# Patent

## Title
Genetic variants contributing to risk of prostate cancer

## Abstract

The present invention is characterized by certain genetic variants being susceptibility variants for prostate cancer. The invention relates to methods of determining increased susceptibility to prostate cancer, as well as methods of determining decreased susceptibility to prostate cancer, using such variants. The invention further relates to kits for determining a susceptibility to prostate cancer.

## Background
RELATED APPLICATIONS
This application is the U.S. National Stage of International Application No. PCT/IS2008/000003, filed Feb. 7, 2008, published in English, and claims priority under 35 U.S.C. §119 or 365 to Iceland Application No. 8654, filed Jun. 22, 2007 and Iceland Application No. IS 8604, filed Feb. 7, 2007.
BACKGROUND OF THE INVENTION
Cancer, the uncontrolled growth of malignant cells, is a major health problem of the modern medical era and is one of the leading causes of death in developed countries. In the United States, one in four deaths is caused by cancer (Jemal, A. et al., CA Cancer J. Clin. 52:23-47 (2002)).
The incidence of prostate cancer has dramatically increased over the last decades and prostate cancer is now a leading cause of death in the United States and Western Europe (Peschel, R. E. and J. W. Colberg, Lancet 4:233-41 (2003); Nelson, W. G. et al., N. Engl. J. Med. 349 (4):366-81 (2003)). Prostate cancer is the most frequently diagnosed noncutaneous malignancy among men in industrialized countries, and in the United States, 1 in 8 men will develop prostate cancer during his life (Simard, J. et al., Endocrinology 143 (6):2029-40 (2002)). Although environmental factors, such as dietary factors and lifestyle-related factors, contribute to the risk of prostate cancer, genetic factors have also been shown to play an important role. Indeed, a positive family history is among the strongest epidemiological risk factors for prostate cancer, and twin studies comparing the concordant occurrence of prostate cancer in monozygotic twins have consistently revealed a stronger hereditary component in the risk of prostate cancer than in any other type of cancer (Nelson, W. G. et al., N. Engl. J. Med. 349 (4):366-81 (2003); Lichtenstein P. et al., N. Engl. J. Med. 343 (2):78-85 (2000)). In addition, an increased risk of prostate cancer is seen in 1st to 5th degree relatives of prostate cancer cases in a nation wide study on the familiarity of all cancer cases diagnosed in Iceland from 1955-2003 (Amundadottir et al., PLoS Medicine 1 (3):e65 (2004)). The genetic basis for this disease, emphasized by the increased risk among relatives, is further supported by studies of prostate cancer among particular populations: for example, African Americans have among the highest incidence of prostate cancer and mortality rate attributable to this disease: they are 1.6 times as likely to develop prostate cancer and 2.4 times as likely to die from this disease than European Americans (Ries, L. A. G. et al., NIH Pub. No. 99-4649 (1999)).
An average 40% reduction in life expectancy affects males with prostate cancer. If detected early, prior to metastasis and local spread beyond the capsule, prostate cancer can be cured (e.g., using surgery). However, if diagnosed after spread and metastasis from the prostate, prostate cancer is typically a fatal disease with low cure rates. While prostate-specific antigen (PSA)-based screening has aided early diagnosis of prostate cancer, it is neither highly sensitive nor specific (Punglia et al., N Engl J Med. 349 (4):335-42 (2003)). This means that a high percentage of false negative and false positive diagnoses are associated with the test. The consequences are both many instances of missed cancers and unnecessary follow-up biopsies for those without cancer. As many as 65 to 85% of individuals (depending on age) with prostate cancer have a PSA value less than or equal to 4.0 ng/mL, which has traditionally been used as the upper limit for a normal PSA level (Punglia et. al., N Engl J Med. 349 (4):335-42 (2003); Cookston, M. S., Cancer Control 8 (2):133-40 (2001); Thompson, I. M. et al., N Engl J. Med. 350:2239-46 (2004)). A significant fraction of those cancers with low PSA levels are scored as Gleason grade 7 or higher, which is a measure of an aggressive prostate cancer.
In addition to the sensitivity problem outlined above, PSA testing also has difficulty with specificity and predicting prognosis. PSA levels can be abnormal in those without prostate cancer. For example, benign prostatic hyperplasia (BPH) is one common cause of a false-positive PSA test. In addition, a variety of noncancer conditions may elevate serum PSA levels, including urinary retention, prostatitis, vigorous prostate massage and ejaculation.
Subsequent confirmation of prostate cancer using needle biopsy in patients with positive PSA levels is difficult if the tumor is too small to see by ultrasound. Multiple random samples are typically taken but diagnosis of prostate cancer may be missed because of the sampling of only small amounts of tissue. Digital rectal examination (DRE) also misses many cancers because only the posterior lobe of the prostate is examined. As early cancers are nonpalpable, cancers detected by DRE may already have spread outside the prostate (Mistry K. J., Am. Board Fam. Pract. 16 (2):95-101 (2003)).
Thus, there is clearly a great need for improved diagnostic procedures that would facilitate early-stage prostate cancer detection and prognosis, as well as aid in preventive and curative treatments of the disease. In addition, there is a need to develop tools to better identify those patients who are more likely to have aggressive forms of prostate cancer from those patients that are more likely to have more benign forms of prostate cancer that remain localized within the prostate and do not contribute significantly to morbidity or mortality. This would help to avoid invasive and costly procedures for patients not at significant risk.
The incidence of prostate cancer has dramatically increased over the last decades. Prostate cancer is a multifactorial disease with genetic and environmental components involved in its etiology. It is characterized by heterogeneous growth patterns that range from slow growing tumors to very rapid highly metastatic lesions.
Although genetic factors are among the strongest epidemiological risk factors for prostate cancer, the search for genetic determinants involved in the disease has been challenging. Studies have revealed that linking candidate genetic markers to prostate cancer has been more difficult than identifying susceptibility genes for other cancers, such as breast, ovary and colon cancer. Several reasons have been proposed for this increased difficulty including: the fact that prostate cancer is often diagnosed at a late age thereby often making it difficult to obtain DNA samples from living affected individuals for more than one generation; the presence within high-risk pedigrees of phenocopies that are associated with a lack of distinguishing features between hereditary and sporadic forms; and the genetic heterogeneity of prostate cancer and the accompanying difficulty of developing appropriate statistical transmission models for this complex disease (Simard, J. et al., Endocrinology 143 (6):2029-40 (2002)).
Various genome scans for prostate cancer-susceptibility genes have been conducted and several prostate cancer susceptibility loci have been reported. For example, HPC1 (1q24-q25), PCAP (1q42-q43), HCPX (Xq27-q28), CAPB (1p36), HPC20 (20q13), HPC2/ELAC2 (17p11) and 16q23 have been proposed as prostate cancer susceptibility loci (Simard, J. et al., Endocrinology 143(6):2029-40 (2002); Nwosu, V. et al., Hum. Mol. Genet. 10 (20):2313-18 (2001)). In a genome scan conducted by Smith et al., the strongest evidence for linkage was at HPC1, although two-point analysis also revealed a LOD score of ≧1.5 at D4S430 and LOD scores≧1.0 at several loci, including markers at Xq27-28 (Ostrander E. A. and J. L. Stanford, Am. J. Hum. Genet. 67:1367-75 (2000)). In other genome scans, two-point LOD scores of ≧1.5 for chromosomes 10q, 12q and 14q using an autosomal dominant model of inheritance, and chromosomes 1q, 8q, 10q and 16p using a recessive model of inheritance, have been reported, as well as nominal evidence for linkage to chr 2q, 12p, 15q, 16q and 16p. A genome scan for prostate cancer predisposition loci using a small set of Utah high risk prostate cancer pedigrees and a set of 300 polymorphic markers provided evidence for linkage to a locus on chromosome 17p (Simard, J. et al., Endocrinology 143 (6):2029-40 (2002)). Eight new linkage analyses were published in late 2003, which depicted remarkable heterogeneity. Eleven peaks with LOD scores higher than 2.0 were reported, none of which overlapped (see Actane consortium, Schleutker et al., Wiklund et al., Witte et. al., Janer et. al., Xu et. al., Lange et al., Cunningham et al.; all of which appear in Prostate, vol. 57 (2003)).
As described above, identification of particular genes involved in prostate cancer has been challenging. One gene that has been implicated is RNASEL, which encodes a widely expressed latent endoribonuclease that participates in an interferon-inducible RNA-decay pathway believed to degrade viral and cellular RNA, and has been linked to the HPC locus (Carpten, J. et al., Nat. Genet. 30:181-84 (2002); Casey, G. et al., Nat. Genet. 32 (4):581-83 (2002)). Mutations in RNASEL have been associated with increased susceptibility to prostate cancer. For example, in one family, four brothers with prostate cancer carried a disabling mutation in RNASEL, while in another family, four of six brothers with prostate cancer carried a base substitution affecting the initiator methionine codon of RNASEL. Other studies have revealed mutant RNASEL alleles associated with an increased risk of prostate cancer in Finnish men with familial prostate cancer and an Ashkenazi Jewish population (Rokman, A. et al., Am J. Hum. Genet. 70:1299-1304 (2002); Rennert, H. et al., Am J. Hum. Genet. 71:981-84 (2002)). In addition, the Ser217Leu genotype has been proposed to account for approximately 9% of all sporadic cases in Caucasian Americans younger than 65 years (Stanford, J. L., Cancer Epidemiol. Biomarkers Prev. 12 (9):876-81 (2003)). In contrast to these positive reports, however, some studies have failed to detect any association between RNASEL alleles with inactivating mutations and prostate cancer (Wang, L. et al., Am. J. Hum. Genet. 71:116-23 (2002); Wiklund, F. et al., Clin. Cancer Res. 10 (21):7150-56 (2004); Maier, C. et al., Br. J. Cancer 92 (6):1159-64 (2005)).
The macrophage-scavenger receptor 1 (MSR1) gene, which is located at 8p22, has also been identified as a candidate prostate cancer-susceptibility gene (Xu, J. et al., Nat. Genet. 32:321-25 (2002)). A mutant MSR1 allele was detected in approximately 3% of men with nonhereditary prostate cancer but only 0.4% of unaffected men. However, not all subsequent reports have confirmed these initial findings (see, e.g., Lindmark, F. et al., Prostate 59 (2):132-40 (2004); Seppala, E. H. et al., Clin. Cancer Res. 9 (14):5252-56 (2003); Wang, L. et al., Nat. Genet. 35 (2):128-29 (2003); Miller, D. C. et al., Cancer Res. 63 (13):3486-89 (2003)). MSR1 encodes subunits of a macrophage-scavenger receptor that is capable of binding a variety of ligands, including bacterial lipopolysaccharide and lipoteicholic acid, and oxidized high-density lipoprotein and low-density lipoprotein in serum (Nelson, W. G. et al., N. Engl. J. Med. 349 (4):366-81 (2003)).
The ELAC2 gene on Chr17p was the first prostate cancer susceptibility gene to be cloned in high risk prostate cancer families from Utah (Tavtigian, S. V., et al., Nat. Genet. 27 (2):172-80 (2001)). A frameshift mutation (1641InsG) was found in one pedigree. Three additional missense changes: Ser217Leu; Ala541Thr; and Arg781His, were also found to associate with an increased risk of prostate cancer. The relative risk of prostate cancer in men carrying both Ser217Leu and Ala541Thr was found to be 2.37 in a cohort not selected on the basis of family history of prostate cancer (Rebbeck, T. R., et al., Am. J. Hum. Genet. 67 (4):1014-19 (2000)). Another study described a new termination mutation (Glu216X) in one high incidence prostate cancer family (Wang, L., et al., Cancer Res. 61 (17):6494-99 (2001)). Other reports have not demonstrated strong association with the three missense mutations, and a recent metaanalysis suggests that the familial risk associated with these mutations is more moderate than was indicated in initial reports (Vesprini, D., et al., Am. J. Hum. Genet. 68(4):912-17 (2001); Shea, P. R., et al., Hum. Genet. 111 (4-5):398-400 (2002); Suarez, B. K., et al., Cancer Res. 61 (13):4982-84 (2001); Severi, G., et al., J. Natl. Cancer Inst. 95 (11):818-24 (2003); Fujiwara, H., et al., J. Hum. Genet. 47 (12):641-48 (2002); Camp, N. J., et al., Am. J. Hum. Genet. 71 (6):1475-78 (2002)).
Polymorphic variants of genes involved in androgen action (e.g., the androgen receptor (AR) gene, the cytochrome P-450c17 (CYP17) gene, and the steroid-5-α-reductase type II (SRD5A2) gene), have also been implicated in increased risk of prostate cancer (Nelson, W. G. et al., N. Engl. J. Med. 349 (4):366-81 (2003)). With respect to AR, which encodes the androgen receptor, several genetic epidemiological studies have shown a correlation between an increased risk of prostate cancer and the presence of short androgen-receptor polyglutamine repeats, while other studies have failed to detect such a correlation. Linkage data has also implicated an allelic form of CYP17, an enzyme that catalyzes key reactions in sex-steroid biosynthesis, with prostate cancer (Chang, B. et al., Int. J. Cancer 95:354-59 (2001)). Allelic variants of SRD5A2, which encodes the predominant isozyme of 5-α-reductase in the prostate and functions to convert testosterone to the more potent dihydrotestosterone, have been associated with an increased risk of prostate cancer and with a poor prognosis for men with prostate cancer (Makridakis, N. M. et al., Lancet 354:975-78 (1999); Nam, R. K. et al., Urology 57:199-204 (2001)).
In short, despite the effort of many groups around the world, the genes that account for a substantial fraction of prostate cancer risk have not been identified. Although twin studies have implied that genetic factors are likely to be prominent in prostate cancer, only a handful of genes have been identified as being associated with an increased risk for prostate cancer, and these genes account for only a low percentage of cases. Thus, it is clear that the majority of genetic risk factors for prostate cancer remain to be found. It is likely that these genetic risk factors will include a relatively high number of low-to-medium risk genetic variants. These low-to-medium risk genetic variants may, however, be responsible for a substantial fraction of prostate cancer, and their identification, therefore, a great benefit for public health. Furthermore, none of the published prostate cancer genes have been reported to predict a greater risk for aggressive prostate cancer than for less aggressive prostate cancer.
Extensive genealogical information for a population containing cancer patients has in a recent study been combined with powerful gene sharing methods to map a locus on chromosome 8q24.21, which has been demonstrated to play a major role in cancer. Various cancer patients and their relatives were genotyped with a genome-wide marker set including 1100 microsatellite markers, with an average marker density of 3-4 cM. (Amundadottir L. T., Nature Genet. 38 (6):652-658 (2006)). Association was detected to a single LD block within the locus between positions 128.414 and 128.506 Mb (NCBI build 34) in Utah CEPH HapMap samples.
SUMMARY OF THE INVENTION
The present invention relates to methods of determining an increased or decreased susceptibility to prostate cancer (e.g., aggressive prostate cancer), by evaluating certain markers that have been found to be associated with increased or decreased susceptibility of prostate cancer (e.g., aggressive prostate cancer). Various applications based on the association of particular polymorphic markers to prostate cancer are described herein.
In a first aspect, the present invention relates to a determining a susceptibility to prostate 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, wherein the at least one polymorphic marker is selected from the polymorphic markers set forth in Tables 11-15, and markers in linkage disequilibrium therewith, and wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer.
In another aspect, the invention relates to a method for determining a susceptibility to prostate cancer in a human individual, comprising determining the presence or absence of at least one allele of at least one polymorphic marker in a genotype dataset derived from the individual, wherein the at least one polymorphic marker is selected from the polymorphic markers set forth in Tables 11-15, and markers in linkage disequilibrium therewith, and wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. The genotype dataset derived from the individual contains a set of genotypes that are based on an analysis of nucleic acid sample from that individual. In other words, the genotypes are characteristic of the individual. In one embodiment, the at least one marker is selected from the group of markers listed in Tables 7-11, and markers in linkage disequilibrium therewith.
In another aspect, the invention relates to a method of predicting aggressive prostate in a human individual who has been diagnosed with, or presents symptoms for, prostate cancer, by 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 derived from the individual, wherein the at least one polymorphic marker is selected from the polymorphic markers set forth in Tables 11-15, and markers in linkage disequilibrium therewith, and wherein the presence of the at least one allele is indicative of aggressive prostate cancer. In one embodiment, the at least one polymorphic marker is selected from the markers set forth in Table 11, and markers in linkage disequilibrium therewith. In another embodiment, the at least one polymorphic marker is selected from rs2710646, and markers in linkage disequilibrium therewith.
In another embodiment, the at least one marker is selected from the group of markers listed in Tables 7-11. In another embodiment, the at least one marker is selected from the markers set forth in SEQ ID NO:1-362. In another embodiment, the at least one marker is selected from the group of markers listed in Tables 1, 4a and 4b, and markers in linkage disequilibrium therewith. In another embodiment, the at least one marker is selected from marker rs3923603 (SEQ ID NO:1), rs4430796 (SEQ ID NO:2), rs7501939 (SEQ ID NO:3), rs1859962 (SEQ ID NO:4), D17S1350 (SEQ ID NO:5), rs5945572 (SEQ ID NO:6), rs5945605 (SEQ ID NO:7), rs2710646 (SEQ ID NO:8), rs3760511 (SEQ ID NO:56), rs7214479 (SEQ ID NO:134), rs6501445 (SEQ ID NO:146), rs983085 (SEQ ID NO:150), rs5945605 (SEQ ID NO:178) and rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In one embodiment, the at least one polymorphic marker is selected from rs2710646 (SEQ ID NO:8) and rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the markers set forth in Table 11. In another embodiment, the at least one polymorphic marker is rs3923603 (SEQ ID NO:1), or markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the markers set forth in Table 7. In another embodiment, the at least one polymorphic marker is rs7501939 (SEQ ID NO:3), or markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the group of markers set forth in Table 8. In a further embodiment, the at least one polymorphic marker is rs1859962 (SEQ ID NO:4), or markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the group of markers set forth in Table 9. In yet another embodiment, the at least one polymorphic marker is rs5945572 (SEQ ID NO:6), or markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the group of markers set forth in Table 10. In certain embodiments, the method further comprises assessing frequency of at least one haplotype in the individual, wherein the presence of the at least one haplotype is indicative of a susceptibility to prostate cancer.
In one embodiment, the at least one marker is selected from the group of markers listed in Table 7, and markers in linkage disequilibrium therewith. In another embodiment the at least one marker is selected from the group of markers listed in Table 8, and markers in linkage disequilibrium therewith. In another embodiment the at least one marker is selected from the group of markers listed in Table 9, and markers in linkage disequilibrium therewith. In another embodiment the at least one marker is selected from the group of markers listed in Table 10, and markers in linkage disequilibrium therewith. In another embodiment the at least one marker is selected from the group of markers listed in Table 11, and markers in linkage disequilibrium therewith.
In another embodiment, the present invention relates to a method of determining a susceptibility to prostate cancer in a human individual, comprising analyzing a nucleic acid sample obtained from the individual for the presence or absence at least one allele of at least one polymorphic marker associated with LD block C02, LD block C04a, the TCF2 gene, LD block C17b and LD block C0Xa, wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the at least marker is selected from the group of markers located within LD block C04a, the TCF2 gene, LD block C17b and/or LD block C0Xa.
In another embodiment, the at least one polymorphism is selected from rs3923603, rs7501939, rs1859962, rs5945572, rs2710646, rs3760511, rs4430796, rs7214479, rs6501455, rs983085, rs5945605, and rs721048, and wherein the presence of allele A in marker rs3923603, allele C in rs7501939, allele G in rs1859962, allele A in rs5945572, allele A in rs2710646, allele C in rs3760511, allele A in rs4430796, allele T in rs7214479, allele A in rs6501455, allele C in rs983085, allele T in rs5945605, or allele A in rs721048 is indicative of increased susceptibility to prostate cancer.
Certain applications of the present invention relate to the TCF2 gene. In one embodiment, the at least one polymorphic marker associated with susceptibility of prostate cancer is associated with the TCF2 gene. In other words, the marker is in linkage disequilibrium with the TCF2 gene. The at least one marker is in one embodiment selected from the group of markers listed in Table 13. In one preferred embodiment, the at least one marker is selected from the markers set forth in Table 4a, Table 4b and/or Table 8. The at least one marker is in another embodiment selected from markers rs7501939 (SEQ ID NO:3) and rs4430796 (SEQ ID NO:2), and markers in linkage disequilibrium therewith. In another embodiment, the at least one polymorphic marker is selected from rs7501939 (SEQ ID NO:3) and rs4430796 (SEQ ID NO:2), and wherein the presence of allele C in marker rs7501939 or allele A in marker rs4430796 is indicative of increased susceptibility of prostate cancer.
In one embodiment, the at least one polymorphic marker is associated with LD block C04a, wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the marker is selected from the markers within LD block C04a as set forth in Table 12. In another embodiment, the marker is selected from the group of markers within LD block C04a set forth in Table 7. In one preferred embodiment, the marker is marker rs3923603 (SEQ ID NO:1), and markers in linkage disequilibrium therewith. In another preferred embodiment, the marker is marker rs3923603, and wherein the presence of allele 1 in rs3923603 is indicative of increased susceptibility of prostate cancer.
In another embodiment, the at least one polymorphic marker is associated with LD block C17b, wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the marker is selected from the markers within LD block C17b as set forth in Table 14. In another embodiment, the marker is selected from the markers within LD block C17b set forth in Table 9. In one preferred embodiment, the at least one polymorphic marker is selected from markers rs1859962 (SEQ ID NO:4) and D17S1350 (SEQ ID NO:5), and markers in linkage disequilibrium therewith. In another preferred embodiment, the marker is selected from markers rs1859962 and D17S1350, and wherein the presence of allele G in marker rs1859962 and allele 0 or allele 2 in marker D17S1350 is indicative of increased susceptibility of prostate cancer. In yet another embodiment, the at least one haplotype is a haplotype comprising allele G at marker rs17763769 and allele 0 or allele 2 at marker D17S1350.
Another embodiment of the invention relates to a method of determining a susceptibility to prostate cancer in an individual, comprising analyzing a nucleic acid sample obtained from the individual for the presence or absence at least one allele of at least one polymorphic marker associated with LD Block C0Xa, wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the at least one polymorphic marker is selected from markers within LD block C0Xa as set forth in Table 15. In another embodiment, the at least one polymorphic marker is selected from markers within LD block C0Xa set forth in Table 10. In one preferred embodiment, the marker is marker rs5945572 (SEQ ID NO:6), and markers in linkage disequilibrium therewith. In another preferred embodiment, the presence of allele 1 in marker rs5945572 is indicative of increased susceptibility of prostate cancer.
In another embodiment, the at least one polymorphic marker is associated with LD Block C02, wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the marker is selected from the markers within LD block C02 as set forth in Table 16. In another embodiment, the marker is selected from the markers within LD block C02 set forth in Table 11. In one preferred embodiment, the at least one polymorphic marker is marker rs2710646 (SEQ ID NO:8) or marker rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In another preferred embodiment, the marker is selected from markers rs2710646 or marker rs721048, and wherein the presence of allele A in marker rs2710646 or allele A in marker rs721048 is indicative of increased susceptibility of prostate cancer.
In another aspect, the present invention relates to a method of diagnosing or determining a susceptibility of prostate cancer in an individual, the method comprising determining the identity of at least one allele of at least one polymorphic marker in a nucleic acid sample obtained from the individual, wherein the at least one marker is selected from the group of markers located within the TCF2 gene, and markers in linkage disequilibrium therewith, wherein the presence of the at least one allele is indicative of a susceptibility of prostate cancer. In one embodiment, the at least one marker is selected from the markers set forth in Table 13. In another embodiment, the at least one marker is selected from the markers set forth in Table 6, and markers in linkage disequilibrium therewith. In another embodiment, the at least one marker is selected from marker rs7501939 (SEQ ID NO:3), rs3760511 (SEQ ID NO:56) and rs4430796 (SEQ ID NO:2), and markers in linkage disequilibrium therewith.
Another aspect of the invention relates to a method of identification a marker for use in assessing susceptibility to prostate cancer, the method comprising identifying at least one polymorphism in linkage disequilibrium with at least one of the polymorphisms listed in Tables 7-11, and determining the genotype status of a sample of individuals diagnosed with, or having a susceptibility to, prostate cancer, and a control sample, wherein significant association to prostate cancer, or a susceptibility to prostate cancer, of at least one allele in the at least one polymorphism is indicative of the polymorphism being useful for assessing susceptibility to prostate cancer. In one embodiment, linkage disequilibrium is characterized by numerical values of r2 of greater than 0.2.
Another aspect of the invention relates to a method of identification of a marker for use in assessing susceptibility to prostate cancer, the method comprising (a) identifying at least one polymorphic marker in linkage disequilibrium with at least one of the markers set forth in any of the Tables 7, 8, 9, 10 or 11 (SEQ ID NO:1-362); (b) determining the genotype status of a sample of individuals diagnosed with, or having a susceptibility to, prostate cancer; and (c) determining the genotype status of a sample of control individuals; wherein a significant difference in frequency of at least one allele in at least one polymorphism in individuals diagnosed with, or having a susceptibility to, prostate cancer, as compared with the frequency of the at least one allele in the control sample is indicative of the at least one polymorphism being useful for assessing susceptibility to prostate cancer. In one embodiment, an increase in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with, or having a susceptibility to, prostate cancer, as compared with the frequency of the at least one allele in the control sample is indicative of the at least one polymorphism being useful for assessing increased susceptibility to prostate cancer. In another embodiment, a decrease in frequency of the at least one allele in the at least one polymorphism in individuals diagnosed with, or having a susceptibility to, prostate cancer, as compared with the frequency of the at least one allele in the control sample is indicative of the at least one polymorphism being useful for assessing decreased susceptibility to, or protection against, prostate cancer.
A further aspect relates to a method of genotyping a nucleic acid sample obtained from a human individual at risk for, or diagnosed with, prostate cancer, comprising determining the presence or absence of at least one allele of at least one polymorphic marker in the sample, wherein the at least one marker is selected from the group consisting of the markers set forth in Tables 7-11 (SEQ ID NO:1-362), and markers in linkage disequilibrium therewith, and wherein the presence or absence of the at least one allele of the at least one polymorphic marker is indicative of a susceptibility of prostate cancer. In one embodiment, genotyping comprises amplifying a segment of a nucleic acid that comprises the at least one polymorphic marker by Polymerase Chain Reaction (PCR), using a nucleotide primer pair flanking the at least one polymorphic marker. In another embodiment, genotyping is performed using a process selected from allele-specific probe hybridization, allele-specific primer extension, allele-specific amplification, nucleic acid sequencing, 5′-exonuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, and single-stranded conformation analysis. In a preferred embodiment, the process comprises allele-specific probe hybridization. In another preferred embodiment, the process comprises DNA sequencing. In a preferred embodiment, the method comprises the steps of (1) contacting copies of the nucleic acid with a detection oligonucleotide probe and an enhancer oligonucleotide probe under conditions for specific hybridization of the oligonucleotide probe with the nucleic acid; wherein (a) the detection oligonucleotide probe is from 5-100 nucleotides in length and specifically hybridizes to a first segment of the nucleic acid whose nucleotide sequence is given by any one of SEQ ID NO:1-SEQ ID NO:362; (b) the detection oligonucleotide probe comprises a detectable label at its 3′ terminus and a quenching moiety at its 5′ terminus; (c) the enhancer oligonucleotide is from 5-100 nucleotides in length and is complementary to a second segment of the nucleotide sequence that is 5′ relative to the oligonucleotide probe, such that the enhancer oligonucleotide is located 3′ relative to the detection oligonucleotide probe when both oligonucleotides are hybridized to the nucleic acid; and (d) a single base gap exists between the first segment and the second segment, such that when the oligonucleotide probe and the enhancer oligonucleotide probe are both hybridized to the nucleic acid, a single base gap exists between the oligonucleotides; (2) treating the nucleic acid with an endonuclease that will cleave the detectable label from the 3′ terminus of the detection probe to release free detectable label when the detection probe is hybridized to the nucleic acid; and measuring free detectable label, wherein the presence of the free detectable label indicates that the detection probe specifically hybridizes to the first segment of the nucleic acid, and indicates the sequence of the polymorphic site as the complement of the detection probe.
Another aspect of the invention relates to the use of an oligonucleotide probe in the manufacture of a reagent for diagnosing and/or assessing susceptibility to prostate cancer in a human individual, wherein the probe hybridizes to a segment of a nucleic acid whose nucleotide sequence is given by any one of SEQ ID NO:1-SEQ ID NO:362, and wherein the probe is 15-500 nucleotides in length.
A further aspect of the invention relates to a method of assessing an individual for probability of response to a therapeutic agent for preventing and/or ameliorating symptoms associated with prostate cancer, 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, wherein the at least one polymorphic marker is selected from the group consisting of the polymorphic markers set forth in Tables 7-11 (SEQ ID NO:1-362), and markers in linkage disequilibrium therewith, wherein the presence of the at least one allele of the at least one marker is indicative of a probability of a positive response to the therapeutic agent.
Yet another aspect relates to method of predicting prognosis of an individual diagnosed with, cancer, the method 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, wherein the at least one polymorphic marker is selected from the group consisting of the polymorphic markers set forth in Tables 7-11, and markers in linkage disequilibrium therewith, wherein the presence of the at least one allele is indicative of a worse prognosis of the cancer in the individual.
A further aspect of the invention relates to a method of monitoring progress of a treatment of an individual undergoing treatment for prostate cancer, the method 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, wherein the at least one polymorphic marker is selected from the group consisting of the polymorphic markers listed in Tables 7-11 (SEQ ID NO:1-362), and markers in linkage disequilibrium therewith, wherein the presence of the at least one allele is indicative of the treatment outcome of the individual.
Certain embodiments of the methods of the invention further comprise assessing at least one biomarker in a sample from the individual. The sample is in certain embodiments a blood sample or a cancer biopsy sample. Other embodiments include a further step of analyzing a sample comprising genomic DNA from a human individual or a genotype dataset derived from a human individual for the presence or absence of at least one at-risk allele of at least one at-risk variant for prostate cancer not in linkage disequilibrium with any one of the markers set forth in Tables 7-11. In one such embodiment, the at least one at-risk variant for prostate cancer is selected from rs10505483, rs1447295, rs6983267 and rs10896450, and markers in linkage disequilibrium therewith. In one such embodiment the presence of allele A in rs10505483, allele A in rs1447295, allele G in rs6983267 and allele G in rs10896450 is indicative of increased susceptibility of prostate cancer.
In certain embodiments, the invention comprises determining the presence or absence of at least one allele of at least two polymorphic markers selected from the group of polymorphic markers set forth in Tables 7-11, and markers in linkage disequilibrium therewith, and wherein the presence of the at least one allele of the at least two polymorphic markers is indicative of an increased susceptibility to prostate cancer. In one embodiment, the method comprises determining the presence of an at-risk allele of markers rs10505483, rs1447295, rs1859962, rs2710646, rs4430796, rs5945572, and rs6983267. In another embodiment, the method comprises determining the presence of an at-risk allele of markers rs10505483, rs1447295, rs1859962, rs2710646, rs4430796, rs5945572, rs6983267 and rs10896450. In one such embodiment, the at-risk allele is allele A in rs10505483, allele A in rs1447295, allele G in rs6983267, allele G in rs10896450, allele G in rs1859962, allele A in rs2710646, allele A in rs4430796, and allele A in rs5945572.
Certain embodiments further comprise analyzing non-genetic information to make risk assessment, diagnosis, or prognosis of the individual. The non-genetic information is in some embodiments selected from age, gender, ethnicity, socioeconomic status, previous disease diagnosis, medical history of subject, family history of cancer, biochemical measurements, and clinical measurements.
The invention also relates to kits for assessing susceptibility to prostate cancer in a human individual. In one embodiment, the kit comprises reagents necessary for selectively detecting at least one allele of at least one polymorphic marker in the genome of the individual, wherein the at least one polymorphic marker is selected from the group consisting of the polymorphic markers listed in Tables 12-16, and markers in linkage disequilibrium therewith, and wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the at least one polymorphic marker is selected from the markers set forth in Tables 7-11. In another embodiment, the at least one polymorphic markers is selected from the group of markers associated with the TCF2 gene. In another embodiment, the kit comprises 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 markers set forth in Tables 7-11 (SEQ ID NO:1-362), and markers in linkage disequilibrium therewith, and wherein the presence of the at least one allele is indicative of a susceptibility to prostate cancer. In one embodiment, the reagents comprise at least one contiguous oligonucleotide that hybridizes to a fragment of the genome of the individual comprising the at least one polymorphic marker, a buffer and a detectable label. In another embodiment, the reagents comprise at least one pair of oligonucleotides that hybridize to opposite strands of a genomic nucleic acid segment obtained from the subject, wherein each oligonucleotide primer pair is designed to selectively amplify a fragment of the genome of the individual that includes one polymorphic marker, and wherein the fragment is at least 30 base pairs in size. In one preferred embodiment, the at least one oligonucleotide is completely complementary to the genome of the individual. In one embodiment, the oligonucleotide is about 18 to about 50 nucleotides in length. In another embodiment, the oligonucleotide is 20-30 nucleotides in length.
In a preferred embodiment, the kit comprises: (a) a detection oligonucleotide probe that is from 5-100 nucleotides in length; (b) an enhancer oligonucleotide probe that is from 5-100 nucleotides in length; and (c) an endonuclease enzyme; wherein the detection oligonucleotide probe specifically hybridizes to a first segment of the nucleic acid whose nucleotide sequence is given by any one of SEQ ID NO:1-SEQ ID NO:362; and wherein the detection oligonucleotide probe comprises a detectable label at its 3′ terminus and a quenching moiety at its 5′ terminus; wherein the enhancer oligonucleotide is from 5-100 nucleotides in length and is complementary to a second segment of the nucleotide sequence that is 5′ relative to the oligonucleotide probe, such that the enhancer oligonucleotide is located 3′ relative to the detection oligonucleotide probe when both oligonucleotides are hybridized to the nucleic acid; wherein a single base gap exists between the first segment and the second segment, such that when the oligonucleotide probe and the enhancer oligonucleotide probe are both hybridized to the nucleic acid, a single base gap exists between the oligonucleotides; and wherein treating the nucleic acid with the endonuclease will cleave the detectable label from the 3′ terminus of the detection probe to release free detectable label when the detection probe is hybridized to the nucleic acid.
The kit can in another embodiment comprise at least one detection oligonucleotide probe that is from 5-100 nucleotides in length and specifically hybridizes (under stringent conditions) to all or a portion of the TCF2 gene, the LD block C02, the LD block C17b, the LD block C0Xa or the LD block C04a, and wherein at least one of said at least one oligonucleotide probes comprises a polymorphism selected from the group of polymorphisms listed in Tables 7-11, and polymorphisms in linkage disequilibrium therewith.
Another aspect relates to a kit comprising at least one reagent for determining the presence in a human nucleic acid of at least one at-risk allele of at least one marker in the TCF2 gene, the LD block C02, the LD block C17b, the LD block C0Xa or the LD block C04a, wherein the presence of the at least one at-risk allele correlates with an increased prevalence of prostate cancer in humans. In one embodiment the at least one reagent comprises at least one contiguous nucleotide sequence that is fully complementary to a region of human nucleic acid that comprises the at least one marker. In another embodiment, the kit comprises at least one allele-specific nucleotide that differentially hybridizes to single-stranded human nucleic acid molecules that contain different alleles of the marker, wherein the marker is selected from the group of markers set forth in any of the Tables 7-11, and wherein the allele-specific oligonucleotide is from 15-200 nucleotides in size.
A further method of the invention relates to detecting the presence of at least one allele of at least one polymorphic marker associated with prostate cancer, the method comprising a step of contacting at least one oligonucleotide probe that specifically hybridizes to an oligonucleotide sequence comprising said polymorphic marker with a test sample comprising genomic DNA from a human individual, and determining whether the at least one oligonucleotide probe hybridizes to the genomic DNA from the test sample. In one embodiment of such a method, the oligonucleotide probe comprises a label. In one preferred embodiment, the label is a fluorescent label. In another embodiment, the oligonucleotide probe further comprises a quencher. In another embodiment, the method comprises contacting two oligonucleotide probes with the test sample, wherein at least one of the oligonucleotide probes contains a fluorescent label and a quencher. The oligonucleotide probes are in preferred embodiments from 15 to 100 nucleotides in size, such as from 18-50 nucleotides, such as 20-30 nucleotides in size.
In certain methods, uses and kits of the invention, the presence of the at least one allele or haplotype is indicative of increased susceptibility to prostate cancer. In some embodiments, the increased susceptibility is characterized by a relative risk of at least 1.1, including a relative risk of at least 1.15, a relative risk of at least 1.2, a relative risk of at least 1.25, a relative risk of at least 1.3, a relative risk of at least 1.4, a relative risk of at least 1.5, a relative risk of at least 1.6, a relative risk of at least 1.7, and a relative risk of at least 2.0.
In other methods and kits, the presence of the at least one allele or haplotype is indicative of decreased susceptibility to prostate cancer. In some embodiments, the decreased susceptibility is characterized by a relative risk of less than 0.9, including a relative risk of less than 0.85, a relative risk of less than 0.8, a relative risk of less than 0.75, a relative risk of less than 0.7, a relative risk of less than 0.6, and a relative risk of less than 0.5.
The various aspects (i.e., uses, methods, kits, media, and apparatus) of the invention can, in a general sense, be reduced to practice using any one or a plurality of the markers described herein to be associated with a susceptibility of prostate cancer. Thus in certain embodiments, the at least one marker to be assessed is selected from the group of markers listed in Tables 7-11, and markers in linkage disequilibrium therewith. In another embodiment, the at least one marker is selected from the group of markers listed in Tables 7-11. In certain other embodiments, the at least one marker is selected from the markers set forth in SEQ ID NO:1-362. In other embodiments, the at least one marker is selected from the group of markers listed in Tables 1, 4a and 4b, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from marker rs3923603 (SEQ ID NO:1), rs4430796 (SEQ ID NO:2), rs7501939 (SEQ ID NO:3), rs1859962 (SEQ ID NO:4), D17S1350 (SEQ ID NO:5), rs5945572 (SEQ ID NO:6), rs5945605 (SEQ ID NO:7), rs2710646 (SEQ ID NO:8), rs3760511 (SEQ ID NO:56), rs7214479 (SEQ ID NO:134), rs6501445 (SEQ ID NO:146), rs983085 (SEQ ID NO:150), rs5945605 (SEQ ID NO:178) and rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In some embodiments, the at least one polymorphic marker is selected from rs2710646 (SEQ ID NO:8) and rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In particular embodiment, the at least one polymorphic marker is selected from the markers set forth in Table 11. In other particular embodiments, the at least one polymorphic marker is rs3923603 (SEQ ID NO:1), or markers in linkage disequilibrium therewith. In certain such embodiments, the at least one polymorphic marker is selected from the markers set forth in Table 7. In certain other embodiments, the at least one polymorphic marker is rs7501939 (SEQ ID NO:3), or markers in linkage disequilibrium therewith. In some of such embodiments, the at least one polymorphic marker is selected from the group of markers set forth in Table 8. In some further embodiments, the at least one polymorphic marker is rs1859962 (SEQ ID NO:4), or markers in linkage disequilibrium therewith. In certain such embodiments, the at least one polymorphic marker is selected from the group of markers set forth in Table 9. In other embodiments, the at least one polymorphic marker is rs5945572 (SEQ ID NO:6), or markers in linkage disequilibrium therewith. In particular embodiments, the at least one polymorphic marker is selected from the group of markers set forth in Table 10. In one embodiment, the at least one marker is selected from the group of markers listed in Table 7, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from the group of markers listed in Table 8, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from the group of markers listed in Table 9, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from the group of markers listed in Table 10, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from the group of markers listed in Table 11, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from markers associated with LD block C02, LD block C04a, the TCF2 gene, LD block C17b or LD block C0Xa, as described herein. In certain such embodiments, the at least marker is selected from the group of markers located within LD block C04a, the TCF2 gene, LD block C17b or LD block C0Xa.
The prostate cancer phenotype applicable for assessment by the methods, uses and kits of the invention is in certain embodiments aggressive prostate cancer, as described further herein. In other words, the markers of the invention are in certain embodiments predictive of aggressive prostate cancer, as defined herein, in an individual. In one embodiment, markers associated with LD block C02 are predictive of aggressive prostate cancer, such as markers selected from the markers set forth in Table 11, or markers in linkage disequilibrium therewith. In certain embodiments, the invention pertains individuals with specific age at onset of disease. In certain embodiments, age at onset of disease is the age at which first diagnosis of the disease is made. In certain other embodiments, age at onset is the age of first symptoms of the disease, which may occur at an earlier age than the actual disease diagnosis. In certain embodiments, age at onset of prostate cancer is early—also sometimes called young onset disease. In particular embodiments, age at onset is before age 70. In other embodiments, age at onset is before age 65. In certain other embodiments, age at onset is before age 60. In other embodiments, age at onset of disease is before age 55.
In certain embodiments of the invention, linkage disequilibrium is characterized by particular cutoff values of the linkage disequilibrium measures r2 and/or |D′|, as discussed in more detail herein. In one embodiment, linkage disequilibrium is characterized by numerical values of r2 of greater than 0.1. In another embodiment, linkage disequilibrium is characterized by values of r2 of greater than 0.2. In other embodiments, linkage disequilibrium is characterized by values of r2 of greater than 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95 or 0.99. In other embodiments, linkage disequilibrium is characterized by numerical values of |D′| of greater than 0.5, such as values of greater than 0.6, values greater than 0.7, values greater than 0.8, values greater than 0.9, or values greater than 0.95. Other non-integer cutoff values in the range of 0.01-1.0 for r2 or |D′| are also contemplated and are also encompassed by the present invention. In particular embodiments, a cutoff of a particular value for r2 and a cutoff of a particular value of |D′| are characteristic of the linkage disequilibrium. In one such embodiment, linkage disequilibrium is characterized by values of r2 of greater than 0.2 and/or |D′| of greater than 0.8. It should be appreciated that other combinations and permutations of the cutoff values of r2 and |D′| are contemplated and are also within the scope of the present invention.



BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments of the invention.
FIG. 1 shows the genomic structure of the TCF2 gene. The gene is characterized by the presence of several splice variants, varying in length from about 5 kb (2 exons) to almost 60 kb (9 exons) of genomic sequence. The positions of SNP markers (dbSNP125, Illumina 300k chip, and HapMap Release 19) are indicated by vertical bars.



DETAILED DESCRIPTION OF THE INVENTION
A description of preferred embodiments of the invention follows.
The present invention discloses polymorphic variants and haplotypes that have been found to be associated with prostate cancer. Particular alleles at polymorphic markers (e.g., the markers of Tables 12-16, e.g., the markers of Tables 7-11, e.g., markers rs3923603 (SEQ ID NO:1), rs4430796 (SEQ ID NO:2), rs7501939 (SEQ ID NO:3), rs1859962 (SEQ ID NO:4), D17S1350 (SEQ ID NO:5), rs5945572 (SEQ ID NO:6), rs5945605 (SEQ ID NO:7), rs2710646 (SEQ ID NO:8), rs721048 (SEQ ID NO:344) and markers in linkage disequilibrium therewith) and haplotypes comprising such alleles have been found to be associated with prostate cancer. Such markers and haplotypes are useful for diagnostic purposes, as described in further detail herein. Further applications of the present invention includes methods for assessing response to prostate cancer therapeutic agents utilizing the polymorphic markers of the invention, as well as kits for assessing susceptibility of an individual to prostate cancer.
DEFINITIONS
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), microsatellites, insertions, deletions, duplications and translocations.
An “allele” refers to the nucleotide sequence of a given locus (position) on a chromosome. A polymorphic marker allele thus refers to the composition (i.e., sequence) of the marker on a chromosome. Genomic DNA from an individual contains two alleles (e.g., allele-specific sequences) for any given polymorphic marker, representative of each copy of the marker on each chromosome. Sequence codes for nucleotides used herein are: A=1, C=2, G=3, T=4.
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.














IUB code
Meaning









A
Adenosine



C
Cytidine



G
Guanine



T
Thymidine



R
G or A



Y
T or C



K
G or T



M
A or C



S
G or C



W
A or T



B
C G or T



D
A G or T



H
A C or T



V
A C or G



N
A C G or T (Any base)










A nucleotide position at which more than one sequence is possible in a population (either a natural population or a synthetic population, e.g., a library of synthetic molecules) is referred to herein as a “polymorphic site”.
A “Single Nucleotide Polymorphism” or “SNP” is a DNA sequence variation occurring when a single nucleotide at a specific location in the genome differs between members of a species or between paired chromosomes in an individual. Most SNP polymorphisms have two alleles. Each individual is in this instance either homozygous for one allele of the polymorphism (i.e. both chromosomal copies of the individual have the same nucleotide at the SNP location), or the individual is heterozygous (i.e. the two sister chromosomes of the individual contain different nucleotides). The SNP nomenclature as reported herein refers to the official Reference SNP (rs) ID identification tag as assigned to each unique SNP by the National Center for Biotechnological Information (NCBI).
A “variant”, as described herein, refers to a segment of DNA that differs from the reference DNA. A “marker” or a “polymorphic marker”, as defined herein, is a variant. Alleles that differ from the reference are referred to as “variant” alleles.
A “microsatellite” is a polymorphic marker that has multiple small repeats of bases that are 2-8 nucleotides in length (such as CA repeats) at a particular site, in which the number of repeat lengths varies in the general population.
An “indel” is a common form of polymorphism comprising a small insertion or deletion that is typically only a few nucleotides long.
A “haplotype,” as described herein, refers to a segment of genomic DNA within one strand 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. Haplotypes are described herein in the context of the marker name and the allele of the marker in that haplotype, e.g., “A rs3923603” refers to the A allele of marker rs3923603 being in the haplotype, and is equivalent to “rs3923603 allele A”. 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 an individual (or group of individuals) being prone to developing a certain state (e.g., a certain trait, phenotype or disease), or 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 prostate 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 prostate 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 the 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 compute-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 “prostate cancer therapeutic agent” refers to an agent that can be used to ameliorate or prevent symptoms associated with prostate cancer.
The term “prostate cancer-associated nucleic acid”, as described herein, refers to a nucleic acid that has been found to be associated to prostate cancer. This includes, but is not limited to, the markers and haplotypes described herein and markers and haplotypes in strong linkage disequilibrium (LD) therewith. In one embodiment, a prostate cancer-associated nucleic acid refers to an LD-block found to be associated with prostate cancer through at least one polymorphic marker located within the LD block or associated with the LD block.
“Aggressive prostate cancer”, as described herein, refers to prostate cancer with combined Gleason grades of 7 or higher OR stage T3 or higher OR node positive OR metastasis positive disease OR death because of prostate cancer. Note that it is sufficient to have one of these criteria to be determined aggressive prostate cancer. These clinical parameters are well known surrogates for increased aggressiveness of the disease.
The term “LD block C04a”, as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 4 between positions 145,601,002 and 145,805,005 of NCBI (National Center for Biotechnology Information) Build 34. The position of the LD block in NCBI Build 35 is between positions 145,380,980 and 145,584,983, and in NCBI Build 36, the LD block is between positions 145,242,825 and 145,446,828. In all these sequence builds, the LD block spans 204,004 bp.
The term “LD block C17b”, as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 17 between positions 69,665,200 and 69,843,150 of NCBI (National Center for Biotechnology Information) Build 34. The position of the LD block in NCBI Build 35 and in NCBI Build 36 is between positions 66,579,672 and 66,757,622. In all these sequence builds, the LD block spans 177,951 bp.
The term “LD block C0Xa, as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome X between positions 50,084,494 and 50,695,908 of NCBI (National Center for Biotechnology Information) Build 34, spanning markers rs972635 and rs4986573. The LD Block is located between positions 51,000,724 and 51,612,138 in NCBI Build 35, while in NCBI Build 36, the LD block is located between positions 51,184,428 and 51,795,842. The block spans 611,414 bp in all these sequence builds.
The term “TCF2”, or “TCF2 gene” as described herein, refers to the human transcription factor 2 gene on chromosome 17q12. Other names for this gene include Hepatocyte nuclear factor 1-beta (HNF-1beta or HNF-1B), Variant hepatic nuclear factor 1 (VHNF1) and Homeoprotein (LFB3)). The TCF2 gene is characterized by several splice variants, the longest of which stretches across a 64 kb region, between positions 36,235,927 and 36,324,014 in NCBI Build 34, and between position 33,114,490 and 33,202,577 in NCBI Build 35.
The term “LD block C02”, as described herein, refers to the Linkage Disequilibrium (LD) block on Chromosome 2 between positions 62,767,002 and 63,881,002 of NCBI (National Center for Biotechnology Information) Build 34. The position of the LD block in NCBI Build 35 is between positions 62,704,119 and 63,818,119, and in NCBI Build 36, the LD block spans positions 62,645,972 and 63,759,972. In all these sequence builds, the LD block spans 1,114,000 bp.
Through association analysis of a population of individuals diagnosed with prostate cancer, it has been discovered that certain alleles at certain polymorphic markers are associated with prostate cancer. A genome-wide analysis for variants associated with prostate cancer (e.g., aggressive prostate cancer) revealed association of prostate cancer to five regions of the genome, on chromosome 2 (2p15), chromosome 4 (4q31.21), chromosome 17 (17q12 and 17q24.3) and chromosome X (Xp11.22). Particular markers have been found to be associated with an increased risk of prostate cancer in these four regions, as shown herein.
As indicated in Table 1, the A allele of marker rs3923603 (SEQ ID NO:1) on chromosome 4q31.21 (also called rs3923603 A allele) was found to be associated with an increased risk of prostate cancer. The marker is located in a linkage disequilibrium region which we call LD block C04a between positions 145,601,002 and 145,805,005 bp on chromosome 4 (NCBI Build 34). On chromosome 17q12, markers rs7501939 2 allele (SEQ ID NO:3) and rs4430796 A allele (SEQ ID NO:2), residing within the TCF2 gene between positions 36,235,927 and 36,324,014 (NCBI Build 34) on chromosome 17 were found to be associated with prostate cancer. Furthermore, rs1859962 3 allele on chromosome 17q24.3 (SEQ ID NO:4) was found to be associated with an increased risk of prostate cancer. A haplotype defined by markers D17S1350 (SEQ ID NO:5; alleles 0 and 2 combined together) and rs1859962 G allele (SEQ ID NO:4) refined the association signal and increases the risk and decreases the P value. Both these markers are located in what we call LD block C17b between positions 69,665,200 and 69,843,150 bp on chromosome 17 (NCBI Build 34). The marker rs5945572 A allele (SEQ ID NO:6) on chromosome Xp11.22 has been found to be associated with an increased risk of prostate cancer. The marker is located in what we call LD block C0Xa between positions 50,084,494 and 50,695,908 bp (NCBI Build 34) on chromosome X. Marker rs2710646 A allele (SEQ ID NO:8) on chromosome 2 has also been found to be associated with increased risk of prostate cancer. This marker is located within a region on chromosome 2 with extensive linkage disequilibrium, between position 62,767,002 and 63,881,002 (NCBI Build 34), which we denote LD block C02.
These markers, and markers that are correlated with these markers are useful in the methods of the present invention. Thus, there are many polymorphic markers at each locus either within or close to the LD blocks defined herein that are in strong LD with the SNP markers shown herein to be associated with prostate cancer (e.g., aggressive prostate cancer). These correlated markers, including known SNPs or other polymorphic markers such as microsatellites or indels, as well as other correlated SNPs or other polymorphic markers, could therefore be used, alone or in combination, as surrogate markers for detecting the association to prostate cancer described herein.
Given that a significant percentage of prostate cancer is a non-aggressive form that will not spread beyond the prostate and cause morbidity or mortality, and treatments of prostate cancer including prostatectomy, radiation, and chemotherapy all have side effects and significant cost, it would be valuable to have diagnostic markers, such as those described herein, that show greater risk for aggressive prostate cancer as compared to the less aggressive form(s).
Replication analysis of the association of markers within the five chromosomal regions (e.g., 2p15, 4q31.21, 17q12, 17q24.3 and Xp11.23) with cancer (e.g., prostate cancer (e.g., aggressive prostate cancer)) shows that the results are applicable to other populations. Several cohorts of Caucasian origin from Nijmegen, the Netherlands, (Radboud University Nijmegen Medical Centre (RUNMC)), Chicago, U.S. (Northwestern University), and Zaragoza, Spain (Zaragoza University Hospital) all showed association of the variants described herein with prostate cancer, with associated risk (measured as odds ratio, OR) being similar or even higher than found in the Icelandic population.
Transcription Factor 2 Gene (TCF2) Association with Prostate Cancer
The present invention in one aspect relates to identification of a prostate cancer-associated gene encoding transcription factor 2 (official gene symbol is TCF2, but other names for this gene are: Hepatocyte nuclear factor 1-beta (HNF-1beta) (HNF-1B), Variant hepatic nuclear factor 1 (VHNF1) and Homeoprotein (LFB3)). Several markers within intron 1 and 2 of TCF2, as well as markers in front of exon 1, such as the SNP markers rs4430796, rs7501939 and rs3760511, have been found to be associated with prostate cancer. The original observation, first found in an Icelandic cohort, of the association of rs7501939 (P=6.8×10−5; Relative risk=1.20; Population attributable risk=20.7%) and of marker rs3760511 (P=2.5×10−5; Relative risk=1.22; Population attributable risk=20.7%) was subsequently replicated in a Dutch prostate cancer cohort and a United States Caucasian cohort.
The TCF2 (HNF1beta) is the only known gene that maps to the region on chromosome 17q12 of the human genome within which an association to prostate cancer has been found. The underlying variation in markers or haplotypes associated with region and cancer may affect expression of the TCF2 gene. It is however also possible that the expression and/or function of the nearby genes, such as DDX52, AP1GBP1, TBC1D3/TBC1D3B (PRC17), are affected by the variants found to be associated to prostate cancer, or variants in linkage disequilibrium therewith. For example, such variation may affect RNA or protein stability or may have structural consequences, such that the region is more prone to somatic rearrangement in haplotype/allele carriers. Thus, the underlying variation could affect uncharacterized genes directly linked to the markers and/or haplotypes described herein, or could possibly also influence neighbouring genes that are not directly associated to the markers and/or haplotypes described herein.
As a result of the discoveries disclosed herein, methods are now available for diagnosis of an increased susceptibility to prostate cancer, as well as for diagnosis of a decreased susceptibility to prostate cancer and/or a protection against prostate cancer. In preferred embodiments of the invention, diagnostic assays are used to identify the presence of particular alleles at chromosomal regions 2p15, 4q31.21, 17q12, 17q24.3 and Xp11.23, in particular the regions defined by LD block C02, LD block C04a, the TCF2 gene, LD block C17b and LD block C0Xa. In additional embodiments of the invention, other markers or SNPs, identified using the methods described herein, can be used for diagnosis of an increased susceptibility to prostate cancer, and also for diagnosis of a decreased susceptibility to prostate cancer or for identification of an allele that is protective against prostate cancer. The diagnostic assays presented below can be used to identify the presence or absence of these particular alleles.
Assessment for Markers and Haplotypes
The genomic sequence within populations is not identical when individuals are compared. Rather, the genome exhibits sequence variability between individuals at many locations in the genome. Such variations in sequence are commonly referred to as polymorphisms, and there are many such sites within each genome For example, the human genome exhibits sequence variations which occur on average every 500 base pairs. The most common sequence variant consists of base variations at a single base position in the genome, and such sequence variants, or polymorphisms, are commonly called Single Nucleotide Polymorphisms (“SNPs”). These SNPs are believed to have occurred in a single mutational event, and therefore there are usually two possible alleles possible at each SNP site; the original allele and the mutated allele. Due to natural genetic drift and possibly also selective pressure, the original mutation has resulted in a polymorphism characterized by a particular frequency of its alleles in any given population. Many other types of sequence variants are found in the human genome, including microsatellites, insertions, deletions, inversions and copy number variations. A polymorphic microsatellite has multiple small repeats of bases (such as CA repeats, TG on the complimentary strand) at a particular site in which the number of repeat lengths varies in the general population. In general terms, each version of the sequence with respect to the polymorphic site represents a specific allele of the polymorphic site. These sequence variants can all be referred to as polymorphisms, occurring at specific polymorphic sites characteristic of the sequence variant in question. In general terms, polymorphisms can comprise any number of specific alleles. Thus in one embodiment of the invention, the polymorphism is characterized by the presence of two or more alleles in any given 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. 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 disease or abnormal 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 in the SNP assay employed. The allele codes for SNPs used herein are as follows: 1=A, 2=C, 3=G, 4=T. The person skilled in the art will however realise that by assaying or reading the opposite DNA strand, the complementary allele can in each case be measured. Thus, for a polymorphic site (polymorphic marker) containing an A/G polymorphism, the assay employed may either measure the percentage or ratio of the two bases possible, i.e. A and G. Alternatively, by designing an assay that determines the opposite strand on the double-stranded DNA template, the percentage or ratio of the complementary bases T/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).
Polymorphic sites (polymorphic markers) can allow for differences in sequences based on substitutions, insertions or deletions. For example, a polymorphic microsatellite has multiple small repeats of bases (such as CA repeats) at a particular site in which the number of repeat lengths varies in the general population. Each version of the sequence with respect to the polymorphic site represents a specific allele of the polymorphic site.
Typically, a reference sequence is referred to for a particular sequence. Alleles that differ from the reference are referred to as “variant” alleles. For example, the genomic DNA sequence from position 145,601,002 to position 145,805,005 bp on Chromosome 4 of NCBI Build 34 (“LD block C04a”) represents a reference sequence. Other reference sequences related to the present invention include the genomic DNA sequence from position 36,235,927 to position 36,324,014 bp on Chromosome 17 of NCBI Build 34 (“TCF2 gene”), the genomic DNA sequence from position 69,665,200 to position 69,843,150 bp on Chromosome 17 of NCBI Build 34 (“LD block C17b”), the genomic DNA sequence from position 50,084,494 to position 50,695,908 bp on Chromosome X of NCBI Build 34 (“LD block C0Xa”), and the genomic DNA sequence from position 62,767,002 to 63,881,002 bp on Chromosome 2 of NCBI Build 34 (“LD block C02”). 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 that define the haplotypes described herein are variants. Additional variants can include changes that affect a polypeptide, e.g., a polypeptide encoded by the sequences represented by LD block C02, LD block C04a, the TCF2 gene sequence, LD block C17b, or and LD block C0Xa. 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, as described in detail herein. Such sequence changes 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 associated with prostate cancer or a susceptibility to prostate cancer 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 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 al., Genome Res. 9 (5): 492-98 (1999); Kutyavin et al., Nucleic Acid Res. 34:e128 (2006)), including PCR, LCR, Nested PCR and other techniques for nucleic acid amplification. Specific methodologies available for SNP genotyping include, but are not limited to, TaqMan genotyping assays and SNPlex platforms (Applied Biosystems), mass spectrometry (e.g., MassARRAY system from Sequenom), minisequencing methods, real-time PCR, Bio-Plex system (BioRad), CEQ and SNPstream systems (Beckman), Molecular Inversion Probe array technology (e.g., Affymetrix GeneChip), and BeadArray Technologies (e.g., Illumina GoldenGate and Infinium assays). By 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 methods described herein, an individual who is at an increased susceptibility (i.e., at risk) for prostate cancer is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring increased susceptibility for prostate cancer is identified (i.e., at-risk marker alleles or haplotypes). In one aspect, the at-risk marker or haplotype is one that confers a significant increased risk (or susceptibility) of prostate cancer. 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 haplotype 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 1.2, including but not limited to: at least 1.2, at least 1.3, at least 1.4, at least 1.5, at least 1.6, at least 1.7, 1.8, at least 1.9, at least 2.0, at least 2.5, at least 3.0, at least 4.0, and at least 5.0. In a particular embodiment, a risk (relative risk and/or odds ratio) of at least 1.2 is significant. In another particular embodiment, a risk of at least 1.3 is significant. In yet another embodiment, a risk of at least 1.4 is significant. In a further embodiment, a risk of at least 1.5 is significant. In another further embodiment, a significant increase in risk is at least 1.7 is significant. However, other cutoffs are also contemplated, e.g., at least 1.15, 1.25, 1.35, and so on, and such cutoffs are also within scope of the present invention. In other embodiments, a significant increase in risk is at least about 20%, including but not limited to about 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 300%, and 500%. In one particular embodiment, a significant increase in risk is at least 20%. In other embodiments, a significant increase in risk is at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% and at least 100%. 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 prostate cancer (affected), compared to the frequency of its presence in a healthy individual (control), and wherein the presence of the marker or haplotype is indicative of increased susceptibility to prostate 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 prostate cancer. Such disease-free control may in one embodiment be characterized by the absence of one or more specific symptoms characteristic of prostate cancer. In another embodiment, the disease-free control group is characterized by the absence of one or more disease-specific risk factors. Such risk factors are in one embodiment at least one environmental risk factor. 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 comprise at least one additional 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.
In other embodiments of the invention, an individual who is at a decreased susceptibility (i.e., at a decreased risk) for prostate cancer is an individual in whom at least one specific allele at one or more polymorphic marker or haplotype conferring decreased susceptibility for prostate cancer 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 prostate cancer. In one embodiment, significant decreased risk is measured as a relative risk (or odds ratio) of less than 0.9, less than 0.8, less than 0.7, less than 0.6, less than 0.5, less than 0.4, less than 0.3, less than 0.2 and less than 0.1. In one particular embodiment, significant decreased risk is less than 0.7. In another embodiment, significant decreased risk is less than 0.5. In yet another embodiment, significant decreased risk is less than 0.3. In another embodiment, the decrease in risk (or susceptibility) is at least 20%, including but not limited to at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% and at least 98%. In one particular embodiment, a significant decrease in risk is at least about 30%. In another embodiment, a significant decrease in risk is at least about 50%. In another embodiment, the decrease in risk is at least about 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.
A genetic variant associated with a disease or a trait (e.g. prostate cancer) can be used alone to predict the risk of the disease for a given genotype. For a biallelic marker, such as a SNP, there are 3 possible genotypes: homozygote for the at risk variant, heterozygote, and non carrier of the at risk variant. Risk associated with variants at multiple loci can be used to estimate overall risk. For multiple SNP variants, there are k possible genotypes k=3n×2p; 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 which 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 a total of eight variants that have been described to associate with prostate cancer (Gudmundsson, J., et al., Nat Genet. 39:631-7 (2007), Gudmundsson, J., et al., Nat Genet. 39:977-83 (2007); Yeager, M., et al, Nat Genet. 39:645-49 (2007), Amundadottir, L., et al., Nat Genet. 38:652-8 (2006); Haiman, C. A., et al., Nat Genet. 39:638-44 (2007)) (see also Table 20 herein). Seven of these loci are on autosomes, and the remaining locus is on chromosome X. The total number of theoretical genotypic combinations is then 37×21=4374. Some of those genotypic classes are very rare, but are still possible, and should be considered for overall risk assessment, as illustrated by the data shown in Table 20 herein. 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.
Linkage Disequilibrium
The natural phenomenon of recombination, which occurs on average once for each chromosomal pair during each meiotic event, represents one way in which nature provides variations in sequence (and biological function by consequence). It has been discovered that recombination does not occur randomly in the genome; rather, there are large variations in the frequency of recombination rates, resulting in small regions of high recombination frequency (also called recombination hotspots) and larger regions of low recombination frequency, which are commonly referred to as Linkage Disequilibrium (LD) blocks (Myers, S. et al., Biochem Soc Trans 34:526-530 (2006); Jeffreys, A. J., et al., Nature Genet. 29:217-222 (2001); May, C. A., et al., Nature Genet. 31:272-275 (2002)).
Linkage Disequilibrium (LD) refers to a non-random assortment of two genetic elements. For example, if a particular genetic element (e.g., “alleles” of a polymorphic marker) occurs in a population at a frequency of 0.50 (50%) and another occurs at a frequency of 0.50 (50%), then the predicted occurrence of a person's having both elements is 0.25 (25%), assuming a random distribution of the elements. However, if it is discovered that the two elements occur together at a frequency higher than 0.25, then the elements are said to be in linkage disequilibrium since they tend to be inherited together at a higher rate than what their independent allele frequencies would predict. Roughly speaking, LD is generally correlated with the frequency of recombination events between the two elements. Allele frequencies can be determined in a population by genotyping individuals in a population and determining the occurrence of each allele in the population. For populations of diploids, e.g., human populations, individuals will typically have two alleles or allelic combinations for each genetic element (e.g., a marker, haplotype or gene).
Many different measures have been proposed for assessing the strength of linkage disequilibrium (LD). Most capture the strength of association between pairs of biallelic sites. Two important pairwise measures of LD are r2 (sometimes denoted Δ2) and |D′|. 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 are present, and it is <1 if all four possible haplotypes are present. So, a value of |D′| that is <1 indicates that historical recombination may have occurred between two sites (recurrent mutation can also cause |D′| to be <1, but for single nucleotide polymorphisms (SNPs) this is usually regarded as being less likely than recombination). The measure r2 represents the statistical correlation between two sites, and takes the value of 1 if only two haplotypes are present.
The r2 measure is arguably the most relevant measure for association mapping, because there is a simple inverse relationship between r2 and the sample size required to detect association between susceptibility loci and SNPs. These measures are defined for pairs of sites, but for some applications a determination of how strong LD is across an entire region that contains many polymorphic sites might be desirable (e.g., testing whether the strength of LD differs significantly among loci or across populations, or whether there is more or less LD in a region than predicted under a particular model). Measuring LD across a region is not straightforward, but one approach is to use the measure r, which was developed in population genetics. 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 can be at least 0.1 such as at least 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 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. Alternatively, linkage disequilibrium as described herein, refers to linkage disequilibrium 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. It is measured by correlation coefficient or |D′| (r2 up to 1.0 and |D′| up to 1.0). 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. 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, japanese, chinese), as defined (http colon-slash-slash www.hapmap.org). In one such embodiment, LD is determined in the CEU population of the HapMap samples. In another embodiment, LD is determined in the YRI 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, to assess all the different polymorphic states. However, due to linkage disequilibrium between polymorphisms, tightly linked polymorphisms are strongly correlated, which reduces the number of polymorphisms that need to be investigated in an association study to observe a significant association. Another consequence of LD is that many polymorphisms may give an association signal due to the fact that these polymorphisms are strongly correlated.
Genomic LD maps have been generated across the genome, and such LD maps have been proposed to serve as framework for mapping disease-genes (Risch, N. & Merkiangas, K, Science 273:1516-1517 (1996); Maniatis, N., et al., Proc Natl Acad Sci USA 99:2228-2233 (2002); Reich, D E et al, 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 al., Nature Genet. 29:229-232 (2001); Gabriel, S. B. et al., Science 296:2225-2229 (2002); Patil, N. et al., Science 294:1719-1723 (2001); Dawson, E. et al., Nature 418:544-548 (2002); Phillips, M. S. et al., 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 al., Science 294:1719-1723 (2001); Dawson, E. et al., Nature 418:544-548 (2002); Zhang, K. et al., Proc. Natl. Acad. Sci. USA 99:7335-7339 (2002)), or as regions between transition zones having extensive historical recombination, identified using linkage disequilibrium (see, e.g., Gabriel, S. B. et al., Science 296:2225-2229 (2002); Phillips, M. S. et al., Nature Genet. 33:382-387 (2003); Wang, N. et al., Am. J. Hum. Genet. 71:1227-1234 (2002); Stumpf, M. P., and Goldstein, D. B., Curr. Biol. 13:1-8 (2003)). More recently, a fine-scale map of recombination rates and corresponding hotspots across the human genome has been generated (Myers, S., et al., Science 310:321-32324 (2005); Myers, S. et al., Biochem Soc Trans 34:526530 (2006)). The map reveals the enormous variation in recombination across the genome, with recombination rates as high as 10-60 cM/Mb in hotspots, while closer to 0 in intervening regions, which thus represent regions of limited haplotype diversity and high LD. The map can therefore be used to define haplotype blocks/LD blocks as regions flanked by recombination hotspots. As used herein, the terms “haplotype block” or “LD block” includes blocks defined by any of the above described characteristics, or other alternative methods used by the person skilled in the art to define such regions.
Haplotype blocks (LD blocks) can be used to map associations between phenotype and haplotype status, using single markers or haplotypes comprising a plurality of markers. The main haplotypes can be identified in each haplotype block, and then a set of “tagging” SNPs or markers (the smallest set of SNPs or markers needed to distinguish among the haplotypes) can then be identified. These tagging SNPs or markers can then be used in assessment of samples from groups of individuals, in order to identify association between phenotype and haplotype. 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, it is likely 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, and as such are useful for use in the methods and kits of the present invention. One or more causative (functional) variants or mutations may reside within the region found to be associating to the disease or trait. Such variants 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 and/or haplotypes of the invention, as described herein, may be used as surrogate markers. The surrogate markers have in one embodiment relative risk (RR) and/or odds ratio (OR) values smaller than for the markers or haplotypes initially found to be associating with the disease, as described herein. 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, as described herein. An example of such an embodiment would be a rare, or relatively rare (such as <10% allelic population frequency) variant in LD with a more common variant (>10% population frequency) initially found to be associating with the disease, such as the variants described herein. Identifying and using such markers for detecting the association discovered by the inventors as described herein 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 linkage region, for example, association of all possible combinations of genotyped markers is studied, provided those markers span a practical region. The combined patient and control groups can be randomly divided into two sets, equal in size to the original group of patients and controls. The marker and haplotype analysis is then repeated and the most significant p-value registered is determined. This randomization scheme can be repeated, for example, over 100 times to construct an empirical distribution of p-values. In a preferred embodiment, a p-value of <0.05 is indicative of a significant marker and/or haplotype association.
Haplotype Analysis
One general approach to haplotype analysis involves using likelihood-based inference applied to NEsted MOdels (Gretarsdottir S., et al., Nat. Genet. 35:131-38 (2003)). The method is implemented in the program NEMO, which allows for many polymorphic markers, SNPs and microsatellites. The method and software are specifically designed for case-control studies where the purpose is to identify haplotype groups that confer different risks. It is also a tool for studying LD structures. In NEMO, maximum likelihood estimates, likelihood ratios and p-values are calculated directly, with the aid of the EM algorithm, for the observed data treating it as a missing-data problem.
Even though likelihood ratio tests based on likelihoods computed directly for the observed data, which have captured the information loss due to uncertainty in phase and missing genotypes, can be relied on to give valid p-values, it would still be of interest to know how much information had been lost due to the information being incomplete. The information measure for haplotype analysis is described in Nicolae and Kong (Technical Report 537, Department of Statistics, University of Statistics, University of Chicago; Biometrics, 60 (2):368-75 (2004)) as a natural extension of information measures defined for linkage analysis, and is implemented in NEMO.
For single marker association to a disease, the Fisher exact test can be used to calculate two-sided p-values for each individual allele. Usually, all p-values are presented unadjusted for multiple comparisons unless specifically indicated. The presented frequencies (for microsatellites, SNPs and haplotypes) are allelic frequencies as opposed to carrier frequencies. To minimize any bias due the relatedness of the patients who were recruited as families for the linkage analysis, first and second-degree relatives can be eliminated from the patient list. Furthermore, the test can be repeated for association correcting for any remaining relatedness among the patients, by extending a variance adjustment procedure described in Risch, N. & Teng, J. (Genome Res., 8:1273-1288 (1998)) for sibships so that it can be applied to general familial relationships, and present both adjusted and unadjusted p-values for comparison. The differences are in general very small as expected. To assess the significance of single-marker association corrected for multiple testing we can carry out a randomization test using the same genotype data. Cohorts of patients and controls can be randomized and the association analysis redone multiple times (e.g., up to 500,000 times) and the p-value is the fraction of replications that produced a p-value for some marker allele that is lower than or equal to the p-value we observed using the original patient and control cohorts.
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, hi and hj, 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.
Linkage Disequilibrium Using NEMO
LD between pairs of markers can be calculated using the standard definition of D′ and r2 (Lewontin, R., Genetics 49:49-67 (1964); Hill, W. G. & Robertson, A. Theor. Appl. Genet. 22:226-231 (1968)). Using NEMO, frequencies of the two marker allele combinations are estimated by maximum likelihood and deviation from linkage equilibrium is evaluated by a likelihood ratio test. The definitions of D′ and r2 are extended to include microsatellites by averaging over the values for all possible allele combination of the two markers weighted by the marginal allele probabilities.
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.
As described herein, certain polymorphic markers and haplotypes comprising such markers are found to be useful for risk assessment of prostate cancer. Risk assessment can involve the use of the markers for diagnosing a susceptibility to prostate cancer. Particular alleles of polymorphic markers are found more frequently in individuals with prostate cancer, than in individuals without diagnosis of prostate cancer. Therefore, these marker alleles have predictive value for detecting prostate cancer, or a susceptibility to prostate cancer, in an individual. Tagging markers within haplotype blocks or LD blocks comprising at-risk markers, as described herein, such as the markers of the present invention, can be used as surrogates for other markers and/or haplotypes within the haplotype block or LD block. Markers with values of r2 equal to 1 are perfect surrogates for the at-risk 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. 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 present invention encompasses the assessment of such surrogate markers for the markers as disclosed herein. Such markers are annotated, mapped and listed in public databases, as well known to the skilled person, or can alternatively be readily identified by sequencing the region or a part of the region identified by the markers of the present invention in a group of individuals, and identify polymorphisms in the resulting group of sequences. As a consequence, the person skilled in the art can readily and without undue experimentation genotype surrogate markers in linkage disequilibrium with the markers and/or haplotypes as described herein. The tagging or surrogate markers in LD with the at-risk variants detected, also have predictive value for detecting association to prostate cancer, or a susceptibility to prostate cancer, in an individual. These tagging or surrogate markers that are in LD with the markers of the present invention can also include other markers that distinguish among haplotypes, as these similarly have predictive value for detecting susceptibility to prostate cancer.
The present invention can in certain embodiments be practiced by assessing a sample comprising genomic DNA from an individual for the presence of variants described herein to be associated with prostate 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 further described herein, and based on the outcome of such assessment, determine whether the individual from whom the sample is derived is at increased or decreased risk (increased or decreased susceptibility) of prostate cancer. Alternatively, the invention can be practiced utilizing a dataset comprising information about the genotype status of at least one polymorphic marker described herein to be associated with prostate cancer (or markers in linkage disequilibrium with at least one marker shown herein to be associated with prostate cancer). In other words, a dataset containing information about such genetic status, for example in the form of genotype counts at a certain polymorphic marker, or a plurality of markers (e.g., an indication of the presence or absence of certain at-risk alleles), or actual genotypes for one or more markers, can be queried for the presence or absence of certain at-risk alleles at certain polymorphic markers shown by the present inventors to be associated with prostate cancer. A positive result for a variant (e.g., marker allele) associated with prostate cancer, as shown herein, is indicative of the individual from which the dataset is derived is at increased susceptibility (increased risk) of prostate cancer.
In certain embodiments of the invention, a polymorphic marker is correlated to prostate cancer by referencing genotype data for the polymorphic marker to a look-up table that comprises correlations between at least one allele of the polymorphism and prostate cancer. In some embodiments, the table comprises a correlation for one polymorphism. In other embodiments, the table comprises a correlation for a plurality of polymorphisms. In both scenarios, by referencing to a look-up table that gives an indication of a correlation between a marker and prostate cancer, a risk for prostate cancer, or a susceptibility to prostate cancer, can be identified in the individual from whom the sample is derived. In some embodiments, the correlation is reported as a statistical measure. The statistical measure may be reported as a risk measure, such as a relative risk (RR), an absolute risk (AR) or an odds ratio (OR).
The markers and haplotypes of the invention, e.g., the markers presented in Tables 7-11, may be useful for risk assessment and diagnostic purposes, either alone or in combination. Thus, even in cases where the increase in risk by individual markers is relatively modest, e.g. on the order of 10-30%, the association may have significant implications. Thus, relatively common variants may have significant contribution to the overall risk (Population Attributable Risk is high), or combination of markers can be used to define groups of individual who, based on the combined risk of the markers, is at significant combined risk of developing the disease
Thus, in one embodiment of the invention, a plurality of variants (genetic markers, biomarkers 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 prostate 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 combined or joint risk analyses, 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.
As described in the above, the haplotype block structure of the human genome has the effect that a large number of variants (markers and/or haplotypes) in linkage disequilibrium with the variant originally associated with a disease or trait may be used as surrogate markers for assessing association to the disease or trait. The number of such surrogate markers will depend on factors such as the historical recombination rate in the region, the mutational frequency in the region (i.e., the number of polymorphic sites or markers in the region), and the extent of LD (size of the LD block) in the region. These markers are usually located within the physical boundaries of the LD block or haplotype block in question as defined using the methods described herein, or by other methods known to the person skilled in the art. However, sometimes marker and haplotype association is found to extend beyond the physical boundaries of the haplotype block as defined. Such markers and/or haplotypes may in those cases be also used as surrogate markers and/or haplotypes for the markers and/or haplotypes physically residing within the haplotype block as defined. As a consequence, markers and haplotypes in LD (typically characterized by r2 greater than 0.1, such as r2 greater than 0.2, including r2 greater than 0.3, also including r2 greater than 0.4) with the markers and haplotypes of the present invention are also within the scope of the invention, even if they are physically located beyond the boundaries of the haplotype block as defined herein.
For the SNP markers described herein, the opposite allele to the allele found to be in excess in patients (at-risk allele) is found in decreased frequency in prostate cancer. These markers and haplotypes in LD and/or comprising such markers, are thus protective for prostate cancer, i.e. they confer a decreased risk or susceptibility of individuals carrying these markers and/or haplotypes developing prostate cancer.
Certain variants of the present invention, including certain haplotypes, comprise, in some cases, a combination of various genetic markers, e.g., SNPs and microsatellites. Detecting haplotypes can be accomplished by methods known in the art and/or described herein for detecting sequences at polymorphic sites. Furthermore, correlation between certain haplotypes or sets of markers and disease phenotype can be verified using standard techniques. A representative example of a simple test for correlation would be a Fisher-exact test on a two by two table.
In specific embodiments, a marker allele or haplotype found to be associated with prostate cancer, (e.g., marker alleles as listed in Tables 7-11, e.g., marker alleles as listed in tables 1-6) is one in which the marker allele or haplotype is more frequently present in an individual at risk for prostate cancer (affected), compared to the frequency of its presence in a healthy individual (control), wherein the presence of the marker allele or haplotype is indicative of prostate cancer or a susceptibility to prostate cancer. In other embodiments, at-risk markers in linkage disequilibrium with one or more markers shown herein to be associated with prostate cancer (e.g., marker alleles as listed in Tables 7-11) are tagging markers that are more frequently present in an individual at risk for prostate cancer (affected), compared to the frequency of their presence in a healthy individual (control), wherein the presence of the tagging markers is indicative of increased susceptibility to prostate cancer. In a further embodiment, at-risk markers alleles (i.e. conferring increased susceptibility) in linkage disequilibrium with one or more markers found to be associated with prostate cancer (e.g., marker alleles as listed in Tables 7-11), are markers comprising one or more allele that is more frequently present in an individual at risk for prostate cancer, compared to the frequency of their presence in a healthy individual (control), wherein the presence of the markers is indicative of increased susceptibility to prostate cancer.
Study Population
In a general sense, the methods and kits of the invention can be utilized from samples containing genomic DNA from any source, i.e. any individual. 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 therefrom. The present invention also provides for assessing markers and/or haplotypes 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 prostate cancer, 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 cancer, e.g., prostate cancer, or related diseases, previous diagnosis of prostate cancer, family history of prostate cancer).
The invention provides for embodiments that include individuals from specific age subgroups, such as those over the age of 40, over age of 45, or over age of 50, 55, 60, 65, 70, 75, 80, or 85. Other embodiments of the invention pertain to other age groups, such as individuals aged less than 85, such as less than age 80, less than age 75, or less than age 70, 65, 60, 55, 50, 45, 40, 35, or age 30. Other embodiments relate to individuals with age at onset of the disease in any of the age ranges described in the above. In one such embodiment, the invention pertains to individuals with age at onset of prostate cancer of less than 65 years. In another such embodiment, the invention pertains to individuals with age at onset of less than 60 years. It is also contemplated that a range of ages may be relevant in certain embodiments, such as age at onset at more than age 45 but less than age 60. Other age ranges are however also contemplated, including all age ranges bracketed by the age values listed in the above. The invention furthermore relates to individuals of either sex, males or females.
The Icelandic population is a Caucasian population of Northern European ancestry. A large number of studies reporting results of genetic linkage and association in the Icelandic population have been published in the last few years. Many of those studies show replication of variants, originally identified in the Icelandic population as being associating with a particular disease, in other populations (Stacey, S. N., et al., Nat. Genet. May 27, 2007 (Epub ahead of print; 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); 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.
The markers of the present invention found to be associated with prostate cancer are believed to show similar association in other human populations, as illustrated by the replication data shown in the Examples herein (Tables 1, 4a and 4b). 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, Portugues, Italian, Polish, Bulgarian, Slavic, Serbian, Bosnian, Chech, Greek and Turkish populations. The invention furthermore in other embodiments can be practiced in specific human populations that include Bantu, Mandenk, Yoruba, San, Mbuti Pygmy, Orcadian, Adygel, Russian, Sardinian, Tuscan, Mozabite, Bedouin, Druze, Palestinian, Balochi, Brahui, Makrani, Sindhi, Pathan, Burusho, Hazara, Uygur, Kalash, Han, Dai, Daur, Hezhen, Lahu, Miao, Oroqen, She, Tujia, Tu, Xibo, Yi, Mongolan, Naxi, Cambodian, Japanese, Yakut, Melanesian, Papuan, Karitianan, Surui, Colmbian, Maya and Pima.
In one preferred embodiment, the invention relates to populations that include black African ancestry such as populations comprising persons of African descent or lineage. Black African ancestry may be determined by self reporting as African-Americans, Afro-Americans, Black Americans, being a member of the black race or being a member of the negro race. For example, African Americans or Black Americans are those persons living in North America and having origins in any of the black racial groups of Africa. In another example, self-reported persons of black African ancestry may have at least one parent of black African ancestry or at least one grandparent of black African ancestry. In another 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.
Utility of Genetic Testing
The person skilled in the art will appreciate and understand that the variants described herein in general do not, by themselves, provide an absolute identification of individuals who will a priori develop prostate cancer. The variants described herein do however indicate increased and/or decreased likelihood that individuals carrying the at-risk or protective variants of the invention will ultimately develop prostate cancer. This information is however extremely valuable in itself, as outlined in more detail in the below, as it can be used to, for example, initiate preventive measures at an early stage, perform regular physical and/or mental exams to monitor the progress and/or appearance of symptoms, or to schedule exams at a regular interval to identify early symptoms, so as to be able to apply treatment at an early stage.
The knowledge of a genetic variant that confers a risk of developing cancer offers the opportunity to apply a genetic-test to distinguish between individuals with increased risk of developing the disease (i.e. carriers of the at-risk variant) and those with decreased risk of developing the disease (i.e. carriers of the protective variant, or non-carriers of the at-risk variant). The core values of genetic testing, for individuals belonging to both of the above mentioned groups, are the possibilities of being able to diagnose the disease at an early stage and provide information to the clinician about prognosis/aggressiveness of the disease in order to be able to apply the most appropriate treatment. For example, the application of a genetic test for prostate cancer (including aggressive or high Gleason grade prostate cancer, and less aggressive or low Gleason grade prostate cancer)) can provide an opportunity for the detection of the disease at an earlier stage which may lead to the application of therapeutic measures at an earlier stage, and thus can minimize the deleterious effects of the symptoms and serious health consequences conferred by cancer. Some advantages of genetic tests for cancer include:
1. To Aid Early Detection
The application of a genetic test for prostate cancer can provide an opportunity for the detection of the disease at an earlier stage which leads to higher cure rates, if found locally, and increases survival rates by minimizing regional and distant spread of the tumor. For prostate cancer, a genetic test will most likely increase the sensitivity and specificity of the already generally applied Prostate Specific Antigen (PSA) test and Digital Rectal Examination (DRE). This can lead to lower rates of false positives (thus minimize unnecessary procedures such as needle biopsies) and false negatives (thus increasing detection of occult disease and minimizing morbidity and mortality due to PCA).
2. To Determine Aggressiveness
Genetic testing can provide information about pre-diagnostic prognostic indicators and enable the identification of individuals at high or low risk for aggressive tumor types that can lead to modification in screening strategies. For example, an individual determined to be a carrier of a high risk allele for the development of aggressive prostate cancer will likely undergo more frequent PSA testing, examination and have a lower threshold for needle biopsy in the presence of an abnormal PSA value.
Furthermore, identifying individuals that are carriers of high or low risk alleles for aggressive tumor types will lead to modification in treatment strategies. For example, if prostate cancer is diagnosed in an individual that is a carrier of an allele that confers increased risk of developing an aggressive form of prostate cancer, then the clinician would likely advise a more aggressive treatment strategy such as a prostatectomy instead of a less aggressive treatment strategy.
As is known in the art, Prostate Specific Antigen (PSA) is a protein that is secreted by the epithelial cells of the prostate gland, including cancer cells. An elevated level in the blood indicates an abnormal condition of the prostate, either benign or malignant. PSA is used to detect potential problems in the prostate gland and to follow the progress of prostate cancer therapy. PSA levels above 4 ng/ml are indicative of the presence of prostate cancer (although as known in the art and described herein, the test is neither very specific nor sensitive).
In one embodiment, the method of the invention is performed in combination with (either prior to, concurrently or after) a PSA assay. In a particular embodiment, the presence of a marker or haplotype, in conjunction with the subject having a PSA level greater than 4 ng/ml, is indicative of a more aggressive prostate cancer and/or a worse prognosis. As described herein, particular markers and haplotypes are associated with high Gleason (i.e., more aggressive) prostate cancer. In another embodiment, the presence of a marker or haplotype, in a patient who has a normal PSA level (e.g., less than 4 ng/ml), is indicative of a high Gleason (i.e., more aggressive) prostate cancer and/or a worse prognosis. A “worse prognosis” or “bad prognosis” occurs when it is more likely that the cancer will grow beyond the boundaries of the prostate gland, metastasize, escape therapy and/or kill the host.
In one embodiment, the presence of a marker or haplotype is indicative of a predisposition to a somatic rearrangement (e.g., one or more of an amplification, a translocation, an insertion and/or deletion) in a tumor or its precursor. The somatic rearrangement itself may subsequently lead to a more aggressive form of prostate cancer (e.g., a higher histologic grade, as reflected by a higher Gleason score or higher stage at diagnosis, an increased progression of prostate cancer (e.g., to a higher stage), a worse outcome (e.g., in terms of morbidity, complications or death)). As is known in the art, the Gleason grade is a widely used method for classifying prostate cancer tissue for the degree of loss of the normal glandular architecture (size, shape and differentiation of glands). A grade from 1-5 is assigned successively to each of the two most predominant tissue patterns present in the examined tissue sample and are added together to produce the total or combined Gleason grade (scale of 2-10). High numbers indicate poor differentiation and therefore more aggressive cancer.
Aggressive prostate cancer is cancer that grows beyond the prostate, metastasizes and eventually kills the patient. As described herein, one surrogate measure of aggressiveness is a high combined Gleason grade. The higher the grade on a scale of 2-10 the more likely it is that a patient has aggressive disease.
As used herein and unless noted differently, the term “stage” is used to define the size and physical extent of a cancer (e.g., prostate cancer). One method of staging various cancers is the TNM method, wherein in the TNM acronym, T stands for tumor size and invasiveness (e.g., the primary tumor in the prostate); N relates to nodal involvement (e.g., prostate cancer that has spread to lymph nodes); and M indicates the presence or absence of metastates (spread to a distant site).
Methods
Methods for risk assessment of prostate cancer are described herein and are encompassed by the invention. The invention also encompasses methods of assessing an individual for probability of response to a therapeutic agent for prostate cancer, methods for predicting the effectiveness of a therapeutic agent for prostate cancer, nucleic acids, polypeptides and antibodies and computer-implemented functions. Kits for assaying a sample from a subject to detect susceptibility to prostate cancer are also encompassed by the invention.
Diagnostic and Screening Methods
In certain embodiments, the present invention pertains to methods of diagnosing, or aiding in the diagnosis of, prostate cancer or a susceptibility to prostate cancer, by detecting particular alleles at genetic markers that appear more frequently in prostate cancer subjects or subjects who are susceptible to prostate cancer. In a particular embodiment, the invention is a method of diagnosing a susceptibility to prostate cancer by detecting at least one allele of at least one polymorphic marker (e.g., the markers described herein). The present invention describes methods whereby detection of particular alleles of particular markers or haplotypes is indicative of a susceptibility to prostate cancer. Such prognostic or predictive assays can also be used to determine prophylactic treatment of a subject prior to the onset of symptoms of prostate 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. 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, made available to the individual can be compared to information from the public literature about disease or trait (e.g., prostate cancer) risk associated with various SNPs. The diagnostic application of disease-associated alleles as described herein, can thus be performed either by the individual, through analysis of his/her genotype data, or by a health professional based on results of a clinical test. In other words, the diagnosis or assessment of a susceptibility based on genetic risk can be made by health professionals, genetic counselors or by the layman, based on information about his/her genotype and publications on various risk factors. 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 addition, in certain other embodiments, the present invention pertains to methods of diagnosing, or aiding in the diagnosis of, a decreased susceptibility to prostate cancer, by detecting particular genetic marker alleles or haplotypes that appear less frequently in prostate cancer patients than in individual not diagnosed with prostate cancer or in the general population.
As described and exemplified herein, particular marker alleles or haplotypes (e.g. markers located within LD block C02, LD block C04a, TCF2, LD block C17b and LD block C0Xa, the markers and haplotypes as listed in Tables 12-16, and markers in linkage disequilibrium therewith, e.g., the markers as listed in Tables 7-11) are associated with prostate cancer (e.g., aggressive prostate cancer). In one embodiment, the marker allele or haplotype is one that confers a significant risk or susceptibility to prostate cancer. In another embodiment, the invention relates to a method of diagnosing a susceptibility to prostate cancer in a human individual, the method 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, wherein the at least one polymorphic marker is selected from the group consisting of the polymorphic markers located within LD block C02, LD block C04a, TCF2, LD block C17b and LD block C0Xa, the markers as listed in Tables 7-11, and markers in linkage disequilibrium therewith, and/or the markers as listed in Tables 11-15. In another embodiment, the invention pertains to methods of diagnosing a susceptibility to prostate cancer in a human individual, by screening for at least one marker allele or haplotype, e.g. markers located within LD block C02, LD block C04a, TCF2, LD block C17b or LD block C0Xa, and markers in linkage disequilibrium therewith. In another embodiment, the marker allele or haplotype is more frequently present in a subject having, or who is susceptible to, prostate cancer (affected), as compared to the frequency of its presence in a healthy subject (control, such as population controls). In another embodiment, the invention relates to a method of determining a susceptibility to prostate cancer in a human individual, the method 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, wherein the at least one polymorphic marker is selected from the group consisting of markers rs3923603 (SEQ ID NO:1), rs4430796 (SEQ ID NO:2), rs7501939 (SEQ ID NO:3), rs1859962 (SEQ ID NO:4), D17S1350 (SEQ ID NO:5), rs5945572 (SEQ ID NO:6), rs5945605 (SEQ ID NO:7), rs2710646 (SEQ ID NO:8), rs3760511 (SEQ ID NO:56), rs7214479 (SEQ ID NO:134), rs6501445 (SEQ ID NO:146), rs983085 (SEQ ID NO:150), rs5945605 (SEQ ID NO:178) and rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In certain embodiments, the significance of association of the at least one marker allele or haplotype is characterized by a p value<0.05. In other embodiments, the significance of association is characterized by smaller p-values, such as <0.01, <0.001, <0.0001, <0.00001, <0.000001, <0.0000001, <0.00000001 or <0.000000001.
In these embodiments, the presence of the at least one marker allele or haplotype is indicative of a susceptibility to prostate cancer. These diagnostic methods involve detecting the presence or absence of at least one marker allele or haplotype that is associated with prostate cancer. The haplotypes described herein include combinations of alleles at various genetic markers (e.g., SNPs, microsatellites). The detection of the particular genetic marker alleles that make up the particular haplotypes can be performed by a variety of methods described herein and/or known in the art. For example, genetic markers can be detected at the nucleic acid level (e.g., by direct nucleotide sequencing or by other means known to the skilled in the art) or at the amino acid level if the genetic marker affects the coding sequence of a protein encoded by a prostate cancer-associated nucleic acid (e.g., by protein sequencing or by immunoassays using antibodies that recognize such a protein). The marker alleles or haplotypes of the present invention correspond to fragments of a genomic DNA sequence associated with prostate cancer. Such fragments encompass the DNA sequence of the polymorphic marker or haplotype in question, but may also include DNA segments in strong LD (linkage disequilibrium) with the marker or haplotype (for example, as determined by a value of r2 greater than 0.2 and/or |D′|>0.8).
In one embodiment, diagnosis of a susceptibility to prostate cancer can be accomplished using hybridization methods, such as Southern analysis, Northern analysis, and/or in situ hybridizations (see Current Protocols in Molecular Biology, Ausubel, F. et al., eds., John Wiley & Sons, including all supplements). A biological sample from a test subject or individual (a “test sample”) of genomic DNA, RNA, or cDNA is obtained from a subject suspected of having, being susceptible to, or predisposed for prostate cancer (the “test subject”). The subject can be an adult, child, or fetus. The test sample can be from any source that contains genomic DNA, such 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. A test sample of DNA from fetal cells or tissue can be obtained by appropriate methods, such as by amniocentesis or chorionic villus sampling. The DNA, RNA, or cDNA sample is then examined. 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 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. In one embodiment, a haplotype can be indicated by a single nucleic acid probe that is specific for the specific haplotype (i.e., hybridizes specifically to a DNA strand comprising the specific marker alleles characteristic of the haplotype). A sequence-specific probe can be directed to hybridize to genomic DNA, RNA, or cDNA. A “nucleic acid probe”, as used herein, can be a DNA probe or an RNA probe that hybridizes to a complementary sequence. One of skill in the art would know how to design such a probe so that sequence specific hybridization will occur only if a particular allele is present in a genomic sequence from a test sample.
To diagnose a susceptibility to prostate cancer, a hybridization sample is formed by contacting the test sample containing an prostate cancer-associated nucleic acid, 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. For example, the nucleic acid probe can comprise all or a portion of the nucleotide sequence of LD block C02, LD block C04a, TCF2, LD block C17b and LD block C0Xa, as described herein, or all or a portion of the nucleotide sequences set forth in SEQ ID NO:1-362 herein, optionally comprising at least one allele of a marker described herein, or at least one haplotype described herein (e.g., the markers and haplotypes as listed in Tables 7-11, and markers in linkage disequilibrium therewith), or the probe can be the complementary sequence of such a sequence. In a particular embodiment, the nucleic acid probe is a portion of the nucleotide sequence of LD block C02, LD block C04a, TCF2, LD block C17b and LD block C0Xa, or all or a portion of the nucleotide sequences set forth in SEQ ID NO:1-362, as described herein, optionally comprising at least one allele of a marker described herein (e.g., the markers and haplotypes as listed in Tables 7-11, and markers in linkage disequilibrium therewith), or at least one allele contained in the haplotypes described herein, or the probe can be the complementary sequence of such a sequence. Other suitable probes for use in the diagnostic assays of the invention are described herein. Hybridization can be performed by methods well known to the person skilled in the art (see, e.g., Current Protocols in Molecular Biology, Ausubel, F. et al., eds., John Wiley & Sons, including all supplements). In one embodiment, hybridization refers to specific hybridization, i.e., hybridization with no mismatches (exact hybridization). In one embodiment, the hybridization conditions for specific hybridization are high stringency.
Specific hybridization, if present, is detected using standard methods. If specific hybridization occurs between the nucleic acid probe and the prostate cancer-associated 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 other 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 prostate 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:e128 (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.
In another hybridization method, Northern analysis (see Current Protocols in Molecular Biology, Ausubel, F. et al., eds., John Wiley & Sons, supra) is used to identify the presence of a polymorphism associated with prostate cancer. For Northern analysis, a test sample of RNA is obtained from the subject by appropriate means. As described herein, specific hybridization of a nucleic acid probe to RNA from the subject is indicative of a particular allele complementary to the probe. For representative examples of use of nucleic acid probes, see, for example, U.S. Pat. Nos. 5,288,611 and 4,851,330
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 al., 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 prostate cancer. Hybridization of the PNA probe is thus diagnostic for prostate cancer or a susceptibility to prostate 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 prostate 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 using quantitative PCR (kinetic thermal cycling). This technique can, for example, utilize commercially available technologies, such as TaqMan® (Applied Biosystems, Foster City, Calif.), to allow the identification of polymorphisms and haplotypes. 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 prostate cancer-associated nucleic acid. 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 at polymorphic sites associated with prostate cancer (e.g. the polymorphic markers and haplotypes of Tables 7-11, and markers in linkage disequilibrium therewith). Therefore, in one embodiment, determination of the presence or absence of a particular marker alleles or haplotypes comprises sequence analysis. For example, a test sample of DNA or RNA can be obtained from the test subject. PCR or other appropriate methods can be used to amplify a portion of a prostate cancer-associated nucleic acid, and the presence of a specific allele can then be detected directly by sequencing the polymorphic site (or multiple polymorphic sites) 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 polymorphisms in a prostate cancer-associated nucleic acid (e.g. the polymorphic markers and haplotypes of Tables 7-11 and markers in linkage disequilibrium therewith). 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 oligonucleotide arrays, also described as “GenechipsTM,” have been generally described in the art (see, e.g., U.S. Pat. No. 5,143,854, PCT Patent Publication Nos. WO 90/15070 and 92/10092). 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 (Fodor, S. et al., Science, 251:767-773 (1991); Pirrung et al., U.S. Pat. No. 5,143,854 (see also published PCT Application No. WO 90/15070); and Fodor. S. et al., published PCT Application No. WO 92/10092 and U.S. Pat. No. 5,424,186, the entire teachings of each of which are incorporated by reference herein). Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261; the entire teachings of which are incorporated by reference herein. In another example, linear arrays can be utilized. Additional descriptions of use of oligonucleotide arrays for detection of polymorphisms can be found, for example, in U.S. Pat. Nos. 5,858,659 and 5,837,832, the entire teachings of both of which are incorporated by reference herein.
Other methods of nucleic acid analysis can be used to detect a particular allele at a polymorphic site associated with prostate cancer (e.g. the polymorphic markers and haplotypes of Tables 7-11, and markers in linkage disequilibrium therewith). 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. Pat. 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 embodiment of the Invention, diagnosis or determination of prostate cancer or a susceptibility to prostate cancer can be made by examining expression and/or composition of a polypeptide encoded by prostate cancer-associated nucleic acid in those instances where the genetic marker(s) or haplotype(s) of the present invention result in a change in the composition or expression of the polypeptide. Thus, diagnosis of a susceptibility to prostate cancer can be made by examining expression and/or composition of one of these polypeptides, or another polypeptide encoded by a prostate cancer-associated nucleic acid, in those instances where the genetic marker or haplotype of the present invention results in a change in the composition or expression of the polypeptide. The haplotypes and markers of the present invention that show association to prostate cancer may play a role through their effect on one or more of these nearby genes. Possible mechanisms affecting these 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.
Thus, in another embodiment, the variants (markers or haplotypes) of the invention showing association to prostate cancer affect the expression of a nearby gene. It is well known that regulatory element affecting gene expression may be located far away, even as far as tenths or even hundreds of kilobases away, from the promoter region of a gene. By assaying for the presence or absence of at least one allele of at least one polymorphic marker of the present invention, it is thus possible to assess the expression level of such nearby genes. It is thus contemplated that the detection of the markers or haplotypes of the present invention can be used for assessing expression for one or more of such genes. In one such embodiment, the gene is the TCF2 gene.
A variety of methods can be used to make such a detection, including enzyme linked immunosorbent assays (ELISA), Western blots, immunoprecipitations and immunofluorescence. A test sample from a subject is assessed for the presence of an alteration in the expression and/or an alteration in composition of the polypeptide encoded by a prostate cancer-associated nucleic acid. An alteration in expression of a polypeptide encoded by a prostate cancer-associated nucleic acid 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 prostate cancer-associated nucleic acid is 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 prostate cancer is made by detecting a particular splicing variant encoded by a prostate cancer-associated nucleic acid, or a particular pattern of splicing variants.
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 polypeptide encoded by a prostate cancer-associated nucleic acid 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, prostate cancer (e.g., a subject that does not possess a marker allele or haplotype 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 prostate 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 prostate cancer-associated nucleic acid 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 prostate cancer-associated nucleic acid 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 one embodiment of this method, the level or amount of polypeptide encoded by a prostate cancer-associated nucleic acid in a test sample is compared with the level or amount of the polypeptide encoded by a prostate cancer-associated nucleic acid in a control sample. A level or amount of the polypeptide in the test sample that is higher or lower than the level or amount of the polypeptide in the control sample, such that the difference is statistically significant, is indicative of an alteration in the expression of the polypeptide encoded by the prostate cancer-associated nucleic acid, and is diagnostic for a particular allele or haplotype responsible for causing the difference in expression. Alternatively, the composition of the polypeptide encoded by a prostate cancer-associated nucleic acid in a test sample is compared with the composition of the polypeptide encoded by a prostate cancer-associated nucleic acid in a control sample. In another embodiment, both the level or amount and the composition of the polypeptide can be assessed in the test sample and in the control sample.
In another embodiment, the diagnosis of a susceptibility to prostate cancer is made by detecting at least one prostate cancer-associated marker allele or haplotype (e.g., the polymorphic markers and haplotypes of Tables 1-10 and markers in linkage disequilibrium therewith) in combination with an additional protein-based, RNA-based or DNA-based assay. The methods of the invention can also be used in combination with an analysis of a subject's family history and risk factors (e.g., environmental risk factors, lifestyle risk factors).
Kits
Kits useful in the methods of diagnosis comprise components useful in any of the methods described herein, including for example, hybridization probes, restriction enzymes (e.g., for RFLP analysis), allele-specific oligonucleotides, antibodies that bind to an altered polypeptide encoded by prostate cancer-associated nucleic acid (e.g., antibodies that bind to a polypeptide encoded by a genomic segment comprising at least one polymorphic marker and/or haplotype of the present invention) or to a non-altered (native) polypeptide encoded by a prostate cancer-associated nucleic acid, means for amplification of a prostate cancer-associated nucleic acid, including amplification of markers associated with prostate cancer, as described herein, means for analyzing the nucleic acid sequence of prostate cancer-associated nucleic acid, means for analyzing the amino acid sequence of a polypeptide encoded by a prostate cancer-associated nucleic acid, etc. The kits can for example include necessary buffers, nucleic acid primers for amplifying nucleic acids of the invention (e.g., one or more of the polymorphic markers as described herein), and reagents for allele-specific detection of the fragments amplified using such primers and necessary enzymes (e.g., DNA polymerase). Additionally, kits can provide reagents for assays to be used in combination with the methods of the present invention, e.g., reagents for use with other prostate cancer diagnostic assays.
In one embodiment, the invention is a kit for assaying a sample from a subject to detect prostate cancer or a susceptibility to prostate 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 (e.g., the polymorphic markers and haplotypes of Tables 7-11 and markers in linkage disequilibrium therewith). 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 one polymorphism, wherein the polymorphism is selected from the group consisting of the polymorphisms as defined in and polymorphic 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 acids flanking polymorphisms (e.g., SNPs or microsatellites) that are indicative of prostate cancer. In another embodiment, the kit comprises one or more labeled nucleic acids capable of detecting one or more specific polymorphic markers or haplotypes associated with prostate cancer, and reagents for detection of the label. Suitable labels include, e.g., a radioisotope, a fluorescent label, an enzyme label, an enzyme co-factor label, a magnetic label, a spin label, an epitope label.
In particular embodiments, the polymorphic marker or haplotype to be detected by the reagents of the kit comprises one or more markers, two or more markers, three or more markers, four or more markers or five or more markers selected from the group consisting of the markers set forth in any one of the Tables 7-15 herein. In another embodiment, the marker or haplotype to be detected comprises at least one of the markers set forth in any of the Tables 7-11 herein. In another embodiment, the marker or haplotype to be detected comprises at least one of the markers rs3923603 (SEQ ID NO:1), rs4430796 (SEQ ID NO:2), rs7501939 (SEQ ID NO:3), rs1859962 (SEQ ID NO:4), D17S1350 (SEQ ID NO:5), rs5945572 (SEQ ID NO:6), rs5945605 (SEQ ID NO:7), rs2710646 (SEQ ID NO:8), rs3760511 (SEQ ID NO:56), rs7214479 (SEQ ID NO:134), rs6501445 (SEQ ID NO:146), rs983085 (SEQ ID NO:150), rs5945605 (SEQ ID NO:178) and rs721048 (SEQ ID NO:344), or markers in linkage disequilibrium therewith.
The kit of the invention in one embodiment comprises at least one oligonucleotide probe that is from 5-100 nucleotides in length and specifically hybridizes (under stringent conditions) to all or a portion of the TCF2 gene, the LD block C02, the LD block C17b, the LD block C0Xa or the LD block C04a, and wherein at least one of said at least one oligonucleotide probes comprises a polymorphism selected from the group of polymorphisms listed in Tables 7-11, and polymorphisms in linkage disequilibrium therewith. In one embodiment, the kit further comprises at least one oligonucleotide pair for amplifying a genomic fragment comprising at least one polymorphism as set forth in SEQ ID NO:1-362, the segment being from 40-500 nucleotides in length. In another embodiment, the oligonucleotide probe comprises a detectable label. In another embodiment, the kit comprises two oligonucleotide probes, wherein one of said probes comprises at least one detectable label and a polymorphism as listed in any of the Tables 7-11. In another embodiment, one of said probes comprises one detectable label, a quencher and a polymorphism as listed in any of the Tables 7-11.
In another embodiment, the invention relates to a kit for assessing susceptibility to prostate cancer, the kit comprising at least one oligonucleotide, from 15 to 200 nucleotides in length, that specifically hybridizes to a nucleotide molecule comprising at least one polymorphic marker as set forth in any of the Tables 7-11, and polymorphic markers in linkage disequilibrium therewith.
In one preferred embodiment, the kit for detecting the markers of the invention comprises a detection oligonucleotide probe, that hybridizes to a segment of template DNA containing a SNP polymorphisms to be detected, an enhancer oligonucleotide probe and an endonuclease. As explained in the above, the detection oligonucleotide probe comprises 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:e128 (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, 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.
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.
In one of such embodiments, the presence of the marker or haplotype is indicative of a susceptibility (increased susceptibility or decreased susceptibility) to prostate cancer. In another embodiment, the presence of the marker or haplotype is indicative of response to a prostate cancer therapeutic agent. In another embodiment, the presence of the marker or haplotype is indicative of prognosis of prostate cancer. In yet another embodiment, the presence of the marker or haplotype is indicative of progress of treatment of prostate cancer. Such treatment may include intervention by surgery, medication or by other means (e.g., lifestyle changes).
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.
Therapeutic Agents
Variants of the present invention (e.g., the markers and/or haplotypes of the invention, e.g., the markers listed in Tables 7-11) can also be used to identify novel therapeutic targets for prostate cancer. For example, genes containing, or in linkage disequilibrium with, variants (markers and/or haplotypes) associated with prostate cancer (e.g., TCF2), 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 prostate cancer, or prevent or delay onset of symptoms associated with prostate 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 of the invention, 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 described and reviewed in Antisense Drug Technology: Principles, Strategies, and Applications, Crooke, ed., Marcel Dekker Inc., New York (2001). In general, antisense nucleic acid molecules are designed to be complementary to a region of mRNA expressed by a gene, so that the antisense molecule hybridizes to the mRNA, thus blocking translation of the mRNA into protein. 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 Layery et al., Curr. Opin. Drug Discov. Devel. 6:561-569 (2003), Stephens et al., Curr. Opin. Mol. Ther. 5:118-122 (2003), Kurreck, Eur. J. Biochem. 270:1628-44 (2003), Dias et al., Mol. Cancer. Ter. 1:347-55 (2002), Chen, Methods Mol. Med. 75:621-636 (2003), Wang et al., Curr. Cancer Drug Targets 1:177-96 (2001), and Bennett, Antisense Nucleic Acid Drug. Dev. 12:215-24 (2002)
The variants described herein can 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 (markers 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 to treat a disease or disorder, such as prostate cancer. 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 of the present invention (e.g., the markers and haplotypes set forth in Tables 7-11) 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′-O-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), Layery, et al., Curr. Opin. Drug Discov. Devel. 6:561-569 (2003), Shi, Trends Genet. 19:9-12 (2003), Shuey et al., Drug Discov. Today 7:1040-46 (2002), McManus et al., Nat. Rev. Genet. 3:737-747 (2002), Xia et al., Nat. Biotechnol. 20:1006-10 (2002), Plasterk et al., curr. Opin. Genet. Dev. 10:562-7 (2000), Bosher et al., Nat. Cell Biol. 2:E31-6 (2000), and Hunter, Curr. Biol. 9:R440-442 (1999).
A genetic defect leading to increased predisposition or risk for development of a disease, including prostate cancer, or a defect causing the disease, may be corrected permanently by administering to a subject carrying the defect a nucleic acid fragment that incorporates a repair sequence that supplies the normal/wild-type nucleotide(s) at the site of the genetic defect. Such site-specific repair sequence may concompass an RNA/DNA oligonucleotide that operates to promote endogenous repair of a subject's genomic DNA. The administration of the repair sequence may be performed by an appropriate vehicle, such as a complex with polyethelenimine, encapsulated in anionic liposomes, a viral vector such as an adenovirus vector, or other pharmaceutical compositions suitable for promoting intracellular uptake of the 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 prostate cancer. Thus, the variants of the invention are useful as targets for the identification and/or development of therapeutic agents. Such methods may include assaying the ability of an agent or compound to modulate the activity and/or expression of a 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. This in turn can be used to identify agents or compounds that inhibit or alter the undesired activity or expression of the encoded nucleic acid product. Assays for performing such experiments 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 prostate 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 of Treatment
As is known in the art, individuals can have differential responses to a particular therapy (e.g., a therapeutic agent or therapeutic method). Pharmacogenomics addresses the issue of how genetic variations (e.g., the variants (markers and/or haplotypes) of the present invention) affect drug response, due to altered drug disposition and/or abnormal or altered action of the drug. Thus, the basis of the differential response may be genetically determined in part. Clinical outcomes due to genetic variations affecting drug response may result in toxicity of the drug in certain individuals (e.g., carriers or non-carriers of the genetic variants of the present invention), or therapeutic failure of the drug. Therefore, the variants of the present invention may determine the manner in which a therapeutic agent and/or method acts on the body, or the way in which the body metabolizes the therapeutic agent. The therapeutic agent is in a preferred embodiment a therapeutic agent for prostate cancer. In certain embodiments, the therapeutic agent is an agent for hormonal therapy, such as an antiandrogen (e.g., flutamide, bicalutamide, nilutamide, cyproterone acetate), an andrenal androgen blocker such as ketoconazole or aminoglutethimide, a GnRH antagonist such as abarelix, a GnRH agonist such as leuprolide, goserelin, triptorelin, or buserelin, or an agent for chemotherapy, such as docetaxel, or a bisphosphonate such as zoledronic acid.
Accordingly, in one embodiment, the presence of a particular allele at a polymorphic site or haplotype is indicative of a different, e.g. a different response rate, to a particular treatment modality. This means that a patient diagnosed with prostate cancer, and carrying a certain allele at a polymorphic or haplotype of the present invention (e.g., the at-risk and protective alleles and/or haplotypes of the invention) would respond better to, or worse to, a specific therapeutic, drug and/or other therapy used to treat the disease. Common treatment modalities for prostate cancer include surgery (prostatectomy), radiation therapy, cryosurgery, and high intensity focused ultrasound (HIFU). Therefore, the presence or absence of the marker allele or haplotype could aid in deciding what treatment should be used for a the patient. For example, for a newly diagnosed patient, the presence of a marker or haplotype of the present invention may be assessed (e.g., through testing DNA derived from a blood sample, as described herein). If the patient is positive for a marker allele or haplotype at (that is, at least one specific allele of the marker, or haplotype, is present), then the physician recommends 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 the disease at an early stage, to select the most appropriate treatment, and provide information to the clinician about prognosis/aggressiveness of the disease in order to be able to apply the most appropriate treatment.
The present invention also relates to methods of monitoring progress or effectiveness of a treatment for prostate cancer, including medication, surgery (prostatectomy), radiation therapy, cryosurgery, and high intensity focused ultrasound (HIFU). This can be done based on the genotype and/or haplotype status of the markers and haplotypes of the present invention, i.e., by assessing the absence or presence of at least one allele of at least one polymorphic marker as disclosed herein, or by monitoring expression of genes that are associated with the variants (markers and haplotypes) of the present invention. The risk gene mRNA or the encoded polypeptide can be measured in a tissue sample (e.g., a peripheral blood sample, or a biopsy sample). Expression levels and/or mRNA levels can thus be determined before and during treatment to monitor its effectiveness. Alternatively, or concomitantly, the genotype and/or haplotype status of at least one risk variant for prostate cancer, as presented herein, is determined before and during treatment to monitor its effectiveness.
Alternatively, biological networks or metabolic pathways related to the markers and haplotypes of the present invention can be monitored by determining mRNA and/or polypeptide levels. This can be done for example, by monitoring expression levels or polypeptides for several genes belonging to the network and/or pathway, in samples taken before and during treatment. Alternatively, metabolites belonging to the biological network or metabolic pathway can be determined before and during treatment. Effectiveness of the treatment is determined by comparing observed changes in expression levels/metabolite levels during treatment to corresponding data from healthy subjects.
In a further aspect, the markers of the present invention can be used to increase power and effectiveness of clinical trials. Thus, individuals who are carriers of at least one at-risk variant of the present invention, i.e. individuals who are carriers of at least one allele of at least one polymorphic marker conferring increased risk of developing prostate 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. This application can improve the safety of clinical trials, but can also enhance the chance that a clinical trial will demonstrate statistically significant efficacy, which may be limited to a certain sub-group of the population. Thus, one possible outcome of such a trial is that carriers of certain genetic variants, e.g., the markers and haplotypes of the present invention, are statistically significantly likely to show positive response to the therapeutic agent, i.e. experience alleviation of symptoms associated with prostate cancer when taking the therapeutic agent or drug as prescribed.
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. 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
The present invention also relates to computer-implemented functions using the polymorphic markers and haplotypes described herein to be associated with prostate cancer. Such functions can be useful for storing, manipulating or otherwise analyzing genotype data that is useful in the methods of the invention.
One such aspect relates to computer-readable media. In general terms, such medium has capabilities of storing (i) identifier information for at least one polymorphic marker or a haplotype; (ii) an indicator of the frequency of at least one allele of said at least one marker, or the frequency of a haplotype, in individuals with prostate cancer; and an indicator of the frequency of at least one allele of said at least one marker, or the frequency of a haplotype, in a reference population. The reference population can be a disease-free population of individuals. Alternatively, the reference population is a random sample from the general population, and is thus representative of the population at large. The frequency indicator may be a calculated frequency, a count of alleles and/or haplotype copies, or normalized or otherwise manipulated values of the actual frequencies that are suitable for the particular medium.
Additional information about the individual can be stored on the medium, such as ancestry information, information about sex, physical attributes or characteristics (including height and weight), biochemical measurements (such as blood pressure, blood is lipid levels, history of previous disease diagnosis (such as cancer, e.g., prostate cancer), family history of cancer, e.g., prostate cancer), or other useful information that is desirable to store or manipulate in the context of the genotype status of a particular individual.
The invention furthermore relates to an apparatus that is suitable for determination or manipulation of genetic data useful for determining a susceptibility to prostate cancer in a human individual. Such an apparatus can include a computer-readable memory, a routine for manipulating data stored on the computer-readable memory, and a routine for generating an output that includes a measure of the genetic data. Such measure can include values such as allelic or haplotype frequencies, genotype counts, sex, age, phenotype information, values for odds ratio (OR) or relative risk (RR), population attributable risk (PAR), or other useful information that is either a direct statistic of the original genotype data or based on calculations based on the genetic data.
The markers and haplotypes shown herein to be associated with increased susceptibility (e.g., increased risk) of prostate cancer, are in certain embodiments useful for interpretation and/or analysis of genotype data. Thus in certain embodiments, an identification of an at-risk allele for prostate cancer, as shown herein, or an allele at a polymorphic marker in LD with any one of the markers shown herein to be associated with prostate cancer, is indicative of the individual from whom the genotype data originates is at increased risk of prostate cancer. In one such embodiment, genotype data is provided for at least one polymorphic marker shown herein to be associated with prostate cancer, or a marker in linkage disequilibrium therewith. The genotype data is subsequently made available to the individual from whom the data originates, for example via a user interface accessible over the internet, together with an interpretation of the genotype data, e.g., in the form of a risk measure (such as an absolute risk (AR), risk ratio (RR) or odds ratio (OR)) for the disease (e.g., prostate cancer), based on the known association between the at least one marker and prostate cancer, as shown herein. 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 individual, 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. In particular embodiments, the results of risk assessment is made available to a third party, e.g., a physician, other healthcare worker or genetic counselor.
Markers Useful in Various Aspects of the Invention
The above-described methods and applications can all be practiced with the markers and haplotypes of the invention that have in more detail been described herein in general terms as being useful for assessing susceptibility to prostate cancer. Thus, these applications can in certain embodiments be reduced to practice using any one, or a plurality of, markers located within, or in linkage disequilibrium with LD block C02, LD block C04a, the TCF2 gene, LD block C17b and/or LD block C0Xa, as defined herein. In other embodiments, the at least one marker is selected from the markers set forth in Tables 7-11, and markers in linkage disequilibrium therewith. In other embodiments, the at least one marker is selected from the markers set forth in tables 11-16. In other embodiments, the at least one marker is selected from marker rs3923603 (SEQ ID NO:1), rs4430796 (SEQ ID NO:2), rs7501939 (SEQ ID NO:3), rs1859962 (SEQ ID NO:4), D17S1350 (SEQ ID NO:5), rs5945572 (SEQ ID NO:6), rs5945605 (SEQ ID NO:7), rs2710646 (SEQ ID NO:8), rs3760511 (SEQ ID NO:56), rs7214479 (SEQ ID NO:134), rs6501445 (SEQ ID NO:146), rs983085 (SEQ ID NO:150), rs5945605 (SEQ ID NO:178) and rs721048 (SEQ ID NO:344), optionally including markers in linkage disequilibrium therewith.
In one embodiment, the at least one polymorphic marker is selected from rs2710646 (SEQ ID NO:8) and rs721048 (SEQ ID NO:344), and markers in linkage disequilibrium therewith. In one such embodiment, the at least one marker is selected from the markers set forth in Table 11. In another embodiment, the at least one polymorphic marker is rs3923603 (SEQ ID NO:1), or markers in linkage disequilibrium therewith. In one embodiment, the at least one polymorphic marker is selected from the markers set forth in Table 7. In another embodiment, the at least one polymorphic marker is rs7501939 (SEQ ID NO:3), or markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the group of markers set forth in Table 8. In yet another embodiment, the at least one polymorphic marker is rs1859962 (SEQ ID NO:4), or markers in linkage disequilibrium therewith. In one such embodiment, the at least one polymorphic marker is selected from the group of markers set forth in Table 9. In a further embodiment, the at least one polymorphic marker is rs5945572 (SEQ ID NO:6), or markers in linkage disequilibrium therewith. In one embodiment, the at least one polymorphic marker is selected from the group of markers set forth in Table 10. In yet another embodiment the at least one polymorphic marker is selected from markers associated with the TCF2 gene by values of the linkage disequilibrium measure r2 of greater than 0.2. In one such embodiment, the at least one polymorphic marker is selected from the markers set forth in Table 13.
In one embodiment, the presence of any one of allele A in marker rs3923603, allele C in rs7501939, allele G in rs1859962, allele A in rs5945572, allele A in rs2710646, allele C in rs3760511, allele A in rs4430796, allele T in rs7214479, allele A in rs6501455, allele C in rs983085, allele T in rs5945605, or allele A in rs721048 is indicative of increased susceptibility to prostate cancer.
Nucleic Acids and Polypeptides
The nucleic acids and polypeptides described herein can be used in any methods or kits as described herein. 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 haplotype described herein). In certain embodiments, the invention includes variants that hybridize under high stringency hybridization and wash conditions (e.g., for selective hybridization) to a nucleotide sequence that comprises the nucleotide sequence of any one of SEQ ID NO:1-362, as set forth herein. In other embodiments, the invention includes variants that hybridize under high stringency hybridization and wash conditions (e.g., for selective hybridization) to a nucleotide sequence that comprises the nucleotide sequence of LD block C02, LD block C04a, the nucleotide sequence encoding the TCF2 gene or a fragment thereof, LD block C17b, or LD block C0Xa (or a nucleotide sequence comprising the complement of any one of SEQ ID NO:1-362, the nucleotide sequence of LD block C02, LD block C04a, the nucleotide sequence encoding the TCF2 gene or a fragment thereof, LD block C17b, or LD block C0Xa). In another embodiment, the variant comprises a nucleotide sequence containing at least one polymorphic marker or haplotype of the present invention (e.g., the markers and haplotypes disclosed in any one of Tables 7-11, and markers in linkage disequilibrium therewith).
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 explained on pages 2.10.1-2.10.16 and pages 6.3.1-6.3.6 in Current Protocols in Molecular Biology (Ausubel, F. et al., “Current Protocols in Molecular Biology”, 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×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).
Other examples include the algorithm of Myers and Miller, CABIOS (1989), ADVANCE and ADAM as described in Torellis, A. and Robotti, C., Comput. Appl. Biosci. 10:3-5 (1994); and FASTA described in Pearson, W. and Lipman, D., Proc. Natl. Acad. Sci. USA, 85:2444-48 (1988).
In another embodiment, the percent identity between two amino acid sequences can be accomplished using the GAP program in the GCG software package (Accelrys, Cambridge, UK).
The present invention also provides isolated nucleic acid molecules that contain a fragment or portion that hybridizes under highly stringent conditions to a nucleic acid that comprises, or consists of, the nucleotide sequence of any one of SEQ ID NO:1-362, LD block C02, LD block C04a, the nucleotide sequence encoding the TCF2 gene or a fragment thereof, LD block C17b, or LD block C0Xa (or a nucleotide sequence comprising the complement of the nucleotide sequence of any one of SEQ ID NO:1-362, LD block C02, LD block C04a, the nucleotide sequence encoding the TCF2 gene or a fragment thereof, LD block C17b, or LD block C0Xa), wherein the nucleotide sequence comprises at least one polymorphic allele contained in the haplotypes (e.g., haplotypes) described herein. The nucleic acid fragments of the invention are at least about 15, at least about 18, 20, 23 or 25 nucleotides, and can be 30, 40, 50, 100, 200, 500, 1000, 10,000 or more nucleotides in length.
The nucleic acid fragments of the invention are used as probes or primers in assays such as those described herein. “Probes” or “primers” are oligonucleotides that hybridize in a base-specific manner to a complementary strand of a nucleic acid molecule. In addition to DNA and RNA, such probes and primers include polypeptide nucleic acids (PNA), as described in Nielsen, P. et al., Science 254:1497-1500 (1991). A probe or primer comprises a region of nucleotide sequence that hybridizes to at least about 15, typically about 20-25, and in certain embodiments about 40, 50 or 75, consecutive nucleotides of a nucleic acid molecule comprising a contiguous nucleotide sequence from any one of SEQ ID NO:1-362, LD block C02, LD block C04a, the nucleotide sequence encoding the TCF2 gene or a fragment thereof, LD block C17b, or LD block C0Xa (or a nucleotide sequence comprising the complement of the nucleotide sequence of any one of SEQ ID NO:1-362, LD block C02, LD block C04a, the nucleotide sequence encoding the TCF2 gene or a fragment thereof, LD block C17b, or LD block C0Xa) and may in one embodiment comprise at least one allele contained in one or more markers and haplotypes described herein (e.g., the markers and haplotypes of Tables 7-11, or markers in linkage disequilibrium therewith), and 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.
In general, the isolated nucleic acid sequences of the invention can be used as molecular weight markers on Southern gels, and as chromosome markers that are labeled to map related gene positions. The nucleic acid sequences can also be used to compare with endogenous DNA sequences in patients to identify prostate cancer or a susceptibility to prostate cancer, and as probes, such as to hybridize and discover related DNA sequences or to subtract out known sequences from a sample (e.g., subtractive hybridization). The nucleic acid sequences can further be used to derive primers for genetic fingerprinting, to raise anti-polypeptide antibodies using immunization techniques, and/or as an antigen to raise anti-DNA antibodies or elicit immune responses.
Antibodies
Polyclonal antibodies and/or monoclonal antibodies that specifically bind one form of the gene product of any gene associated with the variants shown herein to be associated with increased risk of prostate cancer (e.g., TCF2), but not to the other form of the gene product, are also provided. Antibodies are also provided which bind a portion of either the variant or the reference gene product that contains the polymorphic site or sites. 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′)2 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, N.Y.). 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, N.Y. (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 SurfZAPTM Phage Display Kit, Catalog No. 240612). Additionally, examples of methods and reagents particularly amenable for use in generating and screening antibody display library can be found in, for example, U.S. Pat. No. 5,223,409; PCT Publication No. WO 92/18619; PCT Publication No. WO 91/17271; PCT Publication No. WO 92/20791; PCT Publication No. WO 92/15679; PCT Publication No. WO 93/01288; PCT Publication No. WO 92/01047; PCT Publication No. WO 92/09690; PCT Publication No. WO 90/02809; Fuchs et al., Bio/Technology 9: 1370-1372 (1991); Hay et al., Hum. Antibod. Hybridomas 3:81-85 (1992); Huse et al., Science 246: 1275-1281 (1989); and Griffiths et al., EMBO J. 12:725-734 (1993).
Additionally, recombinant antibodies, such as chimeric and humanized monoclonal antibodies, comprising both human and non-human portions, which can be made using standard recombinant DNA techniques, are within the scope of the invention. Such chimeric and humanized monoclonal antibodies can be produced by recombinant DNA techniques known in the art.
In general, antibodies of the invention (e.g., a monoclonal antibody) can be used to isolate a polypeptide of the invention by standard techniques, such as affinity chromatography or immunoprecipitation. A polypeptide-specific antibody can facilitate the purification of natural polypeptide from cells and of recombinantly produced polypeptide expressed in host cells. Moreover, an antibody specific for a polypeptide of the invention can be used to detect the polypeptide (e.g., in a cellular lysate, cell supernatant, or tissue sample) in order to evaluate the abundance and pattern of expression of the polypeptide. Antibodies can be used diagnostically to monitor protein levels in tissue as part of a clinical testing procedure, e.g., to, for example, determine the efficacy of a given treatment regimen. The antibody can be coupled to a detectable substance to facilitate its detection. Examples of detectable substances include various enzymes, prosthetic groups, fluorescent materials, luminescent materials, bioluminescent materials, and radioactive materials. Examples of suitable enzymes include horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; examples of suitable prosthetic group complexes include streptavidin/biotin and avidin/biotin; examples of suitable fluorescent materials include umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, dichlorotriazinylamine fluorescein, dansyl chloride or phycoerythrin; an example of a luminescent material includes luminol; examples of bioluminescent materials include luciferase, luciferin, and aequorin, and examples of suitable radioactive material include 125I, 131I, 35S or 3H.
Antibodies may also be useful in pharmacogenomic analysis. In such embodiments, antibodies against variant proteins encoded by nucleic acids according to the invention, such as variant proteins that are encoded by nucleic acids that contain at least one 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 prostate 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 prostate 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 labelled or labelable antibody and a compound or agent for detecting variant proteins in a biological sample, means for determining the amount or the presence and/or absence of variant protein in the sample, and means for comparing the amount of variant protein in the sample with a standard, as well as instructions for use of the kit.
The present invention will now be exemplified by the following non-limiting examples.
EXEMPLIFICATION
Example 1
Identification of Markers and LD Block Regions Associated with Prostate Cancer
Patients Involved in the Genetics Study
A population based list of all prostate cancer patients that were diagnosed with prostate cancer in Iceland from 1955 to 2005 form the basis for this study. Patients have been invited to join the study since 2001 on an ongoing basis. As of October 2006, blood samples from 1564 prostate cancer patients have been collected. Genomic DNA from 1455 of those samples, as well as samples from 7034 control individuals was extracted and genotyped.
Genotyping
A genome-wide scan of 1455 Icelandic individuals diagnosed with Prostate Cancer and 7034 population controls was performed using Infinium HumanHap300 SNP chips from Illumina for assaying approximately 317,000 single nucleotide polymorphisms (SNPs) on a single chip (Illumina, San Diego, Calif., USA). SNP genotyping for replication in other case-control cohorts was carried using the Centaurus platform (Nanogen).
Statistical Methods for Association and Haplotype Analysis
For single marker association to the disease, Fisher exact test was used to calculate a two-sided P-value for each individual allele. When presenting the results, we used allelic frequencies rather than carrier frequencies for SNPs and haplotypes. Haplotype analyses were performed using a computer program we developed at deCODE called NEMO (NEsted MOdels) (Gretarsdóttir, et al., Nat Genet. 2003 October; 35(2):131-8). NEMO was used both to study marker-marker association and to calculate linkage disequilibrium (LD) between markers, and for case-control haplotype analysis. With NEMO, haplotype frequencies are estimated by maximum likelihood and the differences between patients and controls are tested using a generalized likelihood ratio test. The maximum likelihood estimates, likelihood ratios and P-values are computed with the aid of the EM-algorithm directly for the observed data, and hence the loss of information due to the uncertainty with phase and missing genotypes is automatically captured by the likelihood ratios, and under most situations, large sample theory can be used to reliably determine statistical significance. The relative risk (RR) of an allele or a haplotype, i.e., the risk of an allele compared to all other alleles of the same marker, is calculated assuming the multiplicative model (Terwilliger, J. D. & Ott, J. A haplotype-based ‘haplotype relative risk’ approach to detecting allelic associations. Hum. Hered. 42, 337-46 (1992) and Falk, C. T. & Rubinstein, P. Haplotype relative risks: an easy reliable way to construct a proper control sample for risk calculations. Ann. Hum. Genet. 51 (Pt 3), 227-33 (1987)), together with the population attributable risk (PAR). When controls are considered unaffected (i.e., disease-free), the relative risk is replaced by an estimate for the odds ratio (OR) of the particular marker allele or haplotype.
In the haplotype analysis, it may be useful to group haplotypes together and test the group as a whole for association to the disease. This is possible to do with NEMO. A model is defined by a partition of the set of all possible haplotypes, where haplotypes in the same group are assumed to confer the same risk while haplotypes in different groups can confer different risks. A null hypothesis and an alternative hypothesis are said to be nested when the latter corresponds to a finer partition than the former. NEMO provides complete flexibility in the partition of the haplotype space. In this way, it is possible to test multiple haplotypes jointly for association and to test if different haplotypes confer different risk. As a measure of LD, we use two standard definitions of LD, D′ and R2 (Lewontin, R., Genetics, 49:49-67 (1964) and Hill, W. G. and A. Robertson, Theor. Appl. Genet., 22:226-231 (1968)) as they provide complementary information on the amount of LD. For the purpose of estimating D′ and R2, the frequencies of all two-marker allele combinations are estimated using maximum likelihood methods and the deviation from linkage disequilibrium is evaluated using a likelihood ratio test. The standard definitions of D′ and R2 are extended to include microsatellites by averaging over the values for all possible allele combinations of the two markers weighted by the marginal allele probabilities.
The number of possible haplotypes that can be constructed out of the dense set of markers genotyped over the whole genome is very large and even though the number of haplotypes that are actually observed in the patient and control cohort is much smaller, testing all of those haplotypes for association to the disease is a formidable task. It should be noted that we do not restrict our analysis to haplotypes constructed from a set of consecutive markers, as some markers may be very mutable and might split up an otherwise well conserved haplotype constructed out of surrounding markers
Results
As described herein (Example 1 and Example 2), we have identified five loci that confer an increased risk for particular cancers (e.g., prostate cancer (e.g., aggressive prostate cancer)). In all cases, the loci were identified in a genomewide scan in Icelandic prostate cancer material. Follow-up genotyping was performed in other cohorts to verify the signals. The five loci are located on three different chromosomes: One locus is on chromosome 2 (2p 15), one locus is on chromosome 4 (4q31.21), two loci are on chromosome 17 (17q12 and 17q24.3) and one locus is on chromosome X (Xp11.22). Particular markers and haplotypes, associated with an increased risk of prostate cancer, from these five loci were initially found to be associated with prostate cancer, as depicted in the below. Allele codes for SNPs are as follows: 1=A, 2=C, 3=G, 4=T, and X=any allele.
Locus C02 on Chromosome 2p15
Table 1 shows data for the association of marker rs2710646 to prostate cancer. The original finding in Icelandic cases (1,487 cases and 11,208 controls) is characterized by an Odds Ratio (OR) of 1.16 and a nominal p-value of 0.003 (Table 1). This finding was replicated in cohorts from Holland, Spain and in a cohort of European Americans from Chicago. While the results from the replication cohorts are not statistically significant by themselves, the overall result for the combined cohorts is highly significant (Table 1), illustrating the significance of the finding of association to this chromosomal region.
The rs2710646 marker is located within a region denoted herein as LD block C02, between positions 62,767,002 and 63,881,002 on chromosome 2.
Locus C04a on Chromosome 4q31.21
As indicated in Table 1, the 1 allele of marker rs3923603 on chromosome 4q31.21 (also called rs3923603 1 allele) was found to be associated with an increased risk of prostate cancer. The marker is located in what we call LD block C04a between positions 145601002 and 145805005 bp (NCBI Build 34); the location of the marker is indicated in Table 2. The original finding has been replicated in cohorts from Holland, Spain and the US (European Americans from Chicago), thus providing further support for the significance of the association, with overall p-value of 1.3×10−5 (Table 1).
Locus C0Xa on Chromosome Xp11.22
As indicated in Table 1, markers s5945572 and rs5945605 on chromosome Xp11.22, have been found to be associated with an increased risk of prostate cancer. The markers are located in what we call LD block C0Xa between positions 50033978 and 50259000 bp (NCBI Build 34), and location of the markers is indicated in Table 2. The original finding has been replicated in cohorts from Holland, Spain and the US (European Americans from Chicago), thus providing further support for the significance of the association, with overall p-value of 1.3×10−5 (Table 1).
The opposite alleles to the ones at risk of SNP markers rs5945572 1 allele show significant protection to prostate cancer in carriers.







TABLE 1







Combined association results for the following loci: 2p15, 4q31.21, and


Xp11.23 for prostate cancer in Iceland, the Netherlands, Spain and US











Study population





(N cases/N controls)
Frequency













Location
Variant (allele)
Cases
Controls
OR
P value















4q31.21
Iceland (1,499/11,281)







SG04S719/rs3923603 (A)
0.461
0.432
1.12
0.0048



Holland (974/1450)







SG04S719/rs3923603 (A)
0.430
0.400
1.13
0.040



Spain (453/1,057)







SG04S719/rs3923603 (A)
0.390
0.360
1.13
0.12



European Americans







Chicago (526/503)







SG04S719/rs3923603 (A)
0.379
0.330
1.24
0.020



Combining all above







SG04S719/rs3923603 (A)
—
0.381
1.14
1.30 × 10−5


2p15
Iceland (1,487/11,208)







SG02S799/rs2710646 (A)
0.228
0.203
1.16
0.0031



Holland (997/1459)







SG02S799/rs2710646 (A)
0.206
0.186
1.14
0.082



Spain (455/1,067)







SG02S799/rs2710646 (A)
0.232
0.206
1.17
0.11



European Americans







Chicago (663/529)







SG02S799/rs2710646 (A)
0.196
0.167
1.21
0.070



Combining all above







SG02S799/rs2710646 (A)
—
0.190
1.16
3.13 × 10−5


Xp11.22
Iceland (1,499/11,280)







SG0XS73/rs5945572 (A)
0.412
0.374
1.18
3.53 × 10−3



SG0XS83/rs5945605 (T)
0.389
0.345
1.21
1.76 × 10−3



Holland (999/1,462)







SG0XS73/rs5945572 (A)
0.39
0.348
1.21
0.024



SG0XS83/rs5945605 (T)
0.372
0.335
1.17
0.048



Spain (456/1,077)







SG0XS73/rs5945572 (A)
0.421
0.368
1.28
0.023



SG0XS83/rs5945605 (T)
0.404
0.353
1.29
0.024



Chicago (527/506)







SG0XS73/rs5945572 (A)
0.409
0.358
1.25
0.069



SG0XS83/rs5945605 (T)
0.35
0.287
1.25
0.078



All excluding Iceland







SG0XS73/rs5945572 (A)
—
0.358
1.23
2.62 × 10−4



SG0XS83/rs5945605 (T)
—
0.325
1.22
6.59 × 10−4



All combined (3,481/14,325)







SG0XS73/rs5945572 (A)
—
0.362
1.21
3.34 × 10−6



SG0XS83/rs5945605 (T)
—
0.33
1.22
3.75 × 10−6









Table 2 shows the genomic location of anchor SNP and microsatellite markers from the five chromosomal regions (loci) discussed herein (see SEQ ID NO:1-7). Location of the markers is given with respect to NCBI Build34 of the human genome assembly. The relative position of the markers in basepair position (Build 34 NCBI) is indicated. Further discussion of Chromosome 17 loci are found in Example 2 herein.








TABLE 2







Genomic position of anchor markers











Position


Locus
Name (SEQ ID NO)
Build 34





Chromosome 4q31.21
rs3923603 (SEQ ID NO: 1)
145688956 


Chromosome 17q12
rs4430796 (SEQ ID NO: 2)
36293590


Chromosome 17q12
rs7501939 (SEQ ID NO: 3)
36296706


Chromosome 17q24.3
rs1859962 (SEQ ID NO: 4)
69705876


Chromosome 17q24.3
D17S1350 (SEQ ID NO: 5)
69685886-




69686068*


Chromosome Xp11.22
rs5945572 (SEQ ID NO: 6)
50146489


Chromosome Xp11.22
rs5945605 (SEQ ID NO: 7)
50107288


Chromosome 2p15
rs2710646 (SEQ ID NO: 8)
63109413





*The interval of an amplimer of the microsatellite D17S1350 is given






Example 2
Two Sequence Variants Conferring Risk of Prostate Cancer Identified on Chromosome 17 and One of them, in TCF2, Shown to be Protective Against Type 2 Diabetes
Prostate cancer is the most common non-dermatological cancer of males worldwide, and the second leading cause of cancer-related death in men from western industrialized countries1. Firmly established risk factors for this type of malignancy are age, ethnicity and family history. In addition diet, lifestyle, and circulating androgens may have impact on the risk. Despite a large body of evidence for a genetic component to the risk of prostate cancer, the variants on 8q24 are the only common sequence variants reported so far that account for substantial proportion of the cases2-5.
In the present study we began with a genome-wide SNP association study to search for sequence variants conferring risks of prostate cancer using Icelandic cases and controls. We expanded the data from a study previously reported3, by increasing the number of cases and controls from 1,453 to 1,501, and 3,064 to 11,290, respectively. This corresponds to an approximately 34% increase in effective sample size. After quality filtering, 310,520 SNPs from the Illumina Hap300 chip were tested for association to prostate cancer (see Methods). The results were adjusted for relatedness by applying the method of genomic control6. Apart from the variants on 8q242,3 and SNPs correlated with them, no other SNPs achieved genome-wide significance. However, we assumed that a properly designed follow-up strategy would lead to the identification of additional susceptibility variants for prostate cancer.
Like others7, we believe that results from family-based linkage studies should be taken into account when evaluating the association results of a genome-wide study. However, instead of using linkage scores to formally weight the statistical significance of different SNPs7, we used them to prioritize follow-up studies. The long arm of chromosome 17 has been reported in several linkage studies of prostate cancer8-10 but no susceptibility variants have yet been found11-13. Hence we decided to first focus our efforts on this region.
Six SNPs on chromosome 17q, having the lowest P values (<5×10−4) and ranking from 68 to 100 among the most significantly associated SNPs in our genome-wide analysis, were selected for further analysis (FIG. 1). These SNPs mapped to two distinct regions on chromosome 17q that are both within a region with LOD scores ranging approximately between 1 and 2 but outside the proposed 10 cM candidate gene region reported in a recent linkage analysis10. One locus was on 17q12 (rs7501939 and rs3760511), encompassing the 5′ end of the TCF2 (HNF1β) gene, where the linkage disequilibrium (LD) is weak (based on the Utah CEPH (CEU) HapMap data set). The second locus is in a gene poor area on 17q24.3 (rs1859962, rs7214479, rs6501455 and rs983085) where all four SNPs fall within a strong LD-block (based on the CEU HapMap data set). The two loci are separated by approximately 33 Mb and no LD was observed between the two of them.
We genotyped 5 of the 6 SNPs in three prostate cancer case-control groups of European ancestry from the Netherlands, Spain and the United States (US) (Table 3). The assay for rs983085 on 17q24.3 failed in genotyping but this SNP is almost perfectly correlated with rs6501455 (r2=0.99) and is therefore expected to give comparable results. For each of the replication study groups, the observed effect of 4 of the 5 SNPs were in the same direction as in Iceland. One SNP, rs6501455, showed an opposite effect in the Chicago group. In general, the Dutch samples showed the strongest effects, while the results for the Spanish group were the weakest. When results from all four case-control groups were combined, two SNPs achieved genome-wide significance, rs7501939 allele C (rs7501939 C) at 17q12 (allele specific odds ratio (OR)=1.19, P=4.7×10−9) and rs1859962 allele G (rs1859962 G) at 17q24.3 (OR=1.20, P=2.5×10−1) (Table 2). In an effort to refine the signal at the 17q12 locus, we selected three markers (rs4239217, rs757210, rs4430796) that were in the same LD block as rs7501939 and were substantially correlated with it (r2>0.5) based on the Hapmap CEU data. After genotyping these three refinement SNPs in the different case and control groups, one of them, rs4430796, showed an association to prostate cancer that was even stronger than that of rs7501939. Specifically, with all groups combined, allele A of rs4430796 had an OR of 1.22 with a P of 1.4×10−11 (Table 4a). A joint analysis showed that the effects of rs7501939 and rs3760511 were no longer significant after adjusting for rs4430796 (P=0.88 and 0.58 respectively), while rs4430796 remained significant after adjusting for both rs7501939 and rs3760511 (P=0.0042). At 17q24.3, our attempt at refining the signal did not result with any SNP that was more significant than rs1859962. Among the Illumina SNPs, rs7114479 and rs6501455 were not significant (P>0.75) with adjustment for the effect of rs1859962, whereas, rs1859962 remained significant after adjusting for the other two SNPs (P=7.4×10−4). Henceforth, our focus is mainly on rs4430796 at 17q12 and rs1859962 at 17q24.3. However, at 17q12, since rs7501539 is the most significant among Illumina SNPs and was a part of the original genome-wide scan, we include it in the discussion when appropriate. We also recommend that all replication effort should include at least these three SNPs.
For cases diagnosed at age 65 or younger, the observed OR from the combined analysis was slightly higher, or 1.30 for rs4430796 A, and 1.27 for rs1859962 G. For each copy of the at-risk alleles, patients were diagnosed 2 and 5 months earlier, for rs4430796 and rs1859962 respectively. This observation however, was not statistically significant (P=0.40 and 0.06 for rs4430796 and rs1859962 respectively) and therefore, its confirmation requires further investigation.
The Cancer Genetic Markers of Susceptibility study group (CGEMS), has made results from a genome-wide association analysis of prostate cancer publicly available (https colon-slash-slash cgems.cancer.gov slash). For rs4430796 A allele, the P values range between 4.0×10−4 and 1.7×10−3 depending on the type of analysis performed, and for rs1859962 G the P values range between 0.027 and 0.048. The OR was about 1.2 for both variants which is comparable to the results we find (Table 4). These results from an independent investigation make our observation even more compelling and provide further support for the notion that similar effects could be expected in other populations of European descent.
No interaction was observed between the risk variants on 17q12 and 17q24.3; a multiplicative or log-additive model provided an adequate fit for the joint risk of rs4430796 and rs1859962. Genotype specific ORs were estimated for each locus individually (Table 3). Based on results from all four groups, a multiplicative model for the genotype risk provided an adequate fit for rs4430796 at 17q12. For rs1859962 at the 17q24.3 locus however, the full model provided a significantly better fit than the multiplicative model (P=0.006), a result mainly driven by the Icelandic samples. Specifically, the estimated OR of 1.33 for a heterozygous carrier of rs1859962 G was substantially higher than the 1.20 estimate implied by a multiplicative model. The corresponding PAR was also higher, 21% instead of 16%.
The SNPs rs7501939 and rs4430796 on 17q12 are located in the first and second intron of the TCF2 gene, respectively, which encodes a transcription factor playing an important role in embryonic development of the kidney, pancreas and liver. To the best of our knowledge, genetic variants in TCF2 have not previously been implicated in the risk of prostate cancer but germline mutations have been identified in renal carcinoma14 and epigenetic inactivation by methylation has been found in ovarian cancer and various cancer cell lines15. RNA expression analysis demonstrated expression of TCF2 in both normal and tumor prostate tissue (data not shown). More than 50 different exonic TCF2 mutations have been reported in individuals with renal cysts, maturity-onset diabetes of the young, type 5 (MODY5), pancreatic atrophy, and genital tract abnormalities16,17. We sequenced all 9 exons of TCF2 in 200 Icelandic prostate cancer cases and 200 controls without detecting any mutations explaining our association signal (data not shown).
Evidence has been reported for association of type 2 diabetes mellitus (T2D) to common SNPs in five of the six known MODY genes18,19, including TCF219,20. Interestingly, several epidemiological studies have demonstrated an inverse relationship between T2D and the risk of prostate cancer (see Kasper et al.21 and references therein). A recent meta-analysis estimated the relative risk of prostate cancer to be 0.84 (95% CI, 0.71-0.92) among diabetes patients21. We therefore, decided to investigate a potential association between T2D and the SNPs in TCF2 showing the strongest association to prostate cancer in our data.
The Illumina SNP rs7501939 was typed in 1,380 T2D patients among whom the males were not known to have prostate cancer (according to a nation-wide list of prostate cancer cases diagnosed from 1955 to 2006 held by the Icelandic Cancer Registry). When compared to 9,940 controls neither known to have prostate cancer nor T2D, rs7501939 C showed a protective effect against T2D (OR=0.88, P=0.0045) in these samples. For the same samples, allele A of the refinement SNP rs4430796 gave a comparable result (OR=0.86, P=0.0021). To validate this association, we typed both rs7501939 and rs4430796 in samples from seven additional T2D case-control groups of European-, African-, and Asian ancestry. In all seven case-control groups, rs7501939 C and rs4430796 A showed a negative association to the disease (i.e. OR<1.0) even though some of the estimated effects were very modest and individually non-significant. Three case-control groups (Denmark A, Philadelphia, and West Africans) showed a significant association (P<0.05) and one other group (Hong Kong) gave a marginally significant result (P between 0.05 and 0.10). Combining results from all eight T2D case-control groups, including the Icelandic, gave an OR of 0.91 (P=9.2×10−7) for rs7501939 C, and an OR of 0.91 (P=2.7×10−7) for rs4430796 A (Table 4). Our analysis of the data does not indicate any differential association by gender of rs7501939 or rs4430796 to T2D. In a joint analysis, the effect of rs7501939 was no longer significant after adjusting for rs4430796 (P=0.41), while rs4430796 remained significant with adjustment for rs7501939 (P=0.016). It is noted that the latter was mainly driven by the data from West Africa where the correlation between the two SNPs (r2=0.22 in HapMap Yoruban samples), is substantially lower than that in Caucasians (r2=0.77 in HapMap CEU samples). Still these results are consistent with what was observed for prostate cancer. For T2D, a very recent report20 describes similar findings (OR=0.89, P=5×10−6) for allele G of the SNP rs757210 that is located in the second intron of TCF2 and is substantially correlated with rs4430796 A (D′=0.96; r2=0.62; based on the CEU HapMap data set). This reinforces the finding that one or more variants in TCF2 that confer risk to prostate cancer are protective for T2D. To eliminate the concern that the associations of rs7501939 and rs4430796 to prostate cancer were in part a by-product of an association to T2D, we repeated the prostate cancer association analysis after removing all known diabetics from the Icelandic prostate cancer case-control group. For the resulting 1,444 cases and 9,917 controls, the association for rs7501939 C and rs4430796 A was practically unchanged; rs7501939 had an OR of 1.16 for the Icelandic samples alone (P=4.0×10−4), and 1.19 (P=1.1×10−8) when results of all four case-control groups were combined; whereas rs4430796 had an OR of 1.18 (P=1.9×10−4) in the Icelandic samples and an OR of 1.21 (P=2.1×10−10) in the combined group.
The more distal SNP, rs1859962, on chromosome 17q24.3 is in a 177.5 kb LD-block spanning from 66,579 Mb to 66,757 Mb, (NCBI Build 35), based on the CEU HapMap group. One mRNA (BC039327) and several unspliced ESTs have been localized to this region, but no known genes (www.genome.ucsc.edu, May 2004 Assembly). RT-PCR analysis of various cDNA libraries, including those derived from the prostate, revealed detectable expression of the BC039327 mRNA only in a testis library (data not shown), which was in line with previously reported results22. The closest telomeric gene, located  ̃900 kb away from the LD-block, is SOX9 a SRY related (Sex determining Region Y) transcription factor known to be important for sex determination23. SOX9 is expressed in normal prostate basal cells, and is probably involved in regulating androgen receptor expression in prostate cells24. Various rearrangement breakpoints have been described that affect the expression of SOX9 and lead to a skeletal malformation syndrome called campomelic dysplasia (CD)25. One such translocation breakpoint has been described within the LD-block containing the rs185996222. This translocation is thought to remove a potential cis-acting regulatory element (SOX9cre1) for SOX9 leading to altered expression of SOX926. Overexpression of SOX9 has been shown to suppress growth and tumorigenicity of a prostate tumor cell line27, suggesting that SOX9 might act as a tumor suppressor gene.
In summary, two common variants on chromosome 17q, rs4430796 A and rs1859962 G, were identified to contribute to the risk of prostate cancer in four populations of European descent. Together, based on the combined results, these two variants have an estimated joint PAR of about 36%, which is substantial from a public health point of view. The large PAR is a consequence of the high frequencies of these variants. However, as their relative risks, as estimated by the ORs, are not high, the sibling risk ratio accounted for by them is only approximately 1.009 each and 1.018 jointly. As a consequence, they could only explain a very small fraction of the familial clustering of the disease and could only generate very modest linkage scores. The same is true for the identified variants in the 8q24 region2-5. Hence, it is expected that many more prostate cancer susceptibility variants in the genome remain to be discovered and some of these might also reside in the chromosome 17 linkage region. The vicinity of the TCF2 and SOX9 genes to the variants on 17q could implicate them in the pathogenesis of prostate cancer. Perhaps the most intriguing finding in this study is the discovery of a variant, rs4430796 A, in TCF2 that is associated with increased risk of prostate cancer, but reduced risk of T2D in individuals of European, African and Asian descent. The discovery of a genetic variant in the TCF2 gene, which accounts for at least part of the inverse relationship between these two diseases, provides a step towards an understanding of the complex biochemical checks and balances that result from the pleiotropic impact of singular genetic variants. Previous explanations of the well established inverse relationship between prostate cancer and T2D have centered on the impact of the metabolic and hormonal environment of diabetic men. However, we note that the protective effect of both the TCF2 SNPs against T2D is too modest for its impact on prostate cancer risk to be merely a by-product of its impact on T2D. Indeed, we favor the notion that the primary functional impact of rs4430796 (or a strongly correlated presently unknown variant) is on one or more metabolic or hormonal pathways important for the normal functioning of individuals throughout their lives that incidentally modulate the risk of developing prostate cancer and T2D late in life. Further investigation of the functional impact of the TCF2 variants, both in normal individuals and those with prostate cancer and T2D, may provide knowledge that can be used not only for treatment of these diseases, but no less importantly, for their prevention.
Methods
Icelandic Study Population.
Men diagnosed with prostate cancer were identified based on a nationwide list from the Icelandic Cancer Registry (ICR) that contained all 3,886 Icelandic prostate cancer patients diagnosed from Jan. 1, 1955, to Dec. 31, 2005. The Icelandic prostate cancer sample collection included 1,615 patients (diagnosed from December 1974 to December 2005) who were recruited from November 2000 until June 2006 out of the 1,968 affected individuals who were alive during the study period (a participation rate of about 82%). A total of 1,541 patients were included in a genome wide SNP genotyping effort, using the Infinium II assay method and the Sentrix HumanHap300 BeadChip (Illumina, San Diego, Calif., USA). Thereof, 1,501 (97%) were successfully genotyped according to our quality control criteria and used in the present case-control association analysis. The mean age at diagnosis for the consenting patients was 71 years (median 71 years) and the range was from 40 to 96 years, while the mean age at diagnosis was 73 years for all prostate cancer patients in the ICR. The median time from diagnosis to blood sampling was 2 years (range 0 to 26 years). The 11,290 controls (5,010 males and 6,280 females) used in this study consisted of 758 controls randomly selected from the Icelandic genealogical database and 10,532 individuals from other ongoing genome-wide association studies at deCODE. Specifically, around 1,400 from studies on T2D; about 1,600 from studies on breast cancer; 1,800 from studies on myocardial infarction, and studies on colon cancer, anxiety, addiction, schizophrenia and infectious diseases provided around 700-1,000 controls each. The controls had a mean age of 66 years (median 67) and the range was from 22 to 102 years (see Amundadottir et al. 2 for a more detailed description of the Icelandic study population). The male controls were absent from the nationwide list of prostate cancer patients according to the ICR.
The study was approved by the Data Protection Commission of Iceland and the National Bioethics Committee of Iceland. Written informed consent was obtained from all patients, relatives and controls. Personal identifiers associated with medical information and blood samples were encrypted with a third-party encryption system as previously described28.
Dutch, Spanish, and U.S. Study Populations.
The total number of Dutch prostate cancer cases in this study was 1,013 of which 999 (98%) were successfully genotyped. The Dutch study population was comprised of two recruitment-sets of prostate cancer cases; Group-A was comprised of 390 hospital-based cases recruited from January 1999 to June 2006 at the Urology Outpatient Clinic of the Radboud University Nijmegen Medical Centre (RUNMC); Group-B consisted of 623 cases recruited from June 2006 to December 2006 through a population-based cancer registry held by the Comprehensive Cancer Centre IKO. Both groups were of self-reported European descent. The average age at diagnosis for patients in Group-A was 63 years (median 63 years) and the range was from 43 to 83 years. The average age at diagnosis for patients in Group-B was 65 years (median 66 years) and the range was from 43 to 75 years. The 1,466 control individuals were cancer free and were matched for age with the cases. They were recruited within a project entitled “The Nijmegen Biomedical Study”, in the Netherlands. This is a population-based survey conducted by the Department of Epidemiology and Biostatistics and the Department of Clinical Chemistry of the RUNMC, in which 9,371 individuals participated from a total of 22,500 age and sex stratified, randomly selected inhabitants of Nijmegen. Control individuals from the Nijmegen Biomedical Study were invited to participate in a study on gene-environment interactions in multifactorial diseases, such as cancer. All the 1,466 participants in the present study are of self-reported European descent and were fully informed about the goals and the procedures of the study. The study protocol was approved by the Institutional Review Board of Radboud University and all study subjects gave written informed consent.
The Spanish study population consisted of 464 prostate cancer cases of which 456 (98%) were successfully genotyped. The cases were recruited from the Oncology Department of Zaragoza Hospital in Zaragoza, Spain, from June 2005 to September 2006. All patients were of self-reported European descent. Clinical information including age at onset, grade and stage was obtained from medical records. The average age at diagnosis for the patients was 69 years (median 70 years) and the range was from 44 to 83 years. The 1,078 Spanish control individuals were approached at the University Hospital in Zaragoza, Spain, and were confirmed to be prostate cancer free before they were included in the study. Study protocols were approved by the Institutional Review Board of Zaragoza University Hospital. All subjects gave written informed consent.
The Chicago study population consisted of 557 prostate cancer cases of which 537 (96%) were successfully genotyped. The cases were recruited from the Pathology Core of Northwestern University's Prostate Cancer Specialized Program of Research Excellence (SPORE) from May 2002 to September 2006. The average age at diagnosis for the patients was 60 years (median 59 years) and the range was from 39 to 87 years. The 514 European American controls were recruited as healthy control subjects for genetic studies at the University of Chicago and Northwestern University Medical School, Chicago, US. Study protocols were approved by the Institutional Review Boards of Northwestern University and the University of Chicago. All subjects gave written informed consent.
Statistical Analysis
Association Analysis.
All Icelandic case- and control-samples were assayed with the Infinium HumanHap300 SNP chip (Illumina, San Diego, Calif., USA). This chip contains 317,503 SNPs and provides about 75% genomic coverage in the Utah CEPH (CEU) HapMap samples for common SNPs at r2≧□0.8. For the association analysis, 310,520 SNPs were used since, 6,983 SNPs were deemed unusable due to reasons that include being monomorphic, having low yield (<95%) and failure of Hardy-Weinberg equilibrium (HWE). Samples with a call rate below 98% were excluded from the analysis. Single SNP genotyping for the five SNPs reported here and the four case-control groups was carried out by deCODE Genetics in Reykjavik, Iceland applying the Centaurus (Nanogen) platform to all populations studied. The concordance rate of genotypes from the Illumina platform compared to the Centaurus platform, for the 5 SNPs genotyped by both methods in 1,501 cases and 758 controls form Iceland, turned out to be >99.5%.
For SNPs that were in strong LD, whenever the genotype of one SNP was missing for an individual, the genotypes of the correlated SNP were used to provide partial information through a likelihood approach as we have done before2. This ensured that results presented herein were always based on the same number of individuals, allowing meaningful comparisons of results for highly correlated SNPs. A likelihood procedure described in a previous publication29 and implemented in the NEMO software was used for the association analyses. An attempt was made to genotype all individuals and all SNPs reported herein (Tables 4-5). For each of the SNPs, the yield was higher than 95% in every group. The only exception was in the case of the refinement marker rs4430796 which was not a part of the Human Hap 300 chip. There, using a single SNP assay to genotype, attempts were made for 1,883 of the 11,290 Icelandic controls (yielding 1,860), and in all Icelandic cases and all individuals from the replication groups. Most importantly, for the 17q12 locus, we note that when we evaluated the significance of one SNP (e.g. rs4430796, rs7501939 or rs3760511) with adjustment for one or two other SNPs, whether we used all 11,289 Icelandic controls that had genotypes for at least one of the three markers in Table 4a and handled the missing data by applying a likelihood approach as mentioned above, or by applying logistic regression to only individuals that had genotypes for all three markers, the resulting P values are very similar. We tested the association of an allele to prostate cancer using a standard likelihood ratio statistic that, if the subjects were unrelated, would have asymptotically a chi-square distribution with one degree of freedom under the null hypothesis. Allele-specific ORs and associated P values were calculated assuming a multiplicative model for the two chromosomes of an individual30. For each of the four case-control groups there was no significant deviation from HWE in the controls (P>0.01). When estimating genotype specific OR (Table 5) genotype frequencies in the population were estimated assuming HWE. We feel that this estimate is more stable than an estimate calculated using the observed genotype counts in controls directly. It is however noted that these two approaches gave very similar estimates in this instance. Results from multiple case-control groups were combined using a Mantel-Haenszel model31 in which the groups were allowed to have different population frequencies for alleles, haplotypes and genotypes but were assumed to have common relative risks. All four of the European sample groups include both male and female controls. No significant difference between male and female controls was detected for SNPs presented in Table 2 and 3 for each of the groups after correction for the number of tests performed. We note that for all the three significant variants (rs7501939, rs4430796 and rs1859962) reported in Table 2, no significant differences in frequencies were detected among the disease groups (see description of Icelandic study population) that make up the Icelandic genome wide control sets (P=0.30, 0.55 and 0.88 respectively). The T2D individuals were removed when this test was performed for rs7501939 and rs4430796. A linear regression was used to estimate the relationship between age at onset for prostate cancer and number of copies of at-risk alleles, for rs7501939 and rs1859962, carried by cases and using group as an indicator.
To investigate potential interaction between rs7501939 C and rs1859962 G located at 17q12 and 17q24.3, respectively, we performed two analyses. First we checked for the absence of significant correlation between those alleles among cases. Secondly, by using a logistic regression we demonstrated that the interaction term is not significant (P=0.57).
We note that for the SNP rs757210, Winckler et. al20 report the results for allele A. However, in the main text we provide their corresponding results for the other one (allele G of rs757210) because that allele was the one positively correlated to allele C of rs7501939 reported by us.
Correction for Relatedness and Genomic Control.
Some of the individuals in the Icelandic case-control groups were related to each other, causing the aforementioned chi-square test statistic to have a mean>1 and median>(0.675)2. We estimated the inflation factor in 2 ways: (i) using a previously described procedure where we simulated genotypes through the genealogy of 708,683 Icelanders32 and (ii) by calculating the average of the 310,520 chi-square statistics, which was a method of genomic control6 to adjust for both relatedness and potential population stratification. The inflation factors, estimated by (i) and (ii), were 1.084 and 1.098, respectively. The differences among these 2 estimates were not statistically significant. Results presented are based on adjusting the chi-square statistics by dividing each of them by 1.098.







TABLE 3







Characteristics of prostate cancer cases and controls from four sources
















Mean age at
Age at





Aggressivea
diagnosis
diagnosis <65


Study Population
Cases
Controls
(%)
(range)
years (%)















Iceland
1,501
11,290
50
70.8 (40-96)
22


Nijmegen, the
999
1,466
47
64.2 (43-83)
52


Netherlands







Zaragoza, Spain
456
1,078
37
69.3 (44-83)
19


Chicago, U.S.
537
514
48
59.6 (39-87)
70


Total:
3,493
14,348





aAggressive is determined here as cancers with Gleason scores of 7 or higher AND/OR Stage of T3 or higher AND/OR node positive disease AND/OR metastatic disease.













TABLE 4a







Association results for SNPs on 17q and prostate cancer in Iceland, the


Netherlands, Spain and the US











Study population





(N cases/N controls)
Frequency













Location
Variant (allele)
Cases
Controls
OR (95% CI)
P value





17q12
Iceland (1,501/11,289)







rs7501939 (C)
0.615
0.578
1.17 (1.08-1.27)
1.8 × 10−4



rs3760511 (C)
0.384
0.348
1.17 (1.08-1.27)
1.6 × 10−4



rs4430796 (A)
0.558
0.512
1.20 (1.11-1.31)
1.4 × 10−5



The Netherlands (997/1,464)







rs7501939 (C)
0.648
0.589
1.29 (1.15-1.45)
2.4 × 10−5



rs3760511 (C)
0.362
0.338
1.11 (0.99-1.25)
 0.086



rs4430796 (A)
0.568
0.508
1.28 (1.14-1.43)
3.1 × 10−5



Spain (456/1,078)







rs7501939 (C)
0.583
0.566
1.07 (0.92-1.26)
0.37



rs3760511 (C)
0.277
0.257
1.11 (0.93-1.32)
0.25



rs4430796 (A)
0.469
0.454
1.06 (0.91-1.24)
0.45



Chicago (536/514)







rs7501939 (C)
0.637
0.588
1.15 (1.03-1.47)
 0.021



rs3760511 (C)
0.347
0.294
1.28 (1.06-1.54)
9.4 × 10−3



rs4430796 (A)
0.563
0.477
1.41 (1.19-1.67)
9.4 × 10−5



All excluding Iceland (1,989/3,056)a







rs7501939 (C)
—
0.581
1.21 (1.12-1.32)
5.6 × 10−6



rs3760511 (C)
—
0.296
1.15 (1.05-1.25)
2.4 × 10−3



rs4430796 (A)
—
0.480
1.24 (1.14-1.35)
2.02 × 10−7 



All combined (3,490/14,345)a







rs7501939 (C)
—
0.580
1.19 (1.12-1.26)
4.7 × 10−9



rs3760511 (C)
—
0.309
1.16 (1.09-1.23)
1.4 × 10−6



rs4430796 (A)

0.488
1.22 (1.15-1.30)
 1.4 × 10−11





All P values shown are two-sided. Shown are the corresponding numbers of cases and controls (N), allelic frequencies of variants in affected and control individuals, the allelic odds-ratio (OR) with 95% confidence interval (CI 95%) and P values based on the multiplicative model.


aFor the combined study populations, the reported control frequency was the average, unweighted control frequency of the individual populations, while the OR and the P value were estimated using the Mantel-Haenszel model.













TABLE 4b







Association results for SNPs on 17q and prostate cancer in Iceland, the


Netherlands, Spain and the US











Study population





(N cases/N controls)
Frequency













Location
Variant (allele)
Cases
Controls
OR (95% CI)
P value





17q24.3
Iceland (1,501/11,290)







rs1859962 (G)
0.489
0.453
1.16 (1.07-1.26)
3.1 × 10−4



rs7214479 (T)
0.451
0.415
1.16 (1.07-1.26)
3.3 × 10−4



rs6501455 (A)
0.538
0.501
1.16 (1.07-1.26)
3.0 × 10−4



rs983085 (C)b
0.542
0.504
1.16 (1.07-1.26)
2.0 × 10−4



The Netherlands (999/1,466)







rs1859962 (G)
0.522
0.456
1.30 (1.16-1.46)
6.8 × 10−6



rs7214479 (T)
0.474
0.428
1.20 (1.07-1.35)
1.5 × 10−3



rs6501455 (A)
0.544
0.488
1.25 (1.12-1.40)
1.1 × 10−4



Spain (456/1,078)







rs1859962 (G)
0.512
0.476
1.15 (0.99-1.35)
0.071



rs7214479 (T)
0.455
0.426
1.13 (0.96-1.32)
0.14 



rs6501455 (A)
0.581
0.552
1.13 (0.97-1.32)
0.13 



Chicago (537/510)







rs1859962 (G)
0.513
0.456
1.25 (1.06-1.49)
9.8 × 10−3



rs7214479 (T)
0.460
0.416
1.20 (1.01-1.42)
0.041



rs6501455 (A)
0.549
0.586
0.86 (0.72-1.02)
0.083



All excluding Iceland (1,992/3,054)a







rs1859962 (G)
—
0.463
1.25 (1.15-1.35)
8.3 × 10−8



rs7214479 (T)
—
0.423
1.18 (1.09-1.28)
7.0 × 10−5



rs6501455 (A)
—
0.542
1.12 (1.05-1.20)
6.2 × 10−3



All combined (3,493/14,344)a







rs1859962 (G)
—
0.460
1.20 (1.14-1.27)
 2.5 × 10−10



rs7214479 (T)
—
0.421
1.17 (1.10-1.24)
8.1 × 10−8



rs6501455 (A)
—
0.532
1.14 (1.08-1.21)
6.9 × 10−6





All P values shown are two-sided. Shown are the corresponding numbers of cases and controls (N), allelic frequencies of variants in affected and control individuals, the allelic odds-ratio (OR) with 95% confidence interval (95% CI) and P values based on the multiplicative model.


aFor the combined study populations, the reported control frequency was the average, unweighted control frequency of the individual populations, while the OR and the P value were estimated using the Mantel-Haenszel model.


bThe SNPs, rs983085 and rs6501455 were almost perfectly correlated (r2 = 0.99) but rs983085 failed in genotyping in the non-Icelandic groups.













TABLE 5







Model-free estimates of the genotype odds ratio of rs4430796 (A) at 17q12 and rs1859962


(G) at 17q24.3.










Study group





Variant
Allelic
Genotype odds ratioa















(allele)
OR
00
0X (95% CI)
XX (95% CI)
P valueb
P valuec
PAR





Iceland









rs4430796 (A)
1.20
1
1.12 (0.97-1.29)
1.40 (1.19-1.64)
0.31
8.3 × 10−5
0.14


rs1859962 (G)
1.16
1
1.35 (1.18-1.54)
1.33 (1.13-1.57)
3.4 × 10−3
2.3 × 10−5
0.19


All except Iceland









rs4430796 (A)
1.24
1
1.34 (1.18-1.52)
1.56 (1.32-1.84)
0.12
4.5 × 10−7
0.23


rs1859962 (G)
1.25
1
1.32 (1.17-1.49)
1.57 (1.33-1.84)
0.24
2.9 × 10−7
0.22


All combined









rs4430796 (A)
1.22
1
1.24 (1.13-1.36)
1.48 (1.32-1.66)
0.57
 2.0 × 10−10
0.19


rs1859962 (G)
1.20
1
1.33 (1.21-1.44)
1.45 (1.29-1.62)
6.0 × 10−3
 5.1 × 10−11
0.21





aGenotype odds ratios for heterozygous-(0X) and homozygous carriers (XX) compared with non-carriers (00).


bTest of the multiplicative model (the null hypothesis) versus the full model, one degree of freedom.


cTest of no effect (the null hypothesis) versus the full model, two degrees of freedom. PAR, population attributable risk, OR, odds ratio, CI 95%, 95% confidence interval.













TABLE 6







Association results for SNPs in the TCF2 gene on 17q12 and type 2 diabetes











Study population





(N cases/N controls)
Frequency













Location
Variant (allele)
Cases
Controls
OR (95% CI)
P value















17q12
Icelanda (1,380/9,940)







rs7501939 (C)
0.549
0.582
0.88 (0.80-0.96)
0.0045



rs4430796 (A)
0.482
0.521
0.86 (0.78-0.95)
0.0021



Denmark A (264/596)







rs7501939 (C)
0.525
0.593
0.76 (0.62-0.93)
0.0088



rs4430796 (A)
0.452
0.530
0.73 (0.60-0.90)
0.0032



Denmark B (1,365/4,843)







rs7501939 (C)
0.579
0.596
0.93 (0.85-1.02)
0.11



rs4430796 (A)
0.507
0.528
0.92 (0.85-1.00)
0.062



Philadelphia, US (457/967)







rs7501939 (C)
0.569
0.613
0.83 (0.71-0.98)
0.028



rs4430796 (A)
0.477
0.527
0.82 (0.70-0.96)
0.013



Scotland (3,741/3,718)







rs7501939 (C)
0.607
0.615
0.97 (0.91-1.03)
0.31



rs4430796 (A)
0.517
0.526
0.97 (0.91-1.03)
0.29



The Netherlands (367/915)







rs7501939 (C)
0.563
0.579
0.94 (0.79-1.11)
0.46



rs4430796 (A)
0.494
0.506
0.95 (0.79-1.14)
0.58



Hong Kong (1,495/993)







rs7501939 (C)
0.768
0.791
0.87 (0.76-1.00)
0.054



rs4430796 (A)
0.731
0.754
0.89 (0.78-1.01)
0.073



West Africab (867/1,115)







rs7501939 (C)
0.400
0.437
0.87 (0.77-0.99)
0.042



rs4430796 (A)
0.271
0.313
0.80 (0.69-0.92)
0.0024



All groups combined exclud. Iceland







rs7501939 (C)
—
—
0.91 (0.87-0.95)
3.4 × 10−5



rs4430796 (A)
—
—
0.92 (0.88-0.95)
1.8 × 10−5



All groups combined (9,936/23,087)







rs7501939 (C)
—
—
0.91 (0.87-0.94)
9.2 × 10−7



rs4430796 (A)
—
—
0.91 (0.87-0.94)
2.7 × 10−7





All P values shown are two-sided. Shown are the corresponding numbers of cases and controls (N), allelic frequencies of variants in affected and control individuals, the allelic odds-ratio (OR) with 95% confidence interval (95% CI) and P values based on the multiplicative model.


aKnown prostate cancer patients have been excluded from the Icelandic T2D cases and controls


bResults for the 5 West African tribes have been combined using a Mantel-Haenszel method. The frequency of the variant in West African cases and controls is the weighted average over the 5 tribes.






REFERENCES


    1. Parkin, D. M., Bray, F., Ferlay, J. & Pisani, P. Global cancer statistics, 2002. CA Cancer J Clin 55, 74-108 (2005).
    2. Amundadottir, L. T. et al. A common variant associated with prostate cancer in European and African populations. Nat Genet 38, 652-8 (2006).
    3. Gudmundsson, J. A second cancer susceptibility variant at 8q24 identified through a genome-wide association study. Nature Genetics 39 (2007).
    4. Haiman, C. A. et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nat Genet 39 (2007).
    5. Yeager, M. et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 39 (2007).
    6. Devlin, B. & Roeder, K. Genomic Control for association studies. Biometrics 55, 997-1004 (1999).
    7. Roeder, K., Bacanu, S. A., Wasserman, L. & Devlin, B. Using linkage genome scans to improve power of association in genome scans. Am J Hum Genet. 78, 243-52 Epub 2006 Jan. 3 (2006).
    8. Lange, E. M. et al. Genome-wide scan for prostate cancer susceptibility genes using families from the University of Michigan prostate cancer genetics project finds evidence for linkage on chromosome 17 near BRCA1. Prostate 57, 326-34 (2003).
    9. Xu, J. et al. A combined genomewide linkage scan of 1,233 families for prostate cancer-susceptibility genes conducted by the international consortium for prostate cancer genetics. Am J Hum Genet 77, 219-29 (2005).
    10. Lange, E. M. et al. Fine-mapping the putative chromosome 17q21-22 prostate cancer susceptibility gene to a 10 cM region based on linkage analysis. Hum Genet 121, 49-55 (2007).
    11. Zuhike, K. A. et al. Truncating BRCA1 mutations are uncommon in a cohort of hereditary prostate cancer families with evidence of linkage to 17q markers. Clin Cancer Res 10, 5975-80 (2004).
    12. Kraft, P. et al. Genetic variation in the HSD17B1 gene and risk of prostate cancer. PLoS Genet 1, e68 (2005).
    13. White, K. A., Lange, E. M., Ray, A. M., Wojno, K. J. & Cooney, K. A. Prohibitin mutations are uncommon in prostate cancer families linked to chromosome 17q. Prostate Cancer Prostatic Dis 9, 298-302 (2006).
    14. Rebouissou, S. et al. Germline hepatocyte nuclear factor 1alpha and 1beta mutations in renal cell carcinomas. Hum Mol Genet. 14, 603-14 (2005).
    15. Terasawa, K. et al. Epigenetic inactivation of TCF2 in ovarian cancer and various cancer cell lines. Br J Cancer 94, 914-21 (2006).
    16. Bellanne-Chantelot, C. et al. Large genomic rearrangements in the hepatocyte nuclear factor-1beta (TCF2) gene are the most frequent cause of maturity-onset diabetes of the young type 5. Diabetes 54, 3126-32 (2005).
    17. Edghill, E. L., Bingham, C., Ellard, S. & Hattersley, A. T. Mutations in hepatocyte nuclear factor-1beta and their related phenotypes. J Med Genet 43, 84-90 (2006).
    18. Silander, K. et al. Genetic variation near the hepatocyte nuclear factor-4 alpha gene predicts susceptibility to type 2 diabetes. Diabetes 53, 1141-9 (2004).
    19. Bonnycastle, L. L. et al. Common variants in maturity-onset diabetes of the young genes contribute to risk of type 2 diabetes in Finns. Diabetes 55, 2534-40 (2006).
    20. Winckler, W. et al. Evaluation of Common Variants in the Six Known Maturity-Onset Diabetes of the Young (MODY) Genes for Association With Type 2 Diabetes. Diabetes 56, 685-93 (2007).
    21. Kasper, J. S. & Giovannucci, E. A meta-analysis of diabetes mellitus and the risk of prostate cancer. Cancer Epidemiol Biomarkers Prev 15, 2056-62 (2006).
    22. Hill-Harfe, K. L. et al. Fine mapping of chromosome 17 translocation breakpoints> or =900 Kb upstream of SOX9 in acampomelic campomelic dysplasia and a mild, familial skeletal dysplasia. Am J Hum Genet 76, 663-71 (2005).
    23. Koopman, P. Sex determination: a tale of two Sox genes. Trends Genet 21, 367-70 (2005).
    24. Wang, H. et al. SOX9 is expressed in normal prostate basal cells and regulates androgen receptor expression in prostate cancer cells. Cancer Res 67, 528-36 (2007).
    25. Leipoldt, M. et al. Two novel translocation breakpoints upstream of SOX9 define borders of the proximal and distal breakpoint cluster region in campomelic dysplasia. Clin Genet 71, 67-75 (2007).
    26. Velagaleti, G. V. et al. Position effects due to chromosome breakpoints that map approximately 900 Kb upstream and approximately 1.3 Mb downstream of SOX9 in two patients with campomelic dysplasia. Am J Hum Genet 76, 652-62 (2005).
    27. Drivdahl, R. et al. Suppression of growth and tumorigenicity in the prostate tumor cell line M12 by overexpression of the transcription factor SOX9. Oncogene 23, 4584-93 (2004).
    28. Gulcher, J. R., Kristjansson, K., Gudbjartsson, H. & Stefansson, K. Protection of privacy by third-party encryption in genetic research in Iceland. Eur J Hum Genet 8, 739-42 (2000).
    29. Gretarsdottir, S. et al. The gene encoding phosphodiesterase 4D confers risk of ischemic stroke. Nat Genet. 35, 131-8 (2003).
    30. Falk, C. T. & Rubinstein, P. Haplotype relative risks: an easy reliable way to construct a proper control sample for risk calculations. Ann Hum Genet 51 (Pt 3), 227-33 (1987).
    31. Mantel, N. & Haenszel, W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 22, 719-48 (1959).
    32. Grant, S. F. et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 38, 320-3 Epub 2006 Jan. 15 (2006).


Example 3
Surrogate Markers in Regions Showing Association to Prostate Cancer
There are several SNP markers at each locus, either within or close by the LD block in which the anchor SNP is located, that are in strong LD with the SNP markers listed in the above.
The following therefore contains tables and markers lists that are within the scope of the invention. The tables thus list markers that are in LD, as determined by values of r2 in the HapMap CEU population, of greater than 0.2. The tables furthermore list all markers within the LD blocks defined by the present invention. The markers that have been genotyped and assessed by the HapMap do not represent a full coverage of all relevant markers in the regions defined by the invention as being associating with Prostate Cancer. It is thus possible that other markers, such as the markers listed in the below, can be suitably used to assess susceptibility to Prostate Cancer, as described herein.







TABLE 7







SNPs from Chromosome 4q31.21 (within +/− 500 kb of marker rs3923603


in NCBI Build 35), correlated with rs3923603 by R2 > 0.2.


The SNPs were identified in the Caucasian CEU


HapMap Release 22 dataset (http colon-slash-slash www.hapmap.org).














SEQ ID



Location
Location


Marker
NO
D′
R2
P-value
Build 34
Build 35
















rs6857262
343
0.609387
0.228352
1.59E−06
145603677
145245500


rs6857303
9
0.651749
0.245545
4.02E−07
145613415
145255238


rs7665923
10
0.616231
0.222462
7.95E−07
145620837
145262660


rs11725211
11
0.596965
0.211993
2.63E−06
145629733
145271556


rs13103731
12
0.600312
0.205637
2.89E−06
145634114
145275937


rs11736498
13
0.616231
0.222462
7.95E−07
145635911
145277734


rs11723763
14
0.616231
0.222462
7.95E−07
145635923
145277746


rs6831817
15
0.601493
0.201387
0.00001
145655091
145296914


rs13134172
16
1
0.583822
2.33E−20
145676737
145318560


rs10015396
17
1
1
5.98E−34
145677193
145319016


rs4269130
18
1
1
4.05E−34
145679051
145320874


rs4289393
19
1
0.410609
1.47E−15
145679688
145321511


rs6812128
20
1
0.426434
5.10E−15
145679891
145321714


rs7695923
21
1
0.962477
2.03E−32
145695407
145337230


rs4599356
22
1
1
1.88E−34
145701080
145342903


rs7700104
344
1
1
1.88E−34
145701236
145343059


rs4240360
345
1
1
1.88E−34
145703019
145344842


rs7692784
23
0.881426
0.304643
7.51E−10
145707723
145349546


rs4303930
24
0.881426
0.304643
7.51E−10
145713152
145354975


rs10012731
25
0.871279
0.299448
4.18E−09
145714018
145355841


rs4417920
27
0.873144
0.280005
5.98E−09
145717918
145359741


rs4505762
28
0.872158
0.296753
4.74E−09
145718312
145360135


rs4583707
29
0.881426
0.304643
7.51E−10
145718503
145360326


rs4425324
30
0.838714
0.361322
2.16E−10
145719771
145361594


rs13127749
31
0.827215
0.53055
1.22E−15
145722949
145364772


rs4518185
32
0.74467
0.226665
8.95E−07
145723537
145365360


rs4549337
33
0.711728
0.212389
1.19E−06
145724777
145366600


rs4390989
34
0.714302
0.226767
4.20E−07
145725942
145367765


rs4305469
35
0.73105
0.239544
8.48E−08
145726592
145368415


rs7658605
36
0.711728
0.212389
1.19E−06
145728645
145370468


rs6828217
37
0.73105
0.239544
8.48E−08
145739816
145381639


rs12505797
39
0.73105
0.239544
8.48E−08
145751840
145393663


rs12503177
40
0.73105
0.239544
8.48E−08
145751969
145393792


rs4482709
41
0.711728
0.212389
1.19E−06
145752289
145394112


rs4320096
42
0.720957
0.238928
4.38E−07
145752702
145394525


rs13144823
43
0.711728
0.212389
1.19E−06
145754060
145395883


rs11100843
44
0.784071
0.458305
3.28E−13
145755309
145397132


rs7679401
45
0.729175
0.247715
1.03E−07
145759776
145401599


rs7684835
46
0.713635
0.235294
6.39E−07
145760207
145402030


rs11943703
47
0.821897
0.281314
1.08E−08
145761006
145402829


rs12509569
48
0.701656
0.205626
2.56E−06
145763289
145405112


rs12510585
346
0.711728
0.212389
1.19E−06
145770366
145412189


rs7689179
49
0.705517
0.398424
1.37E−11
145773276
145415099


rs10002827
50
0.506653
0.208441
0.000013
145790721
145432544
















TABLE 8







SNPs from Chromosome 17q12 (within +/− 500 kb of marker


rs7501939 in NCBI Build 35), correlated with rs7501939 by R2 > 0.2.


The SNPs were identified in the Caucasian CEU HapMap


Release 22 dataset (http colon-slash-slash www.hapmap.org).














SEQ








ID


P-
Location
Location


Marker
NO
D′
R2
value
Build 34
Build 35
















rs1016990
51
0.50554
0.229681
2.18E
36284465
33163028


rs3744763
52
0.60072
0.315757
4.11E
36286435
33164998


rs2005705
347
0.855936
0.641047
2.19E
36291850
33170413


rs757210
53
0.963829
0.81187
1.86E
36292065
33170628


rs4430796
2
1
0.765625
2.13E
36293590
33172153


rs4239217
54
0.965889
0.902185
6.03E
36294537
33173100


rs7405696
348
1
0.558059
4.17E
36297585
33176148


rs3760511
55
1
0.291667
9.22E
36301863
33180426


rs7213769
56
0.75348
0.218821
6.62E
36310716
33189279
















TABLE 9







SNPs from Chromosome 17q24.3 within +/− 500 kb of marker


rs1859962 in NCBI Build 35), correlated with rs1859962 by R2 > 0.2.


The SNPs were identified in the Caucasian CEU


HapMap Release 22 dataset (http colon-slash-slash www.hapmap.org).


















Location
Location


Marker
SEQ ID No:
D′
R2
P-value
Build 34
Build 35
















rs4793330
57
0.632925
0.229843
1.41E−06
69667720
66582192


rs1861690
58
0.624718
0.240049
1.86E−07
69669376
66583848


rs7222314
59
0.966081
0.933295
1.68E−31
69702061
66616533


rs17765344
349
1
1
1.90E−37
69703997
66618469


rs8071558
60
1
1
1.90E−37
69704796
66619268


rs8072254
350
1
1
1.90E−37
69704939
66619411


rs984434
61
1
1
1.90E−37
69705250
66619722


rs11650165
62
1
0.633803
1.75E−22
69706741
66621213


rs991429
63
1
1
1.90E−37
69706896
66621368


rs4793528
64
1
0.633803
1.75E−22
69707896
66622368


rs9674957
65
1
0.633803
1.75E−22
69708221
66622693


rs8077906
66
1
0.543014
2.20E−19
69709356
66623828


rs8066875
67
1
0.543014
2.20E−19
69710700
66625172


rs9889335
68
1
1
7.79E−37
69712269
66626741


rs4328484
69
1
0.633803
1.75E−22
69713353
66627825


rs8068266
70
1
0.966667
3.12E−35
69714058
66628530


rs4793529
351
1
0.966667
3.12E−35
69715759
66630231


rs7217652
71
0.950239
0.500909
2.23E−15
69716604
66631076


rs6501437
72
1
0.736328
1.23E−25
69717095
66631567


rs6501438
73
1
0.729608
2.40E−25
69717283
66631755


rs8079315
74
1
0.736328
6.95E−26
69717978
66632450


rs2367256
75
1
0.72559
8.37E−25
69718409
66632881


rs2190697
76
1
0.723359
1.57E−24
69718464
66632936


rs4366746
77
1
0.703014
3.43E−24
69718754
66633226


rs4366747
78
1
0.707267
9.94E−25
69718766
66633238


rs2159034
79
1
0.7114
5.03E−25
69718878
66633350


rs1013999
80
1
0.707267
1.73E−24
69719058
66633530


rs4793530
81
1
0.7114
5.03E−25
69722386
66636858


rs11654749
82
0.599655
0.281972
1.87E−08
69722729
66637201


rs11653132
83
0.630676
0.353293
3.94E−10
69726955
66641427


rs4300694
84
0.583272
0.249279
3.84E−07
69727959
66642431


rs8076830
85
1
0.7114
5.03E−25
69729032
66643504


rs9900242
86
0.607515
0.286639
1.44E−08
69732754
66647226


rs9908442
352
0.607515
0.286639
1.44E−08
69735071
66649543


rs4793334
353
0.56962
0.257808
1.60E−07
69735105
66649577


rs2058083
87
0.607515
0.286639
1.44E−08
69735526
66649998


rs2058084
354
0.607515
0.286639
1.44E−08
69736140
66650612


rs2058085
88
0.607515
0.286639
1.44E−08
69736170
66650642


rs1468481
89
1
0.340617
1.37E−12
69737102
66651574


rs9915190
355
0.607515
0.286639
1.44E−08
69739751
66654223


rs2041114
90
1
0.7114
5.03E−25
69741740
66656212


rs723338
91
1
0.7114
5.03E−25
69742536
66657008


rs2041115
92
1
0.709159
9.41E−25
69743550
66658022


rs8064263
93
1
0.7114
8.79E−25
69743953
66658425


rs9897865
94
1
0.7114
5.03E−25
69744199
66658671


rs11656242
95
0.685791
0.454632
1.88E−13
69744645
66659117


rs9897358
96
1
0.707267
1.73E−24
69744665
66659137


rs11651123
97
0.685791
0.454632
1.88E−13
69744714
66659186


rs11657298
98
0.685791
0.454632
1.88E−13
69744759
66659231


rs11651469
99
0.847433
0.586131
7.42E−18
69745642
66660114


rs11650501
100
0.847433
0.586131
7.42E−18
69745681
66660153


rs719615
101
0.710626
0.471507
9.37E−14
69747033
66661505


rs1558119
102
0.68918
0.470673
1.02E−13
69749095
66663567


rs12150098
103
0.685791
0.454632
1.88E−13
69752957
66667429


rs9910829
104
1
0.7114
5.03E−25
69756700
66671172


rs7220274
105
1
0.7114
5.03E−25
69756890
66671362


rs17224833
106
1
0.7114
5.03E−25
69757598
66672070


rs2108534
107
1
0.726354
4.96E−24
69758023
66672495


rs2108535
108
0.715546
0.497408
4.04E−14
69758268
66672740


rs8182284
109
0.667129
0.419797
1.12E−11
69758514
66672986


rs8182286
110
1
0.7114
8.79E−25
69758677
66673149


rs4793533
111
1
0.7114
8.79E−25
69761594
66676066


rs8069925
112
1
0.7114
5.03E−25
69761985
66676457


rs8068189
113
0.813444
0.558734
7.88E−17
69762018
66676490


rs9901508
114
1
0.7114
5.03E−25
69762322
66676794


rs9907418
115
1
0.7114
5.03E−25
69762342
66676814


rs2367263
116
1
0.687204
3.34E−24
69763411
66677883


rs1859964
117
1
0.687204
3.34E−24
69763694
66678166


rs1859965
118
1
0.682793
6.60E−24
69764218
66678690


rs6501446
119
1
0.687204
3.34E−24
69765181
66679653


rs4793534
120
0.648752
0.40685
4.45E−12
69765416
66679888


rs4239156
121
1
0.687204
3.34E−24
69765504
66679976


rs4793335
122
0.679827
0.431951
9.14E−13
69765814
66680286


rs2097984
123
0.673634
0.410059
4.12E−12
69768349
66682821


rs11654068
124
0.71603
0.434667
2.65E−12
69769659
66684131


rs8079962
125
0.958736
0.654018
6.44E−21
69769825
66684297


rs6501447
356
0.958736
0.654018
6.44E−21
69770221
66684693


rs2886914
126
0.67655
0.427544
1.52E−12
69770936
66685408


rs8076811
127
1
0.633405
1.85E−20
69772530
66687002


rs17178251
128
0.647061
0.404731
8.59E−12
69774002
66688474


rs17765644
357
0.679827
0.431951
9.14E−13
69776615
66691087


rs9913988
358
1
0.687204
3.34E−24
69777181
66691653


rs758106
129
1
0.687204
3.34E−24
69778126
66692598


rs740408
130
0.66562
0.399756
2.17E−11
69778219
66692691


rs4570900
131
0.673728
0.403728
1.72E−10
69783489
66697961


rs4611499
132
0.640666
0.400741
6.36E−12
69785892
66700364


rs7214479
133
0.958736
0.654018
6.44E−21
69788072
66702544


rs1008348
134
0.655348
0.429398
9.50E−13
69788439
66702911


rs6501449
135
0.828717
0.514834
2.28E−14
69789968
66704440


rs6501451
136
1
0.687204
3.34E−24
69790254
66704726


rs6501452
137
0.826939
0.553522
1.68E−14
69790410
66704882


rs11870732
138
0.648752
0.40685
4.45E−12
69792364
66706836


rs17178370
139
0.666131
0.413925
6.76E−12
69792664
66707136


rs7225025
140
0.79739
0.519604
6.69E−15
69794797
66709269


rs17178377
141
0.681069
0.450817
3.20E−13
69795256
66709728


rs11655744
142
1
0.675693
7.14E−23
69796179
66710651


rs2367266
143
1
0.687204
3.34E−24
69797110
66711582


rs1107305
144
0.769646
0.495603
1.89E−14
69797766
66712238


rs6501455
145
0.809767
0.535236
4.42E−16
69798934
66713406


rs7209505
146
0.958131
0.646852
2.29E−20
69800787
66715259


rs2190463
147
0.958736
0.654018
6.44E−21
69804591
66719063


rs2190456
148
0.749106
0.473517
2.05E−12
69808489
66722961


rs983085
149
0.774954
0.507149
3.61E−15
69809184
66723656


rs6501459
150
0.648752
0.40685
4.45E−12
69810578
66725050


rs4793538
151
0.635
0.392093
3.88E−11
69813051
66727523


rs2158905
152
0.774114
0.50285
6.37E−15
69813164
66727636


rs2190457
153
0.652378
0.421393
2.62E−12
69813532
66728004


rs11655567
154
0.774954
0.507149
3.61E−15
69813810
66728282


rs7225458
155
0.648752
0.40685
4.45E−12
69815469
66729941


rs10401004
156
0.917182
0.587347
3.76E−18
69815879
66730351


rs917278
157
0.770798
0.474348
2.58E−14
69819048
66733520


rs1978203
158
0.772049
0.481569
1.74E−14
69819792
66734264


rs1978204
159
0.807013
0.514618
4.54E−15
69820068
66734540


rs737956
160
0.914232
0.592181
5.02E−18
69820991
66735463


rs737957
161
0.913582
0.572582
8.18E−17
69821032
66735504


rs8075481
162
0.918433
0.595451
1.08E−18
69821319
66735791


rs7224058
163
0.638679
0.396624
1.04E−11
69822902
66737374


rs7215307
164
0.601503
0.352766
3.72E−09
69823490
66737962


rs4793541
165
0.774954
0.507149
3.61E−15
69824718
66739190


rs7221080
166
0.776722
0.504519
3.49E−15
69827095
66741567


rs8064388
167
0.772049
0.481569
1.74E−14
69828140
66742612


rs9906756
168
0.916614
0.57259
5.80E−18
69833167
66747639


rs17178530
169
0.769449
0.479973
3.18E−14
69833235
66747707


rs17765886
170
0.763227
0.477295
2.28E−13
69833328
66747800


rs8070461
171
0.910313
0.547145
1.24E−16
69837995
66752467


rs9891216
172
0.874632
0.520827
4.65E−16
69840055
66754527
















TABLE 10







SNPs from Chromosome Xp11.22


(within +/− 1500 kb of rs5945572 in NCBI Build 35),


correlated with rs5945572 by R2 > 0.2.


The SNPs were identified in the Caucasian CEU HapMap


Release 22 dataset (http colon-slash-slash www.hapmap.org).


















Location
Location


Marker
SEQ ID No:
D′
R2
P-value
Build 34
Build 35
















rs972635
173
0.881031
0.424148
6.97E−12
50084494
51184428


rs1875754
174
0.881031
0.424148
6.97E−12
50095192
51195126


rs4907772
175
0.881031
0.424148
6.97E−12
50098347
51198281


rs5945605
7
1
0.76906
2.30E−23
50107288
51207222


rs5945606
177
1
0.757565
1.68E−22
50107963
51207897


rs5945569
178
1
0.874668
3.64E−26
50110017
51209951


rs5987418
179
1
0.870142
2.86E−25
50114019
51213953


rs4907790
180
1
0.876757
3.46E−26
50114517
51214451


rs5945607
181
1
0.874668
5.64E−26
50116514
51216448


rs11798651
183
1
0.835656
7.47E−25
50118401
51218335


rs5945609
184
1
0.874668
3.64E−26
50119272
51219206


rs5945610
185
1
0.874668
3.64E−26
50119351
51219285


rs10284147
186
1
0.805955
1.72E−24
50121302
51221236


rs1327301
187
1
0.919246
1.83E−28
50126863
51226797


rs1327302
188
1
1
2.99E−33
50127421
51227355


rs1327304
189
1
1
2.99E−33
50130982
51230916


rs5987421
359
1
1
2.07E−32
50143296
51243230


rs5945617
190
1
1
2.99E−33
50144027
51243961


rs5945618
191
1
1
2.99E−33
50145357
51245291


rs12854262
192
1
0.226378
1.56E−07
50154146
51254080


rs5945619
193
1
0.923404
2.93E−29
50158478
51258412


rs5945620
194
0.884106
0.44477
5.44E−12
50159476
51259410


rs1891702
195
1
0.923404
2.93E−29
50162267
51262201


rs4907792
196
1
0.923404
2.93E−29
50165589
51265523


rs2384958
197
1
0.923404
2.93E−29
50166203
51266137


rs1936038
198
1
0.887734
3.12E−27
50168227
51268161


rs5945573
199
1
0.887734
5.37E−28
50171156
51271090


rs4907796
200
1
0.887734
5.37E−28
50178055
51277989


rs4907775
201
1
0.887734
5.37E−28
50180006
51279940


rs6614426
202
1
0.887734
5.37E−28
50181230
51281164


rs1970956
203
1
0.887734
5.37E−28
50185184
51285118


rs1970957
204
1
0.887734
5.37E−28
50185256
51285190


rs2153993
205
1
0.887734
5.37E−28
50186545
51286479


rs6614428
206
1
0.887734
5.37E−28
50188982
51288916


rs1541241
208
1
0.887734
5.37E−28
50204595
51304529


rs1541240
209
1
0.887734
5.37E−28
50204917
51304851


rs1592303
210
1
0.887734
3.12E−27
50207088
51307022


rs1110404
211
0.884106
0.44477
5.44E−12
50216553
51316487


rs1110405
212
0.884106
0.44477
5.44E−12
50216780
51316714


rs1343272
214
1
0.887734
5.37E−28
50224746
51324680


rs5987438
215
1
0.887734
1.82E−26
50225787
51325721


rs6614317
216
1
0.887734
3.12E−27
50226607
51326541


rs1936037
217
1
0.887734
5.37E−28
50227182
51327116


rs1936036
218
1
0.887734
5.37E−28
50227627
51327561


rs3131302
219
0.884106
0.44477
5.44E−12
50227660
51327594


rs1361837
220
1
0.887734
5.37E−28
50229870
51329804


rs1936035
221
1
0.887734
5.37E−28
50233065
51332999


rs4457126
222
1
0.887734
5.37E−28
50234705
51334639


rs4308906
223
1
0.887734
5.37E−28
50235272
51335206


rs4907781
225
0.884106
0.44477
5.44E−12
50255769
51355703


rs5945650
226
1
0.853659
7.16E−27
50257193
51357127


rs7064708
227
1
0.887734
5.37E−28
50259241
51359175


rs1936034
228
1
0.887734
5.37E−28
50259756
51359690


rs5945651
229
1
0.887734
5.37E−28
50260300
51360234


rs1541238
230
1
0.887734
5.37E−28
50260441
51360375


rs1984280
231
0.920236
0.808522
1.48E−22
50261077
51361011


rs2721996
232
0.839241
0.264711
1.17E−07
50261165
51361099


rs5945653
233
0.835532
0.255508
1.71E−07
50261315
51361249


rs5945578
234
0.851627
0.563801
1.49E−14
50261944
51361878


rs3955398
235
0.847168
0.539639
8.51E−14
50263639
51363573


rs1419039
236
0.851627
0.563801
1.49E−14
50264982
51364916


rs1419038
237
0.850283
0.571894
7.80E−14
50266006
51365940


rs5987446
238
0.852174
0.549149
6.02E−14
50268126
51368060


rs1419040
239
0.851627
0.563801
1.49E−14
50268841
51368775


rs6614454
240
0.85229
0.562357
1.49E−14
50269140
51369074


rs1541242
241
0.835532
0.255508
1.71E−07
50270359
51370293


rs12688960
360
0.851627
0.563801
1.49E−14
50271345
51371279


rs6521823
242
0.851627
0.563801
1.49E−14
50271564
51371498


rs5987447
243
0.851627
0.563801
1.49E−14
50274370
51374304


rs5945579
244
0.851627
0.563801
1.49E−14
50276001
51375935


rs7057039
245
0.851627
0.563801
1.49E−14
50279417
51379351


rs2185880
246
0.851627
0.563801
1.49E−14
50289478
51389412


rs5987448
247
0.851627
0.563801
1.49E−14
50294562
51394496


rs11091750
248
0.851627
0.563801
1.49E−14
50296878
51396812


rs4907804
249
0.851627
0.563801
1.49E−14
50301057
51400991


rs5945659
361
0.851627
0.563801
1.49E−14
50304407
51404341


rs5945664
251
0.851627
0.563801
1.49E−14
50310726
51410660


rs5945667
252
0.835532
0.255508
1.71E−07
50314369
51414303


rs12558898
253
0.851627
0.563801
1.49E−14
50314532
51414466


rs5945586
254
0.850882
0.562821
2.98E−14
50315435
51415369


rs5945668
255
0.850882
0.562821
2.98E−14
50315732
51415666


rs5945587
256
0.846183
0.492698
1.68E−13
50316246
51416180


rs4544871
259
0.835849
0.254063
1.70E−07
50324147
51424081


rs4473816
260
0.846183
0.492698
1.68E−13
50327396
51427330


rs5945590
261
0.846183
0.492698
1.68E−13
50354050
51453984


rs5945677
262
0.845164
0.491515
3.33E−13
50354977
51454911


rs12394792
263
0.846183
0.492698
1.68E−13
50357758
51457692


rs5951067
264
0.846183
0.492698
1.68E−13
50385612
51485546


rs4986571
265
0.846183
0.492698
1.68E−13
50391482
51491416


rs5951072
266
0.846183
0.492698
1.68E−13
50396019
51495953


rs6614493
267
0.846183
0.492698
1.68E−13
50398854
51498788


rs5951074
268
0.846183
0.492698
1.68E−13
50400135
51500069


rs11796743
269
0.816525
0.215395
1.92E−06
50403001
51502935


rs974285
270
0.846183
0.492698
1.68E−13
50403465
51503399


rs976556
271
0.846183
0.492698
1.68E−13
50407247
51507181


rs3910588
272
0.846183
0.492698
1.68E−13
50410609
51510543


rs3910587
273
0.846183
0.492698
1.68E−13
50410688
51510622


rs1439461
274
0.816525
0.215395
1.92E−06
50415032
51514966


rs2118952
275
0.846183
0.492698
1.68E−13
50419995
51519929


rs4986558
276
0.845347
0.509733
1.30E−13
50425927
51525861


rs5991819
277
0.846183
0.492698
1.68E−13
50428058
51527992


rs5991820
278
0.809912
0.201932
4.54E−06
50430926
51530860


rs2118951
281
0.846183
0.492698
1.68E−13
50435735
51535669


rs12844657
282
0.846183
0.492698
1.68E−13
50439534
51539468


rs11796701
283
0.816525
0.215395
1.92E−06
50440140
51540074


rs9887648
284
0.844693
0.484514
3.99E−13
50440655
51540589


rs12010969
285
0.844693
0.484514
3.99E−13
50441124
51541058


rs5991822
286
0.809912
0.201932
4.54E−06
50443963
51543897


rs5991733
287
0.895028
0.238037
6.89E−07
50444078
51544012


rs12393443
288
0.816525
0.215395
1.92E−06
50444898
51544832


rs5991824
290
0.893717
0.240881
8.45E−07
50452223
51552157


rs4986559
291
0.846183
0.492698
1.68E−13
50455432
51555366


rs7890241
292
0.846183
0.492698
1.68E−13
50457039
51556973


rs4986553
293
0.846183
0.492698
1.68E−13
50463601
51563535


rs5991735
294
0.846183
0.492698
1.68E−13
50469690
51569624


rs5951078
295
0.846183
0.492698
1.68E−13
50470079
51570013


rs5951079
296
0.846183
0.492698
1.68E−13
50472457
51572391


rs4986554
297
0.846183
0.492698
1.68E−13
50480142
51580076


rs1439460
298
0.846183
0.492698
1.68E−13
50490087
51590021


rs5951064
299
0.846183
0.492698
1.68E−13
50490488
51590422


rs5951083
300
0.846183
0.492698
1.68E−13
50497103
51597037


rs7061919
301
0.730286
0.311359
4.35E−07
50498017
51597951


rs7056700
302
0.845232
0.48289
3.96E−13
50498182
51598116


rs12853137
303
0.844696
0.480044
7.80E−13
50498563
51598497


rs12353683
304
0.758564
0.432039
9.01E−12
50502290
51602224


rs7880576
305
0.846183
0.492698
1.68E−13
50509785
51609719


rs4133299
306
0.846183
0.492698
1.68E−13
50517515
51617449


rs5991738
307
0.846183
0.492698
1.68E−13
50517932
51617866


rs1992271
308
0.846183
0.492698
1.68E−13
50526679
51626613


rs5991739
309
0.846183
0.492698
1.68E−13
50527775
51627709


rs4986555
362
0.840674
0.488384
6.28E−13
50528742
51628676


rs1048437
310
0.846183
0.492698
1.68E−13
50532933
51632867


rs5951087
311
0.846183
0.492698
1.68E−13
50535789
51635723


rs7053197
312
0.846183
0.492698
1.68E−13
50542948
51642882


rs5991707
313
0.846183
0.492698
1.68E−13
50547611
51647545


rs11796891
314
0.816525
0.215395
1.92E−06
50550769
51650703


rs5951091
315
0.758564
0.432039
9.01E−12
50565385
51665319


rs7053327
316
0.758564
0.432039
9.01E−12
50570716
51670650


rs7051319
317
0.758564
0.432039
9.01E−12
50575195
51675129


rs5991744
318
0.758564
0.432039
9.01E−12
50577864
51677798


rs11796974
319
0.758564
0.432039
9.01E−12
50583935
51683869


rs4074722
320
0.758564
0.432039
9.01E−12
50595795
51695729


rs4986557
322
0.756348
0.424039
2.02E−11
50611457
51711391


rs6614515
323
0.717503
0.401207
9.50E−11
50611949
51711883


rs4131729
324
0.718465
0.404427
4.94E−11
50614000
51713934


rs5991762
325
0.71325
0.388331
2.35E−10
50614415
51714349


rs11798798
326
0.788135
0.451822
9.61E−12
50629527
51729461


rs5991776
328
0.711179
0.395165
1.72E−10
50640447
51740381


rs5951070
329
0.718465
0.404427
4.94E−11
50640665
51740599


rs11797967
330
0.718465
0.404427
4.94E−11
50643312
51743246


rs5991804
332
0.718465
0.404427
4.94E−11
50659859
51759793


rs5991805
333
0.718465
0.404427
4.94E−11
50662313
51762247


rs12845073
334
0.718465
0.404427
4.94E−11
50664330
51764264


rs12851025
335
0.709544
0.390431
1.96E−10
50664341
51764275


rs5991812
337
0.718465
0.404427
4.94E−11
50670300
51770234


rs5951109
338
0.718465
0.404427
4.94E−11
50675936
51775870


rs5951114
339
0.718465
0.404427
4.94E−11
50692450
51792384


rs4986573
340
0.718465
0.404427
4.94E−11
50695908
51795842
















TABLE 11







All SNP markers, from Chromosome 2p, located between 62684001 bp


and 64597002 bp (Build 35), having correlation with rs2710646 of


R2 > 0.2 or higher in the Caucasian CEU HapMap samples


(http colon-slash-slash www.hapmap.org).














Location





SNP1
SNP2
SNP2
D′
R2
P-value















rs2710646
rs13417654
62772272
0.494791
0.210174
0.000141


rs2710646
rs17025954
62779758
0.810584
0.552754
5.02E−10


rs2710646
rs901529
62792002
1
0.414444
1.02E−11


rs2710646
rs10192894
62813470
1
0.220126
3.08E−08


rs2710646
rs360808
62902009
1
0.308176
5.94E−10


rs2710646
rs7591708
62996584
1
0.308176
5.94E−10


rs2710646
rs17432497
63001871
1
1
1.43E−20


rs2710646
rs12713469
63034461
1
0.254487
6.07E−09


rs2710646
rs13410889
63062312
1
0.261713
4.46E−09


rs2710646
rs721048
63106265
1
1
1.43E−20


rs2710646
rs2553041
63212880
1
1
1.43E−20


rs2710646
rs17657646
63241156
1
0.209486
0.000202


rs2710646
rs10173115
63330054
1
0.205138
6.51E−08


rs2710646
rs17408652
63365024
0.918367
0.779745
6.98E−15


rs2710646
rs7568224
63365530
0.918367
0.779745
6.98E−15


rs2710646
rs17408841
63372508
0.917558
0.721288
4.99E−14


rs2710646
rs2421862
63376507
0.918367
0.779745
6.98E−15


rs2710646
rs13431765
63392263
0.916528
0.672205
4.32E−13


rs2710646
rs17408988
63393109
1
0.22813
2.09E−08


rs2710646
rs7605319
63398029
0.917396
0.722519
8.98E−14


rs2710646
rs13387839
63403105
0.890749
0.434253
3.92E−08


rs2710646
rs12151606
63404709
0.905052
0.381039
2.49E−08


rs2710646
rs2421822
63412016
1
0.236507
1.40E−08


rs2710646
rs13417792
63421734
0.917396
0.722519
8.98E−14


rs2710646
rs10203263
63428935
0.834075
0.597236
2.46E−11


rs2710646
rs6718609
63434343
0.836335
0.646667
2.73E−12


rs2710646
rs1850984
63439476
0.916748
0.670777
2.40E−13


rs2710646
rs13422328
63440711
0.918352
0.778589
7.84E−15


rs2710646
rs17432775
63440997
0.917581
0.72281
4.45E−14


rs2710646
rs2699396
63463324
1
0.240506
1.20E−08


rs2710646
rs1607203
63468803
0.910807
0.459722
3.36E−10


rs2710646
rs2677436
63470469
0.909889
0.43738
6.75E−10


rs2710646
rs2677438
63470610
0.910807
0.459722
3.36E−10


rs2710646
rs2699388
63470671
0.910807
0.459722
3.36E−10


rs2710646
rs2677439
63471532
0.909889
0.43738
6.75E−10


rs2710646
rs970278
63472088
0.909889
0.43738
6.75E−10


rs2710646
rs2945032
63472734
0.909889
0.43738
6.75E−10


rs2710646
rs1607205
63475042
0.909889
0.43738
6.75E−10


rs2710646
rs2030188
63483847
0.908851
0.414564
1.45E−09


rs2710646
rs12713476
63484384
0.909889
0.43738
6.75E−10


rs2710646
rs6725694
63485436
0.908851
0.414564
1.45E−09


rs2710646
rs10207356
63486549
0.909781
0.434899
7.58E−10


rs2710646
rs10173637
63486655
0.911723
0.48437
1.58E−10


rs2710646
rs7596446
63486740
0.911723
0.48437
1.58E−10


rs2710646
rs2421881
63487219
0.911723
0.48437
1.58E−10


rs2710646
rs4416201
63487371
0.909889
0.43738
6.75E−10


rs2710646
rs4671470
63488447
0.911357
0.457872
2.33E−10


rs2710646
rs4671471
63488458
0.909781
0.434899
7.58E−10


rs2710646
rs11684108
63488821
0.910709
0.457239
3.78E−10


rs2710646
rs12713477
63489156
0.908851
0.414564
1.45E−09


rs2710646
rs1517405
63490102
0.908851
0.414564
1.45E−09


rs2710646
rs6724044
63490289
0.909781
0.434899
7.58E−10


rs2710646
rs4671473
63490468
0.909781
0.434899
7.58E−10


rs2710646
rs4671474
63490638
0.909781
0.434899
7.58E−10


rs2710646
rs6545984
63491667
0.910807
0.459722
3.36E−10


rs2710646
rs4611627
63492530
0.909889
0.43738
6.75E−10


rs2710646
rs11125957
63493006
0.909889
0.43738
6.75E−10


rs2710646
rs6744720
63495567
0.908851
0.414564
1.45E−09


rs2710646
rs6748030
63495983
0.909889
0.43738
6.75E−10


rs2710646
rs6705776
63496021
0.909889
0.43738
6.75E−10


rs2710646
rs4428008
63497049
0.909889
0.43738
6.75E−10


rs2710646
rs4560098
63497112
0.909889
0.43738
6.75E−10


rs2710646
rs11691718
63499148
0.908851
0.414564
1.45E−09


rs2710646
rs4671476
63499173
0.908851
0.414564
1.45E−09


rs2710646
rs7591562
63499636
0.908851
0.414564
1.45E−09


rs2710646
rs6545986
63499651
0.908851
0.414564
1.45E−09


rs2710646
rs12713478
63500870
0.908851
0.414564
1.45E−09


rs2710646
rs6708847
63502115
0.908851
0.414564
1.45E−09


rs2710646
rs6709115
63502393
0.908851
0.414564
1.45E−09


rs2710646
rs12994711
63502769
0.908608
0.409551
1.83E−09


rs2710646
rs11675647
63502971
0.908608
0.409551
1.83E−09


rs2710646
rs6713500
63503565
0.90703
0.412904
2.89E−09


rs2710646
rs2090479
63504395
0.908851
0.414564
1.45E−09


rs2710646
rs6734468
63505103
0.908851
0.414564
1.45E−09


rs2710646
rs6706180
63505335
0.908851
0.414564
1.45E−09


rs2710646
rs1517404
63512091
0.908851
0.414564
1.45E−09


rs2710646
rs1400688
63513070
0.908354
0.404437
2.32E−09


rs2710646
rs1400687
63513300
0.908851
0.414564
1.45E−09


rs2710646
rs1517401
63515241
0.909889
0.43738
6.75E−10


rs2710646
rs1400686
63517041
0.909889
0.43738
6.75E−10


rs2710646
rs6545988
63517266
0.909889
0.43738
6.75E−10


rs2710646
rs6708627
63518460
0.909889
0.43738
6.75E−10


rs2710646
rs6545989
63519801
0.905708
0.501477
5.81E−10


rs2710646
rs4671480
63522022
0.909889
0.43738
6.75E−10


rs2710646
rs4671481
63522061
0.909889
0.43738
6.75E−10


rs2710646
rs7558922
63523226
0.909889
0.43738
6.75E−10


rs2710646
rs7585664
63523250
0.738118
0.357933
1.39E−07


rs2710646
rs7599736
63523390
0.909889
0.43738
6.75E−10


rs2710646
rs7559137
63523402
0.909889
0.43738
6.75E−10


rs2710646
rs6545991
63523597
0.909889
0.43738
6.75E−10


rs2710646
rs10169292
63527769
0.909889
0.43738
6.75E−10


rs2710646
rs4671484
63529274
0.909889
0.43738
6.75E−10


rs2710646
rs13021322
63533124
0.888132
0.200734
6.26E−06


rs2710646
rs11691192
63535613
0.885133
0.206097
8.03E−06


rs2710646
rs11686044
63535866
0.909889
0.43738
6.75E−10


rs2710646
rs11691357
63535983
0.909889
0.43738
6.75E−10


rs2710646
rs7561802
63536600
0.909889
0.43738
6.75E−10


rs2710646
rs6545997
63536680
0.909889
0.43738
6.75E−10


rs2710646
rs11687645
63536903
0.889708
0.209097
4.24E−06


rs2710646
rs6717060
63543142
0.907789
0.393501
2.98E−09


rs2710646
rs10209822
63546981
0.909558
0.429859
9.59E−10


rs2710646
rs1996401
63548282
0.888894
0.204698
5.35E−06


rs2710646
rs1517409
63549616
0.910075
0.458982
6.78E−10


rs2710646
rs2176418
63550326
0.910807
0.459722
3.36E−10


rs2710646
rs2176417
63553128
0.908731
0.412069
1.63E−09


rs2710646
rs2421886
63553846
0.909781
0.434899
7.58E−10


rs2710646
rs908622
63554219
0.908851
0.414564
1.45E−09


rs2710646
rs11125967
63559326
0.909781
0.434899
7.58E−10


rs2710646
rs1829261
63561691
0.908731
0.412069
1.63E−09


rs2710646
rs2176416
63564789
0.908851
0.414564
1.45E−09


rs2710646
rs11883730
63566764
0.907789
0.393501
2.98E−09


rs2710646
rs11894445
63566882
0.909781
0.434899
7.58E−10


rs2710646
rs1922421
63567326
0.907789
0.393501
2.98E−09


rs2710646
rs6546001
63568353
0.908851
0.414564
1.45E−09


rs2710646
rs6546002
63568363
0.908608
0.409551
1.83E−09


rs2710646
rs7558796
63571642
0.908731
0.412069
1.63E−09


rs2710646
rs13011799
63571784
0.908851
0.414564
1.45E−09


rs2710646
rs7571697
63572139
0.908851
0.414564
1.45E−09


rs2710646
rs932172
63577431
0.908851
0.414564
1.45E−09


rs2710646
rs932171
63577476
0.908851
0.414564
1.45E−09


rs2710646
rs6741817
63577509
0.908851
0.414564
1.45E−09


rs2710646
rs4671494
63579532
0.907789
0.393501
2.98E−09


rs2710646
rs2292795
63579964
0.908851
0.414564
1.45E−09


rs2710646
rs2292794
63580306
0.908851
0.414564
1.45E−09


rs2710646
rs908621
63580576
0.908851
0.414564
1.45E−09


rs2710646
rs2421887
63580753
0.908851
0.414564
1.45E−09


rs2710646
rs2901580
63580808
0.901703
0.407314
7.51E−09


rs2710646
rs12618974
63581977
0.908851
0.414564
1.45E−09


rs2710646
rs7599208
63597004
0.888894
0.204698
5.35E−06


rs2710646
rs4671503
63611625
0.636105
0.225385
0.000026


rs2710646
rs4671505
63618837
0.633016
0.215614
0.00004


rs2710646
rs1356390
63644159
0.636105
0.225385
0.000026


rs2710646
rs6546005
63646605
0.636105
0.225385
0.000026


rs2710646
rs6736411
63648594
0.636105
0.225385
0.000026


rs2710646
rs7608470
63666012
0.631327
0.210567
0.000044


rs2710646
rs2028887
63690757
0.636105
0.225385
0.000026


rs2710646
rs10176522
63696967
0.636105
0.225385
0.000026


rs2710646
rs6546007
63699329
0.636105
0.225385
0.000026


rs2710646
rs989527
63701325
0.636105
0.225385
0.000026


rs2710646
rs1867849
63701466
0.636105
0.225385
0.000026


rs2710646
rs1446565
63706109
0.631955
0.22009
0.00004


rs2710646
rs1446564
63706216
0.636105
0.225385
0.000026


rs2710646
rs10865338
63707043
0.632602
0.222098
0.000037


rs2710646
rs10168545
63708818
0.636105
0.225385
0.000026


rs2710646
rs9789351
63715148
0.635516
0.223463
0.000028


rs2710646
rs7576316
63715656
0.636105
0.225385
0.000026


rs2710646
rs964903
63716082
0.631327
0.210567
0.000044


rs2710646
rs1031221
63717344
0.636105
0.225385
0.000026


rs2710646
rs6546009
63720276
0.636105
0.225385
0.000026


rs2710646
rs2421952
63724367
0.636105
0.225385
0.000026


rs2710646
rs7570031
63766799
0.636105
0.225385
0.000026


rs2710646
rs7584404
63768876
0.636105
0.225385
0.000026


rs2710646
rs12105140
63771752
0.636105
0.225385
0.000026


rs2710646
rs959195
63778659
0.636105
0.225385
0.000026


rs2710646
rs2028884
63786682
0.588019
0.264196
0.000016


rs2710646
rs10469944
63791206
0.636105
0.225385
0.000026


rs2710646
rs10469945
63791310
0.636105
0.225385
0.000026


rs2710646
rs6546018
63791956
0.636105
0.225385
0.000026


rs2710646
rs6546019
63791974
0.636105
0.225385
0.000026


rs2710646
rs1446569
63792848
0.636105
0.225385
0.000026


rs2710646
rs2305157
63797319
0.636105
0.225385
0.000026


rs2710646
rs1255
63800468
0.636105
0.225385
0.000026


rs2710646
rs7606045
63801062
0.636105
0.225385
0.000026


rs2710646
rs964880
63801466
0.636105
0.225385
0.000026


rs2710646
rs2604613
63807310
0.636105
0.225385
0.000026


rs2710646
rs262472
63808150
0.636105
0.225385
0.000026


rs2710646
rs262473
63808347
0.636105
0.225385
0.000026


rs2710646
rs262493
63819600
0.636105
0.225385
0.000026


rs2710646
rs190519
63832959
0.636105
0.225385
0.000026


rs2710646
rs262501
63833191
0.636105
0.225385
0.000026


rs2710646
rs196125
63833802
0.634912
0.221525
0.000031


rs2710646
rs262502
63834257
0.636105
0.225385
0.000026


rs2710646
rs262503
63834971
0.636105
0.225385
0.000026


rs2710646
rs262504
63835769
0.636105
0.225385
0.000026


rs2710646
rs262505
63836320
0.636105
0.225385
0.000026


rs2710646
rs262476
63845141
0.636105
0.225385
0.000026









LD Structure at Associating Loci

The LD structure of the markers and haplotypes that associates with prostate cancer, in five chromosomal regions, was assessed using HAPMAP data release 19, except for the Chromosome Xp11.22 region, for which the necessary data was not available. For this region, the LD structure was derived from our own analysis of the samples described herein. Regions characterized by high r2 and/or |D′|-values are characteristic of each LD block, and increased recombination rates between adjacent markers are characteristic of the boundaries between LD blocks (leading to rapidly decreasing r2 and |D′|-values). Thus, in general, regions of high LD structure are seen as genomic “blocks” (LD blocks), within which individual markers are in strong LD as indicated by high r2 and |D′|-values.
Analysis of the LD structure in the regions of the present invention defines boundaries within which markers showing association to prostate cancer are residing. It is possible that markers residing within the LD block regions as defined herein and not listed in Tables 7-11 are also associated with the markers disclosed herein to be associated with prostate cancer (e.g., the markers of Table 1, Table 4a and Table 4b), since such markers are in linkage disequilibrium with the anchor markers listed in Table 2. The reason is that not all markers residing within the regions have been tested explicitly for association to prostate cancer, but the presence of the LD blocks as defined herein and the association of markers presented herein (e.g., the markers disclosed in Table 1, Table 4a and Table 4b) suggests that additional markers within the regions and in LD with the anchor markers of Table 2 are also associated with prostate cancer. Tables 12-16 provides a list of publicly known SNP markers in the four regions discussed herein (i.e., LD block C02, LD block C04a, the TCF gene, LD block C17b and LD block COX), based on the public SNP database dbSNP 125, and locations are according to NCBI Build 34).







TABLE 12







Public SNPs from dbSNP Build125 in the LD block C04a region











Location



Name
(Bld 34)






rs6537253
145602078



rs4434191
145602079



rs6844670
145602426



rs4643761
145602575



rs4557214
145602736



rs11723371
145603437



rs11727098
145603582



rs28634998
145603675



rs6857262
145603677



rs11735662
145603753



rs4130880
145604778



rs7682756
145605488



rs4535287
145606058



rs4350961
145606671



rs4613516
145606947



rs3936169
145607497



rs3936168
145607514



rs12500422
145607801



rs13143530
145607817



rs4469024
145607978



rs1132787
145608173



rs11728240
145608328



rs5862686
145608835



rs13125760
145609468



rs11279505
145609583



rs5029339
145609593



rs10031592
145609868



rs4425323
145611159



rs11100830
145611270



rs4645174
145611287



rs6845104
145611416



rs4374581
145611437



rs4484241
145611703



rs4393951
145611767



rs2069004
145611792



rs12108462
145612569



rs10699715
145613243



rs6857303
145613415



rs12645789
145613755



rs6537254
145614238



rs10701183
145614266



rs10643140
145614267



rs6848116
145614813



rs11354655
145614848



rs10024775
145615212



rs6824244
145615607



rs6537255
145616254



rs7673888
145616556



rs7674022
145616585



rs7672629
145616629



rs17292861
145616629



rs13142034
145616659



rs7656975
145616886



rs6835963
145617286



rs12505498
145617846



rs10023823
145617941



rs10012458
145618043



rs28691647
145618141



rs10023998
145618142



rs6857144
145618589



rs6833563
145618590



rs10559726
145618700



rs2667332
145618936



rs6856326
145618954



rs13107492
145618972



rs10015632
145619049



rs17849972
145619313



rs17851030
145619313



rs4867
145619313



rs7658293
145619334



rs7687256
145619335



rs7682260
145619347



rs17858231
145619347



rs17845377
145619347



rs4449373
145619368



rs2305067
145619485



rs4240356
145619877



rs4465992
145620001



rs4465993
145620090



rs7693383
145620147



rs4835576
145620725



rs7665923
145620837



rs17829577
145620837



rs4835577
145620938



rs7694713
145621010



rs4835578
145621019



rs4450876
145621800



rs7696242
145621813



rs4337688
145621833



rs4473613
145621916



rs13142547
145622166



rs13117874
145622214



rs4565031
145622386



rs1143455
145622409



rs11938324
145623187



rs4491970
145623308



rs1373680
145623345



rs1373681
145623376



rs4488896
145623407



rs4392467
145623408



rs1444983
145623467



rs6537257
145624031



rs7673432
145624171



rs7690953
145624193



rs28474907
145624990



rs28711072
145625000



rs28614033
145625007



rs4269129
145625007



rs28521766
145625051



rs4574352
145625244



rs11406104
145625584



rs1470411
145625607



rs1966792
145625831



rs10627192
145626455



rs1444984
145626639



rs1838524
145626769



rs4535288
145626931



rs3733387
145626999



rs3083547
145627034



rs1373682
145627318



rs4464518
145627385



rs11490715
145627539



rs28731264
145627645



rs4286473
145627645



rs11944602
145627857



rs11931075
145627912



rs11935363
145627986



rs11935365
145627996



rs4287959
145628759



rs3194696
145628884



rs4835164
145628903



rs7349621
145628956



rs3209213
145629060



rs4835165
145629080



rs7349651
145629243



rs9997445
145629362



rs7349719
145629368



rs7349654
145629405



rs28556782
145629567



rs11725211
145629733



rs4349551
145630504



rs6419323
145630598



rs4642187
145630598



rs7696658
145630825



rs10708081
145631282



rs4432704
145631283



rs28576209
145631388



rs28465668
145631399



rs4413360
145631434



rs4314231
145631583



rs13138562
145631765



rs13120766
145631855



rs28488827
145631856



rs11731918
145632201



rs6819027
145632210



rs6844008
145632212



rs4355337
145632220



rs11100814
145632334



rs4356869
145632631



rs4629388
145632640



rs4835586
145633205



rs11942789
145633565



rs11943717
145633601



rs11929703
145633721



rs11943767
145633732



rs11929795
145633898



rs6857265
145633963



rs13103731
145634114



rs11939323
145634277



rs28798506
145634353



rs11939410
145634528



rs5001419
145635170



rs5001418
145635172



rs28594673
145635594



rs11736498
145635911



rs11723763
145635923



rs4260483
145635979



rs28444311
145637104



rs11100831
145637270



rs11100832
145637271



rs11414611
145637738



rs11723272
145638003



rs11935223
145638402



rs3806773
145640143



rs3806772
145640145



rs28603191
145640380



rs28533871
145640407



rs28662181
145642227



rs13104399
145642297



rs7681791
145643041



rs3856993
145643693



rs6835623
145643699



rs9995236
145643762



rs7688405
145644032



rs12499911
145644112



rs7659783
145644244



rs13137079
145644268



rs4352423
145644491



rs4336173
145644578



rs4336174
145644587



rs4423827
145644589



rs4336175
145644680



rs28813719
145644820



rs13125462
145644854



rs28823926
145645272



rs13435041
145645302



rs4264778
145645474



rs4359850
145645662



rs28855521
145646293



rs10434188
145646406



rs10452245
145646609



rs7698193
145646927



rs10452246
145647005



rs6537259
145647005



rs13141025
145647297



rs13107394
145647336



rs7438745
145647490



rs7438753
145647552



rs7697694
145648905



rs4383568
145648905



rs4383569
145648906



rs7697695
145648906



rs13149556
145649077



rs6537260
145649632



rs7434993
145649890



rs387988
145650144



rs13119218
145650511



rs12502560
145651217



rs28780480
145651580



rs7677825
145652339



rs13112639
145652653



rs13112856
145652764



rs7688971
145653983



rs7690607
145654148



rs11933261
145654236



rs11732500
145654255



rs7690996
145654353



rs7441048
145654648



rs6831817
145655091



rs11934660
145655438



rs9761473
145656207



rs6838900
145656207



rs11726939
145656500



rs12511610
145656754



rs10008680
145657300



rs10008688
145657340



rs9762726
145657811



rs9685916
145657811



rs6537261
145657811



rs7440632
145657870



rs9761977
145658142



rs9715502
145658142



rs9685368
145658142



rs6419324
145658142



rs4611868
145658142



rs28868097
145659949



rs13113070
145660719



rs7667722
145660969



rs13133352
145661027



rs10857419
145661746



rs12511288
145661773



rs13121373
145662179



rs10024232
145662717



rs11728624
145662770



rs7665326
145663306



rs10030421
145664621



rs12640478
145664920



rs12646795
145665173



rs10673489
145665318



rs5862687
145665695



rs4402988
145666319



rs4432705
145666587



rs6819750
145668098



rs11930911
145668289



rs7439316
145668582



rs7659322
145668785



rs4469025
145668987



rs11727331
145669307



rs4539990
145670484



rs6834247
145670516



rs4303929
145670685



rs9999314
145672034



rs6847842
145672454



rs13134172
145676737



rs10857420
145676917



rs10015396
145677193



rs10213243
145677885



rs4558820
145678132



rs12501242
145679032



rs4269130
145679051



rs4289393
145679688



rs17829589
145679688



rs6812128
145679891



rs13124384
145680376



rs13119039
145680533



rs4835608
145680821



rs11947531
145681127



rs11939413
145682201



rs11942047
145684546



rs13108951
145685301



rs13108952
145685303



rs11100837
145685304



rs10566048
145685313



rs10552752
145686612



rs12508181
145687014



rs4388034
145687324



rs11947279
145688374



rs3923603
145688956



rs7655948
145689137



rs12506435
145689311



rs6838200
145689467



rs7685721
145689488



rs11726495
145691192



rs13115983
145692367



rs6834013
145692585



rs12511121
145695145



rs7695923
145695407



rs13350605
145695845



rs10019883
145695845



rs11100838
145696321



rs4555578
145696570



rs7439076
145696570



rs6537263
145699261



rs4355338
145699261



rs4487295
145700803



rs4599356
145701080



rs11931990
145701176



rs7700104
145701236



rs6148706
145701460



rs4491971
145701618



rs4554017
145701651



rs4379017
145701871



rs10033964
145702395



rs4240360
145703019



rs4835622
145703540



rs4835623
145703584



rs4835624
145703588



rs13111659
145703666



rs4434192
145703996



rs5862688
145706220



rs13127712
145706253



rs13133696
145706387



rs13128382
145706528



rs28568288
145706565



rs7692784
145707723



rs13115407
145708097



rs4585250
145708807



rs28631892
145708869



rs4330310
145708893



rs12644229
145709867



rs28410289
145710108



rs5862689
145710343



rs5862690
145710865



rs5862691
145710867



rs4459948
145711026



rs4452381
145711612



rs4269127
145711675



rs5862692
145711719



rs4345121
145711844



rs4303930
145713152



rs10012731
145714018



rs4491972
145714914



rs12501983
145714922



rs4446264
145715028



rs7440429
145715028



rs11934621
145715496



rs7440593
145715515



rs4491973
145715515



rs4273415
145715657



rs7441555
145715657



rs7436168
145715862



rs4635767
145715862



rs4348046
145715964



rs7441611
145715964



rs4368558
145715977



rs6537264
145715977



rs11100839
145716530



rs6811457
145716935



rs13123634
145717577



rs4417920
145717918



rs4505762
145718312



rs4583707
145718503



rs4505763
145718603



rs4423828
145719032



rs4508836
145719091



rs4425324
145719771



rs4513513
145720198



rs5862693
145720363



rs10657867
145720367



rs6856087
145721074



rs4635768
145721551



rs6832605
145722035



rs13127749
145722949



rs7664319
145722998



rs4518185
145723537



rs4359851
145723864



rs4364211
145723955



rs11734108
145724015



rs13102357
145724472



rs11371652
145724744



rs11450119
145724754



rs4549337
145724777



rs4377535
145724836



rs4386552
145725308



rs4390989
145725942



rs4392468
145726328



rs12513016
145726571



rs4305469
145726592



rs11733518
145727422



rs11934883
145727853



rs11934916
145727905



rs7658605
145728645



rs9308177
145728963



rs5862694
145728965



rs7669619
145729321



rs11938336
145729941



rs10596570
145730167



rs11100840
145730920



rs7349691
145731030



rs11939769
145731398



rs7438710
145731672



rs7435095
145732055



rs4321580
145732055



rs4519751
145732110



rs7435388
145732110



rs6537265
145732883



rs4271962
145733136



rs7690710
145733236



rs4396945
145733269



rs7436364
145733269



rs4626149
145733654



rs7436751
145733654



rs4128281
145733745



rs4481199
145734205



rs7437777
145734205



rs7656927
145734795



rs7436052
145736165



rs7436055
145736179



rs7440160
145736193



rs17869776
145736479



rs13104440
145737381



rs9790335
145737381



rs11100841
145738444



rs28850043
145738464



rs7669987
145739181



rs6828217
145739816



rs4337686
145739883



rs4835625
145739961



rs13351848
145741102



rs10032210
145741102



rs6842847
145741491



rs11934451
145743444



rs13128064
145743514



rs6537266
145743640



rs9759814
145743745



rs9683786
145743745



rs6537267
145743745



rs13115981
145743879



rs6844087
145744012



rs28752136
145744287



rs13136472
145744324



rs6851193
145745138



rs6820942
145745142



rs6851649
145745361



rs6537268
145745365



rs13351745
145745437



rs10031214
145745437



rs4422370
145745834



rs6537269
145745834



rs7437332
145746190



rs4317153
145746190



rs4317154
145746215



rs7437335
145746215



rs28866308
145746499



rs6811004
145746780



rs6829222
145746837



rs12643420
145747495



rs10009929
145748272



rs10007545
145748444



rs28762778
145749228



rs11729636
145749330



rs11100842
145749579



rs28449861
145749755



rs13122044
145750326



rs4287960
145750933



rs4331724
145751306



rs7441314
145751306



rs12505797
145751840



rs12503177
145751969



rs4336169
145752009



rs4269131
145752089



rs4482709
145752289



rs6858517
145752509



rs17829613
145752702



rs4320096
145752702



rs6832567
145752762



rs12504257
145752870



rs10592026
145752872



rs10592027
145752894



rs11937287
145753529



rs13144823
145754060



rs13144992
145754101



rs13105208
145754596



rs13123796
145754808



rs11100843
145755309



rs6854878
145755922



rs7691223
145757447



rs13123322
145758352



rs13123791
145758555



rs7441548
145758930



rs4320097
145758930



rs10303170
145758930



rs10025195
145758951



rs7679401
145759776



rs7684835
145760207



rs10389982
145760419



rs4309786
145760419



rs10012192
145760480



rs13349860
145760480



rs4315730
145760932



rs7440973
145760932



rs11943703
145761006



rs11943710
145761056



rs6537271
145761343



rs10316731
145761343



rs10316736
145761590



rs7696987
145761590



rs13119542
145762396



rs13139062
145762410



rs12647138
145762494



rs12509569
145763289



rs11732756
145763435



rs11729387
145763656



rs13127816
145763979



rs13128959
145764559



rs12506864
145765120



rs12510816
145765121



rs12499055
145765272



rs13102635
145766677



rs4482710
145767508



rs11934670
145768087



rs11725863
145768271



rs7657288
145768833



rs4491974
145769441



rs7436838
145769441



rs10026674
145770126



rs12510585
145770366



rs4613517
145770729



rs11736774
145771275



rs4511953
145771302



rs13114690
145771507



rs13118529
145771638



rs9685252
145771918



rs13121748
145772389



rs7658232
145772816



rs7689179
145773276



rs28396349
145773393



rs11355781
145773626



rs7690204
145773833



rs9994461
145774149



rs11369908
145774396



rs6811546
145774683



rs10707511
145774786



rs6828423
145774804



rs6830134
145774878



rs4597773
145775060



rs4334701
145775065



rs4334702
145775156



rs4597774
145775174



rs4334703
145775290



rs4591542
145775330



rs11933796
145775581



rs10033430
145775585



rs10002790
145775728



rs7656615
145775752



rs7661687
145775787



rs7656994
145775906



rs7656999
145775912



rs7684324
145775965



rs7667380
145776264



rs13125988
145776417



rs9968313
145776461



rs12645983
145777082



rs7692268
145777694



rs7687270
145778329



rs4557215
145778499



rs4285039
145778579



rs4293748
145779895



rs4254717
145779980



rs4438716
145780000



rs5862695
145780041



rs5862696
145780049



rs5001226
145780125



rs7438029
145780146



rs11422979
145780186



rs7700020
145780250



rs7675261
145780366



rs7654269
145780520



rs11736065
145780534



rs7654605
145780672



rs7659591
145780676



rs9684121
145780762



rs7676097
145780790



rs7682399
145780994



rs7664876
145781028



rs7659974
145781096



rs7681225
145781158



rs7681569
145781267



rs11937859
145781545



rs10684018
145781645



rs7666696
145781928



rs7666704
145781933



rs7682836
145781961



rs7687623
145781995



rs7671457
145782025



rs9968341
145782741



rs9968343
145782834



rs4629389
145782873



rs11408501
145783292



rs7673707
145783341



rs7695068
145783493



rs5024787
145783709



rs5024786
145783723



rs4642188
145784396



rs7356365
145784489



rs7356418
145784495



rs7655929
145784862



rs28609938
145784998



rs10012765
145785202



rs10015172
145785249



rs10001335
145785465



rs7662113
145785675



rs13112701
145785731



rs7662334
145785833



rs7692879
145785886



rs11325906
145785926



rs11734478
145786355



rs10028883
145786977



rs9308178
145787077



rs9308179
145787249



rs9308180
145787311



rs4348047
145787525



rs7434323
145787672



rs4586878
145787686



rs4277729
145787740



rs4431168
145787863



rs13146588
145788461



rs4835176
145789014



rs6857355
145789110



rs6835878
145789408



rs9308181
145789824



rs9884309
145789933



rs11100844
145790021



rs11100845
145790038



rs10000202
145790187



rs10002713
145790578



rs10002827
145790721



rs4453896
145790874



rs5862697
145790912



rs4532189
145790951



rs4269132
145791345



rs12711416
145791581



rs28497010
145791894



rs4521297
145792370



rs7674504
145792549



rs4321581
145792660



rs13125170
145792702



rs28488928
145793471



rs13127136
145793772



rs6844385
145793976



rs4388035
145794715



rs6537272
145794726



rs7677370
145796125



rs7682562
145796235



rs13149264
145796979



rs11933270
145796986



rs11933275
145797024



rs11100846
145797191



rs7661384
145798489



rs6819101
145798948



rs5862698
145799143



rs11100847
145799337



rs13105533
145799802



rs28513471
145799903



rs10050184
145800321



rs4410481
145800441



rs4452383
145800549



rs4600870
145800662



rs7438126
145800831



rs28633544
145800888



rs11931474
145800899



rs11945691
145801121



rs4374583
145802263



rs4535289
145802266



rs11100848
145802986



rs6821055
145803625



rs11935505
145804049



rs11100849
145804416
















TABLE 13







Public SNPs from dbSNP Build125 within the TCF2 gene











Location



Name
(Bld 34)






rs12942441
36236581



rs2107135
36236969



rs12936117
36237243



rs2107134
36237340



rs6607285
36237624



rs8068197
36238033



rs3094518
36238441



rs3094517
36238485



rs3094516
36238728



rs3094515
36239203



rs17138522
36239284



rs17624747
36239704



rs739753
36239827



rs17841468
36240323



rs11263755
36241071



rs2057712
36241259



rs17697931
36241357



rs2285741
36241357



rs7222459
36241398



rs7223047
36241769



rs9675203
36241819



rs9912390
36241945



rs10962
36242001



rs17847516
36242001



rs11545641
36242001



rs17138512
36242148



rs11545642
36242383



rs17847515
36242481



rs2688
36242481



rs1058166
36242541



rs17847514
36242651



rs2689
36242651



rs17847513
36242825



rs1800929
36242825



rs2229295
36242826



rs8068014
36242878



rs17847530
36242967



rs3110641
36242967



rs8066605
36243051



rs3094514
36243787



rs5820230
36243977



rs3094513
36244073



rs3110640
36244373



rs3094512
36244490



rs3110639
36244702



rs9675152
36245272



rs3110638
36245313



rs11263756
36245370



rs3049510
36245568



rs5820231
36245571



rs5820232
36245572



rs1110463
36245608



rs10619352
36245796



rs12150371
36245881



rs12935974
36246332



rs7501432
36246343



rs8065402
36246382



rs3940262
36246420



rs11651164
36246836



rs11656043
36246841



rs1859211
36246922



rs8069279
36247107



rs3110637
36247329



rs3094511
36247607



rs3110636
36247626



rs11342001
36247735



rs3110635
36248166



rs10535964
36248170



rs12450532
36248178



rs12453114
36248186



rs11868513
36248242



rs11868535
36248331



rs3110634
36248347



rs3110633
36248619



rs2898655
36248655



rs11464180
36248772



rs3094510
36249319



rs3110632
36249328



rs4795211
36249546



rs4795212
36249793



rs9892033
36249981



rs13341296
36250005



rs9900653
36250005



rs3110630
36250023



rs4795213
36250781



rs7406029
36250781



rs2411156
36250783



rs4502267
36250783



rs4795214
36250783



rs4635384
36251108



rs6422978
36251108



rs13341544
36251132



rs9908712
36251132



rs4795215
36251164



rs6607286
36251164



rs4795216
36251617



rs6607287
36251617



rs12449654
36251626



rs12452659
36251742



rs2411155
36252024



rs2411154
36252106



rs7211959
36252266



rs11656817
36252449



rs7221822
36253596



rs1016991
36253703



rs7207401
36253751



rs11421312
36253836



rs2269845
36254275



rs2269844
36254364



rs11350722
36254468



rs17847522
36254530



rs2269843
36254530



rs9890418
36254543



rs10083829
36254794



rs17847520
36254796



rs2269842
36254796



rs13339672
36254852



rs2269841
36254927



rs12938438
36254935



rs11440398
36254974



rs2269840
36255022



rs8070225
36255431



rs2189303
36255655



rs11381765
36255669



rs8075185
36255766



rs11381764
36255776



rs2074430
36256065



rs11870409
36256161



rs2189302
36256433



rs9905004
36256832



rs17847519
36256847



rs2074429
36256847



rs2074428
36257209



rs17138495
36257493



rs3110619
36257619



rs3094509
36257849



rs12948642
36257978



rs1008284
36258008



rs3094508
36258485



rs7210911
36258850



rs8068474
36259195



rs2189301
36259235



rs17841466
36259274



rs28667231
36259638



rs3094507
36259748



rs2107133
36260447



rs4794757
36260763



rs10701156
36260853



rs17625617
36261045



rs2158254
36261045



rs7207680
36261183



rs11870929
36261640



rs10618694
36262621



rs7221709
36262625



rs3049485
36262676



rs5820233
36262691



rs28497135
36262846



rs7222069
36262859



rs7213333
36263418



rs9892543
36263509



rs2285740
36264278



rs8065904
36264287



rs8067619
36264288



rs3110649
36265730



rs17847518
36266362



rs7225211
36266500



rs3110648
36266584



rs17138480
36266697



rs3110645
36268726



rs3110644
36268746



rs17138478
36268870



rs3110643
36269042



rs3110642
36269170



rs3110631
36270063



rs3094506
36270247



rs2158253
36270333



rs3094505
36270455



rs11649743
36270529



rs7209295
36270746



rs17138476
36271155



rs17138475
36271223



rs3110618
36271269



rs2411153
36271365



rs3110651
36271418



rs3110650
36271455



rs11263757
36271561



rs10661407
36272411



rs10661408
36272412



rs718961
36272649



rs718960
36272829



rs8068344
36273116



rs12942953
36273323



rs12951345
36273413



rs1985643
36274052



rs4795218
36274060



rs2097759
36275003



rs17138469
36275715



rs4794758
36275978



rs7407025
36276360



rs12450628
36277971



rs7223387
36278023



rs11658433
36278457



rs8066151
36278854



rs9914818
36279263



rs11263758
36279553



rs10451318
36279669



rs2107132
36279752



rs916895
36279777



rs11651496
36279794



rs916894
36279811



rs3049482
36280450



rs10571598
36280461



rs9895178
36281866



rs2107131
36282239



rs2898654
36283307



rs11263759
36283391



rs11263760
36283392



rs3786127
36283424



rs2005706
36283835



rs12943407
36283879



rs2002731
36284080



rs1016990
36284465



rs17847529
36284465



rs17847528
36284562



rs17847527
36284588



rs17847526
36284832



rs880411
36284832



rs9891752
36284854



rs880410
36284861



rs1135579
36284865



rs1135581
36284891



rs1135582
36284892



rs17847525
36284897



rs1135583
36284914



rs5820234
36284988



rs9892918
36285339



rs3744764
36285946



rs17847524
36286435



rs3744763
36286435



rs17847523
36286796



rs17847521
36286806



rs9906451
36286806



rs3837868
36288222



rs7405776
36288572



rs9901538
36288991



rs9895048
36289035



rs3216929
36289413



rs4795219
36289813



rs17138459
36289958



rs9909673
36291355



rs17704811
36291780



rs2005705
36291850



rs757211
36292028



rs757210
36292065



rs9912751
36293132



rs11654969
36293319



rs11263761
36293325



rs4430796
36293590



rs4520879
36294416



rs4239217
36294537



rs17847517
36295280



rs11651755
36295390



rs10908278
36295502



rs11657964
36296317



rs4528622
36296558



rs7501939
36296706



rs8064454
36297136



rs12601991
36297183



rs11263762
36297476



rs7405696
36297585



rs7502069
36297890



rs11651052
36297931



rs757209
36298383



rs9901746
36298699



rs11263763
36299115



rs11658063
36299422



rs12453443
36299671



rs5820235
36300747



rs9899145
36301139



rs9913260
36301447



rs9913297
36301489



rs3760511
36301863



rs17626423
36303917



rs12051720
36304974



rs17138380
36306536



rs17626459
36307305



rs17705019
36307818



rs9906050
36307993



rs11868443
36308036



rs7502487
36309930



rs7213769
36310716



rs12949259
36311129



rs11455435
36311670



rs12603084
36312608



rs6607289
36313150



rs6607290
36313188



rs11381897
36313460



rs17138335
36313685



rs9911288
36313836



rs9912022
36314159



rs10567850
36314686



rs17841462
36315087



rs7213140
36315601



rs12944771
36315626



rs11421430
36316133



rs11434768
36316138



rs10674983
36316147



rs8079492
36316421



rs12940061
36316538



rs12940062
36316541



rs12947560
36316560



rs12940076
36316561



rs7219104
36316592



rs11371770
36316677



rs11384996
36316682



rs4795220
36317218



rs4795221
36317219



rs4795222
36317231



rs9908105
36317283



rs10655162
36317587



rs4795223
36317777



rs4794759
36317936



rs9916121
36318357



rs8067696
36318502



rs12600634
36318598



rs11650348
36318941



rs17705177
36319076



rs6607291
36319412



rs6607292
36319632



rs9902483
36319692



rs6607293
36319849



rs6607294
36320128



rs6607295
36320209



rs10565630
36320488



rs11376515
36320666



rs8077061
36320869



rs8077234
36320994



rs12941488
36322154



rs7342854
36322247



rs7342856
36322327



rs7342857
36322332



rs10653005
36322459



rs7342879
36322581



rs9898782
36322636



rs12950378
36323462



rs17138201
36324014
















TABLE 14







Public SNPs from dbSNP Build125 in the LD block C17b region











Location



Name
(Bld 34)






rs7208022
69665297



rs28578535
69665582



rs11871999
69665688



rs7208880
69665972



rs11330720
69666003



rs2367244
69667282



rs4793526
69667313



rs4793527
69667320



rs4793330
69667720



rs12945790
69667864



rs12945793
69667866



rs12942301
69667992



rs8077040
69668296



rs11077543
69668329



rs1861689
69669217



rs1861690
69669376



rs12936362
69669543



rs2367245
69669654



rs2367246
69669660



rs2367247
69669678



rs2367248
69669835



rs4337333
69669883



rs2367249
69669987



rs9898686
69670315



rs9899756
69670442



rs17224342
69671371



rs12938195
69671387



rs7216999
69671422



rs7215968
69671472



rs7217179
69671523



rs12941678
69671533



rs12949308
69671534



rs7215998
69671548



rs12949632
69671634



rs9906150
69671902



rs9914841
69672198



rs4793331
69672482



rs7223432
69672957



rs2216288
69673023



rs10688256
69673156



rs6501435
69673287



rs28415436
69673851



rs2080539
69673914



rs2080540
69674102



rs2098431
69674201



rs12452747
69674232



rs12950552
69674402



rs12944225
69674617



rs11871500
69674867



rs9895751
69675048



rs12944216
69675152



rs28509409
69675646



rs11317800
69675735



rs28479625
69675789



rs28479657
69675792



rs28522706
69676111



rs28608720
69676295



rs11870970
69676459



rs11077544
69676558



rs11077545
69676593



rs994784
69676759



rs16976397
69677105



rs12948425
69677389



rs12938367
69677408



rs7221849
69677626



rs11366111
69677741



rs16976399
69677786



rs7226259
69677831



rs7221739
69677904



rs9892269
69678024



rs9890229
69678201



rs11867272
69678271



rs8068751
69678391



rs8082156
69678793



rs9897691
69679041



rs9900129
69679059



rs9897896
69679093



rs9898123
69679197



rs9899952
69679280



rs9899737
69679909



rs9905626
69680150



rs9905810
69680185



rs9914290
69680203



rs9906277
69680390



rs9908858
69680454



rs9915246
69680633



rs12150666
69680757



rs9915679
69680808



rs9916172
69681232



rs12449832
69681760



rs16976401
69681891



rs11077546
69681919



rs12944455
69682075



rs12943693
69682168



rs8066397
69682265



rs8065642
69682519



rs12600835
69682551



rs11652143
69682700



rs8067786
69682767



rs12946846
69682905



rs2367250
69682988



rs5821824
69683086



rs2108533
69683233



rs28642771
69683621



rs9904895
69683894



rs9909477
69684227



rs9890292
69684255



rs1468480
69684610



rs17765267
69684681



rs7220901
69685291



rs16976403
69685307



rs9907216
69685712



rs7217550
69685774



rs28626361
69685946



rs9302930
69685946



rs10667181
69685947



rs9914095
69686290



rs16967044
69686557



rs9914833
69686630



rs7212301
69687200



rs9891558
69687202



rs11650685
69687640



rs9895083
69687640



rs8080466
69689709



rs12453898
69690615



rs28525974
69691219



rs4793332
69691243



rs17765273
69691691



rs17177838
69692020



rs12450393
69692082



rs12449659
69693931



rs7212237
69694416



rs4564625
69694649



rs16976404
69695256



rs2886910
69695271



rs9915084
69695508



rs9302931
69695924



rs10852729
69696423



rs2159033
69696433



rs2886911
69696503



rs28360962
69696584



rs7214412
69696950



rs9903422
69697320



rs16976409
69697871



rs2215051
69698130



rs2367252
69698367



rs9910903
69698543



rs9911892
69698576



rs2190692
69698699



rs17177858
69698740



rs9910199
69698960



rs28589380
69698962



rs7211190
69700285



rs12948683
69700758



rs11656228
69700772



rs2190693
69700881



rs2190694
69701296



rs12452913
69701467



rs988469
69701741



rs7222314
69702061



rs11077547
69702087



rs28579214
69702168



rs16976411
69702473



rs2190695
69702639



rs991528
69702707



rs11293277
69703131



rs17765332
69703865



rs17765344
69703997



rs9908087
69704060



rs9901566
69704141



rs9908529
69704183



rs8071068
69704466



rs8071558
69704796



rs8072254
69704939



rs10551581
69705007



rs984434
69705250



rs7503885
69705407



rs1859961
69705778



rs1859962
69705876



rs11650165
69706741



rs9891981
69706753



rs991429
69706896



rs4793528
69707896



rs7217073
69708046



rs7216498
69708055



rs9674957
69708221



rs16976415
69708323



rs17824498
69708717



rs8077906
69709356



rs8079414
69709642



rs1859963
69710230



rs8066875
69710700



rs16976418
69710909



rs11357418
69711779



rs9916515
69712100



rs9909964
69712163



rs9889335
69712269



rs9911515
69712481



rs4328484
69713353



rs11077548
69713459



rs8068266
69714058



rs16976420
69714103



rs8072735
69714337



rs12947919
69715210



rs9893698
69715560



rs4793529
69715759



rs11273447
69716092



rs7210934
69716100



rs10522494
69716131



rs6501436
69716413



rs7217652
69716604



rs11418718
69716711



rs11386981
69716721



rs6501437
69717095



rs6501438
69717283



rs8075884
69717686



rs8074361
69717818



rs8079315
69717978



rs8065372
69718014



rs8065379
69718023



rs5821825
69718400



rs2367256
69718409



rs2190696
69718454



rs2190697
69718464



rs10512560
69718543



rs4362433
69718720



rs4366746
69718754



rs4366747
69718766



rs2159034
69718878



rs2159035
69718906



rs1014000
69718968



rs1013999
69719058



rs17824565
69719591



rs17224666
69719603



rs9783828
69719816



rs17765410
69719887



rs9783829
69720122



rs9783825
69720137



rs11270436
69720167



rs9783826
69720230



rs9302932
69720682



rs2367257
69720807



rs2886913
69720814



rs2367258
69720864



rs16976421
69721043



rs4793333
69721433



rs8081862
69721543



rs8078088
69721600



rs11350919
69721604



rs6501439
69722007



rs16976422
69722140



rs6501440
69722155



rs6501441
69722209



rs12451634
69722235



rs6501442
69722265



rs6501443
69722280



rs4793530
69722386



rs7206958
69722630



rs11654749
69722729



rs11651706
69723550



rs28656352
69724006



rs8073978
69724076



rs8074021
69724143



rs11292707
69724148



rs11287955
69724149



rs9906164
69724526



rs11871297
69724691



rs9908315
69724691



rs9906627
69724693



rs11871142
69724693



rs11871327
69724798



rs11868971
69724896



rs13341257
69725212



rs9899619
69725212



rs9907995
69725239



rs13342491
69725239



rs2367259
69725537



rs9901976
69725541



rs28526842
69726195



rs28730401
69726274



rs9916528
69726554



rs11653132
69726955



rs4621009
69727636



rs11654295
69727661



rs4398141
69727827



rs4471727
69727839



rs4300694
69727959



rs7501691
69728181



rs4416055
69728410



rs2367260
69728428



rs2367261
69728429



rs28523317
69728817



rs8076830
69729032



rs16976423
69729222



rs9906588
69729611



rs4793531
69730430



rs7406607
69730430



rs4793532
69730574



rs12103911
69730843



rs11656229
69731166



rs6501444
69731345



rs7208518
69731608



rs28379920
69731668



rs28690329
69731677



rs28579305
69731820



rs11656479
69731840



rs11656520
69732149



rs2367262
69732235



rs9902159
69732480



rs9900242
69732754



rs11653076
69733312



rs11077549
69734431



rs9908442
69735071



rs4793334
69735105



rs17178034
69735366



rs17178041
69735378



rs8081196
69735492



rs2058083
69735526



rs6501445
69735966



rs7224774
69736028



rs2058084
69736140



rs2058085
69736170



rs7225803
69736320



rs11654896
69736454



rs11655964
69736475



rs7224573
69736591



rs11655006
69736706



rs1468481
69737102



rs11657344
69737589



rs7211828
69737632



rs9898184
69737774



rs9906742
69737913



rs7216323
69738056



rs7221230
69738073



rs9905641
69738678



rs9907587
69738834



rs17178062
69739314



rs9915190
69739751



rs8078520
69739851



rs8065751
69739901



rs8080251
69739930



rs17178083
69739997



rs2215052
69740781



rs2159036
69740787



rs16976429
69741127



rs2041113
69741315



rs16976430
69741428



rs2041114
69741740



rs7219029
69742113



rs3079152
69742453



rs5821826
69742455



rs956218
69742459



rs723338
69742536



rs956219
69742830



rs28703253
69742927



rs28475602
69742956



rs28514850
69743159



rs8081602
69743202



rs8081751
69743291



rs8080184
69743366



rs12602284
69743438



rs2041115
69743550



rs8068057
69743778



rs28629932
69743843



rs11351232
69743844



rs8064263
69743953



rs9897626
69744076



rs9897865
69744199



rs11656242
69744645



rs9897358
69744665



rs11651123
69744714



rs11657298
69744759



rs9906121
69744869



rs9900302
69744903



rs11651469
69745642



rs11651501
69745681



rs8068340
69746421



rs8069493
69746422



rs9899201
69746666



rs719615
69747033



rs7216402
69747209



rs7217604
69747260



rs7342907
69747289



rs11275150
69747540



rs3079153
69747512



rs16976435
69747677



rs7219299
69747878



rs7222795
69747986



rs2058086
69748321



rs2058087
69748370



rs9916274
69748656



rs7209594
69748903



rs1558119
69749095



rs10604404
69752277



rs10556506
69752280



rs11077550
69752553



rs9893104
69752632



rs12150098
69752957



rs4629010
69753318



rs4316800
69753509



rs16976438
69753835



rs17824720
69753977



rs7502769
69754543



rs4445939
69754572



rs7502789
69754573



rs4643369
69754602



rs1974561
69754631



rs9747823
69754700



rs9906649
69754803



rs11870806
69755483



rs12601681
69756532



rs9909762
69756612



rs12604060
69756621



rs9910829
69756700



rs7220274
69756890



rs28519385
69757429



rs28688838
69757489



rs17224833
69757598



rs9894739
69757976



rs17765545
69758008



rs2108534
69758023



rs2108535
69758268



rs8182284
69758514



rs8182286
69758677



rs11464001
69760633



rs9913608
69760687



rs8068573
69760970



rs11867357
69761364



rs11330058
69761375



rs10712335
69761382



rs10563340
69761524



rs4793533
69761594



rs8069925
69761985



rs8068189
69762018



rs9900204
69762079



rs9901508
69762322



rs9907418
69762342



rs16967048
69762777



rs2058088
69762984



rs2367263
69763411



rs1859964
69763694



rs1859965
69764218



rs6501446
69765181



rs4793534
69765416



rs4239156
69765504



rs11428263
69765526



rs4438347
69765529



rs7224698
69765533



rs4793335
69765814



rs9891407
69766332



rs9891611
69766373



rs9891269
69766640



rs2108536
69766899



rs9892822
69766928



rs11657351
69767747



rs16976440
69767822



rs7216882
69768093



rs9904271
69768257



rs2097984
69768349



rs9893839
69769593



rs11654068
69769659



rs8079962
69769825



rs6501447
69770221



rs7206969
69770272



rs10686630
69770575



rs10698719
69770684



rs2886914
69770936



rs7217918
69770975



rs7214621
69771488



rs11868953
69771619



rs12944762
69772101



rs9909797
69772305



rs2367264
69772313



rs5821827
69772325



rs5821828
69772327



rs8076235
69772505



rs28432258
69772509



rs8076811
69772530



rs16976442
69772618



rs1859966
69772866



rs17178251
69774002



rs11442732
69774343



rs7225046
69775440



rs17765632
69775827



rs16976443
69776028



rs16976444
69776570



rs7211425
69776601



rs17765644
69776615



rs10652863
69776765



rs9913988
69777181



rs11871129
69777585



rs3079217
69777970



rs758106
69778126



rs7208233
69778192



rs740408
69778219



rs16976445
69778235



rs2886915
69779284



rs28451383
69779302



rs2003060
69779416



rs5821829
69779650



rs5741749
69779684



rs28509323
69779683



rs3079221
69779684



rs28519699
69779685



rs5821830
69779695



rs17224938
69779866



rs2215049
69779914



rs16976446
69779988



rs17765660
69780218



rs7219890
69780630



rs10401046
69780827



rs17824822
69781077



rs17178293
69781121



rs11653243
69781437



rs11658483
69781850



rs9891648
69782075



rs16976448
69782186



rs9898150
69782318



rs16976449
69782374



rs16976451
69782409



rs8064966
69782923



rs9905861
69783360



rs9906098
69783457



rs4570900
69783489



rs4586491
69783636



rs4471728
69783657



rs4632175
69783677



rs4561513
69783679



rs4793535
69784004



rs10717595
69784340



rs9908262
69784558



rs1011729
69784710



rs12943194
69784780



rs1011730
69784868



rs16976453
69785851



rs4611499
69785892



rs7217521
69786434



rs16976455
69786902



rs6501448
69787036



rs7209698
69787510



rs10555877
69787548



rs11274299
69787737



rs10528465
69787774



rs11365486
69787938



rs7208398
69787979



rs7214479
69788072



rs7214488
69788097



rs1008348
69788439



rs7501959
69788446



rs2367265
69788490



rs11394357
69788559



rs8072259
69788643



rs11313840
69789156



rs6501449
69789968



rs10301911
69789968



rs10301948
69790178



rs6501450
69790178



rs6501451
69790254



rs10300397
69790254



rs6501452
69790410



rs10300432
69790428



rs6501453
69790428



rs3079233
69790679



rs8079118
69790903



rs11297510
69791440



rs9893148
69791554



rs9893377
69791625



rs11868131
69792249



rs11870732
69792364



rs16976457
69792601



rs17178370
69792664



rs9899791
69792753



rs16976458
69793386



rs9909806
69793630



rs16976460
69794547



rs17225050
69794612



rs7225025
69794797



rs9890695
69794933



rs2215050
69795232



rs17178377
69795256



rs9912404
69795917



rs11655744
69796179



rs767203
69796402



rs11346937
69797001



rs2367266
69797110



rs1107305
69797766



rs11657389
69798141



rs28707109
69798155



rs9912434
69798171



rs6501454
69798880



rs6501455
69798934



rs11658666
69799137



rs6501456
69799230



rs4872347
69799229



rs8078490
69799267



rs9893953
69800265



rs7209505
69800787



rs10617559
69800819



rs7209069
69800919



rs8070140
69801724



rs4313845
69802271



rs28605967
69802874



rs13342783
69803155



rs9915306
69803155



rs9896356
69803211



rs13342052
69803211



rs10302248
69803464



rs2108337
69803464



rs8067671
69804287



rs2190463
69804591



rs7501860
69804833



rs11657593
69805061



rs9909136
69805273



rs9906656
69805316



rs11654355
69806089



rs6501457
69806375



rs8066265
69806421



rs8066924
69806817



rs8079912
69806928



rs8066934
69807046



rs8067145
69807477



rs2190456
69808489



rs9905278
69808806



rs9906357
69808908



rs983084
69808989



rs983085
69809184



rs11650277
69809918



rs7211370
69810208



rs6501458
69810255



rs7226171
69810292



rs9747127
69810292



rs6501459
69810578



rs12602874
69811325



rs7406314
69811841



rs4793536
69811841



rs4793537
69812013



rs4793538
69813051



rs2158905
69813164



rs2108336
69813235



rs2190457
69813532



rs11655567
69813810



rs7225458
69815469



rs10401004
69815879



rs11291235
69817206



rs7215164
69817444



rs11291234
69817472



rs7221399
69817567



rs9905147
69817884



rs2190458
69818952



rs2190459
69818967



rs917278
69819048



rs917279
69819062



rs2190460
69819088



rs8065046
69819379



rs8065211
69819526



rs1978203
69819792



rs1978204
69820068



rs5025558
69820721



rs3079276
69820892



rs737956
69820991



rs737957
69821032



rs8075168
69821150



rs8075481
69821319



rs9915835
69821324



rs8080004
69821422



rs917280
69821734



rs737958
69821777



rs16976475
69821852



rs917281
69822040



rs737959
69822117



rs11654184
69822459



rs7225026
69822798



rs7223912
69822819



rs7224058
69822902



rs4595838
69823065



rs4793539
69823390



rs7215307
69823490



rs8068921
69823928



rs8072635
69824306



rs4793540
69824317



rs4793541
69824718



rs7221540
69826125



rs10650850
69826133



rs6501460
69827034



rs7221080
69827095



rs6501461
69827609



rs8064388
69828140



rs9892669
69828141



rs11650797
69828297



rs9902909
69828907



rs9909320
69829271



rs9904099
69829611



rs9901939
69829753



rs17178523
69830197



rs8076167
69830206



rs6501462
69830257



rs8077218
69830507



rs8067695
69831434



rs17765868
69831931



rs9898561
69831976



rs9906756
69833167



rs17178530
69833235



rs17765886
69833328



rs9913159
69833376



rs17765897
69833724



rs12946942
69834121



rs8079036
69834162



rs16976482
69834651



rs16976483
69834805



rs1558061
69835309



rs10525593
69835329



rs8074817
69835313



rs10598677
69835341



rs9302933
69835930



rs9916542
69836244



rs9914509
69836395



rs9916166
69836431



rs9895657
69836554



rs12941471
69837058



rs9896822
69837234



rs8066183
69837705



rs8071929
69837713



rs8067475
69837806



rs8068078
69837850



rs8068231
69837908



rs8070461
69837995



rs7220325
69838584



rs2214946
69839003



rs9909596
69839116



rs16976490
69839170



rs9911506
69839258



rs9891216
69840055



rs9898288
69840121



rs8064489
69840682



rs2886917
69841415



rs7217041
69841447



rs9899311
69841854



rs10512561
69841920



rs7222044
69842070



rs2041036
69842261



rs2041037
69842312



rs4505385
69842351



rs7217933
69842529



rs2058005
69842858
















TABLE 15







Public SNPs from dbSNP Build125 chromosome in the LD block


C0Xa region











Location



Name
(Bld 34)






rs12857638
49979931



rs12836093
49980515



rs6614396
49981265



rs5987393
49981474



rs12859093
49981492



rs12558299
49982759



rs28502527
49982859



rs1144821
49983111



rs7060520
49983249



rs7062166
49983256



rs7065932
49983292



rs1144822
49987108



rs1144823
49987225



rs1144824
49988124



rs5945601
49988628



rs28552462
49989770



rs6614399
49989835



rs1151713
49990081



rs1985464
49990289



rs5987414
49991462



rs1151714
49991671



rs1151715
49991957



rs2801628
49992103



rs2625873
49992433



rs2625874
49992518



rs2625875
49992647



rs2801627
49992707



rs12846945
49992824



rs17846448
49992824



rs17859498
49992824



rs2801626
49992830



rs2625876
49992887



rs2801625
49992889



rs17854662
49992891



rs17854580
49992933



rs17854581
49993026



rs2801624
49993520



rs1144825
49993803



rs1144826
49993823



rs1144827
49993865



rs12841743
49995031



rs6614400
49996625



rs1144828
49999653



rs1144829
49999901



rs913224
49999928



rs1144830
50000975



rs1144831
50000982



rs6614401
50001762



rs12835732
50003251



rs2221370
50005157



rs2221369
50005159



rs6614402
50005936



rs12838483
50007226



rs17374021
50007700



rs3904018
50007700



rs4262466
50007700



rs28833510
50008244



rs28867805
50008255



rs6614403
50008283



rs12848124
50008643



rs1503779
50009384



rs12833678
50009385



rs12688009
50013288



rs7882940
50013357



rs1503782
50014081



rs1503783
50014184



rs28600724
50014523



rs1887347
50014934



rs6614404
50015706



rs12846170
50017341



rs7472779
50017631



rs4623626
50017697



rs7472906
50017697



rs6614405
50018413



rs6614406
50018734



rs12835881
50021222



rs12688145
50023165



rs12855323
50023420



rs12856394
50023702



rs12856724
50023791



rs12834749
50024828



rs2089080
50025042



rs4477198
50025042



rs5987416
50025042



rs2102224
50025405



rs12010507
50026965



rs12858098
50027751



rs4524976
50028479



rs7471245
50028479



rs1590666
50028877



rs4388622
50028877



rs12847705
50031298



rs12856477
50032556



rs12834359
50032568



rs1576012
50033094



rs4514178
50033094



rs12837176
50033978



rs12382105
50034001



rs6614292
50034001



rs12843301
50034276



rs1327300
50035847



rs12838882
50036951



rs12857136
50039647



rs12834256
50039777



rs5945568
50040760



rs7882378
50041887



rs12833329
50043125



rs12838967
50044205



rs1587771
50045166



rs10310810
50045420



rs6614407
50045420



rs9780006
50046001



rs12836459
50047884



rs1604763
50049061



rs4587502
50049061



rs1604761
50050548



rs28836241
50051275



rs7886708
50051382



rs1503781
50052580



rs17003292
50052690



rs1503780
50052941



rs7060524
50054360



rs7065124
50054921



rs12838105
50055783



rs7051438
50056677



rs976587
50057233



rs7062237
50059038



rs12834337
50059530



rs2134677
50059658



rs12843640
50061819



rs12837760
50061847



rs6614293
50061856



rs12850320
50062311



rs2134676
50063491



rs17854399
50067247



rs12856054
50067281



rs1875755
50068083



rs2281920
50068474



rs12849304
50069136



rs6614408
50069317



rs7059321
50070408



rs12852369
50070648



rs7059804
50070717



rs12839592
50073335



rs6614409
50074260



rs2134678
50075721



rs12859167
50076589



rs12851320
50078419



rs12834008
50082228



rs17249918
50082583



rs12836006
50082758



rs7064156
50083180



rs972635
50084494



rs12688411
50084797



rs974609
50086161



rs974610
50086341



rs7883508
50087568



rs7883537
50087624



rs12382912
50088010



rs6614410
50088010



rs9281840
50088911



rs4406545
50089273



rs5987410
50089471



rs6614294
50089471



rs6614295
50089616



rs3959290
50091659



rs1142943
50091998



rs7887005
50092059



rs7887184
50092170



rs12844637
50092562



rs12844299
50092744



rs12843029
50092893



rs2694425
50092973



rs2694424
50093036



rs2036490
50094861



rs1875754
50095192



rs7888952
50098151



rs4907789
50098153



rs7392318
50098157



rs4907771
50098161



rs5945602
50098173



rs4907772
50098347



rs12558999
50099863



rs12012935
50101772



rs5945603
50105350



rs12383167
50106377



rs6614296
50106377



rs5945604
50106951



rs6521795
50106951



rs5945605
50107288



rs5945606
50107963



rs12843813
50108909



rs12392073
50109526



rs5945569
50110017



rs3074307
50110485



rs5987418
50114019



rs12850214
50114240



rs4907790
50114517



rs4907791
50114540



rs10127382
50115567



rs13366433
50115567



rs12387530
50116011



rs5987419
50116011



rs5945607
50116514



rs5945570
50116531



rs11091727
50117893



rs5945608
50117893



rs11798651
50118401



rs5945609
50119272



rs5945610
50119351



rs6614297
50120505



rs10284146
50121301



rs10284147
50121302



rs12845998
50126071



rs12846154
50126080



rs12845472
50126481



rs5987395
50126695



rs5945611
50126796



rs1327301
50126863



rs5987396
50127235



rs1327302
50127421



rs12853150
50127455



rs7061990
50127464



rs12856096
50127482



rs5945612
50128059



rs7064089
50128196



rs12836045
50128539



rs12835169
50128540



rs1327303
50130975



rs1327304
50130982



rs5945613
50131486



rs5945614
50134364



rs1854109
50135378



rs10855219
50139404



rs5945571
50139404



rs6614413
50139404



rs2148751
50139634



rs4556264
50139634



rs5945615
50139634



rs12398483
50141270



rs5945616
50141270



rs12851276
50142316



rs12395326
50142524



rs5987420
50142524



rs5987421
50143296



rs5945617
50144027



rs7064195
50144640



rs5945618
50145357



rs12846383
50145415



rs5987422
50145612



rs5945572
50146489



rs12832410
50147431



rs12014647
50150686



rs11091729
50152331



rs7066127
50153285



rs12854262
50154146



rs7059759
50154963



rs12853021
50155066



rs17854581
50155700



rs2768104
50156232



rs2768103
50156299



rs28641581
50156884



rs3209107
50157523



rs12843829
50157531



rs5945619
50158478



rs5987397
50159109



rs5987423
50159123



rs5987424
50159170



rs5945620
50159476



rs5945621
50159687



rs6652209
50159963



rs5945622
50160076



rs6614299
50161112



rs6614300
50161195



rs6614301
50161235



rs12833914
50161256



rs12833917
50161257



rs11338635
50162083



rs1891702
50162267



rs5945623
50162293



rs5987425
50163127



rs4907773
50163450



rs4381122
50164645



rs2008216
50164650



rs4907792
50165589



rs7056117
50165629



rs2384958
50166203



rs10531904
50166570



rs2051095
50166976



rs28367108
50167055



rs5987426
50167347



rs1936038
50168227



rs6614419
50168606



rs5945624
50169734



rs5945625
50170233



rs5945626
50170585



rs5945573
50171156



rs28670322
50171546



rs28618971
50171549



rs12844177
50171726



rs6614421
50171872



rs1071580
50172086



rs6521802
50172147



rs5945627
50172223



rs12384969
50172585



rs11091730
50172689



rs6614423
50172689



rs10855221
50173367



rs5945628
50173367



rs6614424
50173367



rs11091731
50173798



rs5987427
50173798



rs6614302
50173798



rs11091732
50173804



rs5987428
50173804



rs6614425
50173804



rs12398502
50175022



rs5945574
50175022



rs10127076
50175345



rs11798149
50175345



rs5987429
50176499



rs6521803
50176499



rs3207585
50176555



rs6521804
50176772



rs5945629
50176878



rs6521805
50176878



rs12834343
50176887



rs12383673
50177011



rs6614303
50177011



rs12383675
50177052



rs5945630
50177052



rs6614304
50177052



rs12556076
50177154



rs7064349
50177327



rs4907793
50177369



rs6521806
50177369



rs12387490
50177404



rs12556595
50177475



rs4907794
50177686



rs5987430
50177686



rs4907795
50177874



rs7876147
50177982



rs4907796
50178055



rs4907774
50178333



rs12012308
50178919



rs4907775
50180006



rs6614426
50181230



rs12559640
50181590



rs12388771
50183690



rs17003306
50183967



rs3131307
50184759



rs1970956
50185184



rs1970957
50185256



rs5945631
50185690



rs12842085
50186345



rs2153993
50186545









It is important to note that patterns of LD in the genome are usually not “perfect”. In practical terms, this means that linkage disequilibrium between individual markers within blocks, or between entire blocks, is known to occasionally extend beyond the boundaries of LD blocks as defined. Such extended pattern of LD may comprise single markers in neighbouring or even possibly distant regions, or several markers in nearby LD blocks. In such cases, the LD blocks themselves are not “perfect” in the sense that even though the local LD pattern is strongest within the LD block as defined, weaker pattern of LD in these instances is observed beyond the LD block. Such extended pattern of LD is also within scope of the present invention, as markers displaying such LD with the markers of the invention can also be used as surrogate markers in the methods of the invention.
Discussion
As described herein, five loci on chromosome 2p15, 4q31.21, 17q12, 17q24.3 and Xp11.22 have been demonstrated to play a role in cancer (e.g., prostate cancer (e.g., aggressive prostate cancer). Particular markers and/or haplotypes at each locus, including the TCF2 gene on Chromosome 17q12, are present at a higher than expected frequency in subjects having prostate cancer. Based on the markers described herein, which are associated with a propensity for particular forms of cancer, genetic susceptibility assays (e.g., a diagnostic screening test) can be used to identify individuals at risk for cancer.
The TCF2 (HNF1beta) is the only known gene within the genomic region on chromosome 17q12 of the human genome that is found to be associated to prostate cancer. The underlying variation in markers or haplotypes associated with region and cancer may affect expression of the TCF2 gene. It is however also conceivable that the effect extends to, or is mainly limited to, nearby genes, such as DDX52, AP1GBP1, TBC1D3/TBC1D3B (PRC17), and/or other known, unknown or predicted genes within the region found on chromosome 17q12 found to be associated with prostate cancer, or in adjacent regions. Furthermore, such variation may affect RNA or protein stability or may have structural consequences, such that the region is more prone to somatic rearrangement in haplotype/allele carriers. Thus, in general, the underlying variation could affect uncharacterized genes directly linked to the markers and/or haplotypes described herein, or could influence neighbouring genes not directly linked to the markers and/or haplotypes described herein.
The region found to be associated with prostate cancer on chromosome 2, i.e. the LD block C02, contains several reported genes. These include EHBP1 (EH domain-binding protein 1, also called KIAA0903), OTX1 (homolog of orthodenticle (drosophila)), BC093752, LOC51057. EHBP1 has been found to be expressed in all tissues and specific brain regions examined except lung, pancreas, and spleen, in which expression was found to be low. NPF motifs of EHBP1 are disrupted in cortical actin structures, and depletion of EHBP1 in mouse adipocytes by small interfering RNA inhibits endocytosis, suggesting that EHBP1 functions in endocytic trafficking. OTX is a homeobox family gene related to a gene expressed in drosophila head termed ‘orthodenticle.’ OTX transcription factors bind with high affinity to TAATCC/T elements on DNA.
Example 3
Further Evidence for Sequence Variants on 2p15 and Xp11.22 Conferring Susceptibility to Prostate Cancer
Further characterization of association of variants on Chr. 2p15 and Xp11.22 was performed, as shown in Tables 16 and 17. As before, allele A of the SNP rs5945572 (rs5945572 A) showed the most significant P value of markers located on the X chromosome in a GWA analysis of 23,205 Icelandic samples, with an allelic specific odds ratio (OR) of 1.21 (P=3.36×10−4) (Table 16). On chromosome 2p15, allele A of rs2710646 (rs2710646 A) had an OR of 1.16 (P=7.79×10−4) (Table 17) for all Icelandic prostate cancer cases and an OR of 1.33 (P=3.73×10−5) for patients diagnosed with aggressive disease (Table 18). By examining the Utah CEPH (CEU) HapMap data we identified numerous SNPs that are substantially correlated with either of the two anchor SNPs, rs5945572 and rs2710646 (D′≧0.9 and r2>0.4). From this list we selected several SNPs not present on the Illumina Hap300 chip, representing different degrees of correlation with the anchor SNPs, for further genotyping in 1,500 and 800 Icelandic cases and controls, respectively. None of the additional SNPs were found to be more significantly associated to the disease than the anchor SNPs (Table 19).
We proceeded to genotype rs5945572 and rs2710646 in seven prostate cancer study groups of European descent, coming from The Netherlands, Spain, Sweden and the United States (US). However, since the TaqMan assay for rs2710646 on 2p15 failed in design we replaced it with an assay for a fully correlated SNP, rs721048 (LD-characteristics between rs2710646 and rs721048 in Icelanders and in the four HapMap populations (CEU, CHB, JPT, YRI); D′=1; r2≧0.99), which was used for genotyping in the replication study populations. When results from all seven case-control replication groups were combined, using all prostate cancer cases, they were highly significant for both SNPs with OR=1.24 (P=2.57×10−10) for rs5945572 A on Xp11, and OR=1.15 (P=2.23×10−6) for rs721048 A on 2p15 (Table 16 and 17). By combining these data with the Icelandic data the signals at both loci achieved genome-wide significance; rs5945572 A on Xp11.22 had an OR=1.23 (P=3.95×10−13), and rs721048 A on 2p15 had an OR=1.15 (P=7.66×10−9) (Tables 16 and 17). Removing all females (N=14,135) from the control group in the analysis of the combined groups yielded essentially identical results for both loci, (OR=1.23 and 1.15 for rs5945572 A and rs721048 A, respectively). In all of the replication groups, the observed effect for the two loci was in the same direction as in the Icelandic samples, except for rs721048 A which in the Baltimore group showed no effect (OR=1). However, a test of heterogeneity in the OR of the eight different study groups showed no significant difference for the two loci (P=0.89 and 0.19 for Xp11 and 2p15, respectively). We note that in the results released by the Cancer Genetics Markers of Susceptibility study group5 (https colon-slash-slash cgems.cancer.gov slash data slash) the two original anchor SNPs, rs5945572 and rs2710646, show nominal, but not genome-wide, significant association to prostate cancer further supporting the data presented here.
For rs5945572 A on Xp11, the OR seen for cases with younger age at onset (≦65) or aggressive phenotype was the same as for the whole group. The frequency of rs721048 A on the other hand, was significantly higher among patients diagnosed with aggressive prostate cancer than among those with less aggressive disease (OR=1.11; P=2.6×10−3). Comparing the group of patients with aggressive tumor (N=4,787) to controls gave an allelic OR of 1.22 (P=8.7×10−10) when combined for all study groups (Table 18). Specifically, the heterozygous and homozygous carriers of rs721048 A, which are close to 31% and 4% of the general population, have a genotypic OR of 1.22 and 1.49 of being diagnosed with aggressive prostate cancer compared to the non-carriers, respectively. However, rs721048 A, like rs5945572 A, did not show a stronger association in patients with younger age at onset of prostate cancer.
Both of the variants, rs5945572 on Xp11 and rs2710646/rs721048 on 2p15, are located within regions characterized by extended linkage disequilibrium (LD), based on the CEU HapMap results. On Xp11.22, the LD-block spans a region from about 51.001 Mb to 51.612 Mb, or 611 kb (NCBI Build 35). The 2p15 LD-region is even larger, or about 1.1 Mb (62.709 Mb to 63.782 Mb; NCBI Build 35) and contains several genes. Both of the 2p15 SNPs, rs2710646 and rs721048, are located within one of the introns of the EHBP1 gene. This gene is thought to be involved in endocytic trafficking and has not previously been implicated in cancer6. An RT-PCR analysis of various cDNA libraries revealed detectable expression of EHBP1 in several different tissue libraries, including those derived from the prostate (data not shown).
On Xp11, several genes have been localized to the 611 Kb region of interest but none of those have been previously linked with prostate cancer. Possible cancer candidate genes, based on previously described functions, are GSPT2 and MAGED1. GSPT2 is related to GSPT1, a gene encoding a GTP-binding protein that plays an essential role at the G1- to S-phase transition of the cell cycle. MAGED1 has been implicated in programmed cell death through a JNK- and c-Jun-dependent mitochondrial pathway7,8. The genes closest to the SNP rs5945572 are: NUDT10 and NUDT11, along with a single exon transcript LOC340602 with unknown function (www.genome.ucsc.edu, May 2004 Assembly). NUDT10 and 11 belong to a subgroup of phosphohydrolases that preferentially hydrolyze diphosphoinositol polyphosphates (DIPPS)9. It has been proposed that members of this protein superfamily may be involved in vesicle trafficking, stress responses, DNA repair and apoptosis10,11. RT-PCR analysis of various cDNA libraries revealed detectable expression of GSPT2, MAGED1, LOC340602, NUDT10 and 11 in several different tissue libraries, including those derived from the prostate (data not shown). Which one of these genes, if any, confers the risk identified by the association reported here remains to be shown.
Recently, several sequence variants have been identified accounting for a substantial proportion of the population attributable risk (PAR) of prostate cancer1,2,5,12,13. With the identification of the two variants described here, yet another piece has been added to the puzzle of prostate cancer genetic susceptibility. Both the Xp11 and the 2p15 variants are common and confer a moderate risk, resulting in an estimated PAR of about 7% and 5% in individuals of European descent, respectively (see also Table 20).







TABLE 16







Association results for rs5945572 on Xp11.22 and prostate cancer


in Iceland, The Netherlands, Spain, Sweden, and the US









Study population




(N cases/N controls)
Frequency











Variant (allele)
Cases
Controls
OR (95% c.i.)
P value





Iceland(1,833/21,372)a






rs5945572 (A)
0.414
0.368
1.21 (1.09-1.34)
3.36 × 10−4


Holland (991/2,021)


rs5945572 (A)
0.390
0.347
1.20 (1.04-1.39)
0.015


Spain (539/1,594)


rs5945572 (A)
0.432
0.364
1.33 (1.10-1.61)
3.20 × 10−3


Sweden (2,865/1,722)


rs5945572 (A)
0.421
0.379
1.19 (1.05-1.34)
4.84 × 10−3


Baltimore, Maryland (1,516/554)


rs5945572 (A)
0.397
0.334
1.31 (1.07-1.60)
8.39 × 10−3


Chicago, Illinois (656/503)


rs5945572 (A)
0.402
0.354
1.23 (0.98-1.54)
0.072


Nashville, Tennessee (526/612)


rs5945572 (A)
0.392
0.353
1.18 (0.93-1.50)
0.18 


Rochester, Minnesota (1,128/500)b


rs5945572 (A)
0.381
0.306
1.40 (1.11-1.76)
4.39 × 10−3


All excluding Iceland (8,221/7,506)c


rs5945572 (A)
—
0.348
1.24 (1.16-1.33)
 2.57 × 10−10


All combined (10,054/28,879)c


rs5945572 (A)
—
0.351
1.23 (1.16-1.30)
 3.95 × 10−13





All P values shown are two-sided. Shown are the corresponding numbers of cases and controls (N), allelic frequencies of variants in affected and control individuals, the allelic odds-ratio (OR) with 95% confidence interval (95% c.i.) and P values.


aResults presented for Iceland were adjusted for relatedness using a method of genomic control (see Supplementary Methods).


bResults presented for Rochester were adjusted for relatedness by applying a pedigree correction factor (see Supplementary Methods).


cFor the combined study populations, the reported control frequency was the average, unweighted control frequency of the individual populations, while the OR and the P value were estimated using the Mantel-Haenszel model.













TABLE 17







Association results for rs721048 on 2p15 and prostate cancer


in Iceland, The Netherlands, Spain, Sweden and the US









Study population




(N cases/N controls)
Frequency











Variant (allele)
Cases
Controls
OR (95% c.i.)
P value





Iceland(1,854/21,064)a






rs2710646 (A)b
0.227
0.202
1.16 (1.06-1.26)
7.79 × 10−4


rs721048 (A)
0.229
0.204
1.16 (1.06-1.27)
9.22 × 10−4


The Netherlands (998/2,021)


rs721048 (A)
0.205
0.187
1.12 (0.98-1.28)
0.095


Spain (548/1,616)


rs721048 (A)
0.234
0.210
1.15 (0.98-1.35)
0.088


Sweden (2,849/1,731)


rs721048 (A)
0.201
0.186
1.10 (0.98-1.23)
0.090


Baltimore, Maryland (1,521/557)


rs721048 (A)
0.205
0.206
1.00 (0.46-2.20)
0.99 


Chicago, Illinois (665/552)


rs721048 (A)
0.198
0.168
1.22 (1.00-1.49)
0.055


Nashville, Tennessee (526/612)


rs721048 (A)
0.240
0.175
1.49 (1.21-1.83)
1.39 × 10−4


Rochester, Minnesota (1,132/501)c


rs721048 (A)
0.224
0.184
1.28 (1.06-1.55)
0.012


All excluding Iceland (8,239/7,590)d


rs721048 (A)
—
0.188
1.15 (1.09-1.22)
2.23 × 10−6


All combined (10,093/28,654)d


rs721048 (A)
—
0.190
1.15 (1.10-1.21)
7.66 × 10−9





All P values shown are two-sided. Shown are the corresponding numbers of cases and controls (N), allelic frequencies of variants in affected and control individuals, the allelic odds-ratio (OR) with 95% confidence interval (95% c.i.) and P values based on the multiplicative model.


aResults presented for Iceland were adjusted for relatedness using a method of genomic control (see Supplementary Methods).


bThe SNPs rs2710646 and rs721048 are highly correlated (r2 = 0.99) but rs2710646 failed in genotyping in some of the non-Icelandic groups and results are therefore only presented for this marker from the Icelandic study group.


cResults presented for Rochester were adjusted for relatedness by applying a pedigree correction factor (see Supplementary Methods).


dFor the combined study populations, the reported control frequency was the average, unweighted control frequency of the individual populations, while the OR and the P value were estimated using the Mantel-Haenszel model.






REFERENCES


    1. Gudmundsson, J. et al. Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 39, 631-7 (2007).
    2. Gudmundsson, J. et al. Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet 39, 977-83 (2007).
    3. Narod, S. A. et al. The impact of family history on early detection of prostate cancer. Nat Med 1, 99-101 (1995).
    4. Monroe, K. R. et al. Evidence of an X-linked or recessive genetic component to prostate cancer risk. Nat Med 1, 827-9. (1995).
    5. Yeager, M. et al. Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet 39, 645-649 (2007).
    6. Guilherme, A. et al. EHD2 and the novel EH domain binding protein EHBP1 couple endocytosis to the actin cytoskeleton. J Biol Chem 279, 10593-605 (2004).
    7. Salehi, A. H., Xanthoudakis, S. & Barker, P. A. NRAGE, a p75 neurotrophin receptor-interacting protein, induces caspase activation and cell death through a JNK-dependent mitochondrial pathway. J Biol Chem 277, 48043-50 (2002).
    8. Salehi, A. H. et al. NRAGE, a novel MAGE protein, interacts with the p75 neurotrophin receptor and facilitates nerve growth factor-dependent apoptosis. Neuron 27, 279-88 (2000).
    9. Hidaka, K. et al. An adjacent pair of human NUDT genes on chromosome X are preferentially expressed in testis and encode two new isoforms of diphosphoinositol polyphosphate phosphohydrolase. J Biol Chem 277, 32730-8 (2002).
    10. Dubois, E. et al. In Saccharomyces cerevisiae, the inositol polyphosphate kinase activity of Kcs1p is required for resistance to salt stress, cell wall integrity, and vacuolar morphogenesis. J Biol Chem 277, 23755-63 (2002).
    11. Morrison, B. H., Bauer, J. A., Kalvakolanu, D. V. & Lindner, D. J. Inositol hexakisphosphate kinase 2 mediates growth suppressive and apoptotic effects of interferon-beta in ovarian carcinoma cells. J Biol Chem 276, 24965-70 (2001).
    12. Amundadottir, L. T. et al. A common variant associated with prostate cancer in European and African populations. Nat Genet 38, 652-8 (2006).
    13. Haiman, C. A. et al. Multiple regions within 8q24 independently affect risk for prostate cancer. Nat Genet 39, 638-44 (2007).









TABLE 18







Association results for rs721048 on 2q15 and aggressive prostate


cancer in Iceland, The Netherlands, Spain, Sweden and the US









Study population




(N cases/N controls)
Frequency











Variant (allele)
Cases
Controls
OR (95% c.i)
P value







Aggressive prostate cancera














Iceland (694/21,064)b






rs2710646 (A)c
0.251
0.202
1.33 (1.16-1.52)
3.73 × 10−5


rs721048 (A)
0.254
0.205
1.33 (1.16-1.52)
3.71 × 10−5


The Netherlands (466/2,021)


rs721048 (A)
0.211
0.187
1.16 (0.97-1.38)
0.095


Spain (185/1,616)


rs721048 (A)
0.227
0.210
1.11 (0.85-1.46)
0.45


Sweden (1,210/1,731)


rs721048 (A)
0.202
0.186
1.11 (0.97-1.27)
0.12


Baltimore, Maryland (1,003/557)


rs721048 (A)
0.218
0.206
1.08 (0.90-1.30)
0.42


Chicago, Illinois (320/552)


rs721048 (A)
0.208
0.168
1.30 (1.02-1.66)
0.037


Nashville, Tennessee (255/612)


rs721048 (A)
0.257
0.175
1.63 (1.27-2.09)
1.28 × 10−4


Rochester, Minnesota (654/501)d


rs721048 (A)
0.232
0.184
1.34 (1.08-1.66)
7.07 × 10−3


All excluding Iceland (4,093/7,590)e


rs721048 (A)
—
0.188
1.19 (1.11-1.28)
2.12 × 10−6


All combined (4,787/28,654)e


rs721048 (A)
—
0.190
1.22 (1.14-1.30)
 8.68 × 10−10





All P values shown are two-sided. Shown are the corresponding numbers of cases and controls (N), allelic frequencies of variants in affected and control individuals, the allelic odds-ratio (OR) with 95% confidence interval (95% c.i.) and P values based on the multiplicative model.


aAggressive prostate cancer is defined here as: Gleason ≧7 and/or T3 or higher and/or node positive and/or metastatic disease


bResults presented for Iceland were adjusted for relatedness using a method of genomic control


cThe SNPs rs2710646 and rs721048 are highly correlated (r2 = 0.99) but rs2710646 failed in genotyping in some of the non-Icelandic groups and results are therefore only presented for this marker from the Icelandic study group.


dResults presented for Rochester were adjusted for relatedness by applying a pedigree correction factor.


eFor the combined study populations, the reported control frequency was the average, unweighted control frequency of the individual populations, while the OR and the P value were estimated using the Mantel-Haenszel model.













TABLE 19







LD-characteristics and adjusted association results for


markers correlated with markers rs5945572 and rs2710646












Chromosome
Marker-1
Marker-2
D′
r2
P valuea





X
rs1327304
rs5945572
1.00
1.00
0.31


X
rs1327301
rs5945572
1.00
0.92
0.23


X
rs5945650
rs5945572
0.97
0.88
0.86


X
rs1936034
rs5945572
0.97
0.88
0.96


X
rs11798651
rs5945572
0.95
0.83
0.36


X
rs5945607
rs5945572
0.95
0.83
0.35


X
rs4907790
rs5945572
0.95
0.82
0.13


X
rs5945606
rs5945572
0.95
0.81
0.26


X
rs5945605
rs5945572
0.94
0.80
0.15


X
rs1984280
rs5945572
0.90
0.75
0.50


X
rs6521795
rs5945572
0.92
0.54
0.75


X
rs5945620
rs5945572
0.98
0.43
0.36


2
rs2553041
rs2710646
1.00
1.00
0.77


2
rs17432497
rs2710646
1.00
0.98
0.22


2
rs13431765
rs2710646
0.99
0.79
0.33


2
rs2176418
rs2710646
1.00
0.56
0.88


2
rs6545986
rs2710646
0.99
0.55
0.89


2
rs1517405
rs2710646
0.99
0.55
0.92


2
rs959195
rs2710646
0.90
0.47
0.33


2
rs7591708
rs2710646
1.00
0.41
0.60





Shown are P values and LD-characteristics (D′ and r2) from an analysis on 1,500 and 800 Icelandic patients and controls, respectively.


aThe P-value is for the association of Marker-1 to prostate cancer and has been adjusted for Marker-2 (rs5945572 or rs2710646).













TABLE 20





Analysis of combined risk for eight SNP markers associated with prostate


cancer under the multiplicative model. There are 37 × 21 =


4374 possible genotype combinations for the eight markers. Shown are the


20 combinations that confer the lowest risk of prostate cancer (A),


the 20 combinations that confer the highest risk of prostate cancer (B), and the 20 most


common combinations in the general population (C).


Shown are risk estimates with respect to non-carriers of all the at-risk variants


(Risk 1), risk estimates with respect to the population (Risk 2),


frequency of each genotype combination (Freq), cumulative frequency based on sorting


on Risk 2 and start the addition from the lowest risk


combination (Cumulative Freq), and genotype combinations for the eight markers.


The ratio between Risk 1 and Risk 2 values is always


identical, i.e. 1/0.332 = 3.012, since the former is calculated by reference


to the genotype combination that includes non-carriers (i.e.,


homozygotes for the non-risk, or protective, variant) at all the markers.







A.















OR_vs_NC
OR_vs_pop


C08
C08


Risk_1
Risk_2
Freq
Cumulative_Freq
rs10505483
rs1447295





1.488
0.494
1.6986E−03
0.0434
GG
CC


1.476
0.490
6.0132E−03
0.0417
GG
CC


1.476
0.490
1.4297E−03
0.0357
GG
CC


1.464
0.486
1.5963E−03
0.0342
GG
CC


1.464
0.486
5.0612E−03
0.0327
GG
CC


1.461
0.484
1.4622E−03
0.0276
GG
CC


1.452
0.482
5.3743E−03
0.0261
GG
CC


1.440
0.478
1.1309E−03
0.0208
GG
CC


1.414
0.469
3.9367E−04
0.0196
GG
CC


1.403
0.465
1.3937E−03
0.0192
GG
CC


1.392
0.462
1.4799E−03
0.0178
GG
CC


1.380
0.458
1.2456E−03
0.0164
GG
CC


1.322
0.439
8.5747E−05
0.0151
GG
CC


1.270
0.421
3.1168E−03
0.0150
GG
CC


1.230
0.408
8.3915E−04
0.0119
GG
CC


1.220
0.405
2.9707E−03
0.0111
GG
CC


1.210
0.401
3.1545E−03
0.0081
GG
CC


1.200
0.398
2.6551E−03
0.0049
GG
CC


1.150
0.381
7.3111E−04
0.0023
GG
CC


1.000
0.332
1.5584E−03
0.0016
GG
CC
















OR_vs_NC
C17
C02
C17
CX
C08
C11


Risk_1
rs1859962
rs2710646
rs4430796
rs5945572
rs6983267
rs10896450





1.488
TT
TT
GG
AA
TT
AG


1.476
TT
TT
AG
GG
TT
AG


1.476
GT
TT
GG
AA
TT
AA


1.464
TT
TT
GG
GG
TT
GG


1.464
GT
TT
AG
GG
TT
AA


1.461
TT
AT
GG
GG
GT
AA


1.452
GT
TT
GG
GG
TT
AG


1.440
GG
TT
GG
GG
TT
AA


1.414
TT
AT
GG
AA
TT
AA


1.403
TT
AT
AG
GG
TT
AA


1.392
TT
AT
GG
GG
TT
AG


1.380
GT
AT
GG
GG
TT
AA


1.322
TT
AA
GG
GG
TT
AA


1.270
TT
TT
GG
GG
GT
AA


1.230
TT
TT
GG
AA
TT
AA


1.220
TT
TT
AG
GG
TT
AA


1.210
TT
TT
GG
GG
TT
AG


1.200
GT
TT
GG
GG
TT
AA


1.150
TT
AT
GG
GG
TT
AA


1.000
TT
TT
GG
GG
TT
AA










B.















OR_vs_NC
OR_vs_pop


C08
C08


Risk_1
Risk_2
Freq
Cumulative_Freq
rs10505483
rs1447295





53.108
17.618
3.2757E−10
1.0000
AA
AA


46.181
15.320
2.7930E−09
1.0000
AA
AA


44.257
14.681
7.6908E−10
1.0000
AA
AA


43.891
14.560
6.4733E−10
1.0000
AA
AA


43.531
14.441
6.8736E−10
1.0000
AA
AA


43.177
14.323
6.0835E−10
1.0000
AA
AA


41.817
13.872
6.5514E−10
1.0000
AA
AA


40.157
13.321
5.9534E−09
1.0000
AA
AA


38.484
12.766
6.5574E−09
1.0000
AA
AA


38.166
12.661
5.5193E−09
1.0000
AA
AA


37.853
12.557
5.8607E−09
1.0000
AA
AA


37.545
12.455
5.1870E−09
1.0000
AA
AA


36.881
12.234
4.5142E−10
1.0000
AA
AA


36.576
12.133
1.5198E−09
1.0000
AA
AA


36.363
12.063
5.5859E−09
1.0000
AA
AA


36.276
12.034
1.6138E−09
1.0000
AA
AA


36.273
12.033
3.1980E−10
1.0000
AA
AA


35.981
11.936
1.4283E−09
1.0000
AA
AA


35.976
11.934
1.3583E−09
1.0000
AA
AA


35.684
11.837
1.2022E−09
1.0000
AA
AA
















OR_vs_NC
C17
C02
C17
CX
C08
C11


Risk_1
rs1859962
rs2710646
rs4430796
rs5945572
rs6983267
rs10896450





53.108
GG
AA
AA
AA
GG
GG


46.181
GG
AT
AA
AA
GG
GG


44.257
GT
AA
AA
AA
GG
GG


43.891
GG
AA
AA
AA
GG
AG


43.531
GG
AA
AG
AA
GG
GG


43.177
GG
AA
AA
GG
GG
GG


41.817
GG
AA
AA
AA
GT
GG


40.157
GG
TT
AA
AA
GG
GG


38.484
GT
AT
AA
AA
GG
GG


38.166
GG
AT
AA
AA
GG
AG


37.853
GG
AT
AG
AA
GG
GG


37.545
GG
AT
AA
GG
GG
GG


36.881
TT
AA
AA
AA
GG
GG


36.576
GT
AA
AA
AA
GG
AG


36.363
GG
AT
AA
AA
GT
GG


36.276
GT
AA
AG
AA
GG
GG


36.273
GG
AA
AA
AA
GG
AA


35.981
GT
AA
AA
GG
GG
GG


35.976
GG
AA
AG
AA
GG
AG


35.684
GG
AA
AA
GG
GG
AG










C.















OR_vs_NC
OR_vs_pop


C08
C08


Risk_1
Risk_2
Freq
Cumulative_Freq
rs10505483
rs1447295





2.250
0.746
2.0489E−02
0.3088
GG
CC


1.875
0.622
1.2026E−02
0.1699
GG
CC


2.767
0.918
1.1033E−02
0.5291
GG
CC


1.844
0.612
1.0749E−02
0.1397
GG
CC


2.722
0.903
1.0368E−02
0.4943
GG
CC


2.857
0.948
1.0245E−02
0.5675
GG
CC


1.771
0.588
1.0245E−02
0.1038
GG
CC


1.859
0.617
1.0122E−02
0.1512
GG
CC


2.745
0.910
9.7645E−03
0.5151
GG
CC


2.587
0.858
9.6123E−03
0.4239
GG
CC


2.700
0.896
8.7269E−03
0.4735
GG
CC


2.306
0.765
6.4757E−03
0.3493
GG
CC


1.537
0.510
6.3089E−03
0.0583
GG
CC


2.268
0.753
6.0858E−03
0.3284
GG
CC


2.381
0.790
6.0132E−03
0.3765
GG
CC


1.476
0.490
6.0132E−03
0.0417
GG
CC


1.549
0.514
5.9415E−03
0.0643
GG
CC


2.268
0.752
5.7877E−03
0.3175
GG
CC


2.287
0.759
5.7313E−03
0.3423
GG
CC


2.156
0.715
5.6420E−03
0.2436
GG
CC


3.348
1.111
5.5830E−03
0.7050
GG
CC


3.514
1.166
5.5164E−03
0.7574
GG
CC


2.179
0.723
5.5164E−03
0.2596
GG
CC


2.287
0.759
5.4506E−03
0.3348
GG
CC


2.231
0.740
5.4391E−03
0.2839
GG
CC


2.342
0.777
5.3743E−03
0.3589
GG
CC


1.452
0.482
5.3743E−03
0.0261
GG
CC


1.524
0.506
5.3102E−03
0.0517
GG
CC


3.376
1.120
5.2578E−03
0.7129
GG
CC


3.457
1.147
5.1842E−03
0.7372
GG
CC
















OR_vs_NC
C17
C02
C17
CX
C08
C11


Risk_1
rs1859962
rs2710646
rs4430796
rs5945572
rs6983267
rs10896450





2.250
GT
TT
AG
GG
GT
AG


1.875
TT
TT
AG
GG
GT
AG


2.767
GT
TT
AG
AA
GT
AG


1.844
GT
TT
GG
GG
GT
AG


2.722
GT
TT
AG
GG
GT
GG


2.857
GT
TT
AG
GG
GG
AG


1.771
GT
TT
AG
GG
TT
AG


1.859
GT
TT
AG
GG
GT
AA


2.745
GT
TT
AA
GG
GT
AG


2.587
GT
AT
AG
GG
GT
AG


2.700
GG
TT
AG
GG
GT
AG


2.306
TT
TT
AG
AA
GT
AG


1.537
TT
TT
GG
GG
GT
AG


2.268
TT
TT
AG
GG
GT
GG


2.381
TT
TT
AG
GG
GG
AG


1.476
TT
TT
AG
GG
TT
AG


1.549
TT
TT
AG
GG
GT
AA


2.268
GT
TT
GG
AA
GT
AG


2.287
TT
TT
AA
GG
GT
AG


2.156
TT
AT
AG
GG
GT
AG


3.348
GT
TT
AG
AA
GT
GG


3.514
GT
TT
AG
AA
GG
AG


2.179
GT
TT
AG
AA
TT
AG


2.287
GT
TT
AG
AA
GT
AA


2.231
GT
TT
GG
GG
GT
GG


2.342
GT
TT
GG
GG
GG
AG


1.452
GT
TT
GG
GG
TT
AG


1.524
GT
TT
GG
GG
GT
AA


3.376
GT
TT
AA
AA
GT
AG


3.457
GT
TT
AG
GG
GG
GG
















TABLE 21







SEQ ID key to markers.











SEQ ID



SNP
NO













rs3923603
1



rs4430796
2



rs7501939
3



rs1859962
4



D17S1350
5



rs5945572
6



rs5945605
7



rs2710646
8



rs6857303
9



rs7665923
10



rs11725211
11



rs13103731
12



rs11736498
13



rs11723763
14



rs6831817
15



rs13134172
16



rs10015396
17



rs4269130
18



rs4289393
19



rs6812128
20



rs7695923
21



rs4599356
22



rs7692784
23



rs4303930
24



rs10012731
25



rs6537264
26



rs4417920
27



rs4505762
28



rs4583707
29



rs4425324
30



rs13127749
31



rs4518185
32



rs4549337
33



rs4390989
34



rs4305469
35



rs7658605
36



rs6828217
37



rs7441314
38



rs12505797
39



rs12503177
40



rs4482709
41



rs4320096
42



rs13144823
43



rs11100843
44



rs7679401
45



rs7684835
46



rs11943703
47



rs12509569
48



rs7689179
49



rs10002827
50



rs1016990
51



rs3744763
52



rs757210
53



rs4239217
54



rs3760511
55



rs7213769
56



rs4793330
57



rs1861690
58



rs7222314
59



rs8071558
60



rs984434
61



rs11650165
62



rs991429
63



rs4793528
64



rs9674957
65



rs8077906
66



rs8066875
67



rs9889335
68



rs4328484
69



rs8068266
70



rs7217652
71



rs6501437
72



rs6501438
73



rs8079315
74



rs2367256
75



rs2190697
76



rs4366746
77



rs4366747
78



rs2159034
79



rs1013999
80



rs4793530
81



rs11654749
82



rs11653132
83



rs4300694
84



rs8076830
85



rs9900242
86



rs2058083
87



rs2058085
88



rs1468481
89



rs2041114
90



rs723338
91



rs2041115
92



rs8064263
93



rs9897865
94



rs11656242
95



rs9897358
96



rs11651123
97



rs11657298
98



rs11651469
99



rs11651501
100



rs719615
101



rs1558119
102



rs12150098
103



rs9910829
104



rs7220274
105



rs17224833
106



rs2108534
107



rs2108535
108



rs8182284
109



rs8182286
110



rs4793533
111



rs8069925
112



rs8068189
113



rs9901508
114



rs9907418
115



rs2367263
116



rs1859964
117



rs1859965
118



rs6501446
119



rs4793534
120



rs4239156
121



rs4793335
122



rs2097984
123



rs11654068
124



rs8079962
125



rs2886914
126



rs8076811
127



rs17178251
128



rs758106
129



rs740408
130



rs4570900
131



rs4611499
132



rs7214479
133



rs1008348
134



rs6501449
135



rs6501451
136



rs6501452
137



rs11870732
138



rs17178370
139



rs7225025
140



rs17178377
141



rs11655744
142



rs2367266
143



rs1107305
144



rs6501455
145



rs7209505
146



rs2190463
147



rs2190456
148



rs983085
149



rs6501459
150



rs4793538
151



rs2158905
152



rs2190457
153



rs11655567
154



rs7225458
155



rs10401004
156



rs917278
157



rs1978203
158



rs1978204
159



rs737956
160



rs737957
161



rs8075481
162



rs7224058
163



rs7215307
164



rs4793541
165



rs7221080
166



rs8064388
167



rs9906756
168



rs17178530
169



rs17765886
170



rs8070461
171



rs9891216
172



rs972635
173



rs1875754
174



rs4907772
175



rs6521795
176



rs5945606
177



rs5945569
178



rs5987418
179



rs4907790
180



rs5945607
181



rs11091727
182



rs11798651
183



rs5945609
184



rs5945610
185



rs10284147
186



rs1327301
187



rs1327302
188



rs1327304
189



rs5945617
190



rs5945618
191



rs12854262
192



rs5945619
193



rs5945620
194



rs1891702
195



rs4907792
196



rs2384958
197



rs1936038
198



rs5945573
199



rs4907796
200



rs4907775
201



rs6614426
202



rs1970956
203



rs1970957
204



rs2153993
205



rs6614428
206



rs6418006
207



rs1541241
208



rs1541240
209



rs1592303
210



rs1110404
211



rs1110405
212



rs5945643
213



rs1343272
214



rs5987438
215



rs6614317
216



rs1936037
217



rs1936036
218



rs3131302
219



rs1361837
220



rs1936035
221



rs4457126
222



rs4308906
223



rs6521820
224



rs4907781
225



rs5945650
226



rs7064708
227



rs1936034
228



rs5945651
229



rs1541238
230



rs1984280
231



rs2721996
232



rs5945653
233



rs5945578
234



rs3955398
235



rs1419039
236



rs1419038
237



rs5987446
238



rs1419040
239



rs6614454
240



rs1541242
241



rs6521823
242



rs5987447
243



rs5945579
244



rs7057039
245



rs2185880
246



rs5987448
247



rs11091750
248



rs4907804
249



rs5945658
250



rs5945664
251



rs5945667
252



rs12558898
253



rs5945586
254



rs5945668
255



rs5945587
256



rs6521836
257



rs4298665
258



rs4544871
259



rs4473816
260



rs5945590
261



rs5945677
262



rs12394792
263



rs5951067
264



rs4986571
265



rs5951072
266



rs6614493
267



rs5951074
268



rs11796743
269



rs974285
270



rs976556
271



rs3910588
272



rs3910587
273



rs1439461
274



rs2118952
275



rs4986558
276



rs5991819
277



rs5991820
278



rs12399757
279



rs12398578
280



rs2118951
281



rs12844657
282



rs11796701
283



rs9887648
284



rs12010969
285



rs5991822
286



rs5991733
287



rs12393443
288



rs6521844
289



rs5991824
290



rs4986559
291



rs7890241
292



rs4986553
293



rs5991735
294



rs5951078
295



rs5951079
296



rs4986554
297



rs1439460
298



rs5951064
299



rs5951083
300



rs7061919
301



rs7056700
302



rs12853137
303



rs12353683
304



rs7880576
305



rs4133299
306



rs5991738
307



rs1992271
308



rs5991739
309



rs1048437
310



rs5951087
311



rs7053197
312



rs5991707
313



rs11796891
314



rs5951091
315



rs7053327
316



rs7051319
317



rs5991744
318



rs11796974
319



rs4074722
320



rs12388600
321



rs4986557
322



rs6614515
323



rs4131729
324



rs5991762
325



rs11798798
326



rs12395699
327



rs5991776
328



rs5951070
329



rs11797967
330



rs12394739
331



rs5991804
332



rs5991805
333



rs12845073
334



rs12851025
335



rs6413596
336



rs5991812
337



rs5951109
338



rs5951114
339



rs4986573
340



rs7472562
341



rs721048
342



rs6857262
343



rs7700104
344



rs4240360
345



rs12510585
346



rs2005705
347



rs7405696
348



rs17765344
349



rs8072254
350



rs4793529
351



rs9908442
352



rs4793334
353



rs2058084
354



rs9915190
355



rs6501447
356



rs17765644
357



rs9913988
358



rs5987421
359



rs12688960
360



rs5945659
361



rs4986555
362

## Claims

1. A method for determining a susceptibility to prostate cancer in a human individual, the method comprising analyzing nucleic acid from the human individual for the presence of at least one allele of at least one polymorphic marker, wherein the at least one allele comprises rs7501939, allele C,
detecting the presence of allele C of polymorphic marker rs7501939 in the sample, and
determining an increased genetic susceptibility to prostate cancer for the human individual from the presence of the at least one allele in the nucleic acid, and,
performing at least one of a prostate specific antigen (PSA) test and a digital rectal examination (DRE) on the individual determined to have the increased genetic susceptibility.

2. The method of claim 1, wherein the analyzing of the nucleic acid comprises at least one nucleic acid analysis technique selected from: polymerase chain reaction, allele-specific hybridization, allele-specific primer extension, allele-specific amplification, nucleic acid sequencing, 5′-exonuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, and single-stranded conformation analysis.

3. The method of claim 2 wherein the at least one nucleic acid analysis technique is selected from the group consisting of allele-specific probe hybridization and DNA sequencing.

4. The method of claim 1, further comprising calculating a risk measure for prostate cancer that includes a relative risk or odds ratio of at least 1.2 attributable to the presence of allele C of polymorphic marker rs7501939 in the sample, using an apparatus that comprises: a computer readable memory, a processor, and a routine stored on the computer readable memory; wherein the routine is adapted to be executed on the processor to analyze marker information for at least one human individual with respect to the at least one allele of the at least one polymorphic marker, and generate an output based on the marker information, wherein the output comprises a prostate cancer risk measure of the allele as a genetic indicator of the prostate cancer for the human individual.

5. The method of claim 1, further comprising making a communication that includes the risk measure available to the individual or to a third party.

6. The method of claim 5, wherein the communication is made available to the individual or third party by a secured internet interface.

7. The method of claim 1 wherein the human individual has not yet been diagnosed with prostate cancer.

8. A method for determining a susceptibility to prostate cancer in a human individual, the method comprising analyzing nucleic acid from the human individual for the presence of at least one allele of at least one polymorphic marker, wherein the at least one allele comprises rs4430796, allele A,
detecting the presence of allele A of polymorphic marker rs4430796 in the sample, and
determining an increased genetic susceptibility to prostate cancer for the human individual from the presence of the at least one allele in the nucleic acid, and,
performing at least one of a prostate specific antigen (PSA) test and a digital rectal examination (DRE) on the individual determined to have the increased susceptibility.

9. The method of claim 8, wherein the analyzing of the nucleic acid comprises at least one nucleic acid analysis technique selected from: polymerase chain reaction, allele-specific hybridization, allele-specific primer extension, allele-specific amplification, nucleic acid sequencing, 5′-exonuclease digestion, molecular beacon assay, oligonucleotide ligation assay, size analysis, and single-stranded conformation analysis.

10. The method of claim 9 wherein the at least one nucleic acid analysis technique is selected from the group consisting of allele-specific probe hybridization and DNA sequencing.

11. The method of claim 8, further comprising calculating a risk measure for prostate cancer that includes a relative risk or odds ratio of at least 1.2 attributable to the presence of allele A of polymorphic marker rs4430796 in the sample, using an apparatus that comprises: a computer readable memory, a processor, and a routine stored on the computer readable memory; wherein the routine is adapted to be executed on the processor to analyze marker information for at least one human individual with respect to the at least one allele of the at least one polymorphic marker, and generate an output based on the marker information, wherein the output comprises a prostate cancer risk measure of the allele as a genetic indicator of the prostate cancer for the human individual.

12. The method of claim 8, further comprising making a communication that includes the risk measure available to the individual or to a third party.

13. The method of claim 12, wherein the communication is made available to the individual or third party by a secured internet interface.

14. The method of claim 8 wherein the human individual has not yet been diagnosed with prostate cancer.