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

Abstract

Summary Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organism’s function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) 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 sizes 1 . 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 pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

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Affiliated Institutions

• Massachusetts General Hospital US
• Harvard University US
• Harefield Hospital GB
• Vertex Pharmaceuticals (United States) US
• Boston Children's Hospital US
• Institute for Molecular Medicine Finland FI
• Yale University US
• William Harvey Research Institute GB
• Queen Mary University of London GB
• Imperial College London GB
• Wellcome Sanger Institute GB
• Murdoch Children's Research Institute AU
• Garvan Institute of Medical Research AU
• University of Washington US
• Broad Institute US
• Barts Health NHS Trust GB
• MRC London Institute of Medical Sciences GB

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