期刊
NATURE GENETICS
卷 52, 期 1, 页码 56-73出版社
NATURE PORTFOLIO
DOI: 10.1038/s41588-019-0537-1
关键词
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资金
- European Union's Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant [656144]
- PERSPECTIVE project (Government of Canada through Genome Canada)
- PERSPECTIVE project (Government of Canada through Canadian Institutes of Health Research)
- PERSPECTIVE project ('Ministere de l'Economie de la Science et de l'Innovation du Quebec' (Genome Quebec)
- PERSPECTIVE project (Quebec Breast Cancer Foundation)
- NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative
- Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project (NIH) [U19 CA148065, X01HG007492]
- Cancer Research UK [C1287/A10118, C8197/A16565, C1287/A16563, C1287/A10710]
- European Community [223175 (HEALTH-F2-2009-223175)]
- European Union [HEALTH-F2-2009-223175, 633784, 634935]
- Canadian Institutes of Health Research
- Ministry of Economic Development, Innovation and Export Trade of Quebec [PSR-SIIRI-701]
- NIH Cancer Post-Cancer GWAS initiative grant [U19 CA 148065]
- MRC [MC_PC_14105] Funding Source: UKRI
- NATIONAL CANCER INSTITUTE [ZIACP010144] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES [ZIAES049033, ZIAES102245, ZIAES044005] Funding Source: NIH RePORTER
- Marie Curie Actions (MSCA) [656144] Funding Source: Marie Curie Actions (MSCA)
Fine-mapping of causal variants and integration of epigenetic and chromatin conformation data identify likely target genes for 150 breast cancer risk regions. 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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