St-Pierre Julien, Zhang Xinyi, Lu Tianyuan, Jiang Lai, Loffree Xavier, Wang Linbo, Bhatnagar Sahir, Greenwood Celia M T
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
Front Genet. 2022 Oct 25;13:900595. doi: 10.3389/fgene.2022.900595. eCollection 2022.
Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.
遗传风险评分（GRS）和多基因风险评分（PRS）分别是几个或许多遗传变异指示变量的加权和。尽管它们越来越多地被提议用于临床，但构建它们的最佳方法仍在激烈争论中。在这篇评论中，我们展示了几个案例研究，这些研究说明了当不同队列之间或个体亚组之间预期存在疾病风险异质性时，构建或试图提高评分性能所面临的实际挑战。具体而言，我们对比了与选择单核苷酸多态性（SNP）纳入这些评分的几种方法相关的性能。通过将GRS和PRS视为存在测量误差的预测因子，可以深入了解它们的优缺点，并且SNP选择方法在定义此类误差方面起着重要作用。
