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.

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).

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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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