Project Summary Neuropsychiatric conditions such as Autism Spectrum Disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BP) are among the most common long-term diseases in US adults. Genetics play an important role in these conditions, and structural variants (SVs) – chromosomal rearrangements impacting at least 50 base pairs of the genome – contribute particularly to the genetic risk of these diseases. Yet only a small minority of SVs present in the human population has been accounted for in current studies. The goal of this project is to more fully explore the role of SVs in neuropsychiatric conditions. This work leverages the recent creation of two key data sources: 1) extensive catalogues of human structural variation by the 1000 Genomes Project, Genome Aggregation Database, and other projects and 2) the collection of genotyping data in large neuropsychiatric case-control studies by the Psychiatric Genomics Consortium among others. The statistical genetic principles of haplotype phasing and imputation provide the framework to integrate these two resources. I will develop phase-based and imputation-based methods to detect post-zygotic mosaic copy number variations (CNVs) and population polymorphic SVs in genotyping intensity data. By applying these methods in neuropsychiatric case-control cohorts, I will: (i) identify post- zygotic mosaic copy number variants (CNVs) in individuals with ASD; (ii) identify polymorphic SVs in individuals with ASD, SCZ, and BP; and (iii) determine mosaic CNVs and polymorphic SVs that increase risk for neuropsychiatric disorders. Successful completion of these aims will result in a more complete understanding of the role of SVs in neuropsychiatric conditions; indeed, the SVs I will investigate in this project account for much of the genetic diversity in the human population. Furthermore, the computational tools I will develop will be applicable to study other human diseases. These tools will be made publicly available to the research community. Characterizing the role of SVs in neuropsychiatric and other medical conditions will contribute to our understanding of the genetic architectures of these diseases. Long-term, this may help detect at-risk populations, contribute to diagnosis, and ultimately aid in treatment.