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Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array

Abstract

Prostate cancer is the most frequently diagnosed cancer in males in developed countries. To identify common prostate cancer susceptibility alleles, we genotyped 211,155 SNPs on a custom Illumina array (iCOGS) in blood DNA from 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium. Twenty-three new prostate cancer susceptibility loci were identified at genome-wide significance (P < 5 × 10−8). More than 70 prostate cancer susceptibility loci, explaining 30% of the familial risk for this disease, have now been identified. On the basis of combined risks conferred by the new and previously known risk loci, the top 1% of the risk distribution has a 4.7-fold higher risk than the average of the population being profiled. These results will facilitate population risk stratification for clinical studies.

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Figure 1: Composition of the prostate part of the iCOGS chip.
Figure 2: Manhattan plot of associations for new iCOGS loci.
Figure 3: Regional association plots.

References

  1. 1

    Goh, C.L. et al. Genetic variants associated with predisposition to prostate cancer and potential clinical implications. J. Intern. Med. 271, 353–365 (2012).

    CAS  Article  Google Scholar 

  2. 2

    Akamatsu, S. et al. Common variants at 11q12, 10q26 and 3p11.2 are associated with prostate cancer susceptibility in Japanese. Nat. Genet. 44, 426–429 (2012).

    CAS  Article  Google Scholar 

  3. 3

    Gudmundsson, J. et al. Genome-wide association and replication studies identify four variants associated with prostate cancer susceptibility. Nat. Genet. 41, 1122–1126 (2009).

    CAS  Article  Google Scholar 

  4. 4

    Xu, J. et al. Genome-wide association study in Chinese men identifies two new prostate cancer risk loci at 9q31.2 and 19q13.4. Nat. Genet. 44, 1231–1235 (2012).

    CAS  Article  Google Scholar 

  5. 5

    Amin Al Olama, A. et al. A meta-analysis of genome-wide association studies to identify prostate cancer susceptibility loci associated with aggressive and non-aggressive disease. Hum. Mol. Genet. 22, 408–415 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Gudmundsson, J. et al. A study based on whole-genome sequencing yields a rare variant at 8q24 associated with prostate cancer. Nat. Genet. 44, 1326–1329 (2012).

    CAS  Article  Google Scholar 

  7. 7

    Park, J.H. et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat. Genet. 42, 570–575 (2010).

    CAS  Article  Google Scholar 

  8. 8

    Kote-Jarai, Z. et al. Identification of a novel prostate cancer susceptibility variant in the KLK3 gene transcript. Hum. Genet. 129, 687–694 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Kote-Jarai, Z. et al. Multiple novel prostate cancer predisposition loci confirmed by an international study: the PRACTICAL Consortium. Cancer Epidemiol. Biomarkers Prev. 17, 2052–2061 (2008).

    CAS  Article  Google Scholar 

  10. 10

    Koutros, S. et al. Pooled analysis of phosphatidylinositol 3-kinase pathway variants and risk of prostate cancer. Cancer Res. 70, 2389–2396 (2010).

    CAS  Article  Google Scholar 

  11. 11

    Sun, T. et al. Single-nucleotide polymorphisms in p53 pathway and aggressiveness of prostate cancer in a Caucasian population. Clin. Cancer Res. 16, 5244–5251 (2010).

    CAS  Article  Google Scholar 

  12. 12

    Wynendaele, J. et al. An illegitimate microRNA target site within the 3′ UTR of MDM4 affects ovarian cancer progression and chemosensitivity. Cancer Res. 70, 9641–9649 (2010).

    CAS  Article  Google Scholar 

  13. 13

    ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 9, e1001046 (2011).

  14. 14

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  15. 15

    Garcia-Closas, M. et al. Genome-wide association studies identify four ER negative–specific breast cancer risk loci. Nat. Genet. published online; doi:10.1038/ng.2561 (27 March 2013).

    CAS  Article  Google Scholar 

  16. 16

    Couch, F.J. et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet. 9, e1003212 (2013).

    CAS  Article  Google Scholar 

  17. 17

    Volinia, S. et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. USA 103, 2257–2261 (2006).

    CAS  Article  Google Scholar 

  18. 18

    Szarvas, T. et al. Elevated serum matrix metalloproteinase 7 levels predict poor prognosis after radical prostatectomy. Int. J. Cancer 128, 1486–1492 (2011).

    CAS  Article  Google Scholar 

  19. 19

    Richards, T.J. et al. Allele-specific transactivation of matrix metalloproteinase 7 by FOXA2 and correlation with plasma levels in idiopathic pulmonary fibrosis. Am. J. Physiol. Lung Cell. Mol. Physiol. 302, L746–L754 (2012).

    CAS  Article  Google Scholar 

  20. 20

    Thomas, G. et al. A multistage genome-wide association study in breast cancer identifies two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat. Genet. 41, 579–584 (2009).

    CAS  Article  Google Scholar 

  21. 21

    Michailidou, K. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat. Genet. published online; doi:10.1038/ng.2563 (27 March 2013).

    CAS  Article  Google Scholar 

  22. 22

    Haiman, C.A. et al. Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21. Nat. Genet. 43, 570–573 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Callen, D.F. et al. Co-expression of the androgen receptor and the transcription factor ZNF652 is related to prostate cancer outcome. Oncol. Rep. 23, 1045–1052 (2010).

    CAS  Article  Google Scholar 

  24. 24

    Norris, J.D. et al. The homeodomain protein HOXB13 regulates the cellular response to androgens. Mol. Cell 36, 405–416 (2009).

    CAS  Article  Google Scholar 

  25. 25

    Ewing, C.M. et al. Germline mutations in HOXB13 and prostate-cancer risk. N. Engl. J. Med. 366, 141–149 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Edwards, S. et al. Expression analysis onto microarrays of randomly selected cDNA clones highlights HOXB13 as a marker of human prostate cancer. Br. J. Cancer 92, 376–381 (2005).

    CAS  Article  Google Scholar 

  27. 27

    Barbieri, C.E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).

    CAS  Article  Google Scholar 

  28. 28

    Breast Cancer Linkage Consortium. Cancer risks in BRCA2 mutation carriers. The Breast Cancer Linkage Consortium. J. Natl. Cancer Inst. 91, 1310–1316 (1999).

  29. 29

    Macinnis, R.J. et al. A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact. Genet. Epidemiol. 35, 549–556 (2011).

    PubMed  PubMed Central  Google Scholar 

  30. 30

    Schumacher, F.R. et al. Genome-wide association study identifies new prostate cancer susceptibility loci. Hum. Mol. Genet. 20, 3867–3875 (2011).

    CAS  Article  Google Scholar 

  31. 31

    Marchini, J. et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    CAS  Article  Google Scholar 

  32. 32

    Eeles, R.A. et al. Multiple newly identified loci associated with prostate cancer susceptibility. Nat. Genet. 40, 316–321 (2008).

    CAS  Article  Google Scholar 

  33. 33

    Aulchenko, Y.S., Struchalin, M.V. & van Duijn, C.M. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 11, 134 (2010).

    Article  Google Scholar 

  34. 34

    Teo, Y.Y. et al. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 23, 2741–2746 (2007).

    CAS  Article  Google Scholar 

  35. 35

    Giannoulatou, E. et al. GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population. Bioinformatics 24, 2209–2214 (2008).

    CAS  Article  Google Scholar 

  36. 36

    Haldane, J.B. & Slater, E. Assortative mating. Eugen. Rev. 38, 103 (1946).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Aulchenko, Y.S., Ripke, S., Isaacs, A. & van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

Acknowledgments are detailed in the Supplementary Note.

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Contributions

R.A.E. and D.F.E. designed the study. R.A.E. is principal investigator of PRACTICAL. D.F.E. is Scientific Director of the COGS initiative. Z.K.-J. is co-investigator of PRACTICAL. R.A.E., D.F.E., Z.K.-J. and A.A.A.O. wrote the manuscript; the following named coauthors commented on the manuscript. A.A.A.O. and D.F.E. performed the statistical analyses; S.B. collated the data set. J.D. managed the database. Z.K.-J., E.J. Saunders, D.A.L. and M.T. coordinated sample collation and quality control for iCOGS PRACTICAL genotyping. S.J.-L. carried out pathway analysis and constructed regional plots, and T. Dadaev, K.G., M. Guy, R.A.W., E.J. Sawyer and A.M. managed the UKGPCS database and manifests for genotyping. C.L., A.M.D., C.B., D. Conroy, M.J.M., S.A., E.D., A. Lee, D.C.T., F.B. and D.V. carried out iCOGS PRACTICAL genotyping and set quality control standards. M. Ghoussaini selected the iCOGS PRACTICAL SNPs for fine-scale mapping. K.M. and A. Lophatananon collected some of the UKGPCS samples and controls. F.C.H., D.E.N. and J.L.D. are joint principal investigators of ProtecT. B.E.H. and L.L.M. are principal investigators of MEC; C.A.H. and F.S. are co-investigators. S.I.B. and D.A. are principal investigators of the PLCO study; G.A. is the principal investigator for the St. Louis screening center for PLCO; and S.J.C. and M.Y. led the genotyping for PLCO. S.G., R.B.H. and W.R.D. provided samples for PLCO. D.J.H. directs and P. Kraft coordinates data collection and management/analysis for HPFS. M.W. is the principal investigator of CPCS1 and CPCS2. B.G.N., S.F.N. S.E.B., P. Klarskov and M.A.R. have collected samples and data, and contributed to genotyping in this study. J.L.S. is principal investigator of the Fred Hutchinson–based study; E.A.O. collaborated on the study; L.M.F. and S.K. coordinated data collation; and E.M.K. and D.M.K. coordinated the preparation of samples. L.C.-A. is principal investigator of the Utah study; C.T. is the analyst; and R.A.S. is the surgeon. S.L. is a co-investigator of the BPC3 Consortium. H.G. is principal investigator of the CAPS and STHM1 study; J.A., M.A., F.W., S.L.Z. and J.X. have contributed to sample collection, clinical data retrieval, analyses and molecular work. S.A.I. is principal investigator of the USC study, and E.M.J. is principal investigator of SFPCS; M.C. Stem and R.C. led the genotyping of both studies. A.D.J. and A. Shahabi were both involved in genotype data production for the USC and SFPCS studies. A.S.K. is principal investigator of WUGS. B.D. and G.C. collected and collated clinical data and performed sample selection. M.R.T. is the principal investigator of the IPO-Porto study; S.M. and P.P. collected familial and molecular data on cases. L.B.S. and W.J.B. are the principal investigators of SCCS; L.B.S., W.J.B., W.Z. and Q.C. were responsible for the original collection of the samples. W.Z. and Q.C. coordinated sample retrieval, DNA extraction and genotyping. L.B.S. oversaw the assembly of the phenotype data. J.B. and J.A.C. are principal investigators of the Queensland study with input from A.B.S., F.L. and S.S. coordinated the data collation. K.A.C. and E.L. provided imputed data for genotyping in carriers of the mutation encoding the p.Gly84Glu alteration in the HOXB13 region. G.G.G., J.L.H., D.R.E. and G.S. are principal investigators of the Australian studies; M.C. Southey manages the molecular work. J.S. is principal investigator of the Tampere study; T.W. collected and collated clinical data and performed sample selection. T.L.J.T. coordinated sample collection. H.B. is principal investigator of the ESTHER study; D.R. and C.S. contributed to design and data collection; and H.M. is study coordinator. J.Y.P. is principal investigator of the Moffitt study; T.A.S. and H.-Y.L. are contributors to this study. R. Kaneva is principal investigator of the PCMUS study; C.S. provided the samples in the PCMUS study; V.M. oversaw the data collation. C.C. and J.L. are principal investigators of the Poland study; C.C. and D.W. collated the samples. C.M. and W.V. are principal investigators of the Ulm study; A.E.R. identified and collected clinical material, processed samples, undertook genotyping and/or collated data. E.R. is principal investigator of EPIC; F.C., R. Kaaks and D. Campa are investigators in Germany. T.J.K. is principal investigator of the EPIC-Oxford cohort and collected clinical material. R.C.T. collated data. K.-T.K. is principal investigator of the EPIC-Norfolk study. S.N.T. and D.S. are principal investigators of the Mayo Clinic study; S.K.M. coordinated data collation. M.M.G. provided samples for the ACS study. P.D.P.P. and N.P. provided samples for the East Anglia SEARCH study. C.S.C. gave advice about results and contributed to the manuscript. A.C.A. undertook risk prediction analysis for clinical application. D.P.D., A.H., R.A.H., V.S.K., C.C.P., N.J.V.A., C.J.W., A.T., T. Dudderidge, C.O., A.A., A.C., J.V. and A. Siddiq identified and collected clinical material. Other members of the UK Genetic Prostate Cancer Study Collaborators/British Association of Urological Surgeons' Section of Oncology, the UK ProtecT Study Collaborators and the PRACTICAL Consortium (membership lists provided in the Supplementary Note) collected clinical samples, assisted in genotyping and provided data management. Members of the COGS–Cancer Research UK GWAS–ELLIPSE (part of GAME-ON) Initiatives, the Australian Prostate Cancer Bioresource, the UK Genetic Prostate Cancer Study Collaborators/British Association of Urological Surgeons' Section of Oncology, the UK ProtecT Study Collaborators, the PRACTICAL Consortium and CSC collected clinical samples and/or assisted in genotyping and/or provided data management and/or discussion of the data.

Corresponding author

Correspondence to Rosalind A Eeles.

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The authors declare no competing financial interests.

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A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

Supplementary information

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Supplementary Tables 1–8, Supplementary Figures 1–4 and Supplementary Note (PDF 12601 kb)

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Eeles, R., Olama, A., Benlloch, S. et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet 45, 385–391 (2013). https://doi.org/10.1038/ng.2560

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