1. The mutational constraint spectrum quantified from variation in 141,456 humans
- Author
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Karczewski, KJ, Francioli, LC, Tiao, G, Cummings, BB, Alföldi, J, Wang, Q, Collins, RL, Laricchia, KM, Ganna, A, Birnbaum, DP, Gauthier, LD, Brand, H, Solomonson, M, Watts, NA, Rhodes, D, Singer-Berk, M, England, EM, Seaby, EG, Kosmicki, JA, Walters, RK, Tashman, K, Farjoun, Y, Banks, E, Poterba, T, Wang, A, Seed, C, Whiffin, N, Chong, JX, Samocha, KE, Pierce-Hoffman, E, Zappala, Z, O’Donnell-Luria, AH, Vallabh Minikel, E, Weisburd, B, Lek, M, Ware, JS, Vittal, C, Armean, IM, Bergelson, L, Cibulskis, K, Connolly, JM, Covarrubias, M, Donnelly, S, Ferriera, S, Gabriel, S, Gentry, J, Gupta, N, Jeandet, T, Kaplan, D, Llanwarne, C, Munshi, J, Novod, S, Petrillo, N, Roazen, D, Ruano-Rubio, V, Saltzman, A, Schleicher, M, Soto, J, Tibbetts, K, Tolonen, C, Wade, G, Talkowski, ME, Genome Aggregation Database (gnomAD) Consortium, Neale, BM, Daly, MJ, MacArthur, DG, Tampere University, Clinical Medicine, Department of Clinical Chemistry, Wellcome Trust, Rosetrees Trust, Institute for Molecular Medicine Finland, Data Science Genetic Epidemiology Lab, Centre of Excellence in Complex Disease Genetics, Department of Medicine, Clinicum, Gastroenterologian yksikkö, HUS Abdominal Center, University Management, HUS Psychiatry, Department of Psychiatry, HUS Neurocenter, Department of Neurosciences, Neurologian yksikkö, Department of Public Health, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Biostatistics Helsinki, Doctoral Programme in Clinical Research, and Biosciences
- Subjects
Male ,Mutation rate ,ved/biology.organism_classification_rank.species ,VARIANTS ,Genome ,Whole Exome Sequencing ,Cohort Studies ,0302 clinical medicine ,Mutation Rate ,Loss of Function Mutation ,Databases, Genetic ,Exome ,Organism ,Exome sequencing ,0303 health sciences ,Genes, Essential ,Multidisciplinary ,1184 Genetics, developmental biology, physiology ,Brain ,Phenotype ,Multidisciplinary Sciences ,Cardiovascular Diseases ,Science & Technology - Other Topics ,Female ,Proprotein Convertase 9 ,BURDEN ,Medical genomics ,Adult ,General Science & Technology ,Computational biology ,Biology ,3121 Internal medicine ,Article ,03 medical and health sciences ,Humans ,Genetic Predisposition to Disease ,RNA, Messenger ,Model organism ,Gene ,030304 developmental biology ,Science & Technology ,Whole Genome Sequencing ,Genome, Human ,ved/biology ,Genetic Variation ,Reproducibility of Results ,Rare variants ,MODEL ,DE-NOVO MUTATIONS ,Genome Aggregation Database Consortium ,3111 Biomedicine ,030217 neurology & neurosurgery ,Function (biology) ,Genome-Wide Association Study - Abstract
Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases., A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
- Published
- 2020