Large-scale genomic investigations have begun to illuminate the genetic contributions to major psychiatric illnesses. In autism spectrum disorder (ASD), rare large effect variants provide novel causal anchors to understand its neurobiological basis, and to understand convergence and divergence in disease mechanisms. These findings, not unlike other psychiatric disorders, also emphasize extreme genetic heterogeneity. We and others have shown that gene and protein networks provide an organizing framework for understanding heterogeneous psychiatric disease genetic risk in a unified biological context. The emergence of large-scale genomic and epigenetic data from human brain, and growing knowledge of genetic variation involved in ASD and other psychiatric diseases, coupled with advances in methodology, provides an unprecedented opportunity for comprehensive integrative analyses. We bring together a collaborative group of investigators, each with distinct areas of expertise critical for understanding psychiatric diseases, that have not been combined before, to develop a framework for integrative genomic network analysis. The work proposed here represents an ambitious multi-PI project (UCLA/UW, MGH/Harvard, and Johns Hopkins) that brings together 3 principal investigators and collaborators with strong publication records and expertise in all approaches necessary to perform this work using state of the art and novel methodologies. Through close collaboration we aim to develop a comprehensive framework and test optimal methods for integration of gene expression and protein-protein interaction (PPI) networks in the brain with genetic and epigenetic data - networks that will be iteratively refined using experimental data. We will construct networks representing multiple brain regions in adulthood and development through rigorous combination of multiple transcriptomic data sets from ASD and control brain, developing and validating methods for integration of splicing and expression levels within gene networks (Aim 1). These networks will be refined to inform tissue specific PPI inference, validated via experimental tissue-specific PPI (Aim 2). We will further identify causal drivers by integration of genetic and epigenetic data, identifying QTL effects on RNA, splicing and protein levels (Aim 3). We will experimentally validate network regulatory predictions for a subset of putative causal drivers prioritizing network hubs and ASD associated genes (Aim 4). In addition, we will use our networks to predict high likelihood risk genes, whose relationship to ASD will be assessed using data from large-scale sequencing in ASD and related psychiatric disease cohorts, as well as our own focused experimental validation via multiplex inversion probe (MIPs) sequencing (Aim 4). Completion of these aims will lead to more valid and comprehensive CNS networks thereby significantly advancing our understanding of ASD associated variants and causal neurobiological pathways. As is our usual practice, our data, networks, and all code will be made freely available in a web-based format to be of maximum utility to the community.

Public Health Relevance

Advances in genomics and genetics have opened a new window through which to understand psychiatric diseases, such as autism spectrum disorder (ASD), but also reveal that they are all very genetically heterogeneous. Gene networks have shown promise for organizing the complexity and heterogeneity of genetic factors that contribute to ASD. Here, through highly collaborative efforts using multiple public and private data sets we develop a rigorous, multi-level and experimentally tested framework for integrative network studies in neuropsychiatric diseases.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZMH1)
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Arguello, Alexander
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Johns Hopkins University
Biostatistics & Other Math Sci
Biomed Engr/Col Engr/Engr Sta
United States
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GTEx Consortium; Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group; Statistical Methods groups—Analysis Working Group et al. (2017) Genetic effects on gene expression across human tissues. Nature 550:204-213
Li, Xin; Kim, Yungil; Tsang, Emily K et al. (2017) The impact of rare variation on gene expression across tissues. Nature 550:239-243
Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H et al. (2017) Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res 27:1843-1858
Knowles, David A; Davis, Joe R; Edgington, Hilary et al. (2017) Allele-specific expression reveals interactions between genetic variation and environment. Nat Methods 14:699-702