RT Journal Article
SR Electronic
T1 Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 319681
DO 10.1101/319681
A1 Jian Zhou
A1 Christopher Y. Park
A1 Chandra L. Theesfeld
A1 Yuan Yuan
A1 Kirsty Sawicka
A1 Jennifer C. Darnell
A1 Claudia Scheckel
A1 John J Fak
A1 Yoko Tajima
A1 Robert B. Darnell
A1 Olga G. Troyanskaya
YR 2018
UL http://biorxiv.org/content/early/2018/08/03/319681.abstract
AB We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts specific regulatory effects and deleterious disease impact of genetic variants. Applying this framework to 1,790 Autism Spectrum Disorder (ASD) simplex families reveals autism disease causality of noncoding mutations by demonstrating that ASD probands harbor transcriptional (TRDs) and post-transcriptional (RRDs) regulation-disrupting mutations of significantly higher functional impact than unaffected siblings. Importantly, we detect this significant noncoding contribution at each level, transcriptional and post-transcriptional, independently and after multiple hypothesis correction. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development, and reveals a convergent genetic landscape of coding and noncoding (TRD and RRD) de novo mutations in ASD. We demonstrate that sequences carrying prioritized proband de novo mutations possess transcriptional regulatory activity and drive expression differentially, and highlight a link between noncoding mutations and IQ heterogeneity in ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD, prioritizes high impact transcriptional and post-transcriptional regulatory mutations for further study, and is broadly applicable to complex human diseases.