The use of PRS-CSx, as well as the “mult” methods examined in this work, requires a validation dataset to tune hyper-parameters and learn the optimal linear combination of population-specific PRS, and an independent testing dataset where the final PRS can be generated and evaluated. As non-European genomic resources remain limited, independent validation and testing datasets are often difficult to identify, and a single target cohort may be too small to be split into validation and testing sets. To facilitate the use of PRS-CSx, we have released posterior SNP effects and linear combination weights for all the traits and target populations examined in this study. In addition, in certain applications, it may be preferable to calculate PRS for all samples within the target cohort rather than stratifying them into different ancestry groups. For example, returning genomic predictions to patients with recently admixed ancestries in clinical settings would be difficult as ancestries are not distinct entities, and genetic ancestry assignments may be inconsistent with self-reported race/ethnicity, illuminating the complexity of communicating population-stratified PRS results to patients. In these scenarios, PRS-CSx provides an
