, (b) minor allele frequency less than 1% in the population, or (c) showed significant deviation from Hardy-Weinberg equilibrium in the controls (P < 0.001) . Any samples with a call rate below 98% were excluded from the analysis.
Single SNP Genotyping
Single SNP genotyping for all samples was carried out at deCODE genetics in Reykjavik, Iceland, applying the same platform to all populations studied. All single SNP genotyping was carried out using the Centaurus (Nanogen) platform47. The quality of each Centaurus SNP assay was evaluated by genotyping each assay on the CEU samples and comparing the results with the HapMap data44. All assays had mismatch rate <0.5%. Additionally, all markers were re- genotyped on more than 10% of samples typed with the Illumina platform resulting in an observed mismatch in less than <0.5% of samples. Association Analysis
For the quantitative trait association analysis, i.e. smoking quantity measured in cigarettes per day, a classical linear regression, using the genotype as an additive covariate (or expected allele count for imputed SNPs) and CPD categories as a response, was fit to test for association . An additive model for SNP effects was assumed in all instances. The smoking categories are: 1-10 CPD, 11-20 CPD, 21-30 CPD, and 31 CPD and over, and associations with quantitative traits were performed adjusting for sex and year of birth2. We converted the result to CPD by dividing the categorical effect size by 10. The association analysis was performed by most of the ENGAGE studies using SNPTEST45, but Mach46 (KORA), ProbABEL (ERF, Rotterdam, KORA); and GENABEL (TwinUK) were also used.
For case control association analysis, e.g. when comparing PAD and LC and nicotine dependence cases to population controls, we utilized a standard likelihood ratio statistic, implemented in the NEMO software44 to calculate two-sided P values for each individual allele, assuming a multiplicative model for risk, i.e. that the risk of the two alleles a person carries multiplies48. Combined significance levels were calculated by weighing z-scores by the inverse of the square root of each study's effective sample size.
Heterogeneity is tested by comparing the null hypothesis of the effect being the same in all populations to the alternative hypothesis of each population having a different effect using a likelihood ratio test. I2 lies between 0% and 100% and describes the proportion of total variation in study estimates that is due to heterogeneity49.
Correction for Relatedness of the Subjects and Genomic Control
We estimated an inflation factor for each genome-wide association scan by calculating the average of the chi-square statistics, which is a method of genomic control50 to adjust for both relatedness and potential population stratification. The inflation factors for CPD and smoking initiation were estimated within each study by the ratio of the median of the x2-test statistic and its expected value (0.6752), or as 1 if this ratio was less than 1, and all the results presented from association with these traits were adjusted based on these inflation factors.
In-silico Replication Studies
The TAG and OX-GSK consortia provided results for the selected SNPs, using the same methods (i.e. categorical CPD corrected for age and sex) as described above, and provided results from each of the participating populations. Data from samples also present in the ENGAGE analysis were excluded from the in-silico replication stage, and data derived from samples participating in both the TAG and the OX-GSK consortia were entered only once into the analysis.
Table 4. Sample sizes for the ENGAGE GWAS used for the meta-analyses of Smoking Initiation and
CPD.
Smoking Initiation case control
Corogene 579 323
deCODE 15,310 6,077
EGPUT 506 513
ERF 907 378
KORA3 792 831
KORA4 955 800
NFBC 3,219 1,852
NTR-NESDA 2,236 1,207
Rotterdam 3,610 2,624
SUSOD 321 592
TwinUK 537 387
WTCCC-CAD 1,459 466
Total 30,431 16,050
CPD N
Corogene 265
deCODE 15,310
EGP 531
ERF 511
KORA3 183
KORA4 274
NFBC 2, 167
NL-BLC 2,971
NTRNESDA 2,071
Rotterdam 4,759
SUSOD 321
TwinUK 668
WTCCC-CAD 1,235
Total 31,266
Table 5. Genomic regions selected for follow-up (Smoking Initiation)
P-value
Chromosome Start Size (kb) Markers SNP
(ENGAGE)
Chr 1 231,463,228 30 11 rsl2122968 7.2 - 10"7
Chr 5 124,100,202 12 4 rs7705693 1.1 ■10-6
Chr 7 134,321,159 26 6 rs4329203 1.8 ■10-6
Chr 15 79,160,087 9 2 rs868954 7.1 ■10-6
Chr 7 117,282,903 167 55 rsl0487380 8.6 ■10-6
Chr 16 5,549,163 7 9 rs9888773 8.8 ■10-6
Chr 7 38,397,140 4 5 rsl2701627 9.2 ■10-6
Chr 3 141,016,599 13 4 rsl0935356 9.8 ■10-6
Chr 2 45,018,893 32 11 rsl63503 1.0 ■lo-5
Chr 5 166,920,252 68 47 rs2336894 1.1 ■ lo-5
Chr 7 150,546,004 36 3 rsll22979 1.6 ■lo-5
Chr 2 145,884,678 130 3 rsl6824949 2.3 ■ lo-5
Chr 3 65,775,272 13 25 rs868633 3.4 ■lo-5
Chr 1 43,629,135 392 58 rs2251802 5.0 ■lo-5
Chr 11 112,335,772 82 15 rsll214441 6.2 ■lo-5
Table 6. Genomic regions selected for follow-up (CPD).
Size
P-value
Start (kb Markers SNP Genes
(ENGAGE)
Chr 15 76498858 461 193 rsl051730 2.1- 10"3 IREB2, LOCI 23688,
PSMA4,CHRNA5, CHRNA3, CHRNB4,ADAMTS7, MORF4L1
Chr 19 45960916 268 49 rs8102683 1.6 - 10" NUMBL, ADCK4, ITPC,
C190RF54, SNRPA, RAB4B, EGLN2, CYP2A6, CYP2B7P1, CYP2B6, CYP2A13, CYP2F1
Chr 7 32222133 221 103 rs215596 6.3 - 10- PDE1C
Chr 16 81544689 554 47 rs4783307 1.3 - 10" CDH13
Chr 1 51937813 916 45 rsl935289 4.1 - 10" OSBPL9, NRD1, RAB3B,
TXNDC12, BTF3L4, ZFYVE9, CC2D1B, ORCIL, PRPF38A, ZCCHC11
Chr 22 25725385 211 13 rsl l090466 2.1 - 10"
Chr 9 27830794 103 55 rsl0968202 4.6 - 10" LING02
Chr 3 128566720 109 8 rs732548 9.9 - 10" (BC015846)
Chr 2 236118985 17 13 rsl2470301 1.1 - 10" CENTG2
Chr 6 41658150 56 3 rsl2207736 4.2 - 10" FOXP4, MDFI
Chr 11 43574122 222 33 rs7114842 4.0 - 10" HSD17B12
Chr 5 161262342 92 23 rs6887149 5.7 - 10" GABRA1, GABRG2
Chr 16 6461007 353 12 rs8047986 8.3 ■10" A2BP1
Chr 2 33032150 142 18 rsl0490451 1.1 - 10" LTBP1
Chr 8 42643741 84 21 rsl0958726 1.3 - 10" CHRNA6, CHRNB3
Table 7. Association of markers in 4 chromosomal regions with CPD. Results are given for the ENGAGE analysis (ENGAGE), the in-silico replication
obtained by combining results from TAG and OX-GSK {in-silico), and the results of single-SNP assay replications in samples from Iceland, Australia,
Denmark, Germany, and Spain (ISL-AUST-DEN-GER-SPA). Samples that were both in ENGAGE and either TAG or OX-GSK were removed before obtaining
5 the combined in-silico results. Shown are the number of smokers (N), the effect allele (Al) and the other allele (A2), the allele frequencies (Freq), the
chromosome number and position, the estimated allelic effects on CPD and their standard errors in CPD (Effect and SE), the P value for the test of
association (P), the P value for the test for heterogeneity in effect size, and an estimate of the proportion of total variation in study estimates that is due to heterogeneity (I2)
ENGAGE TAG and OX-GSK ISL-AUS-DEN-GER
(meta-analysis) (In silico replication) SPA Combined
Allele
(N = 31,266) (N =45,691) (direct genot) (N=85,997)
(N = 9,040)
SNP Al A2 Freq Chr Position Effect±SE Effect±SE Effect±SE Effect±SE I rsl051730 0.339 15q25 76681394 0.84±0.07 2.1-10"33 0.78±0.06 5.6-10"; 76,972 0.80±0.05 2.4-10-69 0.035 32 rs6474412 T C 0.784 8pll 42669655 0.31±0.08 0.00017 0.30±0.07 2.6- 10"5 0.19±0.18 0.30 84,956 0.29±0.05 1.4-10-' 0.24 13 rsl3280604 A G 0.784 8pll 42678743 0.31±0.08 0.00012 0.30±0.07 2.7- 10"5 76,670 0.31±0.05 1.3-10-' 0.24 14 rs215614 G A 0. .356 7pl4 32313860 0. .38±0. .07 2. .4-10-8 0. .17±0. .06 0. .0036 -0.15±0.16 0.35 86,259 0. .22±0. .04 2. .1-10"7 0, .018 34 rs215605 G T 0, .357 7pl4 32303490 0. ,39±0. .07 1. .7-10-8 0. ,17±0, .06 0. ,0035 77,012 0. ,26±0. .04 5. .4-10-9 0, .12 22 rs7937 T c 0, .560 19ql3 45994546 0. ,34±0. .07 2. .2 10"7 0. .19±0. .06 0. ,0011 0.19±0.14 0.17 86,319 0. ,24±0. .04 2. .4-10"9 0, .45 1 rsl801272 A T 0, .961 19ql3 46046373 1. ,08±0, .27 7. .0-10"5 0. ,41±0, .24 0. ,084 66,380 0. ,68±0. .18 0. .00011 0, .50 0 rs4105144 C T 0. .704 19ql3 46050464 0. ,59±0. .10 1. .2-10"9 0. .31±0. .08 5. .8-10"5 0.27±0.15 0.069 83,317 0. ,39±0. .06 2. .2-10"12 0, .51 0 rs7260329 G A 0, .687 19ql3 46213478 0. ,43±0. .07 1. . l-lO"9 0. ,06±0, .06 0. 36 0.08±0.16 0.65 86,092 0. ,20±0. .04 5. .5-10 6 0, .12 21
10
Table 8. CPD. Association of markers within the regions selected by ENGAGE. Results are given for the ENGAGE discovery sample, the in-silico replication studies using data from the TAG and OX/GSK consortia (see accompanying papers). Shown are the number of smokers (N), the effect allele (Al) and the other allele (A2), the allele frequencies (Freq), the chromosome number and position, the estimated allelic effects on CPD and their standard errors in CPD (Effect and SE), the P value for the test of association (P), the P value for the test for heterogeneity in effect size (Phet), and an estimate of the proportion of total variation in study estimates that is due to heterogeneity (I2)
ENGAGE In silico Combined
SNP Al A2 Chr Position Effect ±SE P Effect ±SE P N Effect ±SE P Phet I2 rsl2130751 G A 1 51937813 0. ,33±0. .08 l. le-05 0. ,05±0. .07 0. .46 76,592 0. , 17±0. .05 0.00074 0.40 4 rsl0888734 A G 1 52038830 0. ,31±0. .06 l. le-06 0. ,08±0. .06 0. .15 77,009 0. , 18±0. .04 2e-05 0.32 9 rs7541944 A C 1 52042644 0. ,24±0. .07 0.00054 0. , 11±0. .06 0. .084 76,961 0. , 16±0. .05 0.00037 0.45 1 rs7551758 G T 1 52046666 0. ,31±0. .06 1.6e-06 0. ,09±0. .06 0. .11 77,007 0. , 18±0. .04 1.3e-05 0.27 12 rs2982846 G T 1 52052238 0. ,30±0. .07 1.7e-05 0. ,08±0. .06 0. .22 76,971 0. , 17±0. .05 0.00019 0.49 0 rs2747525 C A 1 52056606 0. ,32±0. .07 2.3e-06 0. .12±0. .06 0. .047 76,976 0. ,20±0. .04 4.8e-06 0.60 0 rsl l205896 G T 1 52063572 0. ,31±0. .06 9.8e-07 0. ,08±0. .06 0. .16 76,980 0. , 18±0. .04 1.7e-05 0.70 0 rs2077725 A G 1 52066158 0. ,28±0. .07 1.9e-05 0. .12±0. .06 0. .045 76,981 0. , 19±0. .04 1.6e-05 0.62 0 rsl l205897 T C 1 52070739 0. ,31±0. .06 7.7e-07 0. .07±0. .06 0. .2 77,001 0. , 18±0. .04 2.5e-05 0.59 0 rs6702037 G A 1 52083344 0. ,31±0. .06 le-06 0. , 10±0. .06 0. .082 77,022 0. , 19±0. .04 6.3e-06 0.59 0 rs4422953 T C 1 52083470 0. ,29±0. .07 8.7e-06 0. , 13±0. .07 0. .083 57,620 0. ,22±0. .05 8.7e-06 0.48 0 rs6676789 c T 1 52089373 0. ,31±0. .06 1.2e-06 0. , 10±0. .06 0. .079 77,008 0. , 19±0. .04 6.4e-06 0.61 0 rs883058 T A 1 52089819 0. ,34±0. .07 l. le-06 0. , 10±0. .06 0. .11 76,792 0. ,20±0. .05 l . le-05 0.47 0 rs7526552 c T 1 52093147 0. ,32±0. .06 4.4e-07 0. .07±0. .06 0. .2 77,014 0. , 18±0. .04 1.9e-05 0.63 0 rsl l205899 c T 1 52095249 0. ,29±0. .07 2.3e-05 0. ,08±0. .06 0. .21 76,973 0. , 17±0. .05 0.00021 0.54 0 rs6691091 c G 1 52096360 0. ,31±0. .06 l. le-06 0. .07±0. .06 0. .22 77,011 0. , 17±0. .04 4.6e-05 0.41 3 rs6588415 A G 1 52106635 0. ,31±0. .07 3.4e-06 0. , 11±0. .06 0. .071 76,983 0. , 19±0. .04 l . le-05 0.60 0 rsl538881 C T 1 52109627 0. ,31±0. .06 8.3e-07 0. , 10±0. .06 0. .089 77,005 0. , 19±0. .04 6.3e-06 0.54 0 rsl890946 C T 1 52115015 0. ,31±0. .06 1.4e-06 0. .11±0. .06 0. .07 76,993 0. , 19±0. .04 6e-06 0.58 0 rs6663305 A G 1 52115885 0. ,30±0. .07 8.8e-06 0. .12±0. .06 0. .04 76,959 0. ,20±0. .04 8.4e-06 0.76 0 rs736756 C A 1 52117002 0. ,30±0. .06 4.4e-06 0. .09±0. .06 0. .12 76,892 0. , 18±0. .04 2.9e-05 0.72 0 rsl0888738 C T 1 52119626 0. ,26±0. .06 4.5e-05 0. .09±0. .06 0. .11 76,987 0. , 17±0. .04 0.00011 0.96 0 rsl l205902 T G 1 52121357 0. .29±0. .07 1.3e-05 0. .09±0. .06 0. .11 76,995 0. , 18±0. .04 5e-05 0.97 0 rsl954260 T C 1 52163620 0. .29±0. .06 5e-06 0. .12±0. .06 0. .035 77,004 0. ,20±0. .04 4.7e-06 0.98 0 rsl l205911 G A 1 52168900 0. .32±0. .07 1.8e-06 0. , 15±0. .06 0. .014 76,995 0. ,22±0. .04 6.3e-07 0.93 0 rsl2566236 T G 1 52169531 0. .32±0. .07 1.6e-06 0. .14±0. .06 0. .016 77,003 0. ,22±0. .04 7.1e-07 0.94 0 rsl0888740 G A 1 52172685 0. ,31±0. .06 1.9e-06 0. , 12±0. .06 0. .036 77,008 0. ,20±0. .04 2.6e-06 0.97 0 rs2795002 T C 1 52174617 0. ,25±0. .07 0.00039 0. , 10±0. .06 0. .1 76,819 0. .17±0. .05 0.00039 0.89 0 rs6698110 c T 1 52176729 0. ,33±0. .07 5.1e-07 0. , 15±0. .06 0. .011 77,008 0. ,23±0. .04 2.1e-07 0.95 0 rsl935288 T c 1 52178276 0. ,25±0. .07 0.00037 0. , 10±0. .06 0. .1 76,819 0. .17±0. .05 0.00039 0.88 0 rsl935289 c T 1 52179460 0. .41±0. .07 4.1e-08 0. .09±0. .07 0. .16 74,287 0. .22±0. .05 4.6e-06 0.30 10 rs2809943 c T 1 52188728 0. ,25±0. .07 0.00042 0. .11±0. .06 0. .092 76,827 0. .17±0. .05 0.00036 0.92 0 rs2809944 T c 1 52190126 0. ,25±0. .07 0.00035 0. , 10±0. .06 0. .12 76,821 0. , 16±0. .05 0.00045 0.87 0
rs7541308 T G 1 52190709 0 32±0 06 8 4e-07 0.12±0.06 0 039 77 009 0 21 ±0 04 1 7e-06 0 97 0 rs6686975 A G 1 52195722 0 32±0 07 2 8e-06 0.12±0.06 0 06 76 852 0 20±0 05 6 7e-06 0 96 0 rsl0888743 T C 1 52195755 0 39±0 08 3 5e-07 0.12±0.07 0 081 76 321 0 23±0 05 4 2e-06 0 33 9 rs2809948 G A 1 52196682 0 26±0 07 0 00037 0.12±0.07 0 082 76 677 0 18±0 05 0 00025 0 76 0 rsl0888744 G T 1 52204400 0 34±0 09 0 00013 0.10±0.08 0 22 76 230 0 20±0 06 0 00073 0 24 14 rsl7107020 A G 1 52299174 0 58±0 18 0 0016 0.04±0.14 0 77 76 470 0 23±0 11 0 036 0 40 4 rs4394585 G A 1 52485622 0 28±0 07 4 9e-05 0.08±0.06 0 19 76 659 0 16±0 05 0 00028 0 60 0 rs7513934 G A 1 52590776 0 21±0 06 0 00082 0.12±0.06 0 035 76 646 0 16±0 04 0 00015 0 22 15 rsl2044739 C T 1 52601327 0 47±0 15 0 0017 0.07±0.11 0 52 76 711 0 21 ±0 09 0 018 0 18 18 rs9633423 A G 1 52608727 0 33±0 07 1 5e-06 0.10±0.06 0 12 76 702 0 19±0 05 1 7e-05 0 30 10 rs835036 C T 1 52769828 0 30±0 07 4 5e-06 0.11±0.06 0 078 76 518 0 19±0 04 1 4e-05 0 29 11 rs6671552 A G 1 52854173 0 58±0 18 0 001 0.11±0.12 0 33 76 969 0 25±0 10 0 0091 0 25 13 rsl7012387 A G 2 33032150 1 11±0 26 2 8e-05 -0.29±0.20 0 14 72 857 0 17±0 14 0 23 0 065 29 rsl7012390 C T 2 33032718 1 10±0 27 6 4e-05 -0.39±0.20 0 048 72 421 0 08±0 13 0 54 0 050 32 rsl7012393 G A 2 33032785 1 10±0 27 4 7e-05 -0.37±0.20 0 063 72 619 0 11 ±0 14 0 43 0 045 32 rs4952325 A G 2 33038890 0 96±0 25 0 00012 -0.33±0.19 0 085 75 055 0 12±0 13 0 37 0 098 25 rs4952326 C T 2 33041196 0 91±0 23 6 4e-05 -0.31±0.19 0 11 75 055 0 17±0 14 0 2 0 093 25 rsl7012409 A G 2 33042874 0 78±0 23 0 00063 -0.27±0.19 0 15 75 341 0 12±0 13 0 35 0 28 12 rsl l l24300 G T 2 33061280 0 85±0 24 0 00047 -0.19±0.20 0 33 73 131 0 19±0 14 0 18 0 12 23 rsl2468402 G A 2 33065980 1 11±0 27 3 5e-05 -0.19±0.21 0 38 72 577 0 27±0 16 0 078 0 082 27 rsl7012426 C T 2 33070188 0 93±0 24 8 4e-05 -0.17±0.21 0 41 74 746 0 28±0 15 0 06 0 12 23 rsl542343 T c 2 33076523 1 10±0 26 3e-05 -0.08±0.20 0 69 74 472 0 31 ±0 15 0 037 0 042 32 rsl7569615 T G 2 33091430 0 91±0 23 0 00011 -0.16±0.20 0 43 76 686 0 27±0 15 0 064 0 062 29 rs3769554 A G 2 33108941 1 06±0 26 6 6e-05 -0.15±0.20 0 46 74 284 0 27±0 15 0 08 0 056 30 rsl l l24301 T A 2 33109675 0 71±0 22 0 0011 -0.16±0.20 0 44 76 716 0 22±0 14 0 12 0 073 27 rs6727912 T C 2 33142223 1 07±0 25 1 3e-05 -0.26±0.20 0 18 74 764 0 22±0 14 0 12 0 074 28 rs2123915 A C 2 33150083 1 09±0 27 3 8e-05 -0.29±0.21 0 17 72 649 0 20±0 15 0 18 0 11 24 rsl0490451 A G 2 33165965 1 09±0 25 1 le-05 -0.26±0.18 0 16 72 571 0 19±0 14 0 17 0 031 35 rsl l l24303 A G 2 33170204 0 94±0 26 0 00039 -0.27±0.20 0 17 72 866 0 13±0 14 0 35 0 21 16 rsl902030 G A 2 33174095 0 91±0 24 0 00017 -0.17±0.18 0 33 74 485 0 17±0 13 0 19 0 10 25 rsl l687781 A G 2 236118985 0 47±0 13 0 00026 -0.07±0.09 0 46 74 272 0 11 ±0 07 0 13 0 31 10 rs6718421 C T 2 236119282 0 47±0 13 0 00025 -0.07±0.09 0 48 74 536 0 11 ±0 07 0 12 0 35 7 rsl3421279 C G 2 236120581 0 47±0 13 0 00025 -0.11±0.09 0 24 74 268 0 08±0 07 0 24 0 29 11 rsl3021173 T C 2 236121417 0 43±0 10 1 7e-05 -0.04±0.08 0 62 76 965 0 13±0 06 0 029 0 11 23 rs7586914 A G 2 236122252 0 43±0 10 1 5e-05 -0.03±0.08 0 7 76 959 0 14±0 06 0 02 0 043 31 rsl2475206 C T 2 236122627 0 45±0 10 9 4e-06 -0.01±0.08 0 89 76 937 0 16±0 06 0 0092 0 034 32 rsl2470301 G c 2 236122942 0 49±0 10 1 le-06 -0.05±0.08 0 51 74 682 0 15±0 06 0 015 0 020 36 rs7581943 T G 2 236126122 0 47±0 13 0 00024 -0.06±0.10 0 51 74 528 0 12±0 07 0 11 0 39 5 rsl3018365 T G 2 236126752 0 46±0 13 0 00055 -0.07±0.10 0 49 76 383 0 11 ±0 08 0 14 0 40 4 rsl3431390 A G 2 236128971 0 60±0 14 1 4e-05 -0.03±0.10 0 76 74 430 0 18±0 08 0 022 0 36 6 rsl865947 G A 2 236130793 0 47±0 13 0 00027 -0.05±0.10 0 63 74 524 0 13±0 08 0 082 0 44 2 rs6414040 A C 2 236135306 0 33±0 09 0 00019 0.01±0.08 0 92 76 797 0 14±0 06 0 014 0 63 0 rsl2692173 A C 2 236135601 0 47±0 13 0 00033 -0.05±0.10 0 58 74 236 0 13±0 08 0 097 0 37 6
rsl2634857 A G 3 128566720 0 30±0 09 0.00063 -0.05±0.07 0 46 76 984 0 08±0 05 0 13 0 22 15 rs2121851 T C 3 128641324 0 35±0 07 1.6e-06 -0.07±0.06 0 29 76 808 0 10±0 05 0 028 0 045 31 rsl3065243 T C 3 128642649 0 37±0 08 le-06 -0.08±0.06 0 18 76 517 0 09±0 05 0 046 0 014 38 rsl2486396 G A 3 128643205 0 36±0 07 l. le-06 -0.09±0.06 0 18 76 811 0 09±0 05 0 047 0 020 36 rs732548 A G 3 128644442 0 37±0 07 9.9e-07 -0.09±0.06 0 18 76 819 0 09±0 04 0 047 0 016 37 rs732549 T G 3 128644479 0 36±0 07 1.4e-06 -0.09±0.06 0 16 76 788 0 09±0 04 0 057 0 017 37 rs737646 c A 3 128645161 0 36±0 08 2.8e-06 -0.09±0.07 0 18 76 762 0 09±0 05 0 066 0 023 35 rs2594220 T G 3 128676117 0 36±0 10 0.00021 -0.04±0.08 0 66 76 702 0 12±0 06 0 048 0 34 8 rs6859788 A T 5 161262342 0 28±0 07 0.00017 -0.02±0.07 0 82 76 717 0 10±0 05 0 027 0 44 2 rsl870230 C T 5 161270378 0 29±0 08 0.00012 -0.02±0.07 0 81 76 717 0 11 ±0 05 0 027 0 45 1 rsl0068984 A G 5 161274708 0 28±0 08 0.00023 -0.02±0.07 0 81 76 717 0 10±0 05 0 034 0 43 3 rs7723554 T C 5 161276527 0 28±0 07 0.00014 -0.02±0.07 0 8 76 717 0 10±0 05 0 026 0 43 2 rsl3186615 T C 5 161278335 0 28±0 07 0.00024 -0.02±0.07 0 8 76 717 0 10±0 05 0 033 0 42 3 rsl457703 A G 5 161280570 0 28±0 08 0.0002 -0.02±0.07 0 79 76 717 0 10±0 05 0 033 0 43 3 rs7730737 G A 5 161282399 0 28±0 07 0.00016 -0.01±0.07 0 83 76 717 0 10±0 05 0 027 0 43 2 rs6893787 C T 5 161282903 0 29±0 08 0.00016 -0.02±0.07 0 78 76 717 0 10±0 05 0 032 0 44 2 rsl902795 T c 5 161286763 0 31±0 08 0.00019 -0.02±0.07 0 76 76 579 0 11 ±0 05 0 031 0 20 16 rs6556564 G A 5 161290316 0 30±0 08 0.00024 -0.02±0.07 0 75 76 580 0 11 ±0 05 0 033 0 19 17 rsl350374 C T 5 161291288 0 30±0 08 8.7e-05 -0.03±0.07 0 69 76 697 0 10±0 05 0 032 0 41 4 rsl350375 C G 5 161291466 0 30±0 08 7.7e-05 -0.05±0.07 0 44 76 697 0 09±0 05 0 055 0 35 7 rs9313903 C T 5 161291777 0 31±0 08 0.00012 -0.03±0.07 0 72 76 579 0 12±0 05 0 024 0 19 17 rsl585196 G A 5 161293077 0 32±0 08 4.9e-05 -0.02±0.07 0 76 76 655 0 12±0 05 0 019 0 51 0 rsl585198 G A 5 161294336 0 30±0 08 7.1e-05 -0.02±0.07 0 75 76 689 0 11 ±0 05 0 023 0 42 3 rsl902796 A G 5 161301520 0 30±0 07 5.4e-05 -0.02±0.07 0 8 76 689 0 11 ±0 05 0 019 0 36 7 rsl457705 A G 5 161307052 0 33±0 07 1.4e-05 -0.02±0.07 0 8 76 689 0 12±0 05 0 Oi l 0 36 7 rsl0050729 T C 5 161309251 0 32±0 07 2e-05 -0.02±0.07 0 79 76 689 0 12±0 05 0 012 0 37 6 rs6866875 c T 5 161330637 0 31±0 07 4.3e-05 -0.02±0.07 0 71 76 686 0 11 ±0 05 0 022 0 37 6 rs2112596 A G 5 161348056 0 33±0 08 9.9e-06 -0.03±0.07 0 65 76 653 0 11 ±0 05 0 016 0 23 15 rs6887149 A G 5 161353253 0 34±0 08 5.7e-06 -0.04±0.07 0 59 76 637 0 11 ±0 05 0 015 0 23 15 rs3886595 C G 5 161353951 0 31±0 08 2.8e-05 -0.07±0.07 0 29 76 610 0 08±0 05 0 068 0 22 15 rsl862328 A G 5 161354432 0 32±0 07 2.1e-05 -0.05±0.07 0 49 76 610 0 10±0 05 0 031 0 29 11 rsl475365 A C 6 41658150 0 30±0 09 0.00052 0.10±0.07 0 17 76 573 0 17±0 05 0 0013 0 30 10 rsl2207736 G T 6 41681966 0 41±0 09 4.2e-06 0.09±0.08 0 27 76 863 0 22±0 06 0 00015 0 045 30 rs2495229 T c 6 41713808 0 33±0 09 0.00013 0.08±0.08 0 29 76 909 0 19±0 06 0 0011 0 28 11 rsl6875791 G c 7 32222133 0 25±0 07 0.00038 0.02±0.06 0 75 77 003 0 12±0 05 0 012 0 0056 42 rsl860224 A G 7 32222167 0 26±0 07 0.00022 0.01±0.06 0 89 77 004 0 11 ±0 05 0 013 0 0075 40 rsl2672267 G A 7 32225259 0 29±0 08 0.00015 0.02±0.07 0 81 76 799 0 13±0 05 0 0091 0 15 20 rs719585 C G 7 32225610 0 35±0 08 2e-05 0.05±0.07 0 52 76 713 0 17±0 05 0 0013 0 16 19 rs6945244 T C 7 32225798 0 27±0 06 2.2e-05 0.11±0.06 0 059 76 918 0 18±0 04 2 4e-05 0 079 26 rsl2669911 A C 7 32228902 0 35±0 07 1.4e-06 0.13±0.06 0 032 74 808 0 22±0 05 2 7e-06 0 090 25 rsl0233045 A G 7 32231017 0 33±0 07 4. le-06 0.15±0.06 0 016 76 978 0 22±0 05 1 6e-06 0 063 28 rsl0233473 T C 7 32231532 0 34±0 08 1.8e-05 0.05±0.07 0 51 74 826 0 16±0 05 0 0013 0 0048 43 rsl0237329 c T 7 32232250 0 33±0 07 3.6e-06 0.15±0.06 0 015 76 978 0 22±0 05 1 3e-06 0 070 27 rs4141108 T c 7 32233484 0 35±0 08 1.4e-05 0.05±0.07 0 5 76 994 0 17±0 05 0 001 0 0067 41 rsl0269368 G A 7 32238039 0 34±0 08 1.2e-05 0.04±0.07 0 58 76,984 0 16±0 05 0 0014 0 012 38
rsl014242 C T 7 32238830 0 36±0 08 3.6e-06 0 19±0 07 0 0039 76 808 0 26±0 05 2.4e-07 0 21 16 rs7786576 C A 7 32239239 0 38±0 08 l. le-06 0 17±0 07 0 0097 74 630 0 25±0 05 4.2e-07 0 29 11 rs7806224 C T 7 32239632 0 36±0 08 3.4e-06 0 19±0 07 0 0039 76 808 0 26±0 05 2.3e-07 0 22 15 rs9639646 G A 7 32246768 0 40±0 09 8.2e-06 0 06±0 08 0 43 76 819 0 20±0 06 0.00057 0 19 17 rsl0259431 C T 7 32247922 0 38±0 08 1.8e-06 0 19±0 07 0 0038 74 640 0 27±0 05 1.5e-07 0 17 19 rs9639648 A G 7 32248667 0 39±0 09 9.4e-06 0 07±0 08 0 37 76 815 0 20±0 06 0.00045 0 096 24 rsl0263673 T C 7 32249044 0 41±0 09 5.1e-06 0 07±0 08 0 38 74 648 0 20±0 06 0.00037 0 082 26 rs9638875 A T 7 32249439 0 33±0 07 3e-06 0 16±0 06 0 012 76 977 0 23±0 05 7.8e-07 0 080 26 rsl0241729 G A 7 32256404 0 32±0 10 0.00075 0 14±0 09 0 12 76 839 0 21 ±0 06 0.00075 0 16 19 rsl0236197 C T 7 32258286 0 35±0 07 1.5e-06 0 17±0 06 0 0085 74 809 0 24±0 05 3.4e-07 0 070 27 rsl l773343 T c 7 32258841 0 29±0 08 0.00062 0 18±0 07 0 01 74 814 0 22±0 05 3.5e-05 0 15 20 rs7798739 A T 7 32259486 0 35±0 07 1.4e-06 0 16±0 06 0 0098 74 809 0 24±0 05 4.1e-07 0 068 28 rsl3221985 A c 7 32259510 0 29±0 08 0.00062 0 18±0 07 0 0095 74 809 0 22±0 05 3.2e-05 0 15 20 rs929456 G T 7 32260169 0 34±0 07 1.3e-06 0 15±0 06 0 017 76 962 0 23±0 05 7.4e-07 0 084 26 rsl3224417 A G 7 32265118 0 29±0 08 0.00062 0 18±0 07 0 0081 74 813 0 22±0 05 2.6e-05 0 16 19 rsl l762194 A G 7 32266694 0 29±0 08 0.00056 0 19±0 07 0 0073 74 818 0 23±0 05 2.2e-05 0 13 22 rs6948856 A G 7 32268872 0 29±0 08 0.00071 0 19±0 07 0 007 74 773 0 23±0 05 2.4e-05 0 16 19 rs975122 A T 7 32269319 0 29±0 08 0.00056 0 19±0 07 0 0054 74 817 0 23±0 05 1.5e-05 0 17 19 rs7806397 T c 7 32269864 0 35±0 07 7.5e-07 0 16±0 06 0 Oi l 76 979 0 24±0 05 2.6e-07 0 084 26 rs7796692 G A 7 32271390 0 29±0 08 0.00052 0 19±0 07 0 0076 76 983 0 23±0 05 2. le-05 0 21 15 rs7780515 T C 7 32271799 0 35±0 07 9.2e-07 0 16±0 06 0 Oi l 76 970 0 24±0 05 3.6e-07 0 092 25 rs4368879 c T 7 32274450 0 36±0 07 3.2e-07 0 16±0 06 0 012 76 972 0 24±0 05 1.7e-07 0 054 29 rs4370439 c T 7 32274626 0 29±0 08 0.00039 0 18±0 07 0 0091 76 975 0 22±0 05 2. le-05 0 15 20 rsl450869 G T 7 32278197 0 36±0 07 4.7e-07 0 16±0 06 0 0096 76 968 0 24±0 05 1.7e-07 0 063 28 rsl450870 T c 7 32278251 0 37±0 07 2.2e-07 0 13±0 06 0 032 76 960 0 23±0 05 7.1e-07 0 10 24 rs7778162 c T 7 32281009 0 30±0 08 0.00025 0 18±0 07 0 0084 76 975 0 23±0 05 1.4e-05 0 11 23 rs7778443 T c 7 32281215 0 38±0 07 1.3e-07 0 14±0 06 0 027 76 964 0 23±0 05 3.9e-07 0 11 24 rsl0226228 G A 7 32282138 0 36±0 07 5e-07 0 16±0 06 0 Oi l 76 971 0 24±0 05 2.4e-07 0 059 28 rsl476765 G T 7 32286983 0 37±0 07 1.8e-07 0 16±0 06 0 Oi l 76 972 0 25±0 05 1.3e-07 0 050 30 rs9771228 C T 7 32289021 0 38±0 07 1.8e-07 0 18±0 06 0 0046 76 967 0 26±0 05 3.5e-08 0 043 31 rsl2540232 C T 7 32289486 0 36±0 07 4.2e-07 0 16±0 06 0 Oi l 76 972 0 24±0 05 2.2e-07 0 064 28 rs215596 A G 7 32292898 0 41±0 07 6.3e-09 0 13±0 06 0 027 76 973 0 25±0 05 7.9e-08 0 052 30 rsl l768207 C G 7 32293832 0 32±0 08 0.00011 0 14±0 07 0 033 76 996 0 21 ±0 05 5e-05 0 053 29 rs215599 C T 7 32296654 0 40±0 07 2.3e-08 0 13±0 06 0 032 76 953 0 24±0 05 2.2e-07 0 077 26 rsl0271037 T G 7 32296861 0 33±0 08 9.2e-05 0 16±0 07 0 014 77 Oi l 0 22±0 05 1.5e-05 0 074 27 rs215600 G A 7 32300167 0 41±0 07 1.6e-08 0 15±0 06 0 017 76 987 0 25±0 05 7e-08 0 069 27 rs215601 A C 7 32300446 0 40±0 07 1.9e-08 0 13±0 06 0 04 76 976 0 23±0 05 3.1e-07 0 11 23 rs215605 G T 7 32303490 0 39±0 07 1.7e-08 0 17±0 06 0 0035 77 012 0 26±0 04 5.4e-09 0 12 22 rs215607 G A 7 32304862 0 39±0 09 8.6e-06 0 24±0 09 0 0082 57 610 0 31 ±0 06 5.6e-07 0 62 0 rs215610 G A 7 32306119 0 36±0 08 1.8e-05 0 24±0 07 0 00074 77 021 0 28±0 05 l . le-07 0 72 0 rsl2531858 A C 7 32306624 0 34±0 08 2e-05 0 23±0 07 0 00081 77 020 0 28±0 05 1.2e-07 0 77 0 rs215611 C G 7 32307963 0 40±0 07 6.4e-09 0 05±0 06 0 42 76 998 0 19±0 04 1.9e-05 0 0084 40 rs7780009 A G 7 32308068 0 37±0 08 le-05 0 24±0 07 0 00068 77 022 0 29±0 05 6.3e-08 0 72 0 rsl l771526 G A 7 32309143 0 37±0 10 0.00033 0 12±0 10 0 2 76 933 0 23±0 07 0.00089 0 32 9 rs6952609 G A 7 32309860 0 32±0 08 9.2e-05 0 25±0 07 0 00063 76,948 0 28±0 05 2.9e-07 0 65 0
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rsl l072774 C T 15 76739752 0 50±0 10 1.4e-07 0 59±0 08 3.3e- 14 74 816 0 56±0 06 3e-20 0 034 33 rsl7487514 T c 15 76740840 0 59±0 08 7.6e-13 0 65±0 07 1.5e- 19 76 505 0 63±0 05 8.4e-31 0 13 21 rsl2899135 G A 15 76741434 0 60±0 07 1.7e-19 0 59±0 06 2.9e-24 76 807 0 60±0 04 4.7e-42 0 21 16 rsl2148319 A G 15 76743247 0 43±0 10 2.8e-05 0 56±0 09 1.5e- 10 76 675 0 51 ±0 07 2.6e-14 0 060 29 rsl2910237 C T 15 76743393 0 40±0 07 8.4e-09 0 30±0 06 l. le-06 76 656 0 34±0 05 le-13 0 71 0 rsl996371 C T 15 76743861 0 61±0 07 4.6e-20 0 59±0 06 2.9e-24 76 808 0 60±0 04 1.4e-42 0 22 15 rsl2594550 C G 15 76746092 0 44±0 10 4.2e-06 0 46±0 09 8.8e-08 76 985 0 45±0 06 1.7e-12 0 0067 41 rs6495314 C A 15 76747584 0 59±0 07 l. le-18 0 59±0 06 2.1e-24 76 809 0 59±0 04 2.1e-41 0 20 16 rs922691 A G 15 76751049 0 37±0 07 7.5e-08 0 28±0 06 7.2e-06 76 630 0 32±0 05 5.2e-12 0 56 0 rsl2905641 C T 15 76751417 0 36±0 07 2.6e-07 0 31±0 06 6e-07 76 680 0 33±0 05 9.8e-13 0 88 0 rsl l072784 C T 15 76753113 0 25±0 09 0.0065 0 30±0 08 9.3e-05 76 946 0 28±0 06 2e-06 0 24 13 rsl l639372 T c 15 76753710 0 61±0 07 1.8e-19 0 59±0 06 8.1e-24 76 808 0 59±0 04 1.4e-41 0 26 12 rsl2902602 G A 15 76754456 0 61±0 07 l. le-19 0 59±0 06 8.9e-24 76 808 0 60±0 04 le-41 0 22 15 rsl021071 C G 15 76755234 0 62±0 07 6.9e-20 0 25±0 06 2.5e-05 76 808 0 40±0 04 1.4e-19 8 7e-15 76 rsl l072785 T C 15 76755284 0 60±0 07 3.6e-19 0 59±0 06 l. le-23 76 808 0 59±0 04 4e-41 0 26 12 rsl l857532 G T 15 76755323 0 54±0 07 4.3e-16 0 49±0 06 5.1e- 17 76 931 0 51 ±0 04 2.5e-31 0 40 4 rs4886580 G T 15 76756440 0 61±0 07 l. le-19 0 59±0 06 1.3e-23 76 808 0 60±0 04 1.4e-41 0 26 12 rsl6970006 T c 15 76757314 0 64±0 15 l. le-05 0 56±0 12 1.7e-06 74 571 0 59±0 09 9e-l l 0 017 37 rs8032552 T c 15 76758191 0 31±0 08 7.8e-05 0 41±0 07 2.2e-09 77 005 0 37±0 05 9.7e-13 0 24 13 rsl l072787 T c 15 76760032 0 32±0 08 3.2e-05 0 41±0 07 le-09 77 002 0 38±0 05 1.9e-13 0 20 16 rs8043123 c T 15 76760448 0 30±0 08 0.00011 0 40±0 07 3.6e-09 76 995 0 36±0 05 2.3e-12 0 25 13 rs8038920 G A 15 76761600 0 38±0 07 7.2e-08 0 30±0 06 7.3e-07 76 699 0 33±0 05 4.3e-13 0 90 0 rs4887077 T C 15 76765419 0 61±0 07 2.2e-19 0 57±0 06 1.5e-22 76 829 0 58±0 04 3.8e-40 0 27 12 rsl l638372 T C 15 76770614 0 60±0 07 5.3e-19 0 57±0 06 1.9e-22 76 827 0 58±0 04 l . le-39 0 31 10 rs922692 A C 15 76771269 0 60±0 07 2.7e-19 0 57±0 06 8.9e-23 76 813 0 58±0 04 2.6e-40 0 29 11 rsl2910627 C G 15 76781988 0 60±0 07 2.3e-19 0 24±0 06 4.9e-05 76 808 0 39±0 04 7.7e-19 5 4e-14 75 rsl l072791 A C 15 76784131 0 61±0 07 1.4e-19 0 58±0 06 8.6e-23 76 805 0 59±0 04 1.3e-40 0 31 10 rsl l633519 G A 15 76786607 0 35±0 08 5.3e-06 0 36±0 07 9.7e-08 76 992 0 36±0 05 2.4e-12 0 57 0 rsl2899940 T C 15 76788754 0 38±0 08 l. le-06 0 36±0 07 4.9e-08 77 015 0 37±0 05 2.6e-13 0 54 0 rsl l634628 A G 15 76792634 0 38±0 08 6.6e-07 0 36±0 07 4.5e-08 77 018 0 37±0 05 1.6e-13 0 56 0 rsl l072793 A G 15 76793497 0 39±0 08 3.1e-07 0 35±0 07 7.5e-08 76 825 0 37±0 05 1.3e-13 0 44 2 rsl l072794 C T 15 76793637 0 39±0 08 5e-07 0 36±0 07 4.6e-08 77 015 0 37±0 05 1.2e-13 0 61 0 rsl l638490 T c 15 76795005 0 61±0 07 l. le-19 0 58±0 06 9.4e-23 76 805 0 59±0 04 l . le-40 0 37 6 rs4887078 T c 15 76798128 0 41±0 08 2.2e-07 0 36±0 07 7e-08 76 824 0 38±0 05 9.5e-14 0 58 0 rsl l629637 T c 15 76806079 0 61±0 07 9.6e-20 0 58±0 06 1.4e-22 76 994 0 59±0 04 1.4e-40 0 37 6 rs899997 T G 15 76806633 0 39±0 08 2.2e-06 0 35±0 07 3.1e-07 76 742 0 36±0 05 3.5e-12 0 57 0 rs3813565 T G 15 76806665 0 64±0 07 7.1e-20 0 61±0 06 3e-23 76 800 0 63±0 05 2.2e-41 0 20 16 rs4887082 c T 15 76812122 0 59±0 07 8e-18 0 58±0 06 1.6e-21 76 869 0 58±0 05 l . le-37 0 46 1 rsl383634 c T 15 76816451 0 39±0 08 8.1e-07 0 35±0 07 3.4e-07 76 734 0 37±0 05 1.5e-12 0 56 0 rs2219939 A G 15 76816778 0 42±0 08 3.4e-07 0 36±0 07 2.1e-07 75 903 0 38±0 05 4.6e-13 0 61 0 rs4887091 C T 15 76830635 0 39±0 08 2.6e-06 0 34±0 07 6.7e-07 76 910 0 36±0 05 9.4e-12 0 73 0 rs7182567 G A 15 76832109 0 39±0 08 1.2e-06 0 35±0 07 5.6e-07 76 911 0 36±0 05 3.5e-12 0 71 0 rsl2286 A G 15 76838814 0 58±0 07 5.3e-17 0 58±0 06 6.3e-21 76 863 0 58±0 05 2.9e-36 0 48 0 rsl809420 C T 15 76843824 0 56±0 07 5.2e-14 0 55±0 06 4.3e- 19 72 292 0 55±0 05 1.8e-31 0 31 10 rs7174367 G A 15 76851722 0 57±0 07 1.2e-16 0 55±0 06 1.5e- 19 76,866 0 56±0 05 1.7e-34 0 44 1
rs7171916 G c 15 76855006 0. ,54±0..07 3e-14 0.38±0..06 2e-09 76,717 0. ,44±0..05 2.4e-21 1. ,4e-05 57 rsl994016 T c 15 76867289 0. ,53±0. .07 3.4e-14 0. ,40±0. .06 6.2e- l l 76,924 0. ,45±0. .05 5.5e-23 0. .21 15 rsl994017 c T 15 76867361 0. ,33±0. .08 8.8e-06 0. 26±0. .07 0.00012 77,004 0. ,29±0. .05 6.7e-09 0. .90 0 rsl2905740 c T 15 76869419 0. ,32±0. .07 1.7e-05 0. 26±0. .07 0.00013 76,703 0. ,28±0. .05 1.2e-08 0. .90 0 rs2277545 c T 15 76870646 0. ,50±0. .07 7.7e-14 0. ,41±0. .06 2.8e- 12 76,965 0. ,45±0. .04 3.1e-24 0. .16 19 rsl564499 c T 15 76871863 0. ,33±0. .08 9.5e-06 0. 25±0. .07 0.0002 76,709 0. ,28±0. .05 1.3e-08 0. .92 0 rsl2903203 c T 15 76871988 0. ,51±0. .07 2.9e-14 0. ,41±0. .06 3.5e- 12 76,965 0. ,45±0. .04 1.7e-24 0. .15 20 rs2904228 G A 15 76873154 0. ,34±0. .08 7.9e-06 0. 26±0. .07 0.00019 76,667 0. ,29±0. .05 le-08 0. .88 0 rs3743057 C T 15 76876062 0. ,32±0. .07 1.6e-05 0. 23±0. .07 0.00037 76,717 0. ,27±0. .05 3.9e-08 0. .88 0 rs3825807 G A 15 76876166 0. ,50±0. .07 6.2e-14 0. ,41±0. .06 4.2e- 12 76,959 0. ,45±0. .04 3.7e-24 0. .16 19 rs7177699 C T 15 76876789 0. ,50±0. .07 7.6e-14 0. ,42±0. .06 7.9e- 12 75,326 0. ,45±0. .04 7.8e-24 0. .14 21 rs8038189 C G 15 76886081 0. ,38±0. .08 5.8e-07 0. 18±0. .07 0.0083 76,667 0. ,26±0. .05 1.8e-07 0. .34 7 rs922693 A G 15 76886593 0. ,40±0. .08 1.8e-07 0. 26±0. .07 0.00011 76,662 0. ,32±0. .05 2.9e-10 0. .77 0 rsl 1634042 T C 15 76892405 0. ,54±0. .07 3.2e-14 0. 37±0. .08 3e-06 57,483 0. ,46±0. .05 3.3e-18 0. .048 33 rsl383636 A G 15 76893275 0. ,41±0. .08 8.6e-08 0. 26±0. .07 0.00013 76,902 0. ,33±0. .05 1.9e-10 0. .79 0 rs4380028 T C 15 76898148 0. ,31±0. .07 4.6e-06 0. 29±0. .06 6e-07 76,651 0. ,30±0. .04 1.4e-l l 0. .020 36 rs6495335 G T 15 76904188 0. ,33±0. .07 1.9e-06 0. 29±0. .06 5.6e-07 76,645 0. ,31 ±0. .04 5.8e-12 0. .027 34 rs7178051 T c 15 76905351 0. ,35±0. .07 5.2e-07 0. 30±0. .06 6.1e-07 76,895 0. .32±0. .05 2.1e-12 0. .043 31 rs7176187 T c 15 76908428 0. ,32±0. .07 2.3e-06 0. 31±0. .06 1.6e-07 76,701 0. ,31 ±0. .04 1.9e-12 0. .029 34 rsl l852830 A T 15 76912484 0. ,36±0. .06 1.7e-08 0. 36±0. .06 6.1e- 10 76,977 0. ,36±0. .04 5.9e-17 0. .28 11 rs6495337 C G 15 76912744 0. ,30±0. .06 1.4e-06 0. 16±0. .06 0.005 76,999 -0.06±0.04 0.11 1. ,0e-08 66 rs8032771 A G 15 76913114 0. ,37±0. .06 1.6e-08 0. 36±0. .06 6.2e- 10 76,976 0. ,36±0. .04 5.8e-17 0. .30 10 rs4539564 G A 15 76915554 0. ,35±0. .06 5.5e-08 0. 36±0. .06 6.5e- 10 76,973 0. ,35±0. .04 1.9e-16 0. .34 8 rs8035039 A G 15 76916878 0. ,34±0. .06 l. le-07 0. 36±0. .06 6.3e- 10 76,974 0. ,35±0. .04 3.7e-16 0. .34 8 rsl l072810 T C 15 76919261 0. .37±0. .07 l. le-08 0. 35±0. .06 3.8e-09 77,003 0. ,36±0. .04 2.7e-16 0. .65 0 rsl l072811 A C 15 76919385 0. ,38±0. .07 5.1e-09 0. 34±0. .06 7.1e-09 76,998 0. ,36±0. .04 2.5e-16 0. .58 0 rs7403393 C G 15 76922857 0. ,38±0. .07 1.2e-08 0. 27±0. .06 7.6e-06 76,977 0. ,32±0. .04 1.4e-12 0. .035 32 rs7173743 C T 15 76928839 0. .39±0. .07 3e-09 0. 34±0. .06 l. le-08 76,437 0. ,36±0. .04 2.3e-16 0. .53 0 rs7164529 G A 15 76932853 0. ,38±0. .07 8.4e-09 0. 37±0. .06 l. le-09 76,973 0. .37±0. .04 5.1e-17 0. .56 0 rs5029904 G C 15 76939477 0. ,40±0. .07 1.2e-09 0. 25±0. .06 2.2e-05 76,930 0. ,31 ±0. .04 le-12 0. .045 30 rsl2595538 A T 15 76941508 0. ,40±0. .07 2.5e-09 0. 38±0. .06 l. le-09 76,885 0. .39±0. .05 1.6e-17 0. .50 0 rs8029659 A G 15 76954658 0. .37±0. .07 3.2e-07 0. 29±0. .07 1.7e-05 76,722 0. .32±0. .05 3.9e-l l 0. .46 1 rsl7243470 T G 15 76959821 0. ,36±0. .07 1.2e-06 0. 30±0. .07 le-05 76,724 0. .32±0. .05 7.2e-l l 0. .52 0 rsl7832351 A G 15 76960060 0. ,37±0. .07 2.9e-07 0. 30±0. .07 le-05 76,724 0. ,33±0. .05 2e-l l 0. .53 0 rs8047986 C T 16 6461007 0. ,25±0. .06 8.3e-05 0. 13±0. .06 0.026 76,946 0. , 18±0. .04 2. le-05 0. .25 13 rs809704 A T 16 6461769 0. ,26±0. .06 6.6e-05 0. 14±0. .06 0.021 76,946 0. .19±0. .04 1.3e-05 0. .25 13 rs802698 C T 16 6463483 0. ,25±0. .06 0.00011 0. 14±0. .06 0.018 76,695 0. , 19±0. .04 1.5e-05 0. .15 20 rsl640968 A c 16 6463672 0. ,26±0. .06 6.2e-05 0. 14±0. .06 0.019 76,694 0. .19±0. .04 1.2e-05 0. .22 15 rs42347 T A 16 6465592 0. ,25±0. .06 0.00013 0. 13±0. .06 0.027 76,692 0. , 18±0. .04 3e-05 0. .34 7 rs7187508 A C 16 6473550 0. .29±0. .07 2e-05 0. ,08±0. .08 0.33 57,294 0. .19±0. .05 0.00015 0. .18 20 rs813914 T C 16 6474370 0. ,20±0. .06 0.0015 0. ,08±0. .06 0.18 76,710 0. , 13±0. .04 0.0021 0. .61 0 rs3095508 c A 16 6490401 0. ,23±0. .07 0.00052 0. 10±0. .06 0.11 76,717 0. , 15±0. .04 0.0005 0. .42 3
rs811919 G T 16 6492268 0 23±0 07 0 00043 0.09±0.06 0 14 76,717 0 15±0 04 0 00063 0 38 5 rsl l645855 G A 16 6798924 0 51±0 13 0 00012 -0.12±0.11 0 27 76,620 0 12±0 08 0 12 0 17 19 rsl 1648889 A G 16 6804585 0 63±0 16 6 2e-05 -0.13±0.13 0 32 76,408 0 15±0 09 0 11 0 063 29 rs4786123 G C 16 6813922 0 24±0 08 0 0027 -0.08±0.07 0 22 76,517 0 05±0 05 0 37 0 0018 47 rs741591 A G 16 81544689 0 35±0 08 8 le-06 -0.09±0.06 0 13 74,571 0 06±0 04 0 15 0 083 26 rs9928039 G C 16 81555241 0 26±0 06 5 4e-05 0.01±0.06 0 91 77,001 0 12±0 04 0 0051 0 0037 43 rsl2929479 A G 16 81555354 0 22±0 07 0 00082 0.07±0.06 0 23 76,949 0 14±0 04 0 002 0 12 22 rs8064211 G A 16 81558599 0 27±0 06 2 9e-05 0.05±0.06 0 43 77,025 0 14±0 04 0 00092 0 010 39 rs8062451 A G 16 81558637 0 27±0 06 2 4e-05 0.04±0.06 0 45 77,019 0 14±0 04 0 00088 0 012 38 rs8046196 T G 16 81558849 0 24±0 06 0 00024 0.10±0.06 0 1 76,951 0 16±0 04 0 00026 0 029 33 rsl0492868 G C 16 81559356 0 30±0 06 4 5e-06 0.02±0.06 0 74 76,939 0 14±0 04 0 0014 0 0026 44 rs8059783 T G 16 81570343 0 30±0 06 3 4e-06 0.03±0.06 0 59 76,997 0 14±0 04 0 0006 0 0017 46 rs4782742 c A 16 81573151 0 31±0 06 1 8e-06 0.04±0.06 0 53 77,005 0 15±0 04 0 00033 0 0035 43 rs8063602 c A 16 81573922 0 32±0 06 8 2e-07 0.04±0.06 0 53 77,001 0 16±0 04 0 00023 0 0040 43 rs7404645 A G 16 81578029 0 28±0 07 0 00022 0.02±0.07 0 81 76,620 0 12±0 05 0 01 0 0054 42 rs8061888 A G 16 81578930 0 26±0 08 0 00067 -0.07±0.06 0 28 77,007 0 06±0 05 0 19 0 020 36 rsl l l50530 T A 16 81583815 0 29±0 08 0 00015 -0.01±0.07 0 91 76,547 0 12±0 05 0 018 0 0018 46 rs6565099 T C 16 81596483 0 30±0 06 3 le-06 0.00±0.05 1 76,684 0 13±0 04 0 0024 0 0077 41 rs7206011 A T 16 81596720 0 28±0 06 9 2e-06 -0.04±0.06 0 5 76,929 0 10±0 04 0 017 0 047 30 rs7203988 C A 16 81600439 0 28±0 06 7 8e-06 0.00±0.06 0 94 76,992 0 12±0 04 0 0029 0 0061 41 rs6565100 T C 16 81600819 0 22±0 06 0 00037 0.01±0.06 0 91 76,976 0 10±0 04 0 016 0 026 34 rsl2925746 G A 16 81602166 0 48±0 14 0 00046 0.00±0.11 0 96 74,537 0 17±0 08 0 033 0 32 9 rs5000155 T C 16 81615158 0 36±0 10 0 00063 -0.02±0.08 0 77 76,511 0 12±0 06 0 064 0 22 15 rs8057717 A C 16 81617414 0 44±0 10 6 le-06 -0.07±0.08 0 36 76,406 0 12±0 06 0 045 0 049 31 rsl2918209 C T 16 81617613 0 34±0 08 1 9e-05 -0.04±0.07 0 53 76,449 0 11 ±0 05 0 025 0 0021 46 rs9888896 C T 16 81622904 0 32±0 08 5 6e-05 -0.02±0.07 0 79 76,482 0 12±0 05 0 019 0 0037 44 rs6565105 A G 16 81623165 0 22±0 07 0 0012 -0.00±0.06 0 97 76,977 0 09±0 04 0 043 0 13 21 rsl2934188 G A 16 81624733 0 39±0 10 6 4e-05 -0.07±0.08 0 41 76,685 0 10±0 06 0 072 0 37 6 rsl2934355 G A 16 81624826 0 41±0 10 2 3e-05 -0.06±0.08 0 43 76,984 0 12±0 06 0 049 0 37 6 rs4366697 C G 16 81625484 0 37±0 08 1 6e-06 -0.06±0.07 0 37 76,370 0 12±0 05 0 014 0 0011 48 rs4329910 A G 16 81627132 0 33±0 09 0 00024 -0.07±0.08 0 37 77,010 0 09±0 05 0 12 0 32 9 rs4473177 T C 16 81627210 0 34±0 08 3 4e-05 -0.03±0.07 0 64 76,474 0 12±0 05 0 022 0 0026 45 rsl0220997 T C 16 81627755 0 36±0 09 9 9e-05 -0.07±0.08 0 35 76,990 0 09±0 06 0 092 0 38 5 rsl l l50533 T G 16 81628647 0 37±0 09 8 4e-05 -0.08±0.08 0 33 76,988 0 09±0 06 0 099 0 37 6 rs4387591 G C 16 81629923 0 27±0 07 0 00014 -0.02±0.07 0 75 76,640 0 12±0 05 0 Oi l 0 034 32 rs4290460 C A 16 81629963 0 36±0 08 2 6e-05 -0.03±0.07 0 65 76,368 0 12±0 05 0 021 0 0033 44 rs4783307 G T 16 81634135 0 43±0 08 1 3e-08 -0.06±0.06 0 38 76,823 0 14±0 05 0 0035 0 0084 40 rs4523912 G A 16 81634243 0 37±0 09 8 7e-05 -0.08±0.08 0 33 74,783 0 10±0 06 0 082 0 058 29 rs4294808 G A 16 81636082 0 44±0 08 2 5e-08 -0.06±0.07 0 35 76,710 0 14±0 05 0 0049 0 0096 39 rsl7685517 A G 16 82064531 0 33±0 09 0 00014 -0.06±0.07 0 38 77,018 0 09±0 05 0 088 0 33 8 rsl7685702 T C 16 82067235 0 28±0 08 0 00029 -0.05±0.07 0 46 77,011 0 08±0 05 0 088 0 25 13 rs2200793 G A 16 82069004 0 34±0 08 5 4e-05 -0.06±0.07 0 42 77,007 0 10±0 05 0 055 0 42 3 rsl6960622 G A 16 82072047 0 32±0 09 0 00017 -0.05±0.07 0 46 77,000 0 09±0 05 0 075 0 42 3 rsl7758985 T C 16 82075197 0 33±0 08 0 00012 -0.05±0.07 0 46 77,018 0 10±0 05 0 066 0 27 12 rsl7686362 c T 16 82075242 0 35±0 09 3 le-05 -0.05±0.07 0 48 77,020 0 11 ±0 05 0 039 0 29 11
rs923419 A G 16 82075911 0 33±0 09 9.9e-05 -0.05±0.07 0 48 77 017 0 10±0 05 0 06 0 28 11 rsl2930939 T C 16 82095449 0 32±0 09 0.00014 -0.06±0.07 0 44 77 012 0 09±0 05 0 078 0 16 19 rsl387379 T C 16 82097095 0 30±0 09 0.00039 -0.04±0.07 0 59 76 971 0 09±0 05 0 077 0 20 17 rs4238689 A G 16 82097206 0 32±0 08 0.00016 -0.04±0.07 0 54 76 941 0 10±0 05 0 063 0 41 3 rs4782539 C G 16 82097934 0 29±0 08 0.00053 -0.01±0.07 0 91 76 964 0 11 ±0 05 0 039 0 40 4 rs7189644 T C 16 82098347 0 31±0 08 0.00028 -0.03±0.07 0 65 76 954 0 10±0 05 0 058 0 45 1 rs2607420 A G 19 45936727 0 32±0 09 0.00043 0.09±0.07 0 23 30 986 0 17±0 06 0 002 0 14 30 rs2305797 C T 19 45960916 0 26±0 07 0.00025 0.08±0.06 0 24 76 855 0 15±0 05 0 0011 0 037 32 rs2279011 T G 19 45961128 0 29±0 07 l. le-05 0.14±0.06 0 019 76 954 0 20±0 04 3 9e-06 0 51 0 rsl2973666 c G 19 45981237 0 22±0 06 0.0004 0.16±0.06 0 0061 76 997 0 15±0 04 0 00045 0 020 36 rsl2151282 c T 19 45990259 0 29±0 07 2.2e-05 0.09±0.06 0 13 76 771 0 18±0 05 9 8e-05 0 11 24 rs7252227 T G 19 45992955 0 28±0 07 2.2e-05 0.16±0.06 0 006 77 Oi l 0 21 ±0 04 1 3e-06 0 65 0 rs7937 T C 19 45994546 0 34±0 07 2.2e-07 0.19±0.06 0 0011 77 007 0 25±0 04 5 3e-09 0 42 3 rs2644916 c T 19 46001051 0 29±0 08 0.00028 0.19±0.07 0 004 76 997 0 23±0 05 6 4e-06 0 63 0 rs7251418 G A 19 46033429 0 52±0 11 7.7e-07 0.19±0.08 0 02 28 522 0 31 ±0 06 1 4e-06 0 87 0 rs7251570 G A 19 46033590 0 55±0 10 l. le-08 0.25±0.08 0 0017 28 434 0 36±0 06 2 3e-09 0 59 0 rs4343391 C G 19 46036208 0 52±0 11 7.1e-07 0.22±0.09 0 01 28 546 0 33±0 07 4 7e-07 0 93 0 rsl801272 A T 19 46046373 1 08±0 27 7e-05 0.41±0.24 0 084 28 296 0 68±0 18 0 00011 0 50 0 rs4105144 C T 19 46050464 0 59±0 10 1.2e-09 0.31±0.08 5 8e-05 28 508 0 41 ±0 06 5 9e-12 0 87 0 rs8102683 C T 19 46055605 0 62±0 10 1.6e-09 0.26±0.08 0 0021 28 535 0 40±0 06 8 4e-10 0 97 0 rsl496402 A T 19 46057974 0 58±0 10 1.9e-09 0.31±0.08 5 9e-05 28 524 0 41 ±0 06 9 le-12 0 86 0 rsl2461383 G c 19 46062178 0 55±0 09 3.6e-09 0.25±0.07 0 00066 28 206 0 36±0 06 3 le-10 0 74 0 rs3852872 T c 19 46107983 0 34±0 08 1.4e-05 0.15±0.07 0 027 77 022 0 22±0 05 8 4e-06 0 43 3 rs3852873 G T 19 46108100 0 46±0 09 2.7e-07 0.14±0.07 0 053 74 678 0 26±0 06 3 7e-06 0 30 10 rs4090553 C T 19 46118597 0 00±0 01 0.61 0.15±0.07 0 027 77 019 0 01 ±0 01 0 42 0 0088 40 rsl2459565 C G 19 46119379 0 29±0 07 5.4e-05 0.14±0.07 0 032 77 019 0 21 ±0 05 1 9e-05 0 39 5 rs3844443 C T 19 46123775 0 34±0 08 l. le-05 0.12±0.07 0 057 77 020 0 21 ±0 05 2 2e-05 0 40 4 rs3843043 T c 19 46125771 0 33±0 08 2.3e-05 0.15±0.07 0 024 77 024 0 22±0 05 9 4e-06 0 48 0 rs3844444 G A 19 46125887 0 34±0 08 2e-05 0.15±0.07 0 029 74 856 0 22±0 05 1 3e-05 0 41 3 rsl820025 C G 19 46128386 0 35±0 08 l. le-05 0.14±0.07 0 043 77 024 0 22±0 05 1 6e-05 0 38 5 rs8110485 G A 19 46140045 0 33±0 09 0.00012 0.12±0.07 0 094 74 157 0 20±0 05 0 00021 0 0020 46 rsl2611133 T C 19 46140444 0 32±0 08 2.8e-05 0.10±0.07 0 14 74 496 0 19±0 05 0 00013 0 0058 42 rs4124633 T C 19 46141443 0 33±0 08 6.9e-05 0.05±0.07 0 5 74 331 0 16±0 05 0 0023 0 47 0 rs3745220 T C 19 46142449 0 35±0 08 2.4e-05 0.06±0.07 0 41 74 312 0 18±0 05 0 00088 0 43 2 rs4239510 T C 19 46145339 0 31±0 07 2.3e-05 0.09±0.06 0 15 74 711 0 18±0 05 0 00014 0 015 37 rs4803408 c T 19 46148705 0 92±0 25 0.00018 0.18±0.23 0 43 28 243 0 50±0 16 0 0023 0 99 0 rs3889806 T c 19 46151081 0 33±0 07 6.5e-06 0.10±0.06 0 13 74 739 0 19±0 05 5 6e-05 0 0098 39 rsl0417579 T c 19 46156970 0 31±0 07 2.7e-05 0.09±0.06 0 16 74 699 0 17±0 05 0 00021 0 018 36 rsl2459237 T c 19 46158771 1 71±0 49 0.00054 -0.66±0.68 0 33 26 857 0 82±0 38 0 032 0 11 43 rsl l671108 A c 19 46173813 0 29±0 08 0.00015 0.07±0.07 0 32 76 613 0 16±0 05 0 0013 0 86 0 rs7251950 C T 19 46174582 0 33±0 07 1.5e-06 0.07±0.06 0 24 77 024 0 18±0 04 5 7e-05 0 16 19 rsl808002 C T 19 46177034 0 38±0 07 7.5e-08 0.07±0.06 0 28 77 016 0 19±0 05 2 3e-05 0 12 23 rsl808682 G A 19 46181288 0 29±0 08 0.0002 0.07±0.07 0 33 76 606 0 16±0 05 0 0019 0 83 0 rsl0418990 G C 19 46182244 0 32±0 07 2.5e-06 0.07±0.06 0 23 74 810 0 17±0 04 9 5e-05 0 051 30 rs8109525 A G 19 46183758 0 34±0 07 4.8e-07 0.05±0.06 0 44 76,996 0 17±0 04 0 00011 0 21 16
rs2099361 C A 19 46190188 0. ,30±0..07 1.4e-05 0.07±0.06 0..25 76,951 0.16±0..04 0, .00025 0..035 32 rs6508963 T C 19 46190573 0. ,37±0. .07 1.2e-07 0.04±0.06 0. .52 77,006 0. 17±0. .04 0, .00011 0. .18 18 rs2014141 A G 19 46191829 0. ,33±0. .07 2.1e-06 0.03±0.06 0. .56 74,838 0. 15±0. .04 0, .00063 0. .033 33 rs8100458 T C 19 46192053 0. ,37±0. .07 8.4e-08 0.04±0.06 0. .56 77,002 0. 17±0. .04 0, .0001 0. .18 18 rs6508964 G A 19 46194442 0. ,32±0. .07 4.4e-06 0.03±0.06 0. .61 74,830 0. 14±0. .04 0, .0011 0. .050 30 rs4803417 C A 19 46199860 0. ,30±0. .07 4.4e-06 0.02±0.06 0. .78 76,976 0. 14±0. .04 0, .0015 0. .033 32 rs8113196 A C 19 46206192 0. ,30±0. .07 2e-05 0.06±0.06 0. .31 74,667 0. 16±0. .05 0, .00053 0. .012 38 rs8113200 A G 19 46206203 0. ,30±0. .07 2e-05 0.06±0.06 0. .32 74,667 0. 16±0. .05 0, .00055 0. .013 38 rs2279345 T C 19 46207542 0. ,30±0. .07 2.1e-05 0.06±0.06 0. .33 74,667 0. 16±0. .05 0, .00062 0. .012 39 rsl l671243 A C 19 46211555 0. ,29±0. .07 5.1e-05 0.07±0.06 0. .29 74,665 0. 15±0. .05 0, .00074 0. .0052 42 rs7260329 G A 19 46213478 0. ,43±0. .07 l. le-09 0.06±0.06 0. .36 76,898 0. ,21 ±0. .05 3, .4e-06 0. .12 22 rs3786551 C G 19 46213586 0. ,41±0. .08 6.4e-08 0.10±0.06 0. .13 74,731 0. ,22±0. .05 4, .6e-06 0. .31 10 rs3786552 C G 19 46213597 0. ,40±0. .07 5.8e-08 0.10±0.06 0. .13 76,898 0. ,22±0. .05 3, .8e-06 0. .36 6 rs707265 A G 19 46215927 0. ,30±0. .07 le-05 0.05±0.06 0. .4 76,812 0. 16±0. .04 0, .00045 0. .0079 40 rsl552222 T A 19 46217744 0. ,36±0. .10 0.00043 0.11±0.09 0. .22 74, 167 0. ,21 ±0. .07 0, .0015 0. .89 0 rs2113103 G A 19 46220507 0. ,34±0. .10 0.00045 0.12±0.08 0. .15 74,528 0. ,21 ±0. .06 0, .00091 0. .79 0 rs7257703 A G 19 46228462 0. ,32±0. .08 3.9e-05 0.10±0.07 0. .15 74,303 0. 19±0. .05 0, .0002 0. .013 38 rs9608562 C T 22 25725385 0. .42±0. .11 7.9e-05 -0.08±0.10 0. .39 76,572 0. 13±0. .07 0, .055 0. .49 0 rsl2628550 C A 22 25727683 0. .49±0. .12 1.8e-05 0.03±0.10 0. .77 76,733 0. ,23±0. .08 0, .0027 0. .93 0 rs2516082 T C 22 25728512 0. .41±0. .08 6e-07 0.02±0.07 0. .83 77,017 0. 18±0. .05 0, .0007 0. .73 0 rs9608564 c T 22 25728910 0. ,40±0. .08 7.4e-07 0.01±0.07 0. .84 77,017 0. 18±0. .05 0, .00079 0. .72 0 rsl 1090466 G A 22 25729321 0. .42±0. .08 2.1e-07 0.01±0.07 0. .87 77,017 0. 18±0. .05 0, .00053 0. .72 0 rsl2628017 T C 22 25729405 0. .39±0. .08 l. le-06 0.01±0.07 0. .89 77,017 0. 17±0. .05 0, .0011 0. .63 0 rs9613336 c T 22 25730316 0. .39±0. .08 9e-07 0.01±0.07 0. .93 77,017 0. 17±0. .05 0, .0011 0. .69 0 rs9613337 c T 22 25730447 0. ,38±0. .08 1.9e-06 0.00±0.07 0. .97 77,017 0. 16±0. .05 0, .0019 0. .67 0 rs9608565 c T 22 25733729 0. ,40±0. .08 9.7e-07 0.01±0.07 0. .87 77,017 0. 17±0. .05 0, .0011 0. .68 0 rs5761920 A G 22 25767550 0. .32±0. .09 0.0003 -0.07±0.07 0. .33 76,627 0. ,08±0. .05 0, .14 0. .42 3 rs5761921 C T 22 25768386 0. ,30±0. .09 0.00073 -0.06±0.07 0. .37 76,700 0. ,07±0. .05 0, .17 0. .50 0 rsl2160816 T c 22 25770602 0. .29±0. .09 0.00083 -0.02±0.07 0. .74 76,699 0. ,09±0. .05 0, .074 0. .75 0 rs572784 c T 22 25936778 0. ,23±0. .06 0.00033 -0.01±0.06 0. .86 76,954 0. ,09±0. .04 0, .026 0. .33 8
Table 9. Smoking Initiation. Association of markers within the regions selected by ENGAGE. Results are given for the ENGAGE discovery sample, and the in-silico replication studies using data from the TAG and OX/GSK consortia (see accompanying papers). Shown are the number of smokers and never- smokers (N), the effect allele and the other allele, the allele frequencies (Freq), the chromosome number and position, the effect size and standard error (Effect and SE), the P value for the test of association (P), the P value for the test for heterogeneity in effect size (Phet) and an estimate of the proportion of total variation in study estimates that is due to heterogeneity (I2)
ENGAGE In silico Combined
SNP Al A2 Chr Position Effect ±SE P EffectiSE P EffectiSE P Phet I2 rs839756 G C 1 43629135 0. ,52±0. 14 0, ,00024 -0.02±0.16 0, .88 0, ,26±0, , 10 0, ,011 0, .75 0 rsl762343 T A 1 43631524 0. ,49±0. 14 0, ,00035 -0.03±0.11 0, .76 0, , 10±0, ,08 0, , 24 0, .55 0 rs839758 A G 1 43634745 0. ,54±0. 13 5, , 2e-05 0.22±0.11 0, .04 0, ,34±0, ,08 4, ,6e-05 0, .81 0 rs710249 G C 1 43641822 0. ,57±0. 14 7, ,4e-05 0.22±0.11 0, .043 0, ,20±0, ,09 0, ,024 0, .25 13 rs839768 A G 1 43644210 0. ,59±0. 15 5, , 5e-05 0.25±0.11 0, .019 0, ,35±0, ,08 3, ,6e-05 0, .73 0 rs839771 T G 1 43646056 0. ,54±0. 14 0, ,00011 0.28±0.11 0, .0095 0, ,37±0, ,08 1, , 2e-05 0, .76 0 rs839772 G A 1 43646070 0. ,56±0. 14 8, ,4e-05 0.27±0.11 0, .012 0, ,37±0, ,08 1, ,6e-05 0, .75 0 rs2842177 G C 1 43657244 0. ,56±0. 14 8, . le-05 0.25±0.11 0, .019 0, ,23±0, ,09 0, ,011 0, .26 12 rs2782642 A G 1 43658908 0. ,59±0. 15 4, ,9e-05 0.27±0.11 0, .011 0, ,37±0, ,08 1, , 3e-05 0, .75 0 rs2782643 C T 1 43659081 0. ,60±0. 15 4, , le-05 0.28±0.11 0, .01 0, ,37±0, ,09 1, , le-05 0, .75 0 rs2782644 G A 1 43659733 0. ,55±0. 14 0, ,00011 0.27±0.11 0, .011 0, ,37±0, ,08 1, ,6e-05 0, .77 0 rs2782645 G A 1 43661189 0. ,56±0. 14 8, , 5e-05 0.27±0.11 0, .011 0, ,37±0, ,08 1, , 3e-05 0, .76 0 rs2782646 G A 1 43664776 0. ,56±0. 14 8, ,4e-05 0.28±0.11 0, .01 0, ,37±0, ,08 1, , 3e-05 0, .76 0 rs2842179 C T 1 43672214 0. ,59±0. 15 4, ,9e-05 0.27±0.11 0, .013 0, ,37±0, ,08 1, , 7e-05 0, .73 0 rs2039531 A c 1 43673598 0. ,59±0. 15 5, ,9e-05 0.27±0.11 0, .013 0, ,36±0, ,08 1, ,9e-05 0, .74 0 rs2782647 T G 1 43674045 0. ,56±0. 14 7, ,4e-05 0.28±0.11 0, .01 0, ,37±0, ,08 1, , 2e-05 0, .74 0 rs2842180 c T 1 43674503 0. ,58±0. 15 6, , 2e-05 0.26±0.11 0, .016 0, ,36±0, ,09 2, , 5e-05 0, .80 0 rs2782648 G T 1 43676176 0. ,64±0. 15 3, ,4e-05 0.23±0.14 0, .11 0, ,40±0, , 10 9, ,4e-05 0, .57 0 rs2842182 A G 1 43676282 0. ,25±0. 10 0, ,012 -0.01±0.11 0, .92 0, , 13±0, ,07 0, ,081 0, .66 0 rs2842184 A G 1 43676903 0. ,64±0. 15 3e-05 0.23±0.14 0, .11 0, ,40±0, , 10 8, ,8e-05 0, .56 0 rs2782649 T C 1 43678406 0. ,56±0. 14 7, , 3e-05 0.27±0.11 0, .011 0, ,37±0, ,08 1, , 2e-05 0, .74 0 rs2027130 A G 1 43679483 0. .59±0. 15 5, ,6e-05 0.27±0.11 0, .013 0, ,36±0, ,08 1, ,9e-05 0, .74 0 rs2782650 A G 1 43685177 0. .59±0. 15 4, ,8e-05 0.27±0.11 0, .014 0, ,36±0, ,08 1, ,8e-05 0, .73 0 rs2251804 T C 1 43689996 0. ,55±0. 14 0, ,00013 0.25±0.11 0, .022 0, ,34±0, ,08 4, ,8e-05 0, .69 0 rs2251802 G A 1 43690224 0. ,55±0. 14 9, , 7e-05 0.23±0.14 0, .11 0, ,38±0, , 10 0, ,00012 0, .50 0 rs2782651 C G 1 43693273 0. ,61±0. 15 7, , 7e-05 0.03±0.11 0, .78 0, ,08±0, , 10 0, ,42 0, .54 0 rs2842195 G T 1 43693532 0. .62±0. 15 2, ,4e-05 0.27±0.11 0, .012 0, ,36±0, ,08 1, ,4e-05 0, .72 0 rsl334973 T G 1 43693971 0. .07±0. ,05 0, , 16 0.26±0.11 0, .014 0, , 11±0, ,05 0, ,017 0, .20 16 rs2782657 G C 1 43702575 0. .57±0. 15 0, ,0002 O. l liO. l l 0, .35 0, , 12±0, , 10 0, , 24 0, .18 17 rs7413861 C A 1 43711388 0. ,55±0. 15 0, ,0002 0.32±0.11 0, .0038 0, ,40±0, ,09 6, , 3e-06 0, .73 0 rsl l210860 G A 1 43755114 0. ,60±0. 15 7, , 5e-05 0.36±0.11 0, .00093 0, ,44±0, ,09 7, , 3e-07 0, .60 0 rs2152113 C T 1 43756156 0. ,65±0. 15 2, ,6e-05 0.36±0.11 0, .0011 0, ,45±0, ,09 4, , 3e-07 0, .50 0 rsl l577403 G A 1 43762360 0. ,66±0. 16 4, ,4e-05 0.39±0.12 0, .00084 0, ,47±0, ,09 4, , 2e-07 0, .56 0 rs2782640 T C 1 43781620 0. ,64±0. 15 2, , 5e-05 0.34±0.11 0, .0016 0, ,40±0, ,08 1, ,8e-06 0, .50 0 rs2782641 A G 1 43785942 0. .62±0. 15 3, , 2e-05 0.32±0.11 0, .003 0, ,36±0, ,08 8, , 7e-06 0, .63 0 rs2842188 C T 1 43786867 0. .62±0. 15 2, ,6e-05 0.36±0.11 0, .00087 0, ,43±0, ,09 4, , 2e-07 0, .43 2
rs2819333 T A 1 43787160 0. ,60±0.15 4.5e-05 0.32±0.11 0, .0034 0, .29±0. ,09 0.00077 0, .069 27 rs2819334 T C 1 43787322 0. ,58±0. 15 6.8e-05 0.36±0.11 0, .00086 0, .44±0, ,09 5.2e-07 0, .41 3 rs2842187 c T 1 43787536 0. ,60±0. 15 7.1e-05 0.36±0.11 0, .00083 0, .42±0. ,09 9e-07 0, .47 0 rs2842185 T c 1 43792318 0. ,57±0. 15 7.8e-05 0.33±0.10 0, .0013 0, .41±0, ,08 l. le-06 0, .34 7 rsl l210869 A G 1 43798627 0. ,65±0. 15 1.3e-05 0.36±0.11 0, .00092 0, .44±0, ,09 2.8e-07 0, .39 4 rsl887402 G A 1 43808672 0. ,55±0. 15 0.00015 0.35±0.11 0, .00097 0, .42±0. ,09 l. le-06 0, .33 8 rs3791136 C T 1 43822534 0. ,59±0. 15 7.9e-05 0.35±0.11 0, .0012 0, .41±0, ,09 1.3e-06 0, .34 7 rs605709 C T 1 43831054 0. ,64±0. 15 2.4e-05 0.33±0.11 0, .0021 0, .42±0. ,09 1.2e-06 0, .32 9 rsl7371903 A G 1 43843278 0. ,62±0. 15 4.5e-05 0.33±0.11 0, .002 0, .40±0, ,09 2.2e-06 0, .27 11 rs660899 G T 1 43889593 0. ,57±0. 15 0.00022 0.37±0.11 0, .001 0, .43±0. ,09 1.6e-06 0, .60 0 rs489319 C T 1 43904381 0. ,60±0. 15 0.0001 0.33±0.11 0, .0034 0, .41±0, ,09 4e-06 0, .31 9 rs618678 C T 1 43905886 0. ,61±0. 15 9.4e-05 0.33±0.11 0, .0032 0, .42±0. ,09 3.4e-06 0, .32 9 rsl0789442 A c 1 43912662 0. ,61±0. 15 7.5e-05 0.31±0.11 0, .0062 0, .41±0, ,09 6.6e-06 0, .29 10 rs9787076 A c 1 43913736 0. ,61±0. 15 6.8e-05 0.33±0.11 0, .0032 0, .42±0. ,09 2.7e-06 0, .36 6 rs3791034 A G 1 43917717 0. ,60±0. 15 9.2e-05 0.34±0.11 0, .0021 0, .43±0. ,09 2e-06 0, .43 2 rs4660257 T C 1 43920755 0. ,65±0. 16 4.4e-05 0.35±0.11 0, .0019 0, .45±0, ,09 l. le-06 0, .33 8 rsl7401357 G C 1 43926206 0. ,65±0. 16 4e-05 0.12±0.11 0, .27 0, .15±0, , 10 0.12 0, .010 38 rs3791035 G C 1 43927066 0. ,66±0. 16 2.7e-05 0.12±0.11 0, .27 0, .16±0, , 10 0.11 0, .0072 39 rs2270972 C G 1 43930716 0. ,66±0. 15 2.1e-05 0.02±0.12 0, .84 0, .24±0. ,09 0.0088 0, .025 33 rsl2410155 A C 1 43961052 0. ,67±0. 16 1.9e-05 0.34±0.11 0, .0027 0, .45±0, ,09 9.9e-07 0, .30 10 rs3791040 A G 1 43975320 0. ,67±0. 16 2.3e-05 0.45±0.15 0, .003 0, .55±0, , 11 4.5e-07 0, .58 0 rsl2354267 T C 1 44020859 0. ,70±0. 17 2.8e-05 0.32±0.13 0, .013 0, .41±0, , 10 1.8e-05 0, .32 8 rs2884216 G C 1 231463228 0. ,75±0. 15 5.4e-07 0.25±0.11 0, .026 0, .19±0, , 10 0.047 0, .059 27 rsl2122968 G T 1 231463989 0. ,83±0. 15 3.9e-08 0.26±0.11 0, .023 0, .45±0, ,09 5e-07 0, .56 0 rsl033325 C T 1 231465336 0. ,80±0. 15 7.8e-08 0.28±0.11 0, .012 0, .45±0, ,09 3.1e-07 0, .57 0 rsl033322 A G 1 231467047 0. ,77±0. 15 1.7e-07 0.28±0.11 0, .011 0, .44±0, ,09 3.6e-07 0, .54 0 rsl0910122 C T 1 231469297 0. ,73±0. 15 1.8e-06 0.27±0.11 0, .018 0, .41±0, ,09 3.3e-06 0, .52 0 rsl l587411 C T 1 231476321 0. ,77±0. 15 1.6e-07 0.28±0.11 0, .011 0, .45±0, ,09 3.3e-07 0, .61 0 rs6683734 G A 1 231481990 0. .67±0. 16 1.9e-05 0.21±0.12 0, .064 0, .37±0. ,09 4.3e-05 0, .58 0 rs4649294 T C 1 231483051 0. ,60±0. 15 7.8e-05 0.31±0.11 0, .0054 0, .41±0, ,09 3.7e-06 0, .79 0 rsl2044078 T C 1 231483565 0. ,62±0. 15 3.9e-05 0.31±0.11 0, .0055 0, .42±0. ,09 2.3e-06 0, .78 0 rsl0737196 G T 1 231486940 0. ,68±0. 15 4e-06 0.29±0.11 0, .01 0, .43±0. ,09 1.8e-06 0, .69 0 rsl294327 G T 1 231493299 0. ,60±0. 15 6.6e-05 0.20±0.12 0, .089 0, .34±0. ,09 0.00019 0, .57 0 rsl561227 T c 2 45018893 0. , 13±0. ,07 0.051 -0.16±0.11 0, .14 0, .04±0, ,05 0.42 0, .010 38 rs83995 T G 2 45021074 0. ,38±0. 11 0.0003 -0.16±0.11 0, .13 0, .10±0, ,07 0.16 0, .0097 38 rs338070 G C 2 45028793 0. .41±0. 11 0.00025 0.06±0.10 0, .57 0, .05±0, ,09 0.54 0, .0087 39 rsl73076 A G 2 45029089 0. .59±0. 13 3.5e-06 -0.15±0.11 0, .17 0, .12±0. ,07 0.11 0, .011 37 rsl63516 G T 2 45037503 0. .42±0. 11 0.00027 -0.13±0.11 0, .25 0, .10±0, ,07 0.17 0, .032 32 rs4952728 T c 2 45037927 0. .52±0. 13 3.3e-05 -0.09±0.11 0, .4 0, .15±0, ,08 0.053 0, .067 27 rsl63513 G A 2 45038728 0. ,28±0. ,09 0.0023 -0.14±0.11 0, .2 0, .10±0, ,07 0.15 0, .026 33 rsl983312 A G 2 45040631 0. ,49±0. 12 6.6e-05 -0.09±0.11 0, .4 0, .12±0. ,07 0.11 0, .075 26 rs340514 T C 2 45042515 0. .49±0. 12 7.9e-05 -0.13±0.11 0, .23 0, .12±0. ,08 0.11 0, .029 32 rsl63507 A G 2 45047793 0. .41±0. 11 0.00037 -0.23±0.11 0, .042 0, .07±0, ,07 0.33 0, .020 34 rsl63503 G A 2 45050643 0. ,55±0. 15 0.00033 -0.10±0.11 0, .38 0, .12±0. ,09 0.17 0, .12 22 rsl6824949 T G 2 145884678 0. ,60±0. 13 6.1e-06 0.21±0.11 0, .054 0, .35±0. ,08 1.9e-05 0, .11 22
rsl533427 G A 2 145887503 0. ,65±0.15 2e-05 0.18±0.11 0, .1 0.32±0.09 0, ,0002 0, .19 16 rsl0192394 T C 2 146014477 0. ,65±0. 15 2.3e-05 0.17±0.12 0, .14 0.32±0.09 0, ,00032 0, .16 19 rsl473550 T C 3 65775272 0. ,75±0. 16 2e-06 0.08±0.12 0, .52 0.05±0.04 0, , 27 0, .0093 38 rsl473551 G A 3 65775400 0. ,47±0. 13 0, ,00032 0.08±0.12 0, .52 0.23±0.08 0, ,0056 0, .092 24 rs883570 T A 3 65776543 0. ,75±0. 16 1. ,8e-06 0.08±0.12 0, .49 0.02±0.04 0, , 55 0, .0067 40 rs868633 c T 3 65776574 0. ,04±0. ,04 0, , 38 0.09±0.12 0, .48 0.04±0.04 0, , 28 0, .0062 40 rsl473531 c T 3 65776979 0. ,61±0. 14 9, ,8e-06 0.09±0.12 0, .44 0.28±0.09 0, ,001 0, .040 30 rsl473530 T G 3 65777027 0. ,74±0. 16 2, ,6e-06 0.09±0.12 0, .46 0.04±0.04 0, , 27 0, .0095 38 rs6796986 c T 3 65777595 0. ,05±0. ,04 0, , 21 0.10±0.12 0, .43 0.06±0.04 0, , 15 0, .011 37 rsl3059631 G c 3 65777697 0. ,56±0. 12 7, . le-06 0.20±0.12 0, .09 0.08±0.04 0, ,069 0, .013 36 rsl2634960 C T 3 65777960 0. ,59±0. 14 2, , 3e-05 0.10±0.12 0, .38 0.27±0.08 0, ,0013 0, .059 28 rsl2635056 G A 3 65778128 0. ,54±0. 12 9, ,6e-06 0.10±0.12 0, .42 0.06±0.04 0, , 14 0, .011 37 rsl l08716 C A 3 65779300 0. ,43±0. 11 7, ,8e-05 0.10±0.12 0, .4 0.07±0.04 0, , 12 0, .012 37 rsl l08718 G A 3 65779623 0. ,67±0. 14 1. ,8e-06 0.12±0.11 0, .28 0.30±0.08 0, ,00022 0, .069 26 rs2888253 C A 3 65780675 0. ,64±0. 13 2, , le-06 0.13±0.11 0, .25 0.32±0.08 0, ,00011 0, .037 31 rsl2485806 T C 3 65781126 0. ,57±0. 13 1. ,8e-05 O. l liO. l l 0, .31 0.28±0.08 0, ,00042 0, .042 30 rs716244 A T 3 65781513 0. , 10±0. ,06 0, ,089 0.09±0.12 0, .47 0.06±0.05 0, , 26 0, .011 37 rsl495456 C T 3 65781939 0. , 11±0. ,06 0, ,086 0.07±0.12 0, .55 0.10±0.06 0, ,075 0, .019 35 rsl495457 T c 3 65782138 0. ,35±0. 10 0, ,00073 0.09±0.12 0, .44 0.22±0.07 0, ,0033 0, .061 27 rs7624282 T c 3 65782553 0. ,35±0. 12 0, ,0021 0.09±0.12 0, .43 0.21±0.08 0, ,0086 0, .064 27 rs2036069 A G 3 65782762 0. ,58±0. 13 1. ,6e-05 0.13±0.12 0, .27 0.24±0.08 0, ,002 0, .085 25 rs4688248 C A 3 65784256 0. ,40±0. 11 0, ,00026 0.12±0.11 0, .3 0.25±0.07 0, ,00077 0, .044 29 rsl874320 C A 3 65784882 0. ,55±0. 13 2, . le-05 0.09±0.12 0, .45 0.09±0.05 0, ,092 0, .020 34 rs2372151 T A 3 65785178 0. ,20±0. ,08 0, ,0067 O. l liO. l l 0, .32 0.14±0.06 0, ,023 0, .016 35 rs 1495448 T G 3 65785954 0. ,53±0. 13 2, , 5e-05 0.14±0.11 0, .22 0.29±0.08 0, ,0002 0, .043 30 rsl7372566 T A 3 65787443 0. ,36±0. 10 0, ,00032 O. l liO. l l 0, .34 0.19±0.07 0, ,0088 0, .017 35 rsl2490463 A C 3 65788413 0. ,58±0. 13 8e-06 0.08±0.11 0, .49 0.24±0.08 0, ,0025 0, .13 21 rsl0935353 G A 3 141016599 0. ,56±0. 14 4, ,4e-05 -0.13±0.10 0, .22 0.10±0.07 0, , 17 0, .093 24 rsl0935354 G A 3 141026326 0. ,53±0. 14 0, ,00011 -0.10±0.10 0, .33 0.11±0.08 0, , 15 0, .14 20 rsl0935356 C T 3 141026782 0. .59±0. 14 2, ,4e-05 -0.10±0.11 0, .35 0.13±0.08 0, ,094 0, .079 25 rs6777464 C T 3 141029906 0. ,56±0. 14 4, ,8e-05 -0.12±0.10 0, .24 0.10±0.07 0, , 17 0, .10 23 rs6421891 G c 5 124100202 0. ,58±0. 15 0, ,00018 0.05±0.11 0, .63 0.05±0.11 0, ,66 0, .043 30 rs7718029 G A 5 124101912 0. ,56±0. 15 0, ,0002 0.07±0.11 0, .53 0.25±0.09 0, ,0036 0, .17 18 rs4836114 C G 5 124110481 0. ,76±0. 16 3, , 7e-06 -0.05±0.15 0, .74 -0.05±0.09 0, , 55 0, .026 33 rs7705693 C T 5 124112264 0. .72±0. 15 1. ,8e-06 0.04±0.20 0, .86 0.45±0.12 0, ,00012 0, .045 32 rs883322 G T 5 166920252 0. ,48±0. 14 0, ,00046 0.18±0.11 0, .12 0.28±0.09 0, ,00094 0, .086 24 rs888976 A G 5 166920483 0. ,51±0. 13 0, ,00012 0.16±0.11 0, .15 0.29±0.08 0, ,00053 0, .056 28 rs888975 T C 5 166921084 0. .54±0. 17 0, ,0016 0.02±0.12 0, .86 0.22±0.10 0, ,038 0, .0068 40 rsl862347 T C 5 166921163 0. ,62±0. 15 3, ,4e-05 0.16±0.11 0, .14 0.31±0.09 0, ,00036 0, .056 28 rs888974 G T 5 166921545 0. .64±0. 14 8, ,4e-06 0.17±0.11 0, .13 0.31±0.08 0, ,00022 0, .082 25 rsl0071347 A G 5 166921820 0. ,66±0. 15 1. , 3e-05 0.16±0.11 0, .14 0.31±0.09 0, ,00038 0, .057 28 rs2336894 T G 5 166923173 0. ,61±0. 14 9, ,6e-06 0.17±0.11 0, .13 0.33±0.08 0, ,00012 0, .052 28 rs2080976 T C 5 166923617 0. .62±0. 15 3, , 7e-05 0.17±0.11 0, .13 0.31±0.09 0, ,00037 0, .060 27 rs2098651 c G 5 166923848 0. .04±0. ,03 0, , 26 0.16±0.11 0, .14 0.02±0.04 0, ,65 0, .0076 39 rsl3186288 T C 5 166924274 0. ,65±0. 16 3, ,4e-05 0.17±0.11 0, .14 0.31±0.09 0, ,00042 0, .057 28
rs4869056 G A 5 166924656 0. ,04±0. ,05 0, , 35 0, , 18±0.11 0, , 1 0, ,07±0, ,04 0.12 0, .017 35 rs4869058 T A 5 166924825 0. ,58±0. 15 7, . le-05 0, , 17±0. 11 0, , 13 0, ,24±0, ,09 0.0055 0, .021 34 rsl l747772 c T 5 166925286 0. ,56±0. 14 0, ,0001 0, , 16±0. 11 0, , 15 0, ,28±0, ,09 0.00096 0, .067 26 rsl l738110 T G 5 166925338 0. ,56±0. 14 0, ,00011 0, , 17±0. 11 0, , 13 0, ,30±0, ,09 0.00051 0, .036 31 rs986391 G A 5 166926550 0. ,66±0. 16 2, , 3e-05 0, , 13±0. 11 0, ,25 0, ,29±0, ,09 0.00099 0, .022 34 rsl2188010 A T 5 166929044 0. ,50±0. 13 0, ,00015 0, , 18±0. 11 0, ,099 0, ,24±0, ,08 0.0029 0, .032 31 rs4267883 C T 5 166929300 0. ,58±0. 14 6, ,9e-05 0, , 18±0. 11 0, ,099 0, ,30±0, ,09 0.00043 0, .061 27 rs4324704 G c 5 166929514 0. ,64±0. 16 3, , 3e-05 0, ,04±0. 12 0, ,76 0, ,22±0, ,09 0.01 0, .012 37 rsl2188278 A G 5 166930800 0. ,49±0. 14 0, ,00053 0, , 14±0. 11 0, ,21 0, ,26±0, ,09 0.0027 0, .0076 39 rsl0039321 T C 5 166930963 0. ,50±0. 16 0, ,0014 0, , 14±0. 11 0, ,21 0, ,26±0, ,09 0.0046 0, .0056 40 rsl0042499 A G 5 166931157 0. ,36±0. 13 0, ,0065 0, , 14±0. 11 0, ,21 0, ,22±0, ,08 0.0093 0, .0059 40 rsl3153563 T C 5 166932406 0. ,27±0. 11 0, ,012 0, , 12±0. 11 0, ,28 0, , 19±0, ,08 0.012 0, .0024 43 rsl3160227 G A 5 166935946 0. ,41±0. 15 0, ,0066 0, , 12±0. 11 0, ,29 0, ,21±0, ,09 0.015 0, .0021 44 rsl0475853 G T 5 166936147 0. , 14±0. ,08 0, ,092 0, , 14±0. 11 0, ,22 0, , 13±0, ,07 0.042 0, .0031 42 rsl024993 C T 5 166938587 0. ,49±0. 15 0, ,0011 0, , 11±0. 11 0, ,31 0, ,23±0, ,09 0.0092 0, .0038 42 rsl024994 T c 5 166939254 0. ,48±0. 15 0, ,00092 0, , 13±0. 12 0, ,26 0, ,26±0, ,09 0.0039 0, .0067 39 rs9313385 A G 5 166939971 0. ,57±0. 15 0, ,00015 0, , 14±0. 11 0, ,23 0, ,27±0, ,09 0.002 0, .0063 40 rs278016 A G 5 166951589 0. ,56±0. 16 0, ,00035 0, , 14±0. 11 0, ,22 0, ,26±0, ,09 0.0034 0, .0073 39 rsl l750548 G A 5 166958525 0. ,50±0. 15 0, ,00077 0, , 15±0. 11 0, , 16 0, ,27±0, ,09 0.0022 0, .0057 40 rs4869061 C T 5 166962903 0. ,55±0. 16 0, ,00043 0, , 12±0. 11 0, ,28 0, ,24±0, ,09 0.0053 0, .0034 42 rsl l738133 C T 5 166964671 0. ,53±0. 16 0, ,00093 0, , 12±0. 11 0, ,26 0, ,24±0, ,09 0.0074 0, .0028 43 rs4869062 T c 5 166965607 0. ,47±0. 15 0, ,0016 0, , 12±0. 11 0, ,27 0, ,24±0, ,09 0.0066 0, .0029 42 rs4868804 A G 5 166965813 0. ,57±0. 15 0, ,0001 0, , 15±0. 12 0, ,2 0, ,29±0, ,09 0.0012 0, .0049 41 rs898171 G A 5 166966356 0. ,46±0. 14 0, ,0013 0, , 15±0. 12 0, ,2 0, ,26±0, ,09 0.003 0, .0048 41 rs732711 A C 5 166966645 0. ,59±0. 16 0, ,00016 0, , 10±0. 11 0, ,37 0, ,25±0, ,09 0.005 0, .0026 43 rsl0475856 A G 5 166968159 0. ,58±0. 15 0, ,00014 0, , 15±0. 12 0, ,2 0, ,29±0, ,09 0.0015 0, .0049 41 rs981898 A T 5 166968792 0. .57±0. 16 0, ,0004 0, , 15±0. 12 0, ,2 0, ,29±0, ,09 0.002 0, .0060 40 rs6893866 A c 5 166970303 0. ,58±0. 16 0, ,00038 0, , 15±0. 12 0, ,21 0, ,28±0, ,09 0.0031 0, .0042 42 rsl l l34465 G A 5 166970512 0. ,51±0. 16 0, ,0016 0, , 11±0. 12 0, ,35 0, ,24±0, ,09 0.0098 0, .0042 43 rsl 1134466 T C 5 166972188 0. ,61±0. 17 0, ,00025 0, , 12±0. 12 0, ,33 0, ,27±0, , 10 0.0041 0, .0046 41 rsl l738927 A G 5 166972378 0. .59±0. 16 0, ,00025 0, , 11±0. 12 0, ,37 0, ,26±0, ,09 0.0057 0, .0033 42 rsl l743417 G A 5 166975676 0. ,48±0. 16 0, ,0021 0, , 14±0. 12 0, ,25 0, ,25±0, ,09 0.0064 0, .0019 44 rsl459066 C T 5 166977244 0. ,61±0. 17 0, ,00023 0, , 15±0. 13 0, ,22 0, ,29±0, , 10 0.0025 0, .0057 40 rs2336897 C T 5 166982854 0. ,61±0. 17 0, ,00023 0, , 16±0. 13 0, ,2 0, ,30±0, , 10 0.0021 0, .0083 39 rs962065 T c 5 166983151 0. ,56±0. 16 0, ,00058 0, , 16±0. 13 0, ,2 0, ,31±0, , 10 0.002 0, .0087 39 rsl966924 c A 5 166985787 0. ,57±0. 16 0, ,0005 0, , 16±0. 13 0, ,21 0, ,31±0, , 10 0.0021 0, .0099 38 rs2336898 A G 5 166988514 0. .62±0. 17 0, ,0002 0, , 12±0. 13 0, ,34 0, ,29±0, , 10 0.004 0, .0074 39 rsl2701627 G A 7 38397140 0. .02±0. ,02 0, , 13 0, ,01±0. ,08 0, ,93 0, ,02±0, ,02 0.13 0, .0030 42 rs2072507 T C 7 38400511 0. .21±0. ,07 0, ,0032 0, ,02±0. 10 0, ,85 0, , 15±0, ,06 0.011 0, .16 19 rs720667 T C 7 38400790 0. .24±0. ,08 0, ,0017 0, ,03±0. 11 0, ,81 0, , 16±0, ,06 0.0073 0, .18 17 rs720668 c T 7 38400798 0. .64±0. 14 4e-06 0, ,06±0. 11 0, ,61 0, ,27±0, ,08 0.0015 0, .074 26 rs715413 T c 7 38401048 0. ,08±0. ,04 0.057 0, ,02±0. 10 0, ,81 0, ,08±0, ,04 0.062 0, .10 23 rs6976111 A c 7 117282903 0. ,20±0. ,08 0.0089 0, ,42±0. 12 0, ,00053 0, ,27±0, ,07 3.8e-05 0, .0040 41 rs7776980 T c 7 117283555 0. ,53±0. 14 0.00015 0, ,37±0. 12 0, ,0012 0, ,43±0, ,09 le-06 0, .13 21 rsl0252771 G T 7 117284126 0. ,58±0. 14 5.8e-05 0, ,38±0. 12 0, ,001 0, ,44±0, ,09 6.8e-07 0, .12 21
rsl0259910 G T 7 117291218 0. ,57±0.14 6.5e-05 0, ,37±0.11 0, .0011 0, ,43±0, ,09 8, , 3e-07 0, , 12 22 rsl0244364 C T 7 117316877 0. ,57±0. 14 7.1e-05 0, ,38±0. 11 0, .00067 0, ,44±0, ,09 4.4e-07 0, , 12 22 rs727164 A G 7 117319388 0. ,51±0. 14 0.00036 0, ,38±0. 11 0, .00087 0, ,42±0, ,09 2e-06 0, ,076 27 rs6952555 C T 7 117322514 0. ,58±0. 14 6e-05 0, ,37±0. 12 0, .0012 0, ,45±0, ,09 6, , 3e-07 0, , 10 23 rs7807019 G A 7 117330299 0. , 14±0. ,08 0.069 0, ,29±0. 11 0, .0075 0, , 19±0, ,06 0, ,0021 0, ,046 29 rsl7488728 T C 7 117331416 0. ,26±0. 10 0.0088 0, ,26±0. 11 0, .014 0, ,26±0, ,07 0, ,00032 0, , 12 21 rsl0266994 c T 7 117333073 0. ,64±0. 14 7e-06 0, ,26±0. 10 0, .014 0, ,26±0, ,07 0, ,00033 0, ,090 24 rsl477114 T c 7 117334918 0. ,26±0. 10 0.0085 0, ,26±0. 10 0, .014 0, ,26±0, ,07 0, ,00033 0, ,091 24 rs7789130 G T 7 117335533 0. ,64±0. 14 6.7e-06 0, ,26±0. 10 0, .014 0, ,26±0, ,07 0, ,00032 0, ,092 24 rsl2706159 C A 7 117335743 0. ,26±0. 10 0.0083 0, ,25±0. 10 0, .015 0, ,26±0, ,07 0, ,00033 0, ,096 23 rs2193257 G A 7 117337480 0. ,25±0. 10 0.011 0, ,25±0. 10 0, .016 0, ,25±0, ,07 0, ,00049 0, , 11 22 rs6466630 G A 7 117339057 0. ,26±0. 10 0.0089 0, ,25±0. 10 0, .015 0, ,25±0, ,07 0, ,00037 0, ,088 24 rs6964051 C T 7 117341868 0. ,64±0. 14 6.8e-06 0, ,28±0. 11 0, .01 0, ,27±0, ,07 0, ,00024 0, , 10 23 rsl2706160 T c 7 117342153 0. ,26±0. 10 0.009 0, ,28±0. 11 0, .0097 0, ,27±0, ,07 0, ,00024 0, , 10 23 rsl548460 T c 7 117354727 0. , 13±0. ,07 0.087 0, ,27±0. 11 0, .011 0, , 18±0, ,06 0, ,0038 0, ,042 30 rs6466636 T A 7 117356633 0. ,26±0. 10 0.0089 0, ,27±0. 11 0, .011 0, ,25±0, ,07 0, ,00053 0, ,087 24 rsl0272923 c T 7 117359540 0. ,64±0. 14 8.1e-06 0, ,28±0. 11 0, .011 0, ,27±0, ,07 0, ,00028 0, , 10 23 rs6950622 T c 7 117360641 0. ,26±0. 10 0.0087 0, ,28±0. 11 0, .01 0, ,27±0, ,07 0, ,00025 0, , 12 22 rsl l978052 A c 7 117363804 0. ,65±0. 14 7e-06 0, ,28±0. 11 0, .0099 0, ,27±0, ,07 0, ,00025 0, , 11 22 rs6971964 T c 7 117364617 0. ,26±0. 10 0.0096 0, ,29±0. 11 0, .0086 0, ,27±0, ,07 0, ,00022 0, , 11 22 rs2158050 G c 7 117366098 0. ,26±0. 10 0.009 0, ,20±0. 11 0, .08 0, ,24±0, ,07 0, ,0011 0, ,068 26 rsl024432 T G 7 117366565 0. ,26±0. 10 0.0089 0, ,38±0. 14 0, .0072 0, ,30±0, ,08 0, ,00021 0, , 16 22 rsl024433 c T 7 117366759 0. ,65±0. 14 5.6e-06 0, ,33±0. 12 0, .0061 0, ,29±0, ,08 0, ,00015 0, ,40 4 rsl024434 A c 7 117367289 0. ,65±0. 14 5.2e-06 0, ,39±0. 14 0, .0063 0, ,31±0, ,08 0, ,00016 0, , 15 23 rs6950716 T A 7 117367969 0. ,26±0. 10 0.0084 0, ,30±0. 11 0, .0073 0, ,26±0, ,07 0, ,00041 0, ,095 24 rsl0276758 A G 7 117369189 0. ,58±0. 14 4.8e-05 0, ,38±0. 12 0, .0012 0, ,45±0, ,09 5, ,8e-07 0, , 15 19 rs7782815 G A 7 117373015 0. ,60±0. 14 1.8e-05 0, ,41±0. 12 0, .00049 0, ,48±0, ,09 7, , 2e-08 0, ,23 14 rsl0487380 G A 7 117373527 0. .64±0. 15 1.2e-05 0, ,38±0. 12 0, .0012 0, ,48±0, ,09 1, ,8e-07 0, , 16 18 rs6959314 G A 7 117374593 0. ,26±0. 10 0.0096 0, ,30±0. 11 0, .0079 0, ,28±0, ,07 0, ,00021 0, , 12 21 rsl3221302 G A 7 117375933 0. ,26±0. 10 0.0097 0, ,30±0. 11 0, .0082 0, ,27±0, ,07 0, ,00022 0, , 13 21 rs6966339 A G 7 117376444 0. .62±0. 14 1.3e-05 0, ,29±0. 11 0, .0088 0, ,27±0, ,07 0, ,00028 0, , 12 21 rs6943120 C G 7 117378624 0. ,61±0. 14 1.6e-05 0, ,22±0. 11 0, .045 0, , 12±0, ,08 0, , 13 0, ,018 35 rsl989880 G C 7 117379136 0. ,25±0. 10 0.011 0, , 18±0. 11 0, .11 0, ,23±0, ,07 0, ,0019 0, ,071 26 rs6969783 A T 7 117380544 0. .64±0. 14 5.9e-06 0, ,28±0. 11 0, .012 0, ,20±0, ,07 0, ,0078 0, ,035 31 rsl l56954 A G 7 117382074 0. .54±0. 14 0.00015 0, ,39±0. 12 0, .0012 0, ,44±0, ,09 1, ,6e-06 0, ,30 10 rs916784 G A 7 117386750 0. ,44±0. 14 0.0015 0, , 14±0. 11 0, .19 0, ,26±0, ,08 0, ,0014 0, , 19 16 rs7801876 G A 7 117389362 0. .39±0. 14 0.0045 0, , 14±0. 11 0, .19 0, ,25±0, ,08 0, ,0026 0, ,22 14 rsl013278 C G 7 117391056 0. .44±0. 13 0.0011 0, , 13±0. 10 0, .22 0, ,08±0, ,09 0, ,42 0, ,086 24 rsl468183 A G 7 117392950 0. ,45±0. 14 0.0011 0, , 13±0. 11 0, .22 0, ,24±0, ,08 0, ,0036 0, ,24 13 rs7784849 G A 7 117398486 0. .41±0. 14 0.0034 0, , 14±0. 11 0, .19 0, ,25±0, ,08 0, ,0022 0, ,24 13 rsl0280709 T C 7 117400345 0. ,40±0. 14 0.0049 0, , 14±0. 11 0, .21 0, ,25±0, ,08 0, ,0032 0, ,22 14 rsl0237233 c A 7 117402159 0. .41±0. 14 0.0034 0, , 14±0. 11 0, .21 0, ,25±0, ,08 0, ,0025 0, ,21 15 rsl0226992 c T 7 117402553 0. .47±0. 14 0.00059 0, , 13±0. 11 0, .21 0, ,25±0, ,08 0, ,0023 0, ,21 15 rs7793280 T c 7 117405155 0. .39±0. 14 0.006 0, , 14±0. 11 0, .2 0, ,24±0, ,08 0, ,0036 0, ,21 15 rsl2111806 c G 7 117407087 0. ,46±0. 14 0.00077 0, , 13±0. 11 0, .23 0, ,08±0, ,09 0, ,41 0, ,059 27
rs970185 A T 7 117415419 0. ,45±0.14 0.00085 0, , 13±0.11 0, .22 0, , 18±0, ,08 0, ,026 0, .12 21 rs989996 T c 7 117429488 0. ,58±0. 14 3.6e-05 0, ,24±0.11 0, .022 0, ,37±0, ,08 8, ,9e-06 0, .56 0 rsl3438629 A T 7 117435455 0. ,59±0. 14 3e-05 0, ,26±0.11 0, .013 0, ,25±0, ,08 0, ,0033 0, .17 18 rsl0249457 C A 7 117439807 0. ,58±0. 15 0.0001 0, ,27±0.11 0, .011 0, ,38±0, ,08 9, . le-06 0, .55 0 rs739619 C G 7 117443502 0. ,60±0. 14 1.4e-05 0, , 17±0.11 0, .1 0, , 12±0, ,09 0, , 21 0, .063 27 rsl0240110 G C 7 117450234 0. ,59±0. 15 7.6e-05 0, ,22±0.11 0, .033 0, ,34±0, ,08 5, , 3e-05 0, .41 3 rsl0255829 C T 7 117450335 0. ,60±0. 14 2.2e-05 0, ,26±0.11 0, .013 0, ,37±0, ,08 8, , le-06 0, .61 0 rsl7168159 C T 7 134321159 0. ,82±0. ,21 0.0001 0, , 16±0.14 0, .25 0, ,36±0, , 12 0, ,0021 0, .044 30 rsl l983164 T c 7 134328121 0. ,78±0. ,20 0.00014 0, , 17±0.14 0, .25 0, ,36±0, , 11 0, ,0018 0, .12 22 rsl l973318 T c 7 134331846 0. ,87±0. ,22 6.4e-05 0, , 15±0.14 0, .3 0, ,35±0, , 12 0, ,0023 0, .059 28 rs4565407 A G 7 134346538 0. ,86±0. 19 7.8e-06 0, , 14±0.14 0, .31 0, ,39±0, , 11 0, ,00059 0, .074 26 rs4329203 A C 7 134347280 0. ,84±0. 18 2.5e-06 0, , 14±0.14 0, .3 0, ,41±0, , 11 0, ,00022 0, .052 28 rs4415249 C A 7 134347420 0. ,87±0. ,22 6e-05 0, , 14±0.14 0, .34 0, ,35±0, , 12 0, ,0027 0, .043 30 rsl l22979 A G 7 150546004 0. , 11±0. ,05 0.022 -0.04±0.24 0, .87 0, , 10±0, ,05 0, ,032 0, .076 25 rs7812088 A G 7 150550762 0. ,43±0. 14 0.0016 -0.04±0.21 0, .83 0, ,22±0, , 10 0, ,027 0, .083 25 rs7781265 A G 7 150581873 0. ,04±0. ,02 0.077 -0.06±0.20 0, .76 0, ,03±0, ,02 0, ,086 0, .087 24 rsl0891481 G A 11 112335772 0. ,30±0. 10 0.003 0, ,29±0.11 0, .007 0, ,30±0, ,07 6, , 3e-05 0, .18 17 rs7937151 G T 11 112340234 0. ,59±0. 14 2.5e-05 0, ,31±0.11 0, .0043 0, ,33±0, ,08 2, , 7e-05 0, .19 16 rs2155281 A G 11 112343548 0. ,54±0. 13 4e-05 0, ,29±0.11 0, .0069 0, ,28±0, ,07 9, , 3e-05 0, .17 17 rs720023 T A 11 112344077 0. ,27±0. 10 0.0046 0, ,29±0.11 0, .0068 0, ,21±0, ,07 0, ,0029 0, .057 28 rs7948789 G A 11 112344742 0. ,28±0. 10 0.0045 0, ,30±0.11 0, .0063 0, ,29±0, ,07 8, . le-05 0, .15 19 rs7126748 C T 11 112348186 0. ,61±0. 14 1.7e-05 0, ,30±0.11 0, .0059 0, ,28±0, ,07 0, ,00013 0, .13 21 rs7110863 G A 11 112348348 0. ,25±0. ,09 0.0076 0, ,32±0.11 0, .0031 0, ,28±0, ,07 7, , le-05 0, .12 21 rsl 1214441 A T 11 112351923 0. ,60±0. 13 7.7e-06 0, ,29±0.11 0, .0068 0, ,32±0, ,08 9, , 2e-05 0, .090 24 rs2186707 A T 11 112355853 0. ,56±0. 13 2.3e-05 0, ,31±0.11 0, .0052 0, ,31±0, ,08 0, ,00012 0, .11 22 rs2155290 G c 11 112356278 0. ,49±0. 13 0.00011 0, , 15±0.11 0, .17 0, ,21±0, ,08 0, ,0082 0, .023 33 rs2298527 G c 11 112357171 0. ,58±0. 13 1.7e-05 0, , 16±0.11 0, .14 0, , 19±0, ,07 0, ,0076 0, .049 29 rsl940727 T G 11 112357798 0. .49±0. 13 9.3e-05 0, ,31±0.11 0, .005 0, ,38±0, ,08 3, , 7e-06 0, .27 11 rsl940724 G A 11 112358156 0. ,48±0. 12 0.0001 0, ,30±0.11 0, .0054 0, ,38±0, ,08 4, , le-06 0, .29 10 rs7121047 T A 11 112366103 0. ,33±0. 11 0.002 0, ,31±0.11 0, .005 0, ,32±0, ,08 3, , 2e-05 0, .17 17 rsl0891487 A G 11 112374264 0. ,56±0. 13 3e-05 0, ,30±0.11 0, .006 0, ,37±0, ,08 7, ,4e-06 0, .24 13 rs4589334 C G 11 112384666 0. .62±0. 14 8.4e-06 0, ,20±0.11 0, .059 0, , 14±0, ,09 0, , 13 0, .010 37 rs7113596 A C 11 112388971 0. ,44±0. 12 0.00022 0, ,31±0.11 0, .0051 0, ,34±0, ,08 1, , 5e-05 0, .22 14 rsl0732853 G C 11 112392620 0. .42±0. 12 0.00067 0, , 14±0.11 0, .17 0, ,27±0, ,08 0, ,00063 0, .084 25 rs999851 G A 11 112395056 0. .49±0. 13 0.00025 0, ,31±0.11 0, .0048 0, ,37±0, ,08 8, , 3e-06 0, .27 11 rsl940733 A G 11 112397484 0. ,61±0. 14 1.7e-05 0, ,31±0.11 0, .0049 0, ,38±0, ,08 5, , 2e-06 0, .26 12 rs7926312 G A 11 112399066 0. ,55±0. 14 4.8e-05 0, ,31±0.11 0, .0047 0, ,39±0, ,08 2, ,8e-06 0, .26 12 rsl940734 C G 11 112400238 0. ,61±0. 14 1.3e-05 0, ,20±0.11 0, .054 0, , 13±0, ,09 0, , 16 0, .010 38 rsl0750022 T G 11 112401523 0. .52±0. 14 0.00011 0, ,31±0.11 0, .005 0, ,38±0, ,08 5, , le-06 0, .29 10 rsl892983 c T 11 112404424 0. .62±0. 14 1.3e-05 0, ,30±0.11 0, .0065 0, ,38±0, ,08 5e-06 0, .28 11 rsl 1214469 A T 11 112405553 0. .42±0. 12 0.00037 0, ,30±0.11 0, .0053 0, ,24±0, ,08 0.0023 0, .047 29 rs7113099 A T 11 112409545 0. ,61±0. 14 2e-05 0, ,30±0.11 0, .006 0, ,28±0, ,08 0.0008 0, .066 27 rsl940712 C G 11 112411428 0. ,50±0. 13 0.0001 0, , 19±0.10 0, .063 0, , 11±0, ,09 0.22 0, .013 36
rs7948327 A C 11 112415287 0. ,61±0.14 2e-05 0.29±0.11 0, .007 0, ,37±0, ,08 8, , 5e-06 0, .26 12 rs7938812 G T 11 112416214 0. ,61±0. 14 1.4e-05 0.31±0.11 0, .0036 0, ,39±0, ,08 2, , 7e-06 0, .31 9 rs7942723 G T 11 112417049 0. ,61±0. 14 1.8e-05 0.29±0.11 0, .0068 0, ,37±0, ,08 7, , 7e-06 0, .26 12 rs3802847 C T 11 112417513 0. , 14±0. ,06 0.035 0.29±0.11 0, .0075 0, , 17±0, ,06 0, ,0018 0, .054 28 rs3802848 C A 11 112417597 0. ,51±0. 14 0.00017 0.29±0.11 0, .0068 0, ,37±0, ,08 1, . le-05 0, .26 12 rs3802850 C A 11 112417728 0. ,52±0. 14 0.00011 0.29±0.11 0, .007 0, ,37±0, ,08 8, . le-06 0, .27 11 rs2186874 C T 11 112417934 0. ,48±0. 12 8.8e-05 0.29±0.11 0, .0071 0, ,35±0, ,08 1, , 2e-05 0, .21 15 rs2663907 A c 15 79160087 0. ,67±0. 17 6e-05 -0.10±0.15 0, .51 0, ,22±0, , 11 0, ,039 0, .0013 46 rs868954 G A 15 79169043 0. ,71±0. 16 1.4e-05 -0.00±0.01 0, .97 0, ,29±0, , 10 0, ,0049 0, .0015 46 rsl0852660 T C 16 5549163 0. ,57±0. 14 6.1e-05 0.04±0.12 0, .75 0, ,23±0, ,09 0, ,0078 0, .011 37 rs9888783 G C 16 5549316 0. ,63±0. 15 2.4e-05 0.06±0.13 0, .66 0, ,04±0, , 14 0, , 76 0, .0078 39 rs9888773 C G 16 5549451 0. ,63±0. 15 2.5e-05 0.14±0.12 0, .24 0, ,30±0, ,09 0, ,00064 0, .030 32 rs9888774 A G 16 5549464 0. ,62±0. 15 3.1e-05 0.02±0.13 0, .89 0, ,22±0, ,09 0, ,011 0, .013 37 rs2880356 T C 16 5549672 0. ,61±0. 15 4.4e-05 0.02±0.13 0, .9 0, ,22±0, ,09 0, ,013 0, .014 36 rsl7790267 c T 16 5550469 0. ,60±0. 15 5.3e-05 0.02±0.13 0, .91 0, ,21±0, ,09 0, ,015 0, .019 35 rsl969139 T c 16 5551122 0. .59±0. 15 6.5e-05 0.02±0.13 0, .88 0, ,21±0, ,09 0, ,015 0, .023 34 rsl948951 A G 16 5553998 0. ,60±0. 15 3.8e-05 0.05±0.12 0, .68 0, ,24±0, ,09 0, ,0062 0, .014 36 rs7186722 A G 16 5556055 0. ,45±0. 13 0.00041 -0.02±0.10 0, .84 0, , 17±0, ,08 0, ,035 0, .12 21
Table 10. Association with Nicotine Dependence. Shown are the number of cases and controls (N), the frequencies of the effect allele (see Table 7) in cases and controls, the odds ratio and 95% confidence intervals (OR and 95%CI), the P value for the test of association (P). The results for Lung Cancer are shown in Table 11.
N Freq
SNP-Allele Population cases controls cases controls O R (95% CI) P rsl051730-A Iceland 1976 36147 0 384 0 343 1 20 ( 1 12, 1 28) 1 0- 10"7
Chrs 15 NTR-NESDA 835 611 0 286 0 34 1 28 ( 1 09, 1 50) 0 0026
Combined - - - - 1 21 ( 1 14, 1 29) 1 3- 10"9 rs6474412-T Iceland 1979 36202 0 785 0 771 1 09 ( 1 01, 1 18) 0 032
Chrs 8 NTR-NESDA 835 611 0 833 0 846 1 10 (0 89, 1 36) 0 38
Combined - - - - 1 09 ( 1 01, 1 17) 0 020 rsl3280604-A Iceland 1979 36202 0 785 0 771 1 09 ( 1 01, 1 18) 0 032
Chrs 8 NTR-NESDA 835 611 0 833 0 846 1 10 (0 89, 1 36) 0 39
Combined - - - - 1 09 ( 1 01, 1 17) 0 021 rs215614-G Iceland 1979 36202 0 37 0 355 1 07 ( 1 00, 1 14) 0 050
Chrs 7 NTR-NESDA 835 611 0 342 0 341 0 99 (0 86, 1 15) 0 95
Combined - - - - 1 06 (0 99, 1 12) 0 080 rs215605-G Iceland 1979 36163 0 372 0 357 1 07 ( 1 00, 1 14) 0 055
Chrs 7 NTR-NESDA 835 611 0 346 0 345 1 00 (0 84, 1 17) 0 96
Combined - - - - 1 06 (0 99, 1 12) 0 078 rs7937-T Iceland 1975 36121 0 548 0 549 1 00 (0 93, 1 06) 0 92
Chrs 19 NTR-NESDA 835 611 0 529 0 531 1 01 (0 83, 1 23) 0 93
Combined - - - - 1 00 (0 94, 1 06) 0 95 rsl801272-A Iceland 1979 36202 0 96 0 964 0 90 (0 69, 1 18) 0 45
Chrs 19 NTR-NESDA 835 611 0 987 0 981 0 67 (0 30, 1 46) 0 31
Combined - - - - 0 88 (0 68, 1 13) 0 30 rs4105144-C Iceland 1979 36202 0 697 0 706 0 96 (0 87, 1 06) 0 40
Chrs 19 NTR-NESDA 835 611 0 681 0 665 0 93 (0 74, 1 16) 0 51
Combined - - - - 0 96 (0 88, 1 04) 0 30 rs7260329-G Iceland 1969 35982 0 678 0 669 1 04 (0 97, 1 11) 0 25
Chrs 19 NTR-NESDA 835 611 0 67 0 697 1 14 (0 92, 1 40) 0 23
Combined - - - - 1 05 (0 98, 1 12) 0 14
Table 11. Association of SNPs in 4 chromosomal regions with Lung Cancer in four populations. Shown are the number of cases and controls (N), the frequencies of the effect allele (see Table 1) in cases and controls, the odds ratio and 95% confidence intervals (OR and 95%CI), the P value for the test of association (P).
N Freq
Population case control case control OR (95% CI) P
rs6474412-T, chromosome 8pl l
Iceland 839 36,606 0 784 0 770 1 08 (0 96, 1 22) 0 19
Denver 192 856 0 805 0 790 1 09 (0 83, 1 44) 0 53
Spain 351 1,195 0 819 0 764 1 40 (1 13, 1 72) 0 0019
Netherlands 515 769 0 828 0 809 1 13 (0 92, 1 39) 0 23
IARCa 2,506 1,914 0 763 0 778 1 10 (0 99, 1 22) 0 072
Combined 4,403 41,340 - - 1 12 (1 05, 1 20) 0 00060
rs215614-G, chromosome 7pl4
Iceland 839 36,606 0 366 0 355 1 05 (0 95, 1 16) 0 37
Denver 195 864 0 403 0 376 1 12 (0 89, 1 40) 0 33
Spain 450 1,281 0 370 0 335 1 17 (1 00, 1 37) 0 055
Netherlands 502 1,709 0 366 0 367 0 99 (0 86, 1 15) 0 92
IARCa 2,513 1,917 0 344 0 365 1 09 (1 00, 1 19) 0 057
Combined 4,499 42,377 - - 1 07 (1 02, 1 13) 0 011
rs7937-T, chromosome 19ql3
Iceland 836 36,552 0 555 0 549 1 03 (0 93, 1 13) 0 60
Denver 193 864 0 567 0 595 0 89 (0 71, 1 12) 0 32
Spain 453 1,330 0 532 0 512 1 08 (0 93, 1 26) 0 31
Netherlands 528 1,629 0 552 0 548 1 02 (0 89, 1 17) 0 80
IARC 2,518 1,921 0 559 0 580 1 09 (1 00, 1 18) 0 048
Combined 4,528 42,296 - - 1 05 (0 99, 1 10) 0 080
rs4105144-C, chromosome 19ql3
Iceland 839 36,606 0.713 0 705 1 04 (0 90, 1 20) 0 61
Denver 193 848 0.725 0 688 1 20 (0 94, 1 53) 0 14
Spain 437 1,288 0.669 0 620 1 24 (1 06, 1 46) 0 0085
Netherlands 513 1,665 0.638 0 640 0 99 (0 86, 1 15) 0 93
Combined 1,982 40,407 - - 1 09 (1 00, 1 18) 0 040
rs7260329-G, chromosome 19ql3
Iceland 831 36,454 0.688 0 669 1 09 (0 98, 1 21) 0 11
Denver 189 808 0.728 0 694 1 18 (0 92, 1 51) 0 20
Spain 457 1,305 0.702 0 674 1 14 (0 97, 1 35) 0 11
Netherlands 519 1,660 0.701 0 678 1 12 (0 96, 1 30) 0 15
IARC 2,481 1,899 0.662 0 670 1 02 (0 95, 1 10) 0 61
Combined 4,477 42,126 - - 1 06 (1 00, 1 12) 0 041
aFor IARC, results for rs6474412 and rs215614 were not available and here we report results for rs6474414 and rs215605, respectively, both of which are perfect surrogates in the HapMap CEU samples (r2 = l).
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Claims

A method of determining a susceptibility to lung cancer, the method comprising : obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and determining a susceptibility to lung cancer from the sequence data, wherein the at least one polymorphic marker is a marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith.
The method of claim 1, wherein the sequence data is nucleic acid sequence data obtained from a biological sample containing nucleic acid from the human individual .
The method of claim 2, wherein the nucleic acid sequence data is obtained using a method that comprises at least one procedure selected from :
(i) amplification of nucleic acid from the biological sample;
(ii) hybridization assay using a nucleic acid probe and nucleic acid from the biological sample;
(iii) hybridization assay using a nucleic acid probe and nucleic acid obtained by amplification of the biological sample, and
(iv) high-throughput sequencing
The method of claim 1, wherein the sequence data is obtained from a preexisting record.
The method of claim 4, wherein the preexisting record comprises a genotype dataset.
The method of any one of the preceding claims, wherein the determining comprises determining the presence or absence of at least one at-risk allele for lung cancer of the polymorphic marker.
7. The method of any one of the preceding claims, wherein the determining comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to lung cancer.
8. The method of any one of the preceding claims, wherein the at least one polymorphic marker in linkage disequilibrium with rs6474412 is selected from the group consisting of the markers listed in Table 2.
9. The method of any one of the claims 1 to 3, wherein the at least one polymorphic marker in linkage disequilibrium with rs215614 is selected from the group consisting of the markers listed in Table 1.
10. The method of any one of the claims 1 to 3, wherein the at least one polymorphic marker in linkage disequilibrium with rs4105144 is selected from the group consisting of the markers listed in Table 3.
11. The method of claim 2, wherein obtaining nucleic acid sequence data comprises obtaining a biological sample from the human individual and transforming the sample to determine sequence of the at least one polymorphic marker.
12. The method of claim 11, wherein transforming the sample comprises amplifying a nucleic acid segment that comprises the at least one polymorphic marker and determining the sequence of the polymorphic marker.
13. The method of claim 11 or claim 12, wherein determining sequence of the at least one polymorphic marker comprises determining the presence or absence of at least one allele of the at least one polymorphic marker.
14. The method of claim 1, wherein the sequence data is amino acid sequence data obtained from a biological sample comprising polypeptide from the individual.
15. The method of claim 14, comprising determining the presence or absence of an amino acid substitution in a polypeptide from the individual.
16. The method of any one of the preceding claims, wherein determination of a susceptibility comprises comparing the sequence data to a database containing correlation data between the at least one polymorphic marker and susceptibility to lung cancer.
17. The method of any one of the preceding claims, wherein the at least one allele or amino acid substitution is associated with an increased susceptibility of lung cancer in humans.
18. A method of assessing a susceptibility to lung cancer in a human individual, comprising i. obtaining sequence data about the individual for at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans; ii . identifying the presence or absence of at least one allele in the at least one polymorphic marker that correlates with increased occurrence of lung cancer in humans; wherein determination of the presence of the at least one allele identifies the individual as having elevated susceptibility to lung cancer, and wherein determination of the absence of the at least one allele identifies the individual as not having the elevated susceptibility.
19. The method of claim 17 or claim 18, wherein the presence of the at least one allele or amino acid substitution is indicative of increased susceptibility with a relative risk of at least 1.05, at least 1.06, at least 1.07, at least 1.08, at least 1.09, at least 1.10, at least 1.11, at least 1.12, at leas 1.13, at least 1.14 or at least 1.15.
20. The method of claim 19, wherein the at least one allele is selected from the group
consisting of the T allele of rs6474412, the G allele of rs215614, the T allele of rs7937, the C allele of rs4105144 and the G allele of rs7260329.
21. A method of identification of a marker for use in assessing susceptibility to lung cancer in human individuals, the method comprising a. identifying at least one polymorphic marker in linkage disequilibrium with rs6474412, rs215614, or rs4105144; b. obtaining sequence information about the at least one polymorphic marker in a group of individuals diagnosed with lung cancer; and c. obtaining sequence information about the at least one polymorphic marker in a group of control individuals; wherein determination of a significant difference in frequency of at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer as compared with the frequency of the at least one allele in the control group is indicative of the at least one polymorphism is useful for assessing susceptibility to lung cancer.
22. The method of Claim 21, wherein an increase in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group, is indicative of the at least one polymorphism being useful for assessing increased susceptibility to lung cancer, and wherein a decrease in frequency of the at least one allele in the at least one
polymorphism in individuals diagnosed with lung cancer, as compared with the frequency of the at least one allele in the control group, is indicative of the at least one
polymorphism being useful for assessing decreased susceptibility to, or protection against, lung cancer.
23. A method of predicting prognosis of an individual diagnosed with lung cancer, the method comprising obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs215614, rs6474412 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to lung cancer in humans, and predicting prognosis of lung cancer from the sequence data.
24. A method of assessing probability of response of a human individual to a therapeutic agent for preventing, treating and/or ameliorating symptoms associated with lung cancer, comprising : obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different probabilities of response to the therapeutic agent in humans, and determining the probability of a positive response to the therapeutic agent from the sequence data.
25. A kit for assessing susceptibility to lung cancer, the kit comprising : reagents for selectively detecting at least one allele of at least one polymorphic marker in the genome of the individual, wherein the polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith.
26. The kit of claim 25, further comprising a a collection of data comprising correlation data between the at least one polymorphism and susceptibility to lung cancer.
27. The kit of claim 26, wherein the collection of data is on a computer-readable medium .
28. The kit of any one of the claims 25 to 27, wherein the kit comprises reagents for detecting no more than 100 alleles in the genome of the individual.
29. The kit of claim 28, wherein the kit comprises reagents for detecting no more than 20 alleles in the genome of the individual.
30. The kit of any one of the claims 25 to 29, wherein the reagents comprise at least one oligonucleotide probe for selectively detecting at least one allele of the at least one polymorphic marker.
31. The kit of claim 30, wherein the at least one oligonucleotide probe is from 15 to 50
nucleotides in length .
32. Use of an oligonucleotide probe in the manufacture of a diagnostic reagent for diagnosing and/or assessing a susceptibility to lung cancer, wherein the probe is capable of hybridizing to a segment of a nucleic acid whose nucleotide sequence is given by any one of SEQ ID NO: 1-737, and wherein the segment is 15-400 nucleotides in length .
33. The use of claim 32, wherein the segment of the nucleic acid to which the probe is
capable of hybridizing comprises a polymorphic site.
34. A computer-readable medium having computer executable instructions for determining susceptibility to lung cancer, the computer readable medium comprising : sequence data identifying at least one allele of at least one polymorphic marker in the individual ; a routine stored on the computer readable medium and adapted to be executed by a processor to determine risk of developing lung cancer for the at least one polymorphic marker; wherein the at least one polymorphic marker is selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith .
35. The computer-readable medium of claim 34, wherein the medium contains data indicative of at least two polymorphic markers.
36. The computer-readable medium of claim 34 or claim 35, wherein the data indicative of the at least one polymorphic marker comprises sequence data identifying at least one allele of the at least one polymorphic marker.
37. An apparatus for determining a genetic indicator for lung cancer in a human individual, comprising : a processor; a computer readable memory having computer executable instructions adapted to be executed on the processor to analyze marker information for at least one human individual with respect to at least one polymorphic marker selected from the group consisting of rs6474412, rs215614 and rs4105144, and markers in linkage disequilibrium therewith, and generate an output based on the marker information, wherein the output comprises a measure of susceptibility of the at least one marker or haplotype as a genetic indicator of the condition for the human individual .
38. The apparatus of claim 37, wherein the marker information comprises sequence data identifying at least one allele of the at least one marker in the genome of the individual.
39. The apparatus of claim 38, wherein the sequence data comprises a genotype dataset.
40. The apparatus according to Claim 37, wherein the computer readable memory further comprises data indicative of the risk of developing lung cancer associated with at least one allele of at least one polymorphic marker, and wherein a risk measure for the human individual is based on a comparison of the marker information for the human individual to the risk of the condition associated with the at least one allele of the at least one polymorphic marker.
41. The method, kit, use, medium or apparatus according to any of the preceding claims, wherein linkage disequilibrium between markers is characterized by particular numerical values of the linkage disequilibrium measures r2 and/or | D'| .
42. The method, kit, use, medium or apparatus according to any of the preceding claims, wherein linkage disequilibrium between markers is characterized by values of r2 of at least 0.2.
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