Annotating the molecular basis of human disease remains an unsolved challenge, as 93% of disease loci are non-coding and gene-regulatory annotations are highly incomplete^1,2,3. Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000 genetic loci that were associated with 540 traits⁴, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.

注释人类疾病的分子基础仍然是一个悬而未决的挑战，因为 93% 的疾病基因座是非编码的，而且基因调控注释非常不完整^1,2,3。在这里，我们展示了 EpiMap，这是一个包含 800 个样本的 10,000 个表观基因组图谱的纲要，我们用它来定义染色质状态、高分辨率增强子、增强子模块、上游调节因子和下游靶基因。我们使用该资源注释了与 540 个性状相关的 30,000 个基因位点⁴，预测性状相关组织，富集组织增强子中推定的因果核苷酸变异和每个候选组织特异性靶基因。我们将多因素特征划分为具有不同功能丰富和疾病合并症模式的组织特异性促成因素，并揭示了单因素单向和多因素多向位点。得分最高的位点通常有多个预测的驱动变异，通过具有共同靶基因的多个增强子、共同组织中的多个基因或多个基因和多个组织聚合，表明广泛的多效性。我们的结果证明了密集、丰富、高分辨率的表观基因组注释对于研究复杂性状的重要性。