PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Hypertens. Author manuscript; available in PMC 2010 May 1.
Published in final edited form as:
PMCID: PMC2727134
NIHMSID: NIHMS125841

The Association of Cell Cycle Checkpoint 2 Variants and Kidney Function: Findings of the Family Blood Pressure Program and the Atherosclerosis Risk in Communities Study

Abstract

BACKGROUND

Recent experimental evidence suggests that DNA damage and cell cycle regulatory proteins are involved in kidney injury and apoptosis. The checkpoint 2 gene (CHEK2) is an important transducer in DNA damage signaling pathways in response to injury, and therefore, CHEK2 variants may affect susceptibility to kidney disease.

METHODS

We used tag-single-nucleotide polymorphisms (tag-SNPs) to evaluate the association of the CHEK2 with kidney function (estimated glomerular filtration rate, eGFR) in 1,549 African-American and 1,423 white Hypertension Genetic Epidemiology Network (HyperGEN) participants. We performed replication analyses in the Genetic Epidemiology Network of Arteriopathy (GENOA) participants (1,746 African Americans and 1,418 whites), GenNet participants (706 whites), and Atherosclerosis Risk in Communities (ARIC) study participants (3,783 African Americans and 10,936 whites). All analyses were race-stratified and used additive genetic models with adjustments for covariates and for family structure, if needed.

RESULTS

One tag-SNP, rs5762764, was associated with eGFR in HyperGEN P = (0.003) and GENOA white participants (P = 0.009), and it was significantly associated with eGFR in meta-analyses (P = 0.002). The associations were independent of type 2 diabetes.

CONCLUSIONS

These results suggest that CHEK2 variants may influence eGFR in the context of hypertension.

Chronic kidney disease (CKD) is an emerging public health problem with an estimated prevalence of 9.6% (or 19.2 million) of the adult US population.1,2 CKD is a strong risk factor for cardiovascular disease,3 is associated with increased morbidity and mortality,4,5 increased health-care resource use,6 and a decreased quality of life.7 The economic burden associated with the treatment of kidney failure is substantial, accounting for 6.8% of the health-care expenditures in 2004.8,9

Familial aggregation of CKD has been documented.10,11 A high heritability of renal function has been noted in twin studies (0.78 ± 0.03 for glomerular filtration rate—GFR).12 A genetic component for kidney traits has also been identified in studies of type 1 and type 2 diabetic study participants,1317 in hypertensive study participants1821 and in the general population.22,23 Although several monogenic kidney diseases have been identified, little progress has been achieved in identifying susceptibility genes for CKD in individuals with diabetes and/or hypertension,24,25 the two most common conditions associated with kidney failure.8,9 Genetic heterogeneity may introduce analytical challenges for the identification of genetic variants influencing kidney function, and may account for the lack of genome-wide significant findings in a recent genome-wide association study of kidney-related phenotypes.26 Therefore, candidate gene approaches interrogating genes from newly discovered pathways of kidney injury may constitute a complementary approach to large-scale genome-wide gene finding studies.

Genetic and environmental factors (for example, endogenous and exogenous toxins) likely contribute to CKD susceptibility. Programmed cell death or apoptosis of intrinsic kidney cells in response to injury has been described in experimental animal models and in humans.27 Recent research suggests a role for reactive oxygen species and DNA damage in the membrane attack complex C5b-9 injury (passive Heymann nephritis),28 in puromycin nephrosis,29 and in cisplatin nephrotoxicity.30 DNA damage leads to activation of the checkpoint pathways, which mediate cell cycle arrest and apoptosis.31,32 Therefore, genes in the DNA repair and cell cycle checkpoint pathways are excellent candidates for evaluation of association with kidney function.

The checkpoint 2 gene (CHEK2) is an important transducer in DNA damage signaling pathways in response to injury. We have previously identified the CHEK2 gene under a linkage peak for type 2 diabetes among 3,383 Hypertension Genetic Epidemiology Network (HyperGEN) participants.33 The 22q12 chromosome region, where CHEK2 is located, has been identified in linkage and in admixture studies of kidney phenotypes.14,18,34,35 In this study, we tested the hypothesis that CHEK2 contains one or more polymorphic variants (single-nucleotide polymorphisms (SNPs)) that are associated with kidney function in participants of the HyperGEN study. We attempted to replicate our association in three additional populations, the Genetic Epidemiology Network of Arteriopathy (GENOA) study, the GenNet study, and the Atherosclerosis Risk in Communities (ARIC) study.

Methods

Populations, phenotypes, and covariates

Family Blood Pressure Program

The Family Blood Pressure Program (FBPP), composed of four independent networks without overlap in participants (HyperGEN, GENOA, GenNet, and SAPPHIRe), was established to investigate the genetic determinants of high blood pressure (BP) in multiple ethnic groups.36 Families were ascertained based on higher than normal BP or diagnosed hypertension. This study includes only genotyped participants of the FBPP HyperGEN, GENOA, and GenNet studies.

HyperGEN recruited African-American and non-Hispanic white hypertensive siblings and their offspring and/or parents. A family was eligible for participation if at least two siblings had BP measurements ≥140/90 mm Hg or used antihypertensive medications, with the age of diagnosis of <60 years. GENOA recruited African-American, Mexican-American, and non-Hispanic white sibships containing a minimum of two individuals diagnosed with hypertension. GenNet recruited African-American and non-Hispanic white individuals with BP in the upper 20–25% of the age- and gender-specific BP distribution, and all available first degree relatives.

All networks measured a standard set of 95 core phenotypes.36 Clinical, lifestyle information, and medications were obtained via an interview. Body mass index was calculated using weight (kg)/height (m2), obtained during a clinic visit. Tobacco exposure was quantified using questionnaires. Resting BP measurements were obtained using automated Dinamap devices (model 1846 SX/P; GE Medical Systems, Tampa, FL) and hypertension was defined by a BP of at least 140/90 mm Hg or use of antihypertensive medications. Individuals with a fasting plasma glucose of at least 7.0 mmol/l (126 mg/dl) or using medications to treat diabetes were defined as having type 2 diabetes.37 Individuals with age of onset of diabetes <30 years old were considered type 1 diabetics and therefore excluded.

In HyperGEN, serum creatinine was measured by a thin-film adaptation of the amidohydrolase enzymatic method using the Vitros analyzer (Johnson & Johnson Clinical Diagnostics, Raritan, NJ).38 GENOA measured serum creatinine using a picric acid colorimetric assay (Monarch Chemistry System, Lexington, MA), and calibrated serum creatinine to the Cleveland Clinic assay by subtracting 0.17 mg/dl from measured values (S.T. Turner, unpublished data). GenNet measured serum creatinine only in white individuals (using a modified Jaffe reaction). GFR was estimated using the Modification of Diet in Renal Disease equation.39 CKD was defined as an estimated GFR (eGFR) <60 ml/min/1.73 m2.

ARIC

ARIC is a biracial prospective population study of subclinical and clinical atherosclerosis of 15,792 unrelated individuals, aged 45–64 years old at recruitment (1987–1989).40 Study participants were selected as a probability sample from four US communities and were examined at baseline (visit 1) and at every 3 years through January 1999.

Resting BP was recorded in seated individuals using a random-zero sphygmomanometer, and hypertension was defined using the same criteria as for the FBPP. Type 2 diabetes was defined by a fasting plasma glucose of at least 7.0 mmol/l, nonfasting glucose levels of at least 11.1 mmol/l, current use of medications prescribed to treat diabetes (e.g., insulin or sulfonylurea), or a positive response to the question “Has a doctor ever told you that you had diabetes ?”. Smoking was assessed using a questionnaire and anthropometric measures were obtained during clinic visits. Serum creatinine was measured using a modified kinetic Jaffe reaction, and calibrated using regression to the Cleveland Clinic laboratory by subtraction of 0.24 mg/dl.41 eGFR was estimated using serum creatinine from the visit 1 and the Modification of Diet in Renal Disease equation. eGFR values >200 were set to 200 ml/min/1.73 m2.

All participants of the FPPP and ARIC studies provided informed consent. The study was conducted in accordance with the principles of the Declaration of Helsinki.

Selection and genotyping of SNPs

DNA extraction and storage procedures in the FBPP and ARIC studies are described elsewhere.42,43 CHEK2 SNP selection is described in detail in the Supplementary Data online. Briefly, SNPs were chosen using the HapMap phase 1 CEU sample and a pairwise analysis of correlation (r2 = 0.65). The r2 statistics measures the linkage disequilibrium between two markers or how well an allele predicts the allele at the other locus.44 Therefore, it can be used to select a subset of SNPs within a gene (tag-SNPs) for genotyping.45 A selected number of SNPs was genotyped in the replication populations, as part of an ongoing study of diabetes susceptibility (data not shown).

An additional SNP, rs2346397, located 200 kb from CHEK2, was also available for analyses. Genotyping for all samples was performed using Taqman SNP Genotyping Assays (Applied Biosystems, Foster City, CA). The assay call rates were 99% for HyperGEN and GenNet samples, and >95% for GENOA and ARIC samples. We tested for Hardy–Weinberg equilibrium in race-stratified samples by study populations and none of the SNPs had significant deviation of Hardy–Weinberg equilibrium (Supplementary Table S1 online).

Statistical analysis

We tested the association between CHEK2 SNPs and eGFR using general linear mixed models in the FPPP and linear regression models in the ARIC study. eGFR had a normal distribution and therefore was not transformed for analyses. We estimated genotypic means and 95% confidence intervals using empirical covariance matrix structures to account for the correlation within the data due to family relatedness (SAS version 9.1, SAS Institute, Cary, NC). All analyses were linear models, using additive genetic models (1-degree of freedom test) and adjusting for age, age2, sex, age-by-sex interactions, and study center, within each race-stratified population sample (model 1). We further adjusted for systolic BP, hypertension treatment (or use of angiotensin-enzyme converting inhibitor or angiotensin 2 receptor blocker), type 2 diabetes, body mass index, and smoking exposure (model 2). We also explored models excluding individuals with type 2 diabetes (model 3, Supplementary Data online). In the ARIC study, we study the interaction of hypertension and type 2 diabetes on the association of SNPs and eGFR using interaction terms (α = 0.10) and stratified analyses. For family studies, we tested the association using the quantitative trait disequilibrium test implemented in SOLAR, which accounts for linkage and the family structure of these data as well as population structure.46 Because P values were similar in analyses using the quantitative trait disequilibrium test and mixed linear models, but mixed models provided estimates of the effect, we only report results for the mixed models.

Haplotypes were inferred using the Family Base Association Testing software47,48 in family studies, and HAPSTAT49 in studies of unrelated individuals. We performed fixed effects meta-analysis by combining estimated regression coefficients and standard errors across samples (STATA 10), weighting by the inverse variance of each study.50 We also tested for the presence of between-study heterogeneity.51

RESULTS

In our primary sample, we studied 1,423 white and 1,549 African-American HyperGEN participants, mostly hypertensive individuals (Table 1). The mean eGFR were 76.9 ± 16.5 and 92.4 ± 22.3 ml/min/1.73 m2, for white and African-American individuals, respectively. Minor allele frequencies varied by race/ethnicity, with a large difference between white and African-American participants observed for the rs5762764 SNP (Supplementary Table S1 online). Haplotypes with the minor allele for rs5762764 (G allele) were uncommon in African-American individuals (Supplementary Table S2 online).

Table 1
characteristics of individuals participants in the FBPP and ARIC studies

rs5762764 was significantly associated with eGFR in HyperGEN white individuals (P= 0.003, model 1) (Table 2). The presence of one or two copies of minor allele was associated with higher eGFR among white individuals (Table 2). The association was still significant when individuals with type 2 diabetes were excluded (P = 0.01, Supplementary Data online). The SNPs were not associated with eGFR in HyperGEN African-American individuals (Table 2).

Table 2
genotype means and 95% confidence intervals (CIs), and P values for the additive genetic association of SNPs and eGFR by race using generalized linear mixed model: the hyperGEN study

For replication of our findings, we performed analyses of 1,418 white and 1,746 African-American GENOA participants, and 706 GenNet white individuals with available data on eGFR. Minor allele frequencies by race were similar to HyperGEN samples (Supplementary Table S1 online). Participants of the GENOA and GenNet were often hypertensive, and those in GenNet were younger, on average, than those in HyperGEN or GENOA (Table 1).

We observed a significant association of rs5762764 with eGFR among GENOA whites (Table 3). The minor allele for rs5762764 was associated with higher eGFR among these individuals (P = 0.009, model 1). The association was still significant in analyses excluding diabetic individuals (P = 0.006, Supplementary Data online). Although eGFR was higher in GenNet white participants with two copies of the minor allele for rs5762764, the association was not significant (Table 3). None of the SNPs was significantly associated with eGFR among GENOA African Americans.

Table 3
Genotype means and 95% confidence intervals, and P values for the additive genetic association of rs5762764 SNP and eGFR using generalized linear mixed models for the FBPP GENOA and GenNet, and linear models for the ARIC study

We also performed analysis of 10,936 white and 3,783 African-American individuals from the ARIC study. The mean eGFRs and other characteristics of these samples are displayed in Table 1. rs5762764 was not associated with eGFR in white or African-American ARIC participants (Table 3). However, because our results were more consistent among white individuals from samples ascertained for hypertension, we examined interactions of hypertension in the association between SNP-eGFR in the ARIC study. rs5762764 was significantly associated with eGFR among white individuals (P = 0.04, model 1) when accounting for hypertension interaction (P = 0.08 for interaction). The hypertension subsample displayed a genotype effect that was consistent with the other hypertension subsamples from HyperGEN, GENOA, and GenNet (Supplementary Data online).

In meta-analysis, rs5762764 was significant associated with eGFR (P = 0.002), with a combined estimated regression coefficient of 0.60 (Figure 1).

Figure 1
Meta-analyses of the effect of rs5762764 (CHEK2) on estimated glomerular filtration rate (N = 19,847 individuals). ARIC, Atherosclerosis Risk in Communities; CI, confidence interval; ES, estimate; GENOA, Genetic Epidemiology Network of Arteriopathy.

DISCUSSION

Recent experimental evidence suggests that DNA damage and cell cycle regulatory proteins are involved in kidney injury and apoptosis.52 The DNA repair pathway involves multiple mediators that in response to injury lead to cell cycle arrest to allow for DNA repair or to programmed cell death. Genetic polymorphisms of mediators in this pathway may affect the response to injury, repair and cell survival. In this study, we observed an association among polymorphisms in the CHEK2 gene, a transducer in DNA damage signaling pathways, and kidney function. We found some consistency in results among hypertensive enriched samples of the FBPP, and in participants of the population-based ARIC study with hypertension. Although the individual findings were restricted to white individuals, a meta-analysis of effect estimates was consistent with a significant effect across the subpopulation.

In particular, we found that one tag-SNP, rs5762764, was associated with eGFR in white individuals in HyperGEN and GENOA. The rs5762764 minor allele (G) showed a protective effect of increased eGFR for each copy of the minor allele among white FBPP individuals and among hypertensive individuals in the ARIC study. The findings in the FBPP were robust to exclusion of diabetic individuals (Supplementary Data online), suggesting that the genetic effects on kidney function may not be disease specific, but may affect the downstream response to injury in different forms of kidney damage. Alternatively, polymorphisms in CHEK2 may be important in influencing kidney function in hypertension-related kidney injury.

The CHEK2 protein is ubiquitous. CHEK2 is an effector kinase that activates the tumor suppressor protein p53, which leads to cell arrest and apoptosis. Because CHEK2 and other kinases play a key role on cell survival, polymorphisms in the CHEK2 gene may contribute to varying degree of apoptosis of intrinsic kidney cells in response to injury. For example, it may affect the recovery of epithelial tubular cell damage in acute renal failure or it contribute to reduced renal mass in chronic kidney injury. Recent studies suggest that angiotensin II, whose infusion may cause systemic hypertension in animals, mediates oxidative stress in the kidney53 and promotes apoptosis of intrinsic renal cells.54,55 The role of this gene in angiotensin II–induced and hypertension-related kidney injury needs further exploration in experimental models and in population studies.

Replication plays a key role in the assessment of the validity of genotype–phenotype associations, because type 1 error is not expected to replicate more than by chance alone. Our findings are particularly robust because the replication was achieved using the same study design, phenotype (eGFR), genetic marker, and in a population of similar ancestry as has been suggested by the National Cancer Institute and the National Human Genome Research Institute Working Group on guidelines for replication in association studies.56

We also attempted to evaluate the generalizability of our study results to African-American populations. The rs5762764 allele frequency was low in African Americans and haplotypes including the minor allele of this SNP were uncommon in these individuals. Therefore, population-specific linkage disequilibrium and power may account for the lack of association in African Americans. African-American individuals in HyperGEN were also 10 years younger than whites and although we adjusted for age, we may have not fully accounted for the age-differences in the samples. In addition, because we relied on estimating equations for measurement of kidney function, misclassification of the outcome may have occurred.

In conclusion, we have shown that a polymorphism of the CHEK2 gene is associated with measures of kidney function. These results suggest that CHEK2, a protein involved in cell cycle regulatory pathways, may influence kidney function in the context of hypertension.

Supplementary Material

Table 1

Table 2

Text

Acknowledgments

The following investigators are associated with the Family Blood Pressure Program:

GenNet Network: A.B.W. (Network Director), Lillian Gleiberman (Network Coordinator), Anne E. Kwitek, A.C., Richard S. Cooper, Carolina Delgado, Howard J. Jacob, and Nicholas J. Schork.

GENOA Network: E.B. (Network Director), T.H. Mosley, Alanna Morrison, Kathy Klos, Craig Hanis, Sharon Kardia, and S.T.T.

HyperGEN Network: S.C.H. (Network Director), Janet Hood, D.A., J.H.E., R. Curtis Ellison, C.C.G., Gerardo Heiss, Paul Hopkins, Aldi T. Kraja, Jean-Marc Lalouel, M.F.L., Albert Oberman, Michael A. Province, D.C. Rao, Treva Rice, and Robert Weiss.

SAPPHIRe Network: David Curb (Network Director), David Cox, Timothy Donlon, Victor Dzau, John Grove, Kamal Masaki, R.H.M., Richard Olshen, Richard Pratt, Tom Quertermous, Neil Risch, and Beatriz Rodriguez.

National Heart, Lung, and Blood Institute: Dina Paltoo and Cashell E. Jaquish.

Web site: http://www.biostat.wustl.edu/fbpp/FBPP.shtml

The Atherosclerosis Risk in Communities (ARIC) Study is carried out as a collaborative study supported by the National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022. We thank the staff and participants of the ARIC study for their important contributions. The work was also supported by the American Heart Association no. 0675001N (N.F.).

Footnotes

Supplementary material is linked to the online version of the paper at http://www.nature.com/ajh

Disclosure: The authors declared no conflict of interest.

References

1. Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis. 2003;41:1–12. [PubMed]
2. Schoolwerth AC, Engelgau MM, Hostetter TH, Rufo KH, Chianchiano D, McClellan WM, Warnock DG, Vinicor F. Chronic kidney disease: a public health problem that needs a public health action plan. Prev Chronic Dis. 2006;3:A57. [PMC free article] [PubMed]
3. Sarnak MJ. Cardiovascular complications in chronic kidney disease. Am J Kidney Dis. 2003;41:11–17. [PubMed]
4. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296–1305. [PubMed]
5. Collins AJ, Kasiske B, Herzog C, Chavers B, Foley R, Gilbertson D, Grimm R, Liu J, Louis T, Manning W, McBean M, Murray A, St Peter W, Xue J, Fan Q, Guo H, Li Q, Li S, Qiu Y, Roberts T, Skeans M, Snyder J, Solid C, Wang C, Weinhandl E, Zhang R, Arko C, Chen SC, Dalleska F, Daniels F, Dunning S, Ebben J, Frazier E, Hanzlik C, Johnson R, Sheets D, Wang X, Forrest B, Berrini D, Constantini E, Everson S, Eggers P, Agodoa L. Excerpts from the United States Renal Data System 2006 Annual Data Report. Am J Kidney Dis. 2007;49:A6–A7. S1–S296. [PubMed]
6. Hunsicker LG. The consequences and costs of chronic kidney disease before ESRD. J Am Soc Nephrol. 2004;15:1363–1364. [PubMed]
7. Shidler NR, Peterson RA, Kimmel PL. Quality of life and psychosocial relationships in patients with chronic renal insufficiency. Am J Kidney Dis. 1998;32:557–566. [PubMed]
8. US Renal Data System. 2007 Annual Data Report: Atlas of Chronic Kidney Disease & End-Stage Renal Disease in the United States. Am J Kidney Dis. 2008;51:S1–S304.
9. Foley RN, Collins AJ. End-stage renal disease in the United States: an update from the United States Renal Data System. J Am Soc Nephrol. 2007;18:2644–2648. [PubMed]
10. Freedman BI, Wilson CH, Spray BJ, Tuttle AB, Olorenshaw IM, Kammer GM. Familial clustering of end-stage renal disease in blacks with lupus nephritis. Am J Kidney Dis. 1997;29:729–732. [PubMed]
11. Lei HH, Perneger TV, Klag MJ, Whelton PK, Coresh J. Familial aggregation of renal disease in a population-based case-control study. J Am Soc Nephrol. 1998;9:1270–1276. [PubMed]
12. Rao F, Wessel J, Wen G, Zhang L, Rana BK, Kennedy BP, Greenwood TA, Salem RM, Chen Y, Khandrika S, Hamilton BA, Smith DW, Holstein-Rathlou NH, Ziegler MG, Schork NJ, O’Connor DT. Renal albumin excretion: twin studies identify influences of heredity, environment, and adrenergic pathway polymorphism. Hypertension. 2007;49:1015–1031. [PubMed]
13. Imperatore G, Knowler WC, Nelson RG, Hanson RL. Genetics of diabetic nephropathy in the Pima Indians. Curr Diab Rep. 2001;1:275–281. [PubMed]
14. Krolewski AS, Poznik GD, Placha G, Canani L, Dunn J, Walker W, Smiles A, Krolewski B, Fogarty DG, Moczulski D, Araki S, Makita Y, Ng DP, Rogus J, Duggirala R, Rich SS, Warram JH. A genome-wide linkage scan for genes controlling variation in urinary albumin excretion in type II diabetes. Kidney Int. 2006;69:129–136. [PubMed]
15. Iyengar SK, Fox KA, Schachere M, Manzoor F, Slaughter ME, Covic AM, Orloff SM, Hayden PS, Olson JM, Schelling JR, Sedor JR. Linkage analysis of candidate loci for end-stage renal disease due to diabetic nephropathy. J Am Soc Nephrol. 2003;14:S195–S201. [PubMed]
16. Fogarty DG, Hanna LS, Wantman M, Warram JH, Krolewski AS, Rich SS. Segregation analysis of urinary albumin excretion in families with type 2 diabetes. Diabetes. 2000;49:1057–1063. [PubMed]
17. Fogarty DG, Rich SS, Hanna L, Warram JH, Krolewski AS. Urinary albumin excretion in families with type 2 diabetes is heritable and genetically correlated to blood pressure. Kidney Int. 2000;57:250–257. [PubMed]
18. Freedman BI, Beck SR, Rich SS, Heiss G, Lewis CE, Turner S, Province MA, Schwander KL, Arnett DK, Mellen BG. A genome-wide scan for urinary albumin excretion in hypertensive families. Hypertension. 2003;42:291–296. [PubMed]
19. Leon JM, Freedman BI, Miller MB, North KE, Hunt SC, Eckfeldt JH, Lewis CE, Kraja AT, Djousse L, Arnett DK. Genome scan of glomerular filtration rate and albuminuria: the HyperGEN study. Nephrol Dial Transplant. 2007;22:763–771. [PubMed]
20. Chung KW, Ferrell RE, Ellis D, Barmada M, Moritz M, Finegold DN, Jaffe R, Vats A. African American hypertensive nephropathy maps to a new locus on chromosome 9q31–q32. Am J Hum Genet. 2003;73:420–429. [PubMed]
21. Turner ST, Kardia SL, Mosley TH, Rule AD, Boerwinkle E, de Andrade M. Influence of genomic loci on measures of chronic kidney disease in hypertensive sibships. J Am Soc Nephrol. 2006;17:2048–2055. [PubMed]
22. Fox CS, Yang Q, Cupples LA, Guo CY, Larson MG, Leip EP, Wilson PW, Levy D. Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study. J Am Soc Nephrol. 2004;15:2457–2461. [PubMed]
23. Fox CS, Yang Q, Guo CY, Cupples LA, Wilson PW, Levy D, Meigs JB. Genome-wide linkage analysis to urinary microalbuminuria in a community-based sample: the Framingham Heart Study. Kidney Int. 2005;67:70–74. [PubMed]
24. Janssen B, Hohenadel D, Brinkkoetter P, Peters V, Rind N, Fischer C, Rychlik I, Cerna M, Romzova M, de Heer E, Baelde H, Bakker SJ, Zirie M, Rondeau E, Mathieson P, Saleem MA, Meyer J, Koppel H, Sauerhoefer S, Bartram CR, Nawroth P, Hammes HP, Yard BA, Zschocke J, van der Woude FJ. Carnosine as a protective factor in diabetic nephropathy: association with a leucine repeat of the carnosinase gene CNDP1. Diabetes. 2005;54:2320–2327. [PubMed]
25. Freedman BI, Hicks PJ, Sale MM, Pierson ED, Langefeld CD, Rich SS, Xu J, McDonough C, Janssen B, Yard BA, van der Woude FJ, Bowden DW. A leucine repeat in the carnosinase gene CNDP1 is associated with diabetic end-stage renal disease in European Americans. Nephrol Dial Transplant. 2007;22:1131–1135. [PubMed]
26. Hwang SJ, Yang Q, Meigs JB, Pearce EN, Fox CS. A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study. BMC Med Genet. 2007;8(Suppl 1):S10. [PMC free article] [PubMed]
27. Shankland SJ. The podocyte’s response to injury: role in proteinuria and glomerulosclerosis. Kidney Int. 2006;69:2131–2147. [PubMed]
28. Pippin JW, Durvasula R, Petermann A, Hiromura K, Couser WG, Shankland SJ. DNA damage is a novel response to sublytic complement C5b-9-induced injury in podocytes. J Clin Invest. 2003;111:877–885. [PMC free article] [PubMed]
29. Marshall CB, Pippin JW, Krofft RD, Shankland SJ. Puromycin aminonucleoside induces oxidant-dependent DNA damage in podocytes in vitro and in vivo. Kidney Int. 2006;70:1962–1973. [PubMed]
30. Pabla N, Huang S, Mi QS, Daniel R, Dong Z. ATR-Chk2 signaling in p53 activation and DNA damage response during cisplatin-induced apoptosis. J Biol Chem. 2007;283:6572–6583. [PubMed]
31. Niida H, Nakanishi M. DNA damage checkpoints in mammals. Mutagenesis. 2006;21:3–9. [PubMed]
32. Bartek J, Lukas J. DNA damage checkpoints: from initiation to recovery or adaptation. Curr Opin Cell Biol. 2007;19:238–245. [PubMed]
33. Avery CL, Freedman BI, Heiss G, Kraja A, Rice T, Arnett D, Miller MB, Pankow JS, Lewis CE, Myers RH, Hunt SC, Almasy L, North KE. Linkage analysis of diabetes status among hypertensive families: the Hypertension Genetic Epidemiology Network study. Diabetes. 2004;53:3307–3312. [PubMed]
34. Kao WH, Klag MJ, Meoni LA, Reich D, Berthier-Schaad Y, Li M, Coresh J, Patterson N, Tandon A, Powe NR, Fink NE, Sadler JH, Weir MR, Abboud HE, Adler SG, Divers J, Iyengar SK, Freedman BI, Kimmel PL, Knowler WC, Kohn OF, Kramp K, Leehey DJ, Nicholas SB, Pahl MV, Schelling JR, Sedor JR, Thornley-Brown D, Winkler CA, Smith MW, Parekh RS. MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nat Genet. 2008;40:1185–1192. [PMC free article] [PubMed]
35. Kopp JB, Smith MW, Nelson GW, Johnson RC, Freedman BI, Bowden DW, Oleksyk T, McKenzie LM, Kajiyama H, Ahuja TS, Berns JS, Briggs W, Cho ME, Dart RA, Kimmel PL, Korbet SM, Michel DM, Mokrzycki MH, Schelling JR, Simon E, Trachtman H, Vlahov D, Winkler CA. MYH9 is a major-effect risk gene for focal segmental glomerulosclerosis. Nat Genet. 2008;40:1175–1184. [PMC free article] [PubMed]
36. FBPP Investigators. Multi-center genetic study of hypertension: The Family Blood Pressure Program (FBPP) Hypertension. 2002;39:3–9. [PubMed]
37. World Health Organization. Part 1: Diagnosis and Classification of Diabetes Mellitus. Geneva: 1999. Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications. Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications. Report of a WHO Consultation.
38. DeWan AT, Arnett DK, Atwood LD, Province MA, Lewis CE, Hunt SC, Eckfeldt J. A genome scan for renal function among hypertensives: the HyperGEN study. Am J Hum Genet. 2001;68:136–144. [PubMed]
39. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130:461–470. [PubMed]
40. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989;129:687–702. [PubMed]
41. Manjunath G, Tighiouart H, Ibrahim H, MacLeod B, Salem DN, Griffith JL, Coresh J, Levey AS, Sarnak MJ. Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol. 2003;41:47–55. [PubMed]
42. Williams RR, Rao DC, Ellison RC, Arnett DK, Heiss G, Oberman A, Eckfeldt JH, Leppert MF, Province MA, Mockrin SC, Hunt SC. NHLBI family blood pressure program: methodology and recruitment in the HyperGEN network. Hypertension genetic epidemiology network. Ann Epidemiol. 2000;10:389–400. [PubMed]
43. Brown SA, Hutchinson R, Morrisett J, Boerwinkle E, Davis CE, Gotto AM, Jr, Patsch W. Plasma lipid, lipoprotein cholesterol, and apoprotein distributions in selected US communities. The Atherosclerosis Risk in Communities (ARIC) Study. Arterioscler Thromb. 1993;13:1139–1158. [PubMed]
44. Zondervan KT, Cardon LR. The complex interplay among factors that influence allelic association. Nat Rev Genet. 2004;5:89–100. [PubMed]
45. Howie BN, Carlson CS, Rieder MJ, Nickerson DA. Efficient selection of tagging single-nucleotide polymorphisms in multiple populations. Hum Genet. 2006;120:58–68. [PubMed]
46. Boerwinkle E, Chakraborty R, Sing CF. The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann Hum Genet. 1986;50:181–194. [PubMed]
47. Horvath S, Xu X, Lake SL, Silverman EK, Weiss ST, Laird NM. Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics. Genet Epidemiol. 2004;26:61–69. [PubMed]
48. Horvath S, Xu X, Laird NM. The family based association test method: strategies for studying general genotype–phenotype associations. Eur J Hum Genet. 2001;9:301–306. [PubMed]
49. Lin DY, Zeng D, Millikan R. Maximum likelihood estimation of haplotype effects and haplotype-environment interactions in association studies. Genet Epidemiol. 2005;29:299–312. [PubMed]
50. Deeks C, Altman D, Bradburn M. Statistical methods for examining heterogeneity and combing results from several studies in meta-analysis. In: Egger M, Smith G, Altman D, editors. Systematic Reviews in Health Care: Meta-analysis in Context. BMJ Publication Group; London: 2001. pp. 1–487.
51. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560. [PMC free article] [PubMed]
52. Marshall CB, Shankland SJ. Cell cycle regulatory proteins in podocyte health and disease. Nephron Exp Nephrol. 2007;106:e51–e59. [PubMed]
53. Sachse A, Wolf G. Angiotensin II-induced reactive oxygen species and the kidney. J Am Soc Nephrol. 2007;18:2439–2446. [PubMed]
54. Wolf G, Wenzel UO. Angiotensin II and cell cycle regulation. Hypertension. 2004;43:693–698. [PubMed]
55. Schmid U, Stopper H, Schweda F, Queisser N, Schupp N. Angiotensin II induces DNA damage in the kidney. Cancer Res. 2008;68:9239–9246. [PubMed]
56. Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF, Jr, Freimer NB, Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J, Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S, Wijsman EM, Winn DM, Collins FS. Replicating genotype-phenotype associations. Nature. 2007;447:655–660. [PubMed]