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.
Original language | English (US) |
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Pages (from-to) | 1454-1462 |
Number of pages | 9 |
Journal | Nature neuroscience |
Volume | 19 |
Issue number | 11 |
DOIs | |
State | Published - Oct 26 2016 |
All Science Journal Classification (ASJC) codes
- General Neuroscience