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.
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.