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Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder

View ORCID ProfileArjun Krishnan, View ORCID ProfileRan Zhang, View ORCID ProfileVictoria Yao, View ORCID ProfileChandra L. Theesfeld, View ORCID ProfileAaron K. Wong, View ORCID ProfileAlicja Tadych, View ORCID ProfileNatalia Volfovsky, View ORCID ProfileAlan Packer, View ORCID ProfileAlex Lash, View ORCID ProfileOlga G. Troyanskaya
doi: https://doi.org/10.1101/057828
Arjun Krishnan
1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
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Ran Zhang
2Department of Molecular Biology, Princeton University, Princeton, NJ 08544
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Victoria Yao
3Department of Computer Science, Princeton University, Princeton, NJ 08544
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Chandra L. Theesfeld
1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
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Aaron K. Wong
4Simons Center for Data Analysis,Simons Foundation, NY 10010 New York
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Alicja Tadych
1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
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Natalia Volfovsky
4Simons Center for Data Analysis,Simons Foundation, NY 10010 New York
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Alan Packer
4Simons Center for Data Analysis,Simons Foundation, NY 10010 New York
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Alex Lash
4Simons Center for Data Analysis,Simons Foundation, NY 10010 New York
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Olga G. Troyanskaya
1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544
3Department of Computer Science, Princeton University, Princeton, NJ 08544
4Simons Center for Data Analysis,Simons Foundation, NY 10010 New York
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Abstract

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.

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  • ↵* The first two authors are co-first authors and are listed alphabetically

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Posted June 09, 2016.
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Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder
Arjun Krishnan, Ran Zhang, Victoria Yao, Chandra L. Theesfeld, Aaron K. Wong, Alicja Tadych, Natalia Volfovsky, Alan Packer, Alex Lash, Olga G. Troyanskaya
bioRxiv 057828; doi: https://doi.org/10.1101/057828
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Genome-wide characterization of genetic and functional dysregulation in autism spectrum disorder
Arjun Krishnan, Ran Zhang, Victoria Yao, Chandra L. Theesfeld, Aaron K. Wong, Alicja Tadych, Natalia Volfovsky, Alan Packer, Alex Lash, Olga G. Troyanskaya
bioRxiv 057828; doi: https://doi.org/10.1101/057828

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