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.

中文翻译：

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纳入欧洲 GWAS 研究结果提高了东亚人乳腺癌多基因风险预测的准确性

之前的全基因组关联研究（GWAS）主要集中在欧洲（EUR）人群。然而，与欧元相比，源自欧元的多基因风险评分 (PRS) 在非欧元中的表现较差。在这项研究中，我们的目标是改进东亚人 (EAS) 的 PRS 预测。我们引入了重新调整的荟萃分析框架，结合了 EUR ( N  = 122,175) 和 EAS ( N  = 30,801) GWAS 摘要统计数据。为了改进 EAS 中的 PRS 预测，我们使用缩放因子来增加 EAS 数据的权重，以便得到的效应大小估计与 EAS 更相关。然后，我们根据 EAS 和欧元数据的重新调整后的荟萃分析结果得出 EAS 的 PRS。在独立的 EAS 验证数据集中进行评估，与仅从 EAS GWAS、仅 EUR GWAS 和传统方法导出的 PRS 相比，该方法将预测责任调整后的 Nagelkerke 伪R ²分别提高了 40%、41% 和 5% EAS 和 EUR 数据的固定效应荟萃分析。重新调整荟萃分析方法得出的 PRS 的受试者工作特征曲线下面积 (AUC) 为 0.6059，高于 EAS、EUR 以及 EAS 和 EUR 的传统荟萃分析的 AUC = 0.5782、0.5809、0.6008。我们进一步比较了 EUR 和 EAS 之间由具有不同连锁不平衡（LD）分数和次要等位基因频率（MAF）的单核苷酸多态性构建的 PRS，并观察到 ​​EAS 中较低的 LD 分数或 MAF 对应于较差的 PRS 性能（AUC = 0.5677） ，0.5530，分别）高于较高的 LD 分数或 MAF（AUC = 0.589，0.5993，分别）。我们最终使用重新调整的荟萃分析构建了按 EUR 和 EAS 的 LD 分数差异分层的 PRS，并获得了 0.6096 的 AUC，比所研究的其他策略有所改进。