Incorporating Polygenic Risk Scores in the ACE Twin Model to Estimate A-C Covariance

The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, a method to estimate these parameters in the twin model would be useful. Here we explore the possibility of relaxing this assumption by adding polygenic scores to the (univariate) twin model. We demonstrate that this extension renders the additive genetic (A)-common environmental (C) covariance (sigma(AC)) identified. We study the statistical power to reject sigma(AC) = 0 in the ACE model and present the results of simulations.