Mental disorders are complex, debilitating health conditions, yet the neurobiological causes and pathophysiological mechanisms underlying these disorders are not well understood. The emergence of large- scale genome-wide association studies (GWAS) has enabled identification of significant, reliable genetic associations to mental disorders. However, it has been difficult to translate GWAS loci into specific causal driver variants/genes to extract mechanistic insights for identifying actionable targets for therapeutic interventions. Neurobiological intermediate phenotypes (NBIPs) are invaluable in understanding the brain?s structural and functional correlates of elevated risk of psychopathology, although the underlying processes and molecular mechanisms for observed NBIPs are elusive. Recent advances in genetic-based imputation now allow one to infer genetically-regulated portions of intermediate phenotypes from genome-wide genotype data. Our research team has successfully employed brain-specific transcriptomic imputation approaches across mental disorders to identify novel genes and pathways of risk. In this proposal, we seek to increase the biological resolution of the link between neuroimaging genetics and psychiatric genetics by creating novel polygenic models of multimodal neuroimaging based on brain-specific gene expression that can be applied to psychiatric GWAS.
In Aim 1, we will generate brain transcriptomic predictive models of multimodal neuroimaging and replicate them in independent datasets.
In Aim 2, we will conduct Imaging Transcriptome-wide Association Studies (ITWAS) to identify neuroimaging associations with mental disorders at a brain-specific gene-level and distinguish the causal ones. Our integrative analyses will enhance our understanding of NBIPs and mental disorder risk; thus, they will provide mechanistic insights that may drive identification of novel diagnostic, trans-diagnostic and treatment approaches.
Mental disorders have substantial morbidity and mortality, contributing to devastating emotional, social and financial tolls on sufferers and their families. We will study the genetic risk architecture of mental disorders, with a focus on the expressed portion of the human genome and imaging of the brain. We will use computational models, based on brain gene expression, to predict the genetic component of brain imaging of hundreds of thousands of people to better understand the mechanisms that give rise to these disorders.
