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Investigating Genetic Risk Factors Associated with Autism Spectrum Disorder and Language Impairment

Lodi, M. K. (1), Alibutud, R. (1), Hansali, S. (1), Cao, X. (1), Zhou, A (1)., Flax, J. F. (1), Gwin, C. (1), Wilson, S. (1), Robinson, A. (2), Buyske, S. (3), Brzustowicz, L. M. (1), Xing, J. (1), Bartlett, C. W. (2,4) (1) Department of Genetics and the Human Genetics Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ; (2) Battelle Center for Mathematical Medicine, Abigail Research Institute at Nationwide Children's Hospital, Columbus, OH; (3) Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ (4) Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, OH


Poster # 57


Autism spectrum disorder (ASD) is a heritable neurodevelopmental condition that displays heterogeneity in both presentation and etiology and it often presents with concomitant communication difficulties. The hypothesis behind the New Jersey Language and Autism Genetic Study is that genetic heterogeneity for component phenotypes of ASD, such as language impairment, is reduced relative to ASD as a whole. We previously published an initial phase of this study with family recruitment that used very restricted inclusion/exclusion criteria for both autism and language deficits in other family members. Linkage to the language phenotypes of interest, language impairment and reading impairment, was established at two novel chromosomal loci, 15q23-26 and 16p12, respectively. This data indicates shared etiology of ASD and specific language impairment at these two novel loci. In this study, we investigated the association between genomic variants in the two linkage regions and the language and reading impairment phenotypes using whole genome sequencing. We examined different types of genomic variants, including single nucleotide variants (SNVs), structural variants (SVs), and copy number variations (CNVs). To conduct the SNV analysis, we used pVAAST, a bioinformatics tool that incorporates sample pedigree information to perform candidate gene prioritization. We used other bioinformatics tools to analyze different classes of SVs and CNVs, including AnnotSV and GEMINI. Each of these tools incorporated gene prioritization to determine a final set of candidate genes that are likely contributing to an increased risk in developing particular language phenotypes. Additionally, we conducted gene ontology enrichment analysis, pathway enrichment analysis, and protein-protein interaction network analysis to further investigate the functional implications of these candidate genes.

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