Multiethnic polygenic risk scores improve risk prediction in diverse populations

Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multi-ethnic polygenic risk score approach, MultiPRS, that combines training data from European samples and training data from the target population. We applied MultiPRS to predict type 2 diabetes in a Latino cohort using both publicly available European summary statistics in large sample size and Latino training data in small sample size, and observed a >70% relative improvement in prediction accuracy compared to methods that use only one source of training data, consistent with large relative improvements observed in simulations. Notably, this improvement is contingent on the use of ancestry-adjusted coefficients in MultiPRS. Our work reduces the gap in risk prediction accuracy between European and non-European target populations.