Research ArticleAutism

A method to delineate de novo missense variants across pathways prioritizes genes linked to autism

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Science Translational Medicine  19 May 2021:
Vol. 13, Issue 594, eabc1739
DOI: 10.1126/scitranslmed.abc1739

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Making sense of missense variants in autism

Autism spectrum disorder (ASD) is phenotypically and genetically heterogeneous, and the relevance of de novo missense variants to neurodevelopmental outcomes remains unclear. A new study by Koire et al. suggests genes and pathways that could be involved in ASD. These authors assessed bias toward a predicted fitness impact among rare de novo missense variants in functionally related genes. For both established and unexpected genes, the computationally predicted evolutionary impact of these variants correlated with patient IQ. This study suggests a role for this rare variant class in ASD and presents a generalizable approach toward elucidating the genotype-phenotype relationships in complex diseases.

Abstract

Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.

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