Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

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.

journal_name

Nat Genet

journal_title

Nature genetics

authors

Zhou J,Park CY,Theesfeld CL,Wong AK,Yuan Y,Scheckel C,Fak JJ,Funk J,Yao K,Tajima Y,Packer A,Darnell RB,Troyanskaya OG

doi

10.1038/s41588-019-0420-0

subject

Has Abstract

pub_date

2019-06-01 00:00:00

pages

973-980

issue

6

eissn

1061-4036

issn

1546-1718

pii

10.1038/s41588-019-0420-0

journal_volume

51

pub_type

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