Whole Genome Sequencing Data Analysis of Autism. My current research aims to develop a unified statistical framework and accompanying computational software to analyze whole genome sequencing data. In particular, I am interested in applying this framework to study the genetic etiology of autism spectrum disorders (ASD). Advances in next generation sequencing technology have provided a powerful, unbiased way to dissect the complex genetic architecture of ASD. Multiple, independent exome sequencing studies have demonstrated an etiologic role of de novo loss-of-function mutations, as well as the discovery of rare inherited risk mutations. While encouraging, our understanding of the genetic landscape of ASD is still far from complete; Evaluation of rare genetic variants has focused on protein coding regions – overlooking more than 97% of the human DNA – as a result of limited power due to small sample sizes. Another crucial challenge is a lack of analytic strategies that can effectively harness the enormous number of non-coding variants. To address these challenges, I have secured access to genetic data on people with autism, specifically whole genome sequencing data of ASD (N=2,500) through collaborations with the faculty at the Broad Institute, UCLA, and Stanford University. Using this data, we will develop and test effective analytic strategies for the study of whole genome sequencing data.