Risk genetic variants and cis regulation of gene expression in Bipolar Disorder



 DESCRIPTION (provided by applicant): Our understanding of the genetics of bipolar disorder (BD) is advancing at a rapid pace. An increasing number of risk-associated polymorphisms and variants are being found, many of which reside in intergenic, intronic and other non-coding sequences. A major challenge lies in designing testable hypotheses to elucidate the potential function of disease-associated non-coding DNA. Many of these sequences are thought to exert regulatory functions, including long-range enhancer elements physically interacting with transcription start sites separated along the linear genome, sometimes by many kilobases of DNA. This proposal aims to generate a high- resolution quantitative trait loci (QTL) map of open chromatin in discrete cellular populations (neurons and glia) derived from two human cortical brain regions relevant to the pathophysiology of BD. We will then leverage high resolution expression quantitative trait loci (eQTLs), mapped in the same samples and brain regions, to identify BD associated noncoding regions that are simultaneously associated with differential exposure of regulatory regions (open chromatin) and gene expression of nearby genes (eQTLs). Long-range enhancer- promoter interactions of genes potentially regulated by open chromatin sequences will be mapped in human postmortem brain tissue using chromosome conformation capture, an innovative approach in neuroepigenetics. Multiscale network models causally linked to BD will be developed based on existing BD- related large-scale molecular data and the high-impact, high-resolution, complementary datasets generated through the proposed studies. Using iPS-cell-derived cultures of human neuronal cell systems, we will employ high-throughput molecular and cellular screening assays to not only validate the actions of individual genes on molecular and cellular BD-associated processes, but also to validate the molecular networks identified in our studies. The multidimensional approach presented here provides a roadmap to place BD genetic risk variants in molecular contexts to help identify the underlying regulatory and expression mechanisms through which they act. As a service to the BD research community, we will provide dramatically improved general access to large- scale, multidimensional datasets, together with systems level analyses of these datasets.