Dataset Information

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 sequencing¹ to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study². 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.

SUBMITTER: Backman JD

PROVIDER: S-EPMC8596853 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exome sequencing and analysis of 454,787 UK Biobank participants.

Backman Joshua D JD Li Alexander H AH Marcketta Anthony A Sun Dylan D Mbatchou Joelle J Kessler Michael D MD Benner Christian C Liu Daren D Locke Adam E AE Balasubramanian Suganthi S Yadav Ashish A Banerjee Nilanjana N Gillies Christopher E CE Damask Amy A Liu Simon S Bai Xiaodong X Hawes Alicia A Maxwell Evan E Gurski Lauren L Watanabe Kyoko K Kosmicki Jack A JA Rajagopal Veera V Mighty Jason J Jones Marcus M Mitnaul Lyndon L Stahl Eli E Coppola Giovanni G Jorgenson Eric E Habegger Lukas L Salerno William J WJ Shuldiner Alan R AR Lotta Luca A LA Overton John D JD Cantor Michael N MN Reid Jeffrey G JG Yancopoulos George G Kang Hyun M HM Marchini Jonathan J Baras Aris A Abecasis Gonçalo R GR Ferreira Manuel A R MAR

Nature 20211018 7886

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<sup>1</sup> to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study<sup>2</sup>. 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 ...[more]

PMID: 34662886

Similar Datasets

Project description:ImportanceDetection of disease-associated variants in the BRCA1 and BRCA2 (BRCA1/2) genes allows for cancer prevention and early diagnosis in high-risk individuals.ObjectivesTo identify pathogenic and likely pathogenic (P/LP) BRCA1/2 variants in an unselected research cohort, and to characterize the features associated with P/LP variants.Design, setting, and participantsThis is a cross-sectional study of adult volunteers (n = 50 726) who underwent exome sequencing at a single health care system (Geisinger Health System, Danville, Pennsylvania) from January 1, 2014, to March 1, 2016. Participants are part of the DiscovEHR cohort and were identified through the Geisinger MyCode Community Health Initiative. They consented to a research protocol that included sequencing and return of actionable test results. Clinical data from electronic health records and clinical visits were correlated with variants. Comparisons were made between those with (cases) and those without (controls) P/LP variants in BRCA1/2.Main outcomesPrevalence of P/LP BRCA1/2 variants in cohort, proportion of variant carriers not previously ascertained through clinical testing, and personal and family history of relevant cancers among BRCA1/2 variant carriers and noncarriers.ResultsOf the 50 726 health system patients who underwent exome sequencing, 50 459 (99.5%) had no expected pathogenic BRCA1/2 variants and 267 (0.5%) were BRCA1/2 carriers. Of the 267 cases (148 [55.4%] were women and 119 [44.6%] were men with a mean [range] age of 58.9 [23-90] years), 183 (68.5%) received clinically confirmed results in their electronic health record. Among the 267 participants with P/LP BRCA1/2 variants, 219 (82.0%) had no prior clinical testing, 95 (35.6%) had BRCA1 variants, and 172 (64.4%) had BRCA2 variants. Syndromic cancer diagnoses were present in 11 (47.8%) of the 23 deceased BRCA1/2 carriers and in 56 (20.9%) of all 267 BRCA1/2 carriers. Among women, 31 (20.9%) of 148 variant carriers had a personal history of breast cancer, compared with 1554 (5.2%) of 29 880 noncarriers (odds ratio [OR], 5.95; 95% CI, 3.88-9.13; P < .001). Ovarian cancer history was present in 15 (10.1%) of 148 variant carriers and in 195 (0.6%) of 29 880 variant noncarriers (OR, 18.30; 95% CI, 10.48-31.4; P < .001). Among 89 BRCA1/2 carriers without prior testing but with comprehensive personal and family history data, 44 (49.4%) did not meet published guidelines for clinical testing.Conclusions and relevanceThis study found that compared with previous clinical care, exome sequencing-based screening identified 5 times as many individuals with P/LP BRCA1/2 variants. These findings suggest that genomic screening may identify BRCA1/2-associated cancer risk that might otherwise remain undetected within health care systems and may provide opportunities to reduce morbidity and mortality in patients.

Dataset Information

Exome sequencing and analysis of 454,787 UK Biobank participants.

Publications

Exome sequencing and analysis of 454,787 UK Biobank participants.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets