Genome-Wide Meta-Analyses of Plasma Renin Activity and Concentration Reveal Association With the Kininogen 1 and Prekallikrein GenesCLINICAL PERSPECTIVE
Background—The renin–angiotensin–aldosterone system (RAAS) is critical for regulation of blood pressure and fluid balance and influences cardiovascular remodeling. Dysregulation of the RAAS contributes to cardiovascular and renal morbidity. The genetic architecture of circulating RAAS components is incompletely understood.
Methods and Results—We meta-analyzed genome-wide association data for plasma renin activity (n=5275), plasma renin concentrations (n=8014), and circulating aldosterone (n=13289) from ≤4 population-based cohorts of European and European-American ancestry, and assessed replication of the top results in an independent sample (n=6487). Single-nucleotide polymorphisms (SNPs) in 2 independent loci displayed associations with plasma renin activity at genome-wide significance (P<5×10−8). A third locus was close to this threshold (rs4253311 in kallikrein B [KLKB1], P=5.5×10−8). Two of these loci replicated in an independent sample for both plasma renin and aldosterone concentrations (SNP rs5030062 in kininogen 1 [KNG1]: P=0.001 for plasma renin, P=0.024 for plasma aldosterone concentration; and rs4253311 with P<0.001 for both plasma renin and aldosterone concentration). SNPs in the NEBL gene reached genome-wide significance for plasma renin concentration in the discovery sample (top SNP rs3915911; P=8.81×10−9), but did not replicate (P=0.81). No locus reached genome-wide significance for aldosterone. SNPs rs5030062 and rs4253311 were not related to blood pressure or renal traits; in a companion study, variants in the kallikrein B locus were associated with B-type natriuretic peptide concentrations in blacks.
Conclusions—We identified 2 genetic loci (kininogen 1 and kallikrein B) influencing key components of the RAAS, consistent with the close interrelation between the kallikrein–kinin system and the RAAS.
The renin–angiotensin–aldosterone system (RAAS) is a central pathway in cardiovascular and renal physiology. Through a series of enzymatic reactions, the liver-derived protein angiotensinogen is transformed into angiotensin I (this conversion is catalyzed by renin) and subsequently into angiotensin II, which is the key effector of the RAAS, mediating multiple biological effects in the kidneys, the heart, and the vasculature through its local and systemic effects.1–4 Angiotensin II is a potent vasoconstrictor and adversely affects cardiac and vascular remodeling.1–3 On a parallel note, angiotensin II stimulates the production of aldosterone in the adrenal glands, which enhances sodium and water reabsorption in the kidneys.1,2 Thus, the RAAS is a major determinant of fluid and electrolyte hemostasis, blood pressure (BP) regulation and cardiovascular remodeling.1,3 Consequently, RAAS activity influences the development and progression of cardiovascular disease,1,2 and pharmacological inhibition of the RAAS has been shown to improve patient outcomes in a variety of clinical settings.2 Moreover, recent studies suggested a cross talk between adipose tissue and the adrenal gland, and high aldosterone concentrations were reported to be associated with the metabolic syndrome and type 2 diabetes mellitus.5
Clinical Perspective on p 140
Despite its clinical significance, the genetic architecture of the RAAS, as evident in circulating levels of its components, is incompletely understood. Previous studies have established different circulating components of the RAAS as heritable traits.6,7 To improve our understanding of the genetic determinants of the RAAS, we conducted a genome-wide association analysis of plasma renin (investigated by either its activity or concentration) and circulating aldosterone concentrations in ≤4 population-based cohorts with replication in a fifth independent cohort. Given the reported heritability of RAAS components,6,7 we hypothesized that these circulating RAAS biomarkers will be associated with common genetic variation in the general population.
Please find more detailed information about sample description and biomarker measurements in the Data Supplement.
Framingham Heart Study (FHS) Samples
RAAS biomarkers were measured at examination cycle 6 (1995–1998) of the Framingham offspring cohort (Generation 2 [Gen 2])8 and at the first examination cycle (2002–2005) of the Third generation cohort (Generation 3 [Gen 3]).9 At each Heart Study visit, participants were comprehensively characterized with respect to cardiovascular risk factors and subclinical disease measures. All participants provided written informed consent, and the study protocol was approved by the institutional review board at the Boston University Medical Center.
Cooperative Health Research in the Region of Augsburg (KORA) Sample
KORA comprises several population-based cohort studies in the region of Augsburg, Southern Germany.10 This analysis includes data from the follow-up examination KORA F4 (2006–2008) of the KORA S4 survey (1999/2000).10 Participants with missing genotype or phenotype data were excluded from the present analyses, as were participants reporting intake of diuretics or participants with a renin or aldosterone concentration of >1000 ng/L. The final study sample comprised 1786 participants. The studies were approved by the ethics committee of the Bavarian Medical Association (Bayerische Landesärztekammer) and all study participants gave written informed consent.
Study of Health in Pomerania Sample
Study of Health in Pomerania (SHIP) is a population-based cohort study in West Pomerania, the north-east area of Germany.11
The first follow-up examination (SHIP-1) was conducted from March 2003 to July 2006.11 Participants were comprehensively phenotyped with respect to cardiometabolic traits and subclinical disease burden, as detailed elsewhere.11 All participants gave written informed consent. The study protocol was approved by the Ethics Committee of the University of Greifswald.
Supplémentation en Vitamines et Minéraux Antioxydants (SUVIMAX) Study Sample
Participants of the SUVIMAX study were healthy volunteers free of hypertension, cardiovascular disease, or cancer at baseline.12 The recruitment of the SUVIMAX cohort was performed in metropolitan France, and all individuals were of European descent. RAAS biomarkers and genetic information were available in a subsample of 1518 participants.
Prevention of REnal and Vascular ENd-stage Disease (PREVEND) Sample
PREVEND is a general population sample from the city of Groningen (The Netherlands). This longitudinal study was primarily designed to assess the association between urinary albumin excretion and cardiovascular and renal diseases.13 The study design has been described in detail elsewhere.14 Basic vascular risk factors were determined on 2 occasions in the examination center.14 The sample for this analysis comprised 6487 participants.
Measurements of Renin and Aldosterone
In FHS, KORA, and SUVIMAX, blood samples were taken in the early morning from seated participants after an overnight fast. In PREVEND, fasting blood samples were taken between 8 am and 4 pm. In SHIP, blood was drawn throughout the day (between 8 AM and 8 PM) in nonfasting participants, while they were taking their regular medication. Aldosterone measurements were available in all cohorts (FHS Gen 2 and Gen 3, KORA, SHIP, SUVIMAX, and PREVEND; Figure 1); plasma renin concentrations (PRCs) were measured in the FHS Gen 2, KORA, SHIP, and PREVEND samples; plasma renin activity (PRA) was determined in the FHS Gen 3 and SUVIMAX samples (Figure 1). Details on the methods to determine RAAS biomarkers in the different samples are provided in the Data Supplement.
All cohorts were genotyped using genome-wide arrays from Affymetrix or Illumina. Details on the specific arrays used and the quality control measures applied to genetic data are detailed in the Data Supplement.
Meta-Analysis of Genome-Wide Association Data and Replication
The basic study design is displayed in Figure 1. The biomarkers were natural logarithmically transformed before analyses. Within each cohort, the association between ≈2.5 million single-nucleotide polymorphisms (SNPs; exposure) and biomarkers of the RAAS were assessed using linear regression models adjusted for age and sex, assuming an additive genetic model. Aldosterone levels, PRC, and PRA served as dependent variables (each biomarker considered separately). In the FHS sample, a linear mixed effects model was used to adjust for familial correlation.15 For aldosterone levels, genome-wide association results from Gen 2 and Gen 3 cohorts were combined by using Obrien method16 because aldosterone measurements were correlated between Gen 2 and Gen 3. The Genome-Wide Association Study (GWAS) data from ≤4 cohorts (FHS, KORA, SHIP, and SUVIMAX) were meta-analyzed with a sample size–weighted approach (combining sample size–weighted Z statistics from each cohort) using the METAL software.17
Corrections for genomic controls were performed within each cohort, and after the meta-analyses. To display the association results from the meta-analyses for each biomarker graphically, we plotted the P value of each SNP versus its chromosomal position (Manhattan plot). Furthermore, we also plotted the observed versus the expected P-value distribution under the null hypothesis of no association between biomarker and SNPs (quantile–quantile plots) for each biomarker. Genetic variants with a minor allele frequency (MAF) >0.05, imputation quality ratio >0.75, and a P≤5×10−8 for any of the 3 biomarkers were assessed for replication in the PREVEND sample. In PREVEND, only PRC and plasma aldosterone levels were available. Thus, the top loci associated with each RAAS biomarker in the meta-analyses of FHS, KORA, SHIP, and SUVIMAX were tested for association with PRCs and aldosterone levels in PREVEND using a linear regression model adjusting for age and sex (Figure 1). Furthermore, because PRC and aldosterone levels were also available in the discovery samples (Figure 1), we performed a look-up of the top renin activity SNPs with respect to renin and aldosterone concentrations in the individual discovery cohorts.
Finally, we performed fine mapping of the top regions associated with PRA (because these regions reached or were close to genome-wide significance) and of the candidate genes REN and CYP11B2 (±500 kb around the gene) based on imputations to the 1000 genome data set. These 1000 genome imputations were available in FHS, SHIP, and KORA.
Look-Up of Top SNPs in Blacks
We performed a look-up of our top loci, associated with PRA or PRCs in Europeans, to assess their associations with PRA, renin concentration, and B-type natriuretic peptide levels in blacks in the Jackson Heart Study.18
Association of Top SNPs With BP Traits, Renal Traits, and Echocardiographic Left Ventricular Mass in Europeans
In secondary analyses, we tested the successfully replicated SNPs from our GWAS (rs5030062 and rs4253311) for associations with traits known to be influenced by the RAAS, that is, BP, left ventricular mass, and renal traits. For BP, we related our top SNPs to systolic and diastolic BP in the International Consortium for Blood Pressure Genome-Wide Association Studies.19 We also assessed the association of these 2 SNPs with left ventricular mass in the EchoGen Consortium,20 and with renal traits in the CKDGen Consortium21,22 (Figure 1). In the latter consortium, we tested specifically the association of these SNPs with the estimated glomerular filtration rate, with the urinary albumin:creatinine ratio, and with chronic kidney disease (as binary trait).21,22
Pathway analyses provide a potential route to investigate the collective effects of multiple genetic variants on biological systems. For each genetic variant, we assigned an overall SNP score to indicate its association with RAAS-related traits. This SNP score was equivalent to the most significant P value among the 3 RAAS traits examined (PRC, PRA, and circulating aldosterone levels). The genetic variants with the score assigned were then mapped back to the human reference genome (NCBI Build 36, 2006) and we examined their locations relative to RefSeq genes (March 17, 2013), and gene scores were obtained. The gene score was defined as the most significant variant that was located within 110 kb upstream and 40 kb downstream of the gene’s most extreme transcript boundaries. We selected these boundaries because they are expected to encompass the majority of cis-expression quantitative trait loci based on expression data.23 Similar boundaries have been used in earlier studies.24,25 Of the 23 696 genes evaluated, 548 reached a score <1.0×10−4. These genes were then imported into Ingenuity for pathway analyses (Ingenuity Systems, Redwood, CA). Fisher exact test was used to justify the enrichment of each of the canonical pathways.
The discovery sample size comprised 13 289 participants for circulating aldosterone concentrations, 8014 participants for PRCs, and 5275 participants for PRA. Baseline clinical and biochemical characteristics of the study sample are shown in Table 1.
GWAS Meta-Analysis and Replication for Plasma Renin Activity, Plasma Renin Concentrations, and Circulating Aldosterone Levels
We identified 2 independent loci that were associated with PRA at a genome-wide significant level (P<5×10−8). A third locus was close to this threshold (tagged by rs4253311; P=5.5×10−8). The respective Manhattan plot and the quantile–quantile plot for PRA are shown in Figure 2A and 2B. The quantile–quantile plot for PRA in the FHS Gen 3 sample only is provided in Figure I in the Data Supplement. The top SNPs were rs12374220, an intronic variant in the TENM3 gene, rs5030062 in intron 6 of the kininogen 1 [KNG1] gene, and rs4253311 in intron 11 of the kallikrein B[KLKB1] gene (Tables 2 and 3). The 2 latter SNPs could successfully be replicated in an independent sample (PREVEND), that is, they showed nominal statistically significant associations with PRCs and circulating aldosterone levels in PREVEND (Table 3; PRA was not available in PREVEND). However, except for an association of rs5030062 with aldosterone levels in KORA, SNPs rs12374220, rs5030062, and rs4253311 provided no evidence for association with renin concentrations (Table I in the Data Supplement) and aldosterone levels (Table II in the Data Supplement) in the individual discovery cohorts.
Regional plots of the kallikrein B locus and the kininogen 1 locus are shown in Figure 3A and 3B, respectively. Analyzing these 2 regions using a data set imputed to the 1000 genome data26 revealed essentially similar results. Regional plots based on the 1000 genome imputations are shown in Figure IIA and IIB in the Data Supplement.
SNP rs5030062 and rs4253311 explained 0.85% and 0.87% of PRA variance, respectively. PRA, stratified by rs5030062 genotype (Figure IIIA in the Data Supplement) and stratified by rs4253311 genotype (Figure IIIB in the Data Supplement), are displayed. Top loci associated with PRA stratified by sample, for example, in the FHS Gen 3 sample and in the SUVIMAX sample, are provided in Table III in the Data Supplement. The association results for all 3 SNPs were consistent in directionality in FHS and SUVIMAX but reached a higher level of statistical significance in FHS than in SUVIMAX, possibly because of the much larger sample size in FHS.
The Manhattan plot (Figure IVA in the Data Supplement) and the quantile–quantile plot (Figure IVB in the Data Supplement) for the genome-wide analysis for PRC are shown. SNPs in the NEBL gene reached genome-wide significance for PRC in the discovery sample (top SNP rs3915911; meta-analytic P=8.81×10−9), but this observation was not replicated in the PREVEND cohort (P=0.81; Table IV in the Data Supplement).
No SNP reached genome-wide significance (P≤5×10−8) for circulating aldosterone concentrations (Figure VA and VB in the Data Supplement). The top SNP associated with circulating aldosterone levels was SNP rs6986428 on chromosome 8 with a meta-analytic P=4.01×10−6. The top 4 loci associated with circulating aldosterone levels and the replication results are displayed in Table V in the Data Supplement.
Look-Up of Top SNPs in Blacks
In the Jackson Heart Study, rs5030062 (associated with PRA in our sample and in the replication sample PREVEND) was associated with PRC (β=0.100; P=0.017) and SNP rs3915911 (associated with PRC in our discovery sample, but not in the replication cohort; Table IV in the Data Supplement) displayed evidence for association with PRA in blacks (β=0.14; P=0.003). Furthermore, rs3733402, highly correlated with rs4253311 (r2=0.91), and also located in the KLKB1 gene, was associated with circulating B-type natriuretic peptide levels in blacks (companion article by Dr Musani et al27).
Association of Top Loci With Systolic and Diastolic BP, Renal Traits, and Left Ventricular Mass
In secondary analyses, rs5030062 and rs4253311 (the 2 SNPs that reached or were close to genome-wide significance for PRA in the discovery sample and replicated in PREVEND) were assessed for their association with systolic and diastolic BP. In the International Consortium for Blood Pressure Genome-Wide Association Studies,19 neither SNP was associated with systolic or diastolic BP (Table 4). Furthermore, no statistically significant associations could be observed for these 2 SNPs with 3 renal traits (ie, estimated glomerular filtration rate, urinary albumin:creatinine ratio, and chronic kidney disease; Table 4). SNP rs4253311 displayed some evidence for association with left ventricular mass (P=0.03; Table 4).
Association of the Genetic Variation at the Renin Locus and at the CYP11B2 Locus With RAAS Biomarkers
Our genome-wide data set included 13 genetic variants in or close to the REN gene (encoding renin) and 7 SNPs at the CYP11B2 locus (encoding the aldosterone synthase). Genetic variants in the REN gene were not associated with PRA or PRC, nor were SNPs at the CYP11B2 locus associated with circulating aldosterone concentrations (Table VIA–VIC in the Data Supplement). Additional analyses based on the 1000 genome imputation (restricted to variants with an MAF, ≥1%) revealed rs72745753 (P=0.0022; MAF, 1%), rs189709785 (P=0.001687; MAF, 4%), and rs34617726 (P=0.00128; MAF, 14%) as the SNPs in the candidate regions most significantly associated PRC, PRA, and aldosterone, respectively. However, given the number of SNPs tested in the CYP11B2 (n=4275) and the REN (n=3167 [PRC-related analyses] and n=2979 [PCA-related analyses]) regions, these associations were not considered to be statistically significant.
Using genome-wide association data from ≤4 population-based cohorts with replication in an independent large fifth cohort, we identified 2 genetic loci that displayed statistically significant associations with clinically relevant hormones of the RAAS. The main findings of our analyses are summarized below. First, genetic variations in the kininogen 1 and in the kallikrein B genes were associated with PRA in the discovery sample and with PRC and circulating aldosterone concentrations in the replication sample (where PRA was not available). These variants, however, were not associated with PRC or aldosterone levels in the individual discovery cohorts. SNP rs5030062 (in the kininogen 1 gene) was also associated with PRC in blacks. Second, the top SNPs in these genes were not related to BP or renal traits. Third, pathway analyses identified 2 canonical pathways that were significantly enriched for RAAS-related genes: the Gαs signaling pathway and the PKA signaling pathway. Fourth, no genetic variant was associated with circulating aldosterone levels in a genome-wide significant fashion. Fifth, genetic variation in genes encoding renin and the aldosterone synthase, respectively, was not related to PRC or activity or to aldosterone levels.
In the Context of the Published Literature
Possible Mechanism for the Observed Association Between SNPs in the Kallikrein–Kinin System and RAAS Biomarkers
As a key finding, we observed that genetic variation within the kallikrein–kinin system (in the kininogen 1 gene and in the kallikrein B gene) was associated with key biomarkers of the RAAS. The kininogen 1 gene encodes both high and low molecular weight kininogen, the precursors of bradykinin and kallidin (Lys-Bradykinin), respectively.28 The kallikrein B gene encodes plasma prekallikrein, a serine protease that, on transformation to kallikrein, catalyzes the conversion of high molecular weight kininogen to bradykinin (Figure 5)27 and possibly other factors, such as adrenomedullin and endothelin-1.29
Thus, genetic variation in the precursor substance of bradykinin and in the precursor substance of the enzyme (kallikrein) that catalyzes the conversion of high molecular weight kininogen to bradykinin was related to biomarkers of the RAAS in our genome-wide analysis.
These observations are consistent with the concept that the kallikrein–kinin system and the RAAS are tightly interrelated.30 A prime example for this interaction is the angiotensin-converting enzyme (ACE), which catalyzes on one hand the conversion of angiotensin I to II, and on the other hand, degrades bradykinin.30,31 In other words, the ACE affects the concentrations of key effectors of the RAAS (ie, angiotensin II) and the kallikrein–kinin system, (ie, bradykinin) in opposite directions. Also both the systems interact at the receptor level. The AT1 receptor and the bradykinin 2 receptor have been shown to interact by forming heterodimers physically, and this heterodimerization affects downstream signaling.30 Rodents lacking the bradykinin 2 receptor gene display reduced renin mRNA expression as compared with wild-type animals, underscoring that kinins influence renin synthesis through activation of the bradykinin 2 receptor.30,32 Our genetic-epidemiological data add support to this concept by indicating that genetic variation in the kallikrein–kinin system influences interindividual variation of PRA.
Our analytic sample was restricted to participants of European descent. However, our top SNPs associated with PRA on a genome-wide scale also displayed some evidence for association with related traits in participants of black ancestry: SNP rs5030062 (in the kininogen 1 gene) displayed evidence for association with renin concentration in participants of the Jackson Heart Study; and in an companion article, Dr Musani et al report that a close proxy of SNP rs4253311 (rs3733402; r2=0.91) in the kallikrein B gene was associated with circulating B-type natriuretic peptide levels in blacks in a genome-wide significant fashion. Overall, these data lend support to the concept that the kallikrein–kinin system is involved in regulating pathways with high relevance for the cardiovascular system, including the RAAS and the B-type natriuretic peptide pathway. Furthermore, these interrelations seem to be relevant in individuals of both European and African descent, even though the association findings are not identical in these 2 ethnicities (but display important overlap). Experimental data further support the significance of the 2 successfully replicated SNPs for the cardiovascular system. SNP rs5030062 (in the kininogen 1 gene) was predicted to disrupt the binding site of a forkhead box transcription factor,33 which has been shown to be relevant for several cardiovascular traits, including the development of atherosclerotic plaque lesions.34 SNP rs4253311 is in high linkage disequilibrium (r2=0.91) with a missense mutation, rs3733402, which is associated with plasma prekallikrein deficiency.35
Association of Top SNPs With BP, Renal Traits, and Left Ventricular Mass
Because the RAAS is an important modulator of acute and chronic changes in BP and a key determinant of renal function, we assessed the association of the top (genome-wide significant) SNPs in the kininogen 1 gene and in the kallikrein B gene (rs5030062 and rs4253311) with BP and renal traits. Both the SNPs were related to neither systolic or diastolic BP nor renal traits, including urinary albumin:creatinine ratio, estimated glomerular filtration rate, or chronic kidney disease (as a binary trait) in large international consortia.19,21,22 SNP rs4253311 provided evidence for association with left ventricular mass (P=0.03), but this association would not remain statistically significant on correction for the multiple look-ups of several intermediate cardiovascular traits. Thus, in our sample, genetic variation associated with RAAS biomarkers did not translate into observable associations with BP or renal traits. We submit, though, that BP and renal function are complex traits that are regulated by multiple genetic, environmental, and lifestyle factors. Thus, the effect of single genetic variants primarily identified through modest associations with the RAAS pathway, on intermediate cardiovascular disease traits (such as BP or renal function), would not be expected to be prominent.
Association of Genetic Variation in REN and CYP11B2 With Circulating Renin and Aldosterone Levels
In previous studies, genetic variation in the renin (REN) gene itself has been associated with plasma renin activity36,37 and the risk of incident hypertension.36,37 On a parallel note, genetic variation in the CYP11B2 gene, which encodes the aldosterone synthase (MIM 124080), has been related to the ratio of plasma aldosterone concentration/PRA.38 In our analyses, however, a limited number of SNPs in these 2 genes were not associated with PRC, PRA, or circulating aldosterone levels, respectively. This is consistent with results from a previous GWAS in Japanese individuals (n=936).39
Pathway analyses identified the Gαs signaling pathway and the PKA signaling pathway as being over-represented with RAAS-related genes. G-proteins play a central role in signal transduction. Gαs is a stimulatory G-protein subunit activating the adenylate cyclase, which—on such activation—increases intracellular cAMP levels.40–42 cAMP is an important second messenger binding, for example, to PKA, which in turn, influences the transcription of multiple genes.41,42 As reviewed in detail by Kim et al43, the Gαs/cAMP/PKA pathway plays a central role in the regulation of renin secretion, one of the key biomarkers of the RAAS. Mice with targeted deletion of Gαs in juxtaglomerular cells display lower basal renin secretion and expression43 and a blunted response to chronic ACE inhibition or AT1 receptor blockade (impaired "feedback loop"),44 indicating that Gαs signaling is highly relevant to renin secretion and expression under steady-state and dynamic conditions.43
Coadministration of ACE inhibitors or AT1 antagonists with inhibitors of the adenylate cyclase likewise substantially reduces renin expression as compared with ACE inhibitors or AT1 antagonists alone.43,44 Thus, cell-specific disruption of the Gαs gene in juxtaglomerular cells and inhibition of the adenylate cyclase leads to impaired renin secretion in response to chronic blockade of the RAAS.43,44 In addition, it is well established that the sympathetic nervous system is an important determinant of renin secretion and these effects are likewise mediated via the cAMP/PKA pathway.43 Thus, multiple lines of evidence link the RAAS to the Gαs/cAMP/PKA axis and our pathway analyses are in agreement with these experimental data, supporting the significance of Gαs/PKA signaling for the regulation of the RAAS.
Strengths and Limitations
To our knowledge, this analysis is the first comprehensive GWAS analyzing the main circulating biomarkers of the RAAS conjointly. Additional strengths include the considerable samples size, the careful phenotyping, and genotyping in the contributing cohorts as well as their population-based character. As an apparent limitation, PRA was measured in only 2 cohorts (total n=5275) of the discovery sample, and not in the replication sample. Therefore, the genome-wide significant hits for PRA from the discovery sample had to be tested for association with PRCs and circulating aldosterone levels in the replication sample. However, in the absence of large changes in plasma angiotensinogen, measurements of PRA and PRC investigate the same biomarker, circulating active renin, almost exclusively of renal origin, and both biomarkers (renin concentration and renin activity) are strongly correlated.45 In population studies, the control of the physiological factors, which influence plasma renin, mainly posture and sodium intake, are less well controlled than in metabolic studies, and this may lead to an underestimation of the genetic association. However, we do not expect major changes in angiotensinogen in an epidemiological setting. Furthermore, because these 3 biomarkers are tightly interrelated and part of the same pathway, the key finding of our investigation, that genetic variation in the kallikrein–kinin systems is associated with RAAS biomarker levels, is supported by the replication analyses. As an additional limitation, the top SNPs associated with PRA in the discovery cohorts were associated with circulating renin concentrations and aldosterone levels in an independent sample (PREVEND) but not in the individual discovery cohorts themselves. Potential explanations for this discrepancy include the smaller sample sizes and the larger coefficients of variation for renin and aldosterone measurements in some individual discovery cohorts as compared with the PREVEND sample, resulting in larger measurement errors and reduced statistical power to detect modest associations in the discovery as compared with the replication cohort.
In conclusion, we present data from a meta-analysis of large genome-wide association studies for 3 biomarkers of the RAAS in ≤13 289 Europeans and European Americans. We observed that genetic variation in the kallikrein–kinin system was associated with PRA at a genome-wide significant level. This is consistent with experimental data linking the RAAS to the kallikrein–kinin system. Furthermore, pathway analyses identified 2 canonical pathways that were significantly enriched for RAAS-related genes: the Gαs signaling pathway and the PKA signaling pathway, which influence renal renin release. Our observations require replication in other independent cohorts, including samples from other ethnicities and age groups, to address the generalizability of our findings. Furthermore, the significant SNPs warrant further experimental exploration to elucidate in detail the molecular mechanisms underlying the observed associations.
Sources of Funding
A detailed description of funding sources is provided in the Data Supplement.
Drs Wang, Vasan, and Wilson received National Institutes of Health funding related to this article. Dr de Boer received lecture fees from Novartis and Medcon. The other authors report no conflicts.
From The Framingham Heart Study, MA (W.L., M.-H.C., H.L., T.J.W., R.S.V.); Institute of Epidemiology, Christian-Albrechts-University, Kiel, Germany (W.L., S.S.); Section of Preventive Medicine and Epidemiology (R.S.V.) and Department of Neurology (M.-H.C.), Boston University School of Medicine, MA; Interfaculty Institute for Genetics and Functional Genomics (A.T., U.V.), Institute for Community Medicine, Section Study of Health in Pomerania - Clinical-Epidemiological Research (A.T., H.V.), Institute of Clinical Chemistry and Laboratory Medicine (M.N., A.H., H.W.), Department of Internal Medicine B (M.D.), and Institute of Physiology (R.R.), University Medicine Greifswald, Greifswald, Germany; Departments of Cardiology (R.A.d.B., W.H.v.G., D.J.v.V., P.v.d.H.), Internal Medicine (S.J.L.B.), Nephrology (G.N.), and University Medical Center Groningen (S.J.L.B., G.N.), University of Groningen, Groningen, The Netherlands; Department of Medicine, University of Mississippi Medical Center, Jackson (E.R.F., S.K.M., J.G.W.); Institute of Epidemiology I (H.-E.W.), Institute of Epidemiology II (C.M.), and Research Unit Molecular Epidemiology (A.-K.P., R.R), Helmholtz Zentrum München, München, Germany; Department of Internal Medicine IV, University Hospital Freiburg, Freiburg, Germany (Y.L.); Inserm, Clinical Investigation Centre (CIC1418), Paris, France (J.M.); Faculté de Médecine, Université Paris-Descartes, Paris, France (J.M.); Institut National de la Santé et de la Recherche Médicale, U1153, University Paris 13, Bobigny, France (S.H.); Medizinische Klinik und Poliklinik IV, Ludwig Maximilians University Hospital, Munich, Germany (M.B., M.R.).
↵* Drs Wallaschofski, Meneton, van der Harst, Reincke, and Vasan contributed equally to this work.
Guest Editor for this article was Päivi Pajukanta, MD, PhD.
The Data Supplement is available at http://circgenetics.ahajournals.org/lookup/suppl/doi:10.1161/CIRCGENETICS.114.000613/-/DC1.
- Received March 14, 2014.
- Accepted October 27, 2014.
- © 2014 American Heart Association, Inc.
- Weir MR,
- Dzau VJ
- Heeneman S,
- Sluimer JC,
- Daemen MJ
- Kannel WB,
- Feinleib M,
- McNamara PM,
- Garrison RJ,
- Castelli WP
- Splansky GL,
- Corey D,
- Yang Q,
- Atwood LD,
- Cupples LA,
- Benjamin EJ,
- et al
- Völzke H,
- Alte D,
- Schmidt CO,
- Radke D,
- Lorbeer R,
- Friedrich N,
- et al
- de Boer RA,
- Schroten NF,
- Bakker SJ,
- Mahmud H,
- Szymanski MK,
- van der Harst P,
- et al
- Hillege HL,
- Janssen WM,
- Bak AA,
- Diercks GF,
- Grobbee DE,
- Crijns HJ,
- et al
- Chen MH,
- Yang Q
- Willer CJ,
- Li Y,
- Abecasis GR
- Böger CA,
- Chen MH,
- Tin A,
- Olden M,
- Köttgen A,
- de Boer IH,
- et al
- Fox ER,
- Musani SK,
- Barbalic M,
- Lin H,
- Yu B,
- Ogunyankin KO,
- et al
- Segrè AV,
- Groop L,
- Mootha VK,
- Daly MJ,
- Altshuler D
- Musani SK,
- Fox ER,
- Kraja A,
- Bidulescu A,
- Lieb W,
- Lin H,
- Beecham A,
- Chen M,
- Felix JF,
- Fox C,
- Kao WH,
- Kardia SL,
- Liu C,
- Nalls MA,
- Rundek T,
- Sacco RL,
- Smith J,
- Sun YV,
- Wilson G Sr,
- Zhang Z,
- Mosley TH,
- Taylor HA Jr,
- Vasan RS
- Yosipiv IV,
- Dipp S,
- El-Dahr SS
- Ward LD,
- Kellis M
- Bot PT,
- Grundmann S,
- Goumans MJ,
- de Kleijn D,
- Moll F,
- de Boer O,
- et al
- Sun B,
- Williams JS,
- Pojoga L,
- Chamarthi B,
- Lasky-Su J,
- Raby BA,
- et al
- Zurita AR,
- Birnbaumer L
- Lalli E,
- Sassone-Corsi P
- Castrop H,
- Höcherl K,
- Kurtz A,
- Schweda F,
- Todorov V,
- Wagner C
- Chen L,
- Kim SM,
- Eisner C,
- Oppermann M,
- Huang Y,
- Mizel D,
- et al
The renin–angiotensin–aldosterone system (RAAS) is a central pathway in cardiovascular and renal physiology that is frequently targeted pharmacologically in a variety of clinical settings. Despite the clinical significance of the RAAS, environmental and genetic factors influencing circulating RAAS biomarkers are incompletely understood. We evaluated the genetic correlates of 3 important RAAS biomarkers by meta-analyzing genome-wide association data for plasma renin activity (n=5275), plasma renin concentrations (n=8014), and circulating aldosterone levels (n=13 289) from ≤4 population-based discovery cohorts, and assessed replication of the top associations in an independent sample (n=6487). No locus reached genome-wide significance for circulating aldosterone levels. Single-nucleotide polymorphisms in 2 independent loci displayed associations with plasma renin activity in a genome-wide significant fashion (P<5×10−8). A third locus demonstrated a borderline association that was close to this threshold (rs4253311 in the kallikrein B [KLKB1] gene, P=5.5×10−8). Associations for 2 of these loci were successfully replicated in relation to plasma renin concentration and circulating aldosterone levels (single-nucleotide polymorphism rs5030062 in the kininogen 1 gene; and rs4253311 in the KLKB1 gene). Single-nucleotide polymorphisms in the NEBL gene demonstrated a genome-wide significant association with plasma renin concentration in the discovery sample, but the findings were not replicated in the independent sample. Our genetic analyses identified 2 loci (kininogen 1 and kallikrein B) encoding proteins of the kallikrein–kinin system that were associated with key components of the RAAS, providing further evidence for a close interrelation between the kallikrein–kinin system and the RAAS.