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Meta-Analysis
. 2019 Jun;51(6):957-972.
doi: 10.1038/s41588-019-0407-x. Epub 2019 May 31.

A Catalog of Genetic Loci Associated With Kidney Function From Analyses of a Million Individuals

Matthias Wuttke  1   2 Yong Li  1 Man Li  3 Karsten B Sieber  4 Mary F Feitosa  5 Mathias Gorski  6   7 Adrienne Tin  8   9 Lihua Wang  5 Audrey Y Chu  10 Anselm Hoppmann  1 Holger Kirsten  11   12 Ayush Giri  13   14 Jin-Fang Chai  15 Gardar Sveinbjornsson  16 Bamidele O Tayo  17 Teresa Nutile  18 Christian Fuchsberger  19 Jonathan Marten  20 Massimiliano Cocca  21 Sahar Ghasemi  22   23 Yizhe Xu  3 Katrin Horn  11   12 Damia Noce  19 Peter J van der Most  24 Sanaz Sedaghat  25 Zhi Yu  8   26 Masato Akiyama  27   28 Saima Afaq  29   30 Tarunveer S Ahluwalia  31 Peter Almgren  32 Najaf Amin  25 Johan Ärnlöv  33   34 Stephan J L Bakker  35 Nisha Bansal  36   37 Daniela Baptista  38 Sven Bergmann  39   40   41 Mary L Biggs  42   43 Ginevra Biino  44 Michael Boehnke  45 Eric Boerwinkle  46 Mathilde Boissel  47 Erwin P Bottinger  48   49 Thibaud S Boutin  20 Hermann Brenner  50   51 Marco Brumat  52 Ralph Burkhardt  12   53   54 Adam S Butterworth  55   56 Eric Campana  52 Archie Campbell  57 Harry Campbell  58 Mickaël Canouil  47 Robert J Carroll  59 Eulalia Catamo  21 John C Chambers  29   60   61   62   63 Miao-Ling Chee  64 Miao-Li Chee  64 Xu Chen  65 Ching-Yu Cheng  64   66   67 Yurong Cheng  1 Kaare Christensen  68 Renata Cifkova  69   70 Marina Ciullo  18   71 Maria Pina Concas  21 James P Cook  72 Josef Coresh  8 Tanguy Corre  39   40   73 Cinzia Felicita Sala  74 Daniele Cusi  75   76 John Danesh  77 E Warwick Daw  5 Martin H de Borst  35 Alessandro De Grandi  19 Renée de Mutsert  78 Aiko P J de Vries  79 Frauke Degenhardt  80 Graciela Delgado  81 Ayse Demirkan  25 Emanuele Di Angelantonio  82   83 Katalin Dittrich  84   85 Jasmin Divers  86 Rajkumar Dorajoo  87 Kai-Uwe Eckardt  88   89 Georg Ehret  38 Paul Elliott  90   91   92   93 Karlhans Endlich  23   94 Michele K Evans  95 Janine F Felix  25   96   97 Valencia Hui Xian Foo  64 Oscar H Franco  25   98 Andre Franke  80 Barry I Freedman  99 Sandra Freitag-Wolf  100 Yechiel Friedlander  101 Philippe Froguel  47   102 Ron T Gansevoort  35 He Gao  90 Paolo Gasparini  21   52 J Michael Gaziano  103 Vilmantas Giedraitis  104 Christian Gieger  105   106   107 Giorgia Girotto  21   52 Franco Giulianini  108 Martin Gögele  19 Scott D Gordon  109 Daniel F Gudbjartsson  16 Vilmundur Gudnason  110   111 Toomas Haller  112 Pavel Hamet  113   114 Tamara B Harris  115 Catharina A Hartman  116 Caroline Hayward  20 Jacklyn N Hellwege  117   118   119 Chew-Kiat Heng  120   121 Andrew A Hicks  19 Edith Hofer  122   123 Wei Huang  124   125 Nina Hutri-Kähönen  126   127 Shih-Jen Hwang  128   129 M Arfan Ikram  25 Olafur S Indridason  130 Erik Ingelsson  131   132   133   134 Marcus Ising  135 Vincent W V Jaddoe  25   96   97 Johanna Jakobsdottir  136 Jost B Jonas  137   138 Peter K Joshi  58 Navya Shilpa Josyula  139 Bettina Jung  6 Mika Kähönen  140   141 Yoichiro Kamatani  27   142 Candace M Kammerer  143 Masahiro Kanai  27   144 Mika Kastarinen  145 Shona M Kerr  20 Chiea-Chuen Khor  64   87 Wieland Kiess  12   84   85 Marcus E Kleber  81 Wolfgang Koenig  146   147   148 Jaspal S Kooner  61   62   63   149 Antje Körner  12   84   85 Peter Kovacs  150 Aldi T Kraja  5 Alena Krajcoviechova  69   70 Holly Kramer  17   151 Bernhard K Krämer  81 Florian Kronenberg  152 Michiaki Kubo  153 Brigitte Kühnel  105 Mikko Kuokkanen  154   155 Johanna Kuusisto  145   156 Martina La Bianca  21 Markku Laakso  145   156 Leslie A Lange  157 Carl D Langefeld  86 Jeannette Jen-Mai Lee  15 Benjamin Lehne  29 Terho Lehtimäki  158   159 Wolfgang Lieb  160 Lifelines Cohort StudySu-Chi Lim  15   161 Lars Lind  162 Cecilia M Lindgren  163   164 Jun Liu  25 Jianjun Liu  87   165 Markus Loeffler  11   12 Ruth J F Loos  48   166 Susanne Lucae  135 Mary Ann Lukas  167 Leo-Pekka Lyytikäinen  158   159 Reedik Mägi  112 Patrik K E Magnusson  65 Anubha Mahajan  168   169 Nicholas G Martin  109 Jade Martins  170 Winfried März  171   172   173 Deborah Mascalzoni  19 Koichi Matsuda  174 Christa Meisinger  175   176 Thomas Meitinger  147   177   178 Olle Melander  179 Andres Metspalu  112 Evgenia K Mikaelsdottir  16 Yuri Milaneschi  180 Kozeta Miliku  25   96   97 Pashupati P Mishra  158   159 V. A. Million Veteran ProgramKaren L Mohlke  181 Nina Mononen  158   159 Grant W Montgomery  182 Dennis O Mook-Kanamori  78   183 Josyf C Mychaleckyj  184 Girish N Nadkarni  48   185 Mike A Nalls  186   187 Matthias Nauck  23   188 Kjell Nikus  189   190 Boting Ning  191 Ilja M Nolte  24 Raymond Noordam  192 Jeffrey O'Connell  193 Michelle L O'Donoghue  194   195 Isleifur Olafsson  196 Albertine J Oldehinkel  116 Marju Orho-Melander  32 Willem H Ouwehand  77 Sandosh Padmanabhan  197 Nicholette D Palmer  198 Runolfur Palsson  111   130 Brenda W J H Penninx  180 Thomas Perls  199 Markus Perola  200 Mario Pirastu  201 Nicola Pirastu  58 Giorgio Pistis  202 Anna I Podgornaia  10 Ozren Polasek  203   204 Belen Ponte  205 David J Porteous  57   206 Tanja Poulain  12 Peter P Pramstaller  19 Michael H Preuss  48 Bram P Prins  55 Michael A Province  5 Ton J Rabelink  79   207 Laura M Raffield  181 Olli T Raitakari  208   209 Dermot F Reilly  10 Rainer Rettig  210 Myriam Rheinberger  6 Kenneth M Rice  43 Paul M Ridker  108   211 Fernando Rivadeneira  25   212 Federica Rizzi  213   214 David J Roberts  215 Antonietta Robino  21 Peter Rossing  31 Igor Rudan  58 Rico Rueedi  39   40 Daniela Ruggiero  18   71 Kathleen A Ryan  216 Yasaman Saba  217 Charumathi Sabanayagam  64 Veikko Salomaa  200 Erika Salvi  213   218 Kai-Uwe Saum  50 Helena Schmidt  219 Reinhold Schmidt  122 Ben Schöttker  50   51 Christina-Alexandra Schulz  32 Nicole Schupf  220   221   222 Christian M Shaffer  59 Yuan Shi  64 Albert V Smith  111 Blair H Smith  223 Nicole Soranzo  224 Cassandra N Spracklen  181 Konstantin Strauch  225   226 Heather M Stringham  45 Michael Stumvoll  227 Per O Svensson  228   229 Silke Szymczak  100 E-Shyong Tai  15   165   230 Salman M Tajuddin  95 Nicholas Y Q Tan  64 Kent D Taylor  231 Andrej Teren  12   232 Yih-Chung Tham  64 Joachim Thiery  12   53 Chris H L Thio  24 Hauke Thomsen  233 Gudmar Thorleifsson  16 Daniela Toniolo  74 Anke Tönjes  227 Johanne Tremblay  113   234 Ioanna Tzoulaki  90   235 André G Uitterlinden  212 Simona Vaccargiu  201 Rob M van Dam  15   165 Pim van der Harst  236   237   238 Cornelia M van Duijn  25 Digna R Velez Edward  119   239 Niek Verweij  236 Suzanne Vogelezang  25   96   97 Uwe Völker  23   240 Peter Vollenweider  241 Gerard Waeber  241 Melanie Waldenberger  105   106   147 Lars Wallentin  242   243 Ya Xing Wang  138 Chaolong Wang  87   244 Dawn M Waterworth  4 Wen Bin Wei  245 Harvey White  246 John B Whitfield  109 Sarah H Wild  247 James F Wilson  20   58 Mary K Wojczynski  5 Charlene Wong  67 Tien-Yin Wong  64   67 Liang Xu  138 Qiong Yang  191 Masayuki Yasuda  64   248 Laura M Yerges-Armstrong  4 Weihua Zhang  61   90 Alan B Zonderman  95 Jerome I Rotter  231   249   250 Murielle Bochud  73 Bruce M Psaty  251   252 Veronique Vitart  20 James G Wilson  253 Abbas Dehghan  29   90 Afshin Parsa  254   255 Daniel I Chasman  108   211 Kevin Ho  256   257 Andrew P Morris  72   168 Olivier Devuyst  258 Shreeram Akilesh  37   259 Sarah A Pendergrass  260 Xueling Sim  15 Carsten A Böger  6   261 Yukinori Okada  262   263 Todd L Edwards  119   118 Harold Snieder  24 Kari Stefansson  16 Adriana M Hung  119   264 Iris M Heid  7 Markus Scholz  11   12 Alexander Teumer  22   23 Anna Köttgen  265   266 Cristian Pattaro  267
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Free PMC article
Meta-Analysis

A Catalog of Genetic Loci Associated With Kidney Function From Analyses of a Million Individuals

Matthias Wuttke et al. Nat Genet. .
Free PMC article

Abstract

Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.

Conflict of interest statement

competing interests

W. Koenig reports modest consultation fees for advisory board meetings from Amgen, DalCor, Kowa, Novartis, Pfizer and Sanofi and modest personal fees for lectures from Amgen, AstraZeneca, Novartis, Pfizer and Sanofi, all outside the scope of the submitted work. W.M. is employed with Synlab Services and holds shares of Synlab Holding Deutschland. D.O.M.-K. is a part-time research consultant at Metabolon. M.A.N. is supported by a consulting contract between Data Tecnica International and the National Institute on Aging (NIA), National Institutes of Health (NIH) and consults for Illumina, the Michael J. Fox Foundation and University of California Healthcare. O.H.F. works in ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec); Metagenics; and AXA. K.B.S., L.Y.-A., D.M.W. and M.A.L. are full-time employees of GlaxoSmithKline. M.L.O’D. received grant support from GlaxoSmithKline, MSD, Eisai, AstraZeneca, MedCo and Janssen. H.W. received grants and non-financial support from GlaxoSmithKline, during the conduct of the study; grants from Sanofi-Aventis, Eli Lilly, the National Institute of Health, Omthera Pharmaceuticals, Pfizer New Zealand, Elsai Inc. and Dalcor Pharma UK; honoraria and non-financial support from AstraZeneca; and is on advisory boards for Sirtex and Acetilion and received personal fees from CSL Behring and American Regent outside the scope of the submitted work. L. Wallentin received institutional grants from GlaxoSmithKline, AstraZeneca, BMS, Boehringer-Ingelheim, Pfizer, MSD and Roche Diagnostics. D.F.R. and A.I.P. are employees of MSD. M. Scholz received consultancy of and grant support from Merck Serono not related to this project. B.M.P. serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. J. Danesh is a member of the Novartis Cardiovascular and Metabolic Advisory Board and received grant support from Novartis. A.S.B. received grants from MSD, Pfizer, Novartis, Biogen and Bioverativ and personal fees from Novartis. V.S. has participated in a conference trip sponsored by Novo Nordisk and received a honorarium from the same source for participating in an advisory board meeting. A. Köttgen received grant support from Gruenenthal. All other authors declare no conflicts of interest.

Figures

Fig. 1 |
Fig. 1 |. Trans-ancestry GWAS meta-analysis identifies 308 loci associated with eGFR.
Circos plot. The red band corresponds to −log10(P) for association with eGFR (y axis truncated at 30), by chromosomal position. The blue line indicates genome-wide significance (P=5×10−8). Black gene labels indicate new loci, while blue labels indicate known loci. Non-replicating loci are colored in gray (new) or light blue (known). The green band corresponds to measures of heterogeneity related to the index SNPs associated with eGFR. Dot sizes are proportional to I2 or ancestry-related heterogeneity (Panc-het). The blue band corresponds to −log10(P) for association with CKD (y axis truncated at 20), by chromosomal position. The red line indicates genome-wide significance (P = 5×10−8). Radial lines mark regions with Panc-het <1.6×10−4 = 0.05/308 or I2>25%. Inset, effects of all 308 index SNPs on log(eGFR)by minor allele frequency, colored by the associated OR for CKD (red scale for OR≤1, blue scale for OR>1). The largest effects on CKD were observed for rs77924615 at UMOD-PDILT (0R = 0.81, 95% confidence interval (CI) = 0.80, 0.83), rs187355703 at HOXD8 (0R = 0.82, 95% CI = 0.77, 0.87) and rs10254101 at PRKAG2 (0R = 1.11, 95% CI =1.09, 1.11). Triangles highlight SNPs that were associated with CKD (one-sided P<0.05).
Fig. 2 |
Fig. 2 |. Generalizability with respect to other populations and other kidney function markers.
a, Measures of heterogeneity for the 308 eGFR-associated index SNPs. Each variant’s heterogeneity quantified as I2 from the trans-ancestry meta-analysis (y axis) is compared to the ancestry-related heterogeneity from meta-regression (−log10(Panc-het); x axis). Histograms summarize the distribution of the heterogeneity measures on both axes. SNPs with ancestry-related heterogeneity (Panc-het<1.6×10−4 = 0.05/308) are marked in blue and labeled; SNPs with I2>50% are labeled. b, Comparison of genetic effect estimates between CKDGen Consortium discovery (x axis) and MVP replication (y axis). Blue font indicates one-sided P<0.05 in the MVP. Error bars correspond to 95% CIs. The dashed line corresponds to the line of best fit. Pearson’s correlation coefficient r = 0.92 (95% CI = 0.90, 0.94). c, The magnitude of genetic effects on eGFR (x axis) as compared to BUN (y axis) for the 264 replicated eGFR-associated index SNPs. Color coding reflects evidence of kidney function relevance (Methods), which is coded as ‘likely’ (blue), ‘inconclusive’ (gray) or ‘unlikely’ (green). Error bars correspond to 95% CIs. The dashed line corresponds to the line of best fit. Pearson’s correlation coefficient r=−0.65 (95% CI = −0.72, −0.58). d, Association of lower genetically predicted eGFR based on a GRS of 147 SNPs likely to be most relevant for kidney function with ICD-10-based clinical diagnoses for 452,264 individuals from the UK Biobank. Asthma was included as a negative control. Results are displayed as the OR and 95% CI per 10% lower GRS-predicted eGFR (Methods).
Fig. 3 |
Fig. 3 |. Human orthologs of genes with renal phenotypes in genetically manipulated mice are enriched for association signals with eGFR.
a-c, Signals in candidate genes identified on the basis of the mouse phenotypes of abnormal GFR (a), abnormal kidney physiology (b) and abnormal kidney morphology (c). The y axis shows −log10 (P) for association with eGFR in the trans-ancestry meta-analysis for the variant with the lowest P value in each candidate gene. The dashed line corresponds to genome-wide significance (P = 5×10−8), and the solid gray line corresponds to the experiment-wide significance threshold for each nested candidate gene analysis. Orange, genome-wide significance; red, experiment-wide but not genome-wide significance; blue, no significantly associated SNPs. Genes are labeled if they reached experiment- but not genome-wide significance; black font indicates genes not mapping to loci reported in the main analysis. Enrichment P values correspond to the observed number of genes with association signals below the experiment-wide threshold against the number expected on the basis of the complementary cumulative binomial distribution (Methods).
Fig. 4 |
Fig. 4 |. credible set size plotted against variant posterior probability for 3,655 sNPs in 253 99% credible sets according to variant annotation.
a, Exonic variants. SNPs are marked by triangles, with triangle size proportional to CADD score. Red triangles indicate missense SNPs mapping to small credible sets (≤5 SNPs) or to sets containing SNPs with high individual PP of driving the association signal (>50%). b, SNPs with regulatory potential. Symbol color corresponds to regulatory potential as derived from DNase I hypersensitivity analysis in target tissues (Methods). Annotation was restricted to variants with PP>1%; SNPs with PP≥90% contained in credible sets with ≤10 SNPs are labeled. Data are plotted as credible set size (x axis) against variant PP(y axis). Blue and green color coding for gene and SNP labels refers to kidney-function relevance and has the same meaning as in Fig. 2.
Fig. 5 |
Fig. 5 |. colocalization of eGFR association signals with gene expression in kidney tissues.
All eGFR loci were tested for colocalization with all eQTLs where the eQTL cis window overlapped (±100 kb) the sentinel genetic variant. Genes with at least one positive colocalization (PP of one common causal variant (H4)≥80%) in a kidney tissue are shown with the respective sentinel SNP (y axis). Colocalizations across all tissues (x axis) are illustrated as dots, where dot size corresponds to the PP of colocalization. Negative colocalizations (PP for H4<80%) are gray, while positive colocalizations are colored according to the predicted change in expression relative to the allele associated with lower eGFR.
Fig. 6 |
Fig. 6 |. Colocalization of independent eGFR association signals at the UMOD-PDILT locus with urinary uromodulin concentrations (UUCR) supports UMOD as the effector gene.
Association plots show association −log10(P value) (y axis) plotted against chromosomal position (x axis). a, Approximate conditional analyses among European-ancestry individuals support the presence of two independent eGFR-associated signals. b, The association signal for uromodulin (UUCR) levels is similar; r2 = 0.93 between rs34882080 and rs34262842. c,d, Colocalization of association with eGFR (top) and uromodulin (UUCR) levels (bottom) for the independent regions centered on UMOD (c) and PDILT (d) supports a shared underlying variant in both regions with high PP.

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