Dataset Information

The mutational constraint spectrum quantified from variation in 141,456 humans.

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.

SUBMITTER: Karczewski KJ

PROVIDER: S-EPMC7334197 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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The mutational constraint spectrum quantified from variation in 141,456 humans.

Karczewski Konrad J KJ Francioli Laurent C LC Tiao Grace G Cummings Beryl B BB Alföldi Jessica J Wang Qingbo Q Collins Ryan L RL Laricchia Kristen M KM Ganna Andrea A Birnbaum Daniel P DP Gauthier Laura D LD Brand Harrison H Solomonson Matthew M Watts Nicholas A NA Rhodes Daniel D Singer-Berk Moriel M England Eleina M EM Seaby Eleanor G EG Kosmicki Jack A JA Walters Raymond K RK Tashman Katherine K Farjoun Yossi Y Banks Eric E Poterba Timothy T Wang Arcturus A Seed Cotton C Whiffin Nicola N Chong Jessica X JX Samocha Kaitlin E KE Pierce-Hoffman Emma E Zappala Zachary Z O'Donnell-Luria Anne H AH Minikel Eric Vallabh EV Weisburd Ben B Lek Monkol M Ware James S JS Vittal Christopher C Armean Irina M IM Bergelson Louis L Cibulskis Kristian K Connolly Kristen M KM Covarrubias Miguel M Donnelly Stacey S Ferriera Steven S Gabriel Stacey S Gentry Jeff J Gupta Namrata N Jeandet Thibault T Kaplan Diane D Llanwarne Christopher C Munshi Ruchi R Novod Sam S Petrillo Nikelle N Roazen David D Ruano-Rubio Valentin V Saltzman Andrea A Schleicher Molly M Soto Jose J Tibbetts Kathleen K Tolonen Charlotte C Wade Gordon G Talkowski Michael E ME Neale Benjamin M BM Daly Mark J MJ MacArthur Daniel G DG

Nature 20200527 7809

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 annot ...[more]

PMID: 32461654

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Project description:Variation in gene expression is an important contributor to phenotypic diversity within and between species. Although this variation often has a genetic component, identification of the genetic variants driving this relationship remains challenging. In particular, measurements of gene expression usually do not reveal whether the genetic basis for any observed variation lies in cis or in trans to the gene, a distinction that has direct relevance to the physical location of the underlying genetic variant, and which may also impact its evolutionary trajectory. Allelic imbalance measurements identify cis-acting genetic effects by assaying the relative contribution of the two alleles of a cis-regulatory region to gene expression within individuals. Identification of patterns that predict commonly imbalanced genes could therefore serve as a useful tool and also shed light on the evolution of cis-regulatory variation itself. Here, we show that sequence motifs, polymorphism levels, and divergence levels around a gene can be used to predict commonly imbalanced genes in a human data set. Reduction of this feature set to four factors revealed that only one factor significantly differentiated between commonly imbalanced and nonimbalanced genes. We demonstrate that these results are consistent between the original data set and a second published data set in humans obtained using different technical and statistical methods. Finally, we show that variation in the single allelic imbalance-associated factor is partially explained by the density of genes in the region of a target gene (allelic imbalance is less probable for genes in gene-dense regions), and, to a lesser extent, the evenness of expression of the gene across tissues and the magnitude of negative selection on putative regulatory regions of the gene. These results suggest that the genomic distribution of functional cis-regulatory variants in the human genome is nonrandom, perhaps due to local differences in evolutionary constraint.

Dataset Information

The mutational constraint spectrum quantified from variation in 141,456 humans.

Publications

The mutational constraint spectrum quantified from variation in 141,456 humans.

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