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 “auto” version which automatically learns the global shrinkage parameter from the discovery summary statistics, and a “meta” option which integrates population-specific posterior SNP effects using an inverse-variance-weighted meta-analysis within the Gibbs sampler. Combining the “auto” and “meta” algorithms thus generates a trans-ancestry PRS that can be applied to all samples in the target cohort without the need for a validation dataset39. We note that, although simpler to implement, the “meta” option is expected to be less accurate compared with the linear combination approach that optimizes PRS estimation separately in each target population.
