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Psychiatric Gene Networks: Solving the Molecular Puzzle of Psychiatric Disorders


Geetha Senthil, Ph.D.
Division of Neuroscience and Basic Behavioral Science


This initiative will support the application of cutting-edge computational, bioinformatics, network, predictive modeling, systems biology, and experimental approaches to identify and validate novel genetic factors and molecular networks underlying susceptibility to serious mental illnesses, through leveraging existing diverse multi-scale datasets.


The recent convergence of high-throughput genomic technologies, novel statistical methods, and catalogues of common and rare human variation and related function is finally allowing researchers to identify specific genes that contribute to risk of highly complex, polygenic psychiatric diseases. It is increasingly clear that psychiatric disorders are influenced by many genes, most of which individually confer only small (e.g., common variants) to moderate (e.g., rare CNVs and de novo SNVs) risk. Despite these discoveries, there is still a large gap in our understanding of how the combination of risk factors or networks can lead to the development of mental illness.

This initiative encourages investigators to focus on investigating how the interactions of diverse genetic factors contribute to the development of neuropsychiatric phenotypes. In particular, this initiative will support studies that apply a combination of cutting-edge in silico approaches and follow-up validation strategies to: 1) aggregate and mine large psychiatric multi-scale genomic datasets (e.g, DNA/RNA Sequence variants, non-coding functional genomic elements, epigenome, trancriptome, proteome, metabolome, and neuroimaging) in combination with other genomic information to identify novel genetic loci and gene networks associated with psychiatric disorders; 2) determine how these diverse genetic signals converge in molecular networks across psychiatric phenotypes; 3) determine how such interactions are regulated across development and brain regions to arrive at molecular networks driving critical neurobiological process involved in the pathophysiology of psychiatric disorders, and 3) determine how alterations in their function(s) produce neurobiological or behavioral outcomes. These analyses, in turn, will lay the foundation for computational modeling and experimental validation of the complex pathways and networks that ultimately influence the risk for mental illness.

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