To tackle this problem, we developed an approach that combines PRS based on European training data with PRS based on training data from the target population. The method takes advantage of both the accuracy that can be achieved with large training samples (Chatterjee et al., 2013; Dudbridge, 2013) and the accuracy that can be achieved with training data containing the same LD patterns as the target population. In application to predict type 2 diabetes (T2D) in Latino target samples in the SIGMA T2D data set (SIGMA Type 2 Diabetes Consortium et al., 2014), we attained a >70% relative improvement in prediction accuracy (from R2=0.027 to R2=0.047) compared to methods that use only one source of training data. We attained similar relative improvements in simulations. We also obtained a >70% relative improvement in an analysis to predict T2D in a South Asian UK Biobank cohort, and a 30% relative improvement in an analysis to predict height in an African UK Biobank cohort.
