Polygenic risk scores (PRS) use the results of genome-wide association studies (GWAS) to predict quantitative phenotypes or disease risk at an individual level, and provide a potential route to the use of genetic data in personalized medical care. However, a major barrier to the use of PRS is that the majority of GWAS come from cohorts of European ancestry. The predictive power of PRS constructed from these studies is substantially lower in non-European ancestry cohorts, although the reasons for this are unclear. To address this question, we investigate the performance of PRS for height in cohorts with admixed African and European ancestry, allowing us to evaluate ancestry-related differences in PRS predictive accuracy while controlling for environment and cohort differences. We first show that the predictive accuracy of height PRS increases linearly with European ancestry and is partially explained by European ancestry segments of the admixed genomes. We show that recombination rate, differences in allele frequencies, and differences in marginal effect sizes across ancestries all contribute to the decrease in predictive power, but none of these effects explain the decrease on its own. Finally, we demonstrate that prediction for admixed individuals can be improved by using a linear combination of PRS that includes ancestry-specific effect sizes, although this approach is at present limited by the small size of non-European ancestry discovery cohorts.

多基因风险评分（PRS）使用全基因组关联研究（GWAS）的结果来预测个体水平的定量表型或疾病风险，并为在个性化医疗中使用遗传数据提供了一条潜在途径。但是，使用PRS的主要障碍是，大部分GWAS来自欧洲血统。从这些研究中得出的PRS的预测能力在非欧洲血统的人群中要低得多，尽管其原因尚不清楚。为了解决这个问题，我们调查了混合非洲和欧洲血统的队列中PRS身高的表现，使我们能够评估与祖先相关的PRS预测准确性的差异，同时控制环境和队列差异。我们首先显示，高度PRS的预测准确性随欧洲血统的增加而线性增加，并且部分由混合基因组的欧洲血统段解释。我们显示重组率，等位基因频率的差异以及祖先的边缘效应大小的差异均有助于降低预测能力，但这些效应均不能单独解释其降低。最后，我们证明，通过使用PRS的线性组合可以改善混合个体的预测，该组合包括特定于祖先的效应大小，尽管目前该方法受到非欧洲祖先发现队列的小规模的限制。等位基因频率的差异以及各个祖先的边缘效应大小的差异均会导致预测能力的降低，但这些效应均不能单独解释其降低。最后，我们证明，通过使用PRS的线性组合可以改善混合个体的预测，该组合包括特定于祖先的效应大小，尽管目前该方法受到非欧洲祖先发现队列的小规模的限制。等位基因频率的差异以及祖先间边缘效应大小的差异均会导致预测能力的降低，但这些效应均不能单独解释其降低。最后，我们证明，通过使用PRS的线性组合可以改善混合个体的预测，PRS包括特定于祖先的效应大小，尽管目前该方法受到非欧洲祖先发现队列的小规模的限制。