Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Author Jian Zhou, Christopher Park, Chandra Theesfeld, Aaron Wong, Yuan Yuan, Claudia Scheckel, John Fak, Julien Funk, Kevin Yao, Yoko Tajima, Alan Packer, Robert Darnell, Olga Troyanskaya Publication Year 2019 Type Journal Article 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. Keywords RNA, Untranslated, Transcription, Genetic, Humans, Computational Biology, Mutation, Gene Expression Regulation, RNA Processing, Post-Transcriptional, Genes, Reporter, Phenotype, Genomics, Gene Expression, Algorithms, Genetic Predisposition to Disease, Alleles, Genome, Human, Genetic Association Studies, Autism Spectrum Disorder, Deep Learning Journal Nat Genet Volume 51 Issue 6 Pages 973-980 Date Published 2019 Jun ISSN Number 1546-1718 DOI 10.1038/s41588-019-0420-0 Alternate Journal Nat Genet PMCID PMC6758908 PMID 31133750 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML