Abstract
Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease1. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10−8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature (in press).
Ahmed, S. et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat. Genet. 41, 585–590 (2009).
Antoniou, A.C. et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor–negative breast cancer in the general population. Nat. Genet. 42, 885–892 (2010).
Cai, Q. et al. Genome-wide association study identifies breast cancer risk variant at 10q21.2: results from the Asia Breast Cancer Consortium. Hum. Mol. Genet. 20, 4991–4999 (2011).
Cox, A. et al. A common coding variant in CASP8 is associated with breast cancer risk. Nat. Genet. 39, 352–358 (2007).
Easton, D.F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, 1087–1093 (2007).
Fletcher, O. et al. Novel breast cancer susceptibility locus at 9q31.2: results of a genome-wide association study. J. Natl. Cancer Inst. 103, 425–435 (2011).
Ghoussaini, M. et al. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat. Genet. 44, 312–318 (2012).
Haiman, C.A. et al. A common variant at the TERT–CLPTM1L locus is associated with estrogen receptor–negative breast cancer. Nat. Genet. 43, 1210–1214 (2011).
Hein, R. et al. Comparison of 6q25 breast cancer hits from Asian and European genome wide association studies in the Breast Cancer Association Consortium (BCAC). PLoS One 7, e42380 (2012).
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).
Siddiq, A. et al. A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. Hum. Mol. Genet. 21, 5373–5384 (2012).
Stacey, S.N. et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor–positive breast cancer. Nat. Genet. 39, 865–869 (2007).
Stacey, S.N. et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor–positive breast cancer. Nat. Genet. 40, 703–706 (2008).
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).
Turnbull, C. et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat. Genet. 42, 504–507 (2010).
Zheng, W. et al. Genome-wide association study identifies a new breast cancer susceptibility locus at 6q25.1. Nat. Genet. 41, 324–328 (2009).
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, e1–e2 (2013).
Garcia-Closas, M. et al. Genome-wide association studies identify four ER negative–specific breast cancer risk loci. Nat. Genet. 45, 392–398, e1–e2 (2013).
Michailidou, K. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat. Genet. 45, 353–361, e1–e2 (2013).
Cai, Q. et al. Genome-wide association analysis in East Asians identifies breast cancer susceptibility loci at 1q32.1, 5q14.3 and 15q26.1. Nat. Genet. 46, 886–890 (2014).
Long, J. et al. Genome-wide association study in east Asians identifies novel susceptibility loci for breast cancer. PLoS Genet. 8, e1002532 (2012).
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).
Milne, R.L. et al. Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium. Hum. Mol. Genet. 23, 6096–6111 (2014).
Gaudet, M.M. et al. Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk. PLoS Genet. 9, e1003173 (2013).
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).
Orr, N. et al. Fine-mapping identifies two additional breast cancer susceptibility loci at 9q31.2. Hum. Mol. Genet. 24, 2966–2984 (2015).
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).
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).
Couch, F.J. et al. Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer. Nat. Commun. 7, 11375 (2016).
Lawrenson, K. et al. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast–ovarian cancer susceptibility locus. Nat. Commun. 7, 12675 (2016).
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).
Mavaddat, N. et al. Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation carriers: results from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Cancer Epidemiol. Biomarkers Prev. 21, 134–147 (2012).
Amos, C.I. et al. The OncoArray Consortium: a network for understanding the genetic architecture of common cancers. Cancer Epidemiol. Biomarkers Prev. 26, 126–135 (2017).
Antoniou, A.C. et al. A weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genes. Genet. Epidemiol. 29, 1–11 (2005).
Barnes, D.R., Lee, A., Easton, D.F. & Antoniou, A.C. Evaluation of association methods for analysing modifiers of disease risk in carriers of high-risk mutations. Genet. Epidemiol. 36, 274–291 (2012).
Ahsan, H. et al. A genome-wide association study of early-onset breast cancer identifies PFKM as a novel breast cancer gene and supports a common genetic spectrum for breast cancer at any age. Cancer Epidemiol. Biomarkers Prev. 23, 658–669 (2014).
Stevens, K.N. et al. 19p13.1 is a triple-negative-specific breast cancer susceptibility locus. Cancer Res. 72, 1795–1803 (2012).
Willer, C.J., Li, Y. & Abecasis, G.R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Maller, J.B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).
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).
ENCODE Project Consortium. A user's guide to the Encyclopedia of DNA Elements (ENCODE). PLoS Biol. 9, e1001046 (2011).
Kheradpour, P. & Kellis, M. Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Res. 42, 2976–2987 (2014).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Boyle, A.P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012).
He, B., Chen, C., Teng, L. & Tan, K. Global view of enhancer–promoter interactome in human cells. Proc. Natl. Acad. Sci. USA 111, E2191–E2199 (2014).
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).
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).
Forrest, A.R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).
Westra, H.J. et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238–1243 (2013).
James, L.I. et al. Small-molecule ligands of methyl-lysine binding proteins: optimization of selectivity for L3MBTL3. J. Med. Chem. 56, 7358–7371 (2013).
Sukari, A., Muqbil, I., Mohammad, R.M., Philip, P.A. & Azmi, A.S. F-BOX proteins in cancer cachexia and muscle wasting: emerging regulators and therapeutic opportunities. Semin. Cancer Biol. 36, 95–104 (2016).
Ling Zheng, L. et al. Interaction of heat shock protein Cpn10 with the cyclin E/Cdk2 substrate nuclear protein ataxia-telangiectasia (NPAT) is involved in regulating histone transcription. J. Biol. Chem. 290, 29290–29300 (2015).
Easton, D.F. et al. Gene-panel sequencing and the prediction of breast-cancer risk. N. Engl. J. Med. 372, 2243–2257 (2015).
Rogers, S. et al. Cyclin E2 is the predominant E-cyclin associated with NPAT in breast cancer cells. Cell Div. 10, 1 (2015).
Li, Q. et al. Integrative eQTL-based analyses reveal the biology of breast cancer risk loci. Cell 152, 633–641 (2013).
Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G.D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984 (2010).
Wang, K., Li, M. & Bucan, M. Pathway-based approaches for analysis of genomewide association studies. Am. J. Hum. Genet. 81, 1278–1283 (2007).
Wang, K., Li, M. & Hakonarson, H. Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet. 11, 843–854 (2010).
Wang, L., Jia, P., Wolfinger, R.D., Chen, X. & Zhao, Z. Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics 98, 1–8 (2011).
Hacker, B.M. et al. Cloning, chromosomal mapping, and regulatory properties of the human type 9 adenylyl cyclase (ADCY9). Genomics 50, 97–104 (1998).
Melhem-Bertrandt, A. et al. β-blocker use is associated with improved relapse-free survival in patients with triple-negative breast cancer. J. Clin. Oncol. 29, 2645–2652 (2011).
Pon, C.K., Lane, J.R., Sloan, E.K. & Halls, M.L. The β2-adrenoceptor activates a positive cAMP–calcium feedforward loop to drive breast cancer cell invasion. FASEB J. 30, 1144–1154 (2016).
Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).
Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Milne, R.L. & Antoniou, A.C. Genetic modifiers of cancer risk for BRCA1 and BRCA2 mutation carriers. Ann. Oncol. 22 (Suppl. 1), i11–i17 (2011).
Mavaddat, N. et al. Prediction of breast cancer risk based on profiling with common genetic variants. J. Natl. Cancer Inst. 107, djv036 (2015).
Delaneau, O., Marchini, J. & Zagury, J.F. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179–181 (2011).
Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).
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).
Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).
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).
Antoniou, A.C. & Easton, D.F. Polygenic inheritance of breast cancer: implications for design of association studies. Genet. Epidemiol. 25, 190–202 (2003).
Antoniou, A.C. et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br. J. Cancer 98, 1457–1466 (2008).
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).
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).
Ghoussaini, M. et al. Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation. Nat. Commun. 4, 4999 (2014).
Baran, Y. et al. Fast and accurate inference of local ancestry in Latino populations. Bioinformatics 28, 1359–1367 (2012).
Abecasis, G.R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Li, B. & Dewey, C.N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
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).
Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Acknowledgements
We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out.
Genotyping for the OncoArray was funded by the government of Canada through Genome Canada and the Canadian Institutes of Health Research (GPH-129344), the Ministère de l'Économie, de la Science et de l'Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (NIH) (1 U19 CA 148065 for the Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract HHSN268201200008I), Cancer Research UK (C1287/A16563), the Odense University Hospital Research Foundation (Denmark), the National R&D Program for Cancer Control–Ministry of Health and Welfare (Republic of Korea) (1420190), the Italian Association for Cancer Research (AIRC; IG16933), the Breast Cancer Research Foundation, the National Health and Medical Research Council (Australia) and German Cancer Aid (110837).
Genotyping for the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710, C1287/A10118 and C12292/A11174]), NIH grants (CA128978, CA116167 and CA176785) and the Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 (GAME-ON initiative)), an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, the Ministère de l'Économie, Innovation et Exportation du Québec (PSR-SIIRI-701), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund.
Combination of the GWAS data was supported in part by the NIH Cancer Post-Cancer GWAS initiative (1 U19 CA 148065) (DRIVE, part of the GAME-ON initiative). LD score regression analysis was supported by grant CA194393.
BCAC is funded by Cancer Research UK (C1287/A16563) and by the European Union via its Seventh Framework Programme (HEALTH-F2-2009-223175, COGS) and the Horizon 2020 Research and Innovation Programme (633784, B-CAST; 634935, BRIDGES). CIMBA is funded by Cancer Research UK (C12292/A20861 and C12292/A11174).
For a full description of funding and acknowledgments, see the Supplementary Note.
Author information
Authors and Affiliations
Consortia
Contributions
Writing group: R.L.M., K.B.K., K. Michailidou, J. Beesley, S. Kar, S. Lindström, S. Hui, G.D.B., P.D.P.P., F.J.C., D.F.E., P.K., G.C.-T., M.G.-C., M.K.S., A.C.A., J. Simard. Conception and coordination of OncoArray synthesis: D.F.E., A.C.A., J. Simard, C.I.A., J. Byun, S.J.C., E.D., D.J.H., A. Lee, P.D.P.P., J.T., Z.W. OncoArray genotyping: M.A., A.C.A., S.E.B., M.K.B., F.B., G.C.-T., J.M.C., K.F.D., D.F.E., N. Hammell, B. Hicks, K.J., C. Luccarini, L.M., J.M., E.P., J. Romm, M.K.S., X.S., J. Simard, P. Soucy, D.C.T., D.V., J. Vollenweider, L.X., B.Z. OncoArray genotype calling and quality control: X.C., J.D., E.D., D.F.E., K.B.K., J. Lecarpentier, A. Lee, M. Lush. Database management: D. Barrowdale, M.K.B., M.L., L.M., Q.W., R. Keeman, M.K.S. Statistical analysis: K.B.K., K. Michailidou, S. Hui, S. Kar, X.J., A. Rostamianfar, H. Finucane, S. Lindström, D. Barnes, P.K., P.D.P.P., G.D.B., R.L.M., A.C.A., D.F.E. Bioinformatic analysis: J. Beesley, P. Soucy, A. Lemaçon, D. Barnes, F.A.-E., A.D., J. Simard, G.C.-T. Provision of DNA samples and/or phenotypic data: ABCTB Investigators, C.M.A., J. Adlard, S. Agata, S. Ahmed, H.A., J. Allen, K.A., C.B.A., I.L.A., H.A.-C., N.N.A., A.C.A., V.A., N.A., K.J.A., B.A., P.L.A., M.G.E.M.A., J. Azzollini, J. Balmaña, M. Barile, L. Barjhoux, R.B.B., M. Barrdahl, D. Barnes, D. Barrowdale, C. Baynes, M.W.B., J. Beesley, J. Benitez, M. Bermisheva, L. Bernstein, Y.-J.B., K.R.B., M.J.B., C. Blomqvist, W.B., K.B., B. Boeckx, N.V.B., A. Bojesen, S.E.B., M.K.B., B. Bonanni, A. Bozsik, A.R.B., J.S.B., H. Brauch, H. Brenner, B.B.-d.P., C. Brewer, L. Brinton, P.B., A.B.-W., J. Brunet, T.B., B. Burwinkel, S.S.B., A.-L.B.-D., Q.C., T. Caldés, M.A.C., I. Campbell, F.C., O.C., A. Carracedo, B.D.C., J.E.C., L.C., V.C.-M., S.B.C., J.C.-C., S.J.C., X.C., G.C.-T., T.-Y.D.C., J. Chiquette, H.C., K.B.M.C., C.L.C., NBSS Collaborators, T. Conner, D.M.C., J. Cook, E.C.-D., S.C., F.J.C., I. Coupier, D.G.C., A. Cox, S.S.C., K. Cuk, K. Czene, M.B.D., F.D., H.D., R.D., J.D., P.D., O.D., Y.C.D., N.D., S.M.D., C.M.D., S.D., P.-A.D., M. Dumont, A.M.D., L.D., M. Dwek, B.D., T.D., EMBRACE, D.F.E., D.E., R.E., H. Ehrencrona, U.E., B.E., A.B.E., A.H.E., C.E., M.E., L. Fachal, L. Faivre, P.A.F., U.F., J.F., D.F.-J., O.F., H. Flyger, W.D.F., E.F., L. Fritschi, D.F., GEMO Study Collaborators, M. Gabrielson, P. Gaddam, M.D.G., M.G.-D., P.A.G., S.M.G., J. Garber, V.G.-B., M.G.-C., J.A.G.-S., M.M.G., M.G.-V., A. Gehrig, V.G., A.-M.G., G.G.G., G.G., A.K.G., M.S.G., D.E.G., A.G.-N., P. Goodfellow, M.H.G., G.I.G.A., M. Grip, J. Gronwald, A. Grundy, D.G.-K., Q.G., P. Guénel, HEBON, L.H., E. Hahnen, C.A.H., P. Hall, E. Hallberg, U.H., S. Hankinson, T.V.O.H., P. Harrington, S.N.H., J.M.H., C.S.H., A. Hein, S. Helbig, A. Henderson, J.H., P. Hillemanns, S. Hodgson, F.B.H., A. Hollestelle, M.J.H., B. Hoover, J.L.H., C.H., G.H., P.J.H., K.H., D.J.H., N. Håkansson, E.N.I., C.I., M.I., L.I., A.J., P.J., R.J., W.J., U.B.J., E.M.J., N.J., M.J., A.J.-V., R. Kaaks, M. Kabisch, K. Kaczmarek, D.K., K. Kast, R. Keeman, M.J.K., C.M.K., M. Keupers, S. Khan, E.K., J.I.K., J.A.K., I.K., V.-M.K., S.-W.K., P.K., V.N.K., T.A.K., K.B.K., A.K., Y.L., F. Lalloo, K.L., D.L., C. Lasset, C. Lazaro, L.l.M., J. Lecarpentier, M. Lee, A. Lee, E.L., J. Lee, F. Lejbkowicz, F. Lesueur, J. Li, J. Lilyquist, A. Lincoln, A. Lindblom, S. Lindström, J. Lissowska, W.-Y.L., S. Loibl, J. Long, J.T.L., J. Lubinski, C. Luccarini, M. Lush, A.-V.L., R.J.M., T.M., E.M., K.E.M., I.M.K., A. Mannermaa, S. Manoukian, J.E.M., S. Margolin, J.W.M.M., M.E.M., K. Matsuo, D.M., S. Mazoyer, L.M., C. McLean, H.M.-H., A. Meindl, P.M., H.M., K. Michailidou, A. Miller, N.M., R.L.M., G.M., M.M., K. Muir, A.M.M., C. Mulot, S.N., K.L.N., S.L.N., H.N., I.N., D.N., S.F.N., B.G.N., A.N., R.L.N., K. Offit, E.O., O.I.O., J.E.O., H.O., C.O., K. Ong, J.C.O., N.O., A.O., L.O., V.S.P., L.P., S.K.P., T.-W.P.-S., Y.P.-K., R.L., I.S.P., B. Peissel, A.P., J.I.A.P., P.P., J.P., G.P., P.D.P.P., C.M.P., M.P., D.P.-K., B. Poppe, M.E.P., R.P., N.P., D.P., M.A.P., K.P., B.R., P.R., N.R., J. Rantala, C.R.-F., H.S.R., G.R., V.R., K.R., A. Richardson, G.C.R., A. Romero, M.A.R., A. Rudolph, T.R., E.S., J. Sanders, D.P.S., S. Sangrajrang, E.J.S., D.F.S., M.K.S., R.K.S., M.J. Schoemaker, F.S., L. Schwentner, P. Schürmann, C. Scott, R.J.S., S. Seal, L. Senter, C. Seynaeve, M.S., P. Sharma, C.-Y.S., H. Shimelis, M.J. Shrubsole, X.-O.S., L.E.S., J. Simard, C.F.S., C. Sohn, P. Soucy, M.C.S., J.J.S., A.B.S., C. Stegmaier, J. Stone, D.S.-L., G.S., H. Surowy, C. Sutter, A.S., C.I.S., R.M.T., Y.Y.T., J.A.T., M.R.T., M.-I.T., L. Tong, M. Tengström, S.H.T., M.B.T., A.T., M. Thomassen, D.L.T., K. Thöne, M.G.T., L. Tihomirova, M. Tischkowitz, A.E.T., R.A.E.M.T., I.T., D.T., M. Tranchant, T.T., K. Tucker, N.T., H.-U.U., C.V., D.v.d.B., L.V., R.V.-M., A. Vega, A. Viel, J. Vijai, L.W., Q.W., S.W.-G., B.W., C.R.W., J.N.W., C.W., J.W., A.S.W., J.T.W., W.W., R.W., A.W., A.H.W., X.R.Y., D.Y., D.Z., W.Z., A.Z., E.Z., K.K.Z., I.d.-S.-S., kConFab AOCS Investigators, C.J.v.A., E.v.R., A.M.W.v.d.O. All authors read and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
A list of members and affiliations appears in the Supplementary Note.
A list of members and affiliations appears in the Supplementary Note.
A list of members and affiliations appears in the Supplementary Note.
A list of members and affiliations appears in the Supplementary Note.
A list of members and affiliations appears in the Supplementary Note.
A list of members and affiliations appears in the Supplementary Note.
Integrated supplementary information
Supplementary Figure 4 Genomic region around the ER-negative risk-associated variant 2_24739694_CT_T (rs200648189).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Breast cell enhancers overlapping candidate SNPs predicted to target nearby genes by methods including IM-PET and Hnisz are depicted as black bars. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq, and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) chromatin interactions are represented by black arcs. Published GWAS signals from the NHGRI catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 5 Genomic region around the ER-negative risk-associated variant 6_130349119_T_C (rs6569648).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq and histone modification ChIP–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 6 Genomic region around the ER-negative risk-associated variant 8_170692_T_C (rs66823261).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features derived from publicly available transcription factor ChIP–seq that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 7 Genomic region around the ER-negative risk-associated variant 8_124757661_C_T (rs17350191).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 8 Genomic region around the ER-negative risk-associated variant 16_4106788_C_A (rs11076805).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Breast cell enhancers overlapping candidate SNPs predicted to target nearby genes by PreSTIGE53 are depicted as black bars. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq and histone modification ChIP–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 9 Genomic region around the ER-negative risk-associated variant 18_25401204_A_AT (rs36194942).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 10 Genomic region around the ER-negative risk-associated variant 19_11423703_C_G (rs322144).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 11 Genomic region around the ER-negative risk-associated variant 19_30277729_C_T (rs113701136).
One-megabase region showing the statistical significance of all genotyped and imputed SNPs (regional Manhattan plot) and the positions of candidate causal variants in relation to RefSeq annotated genes. Chromatin interactions from ENCODE ChIA-PET experiments in MCF7 cells overlapping candidate variants are depicted as boxes connected by thin lines and are shaded to reflect the confidence score of the interaction. Epigenomic features (derived from publicly available transcription factor ChIP–seq, histone modification ChIP–seq and DNase–seq) that overlap candidate variants are shown as red segments. Density tracks show the summed occurrence of transcription factor ChIP–seq, histone modification ChIP–seq and DHS peaks at each genomic position. Roadmap Epigenomics Project chromatin state models for HMECs and myoepithelial cells grouped into enhancer, promoter or transcribed annotations are shown as yellow, red and green segments, respectively. Transcript levels in MCF7 cells and HMECs are represented by histograms depicting the mean of combined and normalized RNA–seq expression levels at each genomic position. All MCF7 ChIA-PET (ENCODE) and HMEC Hi-C52 chromatin interactions are represented by black and blue arcs, respectively. Published GWAS signals from the NHGRI GWAS catalog are shown as green ticks. The last track shows tested OncoArray SNPs (genotyped or imputed) as black ticks and uninterrogated, common (dbSNP138 EUR MAF > 1%) SNPs as red ticks.
Supplementary Figure 12 Regional eQTL association plot for all variants within 1 Mb of L3MTBL3 and expression of L3MTBL3.
Red dots indicate candidate causal risk variants from the meta-analysis of BCAC ER-negative case–control and CIMBA BRCA1 mutation carrier data.
Supplementary Figure 13 Regional eQTL association plot for all variants within 1 Mb of CDH2 and expression of CDH2.
Red dots indicate candidate causal risk variants from the meta-analysis of BCAC ER-negative case–control and CIMBA BRCA1 mutation carrier data.
Supplementary Figure 14 Enrichment map for pathways enriched in susceptibility to ER-negative breast cancer.
Enriched pathways (enrichment score (ES) ≥ 0.41) are grouped into themes and annotated with genes that appeared to drive the enrichment signal (Online Methods). Shaded circles represent pathways (darker red indicates higher ES, and larger size denotes a greater number of genes in the pathway), and green lines connect those that are most similar in terms of gene set overlap (>70%), with thicker lines denoting greater similarity.
Supplementary Figure 15 Enrichment map of the adenylate cyclase theme, enriched in susceptibility to ER-negative breast cancer.
Shaded circles represent pathways (darker red indicates higher ES, and larger size denotes a greater number of genes in the pathway), and green lines connect those that are most similar in terms of gene set overlap (>70%), with thicker lines denoting greater similarity.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–15 and Supplementary Note
Supplementary Table 1
BCAC studies contributing data on estrogen-receptor-negative cases and controls, by genotyping initiative.
Supplementary Table 2
CIMBA studies contributing data on BRCA1 mutation carriers, by genotyping initiative.
Supplementary Table 3
Ten novel loci associated with risk of estrogen receptor (ER)-negative breast cancer using meta-analysis of BCAC and CIMBA data, by genotyping initiative.
Supplementary Table 4
Results for the two novel ER-negative susceptibility loci at 11q22.3.
Supplementary Table 5
Summary information from bioinformatic and eQTL analyses.
Supplementary Table 6
Data sources for in silico analyses of the ten novel ER-negative breast cancer susceptibility loci.
Supplementary Table 7
Associations for ten novel and ten previously reported (and replicated) loci.
Supplementary Table 8
Associations for ten novel and ten previously reported (and replicated) loci.
Supplementary Table 9
Novel overall breast cancer susceptibility loci from Michailidou et al. (2016): associations with risk of ER-negative breast cancer and breast cancer for BRCA1 mutation carriers.
Supplementary Table 10
Other previously reported (non-ER-negative disease–specific) breast cancer susceptibility loci: associations with risk of ER-negative breast cancer and breast cancer for BRCA1 mutation carriers.
Supplementary Table 11
Associations of 179 breast cancer susceptibility loci with risk for BRCA2 mutation carriers.
Supplementary Table 12
Detailed information about themes and pathways appearing in the enrichment maps (Supplementary Figs. 13 and 14).
Supplementary Table 13
Detailed information about themes and unique genes appearing in the enrichment maps (Supplementary Figs. 13 and 14).
Supplementary Table 14
Pathways enriched in susceptibility to ER-negative breast cancer (enrichment score (ES) ≥ 0.41) but not ER-positive breast cancer (ES < 0).
Supplementary Table 15
Enrichments for ER-negative breast cancer based on summary statistics for combined analysis of BCAC (ER-negative) and CIMBA (BRCA1 mutation carriers) data.
Supplementary Table 16
List of 125 SNPs associated with risk of ER-negative breast cancer.
Supplementary Table 17
List of 39 SNPs associated with breast cancer risk for BRCA1 mutation carriers.
Rights and permissions
About this article
Cite this article
Milne, R., Kuchenbaecker, K., Michailidou, K. et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 49, 1767–1778 (2017). https://doi.org/10.1038/ng.3785
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.3785
This article is cited by
-
Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status
Breast Cancer Research (2024)
-
Assessing the causal associations of sleep apnea with mental health and socioeconomic status: a bidirectional two-sample Mendelian randomization
BMC Medical Genomics (2024)
-
Exploring the causal relationship between immune cells and idiopathic pulmonary fibrosis: a bi-directional Mendelian randomization study
BMC Pulmonary Medicine (2024)
-
Antihypertensive drug targets and breast cancer risk: a two-sample Mendelian randomization study
European Journal of Epidemiology (2024)
-
Clustering of HR + /HER2− breast cancer in an Asian cohort is driven by immune phenotypes
Breast Cancer Research (2024)