Correlations of anthropometric measurements with cardiometabolic risk factors.

<p>Data are correlation coefficients with <i>P</i> values in parentheses.</p

Clinical characteristics of the study cohort.

<p>Data are medians (interquartile range).</p><p>BAI, body adiposity index; BMI, body mass index; Carotid IMT, carotid intima-media thickness; CRP, C-reactive protein; DBP, diastolic blood pressure; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; MCRI, metabolic clearance rate of insulin; M/I, insulin sensitivity index from the euglycemic-hyperinsulinemic clamp; PAI-1, plasminogen activator inhibitor-1; PBF, percent total body fat; SBP, systolic blood pressure; TG, triglycerides.</p

Comparison of correlation coefficients between cardiometabolic risk factors and BMI versus BAI.

<p>Correlation coefficients that are significantly greater are highlighted in bold.</p>a<p><i>P</i> values from Hotelling’s T-test.</p

Relationship of percent body fat (PBF) with body adiposity index (BAI) and body mass index (BMI) in men and women.

<p>A. BAI versus PBF. B. BMI versus PBF. The graphs are generated on untransformed data for the BAI and BMI variables, while the main analyses in our study are based on log transformations of these variables. Therefore, the correlation coefficients (r) here are slightly different than those reported elsewhere in the text.</p

“Global” continental ancestry in Puerto Rican subjects.

<p>A. Principal components analysis of the combined Puerto Rican and HGDP subjects. B. “Global” continental ancestry was estimated using Admixture 1.2 in an analysis supervised by data from HGDP populations as described in Methods (AFR, sub-Saharan African continent, dark blue; EUR, European continent, red; and AMR, Mexico, Central and South America continents, green). HGDP subjects not originating from these three continents are black. Puerto Rican subjects were sorted left-to-right based on ancestry from the African continent.</p

Description of study subjects.

<p>Description of study subjects.</p

NOD2 haplotypes.

<p>Haplotypes were determined using Phase v2.3; SNPs are, in order, rs2066842 (SNP5), rs2066843 (SNP6), rs2066844 (SNP8), rs2066845 (SNP12), and rs5743293 (SNP13). The association of each haplotype was tested by permutation.</p><p>Footnotes:</p><p>1) IBD susceptibility haplotype in Europeans.</p><p>NOD2 haplotypes.</p

Top Associations with Additional SNPs.

<p>Associations with pvalues <5×10⁻⁵ are listed here. Of the ∼115,000 SNPs tested for this table, ∼20,000 SNPs are not in linkage disequilibrium. Since 3 phenotypes were tested, the Bonferroni correction for multiple comparisons is 0.05/(20,000×3) = 8×10⁻⁷.</p><p>Top Associations with Additional SNPs.</p

Association of known IBD SNPs.

<p>“Previously reported SNPs” are those IBD snps added to the design of the ImmunoChip; one per locus is shown here. Since 2 phenotypes were tested for this table (CD and UC) and 100 of the SNPs were not in linkage disequilibrium in Puerto Rico controls, the Bonferroni correction for multiple comparisons is 0.05/(2*100) = 0.00025.</p><p>Association of known IBD SNPs.</p

Variants Identified in a GWAS Meta-Analysis for Blood Lipids Are Associated with the Lipid Response to Fenofibrate

<div><p>A recent large-scale meta-analysis of genome-wide studies has identified 95 loci, 59 of them novel, as statistically significant predictors of blood lipid traits; we tested whether the same loci explain the observed heterogeneity in response to lipid-lowering therapy with fenofibrate. Using data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, n = 861) we fit linear mixed models with the genetic markers as predictors and high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol, and triglyceride concentrations as outcomes. For all four traits, we analyzed both baseline levels and changes in response to treatment with fenofibrate. For the markers that were significantly associated with fenofibrate response, we fit additional models evaluating potential epistatic interactions. All models were adjusted for age, sex, and study center as fixed effects, and pedigree as a random effect. Statistically significant associations were observed between the rs964184 polymorphism near <em>APOA1</em> (P-value≤0.0001) and fenofibrate response for HDL and triglycerides. The association was replicated in the Pharmacogenetics of Hypertriglyceridemia in Hispanics study (HyperTG, n = 267). Suggestive associations with fenofibrate response were observed for markers in or near <em>PDE3A, MOSC1</em>, <em>FLJ36070, CETP</em>, the <em>APOE-APOC1-APOC4-APOC2</em>, and <em>CILP2</em>. Finally, we present strong evidence for epistasis (P-value for interaction =  0.0006 in GOLDN, 0.05 in HyperTG) between rs10401969 near <em>CILP2</em> and rs4420638 in the <em>APOE-APOC1-APOC4-APOC2</em> cluster with total cholesterol response to fenofibrate. In conclusion, we present evidence linking several novel and biologically relevant genetic polymorphisms to lipid lowering drug response, as well as suggesting novel gene-gene interactions in fenofibrate pharmacogenetics.</p> </div