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• W2171315731 abstract "Genome-wide association studies (GWASs) have identified multiple loci associated with cross-sectional eGFR, but a systematic genetic analysis of kidney function decline over time is missing. Here we conducted a GWAS meta-analysis among 63,558 participants of European descent, initially from 16 cohorts with serial kidney function measurements within the CKDGen Consortium, followed by independent replication among additional participants from 13 cohorts. In stage 1 GWAS meta-analysis, single-nucleotide polymorphisms (SNPs) at MEOX2, GALNT11, IL1RAP, NPPA, HPCAL1, and CDH23 showed the strongest associations for at least one trait, in addition to the known UMOD locus, which showed genome-wide significance with an annual change in eGFR. In stage 2 meta-analysis, the significant association at UMOD was replicated. Associations at GALNT11 with Rapid Decline (annual eGFR decline of 3 ml/min per 1.73 m2 or more), and CDH23 with eGFR change among those with CKD showed significant suggestive evidence of replication. Combined stage 1 and 2 meta-analyses showed significance for UMOD, GALNT11, and CDH23. Morpholino knockdowns of galnt11 and cdh23 in zebrafish embryos each had signs of severe edema 72 h after gentamicin treatment compared with controls, but no gross morphological renal abnormalities before gentamicin administration. Thus, our results suggest a role in the deterioration of kidney function for the loci GALNT11 and CDH23, and show that the UMOD locus is significantly associated with kidney function decline. Genome-wide association studies (GWASs) have identified multiple loci associated with cross-sectional eGFR, but a systematic genetic analysis of kidney function decline over time is missing. Here we conducted a GWAS meta-analysis among 63,558 participants of European descent, initially from 16 cohorts with serial kidney function measurements within the CKDGen Consortium, followed by independent replication among additional participants from 13 cohorts. In stage 1 GWAS meta-analysis, single-nucleotide polymorphisms (SNPs) at MEOX2, GALNT11, IL1RAP, NPPA, HPCAL1, and CDH23 showed the strongest associations for at least one trait, in addition to the known UMOD locus, which showed genome-wide significance with an annual change in eGFR. In stage 2 meta-analysis, the significant association at UMOD was replicated. Associations at GALNT11 with Rapid Decline (annual eGFR decline of 3 ml/min per 1.73 m2 or more), and CDH23 with eGFR change among those with CKD showed significant suggestive evidence of replication. Combined stage 1 and 2 meta-analyses showed significance for UMOD, GALNT11, and CDH23. Morpholino knockdowns of galnt11 and cdh23 in zebrafish embryos each had signs of severe edema 72 h after gentamicin treatment compared with controls, but no gross morphological renal abnormalities before gentamicin administration. Thus, our results suggest a role in the deterioration of kidney function for the loci GALNT11 and CDH23, and show that the UMOD locus is significantly associated with kidney function decline. Chronic kidney disease (CKD) is an important public health problem affecting up to 10% of adults worldwide.1Coresh J. Selvin E. Stevens L.A. et al.Prevalence of chronic kidney disease in the United States.JAMA. 2007; 298: 2038-2047Crossref PubMed Scopus (3894) Google Scholar,2Meguid El Nahas A. Bello A.K. Chronic kidney disease: the global challenge.Lancet. 2005; 365: 331-340Abstract Full Text Full Text PDF PubMed Scopus (895) Google Scholar,3KDIGO KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.Kidney Int Suppl. 2013; 3: 1-150Abstract Full Text Full Text PDF Scopus (1596) Google Scholar Faster rates of decline in estimated glomerular filtration rate (eGFR), and entry into CKD stages of increasing severity, are associated with an increased risk of cardiovascular and all-cause mortality.4Matsushita K. Selvin E. Bash L.D. et al.Change in estimated GFR associates with coronary heart disease and mortality.J Am Soc Nephrol. 2009; 20: 2617-2624Crossref PubMed Scopus (210) Google Scholar,5Rifkin D.E. Shlipak M.G. Katz R. et al.Rapid kidney function decline and mortality risk in older adults.Arch Intern Med. 2008; 168: 2212-2218Crossref PubMed Scopus (282) Google Scholar,6Shlipak M.G. Katz R. Kestenbaum B. et al.Rapid decline of kidney function increases cardiovascular risk in the elderly.J Am Soc Nephrol. 2009; 20: 2625-2630Crossref PubMed Scopus (216) Google Scholar,7Turin T.C. Coresh J. Tonelli M. et al.One-year change in kidney function is associated with an increased mortality risk.Am J Nephrol. 2012; 36: 41-49Crossref PubMed Scopus (46) Google Scholar,8Turin T.C. Coresh J. Tonelli M. et al.Short-term change in kidney function and risk of end-stage renal disease.Nephrol Dial Transplant. 2012; 27: 3835-3843Crossref PubMed Scopus (79) Google Scholar,9Turin T.C. Coresh J. Tonelli M. et al.Change in the estimated glomerular filtration rate over time and risk of all-cause mortality.Kidney Int. 2013; 83: 684-691Abstract Full Text Full Text PDF PubMed Scopus (95) Google Scholar Thus, recently issued guidelines on the evaluation and management of patients with CKD have highlighted the importance of evaluating longitudinal measures of renal function in addition to determining eGFR and urinary albumin excretion at discrete time points.3KDIGO KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.Kidney Int Suppl. 2013; 3: 1-150Abstract Full Text Full Text PDF Scopus (1596) Google Scholar Traditional risk factors for CKD include diabetes and hypertension, but these do not fully account for CKD risk.10Fox C.S. Larson M.G. Leip E.P. et al.Predictors of new-onset kidney disease in a community-based population.JAMA. 2004; 291: 844-850Crossref PubMed Scopus (984) Google Scholar There is evidence for considerable clustering of CKD within families11Satko S.G. Sedor J.R. Iyengar S.K. et al.Familial clustering of chronic kidney disease.Semin Dial. 2007; 20: 229-236Crossref PubMed Scopus (84) Google Scholar and the heritability of eGFR has been estimated at up to 36–75% in population-based studies.12O'Seaghdha C.M. Fox C.S. Genome-wide association studies of chronic kidney disease: what have we learned?.Nat Rev Nephrol. 2012; 8: 89-99Crossref Scopus (59) Google Scholar Using genome-wide association studies (GWASs), multiple loci have been identified in association with eGFR and CKD in both European13Chambers J.C. Zhang W. Lord G.M. et al.Genetic loci influencing kidney function and chronic kidney disease.Nat Genet. 2010; 42: 373-375Crossref PubMed Scopus (221) Google Scholar,14Köttgen A. Glazer N.L. Dehghan A. et al.Multiple loci associated with indices of renal function and chronic kidney disease.Nat Genet. 2009; 41: 712-717Crossref PubMed Scopus (479) Google Scholar,15Köttgen A. Pattaro C. Böger C.A. et al.New loci associated with kidney function and chronic kidney disease.Nat Genet. 2010; 42: 376-384Crossref PubMed Scopus (628) Google Scholar,16Pattaro C. Köttgen A. Teumer A. et al.Genome-wide association and functional follow-up reveals new loci for kidney function.PLoS Genet. 2012; 8: e1002584Crossref PubMed Scopus (146) Google Scholar and non-European populations17Liu C.T. Garnaas M.K. Tin A. et al.Genetic association for renal traits among participants of African ancestry reveals new loci for renal function.PLoS Genet. 2011; 7: e1002264Crossref PubMed Scopus (96) Google Scholar,18Okada Y. Sim X. Go M.J. et al.Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations.Nat Genet. 2012; 44: 904-909Crossref PubMed Scopus (212) Google Scholar using data from one time point. However, multiple lines of evidence suggest that there may be unique genetic contributions to renal function decline above and beyond baseline renal function. First, there is substantial variability in the rate of eGFR decline in studies of healthy persons as well as among those with CKD.3KDIGO KDIGO Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.Kidney Int Suppl. 2013; 3: 1-150Abstract Full Text Full Text PDF Scopus (1596) Google Scholar,4Matsushita K. Selvin E. Bash L.D. et al.Change in estimated GFR associates with coronary heart disease and mortality.J Am Soc Nephrol. 2009; 20: 2617-2624Crossref PubMed Scopus (210) Google Scholar,19Cheng T.Y. Wen S.F. Astor B.C. et al.Mortality risks for all causes and cardiovascular diseases and reduced GFR in a middle-aged working population in Taiwan.Am J Kidney Dis. 2008; 52: 1051-1060Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar,20Li L. Astor B.C. Lewis J. et al.Longitudinal progression trajectory of GFR among patients with CKD.Am J Kidney Dis. 2012; 59: 504-512Abstract Full Text Full Text PDF PubMed Scopus (224) Google Scholar Second, we have previously shown that some genetic loci associated with cross-sectional eGFR are also associated with incident CKD (CKDi) even after accounting for baseline eGFR.21Böger C.A. Gorski M. Li M. et al.Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD.PLoS Genet. 2011; 7: e1002292Crossref PubMed Scopus (154) Google Scholar Finally, the genetic background has been shown to affect CKD progression in animal models.22Regner K.R. Harmon A.C. Williams J.M. et al.Increased susceptibility to kidney injury by transfer of genomic segment from SHR onto Dahl S genetic background.Physiol Genomics. 2012; 44: 629-637Crossref PubMed Scopus (11) Google Scholar,23Yu L. Su Y. Paueksakon P. et al.Integrin alpha1/Akita double-knockout mice on a Balb/c background develop advanced features of human diabetic nephropathy.Kidney Int. 2012; 81: 1086-1097Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar Taken together, these data suggest that unique loci may exist for renal function decline in addition to those identified for a one-time measure of eGFR. Thus, we conducted a GWAS meta-analysis among participants from 16 cohorts with serial kidney function measurements within the CKDGen Consortium, followed by independent replication among additional participants from 13 cohorts. Changes in renal function over time were derived from 45,530 individuals who participated in stage 1 meta-analysis of study-specific GWAS, and an additional 18,028 independent individuals who participated in stage 2 meta-analysis (Table 1). Details on study design and genotyping are provided in Supplementary Tables S1 and S2 online, respectively.Table 1Stage 1 and Stage 2 cohort characteristicsnAge at baseline, years% Women, n%HTN at baseline, n%DM at baseline, n%CKD at baseline, neGFR baseline, ml/min per 1.73 m2eGFR follow-up, ml/min per 1.73 m2Duration between baseline and follow-up (years) mean, s.d.Stage 1 discovery cohorts AGES321954.2 (8.98)58.0, 186721.6, 6943.2, 1033.2, 10489.6 (19.3)73.0 (20.0)22.2 (6.7) Amish45846.9 (14.3)54.4, 2498.7, 404.2, 19NA95.9 (24.3)89.2 (19.4)5.2 (2.6) ARIC904954.5 (5.7)52.9, 479326.9, 24278.6, 7802.9, 26089.7 (17.0)83.5 (17.1)A 80.6 (17.1)B8.0 (2.2) ASPS84865.8 (8.1)56.8, 48272.5, 6159.8, 8312.7, 10880.2 (20.2)74.6 (15.1)5.5 (2.0) CHS282071.9 (5.0)61.3, 172934.5, 96611.0, 3107.9, 22477.3 (20.8)75.4 (20.2)5.9 (1.8) CoLaus191853.9 (10.8)54.9, 105335.4, 6796.31, 1215.0, 9591.5 (20.4)84.9 (18.2)5.58 (0.29) FHS (Offspring and Cohort)252358.1 (8.6)55.6, 140536.8, 9278.1, 2068.3, 21088.7 (25.5)79.5 (19.1)11.1 (3.6) GENOA104154.7 (10.3)55.6, 57971.7, 7468.8, 924.3, 4592.1 (20.7)89.8 (22.8)4.0 (1.1) HABC88873.4 (2.8)48.5, 43133.1, 2948.5, 7521.4, 19071.8 (13.2)72.9 (21.2)9.0 (0.3) JUPITER878066.1 (7.8)32.2, 282663.8, 56020.6, 5411.5, 100880.1 (18.1)78.2 (17.7)2.6 (1.0) KORA3164152.5 (10.1)49.5, 81338.3, 6294.3, 713.2, 5391.3 (18.0)83.9 (21.0)10 (0) KORA4180753.8 (8.9)51.3, 92733.7, 6063.7, 663.9, 7089.5 (17.5)85.1 (20.2)7.1 (0.4) MESA232463.2 (10.1)51.7, 120137.3, 8665.5, 13513.3, 31074.2 (13.9)70.7 (15.1)4.8 (0.5) The Rotterdam Study (RS-I)242266.5 (7.0)60.2, 145950.8, 12307.5, 1827.7, 18679.9 (15.5)73.7 (15.8)6.5 (0.4) SHIP320349.2 (15.3)51.8, 165924.3, 7787.0, 2253.7, 11992.4 (19.8)90.6 (23.6)5.3 (0.7) Three Cities (3C)258973.0 (4.5)61.9, 160276.7, 19868.6, 22318.9, 48973.1 (16.1)71.0 (16.8)3.8 (0.3)Stage 2 replication cohorts ADVANCE203467.0 (6.6)31.9, 64955.2, 1123100, 203416.0, 32584.1 (28.1)84.8 (34.5)4.9 (0.9) BMES130462.9 (7.7)60.1, 78467.1, 8755.4, 7123.9, 31282.6 (31.7)75.5 (34.9)10.4 (0.6) COLAUS223853.1 (10.4)53.9,120724.0, 5384.1, 913.5, 7990.5 (19.5)88.7 (18.7)5.5 (0.3) HYPERGENES65153.4 (7.5)45.3, 29513.9, 9100.61, 4107.4 (23.5)103.4 (35.1)5.6 (3.2) KORA3149451.6 (13.3)52.5, 78529.4, 4375.1, 762.6, 3998.0 (20.1)92.4 (21.3)9.6 (0.6) KORA4120049.2 (15.4)52.4, 62913.3, 1594, 485.8, 7097.4 (21.7)92.6 (22.4)7.2 (0.5) NESDA127043.3 (12.3)67.2, 85432.4, 4115.4, 690.9, 1197.8 (20.4)95.5 (18.5)2.0 (0.3) popgen57760.2 (9.4)42.1, 24350.4, 2885.2, 306.1, 3584.5 (17.2)79.9 (18.4)4.8 (0.8) PREVEND (4-year follow-up)79153.0 (13.3)50.8, 40240.8, 3234.2, 336.0, 4789.2 (19.5)106.4 (34.2)4.3 (0.6) PREVEND (9-year follow-up)216948.0 (11.1)48.0, 104028.2, 6123.1, 661.7, 3793.7 (17.7)86.9 (18.6)9.4 (0.84) RS-II124361.8 (5.2)54.6, 67921.4, 2667.8, 978.6, 18681.2 (17.0)73.7 (15.8)10.6 (0.4) SAPHIR137451.6 (6.0)39.0. 53654.9, 7542.6, 360.9, 1391.5 (15.8)88.0 (16.0)4.6 (0.7) YFS168331.9 (4.9)56.0, 9438.3, 1391.1, 180.2, 3105.4 (16.4)100.4 (16.0)6.0 (0)Abbreviations: DM, diabetes; eGFR, estimated glomerular filtration rate; HTN, hypertension; NA, not applicable; s.d., standard deviation.Unless indicated otherwise, values are given as mean (s.d.).A, eGFR at visit 2; B, eGFR at visit 4; CKD, chronic kidney disease (eGFR<60 ml/min per 1.73 m2). Open table in a new tab Download .doc (1.13 MB) Help with doc files Supplementary Information Abbreviations: DM, diabetes; eGFR, estimated glomerular filtration rate; HTN, hypertension; NA, not applicable; s.d., standard deviation. Unless indicated otherwise, values are given as mean (s.d.). A, eGFR at visit 2; B, eGFR at visit 4; CKD, chronic kidney disease (eGFR<60 ml/min per 1.73 m2). At the baseline examination, the prevalence of CKD, defined as eGFR<60 ml/min per 1.73 m2, ranged from 3.2 to 21.4% in stage 1 cohorts and from 0.2 to 23.9% in stage 2 replication cohorts. As expected, cohorts with lower mean age at baseline tended to have a lower baseline prevalence of CKD. Four kidney function decline traits were derived from serial eGFR values in each study participant to model mechanisms underlying different rates of kidney function change over time: (i) annual decline of eGFR (eGFRchange, in ml/min per 1.73 m2 decline per year; a positive value represents a decline in eGFR, whereas a negative value represents a rise in eGFR over time); (ii) CKDi to select individuals with a decline in kidney function to the clinical outcome CKD stage 3 or higher (CKDi, cases defined as those free of CKD at baseline but eGFR<60 ml/min per 1.73 m2 during follow-up); (iii) CKDi with additionally at least a 25% eGFR decline from baseline to select individuals reaching CKD stage 3 after a sizeable decline in kidney function (CKDi25);24Bash L.D. Coresh J. Kottgen A. et al.Defining incident chronic kidney disease in the research setting: The ARIC Study.Am J Epidemiol. 2009; 170: 414-424Crossref PubMed Scopus (89) Google Scholar and (iv) rapid eGFR decline to select individuals with the highest risk of adverse outcomes (Rapid Decline, cases defined as those with annual eGFR decline ⩾3 ml/min per 1.73 m2).5Rifkin D.E. Shlipak M.G. Katz R. et al.Rapid kidney function decline and mortality risk in older adults.Arch Intern Med. 2008; 168: 2212-2218Crossref PubMed Scopus (282) Google Scholar Most cohorts showed a decline in kidney function over time (Table 1). The distribution of all four traits in stage 1 and stage 2 cohorts can be found in Supplementary Table S3 online. The heritability of eGFR change in the Framingham Heart Study was estimated as 38%, after adjusting for age, sex, and baseline eGFR. Stage 1 GWAS meta-analysis was performed in all samples for all four traits. Two secondary association analyses were performed to account for potentially different rates of kidney function decline in those with and those without CKD: (i) eGFRchange stratified by baseline CKD status and (ii) Rapid Decline in only those without baseline CKD; too few individuals with CKD fulfilled the Rapid Decline criteria to perform this analysis. Supplementary Figure S1 online shows the Manhattan and QQ-plots of the stage 1 meta-analysis of each trait. The genomic control factor ranged from 1.007 to 1.05, suggesting negligible evidence for population stratification. In GWAS meta-analysis of stage 1 cohorts, the minor T allele of rs12917707 at the UMOD locus, previously identified by GWAS to be associated with higher eGFR in cross-sectional analysis,14Köttgen A. Glazer N.L. Dehghan A. et al.Multiple loci associated with indices of renal function and chronic kidney disease.Nat Genet. 2009; 41: 712-717Crossref PubMed Scopus (479) Google Scholar was associated with an increase in eGFR over time at a genome-wide significance level (P=2.6 × 10-14, Table 2), and showed at least nominally significant, direction-consistent association with all other analyzed phenotypes (Supplementary Table S4 online). In addition, SNPs at the novel CDH23, GALNTL5/GALNT11, MEOX2, IL1RAP/OSTN, C2orf48/HPCAL1, and NPPB/NPPA loci were associated with at least one of the analyzed traits at a significance level of P<10-6 (Table 2). Thus, a total of seven SNPs were moved forward to stage 2 meta-analysis. These SNPs mostly showed high imputation quality in each cohort or were genotyped de-novo (Supplementary Table S5 online), and showed low between-study heterogeneity (I2<25%).Table 2Genetic association results of SNPs identified in stage 1 meta-analysisDiscovery stage 1Replication stage 2Stage 1 and stage 2 combinedSNPIDTraitChrPosition (b36)LocusCoded alleleNon-coded alleleAF coded alleleβPVal2GCβOne-sided pval1GCβTwo-sided pval1GCTotal sample sizers12917707eGFRchange overall1620275191UMOD, PDILTtg0.18-0.152.6 × 10-14-0.124.7 × 10-5-0.141.8 × 10-1759,373rs11803049eGFRchange CKD111851482NPPB, NPPA, KIAA2013, CLCN6ag0.07-0.573.6 × 10-70.020.43aStudies included: ADVANCE, BMES, COLAUS, RS-II.-0.276.2 × 10-44116rs875860eGFRchange CKD1072979535CDH23tc0.12-0.496.2 × 10-7-0.150.047aStudies included: ADVANCE, BMES, COLAUS, RS-II.-0.314.6 × 10-64116rs11764932Rapid decline overall715699643MEOX2ag0.360.126.8 × 10-80.040.140.093.6 × 10-761,078rs1019173Rapid decline overall7151341480GALNTL5, GALNT11, MLL3, CCT8L1ag0.63-0.123.0 × 10-7-0.060.04-0.102.1 × 10-761,077rs9814367Rapid Decline noCKD3192075180IL1RAP, OSTNtc0.92-0.204.1 × 10-70.020.39-0.137.3 × 10-556,687rs759341CKDi25210297660C2orf48, HPCAL1, RRM2ag0.310.181.5 × 10-60.060.27bStudies included: ADVANCE, BMES, RS-II.0.162.7 × 10-641,122Abbreviations: CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; SNP, single-nucleotide polymorphism.‘Locus’ is based on build 36, hg18. The gene closest to the SNP is listed first and is in boldface if the SNP is located within the gene.a Studies included: ADVANCE, BMES, COLAUS, RS-II.b Studies included: ADVANCE, BMES, RS-II. Open table in a new tab Abbreviations: CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; SNP, single-nucleotide polymorphism. ‘Locus’ is based on build 36, hg18. The gene closest to the SNP is listed first and is in boldface if the SNP is located within the gene. Of the seven loci moved forward for stage 2 meta-analysis, only rs12917707 at UMOD was significantly associated with the stage 1 trait after correcting for multiple testing (P=4.7* × 10-5). Two further SNPs showed suggestive significance (one-sided P<0.05) with their respective stage 1 trait: rs875860 in CDH23 with eGFRchange in those with CKD at baseline, and rs1019173 at GALNTL5/GALNT11 with Rapid Decline (Table 2). There was no significant heterogeneity between studies for these two SNPs (rs875860: I2=9.7%, P=0.34; rs1019173: I2=32.4%, P=0.12) or for the other SNPs analyzed in the stage 2 meta-analysis (I2<30.0%). The SNP rs1019173 is located in an intron in the GALNTL5 gene, and lies in a linkage disequilibrium block spanning the genes GALNT11, MLL3, CCT8L, and part of the GALNTL5 gene (Figure 1a). The SNP in CDH23, rs875860, is an intronic SNP in a linkage disequilibrium block whose boundaries lie within the coding region of the CDH23 gene (Figure 1b). In the combined meta-analysis of these three SNPs from both stage 1 and stage 2 cohorts, there was no evidence of between-study heterogeneity in the combined meta-analysis (I2<25%). Only the SNP at UMOD showed genome-wide significant association (rs12917707, P=1.2 × 10-16) in the combined stage 1 and stage 2 analysis, whereas there was suggestive evidence of significance for the two novel loci identified in stage 1 (rs875860 in CDH23: P=1.5 × 10-6 for the association with eGFRchange in those with CKD; rs1019173 at GALNTL5/GALNT11: odds ratio=0.91 for the A allele, P=2.2 × 10-7 for the association with Rapid Decline). To investigate the role of the two suggestive novel loci in vertebrate kidney development and function and to bolster confidence in the nominally significant statistical associations in the replication studies, we knocked down the corresponding genes in the zebrafish using antisense morpholino (MO) technology. We focused on the CDH23 region and the block containing GALNTL5, GALNT11, MLL3, and CCT8L1. For the latter region, we focused on GALNT11 and MLL3, because there are no zebrafish GALNTL5 and CCT8L1 orthologs. Further, we investigated the effect of MO knockdown of umod. Following MO injection at the 1-cell stage, we performed in situ hybridization for the established renal markers pax2a (global kidney) and nephrin (podocytes) at 48 h post fertilization (h.p.f.). Compared with control embryos, cdh23, galnt11, mll3a, mll3b, and umod morphants did not display significant defects in glomerular or tubule gene expression (Figure 2a, n>25 embryos per MO injection). It is possible that morphant embryos develop a kidney function decline phenotype only after exposure to a nephrotoxin, despite observing no differences in renal marker expression at 48 h.p.f. Accordingly, after MO injection, we injected embryos with gentamicin at 48 h.p.f. and observed edema prevalence and severity over the next 3 days. In control embryos, gentamicin injection predictably resulted in a majority of embryos developing minor (cardiac) edema by 24 h post injection (h.p.i.) (Figure 2b–d). In comparison, cdh23 and galnt11 morphants developed significantly more severe (cardiac, intestinal, and ocular) and more frequent edema (Figure 2b–d). Specifically, whereas 10% of control embryos developed severe edema by 72 h.p.i., 43% of cdh23 morphants (P=0.009) and 55% of galnt11 morphants (P=0.001) developed severe edema at this time point. Additionally, a significant proportion of cdh23 (33%, P=0.035) and galnt11 morphant embryos (46%, P=0.005) injected with gentamicin developed edema earlier compared with controls at 5 h.p.i. In contrast, knockdown of mll3 or umod affected neither kidney development nor susceptibility to gentamicin (Figure 2b and c). Taken together, these data demonstrate that knockdown of cdh23 and galnt11 results in altered renal function after a nephrotoxic insult. We interrogated eSNP databases for evidence of SNPs at the CDH23 and GALNTL5/GALNT11 loci to evaluate an effect on gene expression25Zhang B. Gaiteri C. Bodea L.G. et al.Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease.Cell. 2013; 153: 707-720Abstract Full Text Full Text PDF PubMed Scopus (1091) Google Scholar but found no relevant associations. Similarly, annotation information provided by functional annotation of genetic variants from high-throughput sequencing data (ANNOVAR)26Wang K. Li M. Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.Nucleic Acids Res. 2010; 38: e164Crossref PubMed Scopus (7948) Google Scholar did not yield genetic variants of potential functional interest within 500 kb of and in linkage disequilibrium (r2>0.8 based on HapMap release 22) with the index SNPs. In Caucasian participants of the CRIC study, a prospective study of patients with CKD at baseline,27Lash J.P. Go A.S. Appel L.J. et al.Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.Clin J Am Soc Nephrol. 2009; 4: 1302-1311Crossref PubMed Scopus (423) Google Scholar neither SNPs in GALNTL5/GALNT11 nor SNPs in CDH23 were associated with eGFRchange (n=1476) or time to a composite renal event that consisted of incident end-stage renal disease or halving of eGFR (n=1585, with a total of n=178 events; results not shown). Our key findings are fourfold. First, we estimate the heritability of eGFR decline as being 38% in the general population of European descent, providing a rationale to search for genetic variants associated with kidney function decline. Second, we extend evidence of a known locus (UMOD) previously associated with CKDi and end-stage renal disease21Böger C.A. Gorski M. Li M. et al.Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD.PLoS Genet. 2011; 7: e1002292Crossref PubMed Scopus (154) Google Scholar,28Reznichenko A. Böger C.A. Snieder H. et al.UMOD as a susceptibility gene for end-stage renal disease.BMC Med Genet. 2012; 13: 78Crossref PubMed Scopus (41) Google Scholar by showing genome-wide significant association with kidney function change. Third, we have identified two novel genetic loci (CDH23 and GALNTL5/GALNT11) with suggestive association with kidney function decline phenotypes. Finally, we show that knockdown of the two novel loci in zebrafish renders the nephron susceptible to a nephrotoxic insult. We extend the current literature by performing the first large-scale GWAS of renal function decline traits in the general population. Previous studies analyzing progression of renal disease in African Americans,29Chen Y. Lipkowitz M.S. Salem R.M. et al.Progression of chronic kidney disease: Adrenergic genetic influence on glomerular filtration rate decline in hypertensive nephrosclerosis.Am J Nephrol. 2010; 32: 23-30Crossref PubMed Scopus (12) Google Scholar,30Chen Y. Mahata M. Rao F. et al.Chromogranin A regulates renal function by triggering Weibel-Palade body exocytosis.J Am Soc Nephrol. 2009; 20: 1623-1632Crossref PubMed Scopus (23) Google Scholar,31Chen Y. Salem R.M. Rao F. et al.Common charge-shift mutation Glu65Lys in K+ channel beta(1)-Subunit KCNMB1: pleiotropic consequences for glomerular filtration rate and progressive renal disease.Am J Nephrol. 2010; 32: 414-424Crossref PubMed Scopus (11) Google Scholar,32Fung M.M. Chen Y. Lipkowitz M.S. et al.Adrenergic beta-1 receptor genetic variation predicts longitudinal rate of GFR decline in hypertensive nephrosclerosis.Nephrol Dial Transplant. 2009; 24: 3677-3686Crossref PubMed Scopus (10) Google Scholar individuals of European descent,21Böger C.A. Gorski M. Li M. et al.Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD.PLoS Genet. 2011; 7: e1002292Crossref PubMed Scopus (154) Google Scholar healthy nurses,33Cooper Worobey C. Fisher N.D. Cox D. et al.Genetic polymorphisms and the risk of accelerated renal function decline in women.PLoS One. 2009; 4: e4787Crossref PubMed Scopus (13) Google Scholar and patients with diabetes,34Jorsal A. Tarnow L. Lajer M. et al.The PPAR gamma 2 Pro12Ala variant predicts ESRD and mortality in patients with type 1 diabetes and diabetic nephropathy.Mol Genet Metab. 2008; 94: 347-351Abstract Full Text Full Text PDF PubMed Scopus (21) Google Scholar,35Sandholm N. Salem R.M. McKnight A.J. et al.New susceptibility loci associated with kidney disease in type 1 diabetes.PLoS Genet. 2012; 8: e1002921Crossref PubMed Scopus (178) Google Scholar hypertension,31Chen Y. Salem R.M. Rao F. et al.Common charge-shift mutation Glu65Lys in K+ channel beta(1)-Subunit KCNMB1: pleiotropic consequences for glomerular filtration rate and progressive renal disease.Am J Nephrol. 2010; 32: 414-424Crossref PubMed Scopus (11) Google Scholar IgA nephropathy,36Cheng W. Zhou X. Zhu L. et al.Polymorphisms in the nonmuscle myosin heavy chain 9 gene (MYH9) are associated with the progression of IgA nephropathy in Chinese.Nephrol Dial Transplant. 2011; 26: 2544-2549Crossref PubMed Scopus (32) Google Scholar,37Ferrandi M. Cusi D. Molinari I. et al.Alpha- and beta-Adducin polymorphisms affect podocyte proteins and proteinuria in rodents and decline of renal function in human IgA nephropathy.J Mol Med (Berl). 2010; 88: 203-217Crossref PubMed Scopus (17) Google Scholar and end-stage renal disease21Böger C.A. Gorski M. Li M. et al.Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD.PLoS Genet. 2011; 7: e1002292Crossref PubMed Scopus (154) Google Scholar focused only on candidate genes. The SNP in UMOD has previously been identified in a GWAS of eGFR measured at one time point,14Köttgen A. Glazer N.L. Dehghan A. et al.Multiple loci associated with indices of renal function and chronic kidney disease.Nat Genet. 2009; 41: 712-717Crossref PubMed Scopus (479) Google Scholar and was significantly associated with CKDi and end-stage renal disease in a candidate gene study21Böger C.A. Gorski M. Li M. et al.Association of eGFR-Related Loci Identified by GWAS with Incident CKD and ESRD.PLoS Genet. 2011; 7: e" @default.
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