Whole-genome Deep-learning Analysis Identifies Contribution of Noncoding Mutations to Autism Risk

Overview

Journal Nat Genet

Specialty Genetics

Date 2019 May 29

PMID 31133750

Citations 128

Authors

Jian Zhou

Christopher Y Park

Chandra L Theesfeld

Aaron K Wong

Yuan Yuan

Claudia Scheckel

John J Fak

Julien Funk

Kevin Yao

Yoko Tajima

Alan Packer

Robert B Darnell

Olga G Troyanskaya

Affiliations

Soon will be listed here.

Abstract

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutations-ASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.

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