Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America.
PLoS Genet. 2024 Apr 26;20(4):e1011249. doi: 10.1371/journal.pgen.1011249. eCollection 2024 Apr.
Polygenic scores (PGS) are measures of genetic risk, derived from the results of genome wide association studies (GWAS). Previous work has proposed the coefficient of determination (R2) as an appropriate measure by which to compare PGS performance in a validation dataset. Here we propose correlation-based methods for evaluating PGS performance by adapting previous work which produced a statistical framework and robust test statistics for the comparison of multiple correlation measures in multiple populations. This flexible framework can be extended to a wider variety of hypothesis tests than currently available methods. We assess our proposed method in simulation and demonstrate its utility with two examples, assessing previously developed PGS for low-density lipoprotein cholesterol and height in multiple populations in the All of Us cohort. Finally, we provide an R package 'coranova' with both parametric and nonparametric implementations of the described methods.
多基因评分（PGS）是遗传风险的度量，源自全基因组关联研究（GWAS）的结果。先前的工作提出了决定系数（R2）作为一种合适的度量标准，用于在验证数据集中比较 PGS 的性能。在这里，我们提出了基于相关性的方法来评估 PGS 的性能，方法是改编先前的工作，该工作为比较多个群体中的多个相关度量提供了统计框架和稳健的检验统计量。这个灵活的框架可以扩展到比目前可用的方法更广泛的各种假设检验。我们在模拟中评估了我们提出的方法，并通过两个示例证明了其效用，评估了先前在 All of Us 队列中多个群体中开发的用于低密度脂蛋白胆固醇和身高的 PGS。最后，我们提供了一个 R 包“coranova”，其中包含所描述方法的参数和非参数实现。
