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.

全基因组关联研究已在 150 多个基因组区域中确定了乳腺癌风险变异，但风险背后的机制仍然很大程度上未知。通过将关联分析与计算机基因组特征注释相结合来探索这些区域。我们定义了 205 个独立的风险相关信号，每个信号都有一组可信的因果变异。与此同时，我们使用贝叶斯方法 (PAINTOR)，结合遗传关联、连锁不平衡和丰富的基因组特征来确定具有高因果因果概率的变异。潜在的致病变异在活性基因调控区和转录因子结合位点中明显过多。我们应用 INQUSIT 流程，利用基因表达（表达数量性状位点）、染色质相互作用和功能注释，优先考虑将基因作为这些潜在因果变异的目标。已知的癌症驱动因素、转录因子以及发育、细胞凋亡、免疫系统和 DNA 完整性检查点基因本体通路中的基因在最高置信度目标基因中的比例过高。