UNDERSTANDING THE FUNCTIONAL IMPACTS OF GENETIC VARIANTS IN MENTAL DISORDERS

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 DESCRIPTION (provided by applicant): In the US, 46% are afflicted with a mental illness at some point of life. The management and treatment of mental disorders pose significant economical and social burden to the society. Many rare and common genetic variants, including SNPs and CNVs, are reported to be associated with mental disorders, yet more remain to be discovered. However, despite the large amount of high-throughput genomics data, there is a lack of integrative methods to systematically prioritize variants that confer susceptibility to mental disorders; additionally, despite the presence of many candidate variants for mental disorders, their disease-contributory mechanism remains elusive, so appropriate functional follow-up experiments cannot be designed. Altogether, these problems resulted in a large gap between the copious amount of data about genetic variation and our understanding of their functional impacts on mental diseases, which ultimately delays the development of targeted therapeutic approaches. To address these problems, we propose to develop a suite of novel bioinformatics approaches: (1) NeuroComplex, an information integration approach that leverages phenotype information and multiple sources of prior biological knowledge to rank genes by their likelihood of contributing to specific phenotypic presentations. (2) integrated MEntal-disorder GEnome Score (iMEGES), which leverages a two-layer strategy to predict the impacts of variants in personal genomes on mental disorders. The first layer uses machine learning algorithm to build variant deleteriousness scores for coding, non-coding and structural variants; the second layer integrates variant scores and NeuroComplex to assign pathogenicity scores. (3) a hierarchical model called CombOmics that incorporates cross-omics information to characterizes how variants confers disease risk, in order to help formulate hypothesis of pathogenicity and facilitate the design of functional follow-up experiments. (4) we will develop user-friendly software tools and web applications that implement all the aforementioned bioinformatics approaches, and continuously release and maintain these tools to ensure the maximum benefits to the scientific community.