WO2011095999A1

WO2011095999A1 - Genetic variants for predicting risk of breast cancer

Info

Publication number: WO2011095999A1
Application number: PCT/IS2011/050003
Authority: WO
Inventors: Patrick Sulem; Simon Stacey
Original assignee: Decode Genetics ehf
Current assignee: Decode Genetics ehf
Priority date: 2010-02-05
Filing date: 2011-02-04
Publication date: 2011-08-11
Anticipated expiration: 2012-08-05

Abstract

The invention pertains to certain genetic variants that have been determined to be susceptibility variants of breast cancer. Methods of disease management, including diagnosing increased susceptibility to breast cancer, methods of predicting response to therapy and methods of predicting prognosis using such variants are described. The invention further relates to kits useful in the methods of the invention.

Description

GENETIC VARIANTS FOR PREDICTING RISK OF BREAST

CANCER

BACKGROUND OF THE INVENTION

Breast cancer is by far the most common cancer in women worldwide. Current global incidence is in excess of 1,151,000 new cases diagnosed each year [Parkin, et al., (2005), CA Cancer J Clin, 55, 74-108] . Breast cancer incidence is highest in developed countries, particularly amongst populations of Northern European ethnic origin, and is increasing. In the United States the annual age-standardized incidence rate is approximately 122 cases per 100,000 populations, more than three times the world average. Rates in Northern European countries are similarly high . In the year 2010 it is estimated that 209,060 new cases of invasive breast cancer will be diagnosed in the U.S.A. and 40,230 people will die from the disease [Jemal, et al., (2010), CA Cancer J Clin, 60, 277-300] . To this figure must be added a further 54,010 ductal and lobular carcinoma in-situ diagnoses expected in 2010. From an individual perspective, the lifetime probability of developing breast cancer is 12.1% in U.S. women (i.e., 1 in 8 women will develop breast cancer during their lives) . As with most cancers, early detection and appropriate treatment are important factors. Overall, the 5-year survival rate for breast cancer is 89%. However, in individuals presenting with regionally invasive or metastatic disease, the rate declines to 84% and 23%, respectively [Jemal, et al., (2010), CA Cancer J Clin, 60, 277-300] .

Increasingly, emphasis is falling on the identification individuals who are at high risk for primary or recurrent breast cancer. Such individuals can be managed by more intensive screening, preventative chemotherapies, hormonal therapies and, in cases of individuals at extremely high risk, prophylactic surgery. Mass screening programs constitute a huge economic burden on health services, while preventative therapies have associated risks and quality of life

consequences. Genetic Predisposition to Breast Cancer

The two primary classes of known risk factors for breast cancer are endocrine factors and genetics. Regarding the latter, approximately 12% of breast cancer patients have one or more first degree relatives with breast cancer [(2001), Lancet, 358, 1389-99] . The well known, dominant breast cancer predisposition genes BRCA1 and BRCA2 confer greatly increased breast cancer risk to carriers, with lifetime penetrance estimates ranging from 40-80%. The presence of BRCA1 and BRCA2 mutations can account for the majority of families with 6 or more cases of breast cancer and for a large proportion of families comprising breast and ovarian or male breast cancer. However such families are very rare indeed. BRCA1 and BRCA2 mutations are found much less frequently in families with fewer cases or in families characterized by breast cancer cases only. Together, mutations in BRCA1 and BRCA2 can account for 15-20% of the risk for familial breast cancer. In non-founder populations, if all common BRCA mutations could be detected, between 2-3% of incident breast cancer patients would be expected to harbor a mutation [Gorski, et al., (2005), Breast Cancer Res Treat, 92, 19-24; (2000), Br J Cancer, 83, 1301-8] . This low "chance to find" statistic precludes the responsible use of BRCA mutation testing outside families with an obvious hereditary predisposition (Anon[(2003), J Clin Oncol, 21, 2397-406]) . Rare, high penetrance mutations are known to occur in the TP53 and PTEN genes, however, these together account for no more than 5% of the total genetic risk for breast cancer [Easton, (1999), Breast Cancer Res, 1, 14-7] . Linkage studies have been largely unsuccessful in identifying any more, widespread mutations conferring high risk for breast cancer[Smith, et al., (2006), Genes Chromosomes Cancer, 45, 646-55] .

Recent epidemiological studies have indicated that the majority of breast cancer cases arise in a predisposed, susceptible minority of the population [Antoniou, et al., (2002), Br J Cancer, 86, 76-83; Pharoah, et al., (2002), Nat Genet, 31, 33-6] . Data from twin studies and observations of the constant, high incidence of cancer in the contralateral breast of patients surviving primary breast cancer indicate that a substantial portion of the uncharacterized risk for breast cancer is related to endogenous factors, most probably genetic [Lichtenstein, et al., (2000), N Engl J Med, 343, 78-85; Peto and Mack, (2000), Nat Genet, 26, 411-4] . Knowledge of the genetic factors that underpin this widespread risk is very limited. Segregation analyses predict that the uncharacterized genetic risk for breast cancer is most likely to be polygenic in nature, with risk alleles that confer low to moderate risk and which may interact with each other and with hormonal risk factors. Nevertheless, these studies predict as much as 40-fold differences in relative risk between the highest and lowest quintiles of a distribution that could be defined by genetic profiling that captures these low to moderate risk alleles [Antoniou, et al., (2002), Br J Cancer, 86, 76-83; Pharoah, et al., (2002), Nat Genet, 31, 33-6] . 88% of all breast cancer cases are expected to arise amongst a predisposed 50% of the population and the 12% of the population at highest risk accounts for 50% of all breast cancer cases [Pharoah, et al., (2002), Nat Genet, 31, 33-6; Pharoah, (2003), Recent Results Cancer Res, 163, 7-18; discussion 264- 6] . Much focus is therefore directed towards the identification of such genetically predisposed individuals and developing personalized medical management strategies for them.

It has been shown that there is a significant familial risk of breast cancer in Iceland which extends to at least 5^th degree relatives [Amundadottir, et al., (2004), PLoS Med, 1, e65; Tulinius, et al., (2002), J Med Genet, 39, 457-62] . The contribution of BRCA1 mutations to familial risk in Iceland is thought to be minimal [Arason, et al., (1998), J Med Genet, 35, 446-9; Bergthorsson, et al., (1998), Hum Mutat, Suppl 1, S195-7] . A single founder mutation in the BRCA2 gene (999del5) is present at a carrier frequency of 0.6-0.8% in the general Icelandic population and 7.7-8.6% in female breast cancer patients [Thorlacius, et al., (1997), Am J Hum Genet, 60, 1079-84; Gudmundsson, et al ., (1996), Am J Hum Genet, 58, 749-56] . This single mutation is estimated to account for approximately 40% of the inherited breast cancer risk to first through third degree relatives [Tulinius, et al., (2002), J Med Genet, 39, 457-62] . Although this estimate is higher than the 15-25% of familial risk attributed to all BRCA 1 and 2 mutations combined in non-founder populations, there is still some 60% of Icelandic familial breast cancer risk to be explained. First degree relatives of patients who test negative for BRCA2 999del5 remain at a 1.72 fold the population risk for breast cancer (95% CI 1.49-1.96) [Tulinius, et al., (2002), J Med Genet, 39, 457-62].

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.

Understanding of the genetic factors contributing to the residual genetic risk for breast cancer is limited. Variants in several genes have been confirmed as moderate penetrance breast cancer risk genes; CHEK2, ATM, PALB2, BRIP1, NBS1 and RAD 51 C [Renwick, et al., (2006), Nat Genet, 38, 873-5;; Meijers-Heijboer et al., (2002) Nat Genet, 31, 55-9; Vahteristo et al., (2002) Am J Hum Genet, 71, 432-8; Rahman et al., (2007) Nat Genet, 39, 142-3; Erkko et al., (2007) Nature, 446, 316-9; Seal et al., 2006 Nat Genet, 38, 1239-41; Steffen et al., (2006) Int J Cancer, 119, 472-5; Meindl et al., (2010) Nat Genet, 42, 410-4]. Furthermore, a recent genome-wide association studies have identified low penetrance associations between breast cancer and common genetic variants at the following loci : lpl l (NOTCH1 ), 2q33 (CASP8), 2q35 {IGFBP2, IGFBP5), 3p24 {NEK 10), 5pl2 {MRPS30), 5ql l (MAP3K1 ), 8q24 {MYQ, 9p21 {CDKN2A /B), 10pl5 {ANKRD16, FBX018), 10q21 {ZNF365), 10q22 {ZMIZl), 10q26 (FGFR2), l lpl5 (LSPl, IGF2), l lql3 {CCNDl ), 14q24 {RAD51L1 ), 16ql2 (70X3), 17q23 (COX.... ) [Stacey et al., (2007) Nat Genet, 39, 865-9; Stacey et al., (2008) Nat Genet, 40, 703-6; Easton et al., (2007) Nature, 447, 1087-93; Ahmed et al., (2009) Nat Genet 41, 585-90; Turnbull et al., (2010) Nat Genet, 42, 504-7; Thomas et al ., (2009) Nat Genet, 41, 579-84; Hunter et al., (2007) Nat Genet, 39, 870-4.]

No universally successful method for the prevention or treatment of breast cancer is currently available. Management of breast cancer currently relies on a combination of primary prevention, early diagnosis, appropriate treatments and secondary prevention . There are clear clinical imperatives for integrating genetic testing into all 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. The present invention provides additional genetic variants for breast cancer than can be integrated in prevention programs for breast cancer.

SUMMARY OF THE INVENTION

The present invention is based on the finding by the present inventors that certain genetic variants on chromosome 6 are associated with risk of breast cancer. The invention provides various diagnostic applications based on this surprising finding, including methods, kits, media and apparati useful for determining breast cancer risk.

In a first aspect, the invention provides a method of determining a susceptibility to breast cancer in a human individual, the method comprising steps of (i) 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 breast cancer in humans, and (ii) determining a susceptibility to breast cancer from the sequence data. In another aspect, the invention provides a method of determining a susceptibility to breast cancer in a human individual, the method comprising steps of (i) analyzing sequence data from 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 breast cancer in humans, and (ii) determining a susceptibility to breast cancer from the sequence data.

The at least one polymorphic marker is suitably selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith . In another aspect, the invention provides a method of assessing a susceptibility to breast cancer in a human individual, comprising (i) obtaining sequence information about the individual for at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different

susceptibilities to breast 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 breast cancer in humans; wherein determination of the presence of the at least one allele identifies the individual as having elevated susceptibility to breast cancer, and wherein determination of the absence of the at least one allele identifies the individual as not having the elevated susceptibility.

The invention also provides a method of determining a susceptibility to breast 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 breast cancer in humans, and determining a susceptibility to breast cancer from the sequence data, wherein the at least one polymorphic marker is a marker associated with the human C6orf97 gene and/or the human ESR1 gene. Such a marker is suitably a marker that is in linkage disequilibrium with the human C6orf97 gene and/or the human ESR1 gene, i.e. the marker is in linkage disequilibrium with at least one genetic element, such as a polymorphic marker, within the human C6orf97 gene and/or the human ESR1 gene.

The invention further provides a method of identification of a marker for use in assessing susceptibility to breast cancer in human individuals, the method comprising (a) identifying at least one polymorphic marker in linkage disequilibrium with rs9397435; (b) obtaining sequence information about the at least one polymorphic marker in a group of individuals diagnosed with breast 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 breast 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 susceptibility to breast cancer. In a suitable embodiment, an increase in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with breast 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 breast cancer, and wherein a decrease in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with breast 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, breast cancer. It is known that individuals who have presented with a first primary tumor, may be at increased risk of later developing a second primary tumor. It is useful to be able to identify those individuals who are at increased risk of developing such second primary tumors. Thus, in another aspect of the invention, a method of determining risk of developing at least a second primary tumor in an individual previously diagnosed with breast cancer is provided, the method comprising obtaining sequence data about the 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 risk of developing a second primary tumor in humans previously diagnosed with breast cancer, and determining the risk of developing at least a second primary tumor in the individual from the sequence data, wherein the at least one polymorphic marker is selected from rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith.

Further provided is a method of predicting prognosis of an individual diagnosed with breast 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to breast cancers in humans, and predicting prognosis of breast cancer from the sequence data.

The markers shown herein to be associated with risk of breast cancer may also be useful for determining whether individuals are more or less likely to respond to a particular therapeutic agent for treating breast cancer. Thus, in another aspect the invention relates to a method of assessing probability of response of a human individual to a breast cancer therapeutic agent, 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, 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.

Yet another aspect of the invention relates to methods of monitoring the progress of treatment of individuals undergoing treatment for breast cancer. Such a method suitably comprises steps of obtaining sequence data about a human individual identifying at least one allele of at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different outcome of breast cancer treatment in humans, and determining the probability of a positive treatment outcome from the sequence data.

In another aspect of the invention, a method of establishing a diagnosis is provided, by combining use of diagnostic risk markers of breast cancer as identified by the present inventors, in combination with other diagnostic and clinical methods that are useful for making a diagnosis of breast cancer in an individual. Thus, in one aspect the invention provides a method of diagnosing breast cancer in a human individual, the method comprising (A) obtaining sequence data from the individual, identifying at least one at-risk allele selected from the group consisting of the G allele of rs9397435, the T allele of rs77275268, the G allele of rs9383932, the G allele of rsl2662670, the A allele of rsl2665607, the G allele of rs9383589, the C allele of rs3734805, the A allele of rs6929137, the A allele of rs7752591, the A allele of rs3734804, the C allele of rs6932260 and the A allele of rs852003, and marker alleles in linkage disequilibrium therewith; and (B) one of, or a combination of (i) considering symptoms experienced by the human individual and/or the family history of breast cancer for the human individual; (ii) clinical or self- exam screening of a breast for lumps or other abnormalities; (iii) mammographic screening of a breast for breast cancer; (iv) fine needle aspiration cytology; (v) biopsy of breast tissue; and (vi) determination of the presence or absence of at least one additional genetic risk factor of breast cancer in the individual; whereupon a diagnosis of the presence or absence of breast cancer for the individual is made. Also provided is a method of assessing a subject's risk for breast cancer, the method comprising (a) obtaining sequence information about the individual identifying at least one allele of at least one polymorphic marker in the genome of the individual; (b) representing the sequence information as digital genetic profile data; (c) transforming the digital genetic profile data on a computer processor to generate breast cancer risk assessment report for the subject; and (d) displaying the risk assessment report on an output device; wherein the at least one polymorphic marker comprises at least one marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith . In one suitable embodiment, the digital genetic profile data comprises data indicating the presence or absence of at least one allele of the at least one polymorphic marker.

Kits are also provided . In one embodiment, a kit for assessing susceptibility to breast cancer in humans is provided, the kit comprising reagents for selectively detecting at least one allele of at least one polymorphic marker in the genome of the individual, and a collection of data comprising correlation data between the at least one polymorphism and susceptibility to breast cancer. The at least one marker is suitably selected from the group consisting of the markers rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith. The present invention also provides diag nostic reagents. In one such aspect, the invention relates to the use of an oligonucleotide probe in the manufacture of a diag nostic reagent for diagnosing and/or assessing a susceptibility to breast cancer in hu mans, wherein the probe is capable of hybridizing to a segment of a nucleic acid whose nucleotide sequence is given by any of SEQ ID NO : 1-92 , and wherein the seg ment is 15-300 nucleotides in length . In a suita ble embodiment, the seg ment of the nucleic acid to which the probe is ca pable of hybridizing comprises a polymorphic site. The polymorphic site is suita bly selected from the group consisting of the markers rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibriu m therewith .

The invention also provides computer-im plemented aspects. As is known i n the art, sequence data can conveniently be stored a nd a na lyzed in digita l format, a nd either such sequence data (e.g. , genotype data) or resu lts derived therefrom (e.g., disease-risk estimates) can be provided in digita l format to an end-user. One such aspect relates to a computer-reada ble mediu m having computer executable instructions for determining susceptibility to breast cancer in huma ns, the computer readable mediu m comprising (i) data indicative of at least one polymorphic marker; a nd (ii) a routine stored on the com puter readable medium a nd ada pted to be executed by a processor to determine risk of developing breast ca ncer for the at least one polymorphic marker; wherein the at least one polymorphic ma rker is selected from the grou p consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in lin kage disequilibrium therewith .

Another computer-im plemented aspect relates to an apparatus for determining a genetic indicator for breast cancer in a human individual, com prising (i) a processor; and (ii) a computer readable memory having computer executa ble instructions adapted to be executed on the processor to a na lyze marker i nformation for at least one hu man individua l with respect to at least one polymorphic ma rker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, a nd markers in lin kage disequilibriu m therewith, and generate an output based on the ma rker information, wherein the output comprises a measu re of susceptibility of the at least one marker or ha plotype as a genetic indicator of breast cancer for the hu man individual .

In one em bodiment, the computer reada ble memory further com prises data indicative of the risk of developing breast ca ncer associated with at least one al lele of at least one polymorphic marker, and wherein a risk measure for the human individua l is based on a comparison of the marker information for the human individual to the risk of breast ca ncer associated with the at least one allele of the at least one polymorphic marker. The invention also provides risk assessment reports. One such aspect relates to a risk assessment report of breast cancer for a human individual, comprising (i) at least one personal identifier, and (ii) representation of at least one risk assessment measure of breast cancer for the human subject for at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith. Such reports may be provided in any suitable format, including electronic format (e.g., on a computer-readable medium) or a paper format (e.g., a reported printed or written on paper). A further aspect of the invention is to provide use of variants for selecting individuals for administration of therapeutic agents for treating breast cancer. One such aspect provides use of an agent for treating breast cancer in a human individual that has been tested for the presence of at least one allele of at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith.

Preferably, an at-risk variant for breast cancer is used for selecting individuals who would benefit from administration of the therapeutic agents. Thus, in certain embodiments, the at least one allele is selected from the group consisting of the G allele of rs9397435, the T allele of rs77275268, the G allele of rs9383932, the G allele of rsl2662670, the A allele of rsl2665607, the G allele of rs9383589, the C allele of rs3734805, the A allele of rs6929137, the A allele of rs7752591, the A allele of rs3734804, the C allele of rs6932260 and the A allele of rs852003.

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 for example embodiments that relate to any one or a combination of the markers disclosed herein, 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 an overview of the C6orf97-ESRl breast cancer susceptibility locus. Upper panel (a) shows a view of the genomic region of chromosome 6, nucleotides 151,930,000-152,200,000 taken from the UCSC browser Build 36 assembly (hgl8). The C6orf97 gene and the four RefSeq isoforms of ESR1 are shown. Below them is a histogram of the local recombination rates calculated from HapMap phase II release 22 data. Below that is a track showing the locations of the SNPs that are correlated (r² >_.0.65) with rs2046210 in Han Chinese ("Eq Class SNPs"). Lower panel (b) shows a zoomed view of the region nucleotides 151,960,548-152,013,381, containing the Eq Class SNPs. RefSeq genes and recombination rates are as in panel (a) .

FIG 2 provides dendrograms showing r² relationships between C6orf97-ESRl SNPs in Europeans (CEU) and Yoruban Africans (YRI) . On the left are listed the 37 SNPs that are correlated with an r² > 0.65 with rs2046210 (arrowed) in HapMap Han Chinese (CHB) . In panel (a) the SNPs are arranged in a hierarchical cluster dendrogram based on the r² values between them in the HapMap CEU sample of a European ancestry population . SNPs that were selected for genotyping are highlighted. SNPs indicated *** are present on the Illumina Human Hap300 or

HumanCNV370 chips used in the Icelandic GWAS. Panel (b) shows the same SNPs in a dendrogram based on r² values from Yoruban Africans (HapMap YRI) . Data are derived from HapMap Phase II release 23a .

FIG 3 (A -C) provides dendrograms showing r² relationships between the C6orf9-ESRl SNPs genotyped in each study population . On the left are listed the SNPs that were genotyped in each of the study population samples. The name of the study population sample is indicated on top of each panel. The SNPs are arranged in a hierarchical cluster dendrogram based on the r² values between them derived from the observed genotypes for the SNPs. Note that the scales on the left side of the panels show 1— r² values (i.e. a value of 0 corresponds to an r² of 1) . The scale for the Taiwanese sample is limited in range between 0 and 0.4 (corresponding to an r² range of 1 to 0.6) because all genotyped SNPs had r² values greater than 0.6. The scale for the U .S.A. African American ancestry and the Nigerians ranges from 0.3 to 1.0 (corresponding to an r² range of 0.7 to 0) because no genotyped SNPs had r² values between them of greater than 0.7.

FIG 4 provides a diagram illustrating a computer-implemented system utilizing risk variants as described herein.

FIG 5 provides a diagram illustrating the result of bisulfite sequencing the region surrounding the C/T SNP rs77275268 at position 152, 101,897 (arrowed) showing differential methylation of the C nucleotide in CC homozygotes. The top line shows the reference (non-bisulfite treated) sequence. Panels a-d show sequence traces of bisulfite-treated DNA from four CC homozygous individuals. In samples a and b the C nucleotide is predominantly methylated while a minority is unmethylated. In sample c, the C is predominantly unmethylated and in sample d similar amounts of methylated and unmethylated C are present. At neighboring C nucleotides, the conversion of unmethylated cytosine is complete, indicating that the bisulfite treatment was effective. In addition, we noted that nearby CpGs at positions 152,010,768, 152,010,842, 152,101,940, 152,011,003, and 152,011,013 were also methylated or partially methylated .

FIG 6 provides a diagram illustrating the result of quantitative RT-PCR analysis of ESR1 (ER) PGR (PG) and ERBB2 (HER2) mRNA in tumours with different genotypes for rs9397435. RNA and DNA was isolated from 1,234 frozen tumour specimens. RNA levels were analyzed by RT-PCR and normalized to the mean level of three housekeeping genes. Relative expression levels are calculated as ₂ ^{(mean ct housekeeping " mean ct target)}. Genotypes of rs9397435 were determined by Centaurus assay. Numbers of individuals with each genotype are 1,072 (AA), 151 (AG) and 11 (GG) . Histogram displays the mean relative expression level (calculated as io

^_Qr genotype. Error bars indicated the standard error of the mean relative expression levels. 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", sometimes 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 single nucleotide polymorphisms (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 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. 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) . 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.

The sequence listing presented herein provides flanking sequence for the polymorphic markers described herein, with the polymorphic site indicated in the sequence using the sequence conucleotide ambiguity code as shown above.

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 "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., "3 rs9397435" refers to the 3 allele of marker rs9397435 being in the haplotype, and is equivalent to "rs9397435 allele 3". 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., breast 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 and/or haplotypes of the invention as described herein may be characteristic of increased susceptibility (i .e., increased risk) of breast cancer, as characterized by a relative risk (RR) or odds ratio (OR) of greater than one for the particular allele or haplotype. Alternatively, the markers and/or haplotypes of the invention are characteristic of decreased susceptibility (i.e., decreased risk) of breast 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" is 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 as 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 "breast cancer therapeutic agent" refers to an agent that can be used to ameliorate or prevent symptoms associated with breast cancer.

The term "breast cancer-associated nucleic acid", as described herein, refers to a nucleic acid that has been found to be associated to breast 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 "Breast Cancer", as described herein, refers to any clinical diagnosis of breast cancer, and includes any and all particular subphenotypes of breast cancer. For example, breast cancer is sometimes categorized as estrogen receptor (ER) positive breast or estrogen receptor negative breast cancer; breast cancer is sometimes also categorized as progesterone receptor (PR) positive or negative. Breast cancer is furthermore sometimes diagnosed as invasive ductal, as invasive lobular, as tubular, as medullary, or as otherwise invasive or mixed invasive. Breast cancer can also be categorized as DCIS (Ductal Carcinoma In-Situ) or LCIS (Lobular Carcinoma In-Situ), or otherwise non-invasive. Invasive breast cancer can also be defined as stage 0, stage 1, stage 2 (including stage 2a and stage 2b), stage 3 (including stage 3a, stage 3b and stage 3c) or stage 4 breast cancer. In the present context, "breast cancer" can include any of these subphenotypes of breast cancer, and also includes any other clinically applicable subphenotypes of breast cancer.

The term "estrogen receptor positive breast cancer", or "ER-positive breast cancer", as described herein, refers to tumors determined to be positive for estrogen receptor. In the present context, ER levels of greater than or equal to 10 fmol/mg and/or an immunohistochemical observation of greater than or equal to 10% positive nuclei is considered to be ER positive. Breast cancer that does not fulfill the criteria of being ER positive is defined herein as "ER negative" or "estrogen receptor negative".

The term "progesterone receptor positive breast cancer", or "PR-positive breast cancer", as described herein, refers to tumors determined to be positive for progesterone receptor. In the present context, PR levels of greater than or equal to 10 fmol/mg and/or an

immunohistochemical observation of greater than or equal to 10% positive nuclei is considered to be PR positive. Breast cancer that does not fulfill the criteria of being PR positive is defined herein as "PR negative" or "progesterone receptor negative".

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 corrresponding contiguous bases in a target nucleic acid sequence. The backbone is composed of subunit backbone moieties supporting the purine an 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 ⁿC6orf97" or ⁿC6orf97 gene", as described herein, refers to the chromosome 6 open reading frame 97 gene on human chromosome 6q25.1.

The term "ESRl " or "ESRl gene", as described herein, refers to the estrogen receptor 1 gene on human chromosome 6q25.1.

The term "LD Block C06", as described herein, refers to the linkage disequilibrium (LD) block on human chromosome 6. The LD block, which is flanked by the polymorphic markers rs73620924 and s.152415292, spans positions 151708832 to 152415292 in Build 36 of the human genome assembly (http ://www.ncbi .nlm. nih .gov). Identification of variants on chromosome 6q25.1 as diagnostic markers of breast cancer Through association analysis of a population of individuals diagnosed with breast cancer, the present inventors have discovered that certain alleles at certain polymorphic markers at human chromosome location 6q25.1 are associated with breast cancer. Particular markers within this region were found to be associated with an increased risk of breast cancer. Through genotyping of 2638 samples from Icelandic breast cancer patients and 3506 controls, as well as samples from the USA, Spain, Holland, Sweden, Nigeria and Taiwan, it was discovered that certain variants on chromosome 6q25.1 are associated with risk of breast cancer. While previous reports have suggested that marker rs2046210 confers risk of breast cancer in the Chinese population (Zheng, W. et al. Nat Genet 41 : 324-38 (2009)), the data available to the present inventors suggested that this marker does not confer risk of breast cancer in populations of European or African ancestry.

A detailed analysis of equivalence classes of markers in the 6q25.1 region (see Example 1 for details) surprisingly revealed that variants of a lower population frequency, in very low linkage disequilibrium with the previously identified rs2046210 marker, confer significant risk of breast cancer in Europeans, Africans and Asians. These variants are typified by the markers rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, although the skilled person will appreciate that markers in linkage disequilibrium with these markers may also suitably be used to detect the association. Through extensive ancestry shift refinement mapping, the present inventors have thus identified novel diagnostic markers of breast cancer on chromosome 6q25.1. Methods of determining susceptibility to breast cancer

Accordingly, in a first aspect, the present invention provides a method of determining a susceptibility to breast cancer in a human individual, the method comprising (i) analyzing sequence data from 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 breast cancer in humans, and (ii) determining a susceptibility to breast cancer from the sequence data . In one embodiment, the at least one polymorphic marker is selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith. In another embodiment, the at least one polymorphic marker is selected from the group consisting of rs9397435, rs77275268, rsl2662670, rsl2665607, and rs9383589, and markers in linkage disequilibrium therewith . In a further embodiment, the at least one polymorphic marker is selected from the group consisting of rs9397435, 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.

In certain embodiments, 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 .

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 breast 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.

In certain embodiments, the at least one polymorphic marker is a marker associated with the human ESR1 gene and/or the human C6orf97 gene. In certain embodiment, the marker is a marker within LD block C06, between markers rs73620924 (SEQ ID NO: l) and s.152415292 (SEQ ID NO: 92) . In certain embodiments, the marker is selected from the group consisting of rs9397435, and markers in linkage disequilibrium therewith .

In certain embodiments, markers in linkage disequilibrium with rs9397435 are selected from the group consisting of the markers set forth in Table 1 and Table 8.

In one preferred embodiment, markers in linkage disequilibrium with rs9397435 are selected from the group consisting of the markers rs77275268, rs9397436, rs9397437, rs58343273, s.151995458, rs9397068, s.151997607, rs9383937, s.151999263, s.152000305, and s.152011433. 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 rs9397435 are exemplified by the markers listed in Table 1 and Table 8 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 . 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., rs9397435) are in LD with the anchor marker

characterized by numerical values of D' of 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 of r² of greater than 0.2. The markers provided in Table 1 provides 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 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, greater than 0.95. Other numerical values of r² and/or D' may also be suitably selected 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, surrogate markers of rs9397435 are those markers that have values of r² to rs9397435 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 certain 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 rs9397435 on Chromosome 6. 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 risk alleles for the surrogate markers, i.e. alleles that are correlated with the corresponding allele of the anchor marker, rs9397435-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.

SNP PosB36 Risk Allele D' R² p-value Seq ID NO rs73620924 151708832 A 1.00 0.27 6.00E-04 1

S.151715815 151715815 G 1.00 0.41 2.00E-05 2

S.151715818 151715818 T 1.00 0.41 2.00E-05 3 rs73620938 151716999 c 1.00 0.27 6.00E-04 4 s.151720071 151720071 G 0.53 0.28 1.30E-03 5 s.151780645 151780645 A 1.00 0.27 6.00E-04 6 rs9397408 151801648 A 1.00 0.27 6.00E-04 7

S.151812197 151812197 A 1.00 0.27 6.00E-04 8

S.151819719 151819719 C 1.00 0.27 6.00E-04 9 s.151820577 151820577 A 1.00 0.27 6.00E-04 10 s.151826477 151826477 A 1.00 0.41 2.00E-05 11 s.151855176 151855176 C 1.00 0.27 6.00E-04 12 s.151884553 151884553 T 0.68 0.35 2.20E-04 13 rsl7688135 151890733 A 0.53 0.28 1.30E-03 14 rs4870030 151891535 G 1.00 0.22 2.30E-06 15 rs6900089 151892817 A 0.78 0.26 1.46E-04 16 s.151893270 151893270 T 1.00 0.27 6.00E-04 17 s.151914873 151914873 T 1.00 0.27 6.00E-04 18 rs4869738 151933844 T 0.80 0.21 1.70E-03 19 rs3757318 151955806 A 1.00 0.61 6.40E-10 20 s.151957757 151957757 A 1.00 0.68 2.10E-10 21 s.151959353 151959353 A 0.68 0.40 8.80E-05 22 rsl2662670 151960549 G 1.00 0.68 2.10E-10 23 s.151961264 151961264 C 1.00 0.33 1.50E-07 24 rs9383586 151961265 C 1.00 0.33 1.50E-07 25 rs9383931 151961399 C 1.00 0.33 1.50E-07 26 rs9383932 151961413 G 1.00 0.33 1.50E-07 27 rsl340874 151962465 A 1.00 0.29 3.60E-07 28 rs9397067 151962544 A 1.00 0.29 3.60E-07 29 rs9383587 151962606 A 1.00 0.29 3.60E-07 30 s.151962608 151962608 A 1.00 0.29 3.60E-07 31 rs9383933 151962649 A 1.00 0.29 3.60E-07 32 rs9478226 151962832 T 1.00 0.29 3.60E-07 33 rs9383934 151963030 T 1.00 0.29 3.60E-07 34 rs9371543 151963164 A 1.00 0.29 3.60E-07 35 s.151964315 151964315 T 1.00 0.68 2.10E- 10 36 rsl l l0776 151966300 A 1.00 0.68 2.10E- 10 37 s.151967412 151967412 G 1.00 0.68 2.10E- 10 38 rsl7081488 151967476 A 1.00 0.68 2.10E- 10 39 s.151971362 151971362 A 1.00 0.68 2.10E- 10 40 rsl2663501 151971618 C 1.00 0.68 2.10E- 10 41 rsl2663827 151971942 A 1.00 0.68 2.10E- 10 42 rs3734805 151981043 C 1.00 0.76 5.20E- 11 43 rs9383935 151981541 T 1.00 0.76 5.20E- 11 44 rs9383589 151981953 G 1.00 0.76 5.20E- 11 45 s.151983032 151983032 G 1.00 0.70 1.10E-08 46 rs9383936 151986307 A 1.00 0.76 5.20E- 11 47 rsl2665607 151988322 A 1.00 0.76 5.20E- 11 48 s.151989450 151989450 T 1.00 0.76 5.20E- 11 49 rs9397435 151992913 G 1.00 1.00 O. OOE+00 50 rs9397436 151993695 G 1.00 0.85 1.30E- 10 51 rs9397437 151994025 A 1.00 0.85 1.30E- 10 52 rs58343273 151994873 G 1.00 1.00 4.00E- 13 53 s.151995458 151995458 C 1.00 1.00 4.00E- 13 54 rs9397068 151995552 A 1.00 0.85 1.30E- 10 55 s.151997607 151997607 G 1.00 1.00 4.00E- 13 56 rs9383937 151998812 C 1.00 1.00 4.00E- 13 57 s.151999263 151999263 T 1.00 1.00 4.00E- 13 58 rsl2173570 151999407 T 1.00 0.56 1.70E-09 59 s.152000305 152000305 T 1.00 1.00 4.00E- 13 60 rsl7081533 152000508 c 1.00 0.56 1.70E-09 61 s.152000518 152000518 A 1.00 0.27 6.00E-04 62 rs77275268 152010891 T 1.00 1.00 4.00E- 13 63 s.152011433 152011433 A 1.00 1.00 4.00E- 13 64 rsl0484919 152016115 T 1.00 0.76 5.20E- 11 65 rs6915267 152018778 A 1.00 0.76 5.20E- 11 66 s.152020495 152020495 C 1.00 0.76 5.20E- 11 67 rs9397069 152022400 C 1.00 0.76 5.20E- 11 68 rs9371547 152023782 T 1.00 0.76 5.20E- 11 69 s.152024008 152024008 c 1.00 0.76 5.20E- 11 70 s.152027267 152027267 c 1.00 0.76 5.20E- 11 71 rs9397442 152027696 T 1.00 0.61 6.40E- 10 72 rs9371226 152028287 T 1.00 0.76 5.20E- 11 73 rs9383938 152029050 T 1.00 0.76 5.20E- 11 74 rs7745737 152029499 A 1.00 0.37 5.30E-08 75 rs6913799 152030522 T 1.00 0.76 5.20E- 11 76 rs9479090 152030993 c 1.00 0.76 5.20E- 11 77 s.152031302 152031302 A 1.00 0.76 5.20E- 11 78 s.152038804 152038804 C 1.00 0.27 6.00E-04 79 rs9479098 152045904 A 0.69 0.48 2.90E-05 80 rs9383939 152047871 A 0.69 0.48 2.90E-05 81 rsl0214867 152051347 A 0.52 0.23 2.80E-03 82 s.152054541 152054541 T 0.56 0.22 5.80E-03 83 s.152054542 152054542 T 0.56 0.22 5.80E-03 84 s.152054543 152054543 A 0.56 0.22 5.80E-03 85 s.152054544 152054544 G 0.56 0.22 5.80E-03 86 s.152063519 152063519 T 0.67 0.31 4.60E-04 87 rsl7761320 152193658 A 0.58 0.34 1.28E-04 88 s.152294121 152294121 T 1.00 0.27 6.00E-04 89 s.152302359 152302359 G 0.66 0.24 1.60E-03 90 s.152358887 152358887 C 0.72 0.29 1.90E-03 91 s.152415292 152415292 A 0.72 0.29 1.90E-03 92

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 the human ESR1 gene and/or the human C6orf97 gene. In other words, the marker may be an amino acid substitution in a human ESR1 polypeptide or human C6orf97 polypeptide.

In certain embodiments of the invention, determination of the presence of particular marker alleles or particular haplotypes is predictive of an increased susceptibility of breast cancer in humans. In certain embodiments, determination of the presence of a marker allele selected from the group consisting of the G allele of rs9397435, the T allele of rs77275268, the G allele of rs9383932, the G allele of rsl2662670, the A allele of rsl2665607, the G allele of rs9383589, the C allele of rs3734805, the A allele of rs6929137, the A allele of rs7752591, the A allele of rs3734804, the C allele of rs6932260 and the A allele of rs852003 is indicative of increased risk of breast cancer in the individual. These marker alleles confer increased risk of breast 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. Individuals who are homozygous for at-risk alleles are at particularly high risk of developing breast 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 1.10, 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, at least 1.50, at least 1.55, at least 1.60, at least 1.65, at least 1.70, at least 1.75, and at least 1.80. Other numerical non-integer values greater than unity are also possible to characterize the risk, and such numerical values are also within scope of the invention . Certain embodiments relate to homozygous individuals for a particular markers, 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 the G allele of rs9397435, 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 breast 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, determination of the presence of the alternate allele is indicative of a decreased susceptibility of breast cancer. Individuals who are homozygous for the alternate (protective) allele are at particularly decreased susceptibility or risk.

To identify markers that are useful for assessing susceptibility to breast cancer, it may be useful to compare the frequency of markers alleles in individuals with breast 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 breast cancer. In one embodiment, an increase in frequency of at least one allele in at least one polymorphism in individuals diagnosed with breast 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 breast cancer. In another embodiment, a decrease in frequency of at least one allele in at least one polymorphism in individuals diagnosed with breast 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, breast 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) to a record or database providing a correlation about particular polymorphic marker(s) and susceptibility to disease, such as breast 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 breast cancer. In certain embodiments, the database comprises at least one measure of susceptibility to breast 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 breast cancer for the at least one polymorphic marker. The measure of susceptibility may in the form of relative risk (RR), absolute risk (AR), percentage (%) or other convenient measure for describing genetic susceptibility of individuals. Certain embodiments of the invention relate to markers associated with the human C6orf97 gene and/or the human ESR1 gene. Markers that are associated with 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 segment LD block C06, as defined herein . In certain embodiments, markers associated with the C6orf97 gene are selected from the markers within the human C6orf97 gene. In certain embodiments, markers associated with the ESR1 gene are selected from the markers within the human ESR1 gene.

Certain embodiments of the invention relate to markers located within the LD Block C06 as defined herein . It is however also contemplated that surrogate markers useful for determining susceptibility to breast cancer may be located outside the LD Block C06 as defined in physical terms (genomic locations) . Thus, certain embodiments of the invention are not limited to surrogate markers located within the physical boundaries of the LD Block C06 as defined, but also include useful surrogate markers outside the physical boundaries of the LD block as defined, due to the surrogate markers being in LD with one or more of the markers within LD Block C06 shown herein to be associated with risk of breast cancer.

In certain embodiments of the invention, more than one polymorphic marker is analyzed. 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 may be employed in such embodiments.

One aspect of the invention relates to a method for determining a susceptibility to breast 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 rs9397435, and markers in linkage disequilibrium therewith, and wherein determination of the presence of the at least one allele is indicative of a susceptibility to breast cancer. Determination of the presence of an allele that correlates with breast cancer is indicative of an increased susceptibility to breast cancer. Individuals who are homozygous for such alleles are particularly susceptible to breast cancer. On the other hand, individuals who do not carry such at-risk alleles are at a decreased susceptibility of developing breast cancer. 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 Nucelotide Polymorphisms ("SNPs") . These SNPs are believed to have arisen by a single mutational event, and therefore there are usually two possible alleles possible at each SNPsite; the original allele and the mutated (alternate) 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, 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. All sequence variants can 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 a population. In another embodiment, the polymorphism is characterized by the presence of three or more alleles. 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 9 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 realize 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 opposite strand on the DNA template, the presence of the

complementary bases T and C can be measured . Quantitatively (for example, in terms of relative risk), 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 to increase the possibility that structural changes, such as amplifications or deletions, occur at the somatic level. The polypeptide encoded by the reference nucleotide sequence is the "reference" polypeptide with a particular reference amino acid sequence, and polypeptides encoded by variant alleles are referred to as "variant" polypeptides with variant amino acid sequences. 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 (Chen, X. et a/., Genome Res. 9(5) : 492-98 (1999)), 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 Blochem 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( h; 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:

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, I_g, and the part due to ungenotyped cases, I_u, I = I_g + I_u, and denoting the number of genotyped cases with N, the effective sample size due to the un- genotyped 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 1.10, including but not limited to: 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 1.30, at least 1.40, at least 1.50, at least 1.60, at least 1.70, at least 1.80, at least 1.90, and at least 2.0. In a particular embodiment, a risk (relative risk and/or odds ratio) of at least 1.15 is significant. In another particular embodiment, a risk of at least 1.20 is significant. In yet another embodiment, a risk of at least 1.25 is significant. In a further embodiment, a relative risk of at least 1.30 is significant. In another further embodiment, a significant increase in risk is at least 1.40 is significant. However, other cutoffs are also contemplated, e.g., at least 1.16, 1.17, 1.18, 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 10%, including but not limited to about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, and 100%, 150. In one particular embodiment, a significant increase in risk is at least 10%. In other embodiments, a significant increase in risk is at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 50%, at least 60% and at least 70%. 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 of the present invention 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 or trait (affected), or diagnosed with the disease or trait, 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 or trait (e.g. , breast cancer) . 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, i.e. individuals who have not been diagnosed with breast cancer. Such disease-free control may in one embodiment be characterized by the absence of one or more specific disease-associated symptoms. 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. Representative environmental factors are natural products, minerals or other chemicals which are known to affect, or contemplated to affect, the risk of developing the specific disease or trait. Other environmental risk factors are risk factors related to lifestyle, including but not limited to food and drink habits, geographical location of main habitat, and occupational risk factors. In another embodiment, the risk factors are at least one genetic risk factor.

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.

The person skilled in the art will appreciate that for markers with two alleles present in the population being studied (such as SNPs), and wherein one allele is found in increased frequency in a group of individuals with a trait or disease in the population, compared with controls, the other allele of the marker will be found in decreased frequency in the group of individuals with the trait or disease, compared with controls. In such a case, one allele of the marker (the one found in increased frequency in individuals with the trait or disease) will be the at-risk allele, while the other allele will be a protective allele.

Thus is other embodiments of the invention, an individual who is at a decreased susceptibility (i.e., at a decreased risk) for a disease or trait is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring decreased susceptibility for the disease or trait 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 of less than 0.90, including but not limited to less than 0.85, less than 0.80, less than 0.75, less than 0.7, less than 0.6, less than 0.5, and less than 0.4. In one particular embodiment, significant decreased risk is less than 0.90. In another embodiment, significant decreased risk is less than 0.85. In yet another embodiment, significant decreased risk is less than 0.80. In another embodiment, the decrease in risk (or susceptibility) is at least 10%, including but not limited to at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, and at least 50%. In one particular embodiment, a significant decrease in risk is at least about 15%. In another embodiment, a significant decrease in risk at least about 20%. In another embodiment, the decrease in risk is at least about 25%. 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 or a trait (e.g. breast cancer) can be used alone to predict the risk of the disease for a given genotype. For a bia I le lie 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 2^P; where n is the number autosomal loci and p the number of gonosomal (sex chromosomal) loci. Overall risk assessment calculations 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 were a total of seven variants that have been associated with breast cancer. One such example is provided by the markers rsl3387042, rs4415084, rsl219648, rs3803662, rsl3281615, rs3817198 and rs889312, all of which are used in the marketed deCODE BreastCancer test for breast cancer susceptibility

(http://www.decodediagnostics.com) . The total number of theoretical genotypic combinations is then 3⁷ = 2187. 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 these and other variants associated with breast cancer may be assessed . This includes the variants that are shown and claimed herein to be predictive of breast cancer risk. 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 randombly 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 ai., Biochem Soc Trans 34: 526- 530 (2006); Jeffreys, A.J., et ai., Nature Genet 29 : 217-222 (2001); May, C.A., et ai., 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 0.125, 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 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 particular 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 bein 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 preferred embodiment, the significant r² value is at least 0.4. In yet another preferred embodiment, the significant r² value is at least 0.8. Alternatively, markers in linkage disequilibrium are characterized by values of | D'| of at least 0.2, such as 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.96, 0.97, 0.98, or at least 0.99. Thus, linkage disequilibrium represents a correlation between alleles of distinct markers. In certain embodiments, linkage disequilibrium is defined in terms of values for both the r² and | D'| measures. In one such embodiment, a significant linkage disequilibrium is defined as r² > 0.1 and | D'| >0.8, and markers fulfilling these criteria are said to be in linkage disequilibrium. In another embodiment, a significant linkage disequilibrium is defined as r² > 0.2 and | D'| >0.9. Other combinations and permutations of values of r² and | D'| for determining linkage

disequilibrium are also contemplated, and are also within the scope of the invention. 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 (Yoruba), 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 (Yoruba 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 another embodiment, LD is determined in a European population. 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. 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 ai., Science 294: 1719-1723 (2001); Dawson, E. et ai., Nature 4.28: 544-548 (2002); Zhang, K. et ai., 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 ai., Science 296: 2225-2229 (2002); Phillips, M .S. et ai., Nature Genet. 33: 382-387 (2003); Wang, N . et ai., 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 ai., Science 310: 321-32324 (2005); Myers, S. et ai., 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.

For example, "LD block C06", as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 6 between positions 151708832 and 152415292 of NCBI (National Center for Biotechnology Information) Build 36.

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. Markers shown herein to be associated with breast cancer are such tagging markers. If desired, neighboring haplotype blocks can be assessed concurrently, as there may also exist linkage disequilibrium among the haplotype blocks.

It has thus become apparent that for any given observed association to a polymorphic marker in the genome, that 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. 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 (< 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 region, 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 an 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, h, and h risk(hi)/risk(hj) =

(fi/Pi)/(fj/Pj), where f and 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 necessarily 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 marker rs9397435, allele G has an allelic OR for breast cancer of 1.15 and a frequency (p) around 0.063 in Caucasian populations. The genotype relative risk compared to genotype AA are estimated based on the multiplicative model.

For GG it is 1.15 x 1.15 = 1.32; for AG it is simply the OR 1.15, and for AA it is 1.0 by definition.

The frequency of allele A is q = l - p = l - 0.063 = 0.937. Population frequency of each of the three possible genotypes at this marker is:

Pr(GG) = p² = 0.00397, Pr(AG) = 2pq = 0.118, and Pr(AA) = q² = 0.878 The average population risk relative to genotype AA (which is defined to have a risk of one) is: R = 0.00397 x 1.32 + 0.118x 1.15 + 0.878x 1 = 1.019

Therefore, the risk relative to the general population (RR) for individuals who have one of the following genotypes at this marker is:

RR(GG) = 1.32/1.019 = 1.30, RR(AG) = 1.15/1.019 = 1.13, RR(AA) = 1/1.019 = 0.98. 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

Combined, the overall risk relative to the population for this individual is: 1.03 x 1.30 x0.88x 1.54 = 1.81. In an analogous fashion, overall risk for any plurality of markers (or haplotypes) may be assessed.

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, for a particular disease, if the overall genetic risk relative to the population is 1.8 for an individual, and if the average life-time risk of the dis ease for individuals of his demographic is 20%, then the adjusted lifetime risk for him 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 breast cancer

As described herein, certain polymorphic markers and haplotypes comprising such markers are found to be useful for risk assessment of breast cancer. Risk assessment can involve the use of the markers for diagnosing a susceptibility to breast cancer. Particular alleles of certain polymorphic markers are found more frequently in individuals with breast cancer, than in individuals without diagnosis of breast cancer. Therefore, these marker alleles have predictive value for detecting breast cancer, or a susceptibility to breast cancer, in an individual. Tagging markers in linkage disequilibrium with at-risk variants (or protective variants) described herein can be used as surrogates for these markers (and/or haplotypes) . 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 particular values of r² (e.g., values greater than 0.2) 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 . 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 ma rkers of the present i nvention in a g rou p of individuals, and identify polymorphisms in the resu lting grou p of sequences. As a consequence, the person skilled in the a rt can readily and without u ndue experimentation identify a nd genotype surrogate ma rkers in lin kage

disequili brium with the ma rkers and/or haplotypes as described herein . The tagging or su rrogate markers in LD with the at-risk va riants detected also have predictive value.

The present invention ca n in certain embodiments be practiced by assessing a sample comprisi ng genomic DNA from an individual for the presence of certain va riants described herein to be associated with breast cancer. Such assessment includes steps of detecting the presence or absence of at least one allele of at least one polymorphic marker, using methods well known to the skilled person and fu rther described herein, a nd based on the outcome of such assessment, determi ne whether the individual from whom the sa mple is derived is at increased or decreased risk (increased or decreased susceptibility) of breast cancer. Alternatively, the invention can be practiced utilizing a dataset comprising i nformation about the genotype status of at least one polymorphic marker described herein to be associated with breast cancer (or markers in linkage disequilibriu m with at least one marker shown herein to be associated with breast cancer) . In other words, a dataset containing information about such genetic status, for example in the form of genotype counts at a certain polymorphic ma rker, or a plurality of markers (e.g., a n i ndication of the presence or absence of certai n at-risk alleles), or actual genotypes for one or more markers, can be queried for the presence or absence of certain at-risk a lleles at certain polymorphic ma rkers shown by the present inventors to be associated with breast cancer. A positive result for a va riant (e.g., ma rker allele) associated with increased risk of breast cancer, as shown herein, is indicative of the i ndividua l from which the dataset is derived is at increased susceptibility (increased risk) of breast ca ncer.

In certai n em bodiments of the invention, a polymorphic ma rker is correlated to breast cancer by referencing genotype data for the polymorphic marker to a data base, such as a look-u p table that comprises correlation data between at least one allele of the polymorphism and breast cancer. The correlation data may for exam ple be a value of Relative Risk (RR) or odds ratio (OR) . In some embodiments, the ta ble comprises a correlation for one polymorphism . In other embodiments, the table comprises a correlation for a plu rality of polymorphisms. In both scenarios, by referencing to a look-u p table that gives a n indication of a correlation between a marker (pa rticula r genotype status at the marker) and breast cancer, a risk for breast cancer, or a susceptibility to breast ca ncer, can be identified in the i ndividua l from whom the sa mple is derived . In some em bodi ments, the correlation is reported as a statistical measu re. The statistical measure may be reported as a risk measu re, such as a relative risk (RR), a n absolute risk (AR) or an odds ratio (OR) .

Risk ma rkers may be useful for risk assessment and diag nostic purposes, either alone or i n combination . Results of disease risk assessment based on the markers described herein can also be combined with data for other genetic markers or risk factors for the disease, to establish overa ll risk. Thus, even in cases where the i ncrease in risk by individua l ma rkers 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. For example, combined risk can be assessed based on genotype results for any one of, or combinations of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003. Such combinations can also include other susceptibility markers for breast cancer, such as marker rsl3387042 on chromosome 2q35 (Stacey, SN et al. Nat Genet 39: 865-9 (2007)), marker rs4415084 on chromosome 5pl2 (Stacey, SN et al. Nat Genet 40 : 703-6 (2008)) marker rsl219648 on chromosome 10q26 (Easton, DF et al Nature 447 : 1087-93 (2007); Hunter, DJ et al. Nat Genet 39: 870-4 (2007); Stacey, SN et al. Nat Genet 40 : 703-6 (2008)), marker rs3803662 on chromosome 16ql2 (Stacey, SN et al. Nat Genet 39 : 865-9 (2007); Stacey, SN et al. Nat Genet 40 : 703-6 (2008)), marker rsl3281615 on chromosome 8q24, marker rs3817198 on chromosome l lpl5, and marker rs889312 on chromosome 5ql l (Stacey, SN et al. Nat Genet 40 : 703-6 (2008)) . Alternatively, markers in LD with any one of these markers could be assessed.

Thus, in certain embodiment of the invention, a plurality of variants (markers and/or haplotypes) is 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 breast cancer In such embodiments, the genotype status of a plurality of markers and/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 .

In a general sense, the methods and kits described herein can be utilized from samples containing nucleic acid material (DNA or RNA) from 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 source may be any sample comprising nucleic acid material, including biological samples, or a sample comprising nucleic acid material derived there from . 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 risk of developing the disease, based on other genetic factors, biomarkers, biophysical parameters (e.g ., weight, BMD, blood pressure), or general health and/or lifestyle parameters (e.g., history of breast cancer, history of breast cancer, previous diagnosis of breast cancer or other cancer, family history of cancer, family history of breast cancer) .

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 breast 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. In one embodiment, the invention relates to individuals of Caucasian origin. 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, are polymorphic in one population but not in another. The person skilled in the art will however apply the methods available and as thought herein to practice the present invention in any given human population . This may include assessment of polymorphic markers in the LD region of the present invention, 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.

Models to predict inherited risk for breast cancer

The goal of breast cancer risk assessment is to provide a rational framework for the development of personalized medical management strategies for all women with the aim of increasing survival and quality of life in high-risk women while minimizing costs, unnecessary interventions and anxiety in women at lower risk. Risk prediction models attempt to estimate the risk for breast cancer in an individual who has a given set of risk characteristics (e.g., family history, prior benign breast lesion, previous breast tumor) . The breast cancer risk assessment models most commonly employed in clinical practice estimate inherited risk factors by considering family history. The risk estimates are based on the observations of increased risk to individuals with one or more close relatives previously diagnosed with breast cancer. They do not take into account complex pedigree structures. These models have the further disadvantage of not being able to differentiate between carriers and non-carriers of genes with breast cancer predisposing mutations.

More sophisticated risk models have better mechanisms to deal with specific family histories and have an ability to take into account carrier status for BRCA1 and BRCA2 mutations. For example, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) (Antoniou et al., 2004) takes into account family history based on individual pedigree structures through the pedigree analysis program MENDEL. Information on known BRCA1 and BRCA2 status is also taken into account. The main limitations of the BOADICEA and all other breast cancer risk models currently in use are that they do not incorporate genotypic information from other predisposition genes. Current models depend strongly on family history to act as a surrogate to compensate for the lack of knowledge of non-BRCA genetic

determinants of risk. Therefore the available models are limited to situations where there is a known family history of disease. Lower penetrance breast cancer predisposition genes may be relatively common in the population and may not show such strong tendencies to drive familial clustering as do the BRCA1 and BRCA2 genes. Patients with a relatively high genetic load of predisposition alleles may show little or no family history of disease. There is a need therefore to construct models which incorporate inherited susceptibility data obtained directly through gene- based testing. In addition to making the models more precise, this will reduce the dependency on family history parameters and assist in the extension of the risk profiling into the wider at-risk population where family history is not such a key factor. Integration of Improved Genetic Risk Models into Clinical Management of Breast Cancer

Primary prevention

Clinical primary prevention options currently can be classified as chemopreventative (or hormonal) treatments and prophylactic surgery. Patients identified as high risk can be prescribed long-term courses of chemopreventative therapies. This concept is well accepted in the field of cardiovascular medicine, but is only now beginning to make an impact in clinical oncology. The most widely used oncology chemopreventative is Tamoxifen, a Selective Estrogen Receptor Modulator (SERM) . Initially used as an adjuvant therapy directed against breast cancer recurrence, Tamoxifen now has proven efficacy as a breast cancer preventative agent [Cuzick, et al., (2003), Lancet, 361, 296-300][Martino, et al., (2004), Oncologist, 9, 116-25] . The FDA has approved the use of Tamoxifen as a chemopreventative agent in certain high risk women .

Unfortunately, long term Tamoxifen use increases risks for endometrial cancer approximately 2.5-fold, the risk of venous thrombosis approximately 2.0-fold . Risks for pulmonary embolism, stroke, and cataracts are also increased [Cuzick, et al., (2003), Lancet, 361, 296-300] .

Accordingly, the benefits in Tamoxifen use for reducing breast cancer incidence may not be easily translated into corresponding decreases in overall mortality. Another SERM called Raloxifene may be more efficacious in a preventative mode, and does not carry the same risks for endometrial cancer. However risk for thrombosis is still elevated in patients treated long- term with Raloxifene[Cuzick, et al ., (2003), Lancet, 361, 296-300; Martino, et al., (2004), Oncologist, 9, 116-25] . Moreover, both Tamoxifen and Raloxifene have quality of life issues associated with them. To make a rational risk: benefit analysis of SERM therapy in a

chemopreventative mode, there is a clinical need to identify individuals who are most at risk for breast cancer. Given that a substantial proportion of risk for breast cancer is genetic, there is a clear clinical need for genetic tests to quantify individuals' risks in this context. One can anticipate similar issues arising from any future cancer chemo-preventative therapies that may become available, such as the aromatase inhibitors. Moreover, as chemopreventative therapies become safer, there is an increased need to identify patients who are genetically predisposed, but do not have massively elevated risks associated with BRCAl & 2 mutation carriers.

Patients who are identified as being at high risk for breast cancer are considered for prophylactic surgery; either bilateral mastectomy or oophorectomy or both. Clearly such drastic treatments are recommended only for patients who are perceived to be at extremely high risk. In practice, such risks can currently be identified only in individuals who carry mutations in BRCAl, BRCA2 or genes known to be involved in rare breast cancer predisposition syndromes like p53 in Li- Fraumeni Syndrome, PTEN in Cowden's Syndrome. Estimates of the penetrance of BRCAl and BRCA2 mutations tend to be higher when they are derived from multiple-case families than when they are derived from population-based estimates. This is because different mutation-carrying families exhibit different penetrances for breast cancer (see [Thorlacius, et al., (1997), Am J Hum Genet, 60, 1079-84]for example) . One of the major factors contributing to this variation is the action of as yet unknown predisposition genes whose effects modify the penetrance of BRCA1 and BRCA2 mutations. Therefore the absolute risk to an individual who carries a mutation in the BRCA1 or BRCA2 genes cannot be accurately quantified in the absence of knowledge of the existence and action of modifying genes. Since the treatment options for BRCA1 and BRCA2 carriers can be severe, it is important in this context to quantify the risks to individual BRCA carriers with the greatest accuracy possible. There is a need, therefore, to identify predisposition genes whose effects modify the penetrance of breast cancer in BRCA1 and BRCA2 carriers and to develop improved risk assessment models based on these genes.

Furthermore, there are individuals who are perceived to be at very high risk for breast cancer, perhaps because of a strong family history of breast cancer, but in whom no mutations in known predisposition genes can be identified. Consideration of prophylactic surgery is difficult in such cases because one cannot test the individual to discover whether or not she has inherited a high penetrance predisposition gene. Accordingly, the individual's risk cannot be assessed accurately. There is a clear clinical need, therefore, to identify any high penetrance predisposition genes that remain undiscovered and to develop associated genetic tests for use in primary prevention strategies. Such genes may for example be the genes disclosed herein to be associated with risk of breast cancer. Although the variants shown herein to be associated with risk of breast cancer are fairly common, and conferring a relatively low risk of breast cancer, it is quite possible that higher risk variants exist within one or more of these genes. It is thus contemplated that high- risk genetic variants within, or associated with, the C6orf97 and/or the ESR1 genes could be useful for determining whether an individual is a carrier of a high risk (and high penetrance) genetic factor for breast cancer.

Early Diagnosis

Clinical screening for breast cancer in most western countries consists of periodic clinical breast examination (CBE) and X-ray mammography. There is good evidence to indicate that CBE has little added benefit when used in the context of a good mammographic screening program. In the United Kingdom, women between the ages of 50 and 70 are invited to undergo screening mammography every three years. The situation in the United States varies depending on healthcare provider, however the American Cancer Society recommends annual mammographic screening from age 40. Mammographic screening has proven effectiveness in reducing mortality amongst screened women .

It is unlikely that genetic testing would ever be employed as a means of reducing access to existing mammographic screening programs. However, mammographic screening is not without shortcomings and it is conceivable that genetic testing should be used to select people for augmented screening programs. Mammography is less effective in women under 50 possibly because the density of breast tissue is higher in younger women, making mammographic detection of tumors more difficult. However, breast cancers in predisposed individuals tend to occur at early ages groups and there is a clear association between high breast density and breast cancer risk. Therefore there is a problem with simple increases in mammographic screening for individuals with high predisposition because they would be managed by a technique that performs sub-optimally in the group at highest risk. Recent studies have shown that contrast-enhanced magnetic resonance imaging (CE-MRI) is more sensitive and detects tumors at an earlier stage in this high-risk group than mammographic screening does [Warner, et al., (2004), Jama, 292, 1317-25; Leach, et al., (2005), Lancet, 365, 1769-78] . CE-MRI strategies work particularly well when used in combination with routine X-ray mammography[Leach, et al., (2005), Lancet, 365, 1769-78] . Because CE-MRI requires specialist centers that incur high costs, screening of under-50's must be restricted to those individuals at the highest risk.

Present CE-MRI trials restrict entry to those individuals with BRCA1, BRCA2 or p53 mutations or very strong family histories of disease. The extension of this screening modality to a wider range of high-risk patients would be greatly assisted by the provision of gene-based risk profiling tools. Breast imaging using ultrasound methodologies may also be useful for augmented screening of high risk individuals.

There is good evidence to support the notion that early-onset breast cancers and cancers occurring in genetically predisposed women grow faster than cancers in older, less strongly predisposed women. This comes from observations of higher rates of interval cancers in younger women, that is, cancers that arise in the intervals between screening visits in a well-screened population are higher amongst younger women. Therefore there are suggestions that screening intervals, by whatever method, should be reduced for younger women . There is a paradox here in that more frequent screening using more expensive methodologies seems to be required for an age group in which the overall rates of breast cancer are comparatively low. There is a clear clinical need here to identify those young individuals who are most strongly predisposed to develop the disease early, and channel them into more expensive and extensive screening regimes. The variants disclosed herein to confer risk of breast cancer can be useful for identification of individuals who are at particularly high risk of developing breast cancer. Such individuals are likely to most benefit from early and aggressive screening programs, so as to maximizing the likelihood of early identification of the cancer.

Treatment

Currently, primary breast cancer is treated by surgery, adjuvant chemotherapy, radiotherapy, followed by long term hormonal therapy. Often combinations of three or four therapies are used.

Breast cancer patients with the same stage of disease can have very different responses to adjuvant chemotherapy resulting in a broad variation in overall treatment outcomes. Consensus guidelines (the St Galen and NIH criteria) have been developed for determining the eligibility of breast cancer patients for adjuvant chemotherapy treatment. However, even the strongest clinical and histological predictors of metastasis fail to predict accurately the clinical responses of breast tumors [Goldhirsch, et al., (1998), J Natl Cancer Inst, 90, 1601-8; Eifel, et al., (2001), J Natl Cancer Inst, 93, 979-89] . Chemotherapy or hormonal therapy reduces the risk of metastasis only by approximately 1/3, however 70-80% of patients receiving this treatment would have survived without it. Therefore the majority of breast cancer patients are currently offered treatment that is either ineffective or unnecessary. There is a clear clinical need for improvements in the development of prognostic measures which will allow clinicians to tailor treatments more appropriately to those who will best benefit. It is reasonable to expect that profiling individuals for genetic predisposition may reveal information relevant to their treatment outcome and thereby aid in rational treatment planning . The markers of the present invention, conferring risk of breast cancer, are contemplated to be useful in this context.

Several previous studies exemplify this concept: Breast cancer patients who are BRCA mutation carriers appear to show better clinical response rates and survival when treated with adjuvant chemotherapies [Chappuis, et al., (2002), J Med Genet, 39, 608-10; Goffin, et al., (2003), Cancer, 97, 527-36] . BRCA mutation carriers demonstrate improved responses to platinum chemotherapy for ovarian cancer than non-carriers [Cass, et al ., (2003), Cancer, 97, 2187-95] . Similar considerations may apply to predisposed patients in whom the genes involved are not known . For example, infiltrating lobular breast carcinoma (ILBC) is known to have a strong familial component but the genetic variants involved have not yet been identified. Patients with ILBC demonstrate poorer responses to common chemotherapy regimens [Mathieu, et al., (2004), Eur J Cancer, 40, 342-51] .

Genetic predisposition models may not only aid in the individualization of treatment strategies, but may play an integral role in the design of these strategies. For example, BRCA1 and BRCA2 mutant tumor cells have been found to be profoundly sensitive to poly (ADP-ribose) polymerase (PARP) inhibitors as a result of their defective DNA repair pathway [Farmer, et al., (2005), Nature, 434, 917-21] . This has stimulated development of small molecule drugs targeted on PARP with a view to their use specifically in BRCA carrier patients. From this example it is clear that knowledge of genetic predisposition may identify drug targets that lead to the development of personalized chemotherapy regimes to be used in combination with genetic risk profiling. Similarly, the markers of the present invention may aid in the identification of novel drugs that target, for example, one or more of the C6orf97 and ESR1 genes.

Cancer chemotherapy has well known, dose-limiting side effects on normal tissues particularly the highly proliferative hemopoetic and gut epithelial cell compartments. It can be anticipated that genetically-based individual differences exist in sensitivities of normal tissues to cytotoxic drugs. An understanding of these factors might aid in rational treatment planning and in the development of drugs designed to protect normal tissues from the adverse effects of chemotherapy.

Genetic profiling may also contribute to improved radiotherapy approaches: Within groups of breast cancer patients undergoing standard radiotherapy regimes, a proportion of patients will experience adverse reactions to doses of radiation that are normally tolerated. Acute reactions include erythema, moist desquamation, edema and radiation pneumatitis. Long term reactions including telangiectasia, edema, pulmonary fibrosis and breast fibrosis may arise many years after radiotherapy. Both acute and long-term reactions are considerable sources of morbidity and can be fatal. In one study, 87% of patients were found to have some adverse side effects to radiotherapy while 11% had serious adverse reactions (LENT/SOMA Grade 3-4); [Hoeller, et al., (2003), Int J Radiat Oncol Biol Phys, 55, 1013-8] . The probability of experiencing an adverse reaction to radiotherapy is due primarily to constitutive individual differences in normal tissue reactions and there is a suspicion that these have a strong genetic component. Several of the known breast cancer predisposition genes (e.g. BRCA1, BRCA2, ATM) affect pathways of DNA double strand break repair. DNA double strand breaks are the primary cytotoxic lesion induced by radiotherapy. This has led to concern that individuals who are genetically predisposed to breast cancer through carriage of variants in genes belonging to these pathways might also be at higher risk of suffering excessive normal tissue damage from radiotherapy. It is contemplated that the genetic variants described herein to confer risk of breast cancer, for example through one or more of the C6orf97 and ESR1 genes, may be useful for identifying individuals at particular risk of adverse reaction to radiotherapy.

The existence of constitutively radiosensitive individuals in the population means that radiotherapy dose rates for the majority of the patient population must be restricted, in order to keep the frequency of adverse reactions to an acceptable level. There is a clinical need, therefore, for reliable tests that can identify individuals who are at elevated risk for adverse reactions to radiotherapy. Such tests would indicate conservative or alternative treatments for individuals who are radiosensitive, while permitting escalation of radiotherapeutic doses for the majority of patients who are relatively radioresistant. It has been estimated that the dose escalations made possible by a test to triage breast cancer patients simply into radiosensitive, intermediate and radioresistant categories would result in an approximately 35% increase in local tumor control and consequent improvements in survival rates [Burnet, et al ., (1996), Clin Oncol (R Coll Radiol), 8, 25-34] . Exposure to ionizing radiation is a proven factor contributing to oncogenesis in the breast

[Dumitrescu and Cotarla, (2005), J Cell Mol Med, 9, 208-21] . Known breast cancer

predisposition genes encode pathway components of the cellular response to radiation-induced DNA damage [Narod and Foulkes, (2004), Nat Rev Cancer, 4, 665-76] . Accordingly, there is concern that the risk for second primary breast tumors may be increased by irradiation of normal tissues within the radiotherapy field. There does not appear to be any measurable increased risk for BRCA carriers from radiotherapy, however their risk for second primary tumors is already exceptionally high . There is evidence to suggest that risk for second primary tumors is increased in carriers in breast cancer predisposing alleles of the ATM and CHEK2 genes who are treated with radiotherapy [Bernstein, et al., (2004), Breast Cancer Res, 6, R199-214; Broeks, et al., (2004), Breast Cancer Res Treat, 83, 91-3] . It is expected that the risk of second primary tumors from radiotherapy (and, possibly, from intensive mammographic screening) will be better defined by obtaining accurate genetic risk profiles from patients during the treatment planning stage. Secondary Prevention

Approximately 30% of patients who are diagnosed with a stage 1 or 2 breast cancer will experience either a loco-regional or distant metastatic recurrence of their original tumor.

Patients who have had a primary breast cancer are also at greatly increased risk for being diagnosed with a second primary tumor, either in the contralateral breast or in the ipsilateral breast when breast-conserving surgery has been carried out. Secondary prevention refers to methods used to prevent recurrences or second primary tumors from developing. Methods currently in use comprise; long-term treatment with Tamoxifen or another SERM either alone or alternated with an aromatase inhibitor, risk-reducing mastectomy of the contralateral breast, and risk-reducing oophorectomy (in patients who are at risk for familial breast-ovarian cancer) . Considerations regarding the use of Tamoxifen have been discussed above. With risk-reducing surgical options, it is clear that the risk needs to be quantified as well as possible in order to make an informed cost versus benefit analysis.

There are some indications that patients with known genetic predispositions to breast cancer fare worse than the majority of patients. Patients carrying the CHEK2 gene l lOOdeIC variant have an estimated 2.8-fold increased risk of distant metastasis and a 3.9-fold increased risk of disease recurrence compared to non-carriers [de Bock, et al., (2004), J Med Genet, 41, 731-5] . Patients with BRCAl node-negative tumors have a greater risk of metastasis than similar patients who do not carry a BRCAl mutation [Goffin, et al., (2003), Cancer, 97, 527-36; Moller, et al ., (2002), Int J Cancer, 101, 555-9; Eerola, et al., (2001), Int J Cancer, 93, 368-72] . Genetic profiling can therefore be used to help assess the risk of local recurrence and metastasis, thereby guiding the choice of secondary preventative treatment. Genetic profiling based on the variants described herein may be useful in this context. In certain embodiments, such profiling may be based on one or more of the variants described herein. In other embodiments, such profiling may include one or several other known genetic risk factors for breast cancer. Such risk factors may be well established high-penetrant risk factors, or they may be one or more of the common, lower penetrance risk factors that have been previously described (e.g. , markers rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, or markers in linkage disequilibrium therewith, e.g. markers in Table 4) .

In general, patients with a primary tumor diagnosis are at risk for second primary tumors at a constant annual incidence of 0.7% [Peto and Mack, (2000), Nat Genet, 26, 411-4] . Patients with BRCA mutations are at significantly greater risks for second primary tumors than most breast cancer patients, with absolute risks in the range 40-60%[Easton, (1999), Breast Cancer Res, 1, 14-7] . Carriers of BRCA mutations have a greatly increased risk for second primary tumors

[Stacey, et al ., (2006), PLoS Med, 3, e217; Metcalfe, et al., (2004), J Clin Oncol, 22, 2328-35] . Patients with mutations in the CHEK2 gene have an estimated 5.7-fold increased risk of contralateral breast cancer [de Bock, et al., (2004), J Med Genet, 41, 731-5] . Carriers of the BARD1 Cys557Ser variant are 2.7 fold more likely to be diagnosed with a second primary tumor [Stacey, et al ., (2006), PLoS Med, 3, e217] . Genetic risk profiling can be used to assess the risk of second primary tumors in patients and will inform decisions on how aggressive the preventative measures should be.

Diagnostic and screening methods

In certain embodiments, the present invention pertains to methods of diagnosing, or aiding in the diagnosis of, breast cancer or a susceptibility to breast cancer, by detecting particular alleles at genetic markers that appear more frequently in breast cancer subjects or subjects who are susceptible to breast cancer. In particular embodiments, the invention is a method of determining a susceptibility to breast cancer by detecting at least one allele of at least one polymorphic marker (e.g., the markers described herein) . In other embodiments, the invention relates to a method of diagnosing a susceptibility to breast cancer by detecting at least one allele of at least one polymorphic marker. The present invention describes methods whereby detection of particular alleles of particular markers or haplotypes is indicative of a susceptibility to breast cancer. Such prognostic or predictive assays can also be used to determine prophylactic treatment of a subject prior to the onset of symptoms associated with breast cancer. 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 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) . The layman may also be a service provider who provides a service that comprises analyzing sequence data {e.g., genotype data for particular markers) so as to provide risk assessment measures of particular diseases or traits associated with such markers. 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 thus also be a service provider who interprets genotype information, which may be for example provided by the customer, to provide risk assessment 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 condition, 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. A certain aspect of the invention relates to a method of assessing a subject's risk of breast cancer, comprising steps of (a) obtaining sequence information about the individual identifying at least one allele of at least one polymorphic marker in the genome of the individual; (b) representing the sequence information as digital genetic profile data; (c) transforming the digital genetic profile data on a computer processor to generate breast cancer risk assessment report for the subject; and (d) displaying the risk assessment report on an output device. The sequence information may be obtained by any method, as described in the foregoing . The sequence information is suitably represented as digital genetic profile data, which may for example be in the form of actual genotypes, genotype counts at particular markers, or other indications of the particular genotype status of an individual at one or a plurality of markers (or haplotypes comprising two or more markers) . Transformation of the digital genetic profile data is the risk assessment, whereby the genotype information from the individual is transformed into a risk estimate, based on the known correlation between particular alleles at one or more markers and risk or susceptibility of breast cancer. The output device may be any suitable device for displaying the report, for example a website accessible via the internet, a data carrier, or a printed report.

The invention in a related aspect provides a risk assessment report of breast cancer for a human individual. Such a report comprises (a) at least one personal identifier; and (b) representation of at least one risk assessment measure of breast for the human individual for at least one polymorphic marker - which may be suitably be selected from any of the markers described herein . The personal identifier is any convenient identifier that can be used to identify the individual. The identifier may for example be a name, pseudoname, alias, or other numerical, alphanumerical or other codes that is associated with a unique individual. The identifier may also be an encrypted form of a personal identifier, for example a social security number or the like. In addition, in certain other embodiments, the present invention pertains to methods of diagnosing, or aiding in the diagnosis of, a decreased susceptibility to breast cancer, by detecting particular genetic marker alleles or haplotypes that appear less frequently in breast cancer patients than in individual not diagnosed with breast cancer or in the general population .

In one embodiment, determination of a susceptibility to breast cancer can be accomplished using hybridization methods, (see Current Protocols in Molecular Biology, Ausubel, F. et a/. , 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. The invention can also be reduced to practice using any convenient genotyping method, including commercially available technologies and methods for genotyping particular polymorphic markers.

To determine a susceptibility to breast 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 a nucleotide sequence of LD Block C06 as defined herein; alternatively, the nucleic acid probe can comprise all or a portion of a nucleotide sequence comprising at least one polymorphic marker as described herein, or a nucleotide sequence comprising all or a portion of the human C6orf97 and/or the human ESR1 genes, or the probe can be the complementary sequence of such a sequence. In a particular embodiment, the nucleic acid probe is a portion of a nucleotide sequence as set forth in any one of SEQ ID NO : 1-92, 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 a/., 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. It is also possible to design a single probe containing more than one marker alleles of a particular haplotype (e.g ., a probe containing alleles complementary to 2, 3, 4, 5 or all of the markers that make up a particular haplotype). Detection of the particular markers of the haplotype in the sample is indicative that the source of the sample has the particular haplotype (e.g ., a haplotype) and therefore is susceptible to breast cancer.

In one preferred 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)) . The fluorescent moiety can be Gig Harbor Green or Yakima Yellow, or other suitable fluorescent moieties. The detection probe is designed to hybridize to a short nucleotide sequence that includes the SNP polymorphism to be detected. Preferably, the SNP is anywhere from the terminal residue to -6 residues from the 3' end of the detection probe. The enhancer is a short oligonucleotide probe which hybridizes to the DNA template 3' relative to the detection probe. The probes are designed such that a single nucleotide gap exists between the detection probe and the enhancer nucleotide probe when both are bound to the template. The gap creates a synthetic abasic site that is recognized by an endonuclease, such as Endonuclease IV. The enzyme cleaves the dye off the fully complementary detection probe, but cannot cleave a detection probe containing a mismatch. Thus, by measuring the fluorescence of the released fluorescent moiety, assessment of the presence of a particular allele defined by nucleotide sequence of the detection probe can be performed.

The detection probe can be of any suitable size, although preferably the probe is relatively short. In one embodiment, the probe is from 5-100 nucleotides in length. In another embodiment, the probe is from 10-50 nucleotides in length, and in another embodiment, the probe is from 12-30 nucleotides in length . Other lengths of the probe are possible and within scope of the skill of the average person skilled in the art.

In a preferred embodiment, the DNA template containing the SNP polymorphism is amplified by Polymerase Chain Reaction (PCR) prior to detection . In such an embodiment, the amplified DNA serves as the template for the detection probe and the enhancer probe.

Certain embodiments of the detection probe, the enhancer probe, and/or the primers used for amplification of the template by PCR include the use of modified bases, including modified A and modified G. The use of modified bases can be useful for adjusting the melting temperature of the nucleotide molecule (probe and/or primer) to the template DNA, for example for increasing the melting temperature in regions containing a low percentage of G or C bases, in which modified A with the capability of forming three hydrogen bonds to its complementary T can be used, or for decreasing the melting temperature in regions containing a high percentage of G or C bases, for example by using modified G bases that form only two hydrogen bonds to their complementary C base in a double stranded DNA molecule. In a preferred embodiment, modified bases are used in the design of the detection nucleotide probe. Any modified base known to the skilled person can be selected in these methods, and the selection of suitable bases is well within the scope of the skilled person based on the teachings herein and known bases available from commercial sources as known to the skilled person. Additionally, or alternatively, a peptide nucleic acid (PNA) probe can be used in addition to, or instead of, a nucleic acid probe in the hybridization methods described herein . A PNA is a DNA mimic having a peptide-like, inorganic backbone, such as N-(2-aminoethyl)glycine units, with an organic base (A, G, C, T or U) attached to the glycine nitrogen via a methylene carbonyl linker (see, for example, Nielsen, P., et a/., Bioconjug. Chem. 5: 3-7 (1994)) . The PNA probe can be designed to specifically hybridize to a molecule in a sample suspected of containing one or more of the marker alleles or haplotypes that are associated with breast cancer. 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 or more markers or haplotypes of the present invention . As described herein, identification of a particular marker allele or haplotype associated with breast cancer 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 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) that is encoded by a nucleic acid associated with breast cancer. Further, the expression of the variant(s) can be quantified as physically or functionally different.

In another method 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 associated with breast cancer, and the presence of a specific allele can then be detected directly by sequencing the polymorphic site (or multiple polymorphic sites in a haplotype) of the genomic DNA in the sample.

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 associated with breast cancer. 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. In another aspect, determination of a susceptibility to breast cancer can be made by examining expression and/or composition of a polypeptide in those instances where genetic marker(s) as described herein result in a change in the composition or expression of the polypeptide. Thus, diagnosis of a susceptibility to breast cancer can in such instances be made by examining expression and/or composition of such polypeptides, e.g., one or more of C6orf97 and ESR1 polypeptides. It is also conceivable that the markers described herein that show association to breast cancer may also affect expression of nearby genes. It is well known that regulatory element affecting gene expression may be located far away, even as far as tenths or hundreds of kilobases away, from the promoter region of a gene. By assaying for the polymorphic markers of the present invention, it may thus be possible to assess the expression level of such nearby genes. Possible mechanisms affecting C6orf97 and/or ESR1 genes or other affected genes include, e.g., effects on transcription, effects on RNA splicing, 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 can be used for detecting protein expression levels, including enzyme linked immunosorbent assays (ELISA), Western blots, immunoprecipitations and

immunofluorescence. An alteration in expression of a polypeptide can be, for example, an alteration in the quantitative polypeptide expression {i.e., the amount of polypeptide produced) . An alteration in the composition of a polypeptide encoded by a nucleic acid associated with breast cancer may comprise an alteration in the qualitative polypeptide expression {e.g., expression of a mutant polypeptide or of a different splicing variant) . In one embodiment, diagnosis of a susceptibility to breast cancer is made by detecting a particular splicing variant, or a particular pattern of splicing variants (e.g., splicing variants of one or more of the C6orf97 and ESR1 genes) .

Both such alterations (quantitative and qualitative) can also 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 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, breast cancer. In one embodiment, the control sample is from a subject that does not possess a marker allele as described herein . Similarly, the presence of one or more different splicing variants in the test sample, or the presence of significantly different amounts of different splicing variants in the test sample, as compared with the control sample, can be indicative of a susceptibility to breast cancer. An alteration in the expression or composition of the polypeptide in the test sample, as compared with the control sample, can be indicative of a specific allele in the instance where the allele alters a splice site relative to the reference in the control sample. 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) that is capable of binding to a polypeptide encoded by a nucleic acid associated with breast cancer 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 . In another embodiment, determination of a susceptibility to breast cancer is made by detecting at least one marker or haplotype 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 or to a non-altered (native) polypeptide, means for amplification of nucleic acids, means for analyzing the nucleic acid sequence of a nucleic acid, means for analyzing the amino acid sequence of a polypeptide encoded by a nucleic acid associated with breast cancer, etc. The kits can for example include necessary buffers, nucleic acid primers for amplifying nucleic acids (e.g. , nucleic acids 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 breast cancer diagnostic assays.

In one embodiment, the invention pertains to a kit for assaying a sample from a subject to detect a susceptibility to breast 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 breast cancer risk. In one such embodiment, the polymorphism is selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and polymorphic markers in linkage disequilibrium therewith . In one preferred embodiment, the polymorphism is selected from the group consisting of rs9397435, and markers in linkage disequilibrium therewith . 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 the polymorphisms (e.g., SNPs or microsatellites) . 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 kit comprises reagents for detecting one or more markers, two or more markers, three or more markers, four or more markers or five or more markers. In certain embodiments, the kit comprises reagents for detecting no more than 1000 markers. In certain other embodiments, the kit comprises reagents for detecting no more than 100 markers, no more than 50 markers, no more than 20 markers or no more than 10 markers.

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 breast cancer. The collection of data may be provided on any suitable format. In one embodiment, the collection of data is provided on a computer-readable format.

Therapeutic agents

The risk variants for breast cancer presented herein can be useful in the identification of novel therapeutic targets for breast cancer. For example, genes containing, or in linkage

disequilibrium with, variants (markers and/or haplotypes) associated with breast cancer (e.g., the C6orf97 and/or the ESR1 genes, or their products, as well as genes or their products that are directly or indirectly regulated by or interact with these variant genes or their products, can be targeted for the development of therapeutic agents to treat breast cancer, or prevent or delay onset of symptoms associated with breast 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. In certain embodiments, the nucleotide segment comprises a portion of the C6orf97 and/or the ESR1 genes. In certain other embodiments, the antisense nucleotide is capable of binding to a nucleotide segment of as set forth in any one of SEQ ID NO: 1-92. 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 are from 14-50 nucleotides in length, including 14-40 nucleotides and 14-30 nucleotides. 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 knock- down 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 breast cancer, or a defect causing breast cancer, 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 administered 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.

The present invention provides methods for identifying compounds or agents that can be used to treat breast cancer. It is contemplated that the human C6orf97 gene and/or the human ESR1 gene are useful as targets for the identification and/or development of therapeutic agents for treating breast cancer. In certain embodiments, such methods include assaying the ability of an agent or compound to modulate the activity and/or expression of a C6orf97 and/or ESR1 nucleic acid that includes at least one of the variants (markers and/or haplotypes) of the present invention, or the encoded product of the nucleic acid . Assays for identifying agents or compounds that inhibit or alter undesired activity or expression of encoded nucleic acid products can be performed in cell-based systems or in cell-free systems, as known to the skilled person . Cell-based systems include cells naturally expressing the nucleic acid molecules of interest, or recombinant cells that have been genetically modified so as to express a certain desired nucleic acid molecule.

Variant gene expression in a patient can be assessed by expression of a variant-containing nucleic acid sequence (for example, a gene containing at least one variant of the present invention, which can be transcribed into RNA containing the at least one variant, and in turn translated into protein), or by altered expression of a normal/wild-type nucleic acid sequence due to variants affecting the level or pattern of expression of the normal transcripts, for example variants in the regulatory or control region of the gene. Assays for gene expression include direct nucleic acid assays (mRNA), assays for expressed protein levels, or assays of collateral compounds involved in a pathway, for example a signal pathway. Furthermore, the expression of genes that are up- or down-regulated in response to the signal pathway can also be assayed. One embodiment includes operably linking a reporter gene, such as luciferase, to the regulatory region of the gene(s) of interest.

Modulators of gene expression can in one embodiment be identified when a cell is contacted with a candidate compound or agent, and the expression of mRNA is determined. The expression level of mRNA in the presence of the candidate compound or agent is compared to the expression level in the absence of the compound or agent. Based on this comparison, candidate compounds or agents for treating breast cancer can be identified as those modulating the gene expression of the variant gene. When expression of mRNA or the encoded protein is statistically significantly greater in the presence of the candidate compound or agent than in its absence, then the candidate compound or agent is identified as a stimulator or up-regulator of expression of the nucleic acid . When nucleic acid expression or protein level is statistically significantly less in the presence of the candidate compound or agent than in its absence, then the candidate compound is identified as an inhibitor or down-regulator of the nucleic acid expression .

The invention further provides methods of treatment using a compound identified through drug (compound and/or agent) screening as a gene modulator (i.e. stimulator and/or inhibitor of gene expression) .

Methods of Assessing Probability of Response to Therapeutic Agents, Methods of Monitoring

Progress of Treatment and Methods for Treating Breast Cancer

As is known in the art, individuals can have differential responses to a particular therapy (e.g., a therapeutic agent or therapeutic method) . The basis of the differential response may be genetically determined in part. 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, the presence of a particular allele at a polymorphic site may be indicative of a different response rate to a particular treatment modality. This means that a patient diagnosed with breast cancer, and carrying a certain allele at a polymorphic marker of the present invention would respond better to, or worse to, a specific therapeutic, drug and/or other therapy used to treat breast cancer. Therefore, the presence or absence of the marker allele could aid in deciding what treatment should be used for the patient. For example, for a newly diagnosed patient, sequence information about a marker of the present invention may be obtained (e.g., through testing DNA derived from a blood sample, as described herein) . If the patient is positive for a particular marker allele, then the physician may recommend one particular therapy, while if the patient is negative for the at least one allele of a marker, or a haplotype, 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 at an early stage, to select the most appropriate treatment, and/or provide information to the clinician about prognosis/aggressiveness of disease in order to be able to apply the most appropriate treatment.

As described further herein, current clinical preventive options for breast cancer are mainly chemopreventive (chemotherapy, or hormonal therapy) and prophylactic surgery. The most common chemopreventive is Tamoxifen and Raloxifene; other options include other Selective Estrogen Receptor Modulator (SERM) and aromatase inhibitors. Treatment options also include radiation therapy, for which a proportion of patients experience adverse symptoms. The markers of the invention, as described herein, may be used to assess response to these therapeutic options, or to predict the progress of therapy using any one of these treatment options. Thus, genetic profiling can be used to select the appropriate treatment strategy based on the genetic status of the individual, or it may be used to predict the outcome of the particular treatment option, and thus be useful in the strategic selection of treatment options or a combination of available treatment options.

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 the at-risk variants of the present invention, i.e. individuals who are carriers of at least one allele of at least one polymorphic marker conferring increased risk of developing breast cancer may be more likely to respond to a particular treatment modality. In one embodiment, individuals who carry at-risk variants for gene(s) in a pathway and/or metabolic network for which a particular treatment (e.g., small molecule drug) is targeting are more likely to be responders to the treatment. In another embodiment, individuals who carry at-risk variants for 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.

In a further aspect, the markers and haplotypes of the present invention can be used for targeting the selection of pharmaceutical agents for specific individuals. Personalized selection of treatment modalities, lifestyle changes or combination of the two, can be realized by the utilization of the at-risk variants of the present invention . Thus, the knowledge of an individual's status for particular markers of the present invention, can be useful for selection of treatment options that target genes or gene products affected by the at-risk variants of the invention (e.g., C6orf97 and/or ESR1 , or their gene products) . Certain combinations of variants may be suitable for one selection of treatment options, while other gene variant combinations may target other treatment options. Such combination of variant may include one variant, two variants, three variants, or four or more variants, as needed to determine with clinically reliable accuracy the selection of treatment module. 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.

Fig. 4 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 performs 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 Fig . 4, 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, Fig. 4 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, Fig. 4 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 Fig . 4, provide storage of computer readable instructions, data structures, program modules and other data for the computer 110. In Fig. 4, 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 Fig . 4. The logical connections depicted in Fig. 4 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, Fig. 4 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.

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 Fig . 4. 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 of the polymorphic markers and haplotypes described herein to be associated with breast cancer. 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, etc.), 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 breast cancer, 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 breast cancer, are in certain embodiments useful for interpretation and/or analysis of genotype data (including sequence data identifying particular marker alleles) . Thus in certain embodiments, determination of the presence of an at-risk allele for breast 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 breast cancer. In one such embodiment, genotype data is generated for at least one polymorphic marker shown herein to be associated with breast 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 or analysis 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, as described in the above. 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, a nucleotide sequence comprising all or a portion of LD block C06; a nucleotide sequence of all or a portion of the C6orf97 and/or ESR1 genes; or a nucleotide sequence of all or a portion of the nucleotide sequences as set forth in any one of SEQ ID NO: 1-92; or a nucleotide sequence comprising, or consisting of, the complement of such sequences.

The nucleic acid fragments of the invention are suitably 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 . The fragments are suitably no more than 20,000 nucleotides in length, no more than 5000 nucleotides, no more than 1000 nucleotides, no more than 500 nucleotides, no more than 400 nucleotides, no more than 300 nucleotides, no more than 200 nucleotides, no more than 100 nucleotides, no more than 50 nucleotides or no more than 30 nucleotides in length .

The nucleic acid fragments of the invention may be 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) 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 can 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. 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 a polypeptide of the invention is a molecule that binds to that polypeptide or a fragment thereof, but does not substantially bind other molecules in a sample, e.g. , a biological sample, which naturally contains the polypeptide. 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 invention provides polyclonal and monoclonal antibodies that bind to a polypeptide of the invention. 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 (e.g. a polypetide encoded by the C6orf97 and/or ESR1 genes) 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 in diagnostic applications 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 polymorphic 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 a disease, or in an individual with a predisposition to a disease related to the function of the protein, in particular breast 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 or haplotype as described herein can be used to screen for the presence of the variant protein, for example to screen for a predisposition to breast 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.

Antibodies are further useful for inhibiting variant protein function, for example by blocking the binding of a variant protein to a binding molecule or partner. Such uses can also be applied in a therapeutic context in which treatment involves inhibiting a variant protein's function . An antibody can be for example be used to block or competitively inhibit binding, thereby modulating (i.e., agonizing or antagonizing) the activity of the protein . Antibodies can be prepared against specific protein fragments containing sites required for specific function or against an intact protein that is associated with a cell or cell membrane. For administration in vivo, an antibody may be linked with an additional therapeutic payload, such as radionuclide, an enzyme, an immunogenic epitope, or a cytotoxic agent, including bacterial toxins (diphtheria or plant toxins, such as ricin) . The in vivo half-life of an antibody or a fragment thereof may be increased by pegylation through conjugation to polyethylene glycol.

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 labeled 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

Recent genome-wide association studies (GWAS) have been successful in identifying a number of susceptibility loci for breast cancer and other cancers [1,2,3,4] . In most studies, strong evidence has been obtained for risk association in one particular ancestral group, usually Europeans. SNPs that are represented on the microarray chips used in GWAS protocols are selected in part because they each tag a group of correlated, ungenotyped SNPs through linkage disequilibrium (LD) . There is no particular expectation that a SNP identified in a GWAS is a causative variant, rather it is more likely that any SNP giving a signal in a GWAS does so because it is in LD with a causative variant (or perhaps a set of causative variants, but for convenience we refer here to a single variant) that is not genotyped directly. If the analysis is moved to another ancestral population, then the LD relations between the chip SNP that gave the signal and the causative variant may be disrupted as a result of the differing patterns of LD in different ancestral populations [5] .

It is important to identify SNPs whose property of tagging a causative variant is not disrupted by changes in LD resulting from a shift to another ancestral population . There are two main motivations for this. Firstly and for practical reasons, one might wish to test for risk arising from the susceptibility locus in another ancestral group (or indeed to determine whether a similar causative variant exists at the susceptibility locus in another ancestral group) . Secondly, by moving the analysis into another ancestral population, it might be possible to resolve between SNPs that are so highly correlated in the original ancestral population that their risk associations are indistinguishable. This would aid in the identification of those SNPs that are most strongly correlated with the causative variant, and move the analysis in a stepwise manner towards identification of the causative variant itself. This type of approach, which we term ancestry-shift refinement mapping, has been used previously in studies of type II diabetes and breast cancer [4,6,7,8] . However the interpretation has sometimes been limited by low power in the target ancestral populations or the lack of a comprehensive genotyping strategy.

The estrogen receptor a (ESR1 ) locus has been a focus of attention because of the known roles of estrogen in risk of breast cancer, osteoporosis and other conditions. Many investigations have been conducted looking for risk associations with genetic variants in ESR1 , with generally equivocal results [9] . Comprehensive tag-SNP and meta-analyses found little evidence of breast cancer risk variants in the ESR1 gene itself [10,11] . Recently, a GWAS conducted in a large sample of Chinese from the Shanghi Breast Cancer Study identified an association between rs2046210 and breast cancer [12] . SNP rs2046210 is located 180kb 5 ^' to the major ESR1 transcript initiation sites (and 63kb 5 ^' to the start site of ESR1 isoform 4) . The SNP is about 6kb downstream of the 3 ^' end of C6orf97, a RefSeq gene of unknown function (Figure 1, upper panel) . Zheng et al . [ 12] reported that rs2046210 also confers risk of breast cancer in a population of European ancestry (allelic OR = 1.15, P = 0.01) . However, the evidence from the publically available CGEMS dataset was more equivocal [13] : We estimated from the CGEMS data an alleleic OR of 1.09, P = 0.25 for rs6900157, the best tagger (r² = 0.93 in HapMap CEU) of rs2046210 on the CGEMS Illumina chip. Our own breast cancer GWAS dataset from 1,982 patients and 35,895 controls [3,4] provided no evidence of a risk associated with rs6900157 in Europeans (allelic OR = 1.04, P = 0.36) .

We suspected that the reason for our failure to replicate the Zheng et al. signal in Europeans could be because the LD relationship between the reported SNP rs2046210 and the causative variant might have been disrupted by the ancestry shift from Chinese into Europeans. Here we show that this is indeed the case. By studying a large class of SNPs that are highly correlated in Chinese but not necessarily so in ancestral Europeans and Africans, we were able to identify a class of comparatively rare SNPs (6-7% minor allele frequency [MAF] in Europeans and 1-6% in Africans) that is strongly associated with breast cancer risk in non-Asian ancestral populations. The most strongly associated SNP, rs9397435, fully accounts for the association in all three ancestries.

Results and Discussion:

To examine the possibilities for an ancestry shift refinement of the C6orf97-ESRl signal, we identified 36 SNPs that are well correlated (r² >. 0.65) with the key SNP rs2046210 in Chinese, using the HapMap CHB dataset (Figure 1, lower panel) . Then, using the HapMap CEU dataset, we observed the pattern of correlations between these SNPs in a population of European ancestry. The dendrogram in Figure 2a shows a hierarchical clustering of the 37 SNPs, based on their r² coefficients. We defined equivalence classes as sets of SNPs (or branches of the dendrogram) that show a correlation with an r² >_. 0.8 in CEU. We then selected a set of SNPs for genotyping such that at least one SNP in each equivalence class was included. These SNPs are highlighted in Figure 2a. We forced in some redundant SNPs, partly to cover additional class fractionation in Africans (see below), and partly in order to examine two non-synonomous coding SNPs in the C6orof97 gene : V604I (rs6929137) and V683I (rs3734804) . Single track Centaurus [14] assays were generated for the selected SNPs and validated by typing them in the HapMap CEU, CHB/JPT, and YRI samples.

We then typed the selected SNPs in a series of European ancestry breast cancer case: control samples from Iceland, Holland, Spain, Sweden, and U.S.A. ; a total of 7,899 breast cancer cases and 11,234 controls. Details of the constituent samples sets are given in Table 2. In addition, we typed the selected SNPs in a sample of 1, 126 breast cancer cases and 1, 118 controls of Chinese ancestry from Taiwan. The results are summarized in Table 3 and individual results for each sample set are given in Table 4. We used a likelihood approach to ensure that the same individuals were tested for each SNP, so that the P values for the different SNPs could be compared directly. The results from the Taiwanese sample confirmed the association between breast cancer risk and the key SNP rs2046210 (OR = 1.24, P = 4.3 x 10^~4) that was previously reported in Shanghai Chinese [12] . We also obtained significant signals for a range of SNPs that are highly correlated with rs2046210 in the Taiwanese. However, in the combined European ancestry populations, it was evident that the key SNP rs2046210 confers little or no increased risk of breast cancer (OR = 1.04, P = 0.099, Table 3) . This occurred despite the fact that the minor allele frequency of rs2046210 is quite stable moving from Asians into Europeans. We did observe significant risk estimates in Europeans arising from a group of SNPs with minor allele frequencies in the 6-7% range, tagged by rs9397435, rsl2662670, rsl2665607, rs9383589 and rs3734805 (Table 3) . These SNPs are highly or moderately correlated with each other in Europeans, judging from the HapMap data (Figure 2a) and the observed data from the genotyped samples (Figure 3) . The association P values for these SNPs remained significant if we applied Bonferroni correction for the number of European equivalence classes tested

(significance threshold P = 0.05 divided by 7 classes = 0.007) . Thus, with the ancestry shift into Europeans, the signal appears to segregate off rs2046210 and onto a rarer class of SNPs with the strongest signal coming from rs9383589[G] (OR = 1.15, P = 6.2 x 10^~4) . Thus, if the pathogenic variant that is present in Chinese is also present in Europeans, then in Europeans it appears to be tagged better by rs9383589 than by rs2046210. There was no substantial signal detected from either of the two coding variants in C6orf97 (rs6929137 and rs3734804), ruling them out as potential singular underlying causative variants (Table 3) .

We then examined how the SNPs in the European 6-7% MAF classes were correlated in

Yorubans, using the HapMap data . In YRI, the SNPs split into five separate equivalence classes, with MAFs ranging from about 6% (for the class tagged by rs9397435) to 1% (for the class containing only rsl2665607)(Figure 2b) . We typed these five SNPs in a sample of 851 breast cancer patients and 781 controls from Ibadan, Nigeria. We also included the key SNP rs2046210 and rs6929137, the V604I coding variant which is closely correlated with rs2046210 in Chinese and Europeans but not in Yorubans (Figure 2b) . To confirm the associations observed in the Nigerians, we also typed the SNPs in a small cohort of African American breast cancer patients and controls from the Chicago area . Combined results from the two cohorts are shown in Table 3 and data from each of the two cohorts separately are shown in Table 4. Even though they are in different equivalence classes in Africans, nominally significant ORs were nevertheless observed for rs9397435, rsl2662670, rsl2665607, and rs9383589 (Table 3). Inspection of the results from the Nigerians separately (Table 5, Table 4) and of the LD patterns in the data from the Nigerians and African Americans (Figure 3) did not suggest that the lack of resolution between these SNPs could be explained by European admixture in the African American samples. Neither the key SNP rs2046210 nor the coding variant rs6929137 showed any indication of an association with breast cancer risk in the African ancestry samples, hence they are unlikely to be causative or closely correlated with the causative variant. This is in agreement with Zheng et al who had previously reported that they were unable to see an association between rs2046210 or rs6929137 and risk in a sample of 810 African American breast cancer cases and 1784 controls [8] . If a pathogenic variant is present in all three ancestries, then it might be expected to have a similar effect in all populations. A variant that is in strong LD with a pathogenic variant could also show similar properties, if the LD is maintained in different ancestral populations. Such variants are likely to show the strongest overall disease association when combined over all ancestries. In order to assess the genotyped variants for these characteristics, we used the Mantel-Haenszel model to obtain combined OR estimates and P values for the SNPs that had been typed in all three ancestral populations. The strongest breast cancer association overall, both in terms of OR and P value, was with rs9397435[G], giving an OR of 1.19 and P = 3.90 x 10^~7 (Table 3). The other four SNPs in the European 6-7% MAF class and 1-6% African MAF class also showed substantial signals combined over all three ancestries. None of these five SNPs showed significant heterogeneity in OR estimates over the three ancestries (Table 5). However, all of the SNPs outside these classes (rs6929137 being an exception) did show significant

heterogeneity between all three ancestries, or between Asians and Europeans, indicating that their effects are not consistent in all ancestries.

We then investigated whether the SNP with the strongest overall association could account for the signals observed in all three ancestries. In a multivariate analysis, no SNP retained a significant at-risk signal when adjusted for the effect of rs9397435 (Table 5). Thus there is no evidence for an association signal that is not captured by rs9397435. In Europeans, rs9397435 retained significant ORs when adjusted for the effects of rs2046210, rs6929137, and marginally when adjusted for rsl2662670 (Table 5). No significant OR_adJ was observed when rs9397435 was adjusted for rsl2665607, rs9383589, or rs3734805 in Europeans. We interpret this to mean that no tested SNP is demonstrably closer correlated with the causative variant in Europeans than rs9397435. However rsl2665607, rs9383589, and rs3734805 are also highly correlated with the causative variant and cannot be distinguished from rs9397435 in this respect. Data from the Nigerians supported the exclusion of rs2046210, rs6929137, and the tentative exclusion of rsl2662670 from being the most highly correlated with the causative variant, but no additional resolution was achieved (Table 5). Data from the Taiwanese reconfirmed that little or no resolution is available within the Asian ancestry. We did note a significant protective effect of rs6929137[A] when adjusted for rs9397435 in Asians. This is most likely to be a fluctuation in the data since there is no sign of the effect in the other ancestries and there were no quality issues with the genotyping of rs6929137. However, if the effect were to be confirmed, it would suggest the presence of a second risk variant in Asians, correlated with the rs6929137[G] allele.

The pattern of risk associations was further illuminated by an examination of the common haplotypes generated by the typed SNPs (Table 7). In the Nigerians, the rs9397435[G] allele is present on several different, quite rare haplotypes (Haplotypes E-I). All except one (Haplotype G) have OR point estimates >1.0. Two of these haplotypes (H and I) become more common with the ancestry shift into Europeans and Asians, and are the dominant at-risk haplotypes in those population samples. Conversely, haplotypes E-G become vanishingly rare in Europeans and Asians. In Nigerians, the rs2046210[T] allele is present on all of the common haplotypes carrying rs9397435[G] (Haplotypes E-I). However it is also present on two very common, non-risk haplotypes (A and B), which effectively attenuates the association of rs2046210[T] with disease in Nigerians. In Europeans, Haplotype B is reduced in frequency but Haplotype A is still present at substantial frequencies, again attenuating an association of rs2046210[T] with risk. In Asians, Haplotypes A and B are both very much reduced in frequency while the at-risk Haplotype H has become the dominant haplotype carrying rs2046210[T]. This illustrates why rs2046210[T] is strongly associated with risk in Chinese, but only weakly if at all in the Europeans and Nigerians. To increase the resolution on the haplotype analysis, we generated ancestral maps based on the full HapMap SNP data for the region. This confirmed that the Haplotype group H forms a contiguous branch with greatly increased frequencies in Asians relative to Africans. It also showed that the risk allele rs9397435[G] is present on a diversity of haplotype backgrounds in Africans, of which only some derivatives are represented in Europeans and Asians. Given the dispersion of the African haplotypes containing rs9397435[G], and taking the assumption that all haplotypes carrying this allele are indeed at-risk, there does not appear to be any HapMap SNP that could show a stronger association. We did note, however, that Haplotype G which is the only common haplotype for which we did not observe an OR point estimate > 1, is in an ancestral position in the group H branch of the tree. This raises the possibility that the origin of the causative variant post-dates the mutation event that created rs9397435.

We examined the genomic region containing the 6-7% European MAF class SNPs for correlations between SNP locations and known functional features. The SNPs occur in a region containing a number of ligand-inducible estrogen receptor (ER) binding sites, suggesting that this area may be involved in autoregulation of the ESR1 gene [15,16]. However none of the SNPs actually mapped within the identified ER binding sites. We noted that rs9397435 is located at a site of histone modification marks in human mammary epithelial cells (HMEC) and normal human keratinocytes (NHEK). Peaks of H3K4mel and H3K4me2 (but not H3K4me3) co-localized with rs9397435 [17]. A moderate peak of H3K9ac was also seen at this location in HMEC. This pattern of histone modification has been associated with enhancers but not with transcription initiation sites [18]. None of the other HapMap SNPs in the 6-7% MAF group (including the ungenotyped ones) showed similar associations with histone modification peaks or any other notable bioinformatic features.

To identify additional SNPs that might be causative variants, we accessed the April 2009 release of the 1000 genomes project which includes data on 57 European individuals, 56 Yorubans and 59 Japanese or Han Chinese. We then looked for non-HapMap SNPs that were well correlated {r²> 0.75) with rs9397435 in both Europeans and Asians (no SNPs were this highly correlated with rs9397435 in Yorubans). We identified 10 non-HapMap SNPs having this property of which 8 were listed in dbSNP build 130 and 2 were novel (Table 8). These SNPs are all candidate causative variants. We searched for correlations between these 10 additional SNPs and locations of known functional features. A previously unknown C/T SNP at position 152,010,891 coincides with an experimentally verified binding site of the transcriptional insulator protein CTCF in a variety of cell types including HMEC [19]. The variant changes a CpG sequence to TpG, the latter being correlated with the rs9397435[G] risk allele. Because CTCF binding is sensitive to cytosine methylation of CpG sites, we investigated the novel C/T SNP at 152,010,891 in more detail. We confirmed its existence by Sanger sequencing a sample of Europeans and generated a single- track Centaurus assay for it. The SNP is now listed as rs77275268 in dbSNP build 131. We confirmed its LD relations with rs9397435 in samples of European and Chinese ancestry (Table 9). We also found that rs77275268 exists in Africans at a MAF of 1.3% (in controls) and is most closely correlated there with rs9383589 among the typed HapMap SNPs (Table 9). Like rs9383589, it showed a nominally significant association with breast cancer in the African ancestry samples (OR = 1.97, P = 7.4 x 10^~3) . Bisulfite sequencing of peripheral blood DNA from 29 European individuals who were CC homozygotes for rs77275268 showed that the CpG sequence is partially methylated (Figure 5) . The occurrence of the TpG variant at this position thus precludes facultative methylation and may affect CTCF binding .To investigate a possible impact of the risk variants on gene expression, levels of ESR1, progesterone receptor (PGR) and HER2 (ERBB2) mRNAs were assessed in 1,234 frozen tumour samples. SNP rs9397435 was genotyped using DNA samples from the same tumours. The at-risk GG homozygotes expressed mean levels of ESR1 and PGR mRNA that were four to five-fold higher than the levels in AA homozygotes and AG heterozygotes (Figure 6) . When assessed under our default, multiplicative (co-dominant) inheritance model, these differences were of borderline significance for PGR and not significant for ESR1 (Table 10) . Assessed under a recessive inheritance model, the increases in both ESR1 and PGR mRNA levels in GG homozygotes were significant (P = 0.024 and 0.031 for ESR1 and PGR respectively, (Table 10)) . In comparisons with the full genotype model, neither the multiplicative nor the recessive models could be rejected. ERBB2 mRNA levels did not vary with genotype. These findings raise the possibility that rs9397435[G] (or a correlated SNP) might act to increase expression of ESR1 and, as a consequence, increase PGR expression .

We reviewed the medical records of approximately 8,466 European and Taiwanese patients, including 1,817 individuals from a series of case-only cohorts from Rotterdam (see Methods), whom we genotyped for rs9397435. In Europeans, rs9397435[G] was found to confer significant risk of both estrogen receptor (ER) positive and ER negative breast cancer and of both progesterone receptor positive and negative disease. It was also associated at nominal significance with an early age at first invasive breast cancer (P = 0.015, Table 11) . Elevated univariate OR point estimates were observed for ER negative breast cancer vs controls, advanced stage, poor differentiation grade and lymph node positivity suggesting that the SNP is preferentially associated with these subtypes of breast cancer.

In summary, we have shown that ancestry shift refinement mapping can be useful in the identification of SNPs that associate with risk in populations of different ancestries. This has practical implications for genetic testing and highlights that a comprehensive approach is necessary when investigating whether a risk variant identified in one ancestral population is also present in another ancestry. We also have shown that the refinement available from shifting the ancestry of the study population can offer the potential to discriminate between SNPs that are highly correlated in the original population.

In this particular case, we have shown that the initially reported [12] Chinese breast cancer risk variant rs2046210 cannot be used effectively as a risk marker in Europeans and Africans because it is not in strong LD with a causative variant in all three ancestries. We have identified a variant, rs9397435, that confers risk of breast cancer in all three main ancestral populations. Three other HapMap SNPs rsl2665607, rs9383589, and rs3734805 also are associated with risk in all three ancestries and cannot be distinguished from rs9397435 based on the currently available data. Other, non-HapMap SNPs may prove to be better markers of the breast cancer risk. Both rs9397435 and rs77275268 SNPs are located at sites of potential functional significance. This study demonstrates that, using ancestry-shift refinement mapping, it is possible to move in a progressive, stepwise manner towards the identification of better risk markers and, ultimately, causative variants.

EXAMPLE 2

Samples:

Breast cancer case and control population samples are listed and referenced in Table 2. The Holland (Rotterdam) DNA samples were isolated from 1,817 frozen primary tumour specimens from patient in the following sets: 1099 patients with lymph node negative disease who did not receive any adjuvant chemotherapy, 193 patients with ER positive, lymph node positive disease who were treated with tamoxifen monotherapy, 312 patients with lymph node positive disease who were treated with adjuvant chemotherapy, 316 patients with ER positive tumours who were treated with tamoxifen monotherapy for recurrent disease and 229 patients treated with chemotherapy for advanced disease. Some patients were in both primary and advanced disease groups.

Genotyping:

Genotyping was carried out using Nanogen Centaurus assays [14] . Assays were validated by genotyping on HapMap CEU, YRI and CHB/JPT samles and comparing the genotypes with the published data . Assays were rejected if they showed >_ 1.5% mismatches with the HapMap data.

Genotyping of Icelandic and foreign samples was carried out at the deCODE genetics facility.

Clustering algorithms were applied and manual editing was carried out in a standardized manner for all sample sets. Two standard control DNA samples and water blanks were included on every plate. Genotyping yields were in excess of 98% for all SNP-Sample combinations attempted.

Bisulfite Sequencing: Bisulfite treatment of 1 Mg of each peripheral blood DNA sample was conducted with EpiTect Bisulfite Kit (QIAGEN-59104) according to the manufacturer's protocol. PCR and Sanger sequencing was conducted with standard protocols. Gene Expression Analysis:

RNA isolation and quantitative RT-PCR analysis was carried out according to standard methods. Briefly, tumour material was preserved in liquid nitrogen and RNA isolated from 20-60 cryostat sections of 30μπι using standard methods. cDNA was synthesized using oligo (dT) and random hexamer primers. Real-time quantitative PCR was done on an ABI Prism 7700 Sequence Detection System (Applied Biosystems) . Ct values for the target genes were normalized to the mean Ct values of three housekeeping genes (HMBS, HPRT and B2M) and expressed as: Relative Expression Level = 2^{(mean ct housekeeping " mean ct target)}. Statistical Analysis:

To construct dendrograms, SNPs were arranged in hierarchical clusters based on the r² relationships between them . The clustering was performed using the "stats" package of R software. The "hclust" command was used with the method "average". Pairwise r² values were first re-arranged into a bi-dimensional matrix M that was transformed into a similarity matrix by performing the operation 1-M. The similarity matrix obtained was finally used as a distance matrix and depicted by a dendrogram. In this similarity matrix an original r² value of 1 is thus transformed to 0, representing a distance of 0 from a fully correlated SNP.

We calculated the OR for each SNP allele assuming the multiplicative model, i.e. assuming that the relative risk to a person is the product of the two alleles a person carries. This assumption was tested by calculating genotype-specific relative risks for each SNP in Europeans and comparing them to those determined under the multiplicative model. No significant deviations from the multiplicative model were observed. Therefore, allelic OR and P values are presented in the data tables. P values were calculated with the standard likelihood ratio χ² statistic and confidence intervals were calculated assuming that the estimate of OR has a log-normal distribution . Some Icelandic cases and controls are related to each other, causing the χ² statistic to have a mean > 1. We estimated the inflation factor by simulating genotypes through the Icelandic genealogy and corrected the χ² statistics accordingly. Individuals in the replication sample sets were assumed to be unrelated to each other. The tested SNPs are in LD with each other. Therefore, wherever the genotype of one SNP was missing for an individual, the genotypes of the correlated SNPs were used to infer the missing genotypes using a likelihood approach as described previously[21] . This permitted the equilzation of sample numbers genotyped for each SNP and facilitated the comparisons of P values obtained for different SNPs. Joint analyses of multiple case : control replication groups was carried out using a Mantel-Haenszel model in which the groups are allowed to have different population allele frequencies but were assumed to have common relative risks. Tests of heterogeneity were performed 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. J² takes values between 0% and 100% and describes the proportion of the total variation in estimates that is due to heterogeneity [22] .

For the haplotype analysis using HapMap data, phased haplotypes were generated for the 60 CEU parents, 60 YRI parents and 90 Asian individuals. The phases of alleles in haplotypes was estimated using the EM algorithm, in combination with the family trio information for the CEU and YRI groups (where the genotypes from the 30 children were used to help infer the allelic phase of the haplotypes) .

Quantitative RT-PCR data were analyzed under the multiplicative model by regressing logi₀ transformed Relative Expression Level values against the number of risk alleles carried (0, 1,2) . When testing the recessive model, we used the GG homozygote status as an explanatory variable taking values 0 (AA homozygote or AG heterozygote) or 1 (GG homozygote) . In the full model, we used two explanatory variables; the AG heterozygote status (0 or 1) and the GG homozygote status (0 or 1) .

Bioinformatics:

For the list of candidate SNPs we carried out a search for overlaps between SNP position and known bioinformatic features. We retrieved data from the deCODE inhouse mirror of the UCSC human genome browser and from the UCSC test browser (HG18 build 36) [23] . We also retrieved data from three bioinformatic feature publications [15,16,24] . We accessed all available feature tracks containing genome positional information (approximately 10,000 tracks) and identified those features that overlapped with SNPs. Data from the 1000 Genomes project were obtained from the April 2009 release (http ://www.1000genomes.org) .

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Table 2: Overview of the sample sets used in the study

Sample Set Ancestry Cases Controls Type Reference

Iceland European 2638 3506 Registry Ascertained Case: Population Based Control a

U.S.A. (Mayo Clinic) European 1753 1487 Clinic Ascertained Case: Population Based Control b

Spain European 1009 1719 Clinic Ascertained Case: Population Based Control c

Holland (Nijmegen) European 727 1830 Registry Ascertained Case: Population Based Control c

Sweden (Stockholm) European 818 1750 Clinic Ascertained Case: Population Based Control d

Sweden (Northern) European 954 942 Registry Ascertained Case: Population Based Control e

Holland (Rotterdam) European 1816 NA Case only follow-up cohort f

Nigeria African 851 781 Clinic Ascertained Case: Population Based Control g

U.S.A. (Chicago) African American 300 153 Clinic Ascertained Case: Population Based Control h

Taiwan Asian 1126 1118 Clinic Ascertained Case: Population Based Control i

References:

a Stacey, S.N et al., PLoS Med.2006 Jul;3(7):e217.

b Olson, J.E. et al., Breast Cancer Res Treat. 2007 Apr;102(2):237-47.

c Stacey, S.N. et al., Nat Genet.2007 Jul;39(7):865-9.

d Margolin, S. et al., Genet Test.20048(2): 127-32.

e Kaaks, R. et al., Cancer Causes Control.2002 May;13(4):307-16.

f Wang, Y. et al., Lancet.2005 Feb 19-25;365(9460):671-9.

⁹Adebamowo, C.A. et al., Ann Epidemiol.2003 Jul;13(6):455-61.

hThis paper

' Cheng, T.C. et al., Int J Cancer.2005 Jan 20;113(3):345-53.

Table 3: Association of C6orf97/ESR1 SNPs with Breast Cancer in populations of different ancestries

Frequency

SNP Allele Ancestry³ in OR 95% CI P-value

Controls'³

rs9383932 G Asian 0.378 1.25 (1.11, 1.41) 2.9 x 10^-4

European 0.132 1.06 (1.00, 1.13) 0.066 rs9397435 G Asian 0.326 1.23 (1.09, 1.40) 8.0 x 10^-4

European 0.063 1.15 (1.06, 1.25) 1.2 x 10^~3

African & African American 0.063 1.35 (1.06, 1.71) 0.014

All Ancestries NA 1.19 (1.11, 1.27) 3.9 x 10^~7 rs12662670 G Asian 0.343 1.22 (1.07 1.39) 2.5 x 10^~3

European 0.071 1.12 (1.03, 1.21) 6.4 x 10^~3

African & African American 0.027 1.54 (1.06, 2.23) 0.022

All Ancestries NA 1.16 (1.08, 1.24) 1.9 x 10^~5 rs12665607 A Asian 0.323 1.24 (1.10, 1.40) 6.2 x 10^-4

European 0.072 1.14 (1.05, 1.23) 1.1 x 10^~3

African & African American 0.010 1.94 (1.14, 3.30) 0.015

All Ancestries NA 1.18 (1.10, 1.26) 1.2 x 10^~6 rs9383589 G Asian 0.323 1.20 (1.06, 1.36) 4.2 x 10^~3

European 0.070 1.15 (1.06, 1.25) 6.2 x 10^-4

African & African American 0.016 1.61 (1.05, 2.48) 0.029

All Ancestries NA 1.17 (1.10, 1.25) 2.5 x 10^~6 rs3734805 C Asian 0.324 1.22 (1.07, 1.37) 2.0 x 10^~3

European 0.072 1.13 (1.05, 1.23) 1.8 x 10^~3

African & African American 0.028 1.27 (0.91, 1.78) 0.16

All Ancestries NA 1.16 (1.09, 1.23) 7.8 x 10^~6 rs6929137 A Asian 0.348 1.15 (1.02, 1.30) 0.025

European 0.316 1.04 (0.99, 1.09) 0.082

African & African American 0.538 1.02 (0.89, 1.16) 0.81

All Ancestries NA 1.05 (1.01, 1.09) 0.017 rs2046210 T^c Asian 0.363 1.24 (1.10, 1.40) 4.3 x 10^-4

European 0.337 1.04 (0.99, 1.08) 0.099

African & African American 0.716 0.98 (0.86, 1.11) 0.77

All Ancestries NA 1.06 (1.01, 1.10) 9.8 x 10^~3 rs7752591 A Asian 0.421 1.25 (1.11 1.40) 3.0 x 10^-4

European 0.495 1.03 (0.99, 1.07) 0.16 rs3734804 A Asian 0.422 1.21 (1.08, 1.37) 1.4 x 10^~3

European 0.474 1.00 (0.95, 1.05) 0.97 rs6932260 C Asian 0.422 1.23 (1.09, 1.38) 7.7 x 10^-4

European 0.474 1.00 (0.95, 1.05) 0.96 rs852003 A Asian 0.424 1.26 (1.12, 1.42) 1.2 x 10^-4

European 0.556 1.04 (0.99, 1.08) 0.11 ^a Numbers of Cases and Controls are Asian: 1,126 cases and 1,118 controls; European: 7,899 cases and 11,234 controls; African & African American: 1 ,151 cases and 934 controls; All Ancestries: 10,176 cases and 13,286 controls. Details of the sample sets for each ancestry are in Table 2and association data for each individual sample set are in Table 4

b Quoted Frequency in controls is the simple arithmetic average of all European sample sets for European Ancestry and the frequency in Nigerians for the African & African American Ancestry. NA, not applicable

cThis allele is coded as "A" in Zheng et al.[12] Table 4: Association of C6orf97/ESR1 SNPs with Breast Cancer in each population sample

Sample Set Ancestry SNP allele OR P value N Case Freq Case N Ctr Freq Ctr

Iceland European rs12662670_3 1 .25 2.50E-03 2638 0.076 3506 0.062

Iceland European rs12665607_1 1 .28 7.90E-04 2638 0.076 3506 0.060

Iceland European rs2046210_4 1 .01 7.60E-01 2638 0.320 3506 0.317

Iceland European rs3734804_1 0.98 5.90E-01 2638 0.468 3506 0.473

Iceland European rs3734805_2 1 .30 4.40E-04 2638 0.077 3506 0.060

Iceland European rs6929137_1 1 .03 4.80E-01 2638 0.302 3506 0.296

Iceland European rs6932260_2 0.98 5.40E-01 2638 0.467 3506 0.473

Iceland European rs7752591_1 0.98 5.20E-01 2638 0.473 3506 0.479

Iceland European rs852003_1 1 .04 3.50E-01 2638 0.564 3506 0.555

Iceland European rs9383589_3 1 .30 4.50E-04 2638 0.076 3506 0.060

Iceland European rs9383932_3 1 .13 3.20E-02 2638 0.132 3506 0.119

Iceland European rs9397435_3 1 .29 7.90E-04 2638 0.074 3506 0.059

U.S.A. (Mayo Clinic) European rs12662670_3 1 .04 6.35E-01 1753 0.082 1487 0.079

U.S.A. (Mayo Clinic) European rs12665607_1 1 .1 1 2.35E-01 1753 0.087 1487 0.079

U.S.A. (Mayo Clinic) European rs2046210_4 1 .07 2.28E-01 1753 0.354 1487 0.340

U.S.A. (Mayo Clinic) European rs3734804_1 1 .08 1.13E-01 1753 0.518 1487 0.499

U.S.A. (Mayo Clinic) European rs3734805_2 1 .1 1 2.58E-01 1753 0.085 1487 0.077

U.S.A. (Mayo Clinic) European rs6929137_1 1 .05 3.95E-01 1753 0.332 1487 0.322

U.S.A. (Mayo Clinic) European rs6932260_2 1 .09 9.56E-02 1753 0.520 1487 0.499

U.S.A. (Mayo Clinic) European rs7752591_1 1 .09 8.91 E-02 1753 0.517 1487 0.495

U.S.A. (Mayo Clinic) European rs852003_1 1 .07 1.81 E-01 1753 0.588 1487 0.571

U.S.A. (Mayo Clinic) European rs9383589_3 1 .09 3.44E-01 1753 0.084 1487 0.078

U.S.A. (Mayo Clinic) European rs9383932_3 1 .05 5.20E-01 1753 0.145 1487 0.139

U.S.A. (Mayo Clinic) European rs9397435_3 1 .16 1.15E-01 1753 0.078 1487 0.068

Spain European rs12662670_3 1 .16 1.14E-01 1009 0.099 1719 0.086

Spain European rs12665607_1 1 .14 1.61 E-01 1009 0.106 1719 0.094

Spain European rs2046210_4 1 .1 1 7.24E-02 1009 0.400 1719 0.375

Spain European rs3734805_2 1 .1 1 2.58E-01 1009 0.102 1719 0.093

Spain European rs6929137_1 1 .1 1 8.23E-02 1009 0.377 1719 0.353

Spain European rs7752591_1 1 .09 1.27E-01 1009 0.595 1719 0.574

Spain European rs852003_1 1 .03 6.61 E-01 1009 0.595 1719 0.589

Spain European rs9383589_3 1 .14 1.75E-01 1009 0.101 1719 0.090

Spain European rs9383932_3 1 .00 9.53E-01 1009 0.156 1719 0.157

Spain European rs9397435_3 1.1 1 3.19E-01 1009 0.080 1719 0.073

Holland (Nijmegen) European rs12662670_3 0.95 6.41 E-01 727 0.080 1830 0.084

Holland (Nijmegen) European rs12665607_1 0.93 5.10E-01 727 0.080 1830 0.085

Holland (Nijmegen) European rs2046210_4 0.97 6.67E-01 727 0.345 1830 0.352

Holland (Nijmegen) European rs3734805_2 0.94 5.56E-01 727 0.080 1830 0.086

Holland (Nijmegen) European rs6929137_1 0.97 5.99E-01 727 0.317 1830 0.325

Holland (Nijmegen) European rs7752591_1 1 .07 2.67E-01 727 0.520 1830 0.503

Holland (Nijmegen) European rs852003_1 1 .06 3.91 E-01 727 0.580 1830 0.567

Holland (Nijmegen) European rs9383589_3 0.94 5.67E-01 727 0.081 1830 0.086

Holland (Nijmegen) European rs9383932_3 1 .08 3.48E-01 727 0.159 1830 0.148

Holland (Nijmegen) European rs9397435_3 0.93 5.44E-01 727 0.073 1830 0.078

Sweden (Stockholm) European rs12662670_3 1 .1 1 3.93E-01 818 0.074 1750 0.067

Sweden (Stockholm) European rs12665607_1 1 .12 3.28E-01 818 0.076 1750 0.068

Sweden (Stockholm) European rs2046210_4 1 .07 2.71 E-01 818 0.342 1750 0.327

Sweden (Stockholm) European rs3734805_2 1.09 4.46E-01 818 0.074 1750 0.068

Sweden (Stockholm) European rs6929137_1 1 .07 3.14E-01 818 0.318 1750 0.304

Sweden (Stockholm) European rs7752591_1 1 .09 1.76E-01 818 0.499 1750 0.479

Sweden (Stockholm) European rs852003_1 1 .04 5.03E-01 818 0.565 1750 0.555

Sweden (Stockholm) European rs9383589_3 1 .20 1.35E-01 818 0.071 1750 0.060

Sweden (Stockholm) European rs9383932_3 0.96 6.21 E-01 818 0.125 1750 0.130

Sweden (Stockholm) European rs9397435_3 1 .16 2.09E-01 818 0.070 1750 0.060

Sweden (Northern) European rs12662670_3 1 .03 8.49E-01 954 0.046 942 0.044

Sweden (Northern) European rs12665607_1 1 .1 1 4.90E-01 954 0.052 942 0.047

Sweden (Northern) European rs2046210_4 1 .02 7.32E-01 954 0.314 942 0.309

Sweden (Northern) European rs3734804_1 0.92 2.15E-01 954 0.431 942 0.451

Sweden (Northern) European rs3734805_2 1 .10 5.37E-01 954 0.051 942 0.047

Sweden (Northern) European rs6929137_1 1.03 7.19E-01 954 0.303 942 0.297

Sweden (Northern) European rs6932260_2 0.93 2.68E-01 954 0.433 942 0.451

Sweden (Northern) European rs7752591_1 0.95 4.80E-01 954 0.428 942 0.440

Sweden (Northern) European rs852003_1 0.96 5.50E-01 954 0.490 942 0.500

Sweden (Northern) European rs9383589_3 1 .13 4.37E-01 954 0.051 942 0.045

Sweden (Northern) European rs9383932_3 1 .09 4.47E-01 954 0.103 942 0.096

Sweden (Northern) European rs9397435_3 1 .00 9.92E-01 954 0.039 942 0.039

Nigeria African rs12662670_3 1 .36 1.65E-01 851 0.036 781 0.027

Nigeria African rs12665607_1 1.76 7.90E-02 851 0.017 781 0.010

Nigeria African rs2046210_4 1.00 9.60E-01 851 0.715 781 0.716

Nigeria African rs3734805_2 1.33 1.59E-01 851 0.037 781 0.028

Nigeria African rs6929137_1 0.97 6.74E-01 851 0.530 781 0.538

Nigeria African rs9383589_3 1.66 4.54E-02 851 0.026 781 0.016

Nigeria African rs9397435_3 1.39 1.60E-02 851 0.086 781 0.063

U.S.A. (Chicago) African American rs12662670_3 2.12 3.49E-02 300 0.061 153 0.030

U.S.A. (Chicago) African American rs12665607_1 2.47 7.54E-02 300 0.032 153 0.013

U.S.A. (Chicago) African American rs2046210_4 0.92 5.72E-01 300 0.598 153 0.618

U.S.A. (Chicago) African American rs3734805_2 1.13 7.04E-01 300 0.052 153 0.046

U.S.A. (Chicago) African American rs6929137_1 1.19 2.12E-01 300 0.549 153 0.505

U.S.A. (Chicago) African American rs9383589_3 1.47 3.71 E-01 300 0.033 153 0.023

U.S.A. (Chicago) African American rs9397435_3 1.20 4.83E-01 300 0.085 153 0.072

Taiwan Asian rs12662670_3 1.22 2.46E-03 1126 0.343 1118 0.300

Taiwan Asian rs12665607_1 1.24 6.15E-04 1126 0.372 1118 0.323

Taiwan Asian rs2046210_4 1.24 4.26E-04 1126 0.415 1118 0.363

Taiwan Asian rs3734804_1 1.21 1.44E-03 1126 0.470 1118 0.422

Taiwan Asian rs3734805_2 1.22 2.01 E-03 1126 0.368 1118 0.324

Taiwan Asian rs6929137_1 1.15 2.53E-02 1126 0.380 1118 0.348

Taiwan Asian rs6932260_2 1.23 7.71 E-04 1126 0.473 1118 0.422

Taiwan Asian rs7752591_1 1.25 2.99E-04 1126 0.475 1118 0.421

Taiwan Asian rs852003_1 1.26 1.17E-04 1126 0.482 1118 0.424

Taiwan Asian rs9383589_3 1.20 4.23E-03 1126 0.364 1118 0.323

Taiwan Asian rs9383932_3 1.25 2.92E-04 1126 0.432 1118 0.378

Taiwan Asian rs9397435 3 1.23 7.98E-04 1126 0.374 1118 0.326

Table 5: Conditional analysis of rs9397435 association with breast cancer in European, Nigerian and

Taiwanese population samples

Frequency Unadjusted Adjusted

Primary Adjusted

Population Sample Allele in

SNP

Controls OR P for SNP OR

-3

Combined European rs9397435 G 0.063 1 .15 1 .2 x 10^" rs9383932 1.16 4.2 x 10^~3 rs9397435 G " " " rs12662670 1.14 0.019 rs9397435 G " " " rs12665607 1.08 0.51 rs9397435 G " " " rs9383589 0.99 0.95 rs9397435 G " " " rs3734805 1.1 1 0.34 rs9397435 G " " " rs6929137 1.14 3.9 x 10^~3 rs9397435 G " " " rs2046210 1.14 6.1 x 10^~3 rs9383932 G 0.132 1 .06 0.066 rs9397435 1.00 0.91 rs 12662670 G 0.071 1 .12 6.4 x 10^" -3 rs9397435 1.00 0.96 rs 12665607 A 0.072 -3

1 .14 1 .1 x 10^" rs9397435 1.04 0.69

-4

rs9383589 G 0.070 1 .15 6.2 x 10^" rs9397435 1.13 0.22

-3

rs3734805 C 0.072 1 .13 1 .8 x 10^" rs9397435 1.02 0.87 rs6929137 A 0.316 1 .04 0.082 rs9397435 1.01 0.58 rs2046210 T 0.337 1 .04 0.099 rs9397435 1.02 0.41

Nigerian rs9397435 G 0.063 1 .39 0.016 rs12662670 1.34 0.047 rs9397435 G " " " rs12665607 1.32 0.068 rs9397435 G " " " rs9383589 1.31 0.1 1 rs9397435 G " " " rs3734805 1.43 0.049 rs9397435 G " " " rs6929137 1.39 0.019 rs9397435 G " " " rs2046210 1.44 0.047 rs 12662670 G 0.027 1 .36 0.16 rs9397435 1.17 0.51 rs 12665607 A 0.010 1 .76 0.079 rs9397435 1.33 0.43 rs9383589 G 0.016 1 .66 0.045 rs9397435 1.31 0.37 rs3734805 C 0.028 1 .33 0.16 rs9397435 0.94 0.82 rs6929137 A 0.538 0.97 0.67 rs9397435 0.95 0.56 rs2046210 T 0.716 1 .00 0.96 rs9397435 0.91 0.73

Taiwanese rs9397435 G 0.326 1 .23 8.0 x 10^" -4 rs9383932 1.1 1 0.31 rs9397435 G " " " rs12662670 1.18 0.12 rs9397435 G " " " rs12665607 0.94 0.89 rs9397435 G " " " rs9383589 1.54 0.028 rs9397435 G " " " rs3734805 1.48 0.061 rs9397435 G " " " rs6929137 1.73 8.4 x 10^-4 rs9397435 G " " " rs2046210 1.1 1 0.21 rs9383932 G 0.378 1.25 2.9 x 10^" -4 rs9397435 1.14 0.21

-3

rs 12662670 G 0.300 1 .22 2.5 x 10^" rs9397435 1.06 0.59

-4

rs 12665607 A 0.323 1 .24 6.2 x 10^" rs9397435 1.31 0.52

-3

rs9383589 G 0.323 1 .20 4.2 x 10^" rs9397435 0.79 0.24

-3

rs3734805 C 0.324 1 .22 2.0 x 10^" rs9397435 0.83 0.37 rs6929137 A 0.348 1 .15 0.025 rs9397435 0.7 0.027 rs2046210 T 0.363 1 .24 4.3 x 10^" -4 rs9397435 1.1 1 0.46 Table 6: Heterogeneity in risk estimates for combined population samples

Ancestry³ SNP_Allele Phet I²

All Ancestries rs12662670_3 0.17 43.9

All Ancestries rs12665607_1 0.095 57.5

All Ancestries rs2046210_4 0.014 76.6

All Ancestries rs3734805_2 0.53 0

All Ancestries rs6929137_1 0.30 17.4

All Ancestries rs9383589_3 0.29 18.8

All Ancestries rs9397435_3 0.36 2

European and Asian rs3734804_1 3.4 x 10^-3 88.4

European and Asian rs6932260_2 7.8 x 10^"4 91 .1

European and Asian rs7752591_1 3.1 x 10^"3 88.6

European and Asian rs852003_1 3.0 x 10^"3 88.7

European and Asian rs9383932_3 0.017 82.3

All European rs12662670_3 0.37 7.4

All European rs12665607_1 0.32 15.4

All European rs2046210_4 0.61 0

All European rs3734804_1 0.11 53.9

All European rs3734805_2 0.26 23.2

All European rs6929137_1 0.75 0

All European rs6932260_2 0.11 54.3

All European rs7752591_1 0.2 30.9

All European rs852003_1 0.87 0

All European rs9383589_3 0.26 22.9

All European rs9383932_3 0.65 0

All European rs9397435_3 0.29 19

African and African American rs12662670_3 0.29 10.5

African and African American rs12665607_1 0.57 0

African and African American rs2046210_4 0.63 0

African and African American rs3734805_2 0.68 0

African and African American rs6929137_1 0.19 40.5

African and African American rs9383589_3 0.81 0

African and African American rs9397435 3 0.62 0

^a Values for "All Ancestries" are the heterogeneity values that result from combining the risk estimate for the Taiwanese sample, the combined estimate for all European samples, and the combined estimate for African and African American samples. Values for "European and Asian" are the heterogeneity values that result from combini the risk estimate for the Taiwanese sample with the combined estimate for all European samples. Values for "All European" are the heterogeneity values that result from combining the risk estimates from all of the individual European sample sets. Values for "African and African American are the heterogeneity values that result from combining the risk estimates from the Nigerian and U.S.A. (Chicago) sample sets. Table 7: Frequencies of common haplotypes in European, African and Asian ancestry population samples

Nigeria Iceland U.S.A. European Ancestry Taiwan

Haplotype Frequency in Frequency in Frequency in Frequency in Frequency in Frequency in Frequency in Frequency in

Haplotype⁸ ID Cases Controls Cases Controls Cases Controls Cases Controls

41 1 1441 A 0.346 0.368 0.225 0.232 0.239 0.233 0.012 0.018

431 1441 B 0.263 0.276 0.018 0.022 0.026 0.025 0.024 0.024

431 1421 C 0.164 0.158 0.673 0.679 0.635 0.646 0.550 0.590

41 1 1421 D 0.120 0.127 0.004 0.000 0.006 0.007 0.001 0.005

431 1443 E 0.028 0.015 0.000 0.000 0.002 0.000 0.001 0.000

41 1 1443 F 0.023 0.014 0.000 0.000 0.000 0.000 0.000 0.001

4121443 G 0.01 1 0.013 0.000 0.000 0.000 0.000 0.000 0.000

3123143 H 0.015 0.009 0.065 0.053 0.067 0.059 0.304 0.260

4123143 I 0.000 0.000 0.007 0.004 0.009 0.008 0.049 0.036

331 1421 J 0.000 0.000 0.006 0.004 0.004 0.007 0.023 0.023 ^a The SNPs and at-risk alleles are ordered as: rs12662670_3, rs6929137_1 , rs3734805_2, rs9383589_3, rs12665607_1 , rs2046210_4, rs9397435_3. Haplotpes with an observed control frequency of >0.010 in any one of the population samples are included.

Table 8: Non-HapMap SNPs in strong LD with rs9397435 identified from 1000 Genomes

Project Data³

CEU YRI JPT/CHB Seq ID NO#

SNP Position B36 D^' r² D' r² D^' r²

not listed 151989450 1.00 0.76 nd nd 1 .00 0.96 49 rs9397437 151994025 1.00 0.85 1 .00 0.53 1 .00 1 .00 52 rs58343273 151994873 1.00 1 .00 0.67 0.04 1 .00 1 .00 53 rs9383590 151995458 1.00 1 .00 nd nd 1 .00 1 .00 54 rs60954078 151997607 1.00 1 .00 nd nd nd nd 56 rs12173562 151999263 1.00 1 .00 nd nd 1 .00 1 .00 58 rs6912323 152000305 1.00 1 .00 0.49 0.02 1 .00 1 .00 60 rs77275268 152010891 1.00 1 .00 nd nd 1 .00 0.96 63 rs9371545 15201 1433 1.00 1 .00 nd nd 1 .00 0.96 64 rs9479091 152031302 1.00 0.76 nd nd nd nd 78

Data were obtained from the 1000 Genomes project April 2009 release

(ftp://ftp.1000genomes.ebi.ac.uk/) which includes data on 57 individuals of European ancestry

(CEU), 56 Yorubans (YRI) and 59 Japanese or Han Chinese (JPT/CHB). SNPs were included in

the table if they had an r² value of >0.75 with rs9397435 in both CEU and JPT/CHB or an r² value

of >0.75 in one of the two ancestries and were missing data for the other. ^b nd, no data available.

Table 9: LD relations with the novel chr6:152,010,891 [C/T] SNP (rs77275268)

Sample set SNP1 SNP 2 D^' r²

Iceland rs77275268 rs9397435 1 .00 0.95

Taiwan rs77275268 rs9397435 0.98 0.94

Nigeria rs77275268 rs9397435 0.98 0.23

Nigeria rs77275268 rs 12662670 0.72 0.31

Nigeria rs77275268 rs 12665607 1 .00 0.74

Nigeria rs77275268 rs9383589 0.94 0.76

Nigeria rs77275268 rs3734805 0.94 0.50

Nigeria rs77275268 rs6929137 1.00 0.02

Nigeria rs77275268 rs2046210 1 .00 0.01

Table 10: Levels of ERa, PR and HER2 mRNA in primary tumours, stratified by rs9397435 genotype and assessed under multiplicative and recessive inheritance models

a Numbers of tumours with each genotype are 1072 (AA), 151 (AG) and 1 1 (GG). ^b The fold-effect on expression for each genotype, compared to the expression level in the AA genotype ^c Significance calculated under the models indicated after log₁₀ transformation of the expression levels.

Table 11 : Stratification by clinical variables of breast cancer associations with rs9397435[G] in combined European ancestry population samples³

Number Class 1 Class 2

of

Contributi

Comparison (Class 1 vs Sample Numb Frequen Frequen

Class 2) Sets er cy Number cy OR (95% CI) P Phet

(1.10, 2.5 x

ER negative vs Control 6 1128 0.075 11228 0.063 1.30 1.53) 10^"3 0.75

(1.06, 2.4 x

ER positive vs Control 6 4310 0.071 11228 0.063 1.17 1.29) 10^"3 0.10

(0.78,

ER positive vs ER negative¹¹ 7 5611 0.071 1568 0.076 0.91 1.05) 0.20 0.95

(1.08, 2.5 x

PR negative vs Control 6 1631 0.075 11228 0.063 1.25 1.44) 10^"3 0.85

(1.06, 2.8 x

PR positive vs Control 6 3728 0.071 11228 0.063 1.18 1.31) 10^"3 0.24

(0.84,

PR positive vs PR negative¹¹ 7 4806 0.072 2192 0.074 0.97 1.11) 0.62 0.35

(0.96,

HER2 negative vs Control 4 1733 0.075 8537 0.069 1.11 1.29) 0.15 0.66

(0.99,

HER2 positive vs Control 4 947 0.081 8537 0.069 1.19 1.42) 0.063 0.11

(0.89,

HER2 postive vs Negative¹¹ 5 1138 0.079 2777 0.073 1.09 1.32) 0.40 0.84

Triple Negative No vs (1.07, 9.0 x

Control 4 4042 0.080 8537 0.069 1.19 1.32) 10^"4 0.07

Triple Negative Yes vs (0.91 ,

Control 4 265 0.078 8537 0.069 1.26 1.74) 0.17 0.48

(0.87,

Triple Negative Yes vs No^b 5 437 0.080 4042 0.077 1.10 1.41) 0.42 0.89

(0.85,

In Situ Tumour vs Control 3 566 0.071 6818 0.068 1.08 1.38) 0.52 0.53

(1.06, 1.6 x

Invasive Tumour vs Control 6 5837 0.069 11228 0.063 1.16 1.27) 10^"3 0.20

(0.87,

Invasive vs In Situ Tumour 3 4163 0.076 566 0.071 1.11 1.41) 0.41 0.30

(0.97,

Stage 1 vs Control 6 2659 0.063 11228 0.063 1.10 1.25) 0.14 0.14

(1.04,

Stage 2 vs Control 6 2109 0.072 11228 0.063 1.19 1.35) 0.011 0.96

(1.02,

Stage 3&₄ vs Control 6 748 0.068 11228 0.063 1.25 1.53) 0.029 0.16

(0.99,

Node Negative vs Control⁰ 6 3523 0.063 11228 0.063 1.11 1.24) 0.08 0.04

(1.10, 9.1 x

Node Positive vs Control⁰ 6 2053 0.077 11228 0.063 1.25 1.42) 10-⁴ 0.51

Node Positive vs Node (0.94,

Negative⁰⁰ 7 2718 0.077 4592 0.066 1.10 1.28) 0.23 0.05

Differentiation Grade 1 vs (0.88,

Control 6 1021 0.066 11228 0.063 1.07 1.29) 0.5 0.25

Differentiation Grade 2 vs (1.02,

Control 6 2039 0.073 11228 0.063 1.17 1.34) 0.027 0.24

Differentiation Grade 3 vs (1.07, 4.6 x

Control 6 1386 0.075 11228 0.064 1.25 1.47) 10^"3 0.2

Invasive Ductal Carcinoma (1.10, 1.8 x vs Control 5 3834 0.080 10287 0.067 1.22 1.36) 10-⁴ 0.078

Invasive Lobular Carcinoma (0.88,

vs Control 5 588 0.068 10287 0.068 1.11 1.40) 0.38 0.79

Other Invasive Histology vs (0.91 ,

Control 5 598 0.075 10287 0.067 1.15 1.45) 0.24 0.26

(0.92,

Invasive Ductal vs Lobular⁰

6 4960 0.770 740 0.069 1.11 1.34) 0.26 0.41 Number

of

Contributi

Sample Numb

Trend tests: Sets er Beta (95% CI) P Phet

(-0.02,

Stage 1 to 4^b

7 7190 0.03 0.07) 0.22 0.67

(-0.02,

Grade 1 to 3^b

7 5635 0.03 0.08) 0.2 0.26

Age at first invasive breast (-1.74, - cancer^b 7 7₄50 -0.97 0.19) 0.015 0.8

³ All analyses are univariate. ^b Includes data from the Holland (Rotterdam) case-only sample set. ⁰ For tumours stage 1-₄

Claims

1. A method of determining a susceptibility to breast cancer in a human individual, the method comprising : analyzing sequence data from 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 breast cancer in humans, and determining a susceptibility to breast cancer from the sequence data, wherein the at least one polymorphic marker is selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith .

2. The method of claim 1, wherein determining a susceptibility is performed by analyzing the sequence data using a computer processor.

3. The method of claim 1 or claim 2, wherein the sequence data is nucleic acid sequence data.

4. The method of any one of the preceding claims, comprising obtaining nucleic acid

sequence data about at least two polymorphic markers.

5. The method of any one of the preceding claims, wherein obtaining nucleic acid sequence data comprises obtaining a genotype dataset derived from the human individual. 6. The method of claim 3 or claim 4, wherein the nucleic acid sequence data is obtained from a biological sample containing nucleic acid from the individual .

7. The method of any one of the claims 3 to 6, 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 5, wherein the genotype dataset comprises a look-up table containing at least one risk measure of breast cancer for the at least one polymorphic marker.

The method of any one of the claims 2 to 8, wherein analyzing 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.

The method of claim 1, wherein the sequence data is amino acid sequence data.

The method of claim 10, comprising determining the presence or absence of an amino acid substitution in the amino acid sequence encoded by the polymorphic marker.

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 breast cancer.

The method of any one of the previous claims, wherein the obtaining sequence data comprises obtaining sequence information from a preexisting record.

The method of any one of the claims 1 to 10 and 13 to 14, wherein the at least one polymorphic marker is selected from the group consisting of rs9397435, rs77275268, rsl2665607, rs9383589 and rs3734805, and markers in linkage disequilibrium therewith .

The method of claim 14, wherein the at least one polymorphic marker is selected from the group consisting of rs9397435, and markers in linkage disequilibrium therewith.

The method of claim 15, wherein markers in linkage disequilibrium with rs9397435 are selected from the group consisting of the markers set forth in Table 1 and Table 8

The method of claim 16, wherein markers in linkage disequilibrium with rs9397435 are selected from the group consisting of the markers rs9397436, rs9397437, rs58343273, s.151995458, rs9397068, s.151997607, rs9383937, s.151999263, s.152000305, rs77275268, and s.152011433.

The method of any one of the preceding claims, wherein the at least one allele is associated with an increased susceptibility of breast cancer in humans.

The method of claim 18, wherein the presence of the at least one allele or haplotype is indicative of increased susceptibility with a relative risk of 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 or at least 1.20.

20. The method of claim 18 or claim 19, wherein the at least one allele associated with increased susceptibility of breast cancer is selected from the group consisting of the G allele of rs9397435, the T allele of rs77275268, the G allele of rs9383932, the G allele of rsl2662670, the A allele of rsl2665607, the G allele of rs9383589, the C allele of rs3734805, the A allele of rs6929137, the A allele of rs7752591, the A allele of rs3734804, the C allele of rs6932260 and the A allele of rs852003.

21. The method of any one of the claims 1-17, wherein the at least one allele is associated with a decreased susceptibility of breast cancer in humans.

22. The method of any one of the previous claims, further comprising reporting the

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.

23. A method of assessing a susceptibility to breast cancer in a human individual, comprising i. obtaining sequence information about the individual for at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to breast 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 breast cancer in humans; wherein determination of the presence of the at least one allele identifies the individual as having elevated susceptibility to breast cancer, and wherein determination of the absence of the at least one allele identifies the individual as not having the elevated susceptibility.

24. The method of claim 23, wherein the sequence information is obtained from a biological sample from the individual.

25. The method of claim 23 or claim 24, wherein the at least one polymorphic marker is selected from the group consisting of rs9397435, and markers in linkage disequilibrium therewith. The method of claim 25, wherein the at least one polymorphic marker is selected from the group consisting of the markers set forth in Table 1 and Table 8.

A method of identification of a marker for use in assessing susceptibility to breast cancer in human individuals, the method comprising a. identifying at least one polymorphic marker in linkage disequilibrium with rs9397435; b. obtaining sequence information about the at least one polymorphic marker in a group of individuals diagnosed with breast 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 breast 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 susceptibility to breast cancer. 28. The method of Claim 27, wherein an increase in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with breast 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 breast cancer, and wherein a decrease in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with breast 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, breast cancer.

29. A method of determining risk of developing at least a second primary tumor in an

individual previously diagnosed with breast cancer, the method comprising obtaining sequence data about the 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 risk of developing a second primary tumor in humans previously diagnosed with breast cancer, and determining the risk of developing at least a second primary tumor in the individual from the sequence data, wherein the at least one polymorphic marker is selected from rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith .

30. A method of predicting prognosis of an individual diagnosed with breast 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different susceptibilities to breast cancers in humans, and predicting prognosis of breast cancer from the sequence data.

31. A method of assessing probability of response of a human individual to a breast cancer therapeutic agent, 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, 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.

32. A method of monitoring progress of treatment of an individual undergoing treatment for breast 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, wherein different alleles of the at least one polymorphic marker are associated with different outcome of breast cancer treatment in humans, and determining the probability of a positive treatment outcome from the sequence data.

33. A method of diagnosing breast cancer in a human individual, the method comprising (A) obtaining sequence data from the individual, identifying at least one at-risk allele selected from the group consisting of the G allele of rs9397435, the T allele of rs77275268, the G allele of rs9383932, the G allele of rsl2662670, the A allele of rsl2665607, the G allele of rs9383589, the C allele of rs3734805, the A allele of rs6929137, the A allele of rs7752591, the A allele of rs3734804, the C allele of rs6932260 and the A allele of rs852003, and marker alleles in linkage disequilibrium therewith; and

(B) if a positive determination of at least one at-risk allele in (A) is made, performing at least one of, or a combination of:

(i) considering symptoms experienced by the human individual and/or the family history of breast cancer for the human individual;

(ii) clinical or self-exam screening of a breast for lumps or other abnormalities of the individual;

(iii) mammographic screening of a breast for breast cancer in the indvidual;

(iv) fine needle aspiration cytology of the individual;

(v) biopsy of breast tissue in the individual; and

(vi) determination of the presence or absence of at least one additional genetic risk factor of breast cancer in the individual; whereupon a diagnosis of the presence or absence of breast cancer for the individual is made.

A method of assessing a subject's risk for breast cancer, the method comprising : a) obtaining sequence information about the individual identifying at least one allele of at least one polymorphic marker in the genome of the individual; b) representing the sequence information as digital genetic profile data; c) transforming the digital genetic profile data on a computer processor to generate breast cancer risk assessment report for the subject; and d) displaying the risk assessment report on an output device; wherein the at least one polymorphic marker comprises at least one marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith.

35. The method of claim 34, wherein the digital genetic profile data comprises data indicating the presence or absence of at least one allele of the at least one polymorphic marker. 36. A kit for assessing susceptibility to breast cancer in humans, 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith, and a collection of data comprising correlation data between the at least one polymorphism and susceptibility to breast cancer.

37. The kit of claim 36, wherein the collection of data is on a computer-readable medium .

38. The kit of claim 36 or claim 37, wherein the kit comprises reagents for detecting no more than 100 alleles in the genome of the individual.

39. The kit of claim 38, wherein the kit comprises reagents for detecting no more than 20 alleles in the genome of the individual.

40. Use of an oligonucleotide probe in the manufacture of a diagnostic reagent for diagnosing and/or assessing a susceptibility to breast cancer in humans, wherein the probe is capable of hybridizing to a segment of a nucleic acid whose nucleotide sequence is given by SEQ ID NO : 1-92, and wherein the segment is 15-300 nucleotides in length.

41. The use of claim 40, wherein the segment of the nucleic acid to which the probe is

capable of hybridizing comprises a polymorphic site.

42. The use of claim 41, wherein the polymorphic site is selected from the group consisting of the markers rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith.

43. A computer-readable medium having computer executable instructions for determining susceptibility to breast cancer in humans, the computer readable medium comprising : data indicative of at least one polymorphic marker; a routine stored on the computer readable medium and adapted to be executed by a processor to determine risk of developing breast cancer for the at least one polymorphic marker; wherein the at least one polymorphic marker is selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith .

The computer-readable medium of claim 43, wherein the medium contains data indicative of at least two polymorphic markers.

The computer-readable medium of claim 43 or claim 44, 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.

An apparatus for determining a genetic indicator for breast 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 rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, 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 breast cancer for the human individual .

The apparatus of claim 46, wherein the marker information comprises sequence data identifying at least one allele of the at least one marker in the genome of the individual.

The apparatus of claim 46 or claim 47, wherein the sequence data comprises a genotype dataset.

The apparatus according to any one of the claims 46 to 48, wherein the computer readable memory further comprises data indicative of the risk of developing breast 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 breast cancer associated with the at least one allele of the at least one polymorphic marker.

50. Use of an agent for treating breast cancer in a human individual that has been tested for the presence of at least one allele of at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith .

51. The use of claim 50, wherein the at least one allele is selected from the group consisting of the G allele of rs9397435, the T allele of rs77275268, the G allele of rs9383932, the G allele of rsl2662670, the A allele of rsl2665607, the G allele of rs9383589, the C allele of rs3734805, the A allele of rs6929137, the A allele of rs7752591, the A allele of rs3734804, the C allele of rs6932260 and the A allele of rs852003.

52. A risk assessment report of breast cancer for a human individual, comprising : at least one personal identifier, and representation of at least one risk assessment measure of breast cancer for the human subject for at least one polymorphic marker selected from the group consisting of rs9397435, rs77275268, rs9383932, rsl2662670, rsl2665607, rs9383589, rs3734805, rs6929137, rs7752591, rs3734804, rs6932260 and rs852003, and markers in linkage disequilibrium therewith .

53. 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'| .

54. 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.