Cross-Ancestry Polygenic Prediction: Comparing Methods and Assessing Transferability Across Traits

Abstract

Accurate prediction of disease risk and other complex traits across different populations is essential for clinical and research purposes. However, genetic differences among ancestries, such as allelic frequencies and genetic architecture, can affect the performance of polygenic risk score (PGS) methods in cross-ancestry prediction. To address this issue, we conducted a formal test of seven polygenic prediction methods applicable across ancestries for five traits (BMI, standing height, LDL-, HDL- and total-cholesterol) from the UK Biobank dataset. We demonstrate that, GBLUP and PRS-CSx outperformed other methods for highly polygenic traits like height and BMI. In contrast, PRSice and PolyPred performed best for less polygenic traits like cholesterol, with PRS-CSx being comparable with larger sample sizes. We also observed that utilizing concordant SNPs, which have the same effect direction across diverse ancestries, can improve the accuracy of cross-ancestry PGS models. Furthermore, we found that the transferability of PGS across ancestries varied depending on the trait. Understanding the strengths and limitations of different methods and approaches is important for future methodological development and improvement, enabling better interpretation and application of PGS results in clinical and research settings.