Regular exercise and patterns of response across multiple cardiometabolic traits: the HERITAGE family study.

OBJECTIVES: We investigated whether high responsiveness or low responsiveness to exercise training aggregates in the same individuals across seven cardiometabolic traits. METHODS: A total of 564 adults (29.2% black, 53.7% female) from the HERITAGE family study completed a 20-week endurance training programme (at 55%-75% of participants' maximal oxygen uptake (VO2max)) with VO2max, per cent body fat, visceral adipose tissue, fasting levels of insulin, high-density lipoprotein cholesterol, small low-density lipoprotein particles and inflammatory marker GlycA measured before and after training. For each exercise response trait, we created ethnicity-specific, sex-specific and generation-specific quintiles. High responses were defined as those within the 20th percentile representing the favourable end of the response trait distribution, low responses were defined as the 20th percentile from the least favourable end, and the remaining were labelled as average responses. RESULTS: Only one individual had universally high or low responses for all seven cardiometabolic traits. Almost half (49%) of the cohort had at least one high response and one low response across the seven traits. About 24% had at least one high response but no low responses, 24% had one or more low responses but no high responses, and 2.5% had average responses across all traits. CONCLUSIONS: Interindividual variation in exercise responses was evident in all the traits we investigated, and responsiveness did not aggregate consistently in the same individuals. While adherence to an exercise prescription is known to produce health benefits, targeted risk factors may not improve.

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures.

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.

Deriving stratified effects from joint models investigating gene-environment interactions.

BACKGROUND: Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. RESULTS: In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the "joint" model. We performed simulation studies to assess our estimators' accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators' retained accuracy after filtering SNPs by sample size to mitigate potential bias. CONCLUSIONS: These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.

Genome-wide meta-analysis of variant-by-diuretic interactions as modulators of lipid traits in persons of European and African ancestry.

Hypertension (HTN) is a significant risk factor for cardiovascular morbidity and mortality. Metabolic abnormalities, including adverse cholesterol and triglycerides (TG) profiles, are frequent comorbid findings with HTN and contribute to cardiovascular disease. Diuretics, which are used to treat HTN and heart failure, have been associated with worsening of fasting lipid concentrations. Genome-wide meta-analyses with 39,710 European-ancestry (EA) individuals and 9925 African-ancestry (AA) individuals were performed to identify genetic variants that modify the effect of loop or thiazide diuretic use on blood lipid concentrations. Both longitudinal and cross sectional data were used to compute cohort-specific interaction results, which were then combined through meta-analysis in each ancestry. These ancestry-specific results were further combined through trans-ancestry meta-analysis. Analysis of EA data identified two genome-wide significant (p < 5 × 10-8) loci with single nucleotide variant (SNV)-loop diuretic interaction on TG concentrations (including COL11A1). Analysis of AA data identified one genome-wide significant locus adjacent to BMP2 with SNV-loop diuretic interaction on TG concentrations. Trans-ancestry analysis strengthened evidence of association for SNV-loop diuretic interaction at two loci (KIAA1217 and BAALC). There were few significant SNV-thiazide diuretic interaction associations on TG concentrations and for either diuretic on cholesterol concentrations. Several promising loci were identified that may implicate biologic pathways that contribute to adverse metabolic side effects from diuretic therapy.

Leveraging linkage evidence to identify low-frequency and rare variants on 16p13 associated with blood pressure using TOPMed whole genome sequencing data.

In this study, we investigated low-frequency and rare variants associated with blood pressure (BP) by focusing on a linkage region on chromosome 16p13. We used whole genome sequencing (WGS) data obtained through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program on 395 Cleveland Family Study (CFS) European Americans (CFS-EA). By analyzing functional coding variants and non-coding rare variants with CADD score > 10 residing within the chromosomal region in families with linkage evidence, we observed 25 genes with nominal statistical evidence (burden or SKAT p < 0.05). One of the genes is RBFOX1, an evolutionarily conserved RNA-binding protein that regulates tissue-specific alternative splicing that we previously reported to be associated with BP using exome array data in CFS. After follow-up analysis of the 25 genes in ten independent TOPMed studies with individuals of European, African, and East Asian ancestry, and Hispanics (N = 29,988), we identified variants in SLX4 (p = 2.19 × 10-4) to be significantly associated with BP traits when accounting for multiple testing. We also replicated the associations previously reported for RBFOX1 (p = 0.007). Follow-up analysis with GTEx eQTL data shows SLX4 variants are associated with gene expression in coronary artery, multiple brain tissues, and right atrial appendage of the heart. Our study demonstrates that linkage analysis of family data can provide an efficient approach for detecting rare variants associated with complex traits in WGS data.

VarExp: estimating variance explained by genome-wide GxE summary statistics.

Summary: Many genome-wide association studies and genome-wide screening for gene-environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. This is commonly done using individual-level genotype data but it is challenging when results are derived through meta-analyses. Here, we present R package, 'VarExp', that allows for the estimation of the percentage of phenotypic variance explained using summary statistics only. It allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects and both effects jointly. Its implementation integrates all recent methodological developments and does not need external data to be uploaded by users. Availability and implementation: The R package is available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git. Supplementary information: Supplementary data are available at Bioinformatics online.

The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions.

BACKGROUND: Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci. OBJECTIVES: This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification. METHOD: For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error. RESULTS: In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power. CONCLUSION: SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.

Rare, low frequency and common coding variants in CHRNA5 and their contribution to nicotine dependence in European and African Americans.

The common nonsynonymous variant rs16969968 in the α5 nicotinic receptor subunit gene (CHRNA5) is the strongest genetic risk factor for nicotine dependence in European Americans and contributes to risk in African Americans. To comprehensively examine whether other CHRNA5 coding variation influences nicotine dependence risk, we performed targeted sequencing on 1582 nicotine-dependent cases (Fagerström Test for Nicotine Dependence score⩾4) and 1238 non-dependent controls, with independent replication of common and low frequency variants using 12 studies with exome chip data. Nicotine dependence was examined using logistic regression with individual common variants (minor allele frequency (MAF)⩾0.05), aggregate low frequency variants (0.05>MAF⩾0.005) and aggregate rare variants (MAF<0.005). Meta-analysis of primary results was performed with replication studies containing 12 174 heavy and 11 290 light smokers. Next-generation sequencing with 180 × coverage identified 24 nonsynonymous variants and 2 frameshift deletions in CHRNA5, including 9 novel variants in the 2820 subjects. Meta-analysis confirmed the risk effect of the only common variant (rs16969968, European ancestry: odds ratio (OR)=1.3, P=3.5 × 10(-11); African ancestry: OR=1.3, P=0.01) and demonstrated that three low frequency variants contributed an independent risk (aggregate term, European ancestry: OR=1.3, P=0.005; African ancestry: OR=1.4, P=0.0006). The remaining 22 rare coding variants were associated with increased risk of nicotine dependence in the European American primary sample (OR=12.9, P=0.01) and in the same risk direction in African Americans (OR=1.5, P=0.37). Our results indicate that common, low frequency and rare CHRNA5 coding variants are independently associated with nicotine dependence risk. These newly identified variants likely influence the risk for smoking-related diseases such as lung cancer.

A Review of the Genetics of Hypertension with a Focus on Gene-Environment Interactions.

PURPOSE OF REVIEW: Here, we discuss the interpretation and modeling of gene-environment interactions in hypertension-related phenotypes, with a focus on the necessary assumptions and possible challenges. RECENT FINDINGS: Recently, small cohort studies have discovered several novel genetic variants associated with hypertension-related phenotypes through modeling gene-environment interactions. Several consortia-based meta-analytic efforts have uncovered many novel genetic variants in hypertension without modeling interaction terms, giving promise to future meta-analytic efforts that incorporate gene-environment interactions. Heritability studies and genome-wide association studies have established that hypertension, a prevalent cardiovascular disease, has a genetic component that may be modulated by the environment (such as lifestyle factors). This review includes a discussion of known genetic associations for hypertension/blood pressure, including those resulting from the incorporation of gene-environmental interaction modeling.

Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions.

BACKGROUND: Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability has not been attributed to specific variants. Interactions between genes and BP-associated factors may explain some "missing heritability." Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown. METHODS: We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers ("ever smokers," N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations. RESULTS: Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD > 10) but decreased SBP (7 mmHg) in light smokers (CPD ≤ 10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached P < 1 × 10(-6). DISCUSSION: Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking.

Genome-wide association studies suggest sex-specific loci associated with abdominal and visceral fat.

BACKGROUND: To identify loci associated with abdominal fat and replicate prior findings, we performed genome-wide association (GWA) studies of abdominal fat traits: subcutaneous adipose tissue (SAT); visceral adipose tissue (VAT); total adipose tissue (TAT) and visceral to subcutaneous adipose tissue ratio (VSR). SUBJECTS AND METHODS: Sex-combined and sex-stratified analyses were performed on each trait with (TRAIT-BMI) or without (TRAIT) adjustment for body mass index (BMI), and cohort-specific results were combined via a fixed effects meta-analysis. A total of 2513 subjects of European descent were available for the discovery phase. For replication, 2171 European Americans and 772 African Americans were available. RESULTS: A total of 52 single-nucleotide polymorphisms (SNPs) encompassing 7 loci showed suggestive evidence of association (P<1.0 × 10(-6)) with abdominal fat in the sex-combined analyses. The strongest evidence was found on chromosome 7p14.3 between a SNP near BBS9 gene and VAT (rs12374818; P=1.10 × 10(-7)), an association that was replicated (P=0.02). For the BMI-adjusted trait, the strongest evidence of association was found between a SNP near CYCSP30 and VAT-BMI (rs10506943; P=2.42 × 10(-7)). Our sex-specific analyses identified one genome-wide significant (P<5.0 × 10(-8)) locus for SAT in women with 11 SNPs encompassing the MLLT10, DNAJC1 and EBLN1 genes on chromosome 10p12.31 (P=3.97 × 10(-8) to 1.13 × 10(-8)). The THNSL2 gene previously associated with VAT in women was also replicated (P=0.006). The six gene/loci showing the strongest evidence of association with VAT or VAT-BMI were interrogated for their functional links with obesity and inflammation using the Biograph knowledge-mining software. Genes showing the closest functional links with obesity and inflammation were ADCY8 and KCNK9, respectively. CONCLUSIONS: Our results provide evidence for new loci influencing abdominal visceral (BBS9, ADCY8, KCNK9) and subcutaneous (MLLT10/DNAJC1/EBLN1) fat, and confirmed a locus (THNSL2) previously reported to be associated with abdominal fat in women.

Aggregate blood pressure responses to serial dietary sodium and potassium intervention: defining responses using independent component analysis.

BACKGROUND: Hypertension is a complex trait that often co-occurs with other conditions such as obesity and is affected by genetic and environmental factors. Aggregate indices such as principal components among these variables and their responses to environmental interventions may represent novel information that is potentially useful for genetic studies. RESULTS: In this study of families participating in the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) Study, blood pressure (BP) responses to dietary sodium interventions are explored. Independent component analysis (ICA) was applied to 20 variables indexing obesity and BP measured at baseline and during low sodium, high sodium and high sodium plus potassium dietary intervention periods. A "heat map" protocol that classifies subjects based on risk for hypertension is used to interpret the extracted components. ICA and heat map suggest four components best describe the data: (1) systolic hypertension, (2) general hypertension, (3) response to sodium intervention and (4) obesity. The largest heritabilities are for the systolic (64%) and general hypertension (56%) components. There is a pattern of higher heritability for the component response to intervention (40-42%) as compared to those for the traditional intervention responses computed as delta scores (24%-40%). CONCLUSIONS: In summary, the present study provides intermediate phenotypes that are heritable. Using these derived components may prove useful in gene discovery applications.

Genomic and transcriptomic predictors of triglyceride response to regular exercise.

AIM: We performed genome-wide and transcriptome-wide profiling to identify genes and single nucleotide polymorphisms (SNPs) associated with the response of triglycerides (TG) to exercise training. METHODS: Plasma TG levels were measured before and after a 20-week endurance training programme in 478 white participants from the HERITAGE Family Study. Illumina HumanCNV370-Quad v3.0 BeadChips were genotyped using the Illumina BeadStation 500GX platform. Affymetrix HG-U133+2 arrays were used to quantitate gene expression levels from baseline muscle biopsies of a subset of participants (N=52). Genome-wide association study (GWAS) analysis was performed using MERLIN, while transcriptomic predictor models were developed using the R-package GALGO. RESULTS: The GWAS results showed that eight SNPs were associated with TG training-response (ΔTG) at p<9.9×10(-6), while another 31 SNPs showed p values <1×10(-4). In multivariate regression models, the top 10 SNPs explained 32.0% of the variance in ΔTG, while conditional heritability analysis showed that four SNPs statistically accounted for all of the heritability of ΔTG. A molecular signature based on the baseline expression of 11 genes predicted 27% of ΔTG in HERITAGE, which was validated in an independent study. A composite SNP score based on the top four SNPs, each from the genomic and transcriptomic analyses, was the strongest predictor of ΔTG (R(2)=0.14, p=3.0×10(-68)). CONCLUSIONS: Our results indicate that skeletal muscle transcript abundance at 11 genes and SNPs at a number of loci contribute to TG response to exercise training. Combining data from genomics and transcriptomics analyses identified a SNP-based gene signature that should be further tested in independent samples.

Refinement of a published gene-physical activity interaction impacting HDL-cholesterol: role of sex and lipoprotein subfractions.

Large-scale gene-environment interaction (GxE) discovery efforts often involve compromises in the definition of outcomes and choice of covariates for the sake of data harmonization and statistical power. Consequently, refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). This GxE was originally identified by Kilpeläinen et al., with the strongest cohort-specific signal coming from the Women's Genome Health Study (WGHS). We thus explored this GxE further in the WGHS (N = 23,294), with follow-up in the UK Biobank (UKB; N = 281,380), and the Multi-Ethnic Study of Atherosclerosis (MESA; N = 4,587). Self-reported PA (MET-hrs/wk), genotypes at rs295849 (nearest gene: LHX1), and NMR metabolomics data were available in all three cohorts. As originally reported, minor allele carriers of rs295849 in WGHS had a stronger positive association between PA and HDL-C (pint = 0.002). When testing a range of NMR metabolites (primarily lipoprotein and lipid subfractions) to refine the HDL-C outcome, we found a stronger interaction effect on medium-sized HDL particle concentrations (M-HDL-P; pint = 1.0×10-4) than HDL-C. Meta-regression revealed a systematically larger interaction effect in cohorts from the original meta-analysis with a greater fraction of women (p = 0.018). In the UKB, GxE effects were stronger both in women and using M-HDL-P as the outcome. In MESA, the primary interaction for HDL-C showed nominal significance (pint = 0.013), but without clear differences by sex and with a greater magnitude using large, rather than medium, HDL-P as an outcome. Towards reconciling these observations, further exploration leveraging NMR platform-specific HDL subfraction diameter annotations revealed modest agreement across all cohorts in the interaction affecting medium-to-large particles. Taken together, our work provides additional insights into a specific known gene-PA interaction while illustrating the importance of phenotype and model refinement towards understanding and replicating GxEs.

Genome-Wide Interaction Analysis With DASH Diet Score Identified Novel Loci for Systolic Blood Pressure.

BACKGROUND: The Dietary Approaches to Stop Hypertension (DASH) diet score lowers blood pressure (BP). We examined interactions between genotype and the DASH diet score in relation to systolic BP. METHODS: We analyzed up to 9 420 585 single nucleotide polymorphisms in up to 127 282 individuals of 6 population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (n=35 660) and UK Biobank (n=91 622) and performed European population-specific and cross-population meta-analyses. RESULTS: We identified 3 loci in European-specific analyses and an additional 4 loci in cross-population analyses at Pinteraction<5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency, 0.03) and the DASH diet score (Pinteraction=4e-8; P for heterogeneity, 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (Pinteraction=9.4e-7) and 0.20±0.06 mm Hg (Pinteraction=0.001) in Cohorts for Heart and Aging Research in Genomic Epidemiology and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with cis-expression quantitative trait loci (eQTL) variants (P=4e-273) and cis-DNA methylation quantitative trait loci variants (P=1e-300). Although the closest gene for rs117878928 is MTHFS, the highest narrow sense heritability accounted by single nucleotide polymorphisms potentially interacting with the DASH diet score in this locus was for gene ST20 at 15q25.1. CONCLUSIONS: We demonstrated gene-DASH diet score interaction effects on systolic BP in several loci. Studies with larger diverse populations are needed to validate our findings.

Smoking and genetic risk variation across populations of European, Asian, and African American ancestry--a meta-analysis of chromosome 15q25.

Recent meta-analyses of European ancestry subjects show strong evidence for association between smoking quantity and multiple genetic variants on chromosome 15q25. This meta-analysis extends the examination of association between distinct genes in the CHRNA5-CHRNA3-CHRNB4 region and smoking quantity to Asian and African American populations to confirm and refine specific reported associations. Association results for a dichotomized cigarettes smoked per day phenotype in 27 datasets (European ancestry (N = 14,786), Asian (N = 6,889), and African American (N = 10,912) for a total of 32,587 smokers) were meta-analyzed by population and results were compared across all three populations. We demonstrate association between smoking quantity and markers in the chromosome 15q25 region across all three populations, and narrow the region of association. Of the variants tested, only rs16969968 is associated with smoking (P < 0.01) in each of these three populations (odds ratio [OR] = 1.33, 95% CI = 1.25-1.42, P = 1.1 × 10(-17) in meta-analysis across all population samples). Additional variants displayed a consistent signal in both European ancestry and Asian datasets, but not in African Americans. The observed consistent association of rs16969968 with heavy smoking across multiple populations, combined with its known biological significance, suggests rs16969968 is most likely a functional variant that alters risk for heavy smoking. We interpret additional association results that differ across populations as providing evidence for additional functional variants, but we are unable to further localize the source of this association. Using the cross-population study paradigm provides valuable insights to narrow regions of interest and inform future biological experiments.

Partial normalization of components of metabolic syndrome does not influence prevalent echocardiographic abnormalities: the HyperGEN study.

BACKGROUND AND AIMS: Metabolic syndrome (MetS) is a complex condition characterized by different phenotypes, according to the combinations of risk factors and is associated with cardiovascular abnormalities. Whether control of MetS components by treatment produces improvement in the associated cardiovascular abnormalities is unknown. We investigated whether partial control of components of MetS was associated with less echocardiographic abnormalities than the complete presentation of MetS based on measured components. METHODS AND RESULTS: We evaluated markers of echocardiographic preclinical cardiovascular disease in MetS (ATP III) defined by measured components or by history of treatment, in 1421 African-American and 1195 Caucasian non-diabetic HyperGEN participants, without prevalent cardiovascular disease or serum creatinine >2 mg/dL. Of 2616 subjects, 512 subjects had MetS by measured components and 328 by history. Hypertension was found in 16% of participants without MetS, 6% of those with MetS by history and 42% of those with MetS by measured components. Obesity and central fat distribution had similar prevalence in both MetS groups (both p < 0.0001 vs. No-MetS). Blood pressure was similar in MetS by history and No-MetS, and lower than in MetS by measured components (p < 0.0001). LV mass and midwall shortening, left atrial (LA) dimension and LA systolic force were similarly abnormal in both MetS groups (all p < 0.0001 vs. No-MetS) without difference between them. CONCLUSIONS: There is a little impact of control by treatment of single components of MetS (namely hypertension) on echocardiographic abnormalities. Lower blood pressure in participants with MetS by history was not associated with substantially reduced alterations in cardiac geometry and function.

Measured maximal heart rates compared to commonly used age-based prediction equations in the Heritage Family Study.

OBJECTIVE: The purpose of this study was to examine how well two commonly used age-based prediction equations for maximal heart rate (HRmax ) estimate the actual HRmax measured in Black and White adults from the HERITAGE Family Study. METHODS: A total of 762 sedentary subjects (39% Black, 57% Females) from HERITAGE were included. HRmax was measured during maximal exercise tests using cycle ergometers. Age-based HRmax was predicted using the Fox (220-age) and Tanaka (208 - 0.7 × age) formulas. RESULTS: The standard error of estimate (SEE) of predicted HRmax was 12.4 and 11.4 bpm for the Fox and Tanaka formulas, respectively, indicating a wide-spread of measured-HRmax values are compared to their age-predicted values. The SEE (shown as Fox/Tanaka) was higher in Blacks (14.4/13.1 bpm) and Males (12.6/11.7 bpm) compared to Whites (11.0/10.2 bpm) and Females (12.3/11.2 bpm) for both formulas. The SEE was higher in subjects above the BMI median (12.8/11.9 bpm) and below the fitness median (13.4/12.4 bpm) when compared to those below the BMI median (12.2/11.0 bpm) and above the fitness median (11.4/10.3) for both formulas. CONCLUSION: Our findings show that based on the SEE, the prevailing age-based estimated HRmax equations do not precisely predict an individual's measured-HRmax .

Performance of genotype imputations using data from the 1000 Genomes Project.

Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly using 1KG data for genotype imputations, but only limited evaluations of the performance of this approach are available. In this paper, we empirically evaluated imputation performance using 1KG data by comparing imputation results to those using the HapMap Phase II data that have been widely used. We used three reference panels: the CEU panel consisting of 120 haplotypes from HapMap II and 1KG data (June 2010 release) and the EUR panel consisting of 566 haplotypes also from 1KG data (August 2010 release). We used Illumina 324,607 autosomal SNPs genotyped in 501 individuals of European ancestry. Our most important finding was that both 1KG reference panels provided much higher imputation yield than the HapMap II panel. There were more than twice as many successfully imputed SNPs as there were using the HapMap II panel (6.7 million vs. 2.5 million). Our second most important finding was that accuracy using both 1KG panels was high and almost identical to accuracy using the HapMap II panel. Furthermore, after removing SNPs with MACH Rsq <0.3, accuracy for both rare and low frequency SNPs was very high and almost identical to accuracy for common SNPs. We found that imputation using the 1KG-EUR panel had advantages in successfully imputing rare, low frequency and common variants. Our findings suggest that 1KG-based imputation can increase the opportunity to discover significant associations for SNPs across the allele frequency spectrum. Because the 1KG Project is still underway, we expect that later versions will provide even better imputation performance.