Genetic & Functional Analysis of Variants Associated with Neurocognitive Disorder

University Of Washington, Seattle WA

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Abstract

Though genome-wide association studies have identified a plethora of common variants contributing to neurocognitive disorders, much of the genetic risk remains unexplained. Additionally, specific causal variants and genes are often not characterized, leaving the underlying mechanisms contributing to such disorders largely unknown. Recently, it has become clear that rare variants with large effect sizes, such as de novo copy- number variants (CNVs) and protein-coding mutations, play a role in neurocognitive disorders including nonsyndromic intellectual disability (ID) and autism. In the case of CNVs, implicated variants often encompass tens if not hundreds of genes, with the same lesion associated with a variety of disorders and phenotypes; therefore, the challenge lies in discerning the precise gene(s) within the deleted or duplicated regions that contribute to pathogenicity. Likewise, the major obstacle in identifying disease-causing exonic mutations is discerning high-impact causal variants from a surplus of innocuous variants. Here, I propose the use of genomic approaches to discover and characterize causal genes disrupted by pathogenic CNVs or point mutations that contribute to neurocognitive defects. I will address this goal in three steps: (1) identify a subset of candidate genes contributing to ID and autism using fine-scale mapping within known pathogenic CNVs in affected individuals; (2) identify potentially pathogenic protein-altering mutations in a subset of cases with severe ID and multiple congenital abnormalities; and (3) characterize candidate genes and variants using experimental assays in cell lines and zebrafish to assess their impact on development. The findings of this research will offer insight into the underlying etiology of neurocognitive disorders and pave the way for additional gene discovery and potential treatments. As whole-exome and -genome sequencing screens become standard practice in identifying highly penetrant disease-associated variants, such methods will be vital to ultimately distinguish causal variants from a large list of non-pathogenic coding variants residing in every individual.