Modelling Latent Environmental Modifiers of Polygenic Risk for Late‐Life Depression Using Structural Equation Modelling

Abstract Background Our understanding of the interplay between genetic and environmental 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. Method 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 latent constructs to their observed measurements; these will include disease severity, comorbidities and clinical histories, and behaviours and lifestyle factors such as physical and social activity. Result Our gene‐by‐environment SEM (GESEM) will be initially developed and tested using four well‐characterized clinical cohorts for older adults diagnosed with late‐life depression and treated with antidepressants (CAN‐BIND, IRL‐GREY, STOP‐PD II and IMPACT; n = 1,238). The primary outcome will be antidepressant remission. Multiple PRS will be calculated to capture underlying genetic risk across vulnerable pathways which contribute to comorbidities. This selection will be made based on new, largely unpublished work from our group on the impact of PRS and targeted GxE studies on psychiatric outcomes across the lifespan. Each PRS will be calculated using both clumping and thresholding ( PRSice ‐2) and continuous shrinkage (PRS‐CS‐auto) methods across selected cohorts using well‐powered publicly available GWAS summary statistics. The multilevel GESEM model will include interactions between symptoms and comorbidities (i.e., observed measurements), which are caused by unobserved factors (i.e., latent constructs), and are subject to modification by background PRS. We will compare our GESEM model against existing SEM‐based approaches to GxE, including local SEM (LOSEM). Conclusion An open‐source R package of the analytical code will be created and shared with the research community. This work has the potential to improve upon existing PRS‐based predictive models in a clinical setting.