Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ 07065, USA.
Data and Genome Science, Merck & Co., Inc., Cambridge, MA 02141, USA.
Brief Bioinform. 2023 Jul 20;24(4). doi: 10.1093/bib/bbad181.
Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.
多基因风险评分（PRS）最近被开发出来用于预测复杂性状和药物反应。目前尚不清楚多性状PRS（mtPRS）方法是否通过整合来自多个遗传相关性状的信息，可以提高PRS 分析的预测准确性和效能，与单性状PRS（stPRS）方法相比。在本文中，我们首先回顾了常用的 mtPRS 方法，并发现它们并没有直接对性状之间的潜在遗传相关性进行建模，而这在文献中被证明对多性状关联分析很有用。为了克服这一限制，我们提出了一种 mtPRS-PCA 方法，该方法通过对遗传相关矩阵进行主成分分析（PCA）来对多个性状的 PRS 进行加权合并。为了适应涵盖不同效应方向、信号稀疏性和跨性状相关结构的各种遗传结构，我们进一步提出了一种综合 mtPRS 方法（mtPRS-O），通过 mtPRS-PCA、mtPRS-ML（基于机器学习的 mtPRS）和 stPRS 之间的 Cauchy 组合检验来合并 P 值。我们的广泛模拟研究表明，在性状相关性相似、信号密集、效应方向相似的情况下，mtPRS-PCA 在疾病和药物基因组学（PGx）全基因组关联研究（GWAS）中均优于其他 mtPRS 方法，mtPRS-O 由于其在各种遗传结构下的稳健性，始终优于大多数其他方法。我们进一步将 mtPRS-PCA、mtPRS-O 和其他方法应用于心血管领域随机临床试验的 PGx GWAS 数据，并证明了 mtPRS-PCA 在预测准确性和患者分层方面的性能提高，以及 mtPRS-O 在 PRS 关联检验中的稳健性。
