Figures
Abstract
Chronic kidney disease (CKD) can be a consequence of diabetes, hypertension, immunologic disorders, and other exposures, as well as genetic factors that are still largely unknown. Glomerular filtration rate (GFR), which is widely used to measure kidney function, has a heritability ranging from 25% to 75%, but only 1.5% of this heritability is explained by genetic loci that have been identified to date. In this study we tested for associations between GFR and 234 SNPs in 26 genes from pathways of blood pressure regulation in 3,025 rural Chinese participants of the “Genetic Epidemiology Network of Salt Sensitivity” (GenSalt) study. We estimated GFR (eGFR) using baseline serum creatinine measurements obtained prior to dietary intervention. We identified significant associations between eGFR and 12 SNPs in 6 genes (ACE, ADD1, AGT, GRK4, HSD11B1, and SCNN1G). The cumulative effect of the protective alleles was an increase in mean eGFR of 4 mL/min per 1.73 m2, while the cumulative effect of the risk alleles was a decrease in mean eGFR of 3 mL/min per 1.73 m2. In addition, we identified a significant interaction between SNPs in CYP11B1 and ADRB2. We have identified common variants in genes from pathways that regulate blood pressure and influence kidney function as measured by eGFR, providing new insights into the genetic determinants of kidney function. Complex genetic effects on kidney function likely involve interactions among genes as we observed for CYP11B1 and ADRB2.
Citation: Montasser ME, Shimmin LC, Gu D, Chen J, Gu C, Kelly TN, et al. (2014) Variation in Genes that Regulate Blood Pressure Are Associated with Glomerular Filtration Rate in Chinese. PLoS ONE 9(3): e92468. https://doi.org/10.1371/journal.pone.0092468
Editor: Antonio Carlos Seguro, University of São Paulo School of Medicine, Brazil
Received: December 6, 2013; Accepted: February 21, 2014; Published: March 21, 2014
Copyright: © 2014 Montasser et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The Genetic Epidemiology Network of Salt Sensitivity (GenSalt) is supported by several grants (U01HL072507, R01HL087263, and R01HL090682) from the National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Chronic kidney disease (CKD) is a major risk factor for cardiovascular disease and all-cause mortality, placing a huge burden on the health care system [1], [2]. CKD is a complex trait, regulated by interactions of several environmental and genetic factors [3]. CKD can arise as a consequence of diabetes, hypertension, immunologic disorders (such as lupus or primary glomerulonephritis), and a variety of other exposures. While the genetic causes of monogenic forms of renal diseases are well established, those contributing to the common forms of CKD are still largely unknown [4]. Recent meta analyses of genome wide association studies (GWAS) identified several loci for kidney function indices and CKD, collectively explaining a very small fraction of the variability of these traits, leaving most of their genetic components undetermined [5]. Earlier linkage analyses and candidate gene studies also produced inconsistent or unconfirmed results [6]–[9]. Most study populations were ascertained on the basis of disease status, resulting in enrichment for diabetes, obesity, and hypertension, thus complicating genetic studies of CKD [10], [11].
Genetic studies of CKD can benefit from a pathway-based targeted approach that includes testing for genetics and environmental interactions [12] for an intermediate quantitative trait [13] such as glomerular filtration rate (GFR) that is widely used to measure kidney function. GFR is a complex trait with an estimated heritability of 25–75% [14]. However, only about 1.5% of its variability has been explained by the genetic loci that have been identified so far [5].
In the current study, we examined the genetic factors that may influence GFR in rural Chinese participants of the “Genetic Epidemiology Network of Salt Sensitivity” (GenSalt) study, who did not have clinical evidence of overt CKD. Estimated GFR (eGFR) values were calculated from serum creatinine measurements obtained during a three-day observation period preceding dietary intervention, while the participants consumed their usual diet. We tested single nucleotide polymorphisms (SNPs) in 26 genes from pathways of blood pressure regulation for association with eGFR measures. This genetic study of GFR provides insight into the genetic determinants of kidney function in a general population sample of individuals without CKD, and potentially into the initiation and progression of CKD.
Materials and Methods
Ethics statement
Institutional review boards at Tulane University Health Sciences Center, Washington University School of Medicine, University of Texas School of Public Health, Fu Wai Hospital and Chinese National Human Genome Center at Beijing, Chinese Academy of Medical Sciences approved the GenSalt study. Written informed consents for the baseline observation and for the intervention program were obtained from each participant.
Study population
The GenSalt Study was conducted in Han Chinese families living in six rural villages in Northern China. Families were recruited through 18–60 year old probands who were either prehypertensive or had stage-1 hypertension (SBP 130–160 mm Hg and/or DBP 85–100 mm Hg), but had never been treated with antihypertensive medication. Parents, spouses, siblings, and offspring were invited to participate in the study. Family members were excluded if they had stage-2 hypertension, a history of CVD, diabetes, or heavy alcohol consumption, or were pregnant, on a low sodium diet or taking anti-hypertensive medications. A total of 3,025 individuals in 631 families participated in this study. A large number of demographic, anthropomorphic, and medical variables were measured in GenSalt participants. More information regarding participants recruitment and measurements are available elsewhere [15]. Institutional Review Board approval for this study was obtained at all of the participating institutions and all study participants signed an informed consent document.
Phenotype measurements
Serum creatinine was measured during a 3-day baseline observation period while the study participants consumed their usual diet prior to a GenSalt dietary intervention. During this period, an overnight fasting blood sample was obtained from each participant by venipuncture. This was used to measure serum creatinine by the modified kinetic Jaffe reaction method. GFR was estimated using an amended formulation of the Modification of Diet in Renal Disease (MDRD) study equation, specifically designed for use in healthy individuals [16]: eGFR in mL/min per 1.73 m2 = 216 × (serum creatinine in mg/dL)−0.490 × (age in years)−0.192 × 0.923 (if female).
Gene and SNP selection and genotyping
The GenSalt genotyping effort focused on 26 blood pressure candidate genes. The genes were selected based on their presumed role in blood pressure homeostasis and being a part of blood pressure regulation pathways such as the renin angiotensinsystem (REN, RENBP, AGT, AT2R1, AT2R2, ACE), the aldosterone system (CYP11B1, CYP11B2, MLR, HSD11B1, HSD11B2, CYP3A5), the endothelial system (EDN1, NOS3, SELE), the sympathetic nervous system (GRK4, ADRB2), alternative renin angiotensinsystem pathway (ACE2, APLN, AGTRL1), as well as atrial natriuretic peptide genes (NPR3, NPPA), sodium channels genes (SCNN1B, SCNN1G), and intracellular messengers genes (GNB3, ADD1). We selected 234 SNPs within these genes based on linkage disequilibrium (LD) structure in the Chinese population from the International HapMap project [17]. SNP genotyping was performed using the SNPlex platform (Applied Biosystems, Foster City CA) according to the manufacturer's protocol [18]. We excluded 41 SNPs in three genes from the association analysis due to low call rate (<80%), low minor allele frequency (MAF <0.05), or severe deviation from Hardy-Weinberg Equilibrium (HWE) (p<0.001). Detailed information concerning the remaining 193 SNPs within 24 genes is listed in Table S1.
Statistical analysis
Plink and PedCheck programs were used to assess the Mendelian consistency of SNP genotype data [19], [20]. Programs from the Affected-Sib-Pair Interval Mapping and Exclusion package (ASPEX) and the Graphical Representation of Relationships (GRR) package were used to check for potential misreported relationships within pedigrees [21], [22]. Haploview (Broad Institute, Boston MA) was used for SNP descriptive statistics [23]. The Generalized Estimation Equation (GEE) method was used to test for associations between eGFR and the genetic variants, accounting for familial correlation [24]. Values for eGFR were adjusted for significant covariates including age, age2, age3, gender, BMI, high density lipoprotein cholesterol (HDL-C), hypertension, and field center. Neither smoking nor alcohol consumption was significantly associated with eGFR and were not included as covariates in statistical analysis. GEE analysis was performed with SAS 9.1 using proc genmod, and exchangeable working correlation matrix. False Discovery Rate (FDR) was used to correct for the multiple testing in GEE analysis [25]. For interactions among genes, we used the Generalized Multifactor Dimensionality Reduction program (GMDR) [26] to determine joint effects of each pair of SNPs. The best model identified by GMDR for eGFR was verified using GEE to account for familial correlation. Several web algorithms and data bases were used for SNP annotation and bioinformatics analysis including UCSC [27], SNPnexus [28], [29], PolyPhen-2 [30], SIFT [31], and FastSNP [32].
Results
Table 1 presents the basic characteristics of the 3,025 GenSalt participants who were included in this study. The study sample consisted of healthy free living members of three-generation families with an average age of 50 years, and approximately equal proportions of males and females. Their average values for BMI, HDL-C, serum creatinine, and eGFR were all within normal ranges. Table S1 shows characteristics of the 193 SNPs within 26 loci involved in blood pressure regulation that were chosen according to LD in the Han Chinese population (International HapMap project).
Table 2 presents results of genetic analysis that identified 12 SNPs in six genes that showed significant associations with eGFR (p<0.05). The four significant SNPs in ACE were highly correlated with r2 ranging from 0.78 to 0.96. The three SNPs in the hydroxysteroid 11-beta dehydrogenase 1 gene (HSD11B1) were less correlated (r2 from 0.35 to 0.43), and the two significant SNPs in the alpha-adducin gene (ADD1) were not significantly correlated (r2 = 0.17). The FDR for all significant p-values was 0.59, which means that seven of these 12 significant SNPs might have been false positives. Figure 1 shows the average eGFR values for each of the three genotypes for nine of the significant SNPs (excluding three highly correlated ACE SNPs). Three of the significant SNPs (AGT_rs4762, GRK4_rs2488815, and SCNN1G_rs4299163) can be considered as risk SNPs where the additive effect of the minor allele was associated with lower values of eGFR. The other significant SNPs can be considered as protective, where the minor allele was associated with higher eGFR values.
Figure 2 shows the cumulative effects on mean adjusted eGFR values for carriers of the minor alleles for all nine significant SNPs (Panel A) and separately for the six protective alleles (Panel B) and three risk alleles (Panel C). Carriers with increasing numbers of the minor protective alleles had up to more than 4 mL/min per 1.73 m2higher mean eGFR values (p = 0.001) (Figure 2, Panel B). Carriers with increasing numbers of the minor risk alleles had mean eGFR values that were as much as almost 3 mL/min per 1.73 m2 lower (p = 0.006) (Figure 2, Panel C).
The best fitting trend line and p value from t-tests between those with no minor allele and those with the largest possible number of minor alleles in each category are shown.
We also tested the SNPs for effects of gene by gene interactions (GxG) on eGFR. We identified a joint effect on eGFR between a nonsynonymous SNP in the gene for cytochrome P450, family 11, subfamily B, polypeptide 1 (CYP11B1_rs4541, Ala386Val) and a synonymous SNP in the beta-2-adrenergic receptor gene (ADRB2_rs1042718). Figure 3 shows the joint effects on mean eGFR values of interactions between CYP11B1_rs4541 and ADRB2_rs1042718 genotypes. The mean adjusted eGFR value in homozygotes for the ADRB2_rs1042718 minor allele (AA) depended on their genotype for CYP11B1_rs4541. Homozygotes for the minor allele of ADRB2_rs1042718 (AA) who are also homozygous for the major allele of CYP11B1_rs4541 (CC) had the lowest mean eGFR values. Homozygotes for the minor allele of ADRB2_rs1042718 (AA) who are heterozygous for CYP11B1_rs4541 (CT) had the highest eGFR. The difference between these two joint genotypes was approximately 5 mL/min per 1.73 m2.
The data points represent the eGFR for the nine possible combinations of the three ADRB2 genotypes versus each of the three possible CYP11B1 genotypes. The number of individuals at each point is provided.
Discussion
The overall goal of this study was to conduct a comprehensive examination of the effects of variability in genes from pathways of blood pressure regulation on renal GFR. The GenSalt study cohort was comprised of rural Han Chinese villagers to minimize the genetic heterogeneity that is encountered in most association studies that are conducted in admixed urban populations. None of our study participants were taking antihypertensive medication, so the complexity associated with the antihypertensive drugs is absent from our study. In addition, we employed an amended version of the MDRD eGFR equation that was specifically designed for use in healthy free living individuals and eliminated the underestimation of GFR with the equation that was previously in use [16].
In our analyses of individual SNPs, the Thr207Met polymorphism (rs4762) in the angiotensinogen gene (AGT) showed the strongest association with eGFR (Table 2). AGT plays a role in the renin-angiotensin system (RAS), a primary pathway in blood pressure regulation with strong influences on cardiovascular and renal disease. AGT encodes preangiotensinogen in the liver, which is subsequently cleaved by renin to generate angiotensin I. Angiotensin I converting enzyme (ACE), converts angiontensin I to angiotensin II [33], [34], a potent vasoconstrictor that also affects renal hemodynamics by decreasing renal cortical blood flow, total renal plasma flow, urinary sodium excretion, and GFR [33], [34]. Moreover, angiotensin II increases glomerular capillary pressure, potentially contributing to glomerulosclerosis [35], [36]. AGT_rs4762 (Thr207Met) is a probably damaging SNP [30], [31] as it substitutes a non-polar amino acid (methionine) for a polar amino acid (threonine). Furthermore, threonine at this position in AGT is highly conserved among divergent species ranging from human to zebrafish [37]. Previous studies in Asians have identified associations of AGT_rs4762 with diabetic nephropathy in Taiwanese patients [38] and hypertension in different Asian populations based on meta-analysis [37].
We also found significant associations with eGFR for four correlated SNPs in the ACE gene, another key player in the RAS pathway of blood pressure regulation. Three of these SNPs (ACE_rs4316, ACE_rs4331, ACE_rs4343) are exonic variants, but do not cause amino acid substitutions (synonymous SNPs). ACE_rs4343, was previously reported to be significantly associated with diabetic nephropathy in an Asian Indian population [39].
Our analysis identified a significant association of eGFR with two intronic SNPs (rs3775067 and rs12503220) in the adducin 1 gene (ADD1). ADD1 encodes the alpha subunit of the cytoskeleton protein adducin, which plays an important role in hypertension and renal function via sodium homeostasis [40]. Many previous studies have reported associations of ADD1 variants with hypertension, renal functions and renal diseases, in different populations including Chinese [40]–[48].
Another SNP that showed associations in our study was rs2488815, an intronic SNP in the G protein_coupled receptor kinase 4 (GRK4) gene, a major player in sodium homeostasis and blood pressure regulation [49]. GRK4 is expressed in the renal proximal tubule, where about 70% of renal sodium reabsorption takes place. Increased GRK4 activity leads to decreased dopamine signaling and increased AngII receptor expression and function, both of which increase sodium retention and blood volume which ultimately leads to hypertension [50], [51]. GRK4 variants have been shown to be associated with hypertension and blood pressure traits in different populations including Han Chinese [52].
Three SNPs in the hydroxysteroid 11beta dehydrogenase1 gene (HSD11B1) were associated with eGFR in the current study. HSD11B1 is a NADP dependent enzyme that functions in the proximal tubule and medullary interstitial cells of the human kidney. HSD11B1 plays a role in the metabolism of the endogenous glucocorticoids, which in turn modulate sodium homeostasis, renal blood flow, and GFR [53]. HSD11B1 enzymatic activities are thought to be involved in obesity, hypertension, and other components of the metabolic syndrome. HSD11B1 overexpression in mice has been associated with dose-dependent hypertension and AGT expression in liver [54]–[56].
Our analyses of individual SNPs identified associations of eGFR with an intronic SNP (rs4299163) in the gene encoding the gamma subunit of the epithelial sodium channel gene (SCNN1G). Epithelial sodium channels (ENaC), are the main regulators for sodium transport in the kidney [57], [58], and rare variants in SCNN1G cause Liddle Syndrome, a monogenic form of hypertension [59]. Other variants in SCNN1G cause pseudohypoaldosteronism type 1, a rare inherited form of renal tubular acidosis [60], [61]. Many studies have identified linkage of SBP with the region that contains SCNN1G on chromosome 16 [62]. A fine mapping study of this region detected associations of SBP with three SCNN1G intronic SNPs, including rs4299163 [62].
In addition to analyses of individual SNPs, we tested for interactions among genes (GxG interactions) that influence eGFR. Our GxG analyses identified a joint effect on eGFR between a conservative nonsynonymous SNP rs4541 (Ala386Val) in CYP11B1 and a synonymous SNP rs1042718 in ADRB2. CYP11B1 is one of the cytochrome p450 genes encoding 11β hydroxylase, a protein involved in the synthesis of cortisol in the adrenal cortex [63]. Cortisol is associated with Cushing's syndrome, hypertension of chronic renal failure, hypertension related to low birth weight, and essential hypertension [64], [65]. Glucocorticoid-Remediable Aldosteronism, a rare form of hypertension, is caused by a gene fusion between CYP11B1 and CYP11B2 [66]. ADRB2 encodes the beta 2 adrenergic receptor, a member of the G-protein superfamily receptors, playing a role in metabolism regulation and also in blood pressure regulation by mediating vasodilation and vascular resistance [67], [68]. ADRB2 SNPs were previously found to be associated with hypertension and blood pressure traits in different populations including Han Chinese but many of the results are inconsistent [68]–[70]. ADRB2_rs1042718, the SNP showing significant GxG interaction, is part of a haplotype recently found to be associated with weight, insulin, and homeostasis model assessment (HOMA) score in Korean adolescents [71]. The exact mechanism of interaction between these two coding region SNPs in relation to eGFR warrants further investigation.
We compared the results of our association study with a recently published meta-analysis of kidney function traits in several East Asian populations, including GenSalt [72]. The SNPs with significant associations in our study (Table 2) were not significantly associated with eGFR in the meta-analysis. This discrepancy could stem from differences in the study populations since we included only Han Chinese from Northern China, while the meta-analysis included Japanese, Malay, Indian, Korean, and Chinese. GenSalt participants were relatively healthy free living individuals from three-generation families, while the meta-analysis included hospital and population based cohorts with no exclusion of diseased individuals. In addition, we employed an equation to calculate eGFR that was specifically designed for use in healthy individuals [16], while the meta-analysis used a different equation specific for Japanese individuals [73].
The genes included in this study were all in pathways known to be involved in regulation of blood pressure which may play an important role in regulating kidney function. However, there are established overlaps, such as the RAS pathway which is involved in renal function decline since treatment of patients with renin-angiotensin inhibitors slows the progression of kidney disease [74]–[76]. Such protective effect of RAS blocking on kidney function may be through both blood pressure and non-blood pressure dependent mechanisms. Our study was not designed to determine whether these genes impact GFR independent of their effects on blood pressure, or whether their effects on GFR are linked to the same pathophysiologic cascades as their involvement in hypertension. However, their significant association with kidney function remains after adjustment for hypertension.
In conclusion, we have identified common variants in genes from pathways of blood pressure regulation and their interactions that influence kidney function, providing new insights into the genetic determinants of kidney function. A longitudinal association between these common variants and changes in kidney function remains to be investigated.
Supporting Information
Table S1.
SNP characteristics. Detailed information about the 193 SNPs used in analysis.
https://doi.org/10.1371/journal.pone.0092468.s001
(DOC)
Acknowledgments
GenSalt Study Steering Committee
Dongfeng Gu, Jiang He (Chair), James E Hixson, Cashell E Jaquish, Depei Liu, Dabeeru C. Rao, Paul K Whelton, and Zhijian Yao.
GenSalt Collaborative Research Group
Tulane University Health Sciences Center, New Orleans, USA: Jiang He (PI), Lydia A Bazzano, Chung-Shiuan Chen, Jing Chen, Mei Hao, Lotuce Lee Hamm, Tanika Kelly, Paul Muntner, Kristi Reynolds, Paul K Whelton, Wenjie Yang, and Qi Zhao.
Washington University School of Medicine, St Louis, USA: Dabeeru C. Rao (PI), Matthew Brown, Charles Gu, Hongyan Huang, Treva Rice, Karen Schwander, and Shiping Wang.
University of Texas Health Sciences Center at Houston: James E Hixson (PI) and Lawrence C Shimmin.
National Heart, Lung, and Blood Institute: Cashell E Jaquish.
Chinese Academy of Medical Sciences, Beijing, China: Dongfeng Gu (PI), Jie Cao, Jichun Chen, Jingping Chen, Zhenhan Du, Jianfeng Huang, Hongwen Jiang, Jianxin Li, Xiaohua Liang, Depei Liu, Xiangfeng Lu, Donghua Liu, Qunxia Mao, Dongling Sun, Hongwei Wang, Qianqian Wang, Xigui Wu, Ying Yang, and Dahai Yu.
Shandong Academy of Medical Sciences, Shandong, China: Fanghong Lu (PI), Zhendong Liu, Shikuan Jin, Yingxin Zhao, Shangwen Sun, Shujian Wang, Qengjie Meng, Baojin Liu, Zhaodong Yang, and Chuanrui Wei.
Shandong Center for Diseases Control and Prevention, Shandong, China: Jixiang Ma (PI), Jiyu Zhang, and Junli Tang.
Zhengzhou University: Dongsheng Hu, Hongwei Wen, Chongjian Wang, Minghui Shen, Jingjing Pan, and Liming Yang.
Xinle Traditional Chinese Medicine Hospital, Hebei, China: Xu Ji (PI), Rongyan Li, Haijun Zu, and Junwei Song.
Ganyu Center for Disease Control and Prevention: Delin Wu (PI), Xushan Wang, and Xiaofeng Zhang.
Xi'an Jiaotong University, Shanxi, China: Jianjun Mu (PI), Enrang Chen,Fuqiang Liu, and Guanji Wu.
Chinese National Human Genome Center at Beijing: Zhi-Jian Yao (PI), Shufeng Chen, Dongfeng Gu, Hongfan Li, Laiyuan Wang, and Penghua Zhang.
The Asian Genetic Epidemiology Network (AGEN): Toshihiro Tanaka.
Author Contributions
Conceived and designed the experiments: DG Jing Chen CG TNK CEJ TR DCR Jie Cao Jichun Chen DPL PW LLH JH JEH. Performed the experiments: LCS DG Jing Chen CG TNK TR DCR Jie Cao Jichun Chen JH JEH. Analyzed the data: MEM. Wrote the paper: MEM LCS TR PW LLH JEH.
References
- 1. Centers for Disease Control and Prevention (CDC) (2007) Prevalence of chronic kidney disease and associated risk factors-United States, 1999–2004. MMWR Morb Mortal Wkly Rep 56: 161–165.
- 2. Saran AM, DuBose TD Jr (2008) Cardiovascular disease in chronic kidney disease. Ther Adv Cardiovasc Dis 2: 425–434.
- 3. Arar NH, Voruganti VS, Nath SD, Thameem F, Bauer R, et al. (2008) A genome-wide search for linkage to chronic kidney disease in a community-based sample: The SAFHS. Nephrol Dial Transplant 23: 3184–3191.
- 4. Benoit G, Machuca E, Heidet L, Antignac C (2010) Hereditary kidney diseases: Highlighting the importance of classical mendelian phenotypes. Ann N Y Acad Sci 1214: 83–98.
- 5. Boger CA, Heid IM (2011) Chronic kidney disease: Novel insights from genome-wide association studies. Kidney Blood Press Res 34: 225–234.
- 6. de Borst MH, Benigni A, Remuzzi G (2008) Primer: Strategies for identifying genes involved in renal disease. Nat Clin Pract Nephrol 4: 265–276.
- 7. Freedman BI, Bostrom M, Daeihagh P, Bowden DW (2007) Genetic factors in diabetic nephropathy. Clin J Am Soc Nephrol 2: 1306–1316.
- 8. Maeda S (2008) Genetics of diabetic nephropathy. Ther Adv Cardiovasc Dis 2: 363–371.
- 9. Adler S (2006) Renal disease: Environment, race, or genes? Ethn Dis 16: S2–35-9.
- 10. Fox CS, Yang Q, Cupples LA, Guo CY, Larson MG, et al. (2004) Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: The framingham heart study. J Am Soc Nephrol 15: 2457–2461.
- 11. Schelling JR, Abboud HE, Nicholas SB, Pahl MV, Sedor JR, et al. (2008) Genome-wide scan for estimated glomerular filtration rate in multi-ethnic diabetic populations: The family investigation of nephropathy and diabetes (FIND). Diabetes 57: 235–243.
- 12. Basson J, Simino J, Rao DC (2012) Between candidate genes and whole genomes: Time for alternative approaches in blood pressure genetics. Curr Hypertens Rep 14: 46–61.
- 13. Carlson CS, Eberle MA, Kruglyak L, Nickerson DA (2004) Mapping complex disease loci in whole-genome association studies. Nature 429: 446–452.
- 14. Kiryluk K (2008) Quantitative genetics of renal function: Tackling complexities of the eGFR phenotype in gene mapping studies. Kidney Int 74: 1109–1112.
- 15. GenSalt Collaborative Research Group (2007) GenSalt: Rationale, design, methods and baseline characteristics of study participants. J Hum Hypertens 21: 639–646.
- 16. Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, et al. (2004) Using serum creatinine to estimate glomerular filtration rate: Accuracy in good health and in chronic kidney disease. Ann Intern Med 141: 929–937.
- 17. The International HapMap Consortium (2003) The international HapMap project. Nature 426: 789–796.
- 18. Tobler AR, Short S, Andersen MR, Paner TM, Briggs JC, et al. (2005) The SNPlex genotyping system: A flexible and scalable platform for SNP genotyping. J Biomol Tech 16: 398–406.
- 19. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.
- 20. O'Connell JR, Weeks DE (1998) PedCheck: A program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet 63: 259–266.
- 21.
Hinds DR (1999) The ASPEX package:Affected sib-pair exclusion mapping v1.88.
- 22. Abecasis GR, Cherny SS, Cookson WO, Cardon LR (2001) GRR: Graphical representation of relationship errors. Bioinformatics 17: 742–743.
- 23. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 21: 263–265.
- 24. Hanley JA, Negassa A, Edwardes MD, Forrester JE (2003) Statistical analysis of correlated data using generalized estimating equations: An orientation. Am J Epidemiol 157: 364–375.
- 25. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57: 289–300.
- 26. Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, et al. (2007) A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 80: 1125–1137.
- 27.
University of California Santa Cruz (UCSC) genome browser website. Available: http://genome.ucsc.edu/, 2013.
- 28. Dayem Ullah AZ, Lemoine NR, Chelala C (2012) SNPnexus: A web server for functional annotation of novel and publicly known genetic variants (2012 update). Nucleic Acids Res 40: W65–70.
- 29. Chelala C, Khan A, Lemoine NR (2009) SNPnexus: A web database for functional annotation of newly discovered and public domain single nucleotide polymorphisms. Bioinformatics 25: 655–661.
- 30. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, et al. (2010) A method and server for predicting damaging missense mutations. Nat Methods 7: 248–249.
- 31. Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4: 1073–1081.
- 32. Yuan HY, Chiou JJ, Tseng WH, Liu CH, Liu CK, et al. (2006) FASTSNP: An always up-to-date and extendable service for SNP function analysis and prioritization. Nucleic Acids Res 34: W635–41.
- 33. Phillips JK (2005) Pathogenesis of hypertension in renal failure: Role of the sympathetic nervous system and renal afferents. Clin Exp Pharmacol Physiol 32: 415–418.
- 34. Ritz E, Adamczak M, Zeier M (2003) Kidney and hypertension-causes. update 2003. Herz 28: 663–667.
- 35. Kim S, Iwao H (2000) Molecular and cellular mechanisms of angiotensin II-mediated cardiovascular and renal diseases. Pharmacol Rev 52: 11–34.
- 36. Ichihara A, Kobori H, Nishiyama A, Navar LG (2004) Renal renin-angiotensin system. Contrib Nephrol 143: 117–130.
- 37. Pereira TV, Nunes AC, Rudnicki M, Yamada Y, Pereira AC, et al. (2008) Meta-analysis of the association of 4 angiotensinogen polymorphisms with essential hypertension: A role beyond M235T? Hypertension 51: 778–783.
- 38. Chang HR, Cheng CH, Shu KH, Chen CH, Lian JD, et al. (2003) Study of the polymorphism of angiotensinogen, anigiotensin-converting enzyme and angiotensin receptor in type II diabetes with end-stage renal disease in Taiwan. J Chin Med Assoc 66: 51–56.
- 39. Ahluwalia TS, Ahuja M, Rai TS, Kohli HS, Bhansali A, et al. (2009) ACE variants interact with the RAS pathway to confer risk and protection against type 2 diabetic nephropathy. DNA Cell Biol 28: 141–150.
- 40. Staessen JA, Bianchi G (2005) Adducin and hypertension. Pharmacogenomics 6: 665–669.
- 41. Shioji K, Kokubo Y, Mannami T, Inamoto N, Morisaki H, et al. (2004) Association between hypertension and the alpha-adducin, beta1-adrenoreceptor, and G-protein beta3 subunit genes in the Japanese population; the suita study. Hypertens Res 27: 31–37.
- 42. Wang JG, Staessen JA, Barlassina C, Fagard R, Kuznetsova T, et al. (2002) Association between hypertension and variation in the alpha- and beta-adducin genes in a white population. Kidney Int 62: 2152–2159.
- 43. Ju Z, Zhang H, Sun K, Song Y, Lu H, et al. (2003) Alpha-adducin gene polymorphism is associated with essential hypertension in Chinese: A case-control and family-based study. J Hypertens 21: 1861–1868.
- 44. Yamagishi K, Iso H, Tanigawa T, Cui R, Kudo M, et al. (2004) Alpha-adducin G460W polymorphism, urinary sodium excretion, and blood pressure in community-based samples. Am J Hypertens 17: 385–390.
- 45. Bray MS, Li L, Turner ST, Kardia SL, Boerwinkle E (2000) Association and linkage analysis of the alpha-adducin gene and blood pressure. Am J Hypertens 13: 699–703.
- 46. Bianchi G, Ferrari P, Staessen JA (2005) Adducin polymorphism: Detection and impact on hypertension and related disorders. Hypertension 45: 331–340.
- 47. Huang XH, Sun K, Song Y, Zhang HY, Yang Y, et al. (2007) Association of alpha-adducin gene and G-protein beta3-subunit gene with essential hypertension in Chinese. Zhonghua Yi Xue Za Zhi 87: 1682–1684.
- 48. He X, Zhu DL, Chu SL, Jin L, Xiong MM, et al. (2001) Alpha-adducin gene and essential hypertension in china. Clin Exp Hypertens 23: 579–589.
- 49. Jose PA, Soares-da-Silva P, Eisner GM, Felder RA (2010) Dopamine and G protein-coupled receptor kinase 4 in the kidney: Role in blood pressure regulation. Biochim Biophys Acta 1802: 1259–1267.
- 50. Morris BJ (2009) GRK4 genetics and response to beta-blocker. Am J Hypertens 22: 235–236.
- 51. Harris DM, Cohn HI, Pesant S, Eckhart AD (2008) GPCR signalling in hypertension: Role of GRKs. Clin Sci (Lond) 115: 79–89.
- 52. Zeng C, Villar VA, Eisner GM, Williams SM, Felder RA, et al. (2008) G protein-coupled receptor kinase 4: Role in blood pressure regulation. Hypertension 51: 1449–1455.
- 53. Gong R, Morris DJ, Brem AS (2008) Human renal 11beta-hydroxysteroid dehydrogenase 1 functions and co-localizes with COX-2. Life Sci 82: 631–637.
- 54. Paterson JM, Morton NM, Fievet C, Kenyon CJ, Holmes MC, et al. (2004) Metabolic syndrome without obesity: Hepatic overexpression of 11beta-hydroxysteroid dehydrogenase type 1 in transgenic mice. Proc Natl Acad Sci U S A 101: 7088–7093.
- 55. Stimson RH, Walker BR (2007) Glucocorticoids and 11beta-hydroxysteroid dehydrogenase type 1 in obesity and the metabolic syndrome. Minerva Endocrinol 32: 141–159.
- 56. Morton NM, Seckl JR (2008) 11beta-hydroxysteroid dehydrogenase type 1 and obesity. Front Horm Res 36: 146–164.
- 57. Tesson F, Leenen FH (2007) Still building on candidate-gene strategy in hypertension? Hypertension 50: 607–608.
- 58. Busst CJ, Bloomer LD, Scurrah KJ, Ellis JA, Barnes TA, et al. (2011) The epithelial sodium channel gamma-subunit gene and blood pressure: Family based association, renal gene expression, and physiological analyses. Hypertension 58: 1073–1078.
- 59. Warnock DG (2001) Liddle syndrome: Genetics and mechanisms of na+ channel defects. Am J Med Sci 322: 302–307.
- 60. Rodriguez-Soriano J (2000) New insights into the pathogenesis of renal tubular acidosis—from functional to molecular studies. Pediatr Nephrol 14: 1121–1136.
- 61. Strautnieks SS, Thompson RJ, Gardiner RM, Chung E (1996) A novel splice-site mutation in the gamma subunit of the epithelial sodium channel gene in three pseudohypoaldosteronism type 1 families. Nat Genet 13: 248–250.
- 62. Busst CJ, Scurrah KJ, Ellis JA, Harrap SB (2007) Selective genotyping reveals association between the epithelial sodium channel gamma-subunit and systolic blood pressure. Hypertension 50: 672–678.
- 63. Freel EM, Connell JM (2004) Mechanisms of hypertension: The expanding role of aldosterone. Journal of the American Society of Nephrology 15: 1993–2001.
- 64. Whitworth JA, Brown MA, Kelly JJ, Williamson PM (1995) Mechanisms of cortisol-induced hypertension in humans. Steroids 60: 76–80.
- 65.
Kelly JJ, Mangos G, Williamson PM, Whitworth JA (1998) Cortisol and hypertension. Clin Exp Pharmacol Physiol Suppl 25: S51–6.
- 66. Dluhy RG, Lifton RP (1999) Glucocorticoid-remediable aldosteronism. J Clin Endocrinol Metab 84: 4341–4344.
- 67. Gjesing AP, Andersen G, Burgdorf KS, Borch-Johnsen K, Jorgensen T, et al. (2007) Studies of the associations between functional beta2-adrenergic receptor variants and obesity, hypertension and type 2 diabetes in 7,808 white subjects. Diabetologia 50: 563–568.
- 68. Brodde OE (2008) Beta-1 and beta-2 adrenoceptor polymorphisms: Functional importance, impact on cardiovascular diseases and drug responses. Pharmacol Ther 117: 1–29.
- 69. Yu SF, Zhou WH, Jiang KY, Gu GZ, Wang S (2008) Job stress, gene polymorphism of beta2-AR, and prevalence of hypertension. Biomed Environ Sci 21: 239–246.
- 70. Ge D, Huang J, He J, Li B, Duan X, et al. (2005) beta2-adrenergic receptor gene variations associated with stage-2 hypertension in northern Han Chinese. Ann Hum Genet 69: 36–44.
- 71. Park HS, Shin ES, Lee JE (2008) Genotypes and haplotypes of beta2-adrenergic receptor and parameters of the metabolic syndrome in Korean adolescents. Metabolism 57: 1064–1070.
- 72. Okada Y, Sim X, Go MJ, Wu JY, Gu D, et al. (2012) Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations. Nat Genet 44: 904–909.
- 73. Horio M, Imai E, Yasuda Y, Watanabe T, Matsuo S (2010) Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: Accuracy and use for population estimates. Am J Kidney Dis 56: 32–38.
- 74. Lu X, Roksnoer LC, Danser AH (2013) The intrarenal renin-angiotensin system: Does it exist? implications from a recent study in renal angiotensin-converting enzyme knockout mice. Nephrol Dial Transplant 28: 2977–2982.
- 75. Kobori H, Nangaku M, Navar LG, Nishiyama A (2007) The intrarenal renin-angiotensin system: From physiology to the pathobiology of hypertension and kidney disease. Pharmacol Rev 59: 251–287.
- 76. Zhuo JL, Ferrao FM, Zheng Y, Li XC (2013) New frontiers in the intrarenal renin-angiotensin system: A critical review of classical and new paradigms. Front Endocrinol (Lausanne) 4: 166.