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

TitleWhole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.
Publication TypeJournal Article
Year of Publication2019
AuthorsZhou, 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
JournalNat Genet
Volume51
Issue6
Pagination973-980
Date Published2019 06
ISSN1546-1718
KeywordsAlgorithms, Alleles, Autism Spectrum Disorder, Computational Biology, Deep Learning, Gene Expression, Gene Expression Regulation, Genes, Reporter, Genetic Association Studies, Genetic Predisposition to Disease, Genome, Human, Genomics, Humans, Mutation, Phenotype, RNA Processing, Post-Transcriptional, RNA, Untranslated, Transcription, Genetic
Abstract

<p>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.</p>

DOI10.1038/s41588-019-0420-0
Alternate JournalNat. Genet.
PubMed ID31133750
PubMed Central IDPMC6758908
Grant ListR01 NS081706 / NS / NINDS NIH HHS / United States
UM1 HG008901 / HG / NHGRI NIH HHS / United States
R01 NS034389 / NS / NINDS NIH HHS / United States
R35 NS097404 / NS / NINDS NIH HHS / United States
R01 GM071966 / GM / NIGMS NIH HHS / United States
R56 NS034389 / NS / NINDS NIH HHS / United States