A systematic survey of loss-of-function variants in human protein-coding genes

By Daniel G. MacArthur, S. Balasubramanian, Adam Frankish, N. Huang, James A. Morris, Klaudia Walter, L. Jostins, L. Habegger, Joseph K. Pickrell, Stephen B. Montgomery and 41 more. 2012

1,227 citations recorded by the source corpus.

Defective Gene Detective Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823; see the Perspective by Quintana-Murci) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles. Validation of predicted nonfunctional alleles in the human genome affects the medical interpretation of genomic analyses. Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease–causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

A general framework for estimating the relative pathogenicity of human genetic variants (2014, methodology): “We next compared scaled C-scores with levels of genetic diversity, finding that C-scores are negatively correlated with the DAF of variants identified in the 1000 Genomes Project 14 or the Exome Sequencing Project 24 (ESP) (Fig.”
Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population (2014, methodology): “Analyses of RNA sequencing data To analyze the effects of the LoF variants on gene expression, we used RNA sequencing data from two major studies: the GEUVADIS project [30] with RNA sequencing data from lymphoblastoid cell lines of 462 individuals participants from the 1000 Genomes Project [31]), an”
Settling the score: variant prioritization and Mendelian disease (2017, background): “However, this is a poor approach because the average human harbours hundreds of putative loss-of-function alleles in both heterozygous and homozygous states 4,22,34,35 (TABLE 1) .”
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins (2017, background): “PROVEAN score (16) From version 1 PROV PR Conservation Index (12,13) Improved CI CI Mutant/wildtype log-odd ratio (12) Improved LOR LO Early Folding predictions New EF EF PFAM log-odd score (17) New PF PF Interaction patches annotation (19) New INT IN RVIS (20) New RVIS RV GDI (21) New GDI GD Recess”
Transcript expression-aware annotation improves rare variant discovery and interpretation (2019, background): “…be expecte found in the gnomAD population. cted As such, most or all of these observed pLoF variants are likely sequencing or annotation errors ( 12 ). ely Manual curation of these variants reveals common error mod result in likely misannotation of pLoFs, with diversity of transcript structure,…”
Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects (2016, neutral): “Protein-coding variants represent the most readily interpretable and medically relevant slice of human genetic variation, and have been assessed in large sample sizes through the widespread application of exome sequencing approaches 2 .”
Tools and best practices for data processing in allelic expression analysis (2015, background): “1b): effects of genetic regulatory variants in cis [1–8], nonsense-mediated decay triggered by variants causing a premature stop codon [9–12], and imprinting [13, 14].”