Last updated:
Author(s):
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 Jacob, David Sexton, Paola G. Bronson, Xing Chen, Xinli Hu, Jacqueline I. Goldstein, Daniel King, Christopher Vittal, Timothy Poterba, Duncan S. Palmer, Claire Churchhouse, Daniel P. Howrigan, Wei Zhou, Nicholas A. Watts, Kevin Nguyen, Huy Nguyen, Cara Mason, Christopher Farnham, Charlotte Tolonen, Laura D. Gauthier, Namrata Gupta, Daniel G. MacArthur, Heidi L. Rehm, Cotton Seed, Anthony A. Philippakis, Mark J. Daly, J. Wade Davis, Heiko Runz, Melissa R. Miller, Benjamin M. Neale
Publish date:
15 August 2022
Journal:
Cell Genomics
PubMed ID:
36778668

Abstract

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variations 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 variations 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 394,841 individuals in the UK Biobank with exome-sequence data. We find that the discovery of genetic associations is tightly linked to frequency and is 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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