Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants.

OBJECTIVE: To provide a quantitative map of relationships between metabolic traits, genome-wide association studies (GWAS) variants, metabolic syndrome (MetS), and metabolic diseases through factor analysis and structural equation modeling (SEM). DESIGN AND METHODS: Cross-sectional data were collected on 1,300 individuals from an eastern Adriatic Croatian island, including 14 anthropometric and biochemical traits, and diagnoses of type 2 diabetes, coronary heart disease, gout, kidney disease, and stroke. MetS was defined based on Adult Treatment Panel III criteria. Forty widely replicated GWAS variants were genotyped. Correlated quantitative traits were reduced through factor analysis; relationships between factors, genetic variants, MetS, and metabolic diseases were determined through SEM. RESULTS: MetS was associated with obesity (P < 0.0001), dyslipidemia (P < 0.0001), glycated hemoglobin (HbA1c; P = 0.0013), hypertension (P < 0.0001), and hyperuricemia (P < 0.0001). Of metabolic diseases, MetS was associated with gout (P = 0.024), coronary heart disease was associated with HbA1c (P < 0.0001), and type 2 diabetes was associated with HbA1c (P < 0.0001) and obesity (P = 0.008). Eleven GWAS variants predicted metabolic variables, MetS, and metabolic diseases. Notably, rs7100623 in HHEX/IDE was associated with HbA1c (ß = 0.03; P < 0.0001) and type 2 diabetes (ß = 0.326; P = 0.0002), underscoring substantial impact on glucose control. CONCLUSIONS: Although MetS was associated with obesity, dyslipidemia, glucose control, hypertension, and hyperuricemia, limited ability of MetS to indicate metabolic disease risk is suggested.

Quantitative criteria for improving performance of buccal DNA for high-throughput genetic analysis.

BACKGROUND: DNA from buccal brush samples is being used for high-throughput analyses in a variety of applications, but the impact of sample type on genotyping success and downstream statistical analysis remains unclear. The objective of the current study was to determine laboratory predictors of genotyping failure among buccal DNA samples, and to evaluate the successfully genotyped results with respect to analytic quality control metrics. Sample and genotyping characteristics were compared between buccal and blood samples collected in the population-based Genetic and Environmental Risk Factors for Hemorrhagic Stroke (GERFHS) study (https://gerfhs.phs.wfubmc.edu/public/index.cfm). RESULTS: Seven-hundred eight (708) buccal and 142 blood DNA samples were analyzed for laboratory-based and analysis metrics. Overall genotyping failure rates were not statistically different between buccal (11.3%) and blood (7.0%, p = 0.18) samples; however, both the Contrast Quality Control (cQC) rate and the dynamic model (DM) call rates were lower among buccal DNA samples (p < 0.0001). The ratio of double-stranded to total DNA (ds/total ratio) in the buccal samples was the only laboratory characteristic predicting sample success (p < 0.0001). A threshold of at least 34% ds/total DNA provided specificity of 98.7% with a 90.5% negative predictive value for eliminating probable failures. After genotyping, median sample call rates (99.1% vs. 99.4%, p < 0.0001) and heterozygosity rates (25.6% vs. 25.7%, p = 0.006) were lower for buccal versus blood DNA samples, respectively, but absolute differences were small. Minor allele frequency differences from HapMap were smaller for buccal than blood samples, and both sample types demonstrated tight genotyping clusters, even for rare alleles. CONCLUSIONS: We identified a buccal sample characteristic, a ratio of ds/total DNA <34%, which distinguished buccal DNA samples likely to fail high-throughput genotyping. Applying this threshold, the quality of final genotyping resulting from buccal samples is somewhat lower, but compares favorably to blood. Caution is warranted if cases and controls have different sample types, but buccal samples provide comparable results to blood samples in large-scale genotyping analyses.

Suggestive linkage detected for blood pressure related traits on 2q and 22q in the population on the Samoan islands.

BACKGROUND: High blood pressure or hypertension is a major risk factor involved in the development of cardiovascular diseases. We conducted genome-wide variance component linkage analyses to search for loci influencing five blood pressure related traits including the quantitative traits systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP), the dichotomous trait hypertension (HT) and the bivariate quantitative trait SBP-DBP in families residing in American Samoa and Samoa, as well as in the combined sample from the two polities. We adjusted the traits for a number of environmental covariates such as smoking, alcohol consumption, physical activity and material life style. RESULTS: We found suggestive univariate linkage for SBP on chromosome 2q35-q37 (LOD 2.4) and for PP on chromosome 22q13 (LOD 2.2), two chromosomal regions that recently have been associated with SBP and PP, respectively. CONCLUSION: We have detected additional evidence for a recently reported locus associated with SBP on chromosome 2q and a susceptibility locus for PP on chromosome 22q. However, differences observed between the results from our three partly overlapping genetically homogenous study samples from the Samoan islands suggest that additional studies should be performed in order to verify these results.

Susceptibility loci for adiposity phenotypes on 8p, 9p, and 16q in American Samoa and Samoa.

Obesity is a complex phenotype affected by genetic and environmental influences such as sociocultural factors and individual behaviors. Previously, we performed two separate genome-wide investigations for adiposity-related traits (BMI, percentage body fat (%BF), abdominal circumference (ABDCIR), and serum leptin and serum adiponectin levels) in families from American Samoa and in families from Samoa. The two polities have a common evolutionary history but have lately been influenced by variations in economic development, leading to differences in income and wealth and in dietary and physical activity patterns. We now present a genome-wide linkage scan of the combined samples from the two polities. We adjust for environmental covariates, including polity of residence, education, cigarette smoking, and farm work, and use variance component methods to calculate univariate and bivariate multipoint lod scores. We identified a region on 9p22 with genome-wide significant linkage for the bivariate phenotypes ABDCIR-%BF (1-d.f. lod 3.30) and BMI-%BF (1-d.f. lod 3.31) and two regions with genome-wide suggestive linkage on 8p12 and 16q23 for adiponectin (lod 2.74) and the bivariate phenotype leptin-ABDCIR (1-d.f. lod 3.17), respectively. These three regions have previously been reported to be linked to adiposity-related phenotypes in independent studies. However, the differences in results between this study and our previous polity-specific studies suggest that environmental effects are of different importance in the samples. These results strongly encourage further genetic studies of adiposity-related phenotypes where extended sets of carefully measured environmental factors are taken into account.