| Literature DB >> 27479844 |
Arjun Krishnan1, Ran Zhang2, Victoria Yao3, Chandra L Theesfeld1, Aaron K Wong4, Alicja Tadych1, Natalia Volfovsky4, Alan Packer4, Alex Lash4, Olga G Troyanskaya1,3,5.
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.Entities:
Mesh:
Year: 2016 PMID: 27479844 PMCID: PMC5803797 DOI: 10.1038/nn.4353
Source DB: PubMed Journal: Nat Neurosci ISSN: 1097-6256 Impact factor: 24.884