全基因组关联研究
生物
遗传学
计算生物学
基因组学
数量性状位点
遗传关联
表达数量性状基因座
单核苷酸多态性
基因
基因组
基因型
作者
Edward Mountjoy,Ellen M. Schmidt,Miguel Carmona,Jeremy Schwartzentruber,Gareth Peat,Alfredo Miranda,Luca Fumis,James Hayhurst,Annalisa Buniello,Mohd Anisul Karim,Deil S. Wright,Andrew Hercules,Eliseo Papa,Eric B. Fauman,Jeffrey C. Barrett,John A. Todd,David Ochoa,Ian Dunham,Maya Ghoussaini
出处
期刊:Nature Genetics
[Springer Nature]
日期:2021-10-28
卷期号:53 (11): 1527-1533
被引量:306
标识
DOI:10.1038/s41588-021-00945-5
摘要
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. Open Targets Genetics is a community resource that provides systematic fine mapping at human GWAS loci, enabling users to prioritize genes at disease-associated regions and assess their potential as drug targets.
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