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

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 × 10-7) and multivariate (p < 5 × 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.

A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians

“…While in principle the potential benefits of joint analysis are well established, and have been highlighted in several recent papers (e.g. [35][36][37][38][39][40][41][42]), in practice the vast majority of association studies of multiple phenotypes continue to rely on univariate analyses. We hope that our comparisons provide further motivation to investigators to consider joint multivariate analyses as a tool to help improve power to detect genetic associations.…”

Analysis of multiple phenotypes

“…The studies presented here spanned the genetic epidemiological program: phenotype definition [Baker et al, 2009; Piccolo et al, 2009], inclusion of growth curves for repeated measures [Hamid et al, 2009; Waaijenborg and Zwinderman, 2009], gene discovery (most studies), exploration of etiological complexity and causation [Hamid et al, 2009; Morris et al, 2009; Waaijenborg and Zwinderman, 2009] and clinical prediction [Piccolo et al, 2009]. The results of Hamid et al [2009] are especially noteworthy for quantifying the relatively large effect size of non-genetic factors in complex traits.…”

“…Baker et al [2009] examined the component phenotypes of the World Health Organization's 1999 definition of MetSyn (TG, HDL-C, SBP, DBP, fasting blood glucose, and BMI) in the Offspring Cohort of the FHS. (The World Health Organization definition was chosen over the more recent NCEP-III definition because fasting insulin levels and waist circumference were not included in the FHS SHARe data.)…”

“…Data were available for three generations of FHS participants; repeated measures from several clinical examinations were available for the Original Cohort and the Offspring Cohort. Three groups [Baker et al, 2009;Hamid et al, 2009;Waaijenborg and Zwinderman, 2009] used phenotype data from the Offspring Cohort only, while Morris et al [2009] and Piccolo et al [2009] used data from all cohorts (Table I). Three groups [Hamid et al 2009;Morris et al, 2009;Waaijenborg and Zwinderman, 2009] made use of the repeated measures as described below.…”

“…While metabolic syndrome (MetSyn), a predictor of CVD risk, has been defined in various ways [Day, 2007;Ló pez-Alvarenga et al, 2008], all definitions acknowledge that obesity, type 2 diabetes, and CVD share common risk factors and are themselves intercorrelated. Baker et al [2009] examined the component phenotypes of the World Health Organization's 1999 definition of MetSyn (TG, HDL-C, SBP, DBP, fasting blood glucose, and BMI) in the Offspring Cohort of the FHS. (The World Health Organization definition was chosen over the more recent NCEP-III definition because fasting insulin levels and waist circumference were not included in the FHS SHARe data.)…”