Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome-wide association studies have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, where they likely function through interaction with gene regulatory elements. In this study we first estimated the heritability explained by known common low penetrance risk alleles for EOC. The narrow sense heritability (${\mathit{h}}_{\mathit{g}}^2$) of EOC overall and high-grade serous ovarian cancer (HGSOCs) were estimated to be 5%–6%. Partitioned SNP heritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from Roadmap Epigenomics and ENCODE, and from H3K27Ac ChIP-seq data generated in 26 ovarian cancer and precursor-related cell and tissue types. We identified significant enrichment of risk single-nucleotide polymorphisms (SNPs) in active regulatory elements marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active regulatory elements influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that are predicted to significantly break transcription factor (TF) motifs. The most frequently broken motif was REST (p value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. Overall, these systematic functional annotations with epigenomic data improve interpretation of EOC risk variants and shed light on likely cells of origin.

量化复杂疾病风险变体的功能影响可以提供对疾病生物学基础机制的见解。全基因组关联研究确定了39个与上皮性卵巢癌（EOC）风险相关的区域。这些变体中的绝大多数都位于非编码基因组中，它们可能通过与基因调控元件的相互作用而发挥功能。在这项研究中，我们首先估计了已知的EOC常见的低外显率危险等位基因所解释的遗传力。狭义遗传力（${\mathit{H}}_{\mathit{G}}^2$）EOC的总体和高级别浆液性卵巢癌（HGSOC）估计为5％–6％。跨广泛功能类别的分区SNP遗传力表明，监管要素对EOC遗传力的重大贡献。我们从Roadmap Epigenomics和ENCODE，以及在26种卵巢癌中产生的H3K27Ac ChIP-seq数据和前体相关细胞和组织类型中，整理了77种细胞和组织类型的表观基因组分析数据。我们在HGSOCs中以H3K27Ac标记的活性调控元件中发现了危险单核苷酸多态性（SNP）的大量富集。为了进一步研究活性调节元件中的风险SNP如何影响卵巢癌的易感性，我们使用了motifbreakR来预测转录因子结合位点的破坏。我们在H3K27Ac峰中确定了469个候选因果风险变异体，这些变异体预计会显着破坏转录因子（TF）的基序。最常见的断裂图案是REST（p值= 0.0028），已被报道为肿瘤抑制因子和致癌基因。总体而言，这些具有表观基因组数据的系统功能注释可改善对EOC风险变异的解释，并阐明可能的起源细胞。