Previous genome-wide association studies (GWASs) have been largely focused on European (EUR) populations. However, polygenic risk scores (PRSs) derived from EUR have been shown to perform worse in non-EURs compared with EURs. In this study, we aim to improve PRS prediction in East Asians (EASs). We introduce a rescaled meta-analysis framework to combine both EUR (N = 122,175) and EAS (N = 30,801) GWAS summary statistics. To improve PRS prediction in EASs, we use a scaling factor to up-weight the EAS data, such that the resulting effect size estimates are more relevant to EASs. We then derive PRSs for EAS from the rescaled meta-analysis results of EAS and EUR data. Evaluated in an independent EAS validation data set, this approach increases the prediction liability-adjusted Nagelkerke's pseudo R2 by 40%, 41%, and 5%, respectively, compared with PRSs derived from an EAS GWAS only, EUR GWAS only, and conventional fixed-effects meta-analysis of EAS and EUR data. The PRS derived from the rescaled meta-analysis approach achieved an area under the receiver operating characteristic curve (AUC) of 0.6059, higher than AUC = 0.5782, 0.5809, 0.6008 for EAS, EUR, and conventional meta-analysis of EAS and EUR. We further compare PRSs constructed by single-nucleotide polymorphisms that have different linkage disequilibrium (LD) scores and minor allele frequencies (MAFs) between EUR and EAS, and observe that lower LD scores or MAF in EAS correspond to poorer PRS performance (AUC = 0.5677, 0.5530, respectively) than higher LD scores or MAF (AUC = 0.589, 0.5993, respectively). We finally build a PRS stratified by LD score differences in EUR and EAS using rescaled meta-analysis, and obtain an AUC of 0.6096, with improvement over other strategies investigated.
- breast cancer
- genome-wide association study
- polygenic prediction