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

ABSTRACT: 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 ma

SUBMITTER: Yang X 

PROVIDER: S-EPMC6355816 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature