Human Genetics

, Volume 138, Issue 10, pp 1091–1104 | Cite as

Genetic associations of breast and prostate cancer are enriched for regulatory elements identified in disease-related tissues

  • Hongjie Chen
  • Gleb Kichaev
  • Stephanie A. Bien
  • James W. MacDonald
  • Lu Wang
  • Theo K. Bammler
  • Paul Auer
  • Bogdan Pasaniuc
  • Sara LindströmEmail author
Original Investigation


Although genome-wide association studies (GWAS) have identified hundreds of risk loci for breast and prostate cancer, only a few studies have characterized the GWAS association signals across functional genomic annotations with a particular focus on single nucleotide polymorphisms (SNPs) located in DNA regulatory elements. In this study, we investigated the enrichment pattern of GWAS signals for breast and prostate cancer in genomic functional regions located in normal tissue and cancer cell lines. We quantified the overall enrichment of SNPs with breast and prostate cancer association p values < 1 × 10−8 across regulatory categories. We then obtained annotations for DNaseI hypersensitive sites (DHS), typical enhancers, and super enhancers across multiple tissue types, to assess if significant GWAS signals were selectively enriched in annotations found in disease-related tissue. Finally, we quantified the enrichment of breast and prostate cancer SNP heritability in regulatory regions, and compared the enrichment pattern of SNP heritability with GWAS signals. DHS, typical enhancers, and super enhancers identified in the breast cancer cell line MCF-7 were observed with the highest enrichment of genome-wide significant variants for breast cancer. For prostate cancer, GWAS signals were mostly enriched in DHS and typical enhancers identified in the prostate cancer cell line LNCaP. With progressively stringent GWAS p value thresholds, an increasing trend of enrichment was observed for both diseases in DHS, typical enhancers, and super enhancers located in disease-related tissue. Results from heritability enrichment analysis supported the selective enrichment pattern of functional genomic regions in disease-related cell lines for both breast and prostate cancer. Our results suggest the importance of studying functional annotations identified in disease-related tissues when characterizing GWAS results, and further demonstrate the role of germline DNA regulatory elements from disease-related tissue in breast and prostate carcinogenesis.



This work was supported by CA194393. The breast cancer genome-wide association analyses were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and Grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710) and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Michailidou et al. (2017). The Prostate cancer genome-wide association analyses are supported by the Canadian Institutes of Health Research, European Commission’s Seventh Framework Programme Grant Agreement No. 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C1287/A16563, C5047/A3354, C5047/A10692, C16913/A6135, and The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative Grant: No. 1 U19 CA 148537-01 (the GAME-ON initiative). We would also like to thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The Prostate Cancer Program of Cancer Council Victoria also acknowledge Grant support from The National Health and Medical Research Council, Australia (126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, and Tattersall’s. EAO, DMK, and EMK acknowledge the Intramural Program of the National Human Genome Research Institute for their support. Genotyping of the OncoArray was funded by the US National Institutes of Health (NIH) [U19 CA 148537 for Elucidating Loci Involved in Prostate cancer Susceptibility (ELLIPSE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract number HHSN268201200008I] and by Cancer Research UK Grant A8197/A16565. Additional analytic support was provided by NIH NCI U01 CA188392 (PI: Schumacher). Funding for the iCOGS infrastructure came from: the European Community’s Seventh Framework Programme under Grant Agreement No. 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112—the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233 to D.J.H., U01-CA98710 to S.M.G., U01-CA98216 toE.R., and U01-CA98758 to B.E.H., and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). CAPS GWAS study was supported by the Swedish Cancer Foundation (Grant No. 09-0677, 11-484, 12-823), the Cancer Risk Prediction Center (CRisP;, a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (Grant No. K2010-70X-20430-04-3, 2014-2269). PEGASUS was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

439_2019_2041_MOESM1_ESM.tiff (693 kb)
Supplementary material 1 (TIFF 693 kb) Supplementary Fig. 1 Enrichment of breast cancer GWAS signals with p value < 10−8 in tissue-specific DHS, super enhancer and typical enhancer regions, after removing overlapped SNPs with corresponding annotation in breast cancer cell-line MCF-7
439_2019_2041_MOESM2_ESM.tiff (704 kb)
Supplementary material 2 (TIFF 703 kb) Supplementary Fig. 2 Enrichment of prostate cancer GWAS signals with p value < 10−8 in tissue-specific DHS, super enhancer and typical enhancer regions, after removing overlapped SNPs with corresponding annotation in prostate cancer cell-line LNCaP
439_2019_2041_MOESM3_ESM.xlsx (11.5 mb)
Supplementary material 3 (XLSX 11732 kb)


  1. Akhtar-Zaidi B et al (2012) Epigenomic enhancer profiling defines a signature of colon cancer. Science 336:736–739. CrossRefGoogle Scholar
  2. Al Olama AA et al (2014) A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat Genet 46:1103–1109. CrossRefGoogle Scholar
  3. Al Olama AA et al (2015) Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans. Hum Mol Genet 24:5589–5602. CrossRefGoogle Scholar
  4. Bulik-Sullivan BK et al (2015) LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295. CrossRefGoogle Scholar
  5. Chen L et al (2016) Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167(1398–1414):e1324. CrossRefGoogle Scholar
  6. Consortium EP (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. CrossRefGoogle Scholar
  7. Consortium UK et al (2015) The UK10K project identifies rare variants in health and disease. Nature 526:82–90. CrossRefGoogle Scholar
  8. Corradin O, Scacheri PC (2014) Enhancer variants: evaluating functions in common disease. Genome Med 6:85. CrossRefGoogle Scholar
  9. Dadaev T et al (2018) Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. Nat Commun 9:2256. CrossRefGoogle Scholar
  10. Eeles RA et al (2013) Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet 45:385–391. (391e381–382) CrossRefGoogle Scholar
  11. Ernst J et al (2011) Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473:43–49. CrossRefGoogle Scholar
  12. Finucane HK et al (2015) Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47:1228–1235. CrossRefGoogle Scholar
  13. Gazal S et al (2017) Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat Genet 49:1421–1427. CrossRefGoogle Scholar
  14. Gusev A et al (2014) Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am J Hum Genet 95:535–552. CrossRefGoogle Scholar
  15. Gusev A et al (2016) Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation. Nat Commun 7:10979. CrossRefGoogle Scholar
  16. Han Y et al (2015) Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions. Hum Mol Genet 24:5603–5618. CrossRefGoogle Scholar
  17. Hazelett DJ et al (2014) Comprehensive functional annotation of 77 prostate cancer risk loci. PLoS Genet 10:e1004102. CrossRefGoogle Scholar
  18. Heinz S, Romanoski CE, Benner C, Glass CK (2015) The selection and function of cell type-specific enhancers. Nat Rev Mol Cell Biol 16:144–154. CrossRefGoogle Scholar
  19. Hjelmborg JB et al (2014) The heritability of prostate cancer in the Nordic Twin Study of Cancer. Cancer Epidemiol Biomark Prev 23:2303–2310. CrossRefGoogle Scholar
  20. Hnisz D et al (2013) Super-enhancers in the control of cell identity and disease. Cell 155:934–947. CrossRefGoogle Scholar
  21. Huyghe JR et al (2019) Discovery of common and rare genetic risk variants for colorectal cancer. Nat Genet 51:76–87. CrossRefGoogle Scholar
  22. Iotchkova V et al (2019) GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals. Nat Genet 51:343–353. CrossRefGoogle Scholar
  23. Jiang X et al (2019) Shared heritability and functional enrichment across six solid cancers. Nat Commun 10:431. CrossRefGoogle Scholar
  24. Kar SP et al (2016) Genome-wide meta-analyses of breast, ovarian, and prostate cancer association studies identify multiple new susceptibility loci shared by at least two cancer types. Cancer Discov 6:1052–1067. CrossRefGoogle Scholar
  25. Li Q et al (2013) Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152:633–641. CrossRefGoogle Scholar
  26. Lichtenstein P et al (2000) Environmental and heritable factors in the causation of cancer—analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 343:78–85. CrossRefGoogle Scholar
  27. Maurano MT et al (2012) Systematic localization of common disease-associated variation in regulatory DNA. Science 337:1190–1195. CrossRefGoogle Scholar
  28. Michailidou K et al (2013) Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 45:353–361. (361e351–352) CrossRefGoogle Scholar
  29. Michailidou K et al (2017) Association analysis identifies 65 new breast cancer risk loci. Nature 551:92–94. CrossRefGoogle Scholar
  30. Milne RL et al (2017) Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 49:1767–1778. CrossRefGoogle Scholar
  31. Mucci LA et al (2016) Familial risk and heritability of cancer among twins in Nordic countries. JAMA 315:68–76. CrossRefGoogle Scholar
  32. Page WF, Braun MM, Partin AW, Caporaso N, Walsh P (1997) Heredity and prostate cancer: a study of World War II veteran twins. Prostate 33:240–245CrossRefGoogle Scholar
  33. Pasquali L et al (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46:136–143. CrossRefGoogle Scholar
  34. Polak P et al (2015) Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518:360–364. CrossRefGoogle Scholar
  35. Quiroz-Zarate A et al (2017) Expression quantitative trait loci (QTL) in tumor adjacent normal breast tissue and breast tumor tissue. PLoS One 12:e0170181. CrossRefGoogle Scholar
  36. Rhie SK, Coetzee SG, Noushmehr H, Yan C, Kim JM, Haiman CA, Coetzee GA (2013) Comprehensive functional annotation of seventy-one breast cancer risk Loci. PLoS One 8:e63925. CrossRefGoogle Scholar
  37. Roadmap Epigenomics C et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518:317–330. CrossRefGoogle Scholar
  38. Schumacher FR et al (2018) Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet 50:928–936. CrossRefGoogle Scholar
  39. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30. CrossRefGoogle Scholar
  40. Song L et al (2011) Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res 21:1757–1767. CrossRefGoogle Scholar
  41. Stitzel ML et al (2010) Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci. Cell Metab 12:443–455. CrossRefGoogle Scholar
  42. Thomas G et al (2008) Multiple loci identified in a genome-wide association study of prostate cancer. Nat Genet 40:310–315. CrossRefGoogle Scholar
  43. Thurman RE et al (2012) The accessible chromatin landscape of the human genome. Nature 489:75–82. CrossRefGoogle Scholar
  44. Trynka G, Sandor C, Han B, Xu H, Stranger BE, Liu XS, Raychaudhuri S (2013) Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet 45:124–130. CrossRefGoogle Scholar
  45. Turnbull C et al (2010) Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet 42:504–507. CrossRefGoogle Scholar
  46. Whyte WA et al (2013) Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153:307–319. CrossRefGoogle Scholar
  47. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of EpidemiologyUniversity of WashingtonSeattleUSA
  2. 2.Bioinformatics Interdepartmental ProgramUniversity of California Los AngelesLos AngelesUSA
  3. 3.Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleUSA
  4. 4.Department of Environmental and Occupational Health SciencesUniversity of WashingtonSeattleUSA
  5. 5.Zilber School of Public HealthUniversity of Wisconsin–MilwaukeeMilwaukeeUSA
  6. 6.Departments of Human Genetics, and Pathology and Laboratory Medicine, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA

Personalised recommendations