Unified inference of missense variant effects and gene constraints in the human genome.

PLoS Genetics
Yi-Fei Huang

Abstract

A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows improved performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective c...Continue Reading

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Citations

Jan 23, 2021·Nature Communications·Hongjian QiYufeng Shen

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Methods Mentioned

BETA
exome sequencing
RNA-seq

Software Mentioned

LOESS
PolyPhen
SPIDEX
LRT
LASSIE
phyloP
SIFT
denovo
ClinVar
MatchIt

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