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.
Highlights
Background various organizations have used different criteria to define metabolic syndrome (MetSyn), it is generally agreed that MetSyn consists of a combination of impaired glucose metabolism, insulin resistance, hypertension, obesity, and dyslipidemia that increases the risk of poor cardiovascular outcomes [1]
We aim to establish the relationship between biomarker data for the components of MetSyn based on the World Health Organization (WHO) definition and single-nucleotide polymorphisms (SNPs) within the 50 k SNP candidate gene panel of the offspring cohort of the Framingham Heart Study (FHS), using full-pedigree information
The most striking of these results is for cholesterol ester transfer protein (CETP) on chromosome 16 (p < 1 × 10-10), which has been associated in multiple studies with high-density lipoprotein (HDL)
Summary
Various organizations have used different criteria to define metabolic syndrome (MetSyn), it is generally agreed that MetSyn consists of a combination of impaired glucose metabolism, insulin resistance, hypertension, obesity, and dyslipidemia that increases the risk of poor cardiovascular outcomes [1]. Linkage studies have shown that analyzing the components of MetSyn as a multivariate outcome can give stronger evidence for regions harboring disease-susceptibility loci than analyses of separate univariate phenotypes [3,4,5,6,7,8,9]. We aim to establish the relationship between biomarker data for the components of MetSyn based on the World Health Organization (WHO) definition and single-nucleotide polymorphisms (SNPs) within the 50 k SNP candidate gene panel of the offspring cohort of the Framingham Heart Study (FHS), using full-pedigree information. We will compare the results of our familybased association analyses of MetSyn as a multivariate phenotype to results that consider each component of MetSyn as a univariate trait while accounting for the familial clustering of data in both analysis methods
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