PROJECT SUMMARY Large-scale genetic studies have proven very successful in robustly mapping dozens of risk loci for complex neuropsychiatric disorders such as schizophrenia (SZ), major depressive disorder (MDD) and autism spectrum disorder (ASD). More so than ever before, we have solid leads into the causal biology of these disorders, yet many implicated genes serve no obvious function in classical neurotransmission. As we begin to develop new etiological hypotheses, powerful exploratory technologies will be essential to characterize the networks of interacting factors in which these novel genes operate, to put these findings in biological context. The techniques of functional genomics, which aim to characterize the global effects of a gene or protein of interest, are ideally suited to this purpose. However, functional experiments must be tailored to the predicted function of the target. In this proposal, therefore, we focus on members of single class of genes with exceptional genetic association evidence, strong biological rationale for involvement and, by virtue of their function, lend themselves naturally to network analyses: transcriptional regulators (TRs). Specifically, we aim to characterize and test the CNS genetic networks under control of eight TRs associated with psychiatric disorders, including ZNF804A, SATB2, MEF2C, FOXP1, CHD8, BCL11B, RERE and SOX5. TRs regulate gene expression by binding to specific loci and recruiting the machinery of transcription. Systematically mapping the binding sites for a TR can reveal the network of genes under its control. The most effective technique for this purpose is chromatin immunoprecipitation coupled with next generation sequencing (ChIP-seq). This method has been used extensively by the ENCODE consortium to map binding sites for many TRs. However, TR binding is cell-specific and ENCODE lacks a CNS focus, so comprehensive CNS cell- specific data for many crucial TRs, including those above, is lacking. To address this, we propose ChIP-seq for the above TRs in a panel of CNS cells, including neurons, glia and stem cells. Using the binding sites from ChIP-seq as a starting point, we will characterize the networks regulated by each TR using bioinformatic tools and gene expression data. Then, to leverage this information to advance disease understanding, we will test the TR gene networks for enrichment in neuropsychiatric disorder findings from large-scale GWAS, gene expression and other datasets of cases and controls. Ultimately, we aim to map variants at psychiatric risk loci that could disrupt TR binding, because these may be important functional non-coding variants. This will provide insight into the role of TRs in disease etiology, and should yield testable hypotheses for follow-up studies. We therefore propose the next logical steps in the analysis of high-priority risk genes, by generating new data on their interacting networks and function, both in general terms and in relation to mental disorder etiology.