Supplementary Material for: Power Comparisons of Methods for Joint Association Analysis of Multiple Phenotypes

Background/Aims: Genome-wide association studies (GWAS) have identified many variants that each affect multiple phenotypes, which suggests that pleiotropic effects on human complex phenotypes may be widespread. Therefore, statistical methods that can jointly analyze multiple phenotypes in GWAS may have advantages over analyzing each phenotype individually. Several statistical methods have been developed to utilize such multivariate phenotypes in genetic association studies; however, the performance of these methods under different scenarios is largely unknown. Our goal was to provide researchers with useful guidelines on selecting statistical methods for the application of real data to multiple phenotypes. Methods:In this study, we evaluated the performance of some of the existing methods for association studies using multiple phenotypes. These methods included the O'Brien method (OB), cross-validation method (CV), optimal weight method (OW), Trait-based Association Test that uses Extended Simes procedure (TATES), principal components of heritability (PCH), canonical correlation analysis (CCA), multivariate analysis of variance (MANOVA), and a joint model of multiple phenotypes (MultiPhen). We used simulation studies to compare the powers of these methods under a variety of scenarios, including different numbers of phenotypes, different values of between-phenotype correlation, different minor allele frequencies, and different mean and variance models. Results and Conclusion: Our simulation results show that there is no single method with consistently good performance among all the scenarios. Each method has its own advantages and disadvantages.

研究背景与目的：全基因组关联分析（Genome-wide association studies, GWAS）已发现诸多可同时影响多种表型的遗传变异，这提示人类复杂表型的多效性效应可能广泛存在。因此，可在全基因组关联分析中联合分析多种表型的统计方法，相较于单独分析单种表型具有显著优势。目前已有多款统计方法被开发用于遗传关联研究中的多表型分析，但学界对这些方法在不同场景下的实际表现仍知之甚少。本研究旨在为研究人员提供实用指南，帮助其在实际研究中针对多表型分析选择合适的统计方法。
研究方法：本研究针对多表型关联分析的多款现有统计方法开展性能评估，涉及的方法包括奥布莱恩法（O'Brien method, OB）、交叉验证法（cross-validation method, CV）、最优权重法（optimal weight method, OW）、基于扩展赛姆斯程序的表型关联检验（Trait-based Association Test that uses Extended Simes procedure, TATES）、遗传力主成分法（principal components of heritability, PCH）、典型相关分析（canonical correlation analysis, CCA）、多变量方差分析（multivariate analysis of variance, MANOVA）以及多表型联合模型（joint model of multiple phenotypes, MultiPhen）。本研究通过模拟实验，在多种场景下对比了这些方法的统计效力，涵盖不同表型数量、表型间相关系数水平、次要等位基因频率以及不同均值与方差模型等情形。
结果与结论：模拟实验结果显示，并无任何一种方法可在所有场景下均保持优异性能，各方法均有其适用优势与局限性。