PROJECT SUMMARY: Supplement to Functional genomic resource and integrative model of dopaminergic circuitry associated with psychiatric disease In response to PA-20-272, “Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional),” we propose to validate a small number of additional target genes identified by parent U01 (U01DA048279: Functional genomic resource and integrative model of dopaminergic circuitry associated with psychiatric disease). The parent award aims to construct transcriptome and epigenome (incl. 3D genome/chromosomal conformation) maps for midbrain dopaminergic neurons and for their surrounding nonneuronal cells, and to assess the relationship to known genetic risk factors for complex mental illness, including psychosis with substance abuse co-morbidity. We will apply integrative methods for functional analysis of genetic variation and networks, including but not limited to Bayesian network reconstruction and prediction algorithms of variant causality to identify key drivers of schizophrenia and bipolar disease pathology, and drug addiction co-morbidity. These methods will simultaneously integrate multiple different dimensions of data: DNA variation, RNA expression, chromatin accessibility, 3D structure of the genome, and known pathway and gene network information in the context of clinical phenotype data. The fundamental source of data for the project comes from the current studies on human midbrain functional omics, the CommonMind and PsychENCODE consortia (whole genome sequencing and cortical functional omics data), the Psychiatric Genomics Consortium, and the Million Veterans Project (genetic variation and disease phenotypes). The Million Veterans Project (MVP) has collected genotyping and phenotypic data from ~700,000 individuals, including a subgroup of 50,000 veterans diagnosed with SCZ and BD and a larger group of individuals diagnosed with other neuropsychiatric traits (recurrent depression, suicide and substance abuse). We will make our newly generated transcriptome and epigenome datasets from adult midbrain, as well as the network and predictive models, available to the research community in accordance with NIMH data sharing policies.