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

By Konrad J. Karczewski, L. Francioli, G. Tiao, Beryl B. Cummings, Jessica Alföldi, Qingbo S. Wang, Ryan L. Collins, Kristen M. Laricchia, A. Ganna, Daniel P Birnbaum and 55 more. 2020

8,441 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

GPCRdb in 2021: integrating GPCR sequence, structure and function (2020, background): “cryo-electron microscopy (cryo-EM) (6), deep mutational scanning (7), genome sequencing (8) and signal protein profiling (9,10).”
Accurate proteome-wide missense variant effect prediction with AlphaMissense (2023, background): “G enome sequencing has revealed extensive genetic variation in human populations ( 1 – 3 ). variants Missense variants are genetic variants that alter the amino acid sequence of proteins.”
Missense variants causing Wiedemann-Steiner syndrome preferentially occur in the KMT2A-CXXC domain and are accurately classified using AlphaFold2 (2022, neutral): “1) [10] and the Trans-Omics for 213 Precision Medicine database (TOPmed) [11].”
COSMIC: a curated database of somatic variants and clinical data for cancer (2023, neutral): “For each variant, this is supported by information on its germline frequency in normal populations (gnomAD) ( 21 ), signs of positive selection in cancer cells (dN / dS) ( 22 ), as well as conservation on DNA (GERP) ( 23 ) and protein (SIFT) ( 24 ) level.”
Ensembl 2020 (2019, background): “uk/ eva/) alongside population frequency data from resources including the Genome Aggregation Database (gnomAD) (30).”
A draft human pangenome reference (2022, methodology): “To annotate small variants with allele frequencies from gnomAD (Karczewski et al., 2020), the gnomAD v3.”