Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease–disease and disease–molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 5.7, 11.5). These results are publicly available through a web portal (http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

全基因组关联研究 (GWAS) 已经确定了许多与复杂性状相关的变异，但确定致病基因是一项重大挑战。在本研究中，我们提出了一个开放资源，可在 133,441 个已发表的人类 GWAS 基因座中提供系统的精细定位和基因优先排序。我们将遗传学（GWAS 目录和英国生物银行）与转录组学、蛋白质组学和表观基因组学数据相结合，包括 92 种细胞类型和组织的系统性疾病-疾病和疾病-分子特征共定位结果。我们确定了 729 个基因座精细映射到单编码因果变异并与单个基因共定位。我们使用精细定位的遗传学和功能基因组学数据以及 445 个黄金标准策划的 GWAS 基因座训练了一个机器学习模型，以区分致病基因和邻近基因，优于天真的基于距离的模型。我们的优先基因针对已知批准的药物靶标进行了丰富（比值比 = 8.1，95% 置信区间 = 5.7，11.5）。这些结果可通过门户网站 (http://genetics.opentargets.org) 公开获取，使用户能够轻松地确定疾病相关位点的基因优先级并评估它们作为药物靶点的潜力。