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Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

2020; Elsevier BV; Volume: 99; Issue: 4 Linguagem: Inglês

10.1016/j.kint.2020.09.030

ISSN

1523-1755

Autores

Mathias Gorski, Bettina Jung, Yong Li, Pamela R. Matías‐García, Matthias Wuttke, Stefan Coassin, Chris H. L. Thio, Marcus E. Kleber, Thomas W. Winkler, Veronika Wanner, Jin Fang Chai, Audrey Y. Chu, Massimiliano Cocca, Mary F. Feitosa, Sahar Ghasemi, Anselm Hoppmann, Katrin Horn, Man Li, Teresa Nutile, Markus Scholz, Karsten B. Sieber, Alexander Teumer, Adrienne Tin, Judy Wang, Bamidele O. Tayo, Tarunveer S. Ahluwalia, Peter Almgren, Stephan J. L. Bakker, Bernhard Banas, Nisha Bansal, Mary L. Biggs, Eric Boerwinkle, Erwin P. Böttinger, Hermann Brenner, Robert J. Carroll, John Chalmers, Miao-Li Chee, Miao-Ling Chee, Ching‐Yu Cheng, Josef Coresh, Martin H. de Borst, Frauke Degenhardt, Kai‐Uwe Eckardt, Karlhans Endlich, André Franke, Sandra Freitag‐Wolf, Piyush Gampawar, Ron T. Gansevoort, Mohsen Ghanbari, Christian Gieger, Pavel Hamet, Kevin Ho, Edith Hofer, Bernd Holleczek, Valencia Hui Xian Foo, Nina Hutri‐Kähönen, Shih‐Jen Hwang, M. Arfan Ikram, Navya Shilpa Josyula, Mika Kähönen, Chiea Chuen Khor, Wolfgang Köenig, Holly Kramer, Bernhard K. Krämer, Brigitte Kühnel, Leslie A. Lange, Terho Lehtimäki, Wolfgang Lieb, Ruth J. F. Loos, Mary Ann Lukas, Leo‐Pekka Lyytikäinen, Christa Meisinger, Thomas Meitinger, Olle Melander, Yuri Milaneschi, Pashupati P. Mishra, Nina Mononen, Josyf C. Mychaleckyj, Girish N. Nadkarni, Matthias Nauck, Kjell Nikus, Boting Ning, Ilja M. Nolte, Michelle L. O’Donoghue, Marju Orho‐Melander, Sarah A. Pendergrass, Brenda W.J.H. Penninx, Michael Preuß, Bruce M. Psaty, Laura M. Raffield, Olli T. Raitakari, Rainer Rettig, Myriam Rheinberger, Kenneth Rice, Alexander R. Rosenkranz, Peter Rossing, Jerome I. Rotter, Charumathi Sabanayagam, Helena Schmidt, Reinhold Schmidt, Ben Schöttker, Christina‐Alexandra Schulz, Sanaz Sedaghat, Christian M. Shaffer, Konstantin Strauch, Silke Szymczak, Kent D. Taylor, Johanne Tremblay, Layal Chaker, Pim van der Harst, Peter J. van der Most, Niek Verweij, Uwe Völker, Mélanie Waldenberger, Lars Wallentin, Dawn Waterworth, Harvey D. White, James G. Wilson, Tien Yin Wong, Mark Woodward, Qiong Yang, Masayuki Yasuda, Laura M. Yerges-Armstrong, Yan Zhang, Harold Snieder, Christoph Wanner, Carsten A. Böger, Anna Köttgen, Florian Kronenberg, Cristian Pattaro, Iris M. Heid, Behrooz Z. Alizadeh, H. Marike Boezen, Lude Franke, Pim van der Harst, Gerjan Navis, Marianne G. Rots, Harold Snieder, Morris A. Swertz, Bruce H. R. Wolffenbuttel, Cisca Wijmenga, Gonçalo R. Abecasis, Aris Baras, Michael Cantor, Giovanni Coppola, Aris N. Economides, Luca A. Lotta, John D. Overton, Jeffrey G. Reid, Alan R. Shuldiner, Christina Beechert, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Michael Lattari, Alexander Lopez, John D. Overton, Thomas D. Schleicher, Maria Sotiropoulos Padilla, Karina Toledo, Louis Widom, Sarah E. Wolf, Manasi Pradhan, Kia Manoochehri, Ricardo H. Ulloa, Xiaodong Bai, Suganthi Balasubramanian, Leland Barnard, Andrew Blumenfeld, Gisu Eom, Lukas Habegger, Alicia Hawes, Shareef Khalid, Jeffrey G. Reid, Evan K. Maxwell, William Salerno, Jeffrey Staples, Marcus B. Jones, Lyndon J. Mitnaul,

Tópico(s)

Renal and related cancers

Resumo

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function. Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function. Rapid kidney function decline is an important risk factor for end-stage kidney disease (ESKD), cardiovascular events, and early mortality.1Matsushita 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 (204) Google Scholar,2Coresh J. Turin T.C. Matsushita K. et al.Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality.JAMA. 2014; 311: 2518-2531Crossref PubMed Scopus (608) Google Scholar ESKD is a life-threatening condition with substantial individual and public health burden3Matsushita K. van der Velde M. Astor B.C. et al.Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.Lancet. 2010; 375: 2073-2081Abstract Full Text Full Text PDF PubMed Scopus (2681) Google Scholar, 4Astor B.C. Matsushita K. Gansevoort R.T. et al.Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts.Kidney Int. 2011; 79: 1331-1340Abstract Full Text Full Text PDF PubMed Scopus (491) Google Scholar, 5Go A.S. Chertow G.M. Fan D. et al.Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.N Engl J Med. 2004; 351: 1296-1305Crossref PubMed Scopus (8722) Google Scholar and a major endpoint in clinical nephrology trials. However, identifying and monitoring individuals at risk for ESKD is challenging. Two definitions of rapid decline in creatinine-based eGFR (eGFRcrea) are reported to increase ESKD risk 5- and 12-fold,6Turin 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 (76) Google Scholar,7Andrassy K.M. Comments on "KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.".Kidney Int. 2013; 84: 622-623Abstract Full Text Full Text PDF PubMed Scopus (314) Google Scholar respectively, and thus recommended for clinical use: (i) rapid eGFRcrea decline of >5 ml/min per 1.73 m2 per year and (ii) a ≥25% decline of eGFRcrea along with movement into a lower category of chronic kidney disease.7Andrassy K.M. Comments on "KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.".Kidney Int. 2013; 84: 622-623Abstract Full Text Full Text PDF PubMed Scopus (314) Google Scholar Other surrogate endpoints of ESKD were implemented by interventional trials with a follow-up duration of 1,000,000 individuals identified 264 loci associated with eGFRcrea based on 1 creatinine measurement ("cross-sectional eGFRcrea").17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar However, little is known about whether these or additional genetic factors are associated with rapid kidney function decline ("longitudinal kidney function traits"). Given the substantial organizational and temporal requirements of longitudinal studies, sample sizes for these studies are still limited compared with cross-sectional studies. Our previous longitudinal GWAS based on 61,078 individuals and approximately 3 million genetic variants did not identify any locus for rapid eGFRcrea decline.18Gorski M. Tin A. Garnaas M. et al.Genome-wide association study of kidney function decline in individuals of European descent.Kidney Int. 2015; 87: 1017-1029Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar New studies with longitudinal eGFRcrea measurements and new genomic reference panels enabling a denser and more precise genetic variant imputation now allow for a more powerful investigation. We thus performed a GWAS meta-analysis across 42 longitudinal studies, consisting of 41 studies from the Chronic Kidney Disease Genetics (CKDGen) Consortium and UK Biobank, totaling >270,000 individuals with 2 eGFRcrea measurements across a time period of 1–15 years of follow-up. We implemented 2 definitions of rapid eGFRcrea decline that were feasible in population-based studies while preserving similarity to recommended surrogate clinical endpoints: (i) "Rapid3" cases defined as eGFRcrea decline of >3 ml/min per 1.73 m2 per year compared with "no decline" ("Rapid3" controls, 1 to +1 ml/min per 1.73 m2 per year); and (ii) "CKDi25" cases defined as ≥25% eGFRcrea decline during follow-up together with a movement from eGFRcrea ≥60 ml/min per 1.73 m2 at baseline to eGFRcrea 8 million genetic variants imputed via 1000 Genomes19Auton A. Abecasis G.R. Altshuler D.M. et al.A global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (7682) Google Scholar or Haplotype Reference Consortium20McCarthy S. Das S. Kretzschmar W. et al.A reference panel of 64,976 haplotypes for genotype imputation.Nat Genet. 2016; 48: 1279-1283Crossref PubMed Scopus (1241) Google Scholar reference panels were tested for association with Rapid3 and CKDi25 using logistic regression adjusting for age, sex, and baseline eGFRcrea (Supplementary Table S3, Methods). We meta-analyzed study-specific summary statistics by outcome (34,874 cases, 107,090 controls for Rapid3; 19,901 cases, 175,244 controls for CKDi25; Methods). In our genome-wide approach, we selected genome-wide significant loci (i.e., ≥1 variant with a P value of <5 × 10−8 within ±500 kB; "lead variant" as the variant with the smallest P value); within each locus, we searched for independently associated signals by conditional analyses (Methods). By this, we identified 5 lead variants across 4 loci (P values = 5.94 × 10−9 to 3.51 × 10−33, Figure 2, Table 1): (i) the UMOD-PDILT locus was associated with Rapid3 and CKDi25 and showed a second independent signal for CKDi25 (rs77924615; P-adjusted = 2.98 × 10−10). For CKDi25, the independent odds ratios (ORs) for the 2 UMOD-PDILT lead variants (rs12922822, rs77924615) were 1.06 per adverse allele per variant in a model containing both variants. (ii) One variant in each of the WDR72 and PRKAG2 loci was identified for CKDi25. (iii) A variant near OR2S2 was associated with Rapid3.Table 1Six loci from the genome-wide and candidate-based search for association with Rapid3 or CKDi25RSIDChr:PositionIdentifying analysisLocus nameEA/OAEAFRapid3CKDi25Locus/signal no.Reference variant (R2)ORPORPGenome-wide search (genome-wide significance, P value <5.00 × 10−8)aThe significant lead variants from the GWAS (genome-wide significance, P value < 5.0 × 10−8)rs13329952rs1292282216:20,366,50716:20,367,645Rapid3CKDi25[UMOD-PDILT]t/cc/t0.790.811.1011.1032.35 × 10−171.13 × 10−161.2031.2246.22 × 10−303.51 × 10−331.1rs13329952 (0.91)rs7792461516:20,392,332CKDi25 2ndbStated are OR and P value for Rapid3 and CKDi25 adjusted for the lead variant of the respective primary GWAS (rs13329952 or rs12922822). Unadjusted OR = 1.08 and 1.26 (P value = 1.40 × 10−10 and 1.29 × 10−28) for Rapid3 and CKDi25, respectively.[UMOD-PDILT]g/a0.791.0230.03841.1122.98 × 10−101.2rs7759373415:54,002,606CKDi25[WDR72]t/c0.721.0401.18 × 10−41.1021.42 × 10−112rs560124667:151,406,788CKDi25[PRKAG2]a/g0.271.0411.12 × 10−41.0901.53 × 10−93rs1418097669:35,937,931Rapid3[OR2S2]g/a0.021.2225.94 × 10−91.0650.2524Candidate approach based on 265cA total of 264 reported lead variants plus the lead variant of the 2nd signal in [UMOD-PDILT] from cross-sectional eGFRcrea GWAS.17 reported lead variants from cross-sectional eGFRcrea GWAS (significance P value <0.05/265 ≈ 1.89 × 10−4)dThe significant variants from the candidate-based approach inquiring the 265 variants reported for cross-sectional eGFRcrea17 (Bonferroni-corrected significance, P value < 0.05/265 ≈ 1.89 × 10−4).rs34882080eLead variant of the 2nd signal in [UMOD-PDILT] from cross-sectional eGFRcrea analysis in European ancestry.1716:20,361,441CKDi25; Rapid3[UMOD-PDILT]a/g0.811.1001.11 × 10−151.2162.98 × 10−311.1rs12922822 (0.99)rs7792461516:20,392,332CKDi25; Rapid3[UMOD-PDILT]g/a0.791.0841.40 × 10−101.2561.29 × 10−281.2rs69042815:53,950,578CKDi25[WDR72]a/c0.711.0270.01171.0781.46 × 10−52rs77593734 (0.42)rs102541017:151,415,536CKDi25[PRKAG2]t/c0.281.0375.35 × 10−41.0874.32 × 10−93rs56012466 (0.84)rs8028210310:899,071CKDi25[LARP4B]t/a0.081.0270.1001.1032.97× 10−55rs114507715:45,683,795Rapid3[GATM]t/g0.401.0387.94 × 10−51.0421.93 × 10−36rs1145089 (0.99)RSID, variant identifier on GRCh37; Chr:Position, chromosome and position on GRCh37; identifying analysis, trait and analysis for which the variant was identified with significant association ("2nd" indicating the second signal analysis); locus name, nearest gene, stated in brackets to distinguish from gene and protein names; EA, effect allele: cross-sectional eGFRcrea-lowering allele; EAF, effect allele frequency; locus/signal no., locus number and signal number highlighting that 4 of the 6 candidate-based identified variants capture the same locus/signal as the GWAS; OA, other allele; OR, odds ratio; P, genomic control corrected association P value; reference variant (R2), variant to which the identified variant is compared with in terms of correlation (Spearman correlation coefficient squared).Bold values indicate genome-wide significant P values (<5.00 × 10-8) in the identifying trait in a and a Bonferroni corrected significant P value (<1.89 × 10-4) in d.a The significant lead variants from the GWAS (genome-wide significance, P value < 5.0 × 10−8)b Stated are OR and P value for Rapid3 and CKDi25 adjusted for the lead variant of the respective primary GWAS (rs13329952 or rs12922822). Unadjusted OR = 1.08 and 1.26 (P value = 1.40 × 10−10 and 1.29 × 10−28) for Rapid3 and CKDi25, respectively.c A total of 264 reported lead variants plus the lead variant of the 2nd signal in [UMOD-PDILT] from cross-sectional eGFRcrea GWAS.17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholard The significant variants from the candidate-based approach inquiring the 265 variants reported for cross-sectional eGFRcrea17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar (Bonferroni-corrected significance, P value < 0.05/265 ≈ 1.89 × 10−4).e Lead variant of the 2nd signal in [UMOD-PDILT] from cross-sectional eGFRcrea analysis in European ancestry.17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar Open table in a new tab RSID, variant identifier on GRCh37; Chr:Position, chromosome and position on GRCh37; identifying analysis, trait and analysis for which the variant was identified with significant association ("2nd" indicating the second signal analysis); locus name, nearest gene, stated in brackets to distinguish from gene and protein names; EA, effect allele: cross-sectional eGFRcrea-lowering allele; EAF, effect allele frequency; locus/signal no., locus number and signal number highlighting that 4 of the 6 candidate-based identified variants capture the same locus/signal as the GWAS; OA, other allele; OR, odds ratio; P, genomic control corrected association P value; reference variant (R2), variant to which the identified variant is compared with in terms of correlation (Spearman correlation coefficient squared). Bold values indicate genome-wide significant P values (<5.00 × 10-8) in the identifying trait in a and a Bonferroni corrected significant P value ( 0.84, Supplementary Table S4A). We also conducted a meta-analysis restricting to individuals of African ancestry (2356 cases and 2375 controls for Rapid3; 374 cases and 4183 controls for CKDi25), but limited sample sizes prohibited an informative comparison with EUR results (Supplementary Table S4B, Supplementary Note S1). Overall, we identified 4 loci associated at genome-wide significance for these binary rapid eGFRcrea decline traits. Genetic variants with established association for cross-sectional eGFRcrea are candidates for association with rapid eGFRcrea decline. For our candidate-based approach, we selected the 264 lead variants and the second signal lead variant in the UMOD-PDILT locus reported previously for eGFRcrea17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar and tested these for association with Rapid3 and CKDi25 (judged at Bonferroni-corrected significance; 0.05/265 = 1.89 × 10−4). Among these, we found 6 variants in 5 loci significantly associated with Rapid3 and/or CKDi25 (Table 1), yielding 2 variants that were associated with Rapid3 and/or CKDi25 independently from the 5 GWAS-identified variants, 1 each in LARP4B and GATM, significantly associated with CKDi25 or Rapid3 (Supplementary Note S2, Supplementary Table S5, Supplementary Figure S2). Overall, our genome-wide and candidate-based approaches yielded 7 independent variants in 6 loci associated with at least 1 of the rapid eGFRcrea decline traits. For the OR2S2 locus, the only 2 genome-wide significant variants identified for Rapid3 were highly correlated and showed the largest OR of all 7 identified variants (rs141809766, rs56289282, r2 = 0.95; OR =1.22 and 1.21; P value = 5.94 × 10−9 and 2.11 × 10−8, respectively). Because these variants were not associated with cross-sectional eGFRcrea17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar (P value = 0.16 or 0.18, n = 542,354) and of low frequency in the general population (minor allele frequency [MAF] = 0.02), we evaluated the statistical robustness of this association: (i) the majority of studies showed consistent risk for rs141809766 (Supplementary Figure S3A); (ii) a leave-one-out sensitivity analysis showed no influential single study driving the signal (Supplementary Figure S3B); (iii) when focusing on European ancestry, we found similar results (Supplementary Table S4); (iv) the lack of association with cross-sectional eGFRcrea was confirmed in independent data (UK Biobank, n = 364,686, e.g., rs141809766, P value = 0.65). In summary, these analyses supported this locus as a genuine finding. A challenge in using eGFRcrea to detect genetic variants for kidney function is the fact that it is influenced by both kidney function and creatinine production, the latter being linked to muscle mass.21Schutte J.E. Longhurst J.C. Gaffney F.A. et al.Total plasma creatinine: an accurate measure of total striated muscle mass.J Appl Physiol Respir Environ Exerc Physiol. 1981; 51: 762-766PubMed Google Scholar Alternative biomarkers such as estimated GFR based on cystatin C22Köttgen A. Genome-wide association studies in nephrology research.Am J Kidney Dis. 2010; 56: 743-758Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar (eGFRcys) and blood urea nitrogen17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar (BUN) can be used to support eGFRcrea loci as kidney function loci. We thus evaluated the 7 lead variants for their direction-consistent association with annual change in eGFRcys and BUN in UK Biobank (n = 15,746 or 15,277, respectively; mean follow-up time = 4.3 years): annual decline of eGFRcys and/or annual increase of BUN for the Rapid3/CKDi25-risk increasing allele. For completeness, we also present the 7 variants' association with cross-sectional eGFRcys and BUN (n = 364,819 and 358,791). These analyses with alternative renal biomarkers supported UMOD-PDILT, WDR72, PRKAG2, and OR2S2, but not LARP4B or GATM loci (Table 2, Supplementary Note S3).Table 2Validation of the 7 identified variants association with an alternative renal biomarker in UK BiobankLocus/signal no. [name]RSIDeGFRcys changeaAnnual change of eGFRcys and BUN was calculated as the baseline value minus the follow-up value divided by the years between baseline and follow-up. The age, sex, and baseline eGFRcys/BUN-adjusted residuals were regressed on allele dosage. UKBBBUN changeaAnnual change of eGFRcys and BUN was calculated as the baseline value minus the follow-up value divided by the years between baseline and follow-up. The age, sex, and baseline eGFRcys/BUN-adjusted residuals were regressed on allele dosage. UKBBeGFRcysbThe age- and sex-adjusted residuals of the log eGFRcrea, eGFRcys, and BUN were regressed on allele dosage. UKBBBUNbThe age- and sex-adjusted residuals of the log eGFRcrea, eGFRcys, and BUN were regressed on allele dosage. UKBB (CKDGen)EffectPEffectPEffectPEffectP1.1 [UMOD-PDILT]rs133299520.02710.02−0.00360.45−0.00456.06 × 10−860.0024(0.0040)1.08 × 10−18 (1.62 × 10−22)1.1[UMOD-PDILT]rs129228220.02890.010.00180.53−0.00462.17 × 10−850.0025 (0.0044)1.09 × 10−18 (8.79 × 10−21)1.2 [UMOD-PDILT]rs779246150.02890.01−0.05190.03−0.00511.74 × 10−1080.0029 (0.0053)2.38 × 10−26 (2.57 × 10−42)2 [WDR72]rs775937340.00260.41−0.04290.03−0.00161.88 × 10−160.0014 (0.0026)1.59 × 10−9 (8.46 × 10−17)3 [PRKAG2]rs560124660.02380.02−0.06522.75 × 10−3−0.00391.56 × 10−810.0046 (0.0057)8.73 × 10−80 (1.69 × 10−41)4 [OR2S2]rs1418097660.05370.04−0.12450.020.00050.80−0.00345 (−0.0018)0.70 (0.89)5 [LARP4B]rs802821030.02410.10−0.03620.17−0.00374.87 × 10−290.0026 (0.0026)2.49 × 10−11 (4.90 × 10−7)6 [GATM]rs1145077−0.00960.820.01500.750.00010.74−0.0004 (<0.0001)0.95 (0.46)BUN, blood urea nitrogen; effect, genetic effect; eGFRcys, estimated glomerular filtration rate based on cystatin C; locus/signal no. [name], locus number and signal number [locus name]; P, one-sided association P value; RSID, variant identifier; UKBB, UK Biobank.Association results for annual change in eGFRcys and BUN in UK Biobank (n up to 15,746 or 15,277, respectively). One-sided P values are provided testing the allele that increased the risk of rapid eGFRcrea decline (usually the eGFRcrea-lowering allele, except for the OR2S2 lead variant) into the direction of annual eGFRcys decline and annual BUN increase. For completeness, also shown are association results for cross-sectional eGFRcys and BUN from UK Biobank (n up to 364,819 and 358,791) as well as previously reported BUN results from CKDGen17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar (n = 416,076), where 1-sided P values test the eGFRcrea-lowering allele into the direction of decreased eGFRcys and increased BUN levels.a Annual change of eGFRcys and BUN was calculated as the baseline value minus the follow-up value divided by the years between baseline and follow-up. The age, sex, and baseline eGFRcys/BUN-adjusted residuals were regressed on allele dosage.b The age- and sex-adjusted residuals of the log eGFRcrea, eGFRcys, and BUN were regressed on allele dosage. Open table in a new tab BUN, blood urea nitrogen; effect, genetic effect; eGFRcys, estimated glomerular filtration rate based on cystatin C; locus/signal no. [name], locus number and signal number [locus name]; P, one-sided association P value; RSID, variant identifier; UKBB, UK Biobank. Association results for annual change in eGFRcys and BUN in UK Biobank (n up to 15,746 or 15,277, respectively). One-sided P values are provided testing the allele that increased the risk of rapid eGFRcrea decline (usually the eGFRcrea-lowering allele, except for the OR2S2 lead variant) into the direction of annual eGFRcys decline and annual BUN increase. For completeness, also shown are association results for cross-sectional eGFRcys and BUN from UK Biobank (n up to 364,819 and 358,791) as well as previously reported BUN results from CKDGen17Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (219) Google Scholar (n = 416,076), where 1-sided P values test the eGFRcrea-lowering allele into the direction of decreased eGFRcys and increased BUN levels. Each lead variant represents a signal consisting of correlated variants. Regional association plots (Supplementary Figure S4) illustrate that the 7 rapid eGFRcrea decline signals mostly coincided with the cross-sectional eGFRcrea signal, except for a weaker signal in the WDR72 locus and no corresponding OR2S2 signal for cross-sectional eGFRcrea. Between the 2 traits, Rapid3 and CKDi25, the signals were mostly comparable, except for LARP4B and OR2S2. To prioritize variants at identified signals, we ranked each si

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