Deconstructing Reciprocal Genomic Disorders by Integration of Genome Engineering and Cellular Modeling

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An important goal of human genomic research is the identification and characterization of genetic contributors to neurodevelopmental disease. Recurrent deletions and duplications of specific segments of our genome have emerged as some of the most common mutations identified in patients showing abnormal neurodevelopment. Collectively, these genomic losses and gains explain ~5-10% of autism spectrum disorder, schizophrenia, and intellectual disability. Despite intense interest in these rearrangements and their associated diseases, reciprocal genomic disorders (RGDs), relatively few studies have addressed their functional consequences at the molecular level. This project will leverage genome editing technology to generate a series of cell lines with deletions or duplications of interest against an isogenic background. RNA sequencing will then serve as a platform to investigate global changes in gene expression resulting from each rearrangement over the course of in vitro neuronal differentiation. First, I will establish, differentiate, and characterize cellular models for ten RGDs to describe and compare the transcriptional consequences of deletion and duplication at ten genomic intervals (Aim 1). Second, I will develop a method for parallel genome engineering and apply it to obtain cell lines with altered dosage of smaller segments and single genes within four RGD regions (Aim 2). Third, I will differentiate and characterize cell lines with these smaller rearrangements to identify genetic drivers underlying dysregulation observed in the four corresponding RGDs (Aim 3). This research will provide a detailed assessment and comparison of transcriptional effects of the most common RGD rearrangements. The parallel genome editing strategy will be made freely available to the scientific community, allowing researchers to generate hundreds or thousands of isogenic cell lines differing only by specified mutations of interest. Finally, genetic driver analyses promise to implicate new genes in the etiology of neurodevelopmental disease. Overall, by enhancing our molecular understanding of RGDs, this study will inform efforts to develop effective targeted treatments.