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Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Abstract

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.

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Fig. 1: Flowchart summarizing the study design.
Fig. 2: Determining independent risk signals and CCVs.
Fig. 3: Overlap of CCVs with gene regulatory regions, gene bodies and TFBSs.
Fig. 4: Predicted target genes are enriched in known breast cancer driver genes and transcription factors.
Fig. 5: Predicted target genes by phenotype and significantly enriched pathways.

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Data availability

The credible set of causal variants (determined by either multinomial stepwise regression or PAINTOR) is provided in Supplementary Table 2c. Further information and requests for resources should be directed to M.K.B. (bcac@medschl.cam.ac.uk).

References

  1. Milne, R. L. et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat. Genet. 49, 1767–1778 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    PubMed  PubMed Central  Google Scholar 

  3. Ghoussaini, M. et al. Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation. Nat. Commun. 4, 4999 (2014).

    PubMed  Google Scholar 

  4. Wyszynski, A. et al. An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression. Hum. Mol. Genet. 25, 3863–3876 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Guo, X. et al. Fine-scale mapping of the 4q24 locus identifies two independent loci associated with breast cancer risk. Cancer Epidemiol. Biomark. Prev. 24, 1680–1691 (2015).

    CAS  Google Scholar 

  6. Glubb, D. M. et al. Fine-scale mapping of the 5q11.2 breast cancer locus reveals at least three independent risk variants regulating MAP3K1. Am. J. Hum. Genet. 96, 5–20 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Dunning, A. M. et al. Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170. Nat. Genet. 48, 374–386 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Shi, J. et al. Fine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer. Int. J. Cancer 139, 1303–1317 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Orr, N. et al. Fine-mapping identifies two additional breast cancer susceptibility loci at 9q31.2. Hum. Mol. Genet. 24, 2966–2984 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Darabi, H. et al. Polymorphisms in a putative enhancer at the 10q21.2 breast cancer risk locus regulate NRBF2 expression. Am. J. Hum. Genet. 97, 22–34 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Darabi, H. et al. Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs). Sci. Rep. 6, 32512 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Meyer, K. B. et al. Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1. Am. J. Hum. Genet. 93, 1046–1060 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Betts, J. A. et al. Long noncoding RNAs CUPID1 and CUPID2 mediate breast cancer risk at 11q13 by modulating the response to DNA damage. Am. J. Hum. Genet. 101, 255–266 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. French, J. D. et al. Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers. Am. J. Hum. Genet. 92, 489–503 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Ghoussaini, M. et al. Evidence that the 5p12 variant rs10941679 confers susceptibility to estrogen-receptor-positive breast cancer through FGF10 and MRPS30 regulation. Am. J. Hum. Genet. 99, 903–911 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Horne, H. N. et al. Fine-mapping of the 1p11.2 breast cancer susceptibility locus. PLoS ONE 11, e0160316 (2016).

    PubMed  PubMed Central  Google Scholar 

  17. Zeng, C. et al. Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus. Breast Cancer Res. 18, 64 (2016).

    PubMed  PubMed Central  Google Scholar 

  18. Lin, W. Y. et al. Identification and characterization of novel associations in the CASP8/ALS2CR12 region on chromosome 2 with breast cancer risk. Hum. Mol. Genet. 24, 285–298 (2015).

    CAS  PubMed  Google Scholar 

  19. Bojesen, S. E. et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat. Genet. 45, 371–384.e2 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lawrenson, K. et al. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast–ovarian cancer susceptibility locus. Nat. Commun. 7, 12675 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. Amos, C. I. et al. The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol. Biomark. Prev. 26, 126–135 (2017).

    Google Scholar 

  22. Michailidou, K. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat. Genet. 45, 353–361.e2 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Michailidou, K. et al. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat. Genet. 47, 373–380 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Udler, M. S., Tyrer, J. & Easton, D. F. Evaluating the power to discriminate between highly correlated SNPs in genetic association studies. Genet. Epidemiol. 34, 463–468 (2010).

    PubMed  Google Scholar 

  25. Mavaddat, N., Antoniou, A. C., Easton, D. F. & Garcia-Closas, M. Genetic susceptibility to breast cancer. Mol. Oncol. 4, 174–191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lakhani, S. R. et al. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin. Cancer Res. 11, 5175–5180 (2005).

    CAS  PubMed  Google Scholar 

  27. Taberlay, P. C., Statham, A. L., Kelly, T. K., Clark, S. J. & Jones, P. A. Reconfiguration of nucleosome-depleted regions at distal regulatory elements accompanies DNA methylation of enhancers and insulators in cancer. Genome Res. 24, 1421–1432 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    CAS  PubMed  Google Scholar 

  29. Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    CAS  PubMed  Google Scholar 

  30. Cowper-Sal lari, R. et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat. Genet. 44, 1191–1198 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet. 10, e1004722 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. Quiroz-Zarate, A. et al. Expression quantitative trait loci (QTL) in tumor adjacent normal breast tissue and breast tumor tissue. PLoS ONE 12, e0170181 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. Cancer Genome Atlas Research Networket al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    PubMed Central  Google Scholar 

  34. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Pereira, B. et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7, 11479 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Cancer Genome Atlas Network Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    Google Scholar 

  39. Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    CAS  PubMed  Google Scholar 

  41. Artero-Castro, A. et al. Disruption of the ribosomal P complex leads to stress-induced autophagy. Autophagy 11, 1499–1519 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Wang, X. Y. et al. Musashi1 modulates mammary progenitor cell expansion through proliferin-mediated activation of the Wnt and Notch pathways. Mol. Cell Biol. 28, 3589–3599 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Vijayan, D., Young, A., Teng, M. W. L. & Smyth, M. J. Targeting immunosuppressive adenosine in cancer. Nat. Rev. Cancer 17, 709–724 (2017).

    CAS  PubMed  Google Scholar 

  44. Takebe, N. et al. Targeting Notch, Hedgehog, and Wnt pathways in cancer stem cells: clinical update. Nat. Rev. Clin. Oncol. 12, 445–464 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Thorpe, L. M., Yuzugullu, H. & Zhao, J. J. PI3K in cancer: divergent roles of isoforms, modes of activation and therapeutic targeting. Nat. Rev. Cancer 15, 7–24 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Nusse, R. & Clevers, H. Wnt/β-catenin signaling, disease, and emerging therapeutic modalities. Cell 169, 985–999 (2017).

    CAS  PubMed  Google Scholar 

  47. Massague, J. TGFβ signalling in context. Nat. Rev. Mol. Cell Biol. 13, 616–630 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Meeks, H. D. et al. BRCA2 polymorphic stop codon K3326X and the risk of breast, prostate, and ovarian cancers. J. Natl Cancer Inst. 108, djv315 (2016).

    PubMed  Google Scholar 

  49. CHEK2 Breast Cancer Case-Control Consortium CHEK2*1100delC and susceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from 10 studies. Am. J. Hum. Genet. 74, 1175–1182 (2004).

    Google Scholar 

  50. Schmidt, M. K. et al. Age- and tumor subtype-specific breast cancer risk estimates for CHEK2*1100delC carriers. J. Clin. Oncol. 34, 2750–2760 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Kilpivaara, O. et al. CHEK2 variant I157T may be associated with increased breast cancer risk. Int. J. Cancer 111, 543–547 (2004).

    CAS  PubMed  Google Scholar 

  52. Muranen, T. A. et al. Patient survival and tumor characteristics associated with CHEK2:p.I157T—findings from the Breast Cancer Association Consortium. Breast Cancer Res. 18, 98 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. Killedar, A. et al. A common cancer risk-associated allele in the hTERT locus encodes a dominant negative inhibitor of telomerase. PLoS Genet. 11, e1005286 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. De Basio, A. et al. Unusual roles of caspase-8 in triple-negative breast cancer cell line MDA-MB-231. Int. J. Oncol. 48, 2339–2348 (2016).

    Google Scholar 

  55. Haupt, S. et al. Targeting Mdmx to treat breast cancers with wild-type p53. Cell Death Dis. 6, e1821 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Pandya, P. H., Murray, M. E., Pollok, K. E. & Renbarger, J. L. The immune system in cancer pathogenesis: potential therapeutic approaches. J. Immunol. Res. 2016, 4273943 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. Gionet, N., Jansson, D., Mader, S. & Pratt, M. A. NF-κB and estrogen receptor α interactions: differential function in estrogen receptor-negative and -positive hormone-independent breast cancer cells. J. Cell Biochem. 107, 448–459 (2009).

    CAS  PubMed  Google Scholar 

  58. Fleischer, T. et al. DNA methylation at enhancers identifies distinct breast cancer lineages. Nat. Commun. 8, 1379 (2017).

    PubMed  PubMed Central  Google Scholar 

  59. 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  PubMed  PubMed Central  Google Scholar 

  60. Gaudet, M. M. et al. Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk. PLoS Genet. 9, e1003173 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    CAS  PubMed  Google Scholar 

  62. Antoniou, A. C. et al. RAD51 135G → C modifies breast cancer risk among BRCA2 mutation carriers: results from a combined analysis of 19 studies. Am. J. Hum. Genet. 81, 1186–1200 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Barnes, D. R. et al. Evaluation of association methods for analysing modifiers of disease risk in carriers of high-risk mutations. Genet. Epidemiol. 36, 274–291 (2012).

    PubMed  Google Scholar 

  64. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Zhong, H. & Prentice, R. L. Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies. Biostatistics 9, 621–634 (2008).

    PubMed  PubMed Central  Google Scholar 

  66. Hunter, D. J. et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat. Genet. 39, 870–874 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Baran, Y. et al. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics 28, 1359–1367 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Genomes Project, C. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Google Scholar 

  70. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    PubMed  PubMed Central  Google Scholar 

  72. Li, Q. et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152, 633–641 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed Central  Google Scholar 

  75. Sloan, C. A. et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 44, D726–D732 (2016).

    CAS  PubMed  Google Scholar 

  76. Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Google Scholar 

  77. Stunnenberg, H. G. International Human Epigenome Consortium & Hirst, M. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell 167, 1145–1149 (2016).

    CAS  PubMed  Google Scholar 

  78. Pellacani, D. et al. Analysis of normal human mammary epigenomes reveals cell-specific active enhancer states and associated transcription factor networks. Cell Rep. 17, 2060–2074 (2016).

    CAS  PubMed  Google Scholar 

  79. Cheneby, J., Gheorghe, M., Artufel, M., Mathelier, A. & Ballester, B. ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-Seq experiments. Nucleic Acids Res. 46, D267–D275 (2018).

    CAS  PubMed  Google Scholar 

  80. Pruitt, K. D. et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 42, D756–D763 (2014).

    CAS  PubMed  Google Scholar 

  81. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Wang, J. et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 22, 1798–1812 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Mathelier, A. et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D110–D115 (2016).

    CAS  PubMed  Google Scholar 

  84. Tan, G. & Lenhard, B. TFBSTools: an R/bioconductor package for transcription factor binding site analysis. Bioinformatics 32, 1555–1556 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Grant, C. E., Bailey, T. L. & Noble, W. S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Grassi, E., Zapparoli, E., Molineris, I. & Provero, P. Total binding affinity profiles of regulatory regions predict transcription factor binding and gene expression in human cells. PLoS ONE 10, e0143627 (2015).

    PubMed  PubMed Central  Google Scholar 

  87. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. McLeay, R. C. & Bailey, T. L. Motif enrichment analysis: a unified framework and an evaluation on ChIP data. BMC Bioinformatics 11, 165 (2010).

    PubMed  PubMed Central  Google Scholar 

  89. Kichaev, G. et al. Improved methods for multi-trait fine mapping of pleiotropic risk loci. Bioinformatics 33, 248–255 (2017).

    CAS  PubMed  Google Scholar 

  90. McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

    PubMed  PubMed Central  Google Scholar 

  91. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    CAS  PubMed  Google Scholar 

  93. Stone, E. A. & Sidow, A. Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity. Genome Res. 15, 978–986 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Yeo, G. & Burge, C. B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).

    CAS  PubMed  Google Scholar 

  95. Desmet, F. O. et al. Human Splicing Finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res. 37, e67 (2009).

    PubMed  PubMed Central  Google Scholar 

  96. Beesley, J. et al. Chromatin interactome mapping at 139 independent breast cancer risk signals. Preprint at bioRxiv https://doi.org/10.1101/520916 (2019).

  97. Fullwood, M. J. et al. An oestrogen-receptor-α-bound human chromatin interactome. Nature 462, 58–64 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Corradin, O. et al. Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome Res. 24, 1–13 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. He, B. et al. Global view of enhancer-promoter interactome in human cells. Proc. Natl Acad. Sci. USA 111, e2191–e21999 (2014).

    CAS  PubMed  Google Scholar 

  101. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Moradi Marjaneh, M. et al. High-throughput allelic expression imbalance analyses identify 14 candidate breast cancer risk genes. Preprint at bioRxiv https://doi.org/10.1101/521013 (2019).

  103. Dixon, J. R. et al. Integrative detection and analysis of structural variation in cancer genomes. Nat. Genet. 50, 1388–1398 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Yang, Y. et al. AWESOME: a database of SNPs that affect protein post-translational modifications. Nucleic Acids Res. 47, D874–D880 (2019).

    CAS  PubMed  Google Scholar 

  105. Merico, D., Isserlin, R. & Bader, G. D. Visualizing gene-set enrichment results using the Cytoscape plug-in enrichment map. Methods Mol. Biol. 781, 257–277 (2011).

    CAS  PubMed  Google Scholar 

  106. Vastrik, I. et al. Reactome: a knowledge base of biologic pathways and processes. Genome Biol. 8, R39 (2007).

    PubMed  PubMed Central  Google Scholar 

  107. Schaefer, C. F. et al. PID: the Pathway Interaction Database. Nucleic Acids Res. 37, D674–D679 (2009).

    CAS  PubMed  Google Scholar 

  108. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Romero, P. et al. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 6, R2 (2005).

    PubMed  Google Scholar 

  110. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  Google Scholar 

  111. Kandasamy, K. et al. NetPath: a public resource of curated signal transduction pathways. Genome Biol. 11, R3 (2010).

    PubMed  PubMed Central  Google Scholar 

  112. Thomas, P. D. et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate—a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

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Acknowledgements

We thank all of the individuals who took part in these studies, as well as all of the researchers, clinicians, technicians and administrative staff who enabled this work to be carried out. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant agreement number 656144. Genotyping of the OncoArray was principally funded from three sources: the PERSPECTIVE project (funded 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 the Quebec Breast Cancer Foundation); the NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative and the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project (NIH grants U19 CA148065 and X01HG007492); and Cancer Research UK (C1287/A10118, C8197/A16565 and C1287/A16563). BCAC is funded by Cancer Research UK (C1287/A16563), by the European Community’s Seventh Framework Programme under grant agreement 223175 (HEALTH-F2-2009-223175) (COGS) and by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 633784 (B-CAST) and 634935 (BRIDGES). Genotyping of the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710), the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer’ program, and the Ministry of Economic Development, Innovation and Export Trade of Quebec (grant PSR-SIIRI-701). Combining of the GWAS data was supported in part by NIH Cancer Post-Cancer GWAS initiative grant U19 CA 148065 (DRIVE; part of the GAME-ON initiative). For a full description of funding and acknowledgments, see the Supplementary Note.

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L.Fa., H.A., J.Bee., D.R.B., J.Al., S.Ka., K.A.P., K.Mi., P.So., A.Le., M.Gh., P.D.P.P., J.C.C., M.G.C., M.K.S., R.L.M., V.N.K., J.D.E., S.L.E., A.C.A., G.C.T., J.Si., D.F.E., P.K. and A.M.D. conceived of the study idea. L.Fa., H.A., J.Bee., D.R.B., J.Al., J.D.E., S.L.E., A.C.A., G.C.T., J.Si., D.F.E., P.K. and A.M.D. developed the methodology. J.Bee., J.P.T. and M.L. provided software. L.Fa., H.A., J.Bee., D.R.B., J.Al., S.Ka., C.Tu., M.Mor. and X.J. performed a formal analysis. S.A., K.A., M.R.A., I.L.A., H.A.C., N.N.A., A.A., V.A., K.J.A., B.K.A., B.A., P.L.A., J.Az., J.Ba., R.B.B., D.B., A.B.F., J.Ben., M.B., K.B., A.M.B., C.B., W.B., N.V.B., S.E.B., B.Bo., A.B., H.Bra., H.Bre., I.B., I.W.B., A.B.W., T.B., B.Bu., S.S.B., Q.C., T.C., M.A.C., N.J.C., I.C., F.C., J.S.C., B.D.C., J.E.C., J.C., H.C., W.K.C., K.B.M., C.L.C., J.M.C., S.C., F.J.C., A.C., S.S.C., C.C., K.C., M.B.D., M.D.H., P.D., O.D., Y.C.D., G.S.D., S.M.D., T.D., I.D.S., A.D., S.D., M.Dum., M.Dur., L.D., M.Dw., D.M.E., C.E., M.E., D.G.E., P.A.F., U.F., O.F., G.F., H.F., L.Fo., W.D.F., E.F., L.Fr., D.F., M.Ga., M.G.D., G.Ga., P.A.G., S.M.G., J.Ga., J.A.G., M.M.G., V.G., G.G.G., G.Gl., A.K.G., M.S.G., D.E.G., A.G.N., M.H.G., M.Gr., J.Gr., A.G., P.G., E.H., C.A.H., N.H., P.Ha., U.H., P.A.H., J.M.H., M.H., W.H., C.S.H., B.A.M., J.H., P.Hi., F.B.L., A.H., M.J.H., J.L.H., A.Ho., G.H., P.J.H., E.N.I., C.I., M.I., A.Jag., M.J., A.Jak., P.J., R.J., R.C.J., E.M.J., N.J., M.E.J., A.Juk., A.Jun., R.Ka., D.K., B.Pes., R.Ke., M.J.K., E.K., J.I.K., J.K., C.M.K., Y.K., I.K., V.K., S.Ko., K.K.S., T.K., A.K., K.K., Y.L., D.L., E.L., G.L., J.Le., F.L., A.Li., W.L., J.Lo., A.Lo., J.T.L., J.Lu., R.J.M., T.M., E.M., A.Ma., M.Ma., S.Man., S.Mag., M.E.M., K.Ma., D.M., R.M., L.M., C.M., N.Me., A.Me., P.M., A.Mi., N.Mi., M.Mo., F.M., A.M.M., V.M.M., T.A., S.A.N., R.N., K.L.N., N.Z.N., H.N., P.N., F.C.N., L.N.Z., A.N., K.O., E.O., O.I.O., H.O., N.O., A.O., V.S.P., J.Pa., S.K.P., T.W.P.S., M.T.P., J.Pau., I.S.P., B.Pei., B.Y.K., P.P., J.Pe., D.P.K., K.Pr., R.P., N.P., D.P., M.A.P., K.Py., P.R., S.J.R., J.R., R.R.M., G.R., H.A.R., M.R., A.R., C.M.R., E.S., E.S.H., D.P.S., M.Sa., C.Sa., E.J.S., M.T.S., D.F.S., R.K.S., A.S., M.J.S., B.S., P.Sc., C.Sc., R.J.S., L.S., C.M.D., M.Sh., P.Sh., C.Y.S., X.S., C.F.S., T.P.S., S.S., M.C.S., J.J.S., A.B.S., J.St., D.S.L., C.Su., A.J.S., R.M.T., Y.Y.T., W.J.T., J.A.T., M.R.T., M.Te., S.H., M.B.T., A.T., M.Th., D.L.T., M.G.T., M.Ti., A.E.T., R.A.E., I.T., D.T., G.T.M., M.A.T., N.T., M.Tz., H.U.U., C.M.V., C.J.A., L.E.K., E.J.R., A.Ve., A.Vi., J.V., M.J.V., Q.W., B.W., C.R.W., J.N.W., C.W., H.W., R.W., A.W., A.H.W., D.Y., Y.Z. and W.Z. provided resources. K.Mi., J.D., M.K.B., Q.W., R.Ke., J.C.C. and M.K.S. curated and managed the data. L.Fa., H.A., J.Bee., G.C.T., D.F.E., P.K. and A.M.D. wrote the original draft of the manuscript. D.R.B., J.Al., P.So., A.Le., V.N.K., J.D.E., S.L.E., A.C.A. and J.Si wrote and edited the manuscript. L.Fa., H.A., J.Bee. and C.Tu visualized the results. A.C.A., G.C.T., J.Si., D.F.E., P.K. and A.M.D. supervised the project. L.Fa., P.D.P.P., J.C.C., M.G.C., M.K.S., R.L.M., V.N.K., J.D.E., S.L.E., A.C.A., G.C.T., J.Si., D.F.E., P.K. and A.M.D. acquired funding. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Peter Kraft or Alison M. Dunning.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Note

Reporting Summary

Supplementary Table 1

Breast cancer risk regions identified through GWASs.

Supplementary Table 2

Breast cancer risk signals and CCVs.

Supplementary Table 3

Bio-features enrichment.

Supplementary Table 4

Consensus transcription factor binding motif enrichment.

Supplementary Table 5

Coding, splicing CCVs and overlap of CCVs with variant drivers of local gene expression.

Supplementary Table 6

191 level 1 predicted target genes and regions in which target genes are predicted with high confidence.

Supplementary Table 7

INQUISIT results for coding variants.

Supplementary Table 8

INQUISIT results for promoter variants.

Supplementary Table 9

INQUISIT results for distal variants.

Supplementary Table 10

Pathways significantly enriched in CCV and target genes predicted with HPP.

Supplementary Table 11

Ethical agreements for BCAC studies.

Supplementary Table 12

Ethical agreements for CIMBA studies.

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Fachal, L., Aschard, H., Beesley, J. et al. Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nat Genet 52, 56–73 (2020). https://doi.org/10.1038/s41588-019-0537-1

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