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

• Published in: Cell genomics 2022-09, Vol.2 (9), p.100168-100168, Article 100168
• Main Authors: Karczewski, Konrad J., Solomonson, Matthew, Chao, Katherine R., Goodrich, Julia K., Tiao, Grace, Lu, Wenhan, Riley-Gillis, Bridget M., Tsai, Ellen A., Kim, Hye In, Zheng, Xiuwen, Rahimov, Fedik, Esmaeeli, Sahar, Grundstad, A. Jason, Reppell, Mark, Waring, Jeff, Jacob, Howard, Sexton, David, Bronson, Paola G., Chen, Xing, Hu, Xinli, Goldstein, Jacqueline I., King, Daniel, Vittal, Christopher, Poterba, Timothy, Palmer, Duncan S., Churchhouse, Claire, Howrigan, Daniel P., 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., Seed, Cotton, Philippakis, Anthony A., Daly, Mark J., Davis, J. Wade, Runz, Heiko, Miller, Melissa R., Neale, Benjamin M.
• Format: Article
• Language: English
• Subjects: biobanks; exome sequencing; GWAS; PheWAS; rare variant association studies; rare variants
• ISSN: 2666-979X; 2666-979X
• DOI: 10.1016/j.xgen.2022.100168

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. [Display omitted] •Public release of gene-based association statistics for 4,529 diseases and traits•Genebass, a browser framework to display rare-variant associations•Tight coupling between frequency, natural selection, and power for genetic discovery•Biological signal between SCRIB and white-matter integrity (from MRI) Karczewski et al. generated a massive-scale association dataset between rare genetic mutations and thousands of diseases and traits and released these data in the Genebass browser. They quantify the influence of natural selection and gene function on association discovery and highlight an association between SCRIB and a brain-imaging trait.