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Our understanding of the interplay between genetic andenvironmental factors (Gene x Environment Interaction, or GxE) determining mental health disorders has improved through the proliferation of genome-wide interaction association studies (GWIAS)and targeted GxE analyses. Moreover, multivariate modelling approaches, such as structural equation modelling (SEM) and polygenic risk scores (PRS), offer opportunities for the integration of clinical and genome-wide genotype data in building improved biopsychosocial models of mental illness aetiology and their response to treatment.

We propose to construct a SEM framework to uncover the inter-correlation and directed structure of mental health phenotypes by leveraging the joint predictive capacity of PRS for comorbid traits that share underlying biological and environmental risk pathways. The proposed model will be capable of linking …