Identification of Biological Pathways Associated with Phenotypically-defined Subgroups of Autism Spectrum Disorders. O. J. Veatch1, B. L. Yaspan2, M. A. Pericak-Vance3, J. L. Haines1 1) Ctr Human Gen, Vanderbilt Med Ctr, Nashville, TN; 2) Genentech, Inc., South San Francisco, CA; 3) Hussman Institute for Human Genomics, Miller School of Medicine, Miami, FL.
Autism Spectrum Disorders (ASD) are complex neurodevelopmental disorders with strong evidence for genetic susceptibility. However, effect sizes for implicated loci are small and these loci do not explain the majority of ASD heritability. Difficulty in identifying genetic variation with strong effects may arise from the wide trait variability being explained by underlying genetic heterogeneity. Minimizing phenotypic heterogeneity and applying pathway-based analysis to GWAS data are ways to address these obstacles. We used the Autism Genetic Resource Exchange dataset for our initial modeling. Unsupervised clustering was used to group cases using behavioral and biomarker information, and genetic contributions to cluster assignment were evaluated. We analyzed genome-wide SNP data with the Family-Based Association Test, performing separate analyses based on cluster assignment. Further we used the PARIS pathway analysis program to identify biological pathways of interest. We validated our results using an independent dataset derived from the Autism Genome Project. The phenotypic analysis generated two main clusters based on overall trait severity. We see increased odds for siblings being assigned to the same phenotype cluster (OR1.4, p<0.00001). We also see that cases in a given cluster are more genetically related when compared to the unclustered dataset (Fst0.170.26). We identified 149 genes associated (p<0.001) with the less severe ASD cluster and 166 genes associated (p<0.001) with the more severe cluster. There is minimal overlap (~7%) when comparing genes associated with different clustering-defined subgroups. Genes associated with the less severe cluster relate to immune function, while genes associated with the more severe cluster relate to cellular growth, survival, and mobility. We replicated clustering results in the AGP dataset and see again that unique biological mechanisms are implicated when comparing genes associated with either the more severe or less severe AGP clusters. Our results suggest that meaningful phenotypic subgroup definitions can help clarify the underlying genetic etiology of Autism Spectrum Disorders.
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