An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

作者：Edward Mountjoy, Ellen M. Schmidt, Miguel Carmona, Jeremy Schwartzentruber, Gareth Peat, Alfredo Miranda, Luca Fumis, James Hayhurst, Annalisa Buniello, Mohd Anisul Karim, Daniel J. Wright, Andrew Hercules, Eliseo Papa, Eric B. Fauman, Jeffrey C. Barrett, John A. Todd, David Ochoa, Ian Dunham, Maya Ghoussaini · 发表于：Nature Genetics · 年份：2021 · DOI：10.1038/s41588-021-00945-5 · 被引用次数：657 · 研究领域：Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks、Gene expression and cancer classification

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.