Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies.

ABSTRACT: Many human traits are highly correlated. This correlation can be leveraged to improve the power of genetic association tests to identify markers associated with one or more of the traits. Principal component analysis (PCA) is a useful tool that has been widely used for the multivariate analysis of correlated variables. PCA is usually applied as a dimension reduction method: the few top principal components (PCs) explaining most of total trait variance are tested for association with a predictor of interest, and the remaining components are not analyzed. In this study we review the theoretical basis of PCA and describe the behavior of PCA when testing for association between a SNP and correlated traits. We then use simulation to compare the power of various PCA-based strategies when analyzi

SUBMITTER: Aschard H 

PROVIDER: S-EPMC4067564 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature