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ObjectivesWe investigated whether high responsiveness or low responsiveness to exercise training aggregates in the same individuals across seven cardiometabolic traits.MethodsA 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.ResultsOnly 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.ConclusionsInterindividual 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.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%., The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history.

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naive, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naive Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.

The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study.

The percentage of biomedical, behavioral, and clinical under-represented minority (URM) junior faculty is lower than their respective populations in the United States; whereas racial and ethnic minorities (African Americans, Hispanic Americans/Latinos, American Indians, Alaska Natives, Native

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids., The influence of X chromosome genetic variation on blood lipids and coronary heart disease (CHD) is not well understood. Here, the authors analyse X chromosome sequencing data across 65,322 multi-ancestry individuals, identifying associations of the Xq23 locus with lipid changes and reduced risk of CHD and diabetes mellitus.

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations.

Publisher's version (útgefin grein), Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p, WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. The grant funding of WHI are R21 HL123677, R56 DK104806 and R01 MD012765 to NF. The FamHS was funded by R01HL118305 and R01HL117078 NHLBI grants, and 5R01DK07568102 and 5R01DK089256 NIDDK grant." and "The Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study was supported by the Intramural Research Program of the National Institute on Aging, National Institutes of Health (project # Z01-AG000513 and human subjects protocol number 09-AGN248). Support for GENOA was provided by the National Heart, Lung and Blood Institute (HL119443, HL087660, HL054464, HL054457, and HL054481) of the National Institutes of Health. Ruth loos is supported by the NIH (R01DK110113, U01HG007417, R01DK101855, R01DK107786). The Rotterdam Study GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), and the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006- 01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002- 01254). The ERF study was further supported by ENGAGE consortium and CMSB. Highthroughput analysis of the ERF data was supported by joint grant from Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWORFBR 047.017.043).ERF was further supported by the ZonMw grant (project 91111025), and this work was partially supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (Contract No. N01-HC25195) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6- 4278). This study is also supported by National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) R01 DK078616 to Drs. Meigs, Dupuis and Florez, NIDDK K24 DK080140 to Dr. Meigs, and a Doris Duke Charitable Foundation Clinical Scientist Development Award to Dr. Florez. The HERITAGE Family Study was supported by National Heart, Lung, and Blood Institute grant HL-45670. The Women's Genome Health Study is supported by the National Heart, Lung, and Blood Instutute (HL043851 and HL080467) and the National Cancer Institute (CA047988 and UM1CA182913). Additional support for endpoint collection was provided by the National Heart, Lung, and Blood Institute under ARRA funding (HL099355). HyperGEN (Hypertension Genetic Epidemiology Network): The hypertension network is funded by cooperative agreements (U10) with NHLBI: HL54471, HL54472, HL54473, HL54495, HL54496, HL54497, HL54509, HL54515, and 2 R01 HL55673- 12. The AGES study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C. Caroline Hayward is supported by an MRC University Unit Programme Grant MC_UU_00007/10 (QTL in Health and Disease)”and “Generation Scotland received core funding from the Chief Scientist Office of the Scottish Government Health Directorate CZD/16/6, the Scottish Funding Council HR03006 and the Wellcome Trust through a Strategic Award (reference 104036/Z/14/Z) for Stratifying Resilience and Depression Longitudinally (STRADL). Genotyping was funded by the UK's Medical Research Council. Jose C. Florez, NIDDK K24 DK110550 The MESA project is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881, and DK063491. Additionally, one or more authors are affiliated with the following commercial entities: Interleukin Genetics, GlaxoSmithKline, Daiichi-Sankyo, AstraZeneca, Data Tecnica International LLC, Illumina Inc., University of California Healthcare, Janssen Pharmaceuticals, Goldfinch Bio, and Novo Nordisk. Please see the Competing Interests Statement for additional details. The funders provided support in the form of salaries for authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to “benchmark” results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly liberal. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p-values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to “benchmark” results. In conclusion, we generally recommend the Safe Approach to handle heterogeneity in the LRS based genome-wide interaction meta-analyses.

Mechanistic processes underlying human germline mutations remain largely unknown. Variation in mutation rate and spectra along the genome is informative about the biological mechanisms. We statistically decompose this variation into separate processes using a blind source separation technique. The analysis of a large-scale whole genome sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. Seven of these processes lend themselves to a biological interpretation. One process is driven by bulky DNA lesions that resolve asymmetrically with respect to transcription and replication. Two processes independently track direction of replication fork and replication timing. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions. We also demonstrate that a recently discovered mutagenic process specific to oocytes can be localized solely from population sequencing data. This process is spread across all chromosomes and is highly asymmetric with respect to the direction of transcription, suggesting a major role of DNA damage.

Additional file 1: Figure S1. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of a binary phenotype. Figure S2. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of a quantitative phenotype when relaxing the genotype-environment independence. Figure S3. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of differences between the proportion of exposed individuals for the SNP and the proportion of exposed individuals in the whole sample. Figure S4. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of different phenotypic variance conditionally on the exposition. Figure S5. Comparison of the Type I error rate evaluated between summary obtained using individual-level data (blue) and summary statistics estimated using the pipeline (orange) with respect to the different quintiles of the different sources of bias: G-E correlation(A), misspecification of the proportion of exposed individuals (B) and different phenotypic variance in the strata of the exposure (C). Type I error rate were evaluated for the marginal model in all individuals (left), in unexposed individuals only (middle) and in exposed individuals only (right). The dashed line represents the nominal significance threshold (5%). Figure S6. Proportion of SNPs with discordant significance results between summary statistics obtained using individual-level data and summary statistics estimated using our pipeline with respect to the different quintiles of the different sources of bias: G-E correlation(A), misspecification of the proportion of exposed individuals (B) and different phenotypic variance in the strata of the exposure (C). Type I error rate were evaluated for the marginal model in all individuals (left), in unexposed individuals only (middle) and in exposed individuals only (right).

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

Background and aims GlycA is a relatively new biomarker for inflammation as well as cardiometabolic disease risk. However, the effect of exercise on GlycA is largely unknown. Therefore, the purpose of this study was to examine the effects of regular exercise on the inflammatory marker GlycA across seven studies and 14 exercise interventions. Methods Nuclear magnetic resonance spectroscopy, specifically signal amplitudes originating from the N-acetyl methyl group protons of the N-acetylglucosamine residues on the glycan branches of glycoproteins, was used to quantify GlycA concentrations. GlycA was measured before and after completion of an exercise intervention in 1568 individuals across seven studies and 14 exercise interventions. Random effects inverse variance weighting models were used to pool effects across interventions. Results Combined analysis of unadjusted data showed that regular exercise significantly (p = 2 × 10−6) reduced plasma GlycA (−8.26 ± 1.8 μmol/L). This reduction remained significant (−9.12 ± 1.9 μmol/L, p = 1.22 × 10−6) following adjustment for age, sex, race, baseline BMI, and baseline GlycA. Changes in GlycA were correlated with changes in traditional inflammatory markers, C-reactive protein, interleukin-6, and fibrinogen, however, these correlations were relatively weak (range r: 0.21–0.38, p Conclusions Regular exercise significantly reduced plasma GlycA across 14 different exercise interventions despite differences in exercise programs and study populations. The current study provides a greater understanding of the use of exercise as a potential therapy for the reduction of systemic inflammation. Further research is needed to understand the mechanisms behind the exercise-related reductions in GlycA.

We sought to evaluate whether unbiased machine learning of dense phenotypic data (“phenomapping”) could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure with preserved ejection fraction (HFpEF). In the HyperGEN study, a population- and family-based study of hypertension, we studied 1273 hypertensive patients utilizing clinical, laboratory, and conventional echocardiographic phenotyping of the study participants. We used machine learning analysis of 47 continuous phenotypic variables to identify mutually exclusive groups constituting a novel classification of hypertension. The phenomapping analysis classified study participants into 2 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, and indices of cardiac mechanics (e.g., phenogroup #2 had a decreased absolute longitudinal strain [12.8 ± 4.1 vs. 14.6 ± 3.5%] even after adjustment for traditional comorbidities [p

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency

Asthma prevalence in the United States and the world has been increasing, affecting millions including children (0 - 17 years old) and causing thousands of deaths every year at a societal cost of over billions of dollars. Further, it has been documented that asthma morbidity responds to socioeconomic variations. This study evaluates the relationship between asthma and five air pollutants along with socioeconomic status in Texas counties from 2005 to 2013. Air pollutants investigated were carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particulate matter (PM2.5), and sulfur dioxide (SO2). Exploratory and spatial analyses produced consistent results. Asthma prevalence was positively associated with PM2.5, SO2, living near a park, and living in an urban area. Asthma childhood prevalence rates were positively associated with living in a household with a female head and negatively associated with ethnicity: Caucasian, Hispanic, and African American. Adult prevalence rates were positively associated with living in a household with female head, being on food stamps, and PM2.5. Both the overall and adult rates were positively associated with poverty. Asthma hospitalizations in Texas were positively associated with aerosol particles, sulfur dioxide, and low income. Moreover, the majority of air pollution in Texas is formed by stationary sources, which contradicts recent claims that mobile sources are the main emitters in Texas. Our findings are consistent with those from other geographical locations and suggest that additional studies and measures are required to fully explain the associations detected and underlying cause and effect paradigm.

Objective The goal was to examine lipoprotein subclass responses to regular exercise as measured in 10 exercise interventions derived from six cohorts. Methods Nuclear magnetic resonance spectroscopy was used to quantify average particle size, total and subclass concentrations of very low-density lipoprotein, low-density lipoprotein, and high-density lipoprotein particles (VLDL-P, LDL-P, and HDL-P, respectively) before and after an exercise intervention in 1555 adults from six studies, encompassing 10 distinct exercise programs: APOE (N = 106), DREW (N = 385), GERS (N = 79), HERITAGE (N = 715), STRRIDE I (N = 168) and II (N = 102). Random-effects meta-analyses were performed to evaluate the overall estimate of mean change across the unadjusted and adjusted mean change values from each exercise group. Results Meta-analysis of unadjusted data showed that regular exercise induced significant decreases in the concentration of large VLDL-P, small LDL-P, and medium HDL-P and mean VLDL-P size, with significant increases in the concentration of large LDL-P and large HDL-P and mean LDL-P size. These changes remained significant in meta-analysis with adjustment for age, sex, race, baseline body mass index, and baseline trait value. Conclusions Despite differences in exercise programs and study populations, regular exercise produced putatively beneficial changes in the lipoprotein subclass profile across 10 exercise interventions. Further research is needed to examine how exercise-induced changes in lipoprotein subclasses may be associated with (concomitant changes in) cardiovascular disease risk.

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 (DeltaTG) at p

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.

Objective Identify determinants of plasma adropin concentrations, a secreted peptide translated from the Energy Homeostasis Associated (ENHO) gene linked to metabolic control and vascular function. Methods Associations between plasma adropin concentrations, demographics (sex, age, BMI) and circulating biomarkers of lipid and glucose metabolism were assessed in plasma obtained after an overnight fast in humans. The regulation of adropin expression was then assessed in silico, in cultured human cells, and in animal models. Results In humans, plasma adropin concentrations are inversely related to atherogenic LDL-cholesterol (LDL-C) levels in men (n = 349), but not in women (n = 401). Analysis of hepatic Enho expression in male mice suggests control by the biological clock. Expression is rhythmic, peaking during maximal food consumption in the dark correlating with transcriptional activation by RORα/γ. The nadir in the light phase coincides with the rest phase and repression by Rev-erb. Plasma adropin concentrations in nonhuman primates (rhesus monkeys) also exhibit peaks coinciding with feeding times (07:00 h, 15:00 h). The ROR inverse agonists SR1001 and the 7-oxygenated sterols 7-β-hydroxysterol and 7-ketocholesterol, or the Rev-erb agonist SR9009, suppress ENHO expression in cultured human HepG2 cells. Consumption of high-cholesterol diets suppress expression of the adropin transcript in mouse liver. However, adropin over expression does not prevent hypercholesterolemia resulting from a high cholesterol diet and/or LDL receptor mutations. Conclusions In humans, associations between plasma adropin concentrations and LDL-C suggest a link with hepatic lipid metabolism. Mouse studies suggest that the relationship between adropin and cholesterol metabolism is unidirectional, and predominantly involves suppression of adropin expression by cholesterol and 7-oxygenated sterols. Sensing of fatty acids, cholesterol and oxysterols by the RORα/γ ligand-binding domain suggests a plausible functional link between adropin expression and cellular lipid metabolism. Furthermore, the nuclear receptors RORα/γ and Rev-erb may couple adropin synthesis with circadian rhythms in carbohydrate and lipid metabolism., Graphical abstract, Highlights • In male humans, plasma adropin concentrations are inversely related to low-density circulating cholesterol (LDL-C) levels. • Adropin expression is regulated by core elements of the biological clock (RORA/G, Rev-Erb). • Sterol-sensing by the ROR ligand-binding domain provides a plausible link between adropin expression and lipid metabolism. • In mouse liver, adropin expression is rhythmic and suppressed by exogenous (dietary) cholesterol.

Left ventricular (LV) hypertrophy, highest in prevalence among African Americans, is an established risk factor heart failure. Several genome wide association studies have identified common variants associated with LV-related quantitative-traits in African Americans. To date, however, the effect of rare variants on these traits has not been extensively studied, especially in minority groups. We therefore investigated the association between rare variants and LV traits among 1,934 African Americans using exome chip data from the Hypertension Genetic Epidemiology Network (HyperGEN) study, with replication in 1,090 African American from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We used single-variant analyses and gene-based tests to investigate the association between 86,927 variants and six structural and functional LV traits including LV mass, LV internal dimension-diastole, relative wall thickness, left atrial dimension (LAD), fractional shortening (FS), and the ratio of LV early-to-late transmitral velocity (E/A ratio). Only rare variants (MAF

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. 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.

In order to investigate the associations of SCNN1A, SCNN1G and SCNN1B genes with blood pressure (BP) in the Han Chinese population, we included 2880 participants did not use antihypertensive medication in the month prior to the baseline survey in the current analysis. Forty-four tag-single-nucleotide polymorphisms (SNPs) in epithelial sodium channel (ENaC) genes were selected and genotyped, and nine BP measurements were obtained during the 3-day examination. In the single-marker analyses, we identified significant associations of SCNN1A marker rs13306613 with diastolic BP (DBP) and SCNN1B marker rs12447134 with systolic BP (SBP) under codominant model after Bonferroni's correction (P=2.82 × 10(-5) and 4.63 × 10(-4), respectively). In addition, five SNPs in SCNN1G and four SNPs in SCNN1B achieved nominal significance for SBP, DBP or mean arterial pressure (MAP) under the additive model. For example, the minor C allele of rs5735 in SCNN1G gene was associated with decreased SBP, DBP and MAP (P=0.016, 5.41 × 10(-3) and 4.36 × 10(-3), respectively). Gene-based results showed significant associations of SCNN1G and SCNN1B with BP levels. This study suggested that ENaC genes have important roles in BP regulation in the Han Chinese population. Future studies are warranted to replicate these findings, and functional studies are needed to identify true causal variants in ENaC genes.

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. Am. J. Hum. Biol., 25:695–701, 2013. © 2013 Wiley Periodicals, Inc.

Background— Several consortia have pursued genome-wide association studies for identifying novel genetic loci for blood pressure, lipids, hypertension, etc. They demonstrated the power of collaborative research through meta-analysis of study-specific results. Methods and Results— The Gene-Lifestyle Interactions Working Group was formed to facilitate the first large, concerted, multiancestry study to systematically evaluate gene–lifestyle interactions. In stage 1, genome-wide interaction analysis is performed in 53 cohorts with a total of 149 684 individuals from multiple ancestries. In stage 2 involving an additional 71 cohorts with 460 791 individuals from multiple ancestries, focused analysis is performed for a subset of the most promising variants from stage 1. In all, the study involves up to 610 475 individuals. Current focus is on cardiovascular traits including blood pressure and lipids, and lifestyle factors including smoking, alcohol, education (as a surrogate for socioeconomic status), physical activity, psychosocial variables, and sleep. The total sample sizes vary among projects because of missing data. Large-scale gene–lifestyle or more generally gene–environment interaction (G×E) meta-analysis studies can be cumbersome and challenging. This article describes the design and some of the approaches pursued in the interaction projects. Conclusions— The Gene-Lifestyle Interactions Working Group provides an excellent framework for understanding the lifestyle context of genetic effects and to identify novel trait loci through analysis of interactions. An important and novel feature of our study is that the gene–lifestyle interaction (G×E) results may improve our knowledge about the underlying mechanisms for novel and already known trait loci.

Cigarette smoking has been shown to be a health hazard. In addition to being considered a negative lifestyle behavior, studies have shown that cigarette smoking has been linked to genetic underpinnings of hypertension. Because African Americans have the highest incidence and prevalence of hypertension, we examined the joint effect of genetics and cigarette smoking on health among this understudied population. The sample included African Americans from the genome wide association studies of HyperGEN (N = 1083, discovery sample) and GENOA (N = 1427, replication sample), both part of the FBPP. Results suggested that 2 SNPs located on chromosomes 14 (NEDD8; rs11158609; raw p = 9.80 × 10−9, genomic control-adjusted p = 2.09 × 10−7) and 17 (TTYH2; rs8078051; raw p = 6.28 × 10−8, genomic control-adjusted p = 9.65 × 10−7) were associated with SBP including the genetic interaction with cigarette smoking. These two SNPs were not associated with SBP in a main genetic effect only model. This study advances knowledge in the area of main and joint effects of genetics and cigarette smoking on hypertension among African Americans and offers a model to the reader for assessing these risks. More research is required to determine how these genes play a role in expression of hypertension.

Despite the substantial burden of hypertension in US minority populations, few genetic studies of blood pressure have been conducted in Hispanics and African Americans, and it is unclear whether many of the established loci identified in European-descent populations contribute to blood pressure variation in non-European descent populations. Using the Metabochip array, we sought to characterize the genetic architecture of previously identified blood pressure loci, and identify novel cardiometabolic variants related to systolic and diastolic blood pressure in a multi-ethnic US population including Hispanics (n = 19,706) and African Americans (n = 18,744). Several known blood pressure loci replicated in African Americans and Hispanics. Fourteen variants in three loci (KCNK3, FGF5, ATXN2-SH2B3) were significantly associated with blood pressure in Hispanics. The most significant diastolic blood pressure variant identified in our analysis, rs2586886/KCNK3 (P = 5.2 x 10-9), also replicated in independent Hispanic and European-descent samples. African American and trans-ethnic meta-analysis data identified novel variants in the FGF5, ULK4 and HOXA-EVX1 loci, which have not been previously associated with blood pressure traits. Our identification and independent replication of variants in KCNK3, a gene implicated in primary hyperaldosteronism, as well as a variant in HOTTIP (HOXA-EVX1) suggest that further work to clarify the roles of these genes may be warranted. Overall, our findings suggest that loci identified in European descent populations also contribute to blood pressure variation in diverse populations including Hispanics and African Americans-populations that are understudied for hypertension genetic risk factors.

The relation between parental history of hypertension and blood pressure response to potassium intake is unknown. A 7-day high-sodium followed by a 7-day high-sodium plus potassium dietary-feeding study was conducted from 2003 to 2005 among 1,871 Chinese participants. Those with a maternal history of hypertension had larger systolic blood pressure responses to potassium compared with those without: -4.31 (95% confidence interval (CI): -4.99, -3.62) mm Hg versus -3.35 (95% CI: -4.00, -2.70) mm Hg, respectively (P(difference) = 0.002). A consistent trend was observed for diastolic blood pressure responses: -1.80 (95% CI: -2.41, -1.20) mm Hg versus -1.35 (95% CI: -1.95, -0.74) mm Hg, respectively (P = 0.07). Stronger associations between early onset maternal hypertension and blood pressure responses were noted, with systolic blood pressure decreases of -4.80 (95% CI: -5.65, -3.95) mm Hg versus -3.55 (95% CI: -4.17, -2.93) mm Hg and diastolic blood pressure decreases of -2.25 (95% CI: -3.01, -1.50) mm Hg versus -1.42 (95% CI: -1.99, -0.85) mm Hg among those with early onset maternal hypertension versus those without, respectively (P = 0.001 and 0.009, respectively). Odds ratios for high potassium sensitivity were 1.36 (95% CI: 0.96, 1.92) and 1.60 (95% CI: 1.08, 2.36) for those with maternal hypertension and early onset maternal hypertension, respectively (P = 0.08 and 0.02, respectively). Potassium supplementation could help to reduce blood pressure among those with a maternal history of hypertension.

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.

Blood pressure has a significant genetic component, but less than 3% of the observed variance has been attributed to genetic variants identified to date. Candidate gene studies of rare, monogenic hypertensive syndromes have conclusively implicated several genes altering renal sodium balance, and studies of essential hypertension have inconsistently implicated over 50 genes in pathways affecting renal sodium balance and other functions. Genome-wide linkage scans have replicated numerous quantitative trait loci throughout the genome, and over 50 single nucleotide polymorphisms (SNPs) have been replicated in multiple genome-wide association studies. These studies provide considerable evidence that epistasis and other interactions play a role in the genetic architecture of blood pressure regulation, but candidate gene studies have limited scope to test for epistasis, and genome-wide studies have low power for both main effects and interactions. This review summarizes the genetic findings to date for blood pressure, and it proposes focused, pathway-based approaches involving epistasis, gene-environment interactions, and next-generation sequencing to further the genetic dissection of blood pressure and hypertension.

We examined the genetic association between blood pressure (BP) responses to dietary sodium and potassium intervention and to cold pressor test (CPT) in a large family-based dietary feeding study. The dietary intervention and CPT were conducted among 1906 participants in rural China. The dietary intervention included three 7-day periods of low-sodium feeding (51.3 mmol per day), high-sodium feeding (307.8 mmol per day) and high-sodium feeding plus potassium supplementation (60 mmol per day). BP responses to high-sodium intervention had strong genetic correlations (ρ(G)) with both BP responses to low sodium (ρ(G)=-0.43 to -0.54, P-values=0.0005 to 0.03) and to potassium supplementation (ρ(G)=-0.41 to -0.49, P-values=0.001 to 0.005) interventions. Most environmental correlations between BP responses to various dietary interventions were significant. The ρ(G) between BP responses to CPT and to high-sodium intervention and potassium supplementation were statistically significant. For example, the ρ(G) between maximum BP responses to CPT and BP responses to high-sodium intervention was 0.37 (P=0.006) for systolic BP (SBP) and 0.41 (P=0.002) for diastolic BP (DBP). The ρ(G) between maximum BP responses to CPT and BP responses to potassium intervention was -0.42 (P=0.001) for SBP and -0.46 (P=0.001) for SBP. Our study suggests that there are common genetic determinants that influence BP responses to dietary sodium and potassium interventions and to CPT.

[This corrects the article DOI: 10.1371/journal.pgen.1006728.].

Although beneficial effects of potassium intake on blood pressure (BP) are well established, little is known about genetic factors that underlie interindividual variability in BP response to dietary potassium. In a previous study, we reported the first evidence for significant heritabilities for BP response in a dietary intervention study in rural Chinese. In this report, we extend our genetic studies to examine associations with polymorphisms in genes in vascular endothelial pathways.We genotyped study participants for 23 single nucleotide polymorphisms (SNPs) in endothelin 1 (EDN1), nitric oxide synthase 3, and E selectin (SELE). We tested 17 of these SNPs for associations with BP response to potassium supplementation in 1843 participants. Association tests used population-based [generalized estimation equation (GEE)] and family-based (quantitative transmission disequilibrium test) methods, as well as tests for gene-by-gene (GxG) interaction (generalized multifactor dimensionalilty reduction and GEE).Single SNP analysis identified significant associations for several SNPs in EDN1 with multiple measures of BP response to potassium supplementation. The cumulative effects of the minor EDN1 alleles that showed significant associations were to reduce measures of BP response by 0.5-0.9 mmHg. We found significant evidence for effects of GxG interactions between EDN1 and SELE, even in the absence of individual associations with SELE variants.Our results implicate variability in EDN1 and SELE as genetic factors that influence BP response to potassium intake. Although such epidemiological studies do not allow direct determination of physiologic mechanisms, our findings of joint effects identify EDN1 and SELE as targets for functional studies to determine their interactions in BP response to potassium intake.

A genome-wide linkage scan for endurance training-induced changes in stroke volume detected a quantitative trait locus on chromosome 10p11 in white families of the HERITAGE Family Study. Dense microsatellite mapping narrowed down the linkage region to a 7 Mb area containing 16 known and 14 predicted genes. Association analyses with 90 single nucleotide polymorphisms (SNPs) provided suggestive evidence ( P values from 0.03 to 0.06) for association in the kinesin heavy chain ( KIF5B) gene locus in the whole cohort. The associations at the KIF5B locus were stronger ( P values from 0.001 to 0.008) when the analyses were performed on linkage-informative families only (family-specific logarithm of the odds ratio scores >0.025 at peak linkage location). Resequencing the coding and regulatory regions of KIF5B revealed no new exonic SNPs. However, the putative promoter region was particularly polymorphic, containing eight SNPs with at least 5% minor allele frequency within 1850 bp upstream of the start codon. Functional analyses using promoter haplotype reporter constructs led to the identification of sequence variants that had significant effects on KIF5B promoter activity. Analogous inhibition and overexpression experiments showed that changes in KIF5B expression alter mitochondrial localization and biogenesis in a manner that could affect the ability of the heart to adjust to regular exercise. Our data suggest that KIF5B is a strong candidate gene for the response of stroke volume to regular exercise. Furthermore, training-induced changes in submaximal exercise stroke volume may be due to mitochondrial function and variation in KIF5B expression as determined by functional SNPs in its promoter.

Essential hypertension is a principal cardiovascular risk factor whose origin remains unknown. Classical genetic studies have shown that blood pressure is at least partially heritable, opening a window to understanding the pathophysiology of essential hypertension in the human using modern genetic tools. The Wellcome Trust Case Control Consortium has recently published the results of screening the genomes of 2000 essential hypertension cases and 3000 controls using 500 000 genome-wide single nucleotide polymorphisms (SNPs). None of the variants proved to be genome-wide significant after correction for multiple tests but the most significantly associated SNPs (P

Associations between cholesteryl ester transfer protein (CETP) polymorphisms and high-density lipoprotein cholesterol (HDL-c) levels before and after 20 wk of endurance training were investigated in the HERITAGE Family Study. Plasma HDL-c, HDL2-c, HDL3-c, and apolipoprotein (apo)A1 levels were measured, and 13 CETP single nucleotide polymorphisms (SNPs) were genotyped in 265 blacks and 486 whites. Three haplotypes defined by SNPs at the −1337, −971, and −629 sites were strongly associated with baseline HDL-c levels in whites. Both C−1337T and C−629A were associated with baseline HDL-c ( P < 0.001) and apoA1 ( P < 0.01) when tested separately. However, only C−629A remained significant in a combined model. G−971A was not associated with HDL phenotypes, but showed significant interactions with C−629A ( P = 0.002) on baseline traits. Genotype-by-sex interactions were observed at the −629 locus for HDL3-c ( P = 0.004) and apoA1 ( P = 0.02) training responses in whites. In women, the −629 A/A homozygotes showed greater increases in HDL3-c ( P = 0.02) and apoA1 ( P = 0.02) levels than the other genotypes. Finally, apolipoprotein E (APOE) genotype and the CETP C−629A locus contributed independently and in additive fashion to the HDL traits, explaining 6.0–8.8% of the variance. The CETP −1337T and −629A alleles are associated with higher baseline HDL-c and apoA1 levels. The beneficial effects of endurance training on plasma HDL3-c and apoA1 levels are evident in white women homozygous for the −629A allele. The CETP and APOE genotypes account for up to 9% of the variance in HDL-c phenotypes in the HERITAGE Family Study.