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Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

BackgroundAlzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults.ObjectiveTo identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome.MethodsWeighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up).ResultsTwo proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities.ConclusionsProteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

BackgroundObesity and cardiometabolic dysfunction have been associated with cancer risk and severity. Underlying mechanisms remain unclear.ObjectivesThe aim of this study was to examine associations of obesity and related cardiometabolic traits with incident cancer.MethodsFHS (Framingham Heart Study) and PREVEND (Prevention of Renal and Vascular End-Stage Disease) study participants without prevalent cancer were studied, examining associations of obesity, body mass index (BMI), waist circumference, visceral adipose tissue (VAT) and subcutaneous adipose tissue depots, and C-reactive protein (CRP) with future cancer in Cox models.ResultsAmong 20,667 participants (mean age 50 years, 53% women), 2,619 cancer events were observed over a median follow-up duration of 15 years. Obesity was associated with increased risk for future gastrointestinal (HR: 1.30; 95% CI: 1.05-1.60), gynecologic (HR: 1.62; 95% CI: 1.08-2.45), and breast (HR: 1.32; 95% CI: 1.05-1.66) cancer and lower risk for lung cancer (HR: 0.62; 95% CI: 0.44-0.87). Similarly, waist circumference was associated with increased risk for overall, gastrointestinal, and gynecologic but not lung cancer. VAT but not subcutaneous adipose tissue was associated with risk for overall cancer (HR: 1.22; 95% CI: 1.05-1.43), lung cancer (HR: 1.92; 95% CI: 1.01-3.66), and melanoma (HR: 1.56; 95% CI: 1.02-2.38) independent of BMI. Last, higher CRP levels were associated with higher risk for overall, colorectal, and lung cancer (P < 0.05 for all).ConclusionsObesity and abdominal adiposity are associated with future risk for specific cancers (eg, gastrointestinal, gynecologic). Although obesity was associated with lower risk for lung cancer, greater VAT and CRP were associated with higher lung cancer risk after adjusting for BMI.

BackgroundDysregulation of compensatory mechanisms to regulate blood pressure (BP) upon postural change is a phenotype of BP variability and an emerging risk factor for cardiovascular outcomes.Materials and methodsWe assessed postural change in BP (starting 2 min after standing from a supine position), carotid-femoral pulse wave velocity (cfPWV), and markers of hypertension-mediated organ damage (HMOD) in the heart, kidney, and brain in Framingham Third Generation, Omni-2, and New Offspring Spouse Cohort participants. We related vascular measures (postural change in BP measures and cfPWV) with HMOD in 3,495 participants (mean age 47 years, 53% women) using multivariable logistic and linear regression models.ResultsIn multivariable-adjusted models, we did not observe significant associations of vascular measures with presence of left ventricular hypertrophy, albuminuria, covert brain infarcts, or white matter hyperintensities (Bonferroni-adjusted P-values > 0.05/20 > 0.0025). In multivariable models, greater cfPWV (est. β = 0.11 ± 0.03; P < 0.001), but not postural change in BP measures (Bonferroni-adjusted P-values > 0.05/20 > 0.0025), was associated with higher white matter free water using brain magnetic resonance imaging. In multivariable models, greater postural change in pulse pressure was associated with higher urinary albumin-creatinine ratio (est. β = 0.07 ± 0.02; P < 0.001). No other postural change in BP measure was associated with urinary albumin-creatinine ratio (Bonferroni-adjusted P-values > 0.05/20 > 0.0025). In sex-specific analyses, higher cfPWV was associated with higher urinary albumin-creatinine ratio in men (est. β: 0.11 ± 0.04; P = 0.002) but not in women (est. β: 0.03 ± 0.03; P = 0.44). We also observed marginal to strong effect modification by above vs. at/below median postural change in BP for the association of cfPWV with urinary albumin-creatinine ratio (Bonferroni-adjusted interaction P < 0.001-0.01). Vascular measures were not related to left ventricular mass index or fractional anisotropy (Bonferroni-adjusted P-values > 0.05/20 > 0.0025).ConclusionBaroreflex dysfunction is associated with greater subclinical kidney damage. Additionally, relations of higher aortic stiffness with greater kidney damage may be modified by associated baroreflex dysregulation.

ObjectiveLp(a) (lipoprotein[a]) is an independent risk factor for cardiovascular diseases and plasma levels are primarily determined by variation at the LPA locus. We performed a genome-wide association study in the UK Biobank to determine whether additional loci influence Lp(a) levels. Approach and Results: We included 293 274 White British individuals in the discovery analysis. Approximately 93 095 623 variants were tested for association with natural log-transformed Lp(a) levels using linear regression models adjusted for age, sex, genotype batch, and 20 principal components of genetic ancestry. After quality control, 131 independent variants were associated at genome-wide significance (P≤5×10-8). In addition to validating previous associations at LPA, APOE, and CETP, we identified a novel variant at the APOH locus, encoding β2GPI (beta2-glycoprotein I). The APOH variant rs8178824 was associated with increased Lp(a) levels (β [95% CI] [ln nmol/L], 0.064 [0.047-0.081]; P=2.8×10-13) and demonstrated a stronger effect after adjustment for variation at the LPA locus (β [95% CI] [ln nmol/L], 0.089 [0.076-0.10]; P=3.8×10-42). This association was replicated in a meta-analysis of 5465 European-ancestry individuals from the Framingham Offspring Study and Multi-Ethnic Study of Atherosclerosis (β [95% CI] [ln mg/dL], 0.16 [0.044-0.28]; P=0.0071).ConclusionsIn a large-scale genome-wide association study of Lp(a) levels, we identified APOH as a novel locus for Lp(a) in individuals of European ancestry. Additional studies are needed to determine the precise role of β2GPI in influencing Lp(a) levels as well as its potential as a therapeutic target.

In addition to their fundamental role in clearance, the kidneys release select molecules into the circulation, but whether any of these anabolic functions provides insight on kidney health is unknown. Using aptamer-based proteomics, we characterized arterial (A)-to-renal venous (V) gradients for >1,300 proteins in 22 individuals who underwent invasive sampling. Although most of the proteins that changed significantly decreased from A to V, consistent with renal clearance, several were found to increase, the most significant of which was testican-2. To assess the clinical implications of these physiologic findings, we examined proteomic data in the Jackson Heart Study (JHS), an African-American cohort (n = 1,928), with replication in the Framingham Heart Study (FHS), a White cohort (n = 1,621). In both populations, testican-2 had a strong, positive correlation with estimated glomerular filtration rate (eGFR). In addition, higher baseline testican-2 levels were associated with a lower rate of eGFR decline in models adjusted for age, gender, hypertension, type 2 diabetes, body mass index, baseline eGFR, and albuminuria. Glomerular expression of testican-2 in human kidneys was demonstrated by immunohistochemistry, immunofluorescence, and electron microscopy, while single-cell RNA sequencing of human kidneys showed expression of the cognate gene, SPOCK2, exclusively in podocytes. In vitro, testican-2 increased glomerular endothelial tube formation and motility, raising the possibility that its secretion has a functional role within the glomerulus. Taken together, our findings identify testican-2 as a podocyte-derived biomarker of kidney health and prognosis.

Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.

Aims: New tools are needed to identify heart failure (HF) risk earlier in its course. We evaluated the association of multidimensional cardiopulmonary exercise testing (CPET) phenotypes with subclinical risk markers and predicted long‐term HF risk in a large community‐based cohort. Methods and results: We studied 2532 Framingham Heart Study participants [age 53 ± 9 years, 52% women, body mass index (BMI) 28.0 ± 5.3 kg/m2, peak oxygen uptake (VO2) 21.1 ± 5.9 kg/m2 in women, 26.4 ± 6.7 kg/m2 in men] who underwent maximum effort CPET and were not taking atrioventricular nodal blocking agents. Higher peak VO2 was associated with a lower estimated HF risk score (Spearman correlation r: −0.60 in men and −0.55 in women, P < 0.0001), with an observed overlap of estimated risk across peak VO2 categories. Hierarchical clustering of 26 separate CPET phenotypes (values residualized on age, sex, and BMI to provide uniformity across these variables) identified three clusters with distinct exercise physiologies: Cluster 1—impaired oxygen kinetics; Cluster 2—impaired vascular; and Cluster 3—favourable exercise response. These clusters were similar in age, sex distribution, and BMI but displayed distinct associations with relevant subclinical phenotypes [Cluster 1—higher subcutaneous and visceral fat and lower pulmonary function; Cluster 2—higher carotid‐femoral pulse wave velocity (CFPWV); and Cluster 3—lower CFPWV, C‐reactive protein, fat volumes, and higher lung function; all false discovery rate < 5%]. Cluster membership provided incremental variance explained (adjusted R2 increment of 0.10 in women and men, P < 0.0001 for both) when compared with peak VO2 alone in association with predicted HF risk. Conclusions: Integrated CPET response patterns identify physiologically relevant profiles with distinct associations to subclinical phenotypes that are largely independent of standard risk factor‐based assessment, which may suggest alternate pathways for prevention. [ABSTRACT FROM AUTHOR]

AimsWhile heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF) are well described, determinants and outcomes of heart failure with mid-range ejection fraction (HFmrEF) remain unclear. We sought to examine clinical and biochemical predictors of incident HFmrEF in the community.Methods and resultsWe pooled data from four community-based longitudinal cohorts, with ascertainment of new heart failure (HF) classified into HFmrEF [ejection fraction (EF) 41-49%], HFpEF (EF ≥50%), and HFrEF (EF ≤40%). Predictors of incident HF subtypes were assessed using multivariable Cox models. Among 28 820 participants free of HF followed for a median of 12 years, there were 200 new HFmrEF cases, compared with 811 HFpEF and 1048 HFrEF. Clinical predictors of HFmrEF included age, male sex, systolic blood pressure, diabetes mellitus, and prior myocardial infarction (multivariable adjusted P ≤ 0.003 for all). Biomarkers that predicted HFmrEF included natriuretic peptides, cystatin-C, and high-sensitivity troponin (P ≤ 0.0004 for all). Natriuretic peptides were stronger predictors of HFrEF [hazard ratio (HR) 2.00 per 1 standard deviation increase, 95% confidence interval (CI) 1.81-2.20] than of HFmrEF (HR 1.51, 95% CI 1.20-1.90, P = 0.01 for difference), and did not differ in their association with incident HFmrEF and HFpEF (HR 1.56, 95% CI 1.41-1.73, P = 0.68 for difference). All-cause mortality following the onset of HFmrEF was worse than that of HFpEF (50 vs. 39 events per 1000 person-years, P = 0.02), but comparable to that of HFrEF (46 events per 1000 person-years, P = 0.78).ConclusionsWe found overlap in predictors of incident HFmrEF with other HF subtypes. In contrast, mortality risk after HFmrEF was worse than HFpEF, and similar to HFrEF.

BackgroundAtrial fibrillation (AF) has a substantial genetic basis. Identification of individuals at greatest AF risk could minimize the incidence of cardioembolic stroke.MethodsTo determine whether genetic data can stratify risk for development of AF, we examined associations between AF genetic risk scores and incident AF in 5 prospective studies comprising 18 919 individuals of European ancestry. We examined associations between AF genetic risk scores and ischemic stroke in a separate study of 509 ischemic stroke cases (202 cardioembolic [40%]) and 3028 referents. Scores were based on 11 to 719 common variants (≥5%) associated with AF at P values ranging from

Untargeted liquid-chromatography–mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ~3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts.Graphical Abstract

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

Aortic stiffness is associated with cognitive decline and cerebrovascular disease late in life, although these associations have not been examined in young adults. Understanding the effects of aortic stiffness on the brain at a young age is important both from a pathophysiological and public health perspective. The aim of this study was to examine the cross-sectional associations of aortic stiffness with cognitive function and brain aging in the Framingham Heart Study Third Generation cohort (47% men; mean age, 46 years). Participants completed the assessment of aortic stiffness (carotid-femoral pulse wave velocity), a neuropsychological test battery assessing multiple domains of cognitive performance and magnetic resonance imaging to examine subclinical markers of brain injury. In adjusted regression models, higher aortic stiffness was associated with poorer processing speed and executive function (Trail Making B-A; β±SE, -0.08±0.03; P

ObjectiveWe tested whether abnormal arterial stiffness and blood pressure would be associated with progression of brain aging measured by brain MRI and neurocognitive testing.MethodsFramingham Offspring Cohort participants (n = 1,223, 61 ± 9 years, 56% women) without previous stroke or dementia underwent applanation tonometry, brain MRI, and neurocognitive testing at examination 7 (1998-2001). Follow-up brain MRI and neurocognitive testing was performed at examination 8 (2005-2008, mean interval 6.4 ± 1.3 years). We related examination 7 inverse-transformed carotid-femoral pulse wave velocity (iCFPWV), central pulse pressure (CPP), and mean arterial pressure to changes in the following variables between examinations 7 and 8: total cerebral brain volume, white matter hyperintensity volume, and performance on executive function and abstraction tasks, the Trail Making Test, Parts B and A (ΔTrails B-A), and Similarities tests.ResultsHigher baseline iCFPWV and CPP were associated with greater progression of neurocognitive decline (iCFPWV and ΔTrails B-A association: SD unit change in outcome variable per SD change in tonometry variable [β] ± SE = 0.10 ± 0.04, p = 0.019; CPP and ΔSimilarities association: -0.08 ± 0.03, p = 0.013). Higher mean arterial pressure, but not iCFPWV or CPP, was associated with increase in white matter hyperintensity volume ([β ± SE] 0.07 ± 0.03, p = 0.017). No tonometry measures were associated with change in cerebral brain volume.ConclusionsIn middle-aged and older adults without evidence of clinical stroke or dementia, elevated arterial stiffness and pressure pulsatility are associated with longitudinal progression of subclinical vascular brain injury and greater neurocognitive decline. Treatments to reduce arterial stiffness may potentially reduce the progression of neurovascular disease and cognitive decline.

Osteoprotegerin (OPG) is involved in bone homeostasis and tumor cell survival. Circulating OPG levels are also important biomarkers of various clinical traits, such as cancers and atherosclerosis. OPG levels were measured in serum or in plasma. In a meta-analysis of genome-wide association studies in up to 10 336 individuals from European and Asian origin, we discovered that variants >100 kb upstream of the TNFRSF11B gene encoding OPG and another new locus on chromosome 17q11.2 were significantly associated with OPG variation. We also identified a suggestive locus on chromosome 14q21.2 associated with the trait. Moreover, we estimated that over half of the heritability of OPG levels could be explained by all variants examined in our study. Our findings provide further insight into the genetic regulation of circulating OPG levels.

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation.

Although age-dependent effects on blood pressure (BP) have been reported, they have not been systematically investigated in large-scale genome-wide association studies (GWASs). We leveraged the infrastructure of three well-established consortia (CHARGE, GBPgen, and ICBP) and a nonstandard approach (age stratification and metaregression) to conduct a genome-wide search of common variants with age-dependent effects on systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse (PP) pressure. In a two-staged design using 99,241 individuals of European ancestry, we identified 20 genome-wide significant (p ≤ 5 × 10(-8)) loci by using joint tests of the SNP main effect and SNP-age interaction. Nine of the significant loci demonstrated nominal evidence of age-dependent effects on BP by tests of the interactions alone. Index SNPs in the EHBP1L1 (DBP and MAP), CASZ1 (SBP and MAP), and GOSR2 (PP) loci exhibited the largest age interactions, with opposite directions of effect in the young versus the old. The changes in the genetic effects over time were small but nonnegligible (up to 1.58 mm Hg over 60 years). The EHBP1L1 locus was discovered through gene-age interactions only in whites but had DBP main effects replicated (p = 8.3 × 10(-4)) in 8,682 Asians from Singapore, indicating potential interethnic heterogeneity. A secondary analysis revealed 22 loci with evidence of age-specific effects (e.g., only in 20 to 29-year-olds). Age can be used to select samples with larger genetic effect sizes and more homogenous phenotypes, which may increase statistical power. Age-dependent effects identified through novel statistical approaches can provide insight into the biology and temporal regulation underlying BP associations.

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.