Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 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 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

doi:10.1016/j.xgen.2022.100168

Systematic single-variant and gene-based association testing of thousands of phenotypes in 394,841 UK Biobank exomes

Cell Genom. 2022 Aug 15;2(9):100168. doi: 10.1016/j.xgen.2022.100168. eCollection 2022 Sep 14.

Authors

Konrad J Karczewski^{1

2

3}, Matthew Solomonson^{1

2}, Katherine R Chao^{1

2}, Julia K Goodrich^{1

2}, Grace Tiao^{1

2}, Wenhan Lu^{1

2

3}, Bridget M Riley-Gillis⁴, Ellen A Tsai⁵, Hye In Kim⁶, Xiuwen Zheng⁴, Fedik Rahimov⁴, Sahar Esmaeeli⁴, A Jason Grundstad⁴, Mark Reppell⁴, Jeff Waring⁴, Howard Jacob⁴, David Sexton⁵, Paola G Bronson⁵, Xing Chen⁶, Xinli Hu⁶, Jacqueline I Goldstein^{1

2

3}, Daniel King^{1

2

3}, Christopher Vittal^{1

2

3}, Timothy Poterba^{1

2

3}, Duncan S Palmer^{1

2

3}, Claire Churchhouse^{1

2

3}, Daniel P Howrigan^{1

2

3}, Wei Zhou^{1

2}, Nicholas A Watts^{1

2}, Kevin Nguyen^{1

2}, Huy Nguyen^{1

2}, Cara Mason⁷, Christopher Farnham⁷, Charlotte Tolonen⁷, Laura D Gauthier⁷, Namrata Gupta⁷, Daniel G MacArthur^{1

2}, Heidi L Rehm^{1

2}, Cotton Seed^{1

2

3}, Anthony A Philippakis⁷, Mark J Daly^{1

2

3

8}, J Wade Davis⁴, Heiko Runz⁵, Melissa R Miller⁶, Benjamin M Neale^{1

2

3}

Affiliations

¹ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
³ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁴ Genomics Research Center, AbbVie, North Chicago, IL 60064, USA.
⁵ Biogen, Inc., Cambridge, MA 02142, USA.
⁶ Worldwide Research Development and Medical, Pfizer, Inc., Cambridge, MA 02139, USA.
⁷ Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁸ Institute for Molecular Medicine Finland, Helsinki, Finland.

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.

Keywords: GWAS; PheWAS; biobanks; exome sequencing; rare variant association studies; rare variants.

Grants and funding

R01 DK075787/DK/NIDDK NIH HHS/United States