生物
遗传学
自闭症
突变
自闭症谱系障碍
先证者
基因组
基因组学
功能基因组学
非编码DNA
基因
人类遗传学
神经发育障碍
心理学
发展心理学
作者
Jian Zhou,Christopher Y. Park,Chandra L. Theesfeld,Aaron K. Wong,Yuan Yuan,Claudia Scheckel,John Fak,Julien Funk,Kevin Yao,Yoko Tajima,Alan Packer,Robert B. Darnell,Olga G. Troyanskaya
出处
期刊:Nature Genetics
[Springer Nature]
日期:2019-05-27
卷期号:51 (6): 973-980
被引量:228
标识
DOI:10.1038/s41588-019-0420-0
摘要
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.
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