Modelling Latent Environmental Modifiers of Polygenic Risk for Late‐Life Depression Using Structural Equation Modelling
Modelling Latent Environmental Modifiers of Polygenic Risk for Late‐Life Depression Using Structural Equation Modelling
Samar S. M. Elsheikh,V. Marshe,4 Authors,Daniel J. Mueller
TLDR
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.
Abstract
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.