Systematic single-variant and gene-based association testing of 3,700 phenotypes in 281,850 UK Biobank exomes

Author:

Karczewski Konrad J.ORCID,Solomonson MatthewORCID,Chao Katherine R.ORCID,Goodrich Julia K.ORCID,Tiao GraceORCID,Lu Wenhan,Riley-Gillis Bridget M.,Tsai Ellen A.ORCID,Kim Hye In,Zheng XiuwenORCID,Rahimov Fedik,Esmaeeli Sahar,Grundstad A. Jason,Reppell Mark,Waring Jeff,Jacob Howard,Sexton David,Bronson Paola G.ORCID,Chen XingORCID,Hu Xinli,Goldstein Jacqueline I.ORCID,King Daniel,Vittal Christopher,Poterba TimothyORCID,Palmer Duncan S.ORCID,Churchhouse ClaireORCID,Howrigan Daniel P.ORCID,Zhou Wei,Watts Nicholas A.,Nguyen Kevin,Nguyen Huy,Mason Cara,Farnham Christopher,Tolonen Charlotte,Gauthier Laura D.,Gupta Namrata,MacArthur Daniel G.,Rehm Heidi L.ORCID,Seed Cotton,Philippakis Anthony A.,Daly Mark J.ORCID,Davis J. Wade,Runz HeikoORCID,Miller Melissa R.,Neale Benjamin M.ORCID

Abstract

AbstractGenome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variation in human disease has not been explored at scale. Exome sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variation across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 3,700 phenotypes using single-variant and gene tests of 281,850 individuals in the UK Biobank with exome sequence data. We find that the discovery of genetic associations is tightly linked to frequency as well as correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside a browser framework for rapidly exploring rare variant association results.

Publisher

Cold Spring Harbor Laboratory

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