CHILDHOOD OBESITY: REGULATION OF ENERGY BALANCE AND BODY COMPOSITION
Location: Children Nutrition Research Center (Houston, Tx)
Title: Obesity, hypertension and genetic variation in the TIGER Study
| Sailors, Mary - |
| Rodin, Andrei - |
| Jackson, Andrew - |
| Bray, Molly - |
Submitted to: Obesity
Publication Type: Abstract Only
Publication Acceptance Date: September 1, 2008
Publication Date: October 1, 2008
Citation: Sailors, M., Rodin, A., Jackson, A., Bray, M.S. 2008. Obesity, hypertension and genetic variation in the TIGER Study [abstract]. Obesity. 16(S1):S317.
Obesity and hypertension are multifactoral conditions in which the onset and severity of the conditions are influenced by the interplay of genetic and environmental factors. We hypothesize that multiple genes and environmental factors account for a significant amount of variation in BMI and blood pressure (BP) in a healthy, young, multiracial cohort (n=311). Bayesian Networks (BN) were utilized as an exploratory strategy to identify putative SNPs and gene-environment interactions associated with BMI and BP variation. Specifically, we were looking to identify SNPs that were associated with both BMI and BP. BN were created separately by race and included 45 SNPs, BMI, BP, gender, age, physical activity, and smoking status. Continuous variables were categorized to produce the most parsimonious model. SNPs that were found to be putatively associated with BMI and BP (under the Markov Blanket) in the BN were further investigated by more traditional statistical techniques (logistic regression). Obesity was classified as those having a BMI = 30 kg/m2 and high normal or hypertensive status (HYT) was based on the JNC VI definitions. Haplotypes were calculated using PHASE (v2.1) and analyzed in a regression model if indicated by the BN. BN analysis showed 12 SNPs to be of interest for BP, 6 SNPs to be of interest for BMI, and 3 SNPs were found to be associated with both BMI and BP. Logistic regression analysis revealed multiple variants in PGC1 to be associated with HYT in whites (n=115) and one variant in PGC1 to be associated with HYT in AA (n=107). One variant in CYP2E1 and one variant in PGC1 was associated with obesity in whites only. Haplotype analysis revealed two haplotypes in PGC1 to be significantly associated with HYT in whites (CGGACA: OR=3.37,p=0.01; AAAGAA: OR=0.19,p<0.01). Interestingly, these haplotypes had opposite effects on HYT and consisted of opposing alleles. Despite being modestly correlated, when followed up with traditional statistical techniques, SNPs common to BMI and BP in the BN analysis were not significantly associated to either trait. Variants within PGC1 were found to be significantly associated with HYT in whites. The BN approach is a useful tool for dissecting the inter-relationships between multiple SNPs and phenotypes.