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BMC Proc. 2009 Dec 15;3 Suppl 7:S42.

Multivariate association analysis of the components of metabolic syndrome from the Framingham Heart Study.

Author information

1
Department of Epidemiology and Biostatistics, Division of Genetic and Molecular Epidemiology, Case Western Reserve University, 11400 Euclid Avenue, Suite 260, Cleveland, Ohio 44106, USA.
2
Center for Clinical Investigation, Case Western Reserve University, 11400 Euclid Avenue, Suite 260, Cleveland, Ohio 44106, USA.
#
Contributed equally

Abstract

Metabolic syndrome, by definition, is the manifestation of multiple, correlated metabolic impairments. It is known to have both strong environmental and genetic contributions. However, isolating genetic variants predisposing to such a complex trait has limitations. Using pedigree data, when available, may well lead to increased ability to detect variants associated with such complex traits. The ability to incorporate multiple correlated traits into a joint analysis may also allow increased detection of associated genes. Therefore, to demonstrate the utility of both univariate and multivariate family-based association analysis and to identify possible genetic variants associated with metabolic syndrome, we performed a scan of the Affymetrix 50 k Human Gene Panel data using 1) each of the traits comprising metabolic syndrome: triglycerides, high-density lipoprotein, systolic blood pressure, diastolic blood pressure, blood glucose, and body mass index, and 2) a composite trait including all of the above, jointly. Two single-nucleotide polymorphisms within the cholesterol ester transfer protein (CETP) gene remained significant even after correcting for multiple testing in both the univariate (p < 5 x 10-7) and multivariate (p < 5 x 10-9) association analysis. Three genes met significance for multiple traits after correction for multiple testing in the univariate analysis, while five genes remained significant in the multivariate association. We conclude that while both univariate and multivariate family-based association analysis can identify genes of interest, our multivariate approach is less affected by multiple testing correction and yields more significant results.

PMID:
20018034
PMCID:
PMC2795941

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