Exome sequencing and analysis of 454,787 UK Biobank participants

Abstract 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 sequencing 1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study 2 . We identified 12 million coding variants, including around 1 million loss-of-function and around 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 ≤ 2.18 × 10 −11 . Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as 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 ). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene–trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.