Joint Analysis of Multiple Phenotypes in Association Studies based on Cross-Validation Prediction Error

In genome-wide association studies (GWAS), joint analysis of multiple phenotypes could have increased statistical power over analyzing each phenotype individually to identify genetic variants that are associated with complex diseases. With this motivation, several statistical methods that jointly analyze multiple phenotypes have been developed, such as O’Brien’s method, Trait-based Association Test that uses Extended Simes procedure (TATES), multivariate analysis of variance (MANOVA), and joint model of multiple phenotypes (MultiPhen). However, the performance of these methods under a wide range of scenarios is not consistent: one test may be powerful in some situations, but not in the others. Thus, one challenge in joint analysis of multiple phenotypes is to construct a test that could maintain good performance across different scenarios. In this article, we develop a novel statistical method to test associations between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Extensive simulations are conducted to evaluate the type I error rates and to compare the power performance of MultP-PE with various existing methods. The simulation studies show that MultP-PE controls type I error rates very well and has consistently higher power than the tests we compared in all simulation scenarios. We conclude with the recommendation for the use of MultP-PE for its good performance in association studies with multiple phenotypes.