Genome-wide association studies (GWASs) of prostate cancer have identified >250 significant risk loci, but the causal variants and mechanisms for these loci remain largely unknown. Here, we sought to identify and characterize risk-harboring regulatory elements by integrating epigenomes from primary prostate tumor and normal tissues of 27 individuals across the H3K27ac, H3K4me3, and H3K4me2 histone marks and FOXA1 and HOXB13 transcription factors. We identified 7,371 peaks with significant allele specificity (allele-specific quantitative trait locus [asQTL] peaks). Showcasing their relevance to prostate cancer risk, H3K27ac T-asQTL peaks were the single annotation most enriched for prostate cancer GWAS heritability (40×), significantly higher than corresponding non-asQTL H3K27ac peaks (14×) or coding regions (14×). Surprisingly, fine-mapped GWAS risk variants were most significantly enriched for asQTL peaks observed in tumors, including asQTL peaks that were differentially imbalanced with respect to tumor-normal states. These data pinpointed putative causal regulatory elements at 20 GWAS loci, of which 11 were detected only in the tumor samples. More broadly, tumor-specific asQTLs were enriched for expression QTLs in benign tissues as well as accessible regions found in stem cells, supporting a hypothesis where some germline variants become reactivated during or after transformation and can be captured by epigenomic profiling of the tumor. Our study demonstrates the power of allele specificity in chromatin signals to uncover GWAS mechanisms, highlights the relevance of tumor-specific regulation in the context of cancer risk, and prioritizes multiple loci for experimental follow-up.

前列腺癌的全基因组关联研究 (GWAS) 已确定 > 250 个重要的风险基因座，但这些基因座的因果变异和机制仍然很大程度上未知。在这里，我们试图通过整合来自 H3K27ac、H3K4me3 和 H3K4me2 组蛋白标记以及FOXA1和HOXB13的 27 个个体的原发性前列腺肿瘤和正常组织的表观基因组来识别和表征具有风险的调节元件转录因子。我们确定了 7,371 个具有显着等位基因特异性的峰（等位基因特异性数量性状基因座 [asQTL] 峰）。H3K27ac T-asQTL 峰展示了它们与前列腺癌风险的相关性，是前列腺癌 GWAS 遗传力（40×）最丰富的单一注释，显着高于相应的非 asQTL H3K27ac 峰（14×）或编码区（14×）。令人惊讶的是，精细映射的 GWAS 风险变体最显着地富集了在肿瘤中观察到的 asQTL 峰，包括相对于肿瘤正常状态差异不平衡的 asQTL 峰。这些数据确定了 20 个 GWAS 基因座的假定因果调节元件，其中 11 个仅在肿瘤样本中检测到。更广泛地，肿瘤特异性 asQTL 在良性组织以及干细胞中发现的可接近区域中的表达 QTL 富集，这支持了一些种系变体在转化期间或之后重新激活并且可以通过肿瘤的表观基因组分析捕获的假设。我们的研究证明了染色质信号中等位基因特异性的力量，可以揭示 GWAS 机制，突出肿瘤特异性调控在癌症风险背景下的相关性，并优先考虑多个基因座进行实验随访。