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

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      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 3 , 14 , 15 , 16 , 17 , 17 , 18 , 17 , 17 , 19 , 4 , 20 , 21 , 22 , 23 , 24 , 11 , 25 , 26 , 26 , 27 , 26 , 27 , 25 , 26 , 28 , 29 , 25 , 26 , 30 , 31 , 32 , 17 , 9 , 6 , 2 , 3 , 11 , 33 , * , 18 , * , The CKDGen Consortium
      PLoS Genetics
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          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/1.73m 2 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.

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          Most cited references35

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          Chronic kidney disease as a global public health problem: approaches and initiatives - a position statement from Kidney Disease Improving Global Outcomes.

          Chronic kidney disease (CKD) is increasingly recognized as a global public health problem. There is now convincing evidence that CKD can be detected using simple laboratory tests, and that treatment can prevent or delay complications of decreased kidney function, slow the progression of kidney disease, and reduce the risk of cardiovascular disease (CVD). Translating these advances to simple and applicable public health measures must be adopted as a goal worldwide. Understanding the relationship between CKD and other chronic diseases is important to developing a public health policy to improve outcomes. The 2004 Kidney Disease Improving Global Outcomes (KDIGO) Controversies Conference on 'Definition and Classification of Chronic Kidney Disease' represented an important endorsement of the Kidney Disease Outcome Quality Initiative definition and classification of CKD by the international community. The 2006 KDIGO Controversies Conference on CKD was convened to consider six major topics: (1) CKD classification, (2) CKD screening and surveillance, (3) public policy for CKD, (4) CVD and CVD risk factors as risk factors for development and progression of CKD, (5) association of CKD with chronic infections, and (6) association of CKD with cancer. This report contains the recommendations from the meeting. It has been reviewed by the conference participants and approved as position statement by the KDIGO Board of Directors. KDIGO will work in collaboration with international and national public health organizations to facilitate implementation of these recommendations.
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            Multiple loci associated with indices of renal function and chronic kidney disease.

            Chronic kidney disease (CKD) has a heritable component and is an important global public health problem because of its high prevalence and morbidity. We conducted genome-wide association studies (GWAS) to identify susceptibility loci for glomerular filtration rate, estimated by serum creatinine (eGFRcrea) and cystatin C (eGFRcys), and CKD (eGFRcrea < 60 ml/min/1.73 m(2)) in European-ancestry participants of four population-based cohorts (ARIC, CHS, FHS, RS; n = 19,877; 2,388 CKD cases), and tested for replication in 21,466 participants (1,932 CKD cases). We identified significant SNP associations (P < 5 × 10(-8)) with CKD at the UMOD locus, with eGFRcrea at UMOD, SHROOM3 and GATM-SPATA5L1, and with eGFRcys at CST and STC1. UMOD encodes the most common protein in human urine, Tamm-Horsfall protein, and rare mutations in UMOD cause mendelian forms of kidney disease. Our findings provide new insights into CKD pathogenesis and underscore the importance of common genetic variants influencing renal function and disease.
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              Sample size requirements for matched case-control studies of gene-environment interaction.

              Consideration of gene-environment (GxE) interaction is becoming increasingly important in the design of new epidemiologic studies. We present a method for computing required sample size or power to detect GxE interaction in the context of three specific designs: the standard matched case-control; the case-sibling, and the case-parent designs. The method is based on computation of the expected value of the likelihood ratio test statistic, assuming that the data will be analysed using conditional logistic regression. Comparisons of required sample sizes indicate that the family-based designs (case-sibling and case-parent) generally require fewer matched sets than the case-control design to achieve the same power for detecting a GxE interaction. The case-sibling design is most efficient when studying a dominant gene, while the case-parent design is preferred for a recessive gene. Methods are also presented for computing sample size when matched sets are obtained from a stratified population, for example, when the population consists of multiple ethnic groups. A software program that implements the method is freely available, and may be downloaded from the website http://hydra.usc.edu/gxe. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                September 2011
                September 2011
                29 September 2011
                : 7
                : 9
                : e1002292
                Affiliations
                [1 ]Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
                [2 ]Department of Epidemiology and Preventive Medicine, University Hospital Regensburg, Regensburg, Germany
                [3 ]Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
                [4 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [5 ]Clinical Chemistry, University Medical Center Freiburg, University of Freiburg, Freiburg, Germany
                [6 ]Division of Nephrology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [7 ]Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
                [8 ]Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
                [9 ]University of Würzburg, Department of Medicine 1, Division of Nephrology, University Hospital Würzburg, Würzburg, Germany
                [10 ]Division of Nephrology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
                [11 ]NHLBI's Framingham Heart Study and the Center for Population Studies, Framingham, Massachusetts, United States of America
                [12 ]Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
                [13 ]Swiss Institute of Bioinformatics, Lausanne, Switzerland
                [14 ]Institute of Medical Informatics, Biometry, and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
                [15 ]Klinikum Großhadern, Munich, Germany
                [16 ]Diabetes Klinik Bad Mergentheim, Bad Mergentheim, Germany
                [17 ]Innsbruck Medical University, Division of Genetic Epidemiology, Innsbruck, Austria
                [18 ]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
                [19 ]First Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria
                [20 ]Renal Division, University Hospital of Freiburg, Freiburg, Germany
                [21 ]Department of Medicine, University of Washington, Seattle, Washington, United States of America
                [22 ]Division of General Internal Medicine, San Francisco VA Medical Center, San Francisco, California, United States of America
                [23 ]Department of Medicine, Epidemiology, and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
                [24 ]Department of Medicine, San Francisco General Hospital and University of California San Francisco, San Francisco, California, United States of America
                [25 ]Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
                [26 ]Member of Netherlands Consortium for Healthy Aging (NCHA), Netherlands Genomics Initiative (NGI), Leiden, The Netherlands
                [27 ]Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
                [28 ]Service of Medical Genetics, Centre Hospitalier Universitaire Vaudois and Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
                [29 ]University Medical Centre Mannheim, 5th Department of Medicine, Mannheim, Germany
                [30 ]University Institute of Social and Preventive Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
                [31 ]Cardiovascular Health Research Unit, Departments of Epidemiology and Medicine, University of Washington, Seattle, Washington, United States of America
                [32 ]Institute of Physiology, University of Greifswald, Greifswald, Germany
                [33 ]Division of Endocrinology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
                Stanford University Medical Center, United States of America
                Author notes

                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.

                Article
                PGENETICS-D-11-00374
                10.1371/journal.pgen.1002292
                3183079
                21980298
                e5a82287-e002-4daf-863f-17c3b7151c32
                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                History
                : 21 February 2011
                : 22 July 2011
                Page count
                Pages: 8
                Categories
                Research Article
                Biology
                Genetics
                Human Genetics
                Genetic Association Studies
                Genetic Testing
                Genome-Wide Association Studies
                Personalized Medicine
                Genetics of Disease
                Genome-Wide Association Studies
                Medicine
                Clinical Genetics
                Epidemiology
                Biomarker Epidemiology
                Clinical Epidemiology
                Genetic Epidemiology
                Nephrology
                Chronic Kidney Disease
                Dialysis

                Genetics
                Genetics

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