Applications of novel statistical methods to CNVs in autism and schizophrenia

? DESCRIPTION (provided by applicant): We propose to develop and apply state-of-the-art statistical methods to identify clusters of rare disease risk variants within large copy-number variable (CNV) regions previously implicated in autism spectrum disorders (ASD) and schizophrenia (SCZ). Although many large CNV regions have been implicated in risk to psychiatric disorders such as ASD and SCZ, the underlying disease genes in these regions are mostly unknown, because these CNVs are large and contain many genes. Furthermore, these CNVs have not been comprehensively investigated using the large whole-exome sequencing (WES) datasets that have become recently available for ASD and SCZ, with more than 20,000 WES samples combined. We propose to take advantage of these new WES data for ASD and SCZ and propose a systematic investigation of the CNVs implicated in these disorders to identify the underlying disease gene(s) within these CNVs. The problem of identifying rare disease risk variants within these CNVs is of great importance to the field, as rare and large CNVs are the most replicable association so far for these psychiatric disorders. Based on previous work from our group, and taking advantage of some of the largest WES studies for ASD and SCZ, the novel scan statistic approaches we propose to develop promise to help substantially in elucidating the disease genes in these CNV regions. In addition, software implementing these methods will be made publicly available for other researchers interested in pursuing similar work. We believe that the proposed research is very timely and has the potential to be of great public health importance through direct application to autism and schizophrenia, and more broadly to other mental diseases.