Investigation of Genetic and Clinical Heterogeneity in Subtypes of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and genetically heterogeneous; core symptoms range from mild to severe, and the presentation and severity of co-morbidities vary greatly. Hundreds of genes and genomic loci have been identified to contribute to ASD susceptibility. Several genetic models have been proposed to explain the contribution of rare and/or common variants to ASD susceptibility. Subdividing ASD-affected individuals into more genetically or phenotypically homogeneous subgroups can help elucidate the relationship between clinical and genetic heterogeneity in ASD. In my thesis, I investigate the genetic and clinical heterogeneity in ASD subtypes using extensive phenotype information and rich genetic data from whole-genome sequencing (WGS). First, I developed a robust WGS analysis workflow to apply to subsequent studies. I contributed to identifying ASD candidate gene using a de novo variant approach by compiling a database of de novo variants from 4,807 ASD families. I conducted a comprehensive genetic and clinical characterization of one of these candidate genes, KMT2A, which revealed a characteristic phenotypic subtype of ASD. Lastly, I examined the genetic contribution of rare and common variants in ASD subtypes categorized based on morphology. Although rare variants have different effect sizes, traditional rare variant burden analyses assume they have similar impacts on ASD risk. To address this limitation, I developed a gene set-based rare variant score (GRS), which is a weighted sum of the number of rare variants in ASD-relevant gene sets and noncoding loci, weighted based on effect sizes. Using this approach, I found a greater burden of ASD-relevant rare variants in ASD probands with more morphological anomalies compared to those with fewer anomalies. In contrast, those with fewer morphological anomalies had a significant over-transmission of common variant risk. These findings reveal distinct contributions of rare and common variants in different morphological ASD subtypes, supporting a combined polygenic model for rare and common variants in ASD. The novel GRS method can be applied to future studies to better measure the impact of rare variants on susceptibility to ASD and other complex disorders. Data and WGS methods presented in this thesis will guide clinical genetic testing, genetic counseling and clinical management.

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