The additive effects of thousands of common genetic variants explain most genetic risk for autism spectrum disorder (ASD) in the population. Common variant risk is relevant to all groups of ASD cases, even the approximately 20 percent of ASD individuals who carry a large-effect de novo event. However, it is mostly unknown which genes, pathways, cell types and cellular processes mediate these effects.

This limited progress reflects two primary challenges: (1) small genome-wide association study (GWAS) sample sizes for ASD and (2) inadequate methods to identify causal variants, genes, pathways and cell types based on GWAS results. Over the next several years, SPARK (Simons Foundation Powering Autism Research) will release publicly available genotype and exome sequencing data from 50,000 individuals with ASD and their family members. As these data become available, our ability to interpret them will become another critical bottleneck. In particular, new statistical approaches are needed to prioritize causal variants, genes, pathways and cell types from GWAS data; to identify convergent molecular mechanisms shared between GWAS findings and the many ASD risk genes identified through exome sequencing; and to understand the molecular basis of ASD heterogeneity.

In this project, Elise Robinson and colleagues plan to develop and deploy the statistical methods needed to interpret genomic data from SPARK and other large ASD datasets. They plan to identify causal variants, genes and pathways from ASD GWAS results and characterize how they differ or converge with those that have been identified using exome sequencing data. Lastly, they plan to contrast the molecular basis of ASD with and without co-occurring intellectual disability.