Our long-term goal is to use genetic tools to improve understanding, prevention, and treatment of neurodevelopmental disorders. The objective of this proposal is to use genetic-expression networks to identify genes responsible for CNV-associated behavioral phenotypes. The central hypothesis is that expression dysregulation of one or multiple genes in a CNV region may contribute to phenotypic expression and this can be explored by expression quantitative trait locus (eQTL) and association analysis. In support of this hypothesis, preliminary data show that 16p11.2-related phenotypes show brain-specific association with eQTL networks of 16p11.2 genes. This proposal comprises a novel transcriptomics approach to identify gene expression dysregulation effects underlying neurobehavioral phenotypes by: 1) generating eQTL networks for each gene within or bordering a CNV of interest, 2) testing each eQTL network for association with relevant phenotypes in idiopathic disease datasets and EMR-linked biorepositories, and 3) utilizing eQTL network and association results to address biological questions. We expect to gain insight into how CNVs containing many brain genes influence neurodevelopment. Our unique approach enabled by preliminary data and powerful biorepositories may identify biological pathways through which CNVs cause neuropathogenicity and by extension may lead to novel hypothesized modifiers of relevant endophenotypes that could be explored in the future.
The 22q11.2 copy number variant (CNV) and 16p11.2 CNV are some of the most common and strong risk factors for schizophrenia and autism spectrum disorders (ASDs), as well as other behavioral phenotypes. We will take advantage of advances in transcriptomic data to identify genes that might be important in the behavioral phenotypes associated with these CNVs and by which mechanisms they operate. Understanding of pathogenic mechanisms for major impact CNV regions can provide insight leading to improvement in treatment strategies for behavioral disorders.