Computational Discovery of Synergistic Mechanisms Responsible for Psychiatric Dis

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DESCRIPTION (provided by applicant): The biological mechanisms responsible for psychiatric disorders are largely unknown. Given the limited success of identifying significant individual risk conferring genetic variants, it is believed that discovery of responsible epistatic interactions among multiple genetic variants reflecting molecular elements in complex pathways will elucidate novel disease mechanisms. We will perform our research aimed at discovering such mechanisms through collaboration between our computational and genetic laboratories. We will develop systems-based computational and visual tools to discover such interactions and apply them to publically available as well as in-house genome-wide association data for two diseases: schizophrenia and bipolar disorder. We will validate the statistical significance of our results and replicate them in silico on independent data. Our computational methodology will be designed to analyze both single nucleotide polymorphisms (SNPs) as well as copy number variations (CNVs) using quantitative measures of the synergy inherent in pairs of genetic variants indicating possible joint involvement in pathways. We will biologically interpret the resulting computational outputs and attempt to genetically validate the identified interactions. If the resulting biological hypotheses involving two genes are deemed promising, we will test those using in vitro neurobiological experiments. If enough evidence is accrued to support the possibility of biological epistasis, we will eventually generate transgenic animal models for either one or both genes in an interacting pair using genetic or pharmacological approaches when feasible, designed to confirm a biological interaction and dissect the underlying mechanistic basis. The relevance of our proposed research to public health is evidenced by its potential to enhance our understanding of etiological mechanisms responsible for schizophrenia and bipolar disorder. In turn, these discoveries will be helpful for the development of highly needed diagnostic and therapeutic methods for these diseases.