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Exome sequencing and analysis of 454,787 UK Biobank participants
Nature  (IF49.962),  Pub Date : 2021-10-18, DOI: 10.1038/s41586-021-04103-z
Joshua D. Backman, Alexander H. Li, Anthony Marcketta, Dylan Sun, Joelle Mbatchou, Michael D. Kessler, Christian Benner, Daren Liu, Adam E. Locke, Suganthi Balasubramanian, Ashish Yadav, Nilanjana Banerjee, Christopher Gillies, Amy Damask, Simon Liu, Xiaodong Bai, Alicia Hawes, Evan Maxwell, Lauren Gurski, Kyoko Watanabe, Jack A. Kosmicki, Veera Rajagopal, Jason Mighty, Marcus Jones, Lyndon Mitnaul, Eli Stahl, Giovanni Coppola, Eric Jorgenson, Lukas Habegger, William J. Salerno, Alan R. Shuldiner, Luca A. Lotta, John D. Overton, Michael N. Cantor, Jeffrey G. Reid, George Yancopoulos, Hyun M. Kang, Jonathan Marchini, Aris Baras, Gonçalo R. Abecasis, Manuel A. Ferreira

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein altering variants and their consequences in 454,787 UK Biobank study participants2. We identified 12 million coding variants, including ~1 million loss-of-function and ~1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P≤2.18x10-11. Rare variant associations were enriched in GWAS loci, but most (91%) were independent of common variant signals. We discover several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as novel risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). 81% of signals available and powered for replication were confirmed in an independent cohort; furthermore, association signals were generally consistent across European, Asian and African ancestry individuals. We illustrate the ability of exome sequencing to identify novel gene-trait associations, elucidate gene function, and pinpoint effector genes underlying GWAS signals at scale.