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

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  • Martin, Lisa J
    • Other Affiliation: Center for Epidemiology and Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, 45229 USA
  • North, Kari
    • Affiliation: Gillings School of Global Public Health, Department of Epidemiology
  • Dyer, Tom
    • Other Affiliation: Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, 78245 USA
  • Blangero, John
    • Other Affiliation: Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, 78245 USA
  • Comuzzie, Anthony G
    • Other Affiliation: Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, 78245 USA
  • Williams, Jeff
    • Other Affiliation: Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas, 78245 USA
Abstract
  • 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.
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  • Article
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  • In Copyright
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  • Lisa J Martin et al.; licensee BioMed Central Ltd.
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  • English
Is the article or chapter peer-reviewed?
  • Yes
Bibliographic citation
  • BMC Genetics. 2003 Dec 31;4(Suppl 1):S95
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  • BioMed Central Ltd
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  • Open Access
Date uploaded
  • September 5, 2012

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