Abtract: Polygenic risk scores are becoming increasingly predictive of complex traits, but subpar performance in non-European populations raises concerns about their potential clinical applications. We develop a powerful and scalable method to calculate PRS using GWAS summary-statistics from multi-ancestry training samples by integrating multiple techniques, including clumping and thresholding, empirical Bayes and super learning. We evaluate the performance of the proposed method and a variety of alternatives using large-scale simulated GWAS on ~19 million common variants and large 23andMe Inc. datasets, including up to 800K individuals from four non-European populations, across seven complex traits. Results show that the proposed method can substantially improve the performance of PRS in non-European populations relative to simple alternatives and has comparable or superior performance relative to more advanced and less flexible methods that require substantially more computational time. Further, our simulation studies provide novel insights to sample size requirements and the effect of SNP density on multi-ancestry risk prediction.