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 Genetjournal_title
Nature geneticsauthors
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 OGdoi
10.1038/s41588-019-0420-0subject
Has Abstractpub_date
2019-06-01 00:00:00pages
973-980issue
6eissn
1061-4036issn
1546-1718pii
10.1038/s41588-019-0420-0journal_volume
51pub_type
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