Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes

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 54 more. 2019

1,088 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 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. 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.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “…mutational ﬁtness landscape provides deep insight into biology: determining protein activity (Fowler & Fields, 2014), linking genetic variation to human health (Lek et al., 2016; Bycroft et al., 2018; Karczewski et al., 2019), and enabling for rational protein engineering (Slaymaker et al., 2016).”
Mendelian Gene Discovery: Fast and Furious with No End in Sight. (2019, background): “Use of transcriptome sequencing (RNA-seq) to identify abnormally spliced transcripts and/or assess transcript abundance facilitates identiﬁcation of non-coding variants, deep intronic splice variants, 16–18 and to a lesser extent, synonymous variants with unexpected effects on splicing.”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, background): “We next investigated the ability of both DMS experiments and VEPs to distinguish pathogenic human missense mutations, taken from the ClinVar database (Landrum et al , 2014), from missense variants observed in the human population, taken from gnomAD (preprint: Karczewski et al , 2019).”
Transcript expression-aware annotation improves rare variant discovery and interpretation (2019, methodology): “putative loss of function (pLoF) variants in haploinsufficient disease genes in the Genome Aggregation Database (gnomAD)(1), we show that one explanation for this paradox involves alternative mRNA splicing,”
Evaluating drug targets through human loss-of-function genetic variation (2019, neutral): “1b), as previously reported 7,10 , validating the usefulness of constraint as a measure of gene essentiality.”
Efficient phasing and imputation of low-coverage sequencing data using large reference panels (2020, background): “21 ) for the appropriate populations.”
The DisGeNET knowledge platform for disease genomics: 2019 update (2019, neutral): “Genes are referred by their NCBI identifiers, and are annotated with the official gene symbol, the UniProt accession, the protein class, and with their value of pLI (probability of being loss-of-function intolerant), a gene constraint metric from the gnomAD consortium (19).”
PRIM1 deficiency causes a distinctive primordial dwarfism syndrome (2020, neutral): “2017) while constraint metrics from the gnomAD consortium suggest that loss of a single copy is unlikely to result in developmental disease (Table S2) (Karczewski et al. 2019).”