“Exome Sequencing and Analysis of 454,787 UK Biobank Participants”, 2021 (; similar):
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 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study. 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). 6 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.