This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors .

Proceedings

Exploring pleiotropy using principal components

Jeannette T Bensen^{* 1} , Leslie A Lange^{* 2} , Carl D Langefeld² , Bao-Li Chang¹ , Eugene R Bleecker¹ , Deborah A Meyers¹ and Jianfeng Xu^1,2

¹Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA

²Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA

author email corresponding author email* Contributed equally

BMC Genetics 2003, 4(Suppl 1):S53doi:10.1186/1471-2156-4-S1-S53

Published:	31 December 2003

Abstract

A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself.