An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

ABSTRACT:

Background

In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher

SUBMITTER: Yang JJ 

PROVIDER: S-EPMC4704475 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature