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

Nature. 2020 May;581(7809):434-443. doi: 10.1038/s41586-020-2308-7. Epub 2020 May 27.

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Brain / metabolism
  • Cardiovascular Diseases / genetics
  • Cohort Studies
  • Databases, Genetic
  • Exome / genetics*
  • Female
  • Genes, Essential / genetics*
  • Genetic Predisposition to Disease / genetics
  • Genetic Variation / genetics*
  • Genome, Human / genetics*
  • Genome-Wide Association Study
  • Humans
  • Loss of Function Mutation / genetics
  • Male
  • Mutation Rate
  • Proprotein Convertase 9 / genetics
  • RNA, Messenger / genetics
  • Reproducibility of Results
  • Whole Exome Sequencing
  • Whole Genome Sequencing

Substances

  • RNA, Messenger
  • PCSK9 protein, human
  • Proprotein Convertase 9