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

TLDR

Genetic loss‑of‑function variants reveal gene essentiality, but annotation errors and extreme rarity require careful curation and very large sample sizes. The authors aim to aggregate 125,748 exomes and 15,708 genomes into the Genome Aggregation Database (gnomAD). They compile data from diverse human sequencing studies to build gnomAD, enabling large‑scale variant analysis. The database contains 443,769 high‑confidence pLoF variants, and using an improved mutation‑rate model the authors classify genes by tolerance to inactivation, validate this spectrum with model organisms and engineered cells, and show it enhances gene‑discovery power for common and rare diseases.

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.