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.