Advertisement
Research Article

Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD

  • Carsten A. Böger,

    Affiliation: Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany

    X
  • Mathias Gorski,

    Affiliations: Department of Epidemiology and Preventive Medicine, University Hospital Regensburg, Regensburg, Germany, Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    X
  • Man Li,

    Affiliation: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

    X
  • Michael M. Hoffmann,

    Affiliation: Clinical Chemistry, University Medical Center Freiburg, University of Freiburg, Freiburg, Germany

    X
  • Chunmei Huang,

    Affiliation: Division of Nephrology, Massachusetts General Hospital, Boston, Massachusetts, United States of America

    X
  • Qiong Yang,

    Affiliation: Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America

    X
  • Alexander Teumer,

    Affiliation: Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany

    X
  • Vera Krane,

    Affiliation: University of Würzburg, Department of Medicine 1, Division of Nephrology, University Hospital Würzburg, Würzburg, Germany

    X
  • Conall M. O'Seaghdha,

    Affiliations: Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America

    X
  • Zoltán Kutalik,

    Affiliations: Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland, Swiss Institute of Bioinformatics, Lausanne, Switzerland

    X
  • H.-Erich Wichmann,

    Affiliations: Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany, Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany, Klinikum Großhadern, Munich, Germany

    X
  • Thomas Haak,

    Affiliation: Diabetes Klinik Bad Mergentheim, Bad Mergentheim, Germany

    X
  • Eva Boes,

    Affiliation: Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria

    X
  • Stefan Coassin,

    Affiliation: Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria

    X
  • Josef Coresh,

    Affiliation: Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

    X
  • Barbara Kollerits,

    Affiliation: Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria

    X
  • Margot Haun,

    Affiliation: Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria

    X
  • Bernhard Paulweber,

    Affiliation: First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria

    X
  • Anna Köttgen,

    Affiliations: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America, Renal Division, University Hospital of Freiburg, Freiburg, Germany

    X
  • Guo Li,

    Affiliation: Department of Medicine, University of Washington, Seattle, Washington, United States of America

    X
  • Michael G. Shlipak,

    Affiliations: Division of General Internal Medicine, San Francisco VA Medical Center, San Francisco, California, United States of America, Department of Medicine, Epidemiology, and Biostatistics, University of California San Francisco, San Francisco, California, United States of America

    X
  • Neil Powe,

    Affiliation: Department of Medicine, San Francisco General Hospital and University of California San Francisco, San Francisco, California, United States of America

    X
  • Shih-Jen Hwang,

    Affiliation: NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America

    X
  • Abbas Dehghan,

    Affiliations: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands, Member of Netherlands Consortium for Healthy Aging (NCHA), Netherlands Genomics Initiative (NGI), Leiden, The Netherlands

    X
  • Fernando Rivadeneira,

    Affiliations: Member of Netherlands Consortium for Healthy Aging (NCHA), Netherlands Genomics Initiative (NGI), Leiden, The Netherlands, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands

    X
  • André Uitterlinden,

    Affiliations: Member of Netherlands Consortium for Healthy Aging (NCHA), Netherlands Genomics Initiative (NGI), Leiden, The Netherlands, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands

    X
  • Albert Hofman,

    Affiliations: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands, Member of Netherlands Consortium for Healthy Aging (NCHA), Netherlands Genomics Initiative (NGI), Leiden, The Netherlands

    X
  • Jacques S. Beckmann,

    Affiliation: Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland

    X
  • Bernhard K. Krämer,

    Affiliation: University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany

    X
  • Jacqueline Witteman,

    Affiliations: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands, Member of Netherlands Consortium for Healthy Aging (NCHA), Netherlands Genomics Initiative (NGI), Leiden, The Netherlands

    X
  • Murielle Bochud,

    Affiliation: University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

    X
  • David Siscovick,

    Affiliation: Cardiovascular Health Research Unit, Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington, United States of America

    X
  • Rainer Rettig,

    Affiliation: Institute of Physiology, University of Greifswald, Greifswald, Germany

    X
  • Florian Kronenberg,

    Affiliation: Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria

    X
  • Christoph Wanner,

    Affiliation: University of Würzburg, Department of Medicine 1, Division of Nephrology, University Hospital Würzburg, Würzburg, Germany

    X
  • Ravi I. Thadhani,

    Affiliation: Division of Nephrology, Massachusetts General Hospital, Boston, Massachusetts, United States of America

    X
  • Iris M. Heid,

    Affiliations: Department of Epidemiology and Preventive Medicine, University Hospital Regensburg, Regensburg, Germany, Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    X
  • Caroline S. Fox equal contributor mail,

    equal contributor Contributed equally to this work with: Caroline S. Fox, W. H. Kao

    foxca@nhlbi.nih.gov (CSF); wkao@jhsph.edu (WHK)

    Affiliations: NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America, Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America

    X
  • W. H. Kao equal contributor mail,

    equal contributor Contributed equally to this work with: Caroline S. Fox, W. H. Kao

    foxca@nhlbi.nih.gov (CSF); wkao@jhsph.edu (WHK)

    Affiliation: Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America

    X
  • The CKDGen Consortium
  • Published: September 29, 2011
  • DOI: 10.1371/journal.pgen.1002292

Abstract

Family studies suggest a genetic component to the etiology of chronic kidney disease (CKD) and end stage renal disease (ESRD). Previously, we identified 16 loci for eGFR in genome-wide association studies, but the associations of these single nucleotide polymorphisms (SNPs) for incident CKD or ESRD are unknown. We thus investigated the association of these loci with incident CKD in 26,308 individuals of European ancestry free of CKD at baseline drawn from eight population-based cohorts followed for a median of 7.2 years (including 2,122 incident CKD cases defined as eGFR <60ml/min/1.73m2 at follow-up) and with ESRD in four case-control studies in subjects of European ancestry (3,775 cases, 4,577 controls). SNPs at 11 of the 16 loci (UMOD, PRKAG2, ANXA9, DAB2, SHROOM3, DACH1, STC1, SLC34A1, ALMS1/NAT8, UBE2Q2, and GCKR) were associated with incident CKD; p-values ranged from p = 4.1e-9 in UMOD to p = 0.03 in GCKR. After adjusting for baseline eGFR, six of these loci remained significantly associated with incident CKD (UMOD, PRKAG2, ANXA9, DAB2, DACH1, and STC1). SNPs in UMOD (OR = 0.92, p = 0.04) and GCKR (OR = 0.93, p = 0.03) were nominally associated with ESRD. In summary, the majority of eGFR-related loci are either associated or show a strong trend towards association with incident CKD, but have modest associations with ESRD in individuals of European descent. Additional work is required to characterize the association of genetic determinants of CKD and ESRD at different stages of disease progression.

Author Summary

Chronic kidney disease (CKD) affects about 6%–11% of the general population, and progression to end stage renal disease (ESRD) has a significant public health impact. Family studies suggest that the risk for CKD and ESRD is heritable. Unraveling the genetic underpinning of risk for these diseases may lead to the identification of novel mechanisms and thus diagnostic and therapeutic tools. We have previously identified 16 genetic markers in association with kidney function and prevalent CKD in general population studies. However, little is known about the relevance of these SNPs to the initial development of CKD or to ESRD risk. Therefore, we have now analyzed the association of these markers with the initiation of CKD in more than 26,000 individuals from the general population using serial estimations of kidney function, and with ESRD in four case-control studies in subjects of European ancestry (3,775 cases, 4,577 controls). We show that many of the 16 markers are also associated or show a strong trend towards association with initiation of CKD, while only 2 markers are nominally associated with ESRD. Further work is required to characterize the association of genetic determinants of different stages of CKD progression.

Introduction

Chronic kidney disease (CKD) and end stage renal disease (ESRD) are associated with significant cardiovascular morbidity and mortality, with substantial economic burden [1][4]. Diabetes and hypertension are the primary risk factors for CKD and ESRD [5][8] but do not fully account for CKD and ESRD risk [9][11]. Studies indicate familial aggregation of ESRD [12]. In African Americans, high risk common variants in the MYH9/APOL1 locus account for much of the excess genetic risk for non-diabetic ESRD compared to their counterparts of European descent. In contrast, comparable genetic risk loci of severe renal phenotypes have not been identified in individuals of European ancestry [13][15].

Recently, 16 genetic risk loci associated with estimated glomerular filtration rate (eGFR) and prevalent CKD were identified and replicated by genome wide association studies (GWAS) in about 70,000 individuals of European ancestry in the CKDGen consortium [16], [17]. Two of these loci were also identified by an independent consortium [18]. However, these studies focused on eGFR and prevalent CKD (defined as eGFR <60 ml/min/1.73m2) at one time point, which encompasses the entire spectrum of CKD, and does not does not address the question of whether these genetic factors are involved in the initiation of CKD or in the progression to ESRD, the most advanced stage of CKD. We thus sought to analyze the association of the previously identified 16 eGFR-associated loci with the development of CKD and with ESRD in a total of over 34,000 individuals of European descent.

Results

Association of SNPs with Incident CKD

Overall, 26,308 individuals of European descent, from eight population-based prospective studies, who were free of CKD at baseline were included in the incident CKD analysis (Table 1). At baseline, mean age ranged from 40.5 to 71.7 years. After a median follow-up of 7.2 years, 2122 participants developed incident CKD.

thumbnail

Table 1. Cohort characteristics of the incident CKD analysis (n = 26,308).

doi:10.1371/journal.pgen.1002292.t001

Of the 16 SNPs analyzed, 11 were associated with incident CKD (Table 2): SNPs in UMOD, PRKAG2, ANXA9, DAB2, SHROOM3, DACH1, STC1, SLC34A1, ALMS1/NAT8, UBE2Q2 and GCKR showed p-values ranging from p = 4.1×10−9 in UMOD to p = 0.03 in GCKR. The odds ratios (OR) for incident CKD of the minor alleles at each of the 11 loci ranged from 0.76 per copy of the T allele (allele frequency 18%) at the UMOD locus to 1.19 per copy of the A allele (allele frequency 22%) at PRKAG2. After additional adjustment for baseline eGFR, 6 SNPs (at the UMOD, PRKAG2, ANXA9, DAB2, DACH1 and STC1 loci) remained significantly associated with incident CKD, with minimal attenuation of effect size (Table 2).

thumbnail

Table 2. Results for incident CKD and ESRD, CKDGen consortium.

doi:10.1371/journal.pgen.1002292.t002

At each of the significant loci, the direction and the magnitude of the association was similar to those from the discovery analyses of eGFR and prevalent CKD [17]. For example, at the UMOD locus, each copy of the minor T allele at rs12917707 was associated with a 24% reduced risk for incident CKD, while in the CKDGen consortium the same allele was associated with higher eGFR [17]. Though the associations between incident CKD and SNPs in SLC7A9, ATXN2, PIP5K1B and VEGFA were not significant, the direction and magnitude of associations were consistent with our previous findings for the phenotypes eGFR and prevalent CKD [16], [17]. TFDP2 was the only locus where we did not observe association with incident CKD. Of the 16 SNPs tested, 15 had the same direction of association with incident CKD as their original associations with prevalent CKD. The probability of observing this many SNPs with consistency in direction of associations is 0.0002. We did not observe evidence for heterogeneity between studies at any of the 16 loci (test for heterogeneity p>0.05 for all SNPs).

Association of SNPs with ESRD

For the ESRD analysis, we included four case-control studies with a total of 3775 ESRD patients and 4577 controls of European descent without CKD (Table 3). Mean age ranged from 50.7 to 66.2 years in cases and from 47.7 to 62.1 years in controls. Although the direction and magnitude of association for 8 SNPs (at the UMOD, GCKR, PIP5K1B, PRKAG2, STC1, VEGFA, SHROOM3, and ALMS1/NAT8 loci) were consistent with our previous findings for eGFR and prevalent CKD [16], [17], only two SNPs showed nominally significant associations with ESRD (Table 2): rs1260326 in GCKR (OR = 0.93; p-value = 0.03) and rs12917707 in UMOD (OR = 0.92; p-value = 0.04). The lack of association was not likely due to heterogeneity of ESRD cases as only two SNPs showed moderate heterogeneity in their associations with ESRD (Table 2): rs4744712 at the PIP5K1B locus (p = 0.04 for heterogeneity) and rs626277 at the DACH1 locus (p = 0.02 for heterogeneity).

thumbnail

Table 3. Characteristics of the ESRD case-control studies (n = 3,775 cases, n = 4,577 controls).§

doi:10.1371/journal.pgen.1002292.t003

Discussion

Among individuals of European Ancestry, most genetic loci associated with the quantitative trait eGFR are also associated with risk for initiation of CKD, with more than half of these associations independent of eGFR at the baseline examination. In contrast, only two SNPs were nominally associated with ESRD.

To date, the genetic loci showing significant and replicated associations with ESRD are limited [13][15], [19][26], and genetic studies for incident CKD or for renal function decline in established kidney disease are only recently emerging [27][29]. The loci we analyzed were identified in association with renal function cross-sectionally and with prevalent CKD by GWAS in the general population. Typical of many SNPs uncovered in GWAS, the majority of these SNPs reside in intronic regions with unknown functional consequences, although several are associated with cis expression levels in liver tissue or leukocytes (Table S3) [16], [17]. These newly identified loci are non-overlapping with those previously identified in individuals of European or Asian descent with advanced diabetic nephropathy [19][26], or in African Americans with non-diabetic ESRD [13][15].

For the ESRD analysis, we had adequate power to detect effects that were similar to those for prevalent CKD in the discovery GWAS, where odds ratios ranged from 0.8 to 1.19 [16], [17]. In the present study, where associations were observed, the odds ratios for ESRD tended to be smaller and ranged from 0.92 to 1.11. There are several potential explanations for this effect dilution. First, the mechanisms involved in the initiation of CKD, the progression of CKD, and the incidence of ESRD may differ [30][33]. Experimental animal data and gene expression profiling in human kidney biopsies suggest differential biological pathways contributing to kidney disease initiation and progression [34][36]. Second, the majority of patients with CKD die of cardiovascular disease before developing ESRD [37][39]. Thus, the genetic findings for kidney function in the general population may not apply to the highly selected group of dialysis populations. Finally, the process of progression from CKD to ESRD often involves repeated insults including episodes of acute kidney injury by diagnostic and operative procedures and therapies [40][43], cardiac function deterioration [44], variation in access to adequate health care [45], [46] and other non-genetic factors [47]. Jointly, these factors may further decrease the relative impact of the small effects of SNPs derived from GWAS of eGFR in the general population at the earliest stage of disease initiation.

The observed small effect sizes for ESRD in our study are in contrast to the large effect sizes observed in relatively small cohorts of individuals of African descent for variants in the MYH9/APOL1 locus, where odds ratios for ESRD ranged from 7.3 for the G1–G2 haplotype at the APOL1 locus to 2.38 for the E1 haplotype in the MYH9 locus [13][15]. However, the strong effect at this locus is an exceptional case and may be a consequence of a pronounced positive selection against vulnerability for Trypanosoma brucei rhodesiense infection at the price of a higher susceptibility for non-diabetic ESRD in African Americans not observed in other ethnicities. The establishment of large cohorts is thus needed for performing GWAS of CKD initiation and progression as well as ESRD to overcome the challenge of identifying novel loci significantly associated with these phenotypes with small effect sizes.

The strength of our work lies in the large number of individuals studied. Further, we exclusively analyzed candidate SNPs identified by the unbiased method of GWAS [16], [17]. However, some limitations warrant mention. First, seven of the eight cohorts used for the incident CKD analysis were also part of the CKDGen discovery effort; thus the two samples are not entirely “independent”. However, the phenotype studied differs substantially: in Köttgen et al [17], we used prevalent eGFR data including those with CKD, while follow-up data in those without CKD at the baseline examination was used for the present incident CKD analysis. In the present work, we demonstrate robustness of our findings independent of baseline GFR. Second, we relied on only two serum creatinine measurements to define incident CKD, which may have introduced misclassification and biased our findings towards the null. Third, we did not account for pharmacological treatment with inhibitors of the renin-angiotensin-aldosterone system. Since these drugs may affect kidney function independently of kidney damage, their use may have diluted observable genetic effects [48]. Fourth, our study was not designed to detect fluctuations in eGFR. Furthermore, the etiology of ESRD in the cases we examined may vary between studies, though we observed a low degree of heterogeneity. Finally, our sample consisted of individuals of European ancestry; findings may not be generalizable to other ethnicities.

SNPs associated with eGFR in population-based studies are associated with incident CKD, whereas modest associations were observed with ESRD. Additional work is necessary to characterize the genetic underpinnings across the full range of kidney disease phenotypes, which could ultimately lead to novel diagnostic and therapeutic strategies.

Materials and Methods

Ethics statement

In all studies, all participants gave informed consent. All studies were approved by their appropriate Research Ethics Committees.

Study design and phenotype definition

In population based cohorts, serum creatinine measurements were calibrated to the National Health and Nutrition Examination Study (NHANES) standards in all studies to account for between-laboratory variation across studies, as described previously [10], [16], [17]. Using calibrated serum creatinine, we calculated the estimated glomerular filtration rate (eGFR) with the 4-variable MDRD equation [49].

For incident CKD, we analyzed studies of incident CKD in eight population-based cohorts in the CKDGen consortium with follow-up available: ARIC, CHS, CoLaus, FHS, KORA S3/F3, KORA S4/F4, the Rotterdam Study and SHIP. Each study's design is shown in Text S1. Incident CKD cases were defined as those free of CKD at baseline (defined as eGFR≥60 ml/min/1.73m2) but with a follow-up eGFR<60 ml/min/1.73m2. Controls were those free of CKD at baseline and at follow-up.

For the ESRD analysis, we performed four case control studies of ESRD. Cases were ESRD patients from six cohorts of ESRD patients: CHOICE, ArMORR, GENDIAN, 4D, MMKD and FHKS. Controls were those free of CKD (defined as eGFR≥60 ml/min/1.73m2) in three population-based cohorts (KORA F3, KORA F4, SAPHIR) and one type 2 diabetes cohort (GENDIAN). Each study's design is shown in Text S1.

Statistical methods

In each study, we performed age- and sex adjusted logistic regression of incident CKD, with and without additional adjusting for baseline eGFR, or ESRD status with each SNP. In multicenter studies further adjustment for study-center was performed to account for possible differences between recruiting centers. For family-based studies, we applied logistic regression via generalized estimating equations (GEE) to account for the familial relatedness. Study-specific results were then combined by meta-analysis using a fixed effects model, using METAL (http://www.sph.umich.edu/csg/abecasis/Me​tal/index.html) [50]. When significant heterogeneity between studies was observed (p for heterogeneity between studies <0.05) we used the random effects model [51]. Statistical significance was defined as a one-sided p-value <0.05 for each SNP without adjustment for multiple testing since all SNPs examined had strong prior probabilities of being associated with the outcomes and the same alleles were hypothesized to be associated with lower eGFR, incident CKD, and ESRD.

Power estimation

We used the QUANTO software for power estimation, assuming an additive genetic model (http://hydra.usc.edu/GxE) [52]. For the ESRD analysis and for SNPs with minor allele frequency ranging from 0.2 to 0.4 we had 80–100% power to detect an OR ≥ 1.10, whereas power was borderline for an OR of 1.05 to 1.09. For example, for the SNP rs12917707 at UMOD, we had 100% power to detect an association with ESRD in the 3775 ESRD cases and 4577 controls assuming that the effect in ESRD would be the same or larger than the effect observed for prevalent CKD previously [16], [17].

Genotyping methods and quality control

For the incident CKD analysis, we used the allele dosage information of each of the 16 SNPs from each study's genome wide data set imputed to HAPMAP CEU samples described previously [17], [18]. Imputation provides a common SNP panel across all studies to facilitate a meta-analysis across all contributing SNPs. Information on each study's genotyping and imputation platform and quality control procedures are shown in Table S1. Table S2 summarizes each SNPs imputation quality.

De novo genotyping of the 16 SNPs was performed in each of the ESRD case-control studies as described previously [17]. Briefly, genotyping was performed either on a MassARRAY system using Assay Design v.3.1.2 and the iPLEX™ chemistry (Sequenom, San Diego, USA) at the Helmholtz Zentrum in Munich, Germany (ArMORR, GENDIAN, 4D, MMKD, FHKS, KORA S3/F3-subset without GWAS data, KORA S4/F4-subset without GWAS data, SAPHIR); by using 5′ nuclease allelic discrimination assays on 7900HT Fast Real-Time Taqman PCR genotyping systems (Applied Biosystems, Foster City, CA, USA) at the Innsbruck Medical University (ArMORR, GENDIAN, 4D, MMKD, FHKS, KORA F3-subset without GWAS data, KORA F4-subset without GWAS data, SAPHIR); or as part of a larger panel of 768 SNPs genotyped on the Illumina Bead Station (CHOICE). The SNPs rs347685, rs11959928, rs4744712 and rs12460876 were not available for de novo genotyping on the Sequenom platform, thus the proxy SNPs rs6773343, rs11951093, rs1556751 and rs8101881, with pairwise r2 of 1.0, 0.87, 0.87 and 1.0 respectively [53], were included in the MassARRAY multiplex PCR.

For the obtained duplicate genotypes (9–22% of the subjects in GENDIAN, 4D, MMKD, FHKS, KORA F3-subset without GWAS data, KORA F4-subset without GWAS data, and SAPHIR; no duplicate genotyping possible due to limited DNA-availability in CHOICE and ArMORR) concordance was 96–100% (median: 100%). SNPs with a per-study call rate <90% or with a per-study HWE p value <0.0001 were excluded from further analysis (rs6773343 and rs653178 in GENDIAN cases; rs13538, rs267734, rs10109414, rs1394125 in ArMORR, rs6773343, rs10109414, rs1556751, rs653178, rs8101881 in CHOICE). In addition, individual samples with <80% successfully genotyped SNPs were excluded from further analysis. After these exclusions, call rates ranged from 91–100% (mean: 98%) across all studies and all SNPs.

Supporting Information

Table S1.

Genotyping and Imputation Platforms Used by Studies in the incident CKD analysis.

doi:10.1371/journal.pgen.1002292.s001

(DOC)

Table S2.

Imputation quality scores of SNPs across incident CKD cohorts.

doi:10.1371/journal.pgen.1002292.s002

(DOC)

Table S3.

Location and function of analyzed SNPs.

doi:10.1371/journal.pgen.1002292.s003

(DOC)

Text S1.

Study-specific details.

doi:10.1371/journal.pgen.1002292.s004

(DOC)

Acknowledgments

Atherosclerosis Risk in Communities Study (ARIC)

The authors thank the staff and participants of the ARIC study for their important contributions.

Cardiovascular Health Study (CHS)

A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm.

CoLaus

The CoLaus authors thank Yolande Barreau, Mathieu Firmann, Vladimir Mayor, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey, and Sylvie Mermoud for data collection.

Framingham Heart Study (FHS)

This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project.

The Rotterdam Study

We thank Pascal Arp, Mila Jhamai, Dr Michael Moorhouse, Marijn Verkerk, and Sander Bervoets for their help in creating the GWAS database. The authors are very grateful to the participants and staff from the Rotterdam Study, the participating general practioners, and the pharmacists. We would like to thank Dr. Tobias A. Knoch, Luc V. de Zeeuw, Anis Abuseiris, and Rob de Graaf, as well as their institution the Erasmus Computing Grid, Rotterdam, The Netherlands.

Accelerated Mortality on Renal Replacement (ArMORR)

The authors thank the participants, nurse and laboratory staff, medical doctors and directors of the ArMORR study for their important contributions to the study.

4D

We thank all investigators and study nurses who participated in the 4D study (www.nephrologie.uni-wuerzburg.de).

CHOICE

The authors thank the participants, staff, laboratory and medical directors from the participating clinics of DCI for their contributions to this study.

Family Heart and Kidney Study (FHKS)

We appreciate the collaboration with the following members of the Family Heart and Kidney Study (FHKS) Group: Paul König (Innsbruck University Hospital, Innsbruck, Austria); Michael Koch and Iva Poludniak (Nephrologisches Zentrum Mettmann, Germany); Ulrich Neyer, Susanne Linder, Christine Stüttler-Gut, Kathrin Berchtold, Hannelore Sprenger-Mähr, Sabina Smodek, Lisa Schuler (Feldkirch Hospital, Feldkirch, Austria); Martin Auinger (Krankenhaus Hietzing, Vienna, Austria); Martin Wiesholzer (St. Pölten Hospital, Austria); Nikolaus Zambelis and Wilfried V. Jilly (Pörtschach am Wörther See and Klagenfurt, Austria); Josef Kovarik, Ursula Lang and Heinz Fuhrmann (Wilhelminenspital Vienna, Austria); Ludwig Knabl (Zams Hospital, Austria).

GENDIAN

The support of the physicians, the patients, and the staff of the Diabetes Zentrum Mergentheim (Head: Prof. Dr. Thomas Haak), the diabetes outpatient clinic Dr Nusser+Dr Kreisel, the dialysis centers KfH Amberg, KfH Bayreuth, KfH Deggendorf, KfH Donauwörth, KfH Freising, KfH Freyung, KfH Fürth, KfH Hof, KfH Ingolstadt, KfH Kelheim, KfH München Elsenheimerstraße, KfH München-Schwabing, KfH Neumarkt, KfH Neusäß, KfH Oberschleißheim, KfH Passau, KfH Plauen, KfH Regensburg Günzstraße, KfH Regensburg Caritas-Krankenhaus, KfH Straubing, KfH Sulzbach-Rosenberg, KfH Weiden, Dialysezentrum Augsburg Dr. Kirschner, Dialysezentrum Bad Alexandersbad, KfH Bamberg, Dialysezentrum Emmering, Dialysezentrum Klinikum Landshut, Dialysezentrum Landshut, Dialysezentrum Pfarrkirchen, Dialysezentrum Schwandorf, Dr. Angela Götz, the medical doctoral students Emilia Ruff and Johanna Christ and the Study Nurse Ingrid Lugauer. The expert technical assistance of Claudia Strohmeier is gratefully acknowledged.

Mild to Moderate Kidney Disease Study (MMKD)

We appreciate the collaboration with the following members of the Mild and Moderate Kidney Disease (MMKD) Study Group: Erich Kuen, Division of Genetic Epidemiology, Innsbruck Medical University (Innsbruck, Austria); Paul König, Innsbruck University Hospital (Innsbruck, Austria); Günter Kraatz, Ernst Moritz Arndt University (Greifswald, Germany); Johannes F.E. Mann, München Schwabing Hospital (Munich, Germany); Gerhard A. Müller, Georg August University (Göttingen, Germany); Ulrich Neyer, Feldkirch Hospital (Feldkirch, Austria); Hans Köhler, Medizinische Universitätskliniken des Saarlandes (Homburg/Saar, Germany); Peter Riegler, Bozen Hospital (Bozen, Italy).

Author Contributions

Conceived and designed the experiments: CAB MG MMH CH AT VK H-EW JC EB BP AK MGS NP JW AD JSB BKK MB DS RR FK CW RIT IMH CSF WHK. Wrote the paper: CAB MG ML MMH CH QY AD DS FK CSF WHK IMH MB RR RIT. Study management: CAB MMH CH VK H-EW TH JC BK BP MH AK GL MGS NP JW JSB BKK AD MB DS RR FK CW RIT IMH CSF WHK. Subject recruitment: CAB CH VK H-EW TH JC BK BP SC EB MGS NP JW BKK MB DS RR FK CW RIT CSF WHK. Interpretation of results: CAB MG ML MMH CH AT CMO ZK AK GL EB SC BP BK MGS AD MB DS CW VK RR FK IMH CSF WHK. Critical review of manuscript: CAB MG ML MMH CH QY AT VK CMO ZK H-EW TH EB SC JC BK MH BP AK GL MGS NP S-JH CMO JW AH AU FR JSB BKK AD MB DS RR FK CW RIT IMH CSF WHK. Statistical methods and analysis: CAB MG ML MMH CH QY AT CMO ZK BK GL AD MB DS FK IMH CSF WHK. Genotyping: CAB MMH H-EW SC FK MH MGS DS JW AD JSB MB RIT IMH CSF WHK. Bio-informatics: CAB MG ML MMH AT CMO QY ZK SC AK GL AD MB DS IMH CSF WHK.

References

  1. 1. Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, et al. (2007) Chronic kidney disease as a global public health problem: approaches and initiatives - a position statement from Kidney Disease Improving Global Outcomes. Kidney Int 72: 247–59.
  2. 2. Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, et al. (2010) Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet 375: 2073–81.
  3. 3. Baumeister SE, Böger CA, Krämer BK, Doring A, Eheberg D, et al. (2010) Effect of chronic kidney disease and comorbid conditions on health care costs: A 10-year observational study in a general population. Am J Nephrol 31: 222–9.
  4. 4. Meguid El Nahas A, Bello AK (2005) Chronic kidney disease: the global challenge. Lancet 365: 331–40.
  5. 5. Adler AI, Stevens RJ, Manley SE, Bilous RW, Cull CA, et al. (2003) Development and progression of nephropathy in type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS 64). Kidney Int 63: 225–32.
  6. 6. Hsu CY, McCulloch CE, Darbinian J, Go AS, Iribarren C (2005) Elevated blood pressure and risk of end-stage renal disease in subjects without baseline kidney disease. Arch Intern Med 165: 923–8.
  7. 7. Kastarinen M, Juutilainen A, Kastarinen H, Salomaa V, Karhapaa P, et al. Risk factors for end-stage renal disease in a community-based population: 26-year follow-up of 25,821 men and women in eastern Finland. J Intern Med 267: 612–20.
  8. 8. Ritz E, Stefanski A (1996) Diabetic nephropathy in type II diabetes. Am J Kidney Dis 27: 167–94.
  9. 9. Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, et al. (2007) Prevalence of chronic kidney disease in the United States. JAMA 298: 2038–47.
  10. 10. Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, et al. (2004) Predictors of new-onset kidney disease in a community-based population. JAMA 291: 844–50.
  11. 11. Fox CS, Muntner P (2008) Trends in diabetes, high cholesterol, and hypertension in chronic kidney disease among U.S. adults: 1988-1994 to 1999-2004. Diabetes Care 31: 1337–42.
  12. 12. Satko SG, Sedor JR, Iyengar SK, Freedman BI (2007) Familial clustering of chronic kidney disease. Semin Dial 20: 229–36.
  13. 13. Genovese G, Friedman DJ, Ross MD, Lecordier L, Uzureau P, et al. (2010) Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 329: 841–5.
  14. 14. Kao WH, Klag MJ, Meoni LA, Reich D, Berthier-Schaad Y, et al. (2008) MYH9 is associated with nondiabetic end-stage renal disease in African Americans. Nat Genet 40: 1185–92.
  15. 15. Kopp JB, Smith MW, Nelson GW, Johnson RC, Freedman BI, et al. (2008) MYH9 is a major-effect risk gene for focal segmental glomerulosclerosis. Nat Genet 40: 1175–84.
  16. 16. Köttgen A, Glazer NL, Dehghan A, Hwang SJ, Katz R, et al. (2009) Multiple loci associated with indices of renal function and chronic kidney disease. Nat Genet 41: 712–7.
  17. 17. Köttgen A, Pattaro C, Böger CA, Fuchsberger C, Olden M, et al. (2010) New loci associated with kidney function and chronic kidney disease. Nat Genet 42: 376–84.
  18. 18. Chambers JC, Zhang W, Lord GM, van der Harst P, Lawlor DA, et al. (2010) Genetic loci influencing kidney function and chronic kidney disease. Nat Genet 42: 373–5.
  19. 19. Ma RC, Tam CH, Wang Y, Luk AO, Hu C, et al. (2010) Genetic variants of the protein kinase C-beta 1 gene and development of end-stage renal disease in patients with type 2 diabetes. JAMA 304: 881–9.
  20. 20. Shimazaki A, Kawamura Y, Kanazawa A, Sekine A, Saito S, et al. (2005) Genetic variations in the gene encoding ELMO1 are associated with susceptibility to diabetic nephropathy. Diabetes 54: 1171–8.
  21. 21. Pezzolesi MG, Katavetin P, Kure M, Poznik GD, Skupien J, et al. (2009) Confirmation of genetic associations at ELMO1 in the GoKinD collection supports its role as a susceptibility gene in diabetic nephropathy. Diabetes 58: 2698–702.
  22. 22. Pezzolesi MG, Poznik GD, Mychaleckyj JC, Paterson AD, Barati MT, et al. (2009) Genome-wide association scan for diabetic nephropathy susceptibility genes in type 1 diabetes. Diabetes 58: 1403–10.
  23. 23. Alkhalaf A, Bakker SJ, Bilo HJ, Gans RO, Navis GJ, et al. A polymorphism in the gene encoding carnosinase (CNDP1) as a predictor of mortality and progression from nephropathy to end-stage renal disease in type 1 diabetes mellitus. Diabetologia 53: 2562–8.
  24. 24. Freedman BI, Bostrom M, Daeihagh P, Bowden DW (2007) Genetic factors in diabetic nephropathy. Clin J Am Soc Nephrol 2: 1306–16.
  25. 25. He B, Osterholm AM, Hoverfalt A, Forsblom C, Hjorleifsdottir EE, et al. (2009) Association of genetic variants at 3q22 with nephropathy in patients with type 1 diabetes mellitus. Am J Hum Genet 84: 5–13.
  26. 26. Zhang D, Efendic S, Brismar KGu HF Effects of MCF2L2, ADIPOQ and SOX2 genetic polymorphisms on the development of nephropathy in type 1 Diabetes Mellitus. BMC Med Genet 11: 116.
  27. 27. Köttgen A, Hwang SJ, Rampersaud E, Coresh J, North KE, et al. (2008) TCF7L2 variants associate with CKD progression and renal function in population-based cohorts. J Am Soc Nephrol 19: 1989–99.
  28. 28. Liu M, Shi S, Senthilnathan S, Yu J, Wu E, et al. (2010) Genetic variation of DKK3 may modify renal disease severity in ADPKD. J Am Soc Nephrol 21: 1510–20.
  29. 29. Wheeler HE, Metter EJ, Tanaka T, Absher D, Higgins J, et al. (2009) Sequential use of transcriptional profiling, expression quantitative trait mapping, and gene association implicates MMP20 in human kidney aging. PLoS Genet 5: e1000685. doi:10.1371/journal.pgen.1000685.
  30. 30. Brancati FL, Whelton PK, Randall BL, Neaton JD, Stamler J, et al. (1997) Risk of end-stage renal disease in diabetes mellitus: a prospective cohort study of men screened for MRFIT. Multiple Risk Factor Intervention Trial. JAMA 278: 2069–74.
  31. 31. (2002) K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 39: S1–266.
  32. 32. Bash LD, Astor BC, Coresh J (2010) Risk of incident ESRD: a comprehensive look at cardiovascular risk factors and 17 years of follow-up in the Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 55: 31–41.
  33. 33. Kronenberg F (2009) Emerging risk factors and markers of chronic kidney disease progression. Nat Rev Nephrol 5: 677–89.
  34. 34. Pillebout E, Burtin M, Yuan HT, Briand P, Woolf AS, et al. (2001) Proliferation and remodeling of the peritubular microcirculation after nephron reduction: association with the progression of renal lesions. Am J Pathol 159: 547–60.
  35. 35. Viau A, El Karoui K, Laouari D, Burtin M, Nguyen C, et al. (2010) Lipocalin 2 is essential for chronic kidney disease progression in mice and humans. J Clin Invest 120: 4065–76.
  36. 36. Schmid H, Boucherot A, Yasuda Y, Henger A, Brunner B, et al. (2006) Modular activation of nuclear factor-kappaB transcriptional programs in human diabetic nephropathy. Diabetes 55: 2993–3003.
  37. 37. Al-Aly Z, Zeringue A, Fu J, Rauchman MI, McDonald JR, et al. (2010) Rate of Kidney Function Decline Associates with Mortality. J Am Soc Nephrol 21: 1961–9.
  38. 38. Dalrymple LS, Katz R, Kestenbaum B, Shlipak MG, Sarnak MJ, et al. (2011) Chronic Kidney Disease and the Risk of End-Stage Renal Disease versus Death. J Gen Intern Med 26: 379–85.
  39. 39. Agarwal R, Bunaye Z, Bekele DM, Light RP (2008) Competing risk factor analysis of end-stage renal disease and mortality in chronic kidney disease. Am J Nephrol 28: 569–75.
  40. 40. Borthwick E, Ferguson APerioperative acute kidney injury: risk factors, recognition, management, and outcomes. BMJ 341: c3365.
  41. 41. Kelly KJ, Dominguez JHRapid Progression of Diabetic Nephropathy Is Linked to Inflammation and Episodes of Acute Renal Failure. Am J Nephrol 32: 469–75.
  42. 42. van Kuijk JP, Flu WJ, Chonchol M, Hoeks SE, Winkel TA, et al. (2010) Temporary perioperative decline of renal function is an independent predictor for chronic kidney disease. Clin J Am Soc Nephrol 5: 1198–204.
  43. 43. James MT, Ghali WA, Tonelli M, Faris P, Knudtson ML, et al. Acute kidney injury following coronary angiography is associated with a long-term decline in kidney function. Kidney Int 78: 803–9.
  44. 44. Ronco C, McCullough PA, Anker SD, Anand I, Aspromonte N, et al. Cardiorenal syndromes: an executive summary from the consensus conference of the Acute Dialysis Quality Initiative (ADQI). Contrib Nephrol 165: 54–67.
  45. 45. Winkelmayer WC, Owen WF Jr, Levin R, Avorn J (2003) A propensity analysis of late versus early nephrologist referral and mortality on dialysis. J Am Soc Nephrol 14: 486–92.
  46. 46. Ward MM (2009) Access to care and the incidence of end-stage renal disease due to diabetes. Diabetes Care 32: 1032–6.
  47. 47. Soderland P, Lovekar S, Weiner DE, Brooks DR, Kaufman JSChronic kidney disease associated with environmental toxins and exposures. Adv Chronic Kidney Dis 17: 254–64.
  48. 48. Mann JF, Schmieder RE, McQueen M, Dyal L, Schumacher H, et al. (2008) Renal outcomes with telmisartan, ramipril, or both, in people at high vascular risk (the ONTARGET study): a multicentre, randomised, double-blind, controlled trial. Lancet 372: 547–53.
  49. 49. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, et al. (2006) Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 145: 247–54.
  50. 50. Willer CJ, Li Y, Abecasis GRMETAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26: 2190–1.
  51. 51. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7: 177–88.
  52. 52. Gauderman WJ (2002) Sample size requirements for matched case-control studies of gene-environment interaction. Stat Med 21: 35–50.
  53. 53. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, et al. (2008) SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24: 2938–9.