PRS-CSx is expected to provide larger power gains when the GWAS in the target population has lower statistical power, while well-powered GWAS from other populations are available. This often happens when predicting into a non-EUR population, where ancestry-matched GWAS have limited sample sizes but large-scale EUR GWAS already exist. By integrating EUR and non-EUR GWAS, PRS-CSx can significantly improve the prediction accuracy in non-EUR populations, which alleviates the imminent challenge of polygenic prediction in under-represented populations. In contrast, PRS-CSx may provide limited increase in prediction accuracy when a well-powered GWAS in the target population already exists and GWAS from other populations have smaller sample sizes and lower statistical power. In practice, this happens almost exclusively for predictions in the EUR population. We note that while PRS-CSx increased the prediction in non-European populations for the majority of the traits examined in this study, the amount of improvement in prediction accuracy over alternative methods varied across traits. Future research is needed to dissect the effects of potential factors on the accuracy of cross-ancestry polygenic prediction and to better understand the behavior of different prediction algorithms for individual traits.
