Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes

By S. Petrovski, Quanli Wang, E. Heinzen, A. Allen, David B. Goldstein. 2013

881 citations recorded by the source corpus.

A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity (2015, methodology): “Reflecting 96% of the CCDS release 9 genes scored in Petrovski et al (2013).”
Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder (2016, methodology): “Evolutionary constraint estimates of genes were obtained from two different studies 58,59 , one providing a quantitative (RVIS) score for each gene, and the other providing a single set of 1,003 constrained genes.”
Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci (2015, background): “04, p = 1 3 10 (5), logistic regression) (Petrovski et al., 2013; Samocha et al., 2014), one dataset of genes that are targeted by the Fragile X protein FMRP in mouse brain (p = 4 3 10 (8), logistic”
Genomic diagnosis for children with intellectual disability and/or developmental delay (2016, background): “Variation detected through our studies has already helped lead to the discovery of at least one new disease association, as we identified two patients that harbor de novo variants in EBF3, a highly conserved transcription factor involved in neurodevelopment that is relatively intolerant to mutations”
Settling the score: variant prioritization and Mendelian disease (2017, methodology): “For example, the Residual Variation Intolerance Score (RVIS) uses ~6,500 exomes from the NHLBI Exome Sequencing Project and a linear model comparing the number of common functional variants observed in a gene against the total number of variants observed in the gene 55 .”
DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins (2017, background): “Summary of the features used in DEOGEN2 Feature Status Short name Code 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 anno”
The human gene damage index as a gene-level approach to prioritizing exome variants (2015, methodology): “…indels/splice variants in the genes with a MAF < 0.01 and a high sequencing quality, in 84 PID patients with known disease-causing mutations, after removal of the disease-causing mutation, for all of the patients ’ genes. in cases vs. controls by the hot zone approach (12, 36, 37).”
Regional missense constraint improves variant deleteriousness prediction (2017, background): “…provide the opportunity to better understand patterns and rates of variation within the human population and have permitted the identification of genes and regions within genes that are intolerant of nonsynonymous variation (constrained) 14-16 and therefore more likely to be associated to disease.”