A principal components-based clustering method to identify variants associated with complex traits.

ABSTRACT:

Background

Multivariate methods ranging from joint SNP to principal components analysis (PCA) have been developed for testing multiple markers in a region for association with disease and disease-related traits. However, these methods suffer from low power and/or the inability to identify the subset of markers contributing to evidence for association under various scenarios.

Methods

We introduce orthoblique principal components-based clustering (OPCC) as an alternative approach to identify specific subsets of markers showing association with a quantitative outcome of interest. We demonstrate the utility of OPCC using simulation studies and an example from the literature on type 2 diabetes.

Results

Compared to traditional methods, OPCC has similar or improved power und

SUBMITTER: Black MH 

PROVIDER: S-EPMC3089426 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature