Systematic single-variant and gene-based association testing of thousands of phenotypes in 426,370 UK Biobank exomes

Konrad J. Karczewski; Matthew Solomonson; Katherine R. Chao; Julia K. Goodrich; Grace Tiao; Wenhan Lu; Bridget M. Riley-Gillis; Ellen A. Tsai; Hye In Kim; Xiuwen Zheng; Fedik Rahimov; Sahar Esmaeeli; A. Jason Grundstad; Mark Reppell; Jeff Waring; Howard J. Jacob; David Sexton; Paola G. Bronson; Xing Chen; Xinli Hu; Jacqueline I. Goldstein; Daniel A. King; Christopher Vittal; Timothy Poterba; Duncan S. Palmer; Claire L. Churchhouse; Daniel P. Howrigan; Wei Zhou; Nicholas A. Watts; Kevin K. Nguyen

doi:10.1016/j.xgen.2022.100168

Genetic & Genomic MedicineCell GenomicsAug 15, 2022

Peer-reviewed journal article

Systematic single-variant and gene-based association testing of thousands of phenotypes in 426,370 UK Biobank exomes

Konrad J. Karczewski iD, Matthew Solomonson iD, Katherine R. Chao iD, Julia K. Goodrich iD, Grace Tiao iD, Wenhan Lu iD, Bridget M. Riley-Gillis iD, Ellen A. Tsai iD, Hye In Kim iD, Xiuwen Zheng iD, Fedik Rahimov iD, Sahar Esmaeeli, A. Jason Grundstad, Mark Reppell iD, Jeff Waring, Howard J. Jacob iD, David Sexton iD, Paola G. Bronson iD, Xing Chen iD, Xinli Hu iD, Jacqueline I. Goldstein iD, Daniel A. King iD, Christopher Vittal, Timothy Poterba iD, Duncan S. Palmer iD, , Benjamin M. Neale iD

Abstract

Genome-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 4,529 phenotypes using single-variant and gene tests of 426,370 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 the Genebass browser for rapidly exploring rare variant association results.

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