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

Proceedings

Phenotypic, genetic, and genome-wide structure in the metabolic syndrome

Lisa J Martin¹ , Kari E North² , Tom Dyer³ , John Blangero³ , Anthony G Comuzzie³ and Jeff Williams³

¹Center for Epidemiology and Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229 USA

²Department of Epidemiology, University of North Carolina, Chapel Hill North, Carolina, 27514 USA

³Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, 78245 USA

author email corresponding author email

BMC Genetics 2003, 4(Suppl 1):S95doi:10.1186/1471-2156-4-S1-S95

Published:	31 December 2003

Abstract

Background

Insulin resistance, obesity, dyslipidemia, and high blood pressure characterize the metabolic syndrome. In an effort to explore the utility of different multivariate methods of data reduction to better understand the genetic influences on the aggregation of metabolic syndrome phenotypes, we calculated phenotypic, genetic, and genome-wide LOD score correlation matrices using five traits (total cholesterol, high density lipoprotein cholesterol, triglycerides, systolic blood pressure, and body mass index) from the Framingham Heart Study data set prepared for the Genetic Analysis Workshop 13, clinic visits 10 and 1 for the original and offspring cohorts, respectively. We next applied factor analysis to summarize the relationship between these phenotypes.

Results

Factors generated from the genetic correlation matrix explained the most variation. Factors extracted using the other matrices followed a different pattern and suggest distinct effects.

Conclusions

Given these results, different methods of multivariate data reduction may provide unique clues on the clustering of this complex syndrome.