Modeling the interaction of physiological and environmental stressors on common variants to psychiatric traits

PROJECT SUMMARY Psychiatric and neurodegenerative disorders are highly heritable and debilitating brain diseases that together affect nearly fifty million Americans, caused by the complex interaction of genetic and environmental risk factors. Although genomic studies indicate that much of disease risk reflects the aggregate impact of hundreds of genetic variants, to date, a substantial proportion of both the heritable and environmental components remain unexplained. A major challenge in the field has been illuminating the pathways connecting genetic variants (the vast majority of which fall in non-coding sequences) to target genes and causal cellular phenotypes, particularly in a cell-type-specific and context-dependent manner. We previously uncovered an unexpected combinatorial effect between risk genes that was not predicted from single gene perturbations, one that concentrated on synaptic function and linked the rare and common variant genes implicated in psychiatric disease risk. Based on our preliminary analyses and the work of others, we hypothesize that impact of genetic variants and stress converge and interact to impact critical neuronal and glia functions. Here our objective is to evaluate psychiatric and neurodegenerative risk variants, investigating convergent relationships between risk variants and stress across the major cell types of the brain. To do this, we will functionally dissect the impact of genetic variants significantly associated with brain disease, exploring their regulatory impact across cell types (neurons, astrocytes, glia) and contexts (physiological and environmental stressors) (Aim 1). To extend these insights, we will explore additive effects between risk variants and stressors at the level of network expression and cellular function (Aim 2). Finally, to test the extent that these insights might result in clinically actionably information, we will the clinical consequences of gene-environment interactions across two large healthcare and population- based biobanks (Aim 3). The translational impact of our work includes potential improvements to additive polygenic risk scores, prioritization of convergent genes for mechanistic follow-up, and identification of pathways that might serve as potential therapeutic targets. Our overarching goal is to advance the field towards an era of precision medicine, whereby not just each patient’s genetic variants, but also the expected interactions between them, can be used to predict disease trajectory and potential therapeutic interventions.