Principal-component-based multivariate regression for genetic association studies of metabolic syndrome components.

ABSTRACT:

Background

Quantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers.

Results

We propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the princip

SUBMITTER: Mei H 

PROVIDER: S-EPMC2991276 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature