PT  - JOURNAL ARTICLE
AU  - Arjun Krishnan
AU  - Ran Zhang
AU  - Victoria Yao
AU  - Chandra L. Theesfeld
AU  - Aaron K. Wong
AU  - Alicja Tadych
AU  - Natalia Volfovsky
AU  - Alan Packer
AU  - Alex Lash
AU  - Olga G. Troyanskaya
TI  - Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder
AID  - 10.1101/057828
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 057828
4099  - http://biorxiv.org/content/early/2016/06/09/057828.short
4100  - http://biorxiv.org/content/early/2016/06/09/057828.full
AB  - Autism spectrum disorder (ASD) is a range of major neurodevelopmental disabilities with a strong genetic basis. Yet, owing to extensive genetic heterogeneity, multiple modes of inheritance and limited study sizes, sequencing and quantitative genetics approaches have had limited success in characterizing the complex genetics of ASD. Currently, only a small fraction of potentially causal genes—about 65 genes out of an estimated severalhundred—are known based on strong genetic evidence. Hence, there isa critical need for complementary approaches to further characterize the genetic basis of ASD, enabling development of better screening and therapeutics. Here, we use a machine-learning approach based on a human brain-specific functional gene interaction network to present a genome-wide prediction of autism-associated genes, including hundreds of candidate genes for which there is minimal or no prior genetic evidence. Our approach is validated in an independent case-control sequencing study of approximately 2,500families. Leveraging these genome-wide predictions and the brain-specificnetwork, we demonstrate that the large set of ASD genes converges on a smaller number of key cellular pathways and specific developmental stages of the brain. Specifically, integration with spatiotemporal transcriptome expression data implicates early fetal and midfetal stages of the developing human brain in ASD etiology. Likewise, analysis of the connectivity of topautism genes in the brain-specific interaction network reveals the breadthof autism-associated functional modules, processes, and pathways in the brain. Finally, we identify likely pathogenic genes within the most frequent autism-associated copy-number-variants (CNVs) and propose genes and pathways that are likely mediators of autism across multiple CNVs. All the predictions, interactions, and functional insights from this work are available to biomedical researchers at asd.princeton.edu.