Search

Background: Cardiometabolic (CM) risk factors are heritable and cluster in individuals. We hypothesized that CM risk factors are associated with multiple shared and unique mRNA and microRNA (miRNA) signatures. We examined associations of mRNA and miRNA levels with 6 CM traits: body mass index, HDL-cholesterol and triglycerides, fasting glucose, and systolic and diastolic blood pressures through cross-sectional analysis of 2812 Framingham Heart Study who had whole blood collection for RNA isolation for mRNA and miRNA expression studies and who consented to genetic research. We excluded participants taking medication for hypertension, dyslipidemia, or diabetes. We measured mRNA (n = 17,318; using the Affymetrix GeneChip Human Exon 1.0 ST Array) and miRNA (n = 315; using qRT-PCR) expression in whole blood. We used linear regression for mRNA analyses and a combination of linear and logistic regression for miRNA analyses. We conducted miRNA-mRNA coexpression and gene ontology enrichment analyses to explore relations between pleiotropic miRNAs, mRNA expression, and CM trait clustering. Results: We identified hundreds of significant associations between mRNAs, miRNAs, and individual CM traits. Four mRNAs (FAM13A, CSF2RB, HIST1H2AC, WNK1) were associated with all 6 CM traits (FDR < 0.001) and four miRNAs (miR-197-3p, miR-328, miR-505-5p, miR-145-5p) were associated with four CM traits (FDR < 0.05). Twelve mRNAs, including WNK1, that were coexpressed with the four most pleiotropic miRNAs, were also miRNA targets. mRNAs coexpressed with pleiotropic miRNAs were enriched for RNA metabolism (miR-505-5p), ubiquitin-dependent protein catabolism (miR-197-3p, miR-328) and chromatin assembly (miR-328). Conclusions: We identified mRNA and miRNA signatures of individual CM traits and their clustering. Implicated transcripts may play causal roles in CM risk or be downstream consequences of CM risk factors on the transcriptome. Studies are needed to establish whether or not pleiotropic circulating transcripts illuminate causal pathways for CM risk. [ABSTRACT FROM AUTHOR]

Background: Prior studies reported conflicting findings on the association between metabolic syndrome and inflammatory biomarkers. We tested the cross-sectional associations between metabolic syndrome and nine inflammatory markers. Methods: We measured C-reactive protein, CD40 ligand, interleukin-6, intercellular adhesion molecule-1, monocyte chemoattractant protein-1, osteoprotegerin, P-selectin, tumor necrosis factor-alpha, and tumor necrosis factor receptor-2 in 2570 Framingham Offspring Study participants free of diabetes and cardiovascular disease at examination 7. Metabolic syndrome was defined by National Cholesterol Education Program criteria. We performed multivariable linear regressions for each biomarker with metabolic syndrome as the exposure adjusting for age, sex, smoking, aspirin use, and hormone replacement. We subsequently added to the models components of the metabolic syndrome as continuous traits plus lipid lowering and hypertension treatments. We considered P<0.05 as statistically significant. Results: Metabolic syndrome was present in 984 (38%) participants and was statistically significantly associated with each biomarker (all P<0.02) except osteoprotegerin. After adjusting for its component variables, the metabolic syndrome was associated only with P-selectin (1.06 fold higher in metabolic syndrome, 95% CI 1.02, 1.10, p = 0.005). Conclusions: Metabolic syndrome was associated with multiple inflammatory biomarkers. However, adjusting for each of its components eliminated the association with most inflammatory markers, except P-selectin. Our results suggest that the relation between metabolic syndrome and inflammation is largely accounted for by its components. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data. We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. 10-6 for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulations are performed to evaluate type I error and power of the proposed method compared to LME adjusting for 10 principal components (PC) of the genotype data. We also apply the three-stage approach to the GWA analyses of uric acid in Framingham Heart Study's SNP Health Association Resource (SHARe) project. Results: Our simulations show that whether or not population admixture is present, the three-stage approach has no inflated type I error. In terms of power, using LME adjusting PC is only slightly more powerful than the three-stage approach. When applied to the GWA analyses of uric acid in the SHARe project of FHS, the three-stage approach successfully identified and confirmed three SNPs previously reported as genome-wide significant signals. Conclusions: For GWA analyses of quantitative traits with family data, our three-stage approach provides another appealing solution to population admixture, in addition to LME adjusting for genetic PC. [ABSTRACT FROM AUTHOR]

Background: The Framingham Heart Study (FHS) recently obtained initial results from the first genome-wide association scan for renal traits. The study of 70,987 single nucleotide polymorphisms (SNPs) in 1,010 FHS participants provides a list of SNPs showing the strongest associations with renal traits which need to be verified in independent study samples. Methods: Sixteen SNPs were selected for replication based on the most promising associations with chronic kidney disease (CKD), estimated glomerular filtration rate (eGFR), and serum cystatin C in FHS. These SNPs were genotyped in 15,747 participants of the Atherosclerosis in Communities (ARIC) Study and evaluated for association using multivariable adjusted regression analyses. Primary outcomes in ARIC were CKD and eGFR. Secondary prospective analyses were conducted for association with kidney disease progression using multivariable adjusted Cox proportional hazards regression. The definition of the outcomes, all covariates, and the use of an additive genetic model was consistent with the original analyses in FHS. Results: The intronic SNP rs6495446 in the gene MTHFS was significantly associated with CKD among white ARIC participants at visit 4: the odds ratio per each C allele was 1.24 (95% CI 1.09-1.41, p = 0.001). Borderline significant associations of rs6495446 were observed with CKD at study visit 1 (p = 0.024), eGFR at study visits 1 (p = 0.073) and 4 (lower mean eGFR per C allele by 0.6 ml/min/1.73 m², p = 0.043) and kidney disease progression (hazard ratio 1.13 per each C allele, 95% CI 1.00-1.26, p = 0.041). Another SNP, rs3779748 in EYA1, was significantly associated with CKD at ARIC visit 1 (odds ratio per each T allele 1.22, p = 0.01), but only with eGFR and cystatin C in FHS. Conclusion: This genome-wide association study provides unbiased information implicating MTHFS as a candidate gene for kidney disease. Our findings highlight the importance of replication to identify common SNPs associated with renal traits. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) and its most common manifestations - including coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) - are major causes of morbidity and mortality. In many industrialized countries, cardiovascular disease (CVD) claims more lives each year than any other disease. Heart disease and stroke are the first and third leading causes of death in the United States. Prior investigations have reported several single gene variants associated with CHD, stroke, HF, and AF. We report a community-based genome-wide association study of major CVD outcomes. Methods: In 1345 Framingham Heart Study participants from the largest 310 pedigrees (54% women, mean age 33 years at entry), we analyzed associations of 70,987 qualifying SNPs (Affymetrix 100K GeneChip) to four major CVD outcomes: major atherosclerotic CVD (n = 142; myocardial infarction, stroke, CHD death), major CHD (n = 118; myocardial infarction, CHD death), AF (n = 151), and HF (n = 73). Participants free of the condition at entry were included in proportional hazards models. We analyzed model-based deviance residuals using generalized estimating equations to test associations between SNP genotypes and traits in additive genetic models restricted to autosomal SNPs with minor allele frequency ≥0.10, genotype call rate ≥0.80, and Hardy-Weinberg equilibrium p-value ≥ 0.001. Results: Six associations yielded p < 10-5. The lowest p-values for each CVD trait were as follows: major CVD, rs499818, p = 6.6 × 10-6; major CHD, rs2549513, p = 9.7 × 10-6; AF, rs958546, p = 4.8 × 10-6; HF: rs740363, p = 8.8 × 10-6. Of note, we found associations of a 13 Kb region on chromosome 9p21 with major CVD (p 1.7 - 1.9 × 10-5) and major CHD (p 2.5 - 3.5 × 10-4) that confirm associations with CHD in two recently reported genome-wide association studies. Also, rs10501920 in CNTN5 was associated with AF (p = 9.4 × 10-6) and HF (p = 1.2 × 10-4). Complete results for these phenotypes can be found at the dbgap website http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Conclusion: No association attained genome-wide significance, but several intriguing findings emerged. Notably, we replicated associations of chromosome 9p21 with major CVD. Additional studies are needed to validate these results. Finding genetic variants associated with CVD may point to novel disease pathways and identify potential targeted preventive therapies. [ABSTRACT FROM AUTHOR]

Background: About one quarter of adults are hypertensive and high blood pressure carries increased risk for heart disease, stroke, kidney disease and death. Increased arterial stiffness is a key factor in the pathogenesis of systolic hypertension and cardiovascular disease. Substantial heritability of blood-pressure (BP) and arterial-stiffness suggests important genetic contributions. Methods: In Framingham Heart Study families, we analyzed genome-wide SNP (Affymetrix 100K GeneChip) associations with systolic (SBP) and diastolic (DBP) BP at a single examination in 1971-1975 (n = 1260), at a recent examination in 1998-2001 (n = 1233), and long-term averaged SBP and DBP from 1971-2001 (n = 1327, mean age 52 years, 54% women) and with arterial stiffness measured by arterial tonometry (carotid-femoral and carotid-brachial pulse wave velocity, forward and reflected pressure wave amplitude, and mean arterial pressure; 1998-2001, n = 644). In primary analyses we used generalized estimating equations in models for an additive genetic effect to test associations between SNPs and phenotypes of interest using multivariable-adjusted residuals. A total of 70,987 autosomal SNPs with minor allele frequency ≥ 0.10, genotype call rate ≥ 0.80, and Hardy-Weinberg equilibrium p ≥ 0.001 were analyzed. We also tested for association of 69 SNPs in six renin-angiotensin-aldosterone pathway genes with BP and arterial stiffness phenotypes as part of a candidate gene search. Results: In the primary analyses, none of the associations attained genome-wide significance. For the six BP phenotypes, seven SNPs yielded p values < 10-5. The lowest p-values for SBP and DBP respectively were rs10493340 (p = 1.7 × 10-6) and rs1963982 (p = 3.3 × 10-6). For the five tonometry phenotypes, five SNPs had p values < 10-5; lowest p-values were for reflected wave (rs6063312, p = 2.1 × 10-6) and carotid-brachial pulse wave velocity (rs770189, p = 2.5 × 10-6) in MEF2C, a regulator of cardiac morphogenesis. We found only weak association of SNPs in the renin-angiotensinaldosterone pathway with BP or arterial stiffness. Conclusion: These results of genome-wide association testing for blood pressure and arterial stiffness phenotypes in an unselected community-based sample of adults may aid in the identification of the genetic basis of hypertension and arterial disease, help identify high risk individuals, and guide novel therapies for hypertension. Additional studies are needed to replicate any associations identified in these analyses. [ABSTRACT FROM AUTHOR]

Background: Echocardiographic left ventricular (LV) measurements, exercise responses to standardized treadmill test (ETT) and brachial artery (BA) vascular function are heritable traits that are associated with cardiovascular disease risk. We conducted a genome-wide association study (GWAS) in the community-based Framingham Heart Study. Methods: We estimated multivariable-adjusted residuals for quantitative echocardiography, ETT and BA function traits. Echocardiography residuals were averaged across 4 examinations and included LV mass, diastolic and systolic dimensions, wall thickness, fractional shortening, left atrial and aortic root size. ETT measures (single exam) included systolic blood pressure and heart rate responses during exercise stage 2, and at 3 minutes post-exercise. BA measures (single exam) included vessel diameter, flow-mediated dilation (FMD), and baseline and hyperemic flow responses. Generalized estimating equations (GEE), family-based association tests (FBAT) and variance-components linkage were used to relate multivariable-adjusted trait residuals to 70,987 SNPs (Human 100K GeneChip, Affymetrix) restricted to autosomal SNPs with minor allele frequency ≥0.10, genotype call rate ≥0.80, and Hardy-Weinberg equilibrium p ≥ 0.001. Results: We summarize results from 17 traits in up to 1238 related middle-aged to elderly men and women. Results of all association and linkage analyses are web-posted at http://ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. We confirmed modest-to-strong heritabilities (estimates 0.30-0.52) for several Echo, ETT and BA function traits. Overall, p < 10-5 in either GEE or FBAT models were observed for 21 SNPs (nine for echocardiography, eleven for ETT and one for BA function). The top SNPs associated were (GEE results): LV diastolic dimension, rs1379659 (SLIT2, p = 1.17*10-7); LV systolic dimension, rs10504543 (KCNB2, p = 5.18*10-6); LV mass, rs10498091 (p = 5.68*10-6); Left atrial size, rs1935881 (FAM5C, p = 6.56*10-6); exercise heart rate, rs6847149 (NOLA1, p = 2.74*10-6); exercise systolic blood pressure, rs2553268 (WRN, p = 6.3*10-6); BA baseline flow, rs3814219 (OBFC1, 9.48*10-7), and FMD, rs4148686 (CFTR, p = 1.13*10-5). Several SNPs are reasonable biological candidates, with some being related to multiple traits suggesting pleiotropy. The peak LOD score was for LV mass (4.38; chromosome 5); the 1.5 LOD support interval included NRG2. Conclusion: In hypothesis-generating GWAS of echocardiography, ETT and BA vascular function in a moderate-sized community-based sample, we identified several SNPs that are candidates for replication attempts and we provide a webbased GWAS resource for the research community. [ABSTRACT FROM AUTHOR]

Background: Heritable electrocardiographic (ECG) and heart rate variability (HRV) measures, reflecting pacemaking, conduction, repolarization and autonomic function in the heart have been associated with risks for cardiac arrhythmias. Whereas several rare monogenic conditions with extreme phenotypes have been noted, few common genetic factors contributing to interindividual variability in ECG and HRV measures have been identified. We report the results of a community-based genomewide association study of six ECG and HRV intermediate traits. Methods: Genotyping using Affymetrix 100K GeneChip was conducted on 1345 related Framingham Heart Study Original and Offspring cohort participants. We analyzed 1175 Original and Offspring participants with ECG data (mean age 52 years, 52% women) and 548 Offspring participants with HRV data (mean age 48 years, 51% women), in relation to 70,987 SNPs with minor allele frequency ≥ 0.10, call rate ≥ 80%, Hardy-Weinberg p-value ≥ 0.001. We used generalized estimating equations to test association of SNP alleles with multivariable-adjusted residuals for QT, RR, and PR intervals, the ratio of low frequency to high frequency power (LF/HFP), total power (TP) and the standard deviation of normal RR intervals (SDNN). Results: Associations at p < 10-3 were found for 117 (QT), 105 (RR), 111 (PR), 102 (LF/HF), 121 (TP), and 102 (SDNN) SNPs. Several common variants in NOS1AP (4 SNPs with p-values < 10-3; lowest p-value, rs6683968, p = 1 × 10-4) were associated with adjusted QT residuals, consistent with our previously reported finding for NOS1AP in an unrelated sample of FHS Offspring and other cohorts. All results are publicly available at NCBI's dbGaP at http://www.ncbi.nlm.nih.gov/ projects/gap/cgi-bin/study.cgi?id=phs000007. Conclusion: In the community-based Framingham Heart Study none of the ECG and HRV results individually attained genomewide significance. However, the presence of bona fide QT-associated SNPs among the top 117 results for QT duration supports the importance of efforts to validate top results from the reported scans. Finding genetic variants associated with ECG and HRV quantitative traits may identify novel genes and pathways implicated in arrhythmogenesis and allow for improved recognition of individuals at high risk for arrhythmias in the general population. [ABSTRACT FROM AUTHOR]

Background: Systemic biomarkers provide insights into disease pathogenesis, diagnosis, and risk stratification. Many systemic biomarker concentrations are heritable phenotypes. Genome-wide association studies (GWAS) provide mechanisms to investigate the genetic contributions to biomarker variability unconstrained by current knowledge of physiological relations. Methods: We examined the association of Affymetrix 100K GeneChip single nucleotide polymorphisms (SNPs) to 22 systemic biomarker concentrations in 4 biological domains: inflammation/oxidative stress; natriuretic peptides; liver function; and vitamins. Related members of the Framingham Offspring cohort (n = 1012; mean age 59 ± 10 years, 51% women) had both phenotype and genotype data (minimum-maximum per phenotype n = 507-1008). We used Generalized Estimating Equations (GEE), Family Based Association Tests (FBAT) and variance components linkage to relate SNPs to multivariable-adjusted biomarker residuals. Autosomal SNPs (n = 70,987) meeting the following criteria were studied: minor allele frequency ≥ 10%, call rate ≥ 80% and Hardy-Weinberg equilibrium p ≥ 0.001. Results: With GEE, 58 SNPs had p < 10-6: the top SNPs were rs2494250 (p = 1.00*10-14) and rs4128725 (p = 3.68*10-12) for monocyte chemoattractant protein-1 (MCP1), and rs2794520 (p = 2.83*10-8) and rs2808629 (p = 3.19*10-8) for C-reactive protein (CRP) averaged from 3 examinations (over about 20 years). With FBAT, 11 SNPs had p < 10-6: the top SNPs were the same for MCP1 (rs4128725, p = 3.28*10-8, and rs2494250, p = 3.55*10-8), and also included B-type natriuretic peptide (rs437021, p = 1.01*10-6) and Vitamin K percent undercarboxylated osteocalcin (rs2052028, p = 1.07*10-6). The peak LOD (logarithm of the odds) scores were for MCP1 (4.38, chromosome 1) and CRP (3.28, chromosome 1; previously described) concentrations; of note the 1.5 support interval included the MCP1 and CRP SNPs reported above (GEE model). Previous candidate SNP associations with circulating CRP concentrations were replicated at p < 0.05; the SNPs rs2794520 and rs2808629 are in linkage disequilibrium with previously reported SNPs. GEE, FBAT and linkage results are posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/ study.cgi?id=phs000007. Conclusion: The Framingham GWAS represents a resource to describe potentially novel genetic influences on systemic biomarker variability. The newly described associations will need to be replicated in other studies. [ABSTRACT FROM AUTHOR]

Background: The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies. Methods: Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests. Results: The participants were white of European descent and from the FHS Original and Offspring Cohorts (examination 1 Offspring mean age 32 ± 9 years, 54% women). This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequency ≥ 10%, genotype call rate ≥ 80%, Hardy-Weinberg equilibrium p-value ≥ 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http:// www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Conclusion: We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication. [ABSTRACT FROM AUTHOR]

Background: High-throughput genotyping generates vast amounts of data for analysis; results can be difficult to summarize succinctly. A single project may involve genotyping many genes with multiple variants per gene and analyzing each variant in relation to numerous phenotypes, using several genetic models and population subgroups. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association. Results: To facilitate visual display and summary of large numbers of association tests of genetic loci with multiple phenotypes, we developed a Phenotype-Genotype Association (PGA) grid display. A database-backed web server was used to create PGA grids from phenotypic and genotypic data (sample sizes, means and standard errors, P-value for association). HTML pages were generated using Tcl scripts on an AOLserver platform, using an Oracle database, and the ArsDigita Community System web toolkit. The grids are interactive and permit display of summary data for individual cells by a mouse click (i.e. least squares means for a given SNP and phenotype, specified genetic model and study sample). PGA grids can be used to visually summarize results of individual SNP associations, gene-environment associations, or haplotype associations. Conclusion: The PGA grid, which permits interactive exploration of large numbers of association test results, can serve as an easily adapted common and useful display format for large-scale genetic studies. Doing so would reduce the problem of publication bias, and would simplify the task of summarizing large-scale association studies. [ABSTRACT FROM AUTHOR]

Background: Animal studies suggest that local adipocyte-mediated activity of the renin-angiotensin-aldosterone system (RAAS) contributes to circulating levels, and may promote the development of obesity-related hypertension in rodents. Methods: We examined relations of systemic RAAS activity, as assessed by circulating plasma renin activity (PRA), serum aldosterone level, and aldosterone:renin ratio (ARR), with specific regional adiposity measures in a large, community-based sample. Third Generation Framingham Heart Study participants underwent multidetector computed tomography assessment of SAT and VAT volumes during Exam 1 (2002 and 2005). PRA and serum aldosterone were measured after approximately 10 minutes of supine rest; results were log-transformed for analysis. Correlation coefficients between log-transformed RAAS measures and adiposity measurements were calculated, adjusted for age and sex. Partial correlations between log-transformed RAAS measures and adiposity measurements were also calculated, adjusted for standard CVD risk factors. Results: Overall, 992 women and 897 men were analyzed (mean age 40 years; 7% hypertension; 3% diabetes). No associations were observed with SAT (renin r = 0.04, p = 0.1; aldosterone r = -0.01, p = 0.6) or VAT (renin r = 0.03, p = 0.2; aldosterone r = -0.03, p = 0.2). Similar results were observed for ARR, in sex-stratified analyses, and for BMI and waist circumference. Non-significant partial correlations were also observed in models adjusted for standard cardiovascular risk factors. Conclusions: Regional adiposity measures were not associated with circulating measures of RAAS activity in this large population-based study. Further studies are required to determine whether adipocyte-derived RAAS components contribute to systemic RAAS activity in humans.

Background: Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data. We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. \(10^{-6}\) for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulations are performed to evaluate type I error and power of the proposed method compared to LME adjusting for 10 principal components (PC) of the genotype data. We also apply the three-stage approach to the GWA analyses of uric acid in Framingham Heart Study's SNP Health Association Resource (SHARe) project. Results: Our simulations show that whether or not population admixture is present, the three-stage approach has no inflated type I error. In terms of power, using LME adjusting PC is only slightly more powerful than the three-stage approach. When applied to the GWA analyses of uric acid in the SHARe project of FHS, the three-stage approach successfully identified and confirmed three SNPs previously reported as genome-wide significant signals. Conclusions: For GWA analyses of quantitative traits with family data, our three-stage approach provides another appealing solution to population admixture, in addition to LME adjusting for genetic PC.

Background: High-throughput genotyping generates vast amounts of data for analysis; results can be difficult to summarize succinctly. A single project may involve genotyping many genes with multiple variants per gene and analyzing each variant in relation to numerous phenotypes, using several genetic models and population subgroups. Hundreds of statistical tests may be performed for a single SNP, thereby complicating interpretation of results and inhibiting identification of patterns of association. Results: To facilitate visual display and summary of large numbers of association tests of genetic loci with multiple phenotypes, we developed a Phenotype-Genotype Association (PGA) grid display. A database-backed web server was used to create PGA grids from phenotypic and genotypic data (sample sizes, means and standard errors, P-value for association). HTML pages were generated using Tcl scripts on an AOLserver platform, using an Oracle database, and the ArsDigita Community System web toolkit. The grids are interactive and permit display of summary data for individual cells by a mouse click (i.e. least squares means for a given SNP and phenotype, specified genetic model and study sample). PGA grids can be used to visually summarize results of individual SNP associations, gene-environment associations, or haplotype associations. Conclusion: The PGA grid, which permits interactive exploration of large numbers of association test results, can serve as an easily adapted common and useful display format for large-scale genetic studies. Doing so would reduce the problem of publication bias, and would simplify the task of summarizing large-scale association studies.

Background: Animal studies suggest that local adipocyte-mediated activity of the renin-angiotensin-aldosterone system (RAAS) contributes to circulating levels, and may promote the development of obesity-related hypertension in rodents., Methods: We examined relations of systemic RAAS activity, as assessed by circulating plasma renin activity (PRA), serum aldosterone level, and aldosterone:renin ratio (ARR), with specific regional adiposity measures in a large, community-based sample. Third Generation Framingham Heart Study participants underwent multidetector computed tomography assessment of SAT and VAT volumes during Exam 1 (2002 and 2005). PRA and serum aldosterone were measured after approximately 10 minutes of supine rest; results were log-transformed for analysis. Correlation coefficients between log-transformed RAAS measures and adiposity measurements were calculated, adjusted for age and sex. Partial correlations between log-transformed RAAS measures and adiposity measurements were also calculated, adjusted for standard CVD risk factors., Results: Overall, 992 women and 897 men were analyzed (mean age 40 years; 7% hypertension; 3% diabetes). No associations were observed with SAT (renin r = 0.04, p = 0.1; aldosterone r = -0.01, p = 0.6) or VAT (renin r = 0.03, p = 0.2; aldosterone r = -0.03, p = 0.2). Similar results were observed for ARR, in sex-stratified analyses, and for BMI and waist circumference. Non-significant partial correlations were also observed in models adjusted for standard cardiovascular risk factors., Conclusions: Regional adiposity measures were not associated with circulating measures of RAAS activity in this large population-based study. Further studies are required to determine whether adipocyte-derived RAAS components contribute to systemic RAAS activity in humans.