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Meta-Analysis
. 2022 Jun 13;5(1):580.
doi: 10.1038/s42003-022-03448-z.

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Thomas W Winkler #  1 , Humaira Rasheed #  2   3   4 , Alexander Teumer #  5   6   7 , Mathias Gorski #  8   9 , Bryce X Rowan #  10   11 , Kira J Stanzick  8 , Laurent F Thomas  2   12   13 , Adrienne Tin  14   15 , Anselm Hoppmann  16 , Audrey Y Chu  17 , Bamidele Tayo  18 , Chris H L Thio  19 , Daniele Cusi  20   21 , Jin-Fang Chai  22 , Karsten B Sieber  23 , Katrin Horn  24   25 , Man Li  26 , Markus Scholz  24   25 , Massimiliano Cocca  27 , Matthias Wuttke  16   28 , Peter J van der Most  19 , Qiong Yang  29 , Sahar Ghasemi  5   6   30 , Teresa Nutile  31 , Yong Li  16 , Giulia Pontali  32   33 , Felix Günther  8   34 , Abbas Dehghan  35   36 , Adolfo Correa  14 , Afshin Parsa  37   38 , Agnese Feresin  39 , Aiko P J de Vries  40 , Alan B Zonderman  41 , Albert V Smith  42   43 , Albertine J Oldehinkel  44 , Alessandro De Grandi  32 , Alexander R Rosenkranz  45 , Andre Franke  46 , Andrej Teren  25   47 , Andres Metspalu  48 , Andrew A Hicks  32 , Andrew P Morris  49   50   51 , Anke Tönjes  52 , Anna Morgan  27 , Anna I Podgornaia  17 , Annette Peters  53   54   55 , Antje Körner  25   56   57 , Anubha Mahajan  50 , Archie Campbell  58 , Barry I Freedman  59 , Beatrice Spedicati  39 , Belen Ponte  60 , Ben Schöttker  61   62 , Ben Brumpton  2   63   64 , Bernhard Banas  9 , Bernhard K Krämer  65 , Bettina Jung  7   9   66 , Bjørn Olav Åsvold  2   67 , Blair H Smith  68 , Boting Ning  29 , Brenda W J H Penninx  69 , Brett R Vanderwerff  70   71 , Bruce M Psaty  72   73   74 , Candace M Kammerer  75 , Carl D Langefeld  76 , Caroline Hayward  77 , Cassandra N Spracklen  78   79 , Cassianne Robinson-Cohen  11   80 , Catharina A Hartman  44 , Cecilia M Lindgren  81   82   83 , Chaolong Wang  84   85 , Charumathi Sabanayagam  86   87 , Chew-Kiat Heng  88   89 , Chiara Lanzani  90 , Chiea-Chuen Khor  84   86 , Ching-Yu Cheng  86   87   91 , Christian Fuchsberger  32 , Christian Gieger  53   54   92 , Christian M Shaffer  93 , Christina-Alexandra Schulz  94 , Cristen J Willer  95   96   97 , Daniel I Chasman  98   99 , Daniel F Gudbjartsson  100   101 , Daniela Ruggiero  31   102 , Daniela Toniolo  103 , Darina Czamara  104 , David J Porteous  58   105 , Dawn M Waterworth  23 , Deborah Mascalzoni  32   106 , Dennis O Mook-Kanamori  107   108 , Dermot F Reilly  17 , E Warwick Daw  109 , Edith Hofer  110   111 , Eric Boerwinkle  112 , Erika Salvi  113 , Erwin P Bottinger  114   115 , E-Shyong Tai  22   116   117 , Eulalia Catamo  27 , Federica Rizzi  21   118 , Feng Guo  61 , Fernando Rivadeneira  119   120 , Franco Guilianini  98 , Gardar Sveinbjornsson  100 , Georg Ehret  121 , Gerard Waeber  122 , Ginevra Biino  123 , Giorgia Girotto  27   39 , Giorgio Pistis  124 , Girish N Nadkarni  114   125 , Graciela E Delgado  126 , Grant W Montgomery  127 , Harold Snieder  19 , Harry Campbell  128 , Harvey D White  129 , He Gao  35 , Heather M Stringham  130 , Helena Schmidt  131 , Hengtong Li  86 , Hermann Brenner  61   62 , Hilma Holm  100 , Holgen Kirsten  24   25 , Holly Kramer  18   132 , Igor Rudan  128 , Ilja M Nolte  19 , Ioanna Tzoulaki  35   36   133 , Isleifur Olafsson  134 , Jade Martins  104 , James P Cook  49 , James F Wilson  77   128 , Jan Halbritter  52   135 , Janine F Felix  120   136 , Jasmin Divers  76 , Jaspal S Kooner  137   138   139   140 , Jeannette Jen-Mai Lee  22 , Jeffrey O'Connell  38 , Jerome I Rotter  141 , Jianjun Liu  84   116 , Jie Xu  142 , Joachim Thiery  25   143 , Johan Ärnlöv  144   145 , Johanna Kuusisto  146   147 , Johanna Jakobsdottir  148   149 , Johanne Tremblay  150   151 , John C Chambers  35   137   138   139   152 , John B Whitfield  153 , John M Gaziano  154   155 , Jonathan Marten  77 , Josef Coresh  15 , Jost B Jonas  142   156   157   158 , Josyf C Mychaleckyj  159 , Kaare Christensen  160 , Kai-Uwe Eckardt  161   162 , Karen L Mohlke  78 , Karlhans Endlich  6   163 , Katalin Dittrich  56   57 , Kathleen A Ryan  164 , Kenneth M Rice  165 , Kent D Taylor  141 , Kevin Ho  166   167 , Kjell Nikus  168   169 , Koichi Matsuda  170 , Konstantin Strauch  171   172   173 , Kozeta Miliku  120   136 , Kristian Hveem  2 , Lars Lind  174 , Lars Wallentin  175   176 , Laura M Yerges-Armstrong  23 , Laura M Raffield  78 , Lawrence S Phillips  177   178 , Lenore J Launer  179 , Leo-Pekka Lyytikäinen  180   181 , Leslie A Lange  182 , Lorena Citterio  90 , Lucija Klaric  77 , M Arfan Ikram  183 , Marcus Ising  184 , Marcus E Kleber  126   185 , Margherita Francescatto  39 , Maria Pina Concas  27 , Marina Ciullo  31   102 , Mario Piratsu  186 , Marju Orho-Melander  94 , Markku Laakso  146   147 , Markus Loeffler  24   25 , Markus Perola  187   188 , Martin H de Borst  189 , Martin Gögele  32 , Martina La Bianca  27 , Mary Ann Lukas  190 , Mary F Feitosa  109 , Mary L Biggs  72   165 , Mary K Wojczynski  109 , Maryam Kavousi  183 , Masahiro Kanai  191   192 , Masato Akiyama  191   193 , Masayuki Yasuda  86   194 , Matthias Nauck  6   195 , Melanie Waldenberger  53   92   196 , Miao-Li Chee  86 , Miao-Ling Chee  86 , Michael Boehnke  130 , Michael H Preuss  114 , Michael Stumvoll  52 , Michael A Province  109 , Michele K Evans  41 , Michelle L O'Donoghue  197   198 , Michiaki Kubo  199 , Mika Kähönen  200   201 , Mika Kastarinen  147 , Mike A Nalls  202   203 , Mikko Kuokkanen  188   204   205 , Mohsen Ghanbari  183   206 , Murielle Bochud  207 , Navya Shilpa Josyula  208 , Nicholas G Martin  153 , Nicholas Y Q Tan  86 , Nicholette D Palmer  209 , Nicola Pirastu  128 , Nicole Schupf  210 , Niek Verweij  211 , Nina Hutri-Kähönen  212 , Nina Mononen  180   181 , Nisha Bansal  213   214 , Olivier Devuyst  215 , Olle Melander  94 , Olli T Raitakari  216   217   218 , Ozren Polasek  219   220 , Paolo Manunta  90 , Paolo Gasparini  27   39 , Pashupati P Mishra  180   181 , Patrick Sulem  100 , Patrik K E Magnusson  221 , Paul Elliott  35   36   222   223 , Paul M Ridker  98   99 , Pavel Hamet  150   224 , Per O Svensson  225   226 , Peter K Joshi  128 , Peter Kovacs  52   227 , Peter P Pramstaller  32 , Peter Rossing  228   229 , Peter Vollenweider  122 , Pim van der Harst  211   230 , Rajkumar Dorajoo  84 , Ralene Z H Sim  86 , Ralph Burkhardt  25   143   231 , Ran Tao  10   232 , Raymond Noordam  233 , Reedik Mägi  48 , Reinhold Schmidt  110 , Renée de Mutsert  108 , Rico Rueedi  234   235 , Rob M van Dam  22   236 , Robert J Carroll  93 , Ron T Gansevoort  189 , Ruth J F Loos  114   237   238 , Sala Cinzia Felicita  103 , Sanaz Sedaghat  183 , Sandosh Padmanabhan  239 , Sandra Freitag-Wolf  240 , Sarah A Pendergrass  241 , Sarah E Graham  95 , Scott D Gordon  153 , Shih-Jen Hwang  242   243 , Shona M Kerr  77 , Simona Vaccargiu  186 , Snehal B Patil  70   71   96 , Stein Hallan  12   244 , Stephan J L Bakker  189 , Su-Chi Lim  22   245 , Susanne Lucae  184 , Suzanne Vogelezang  120   136 , Sven Bergmann  234   235 , Tanguy Corre  207   234   235 , Tarunveer S Ahluwalia  228   246 , Terho Lehtimäki  180   181 , Thibaud S Boutin  77 , Thomas Meitinger  196   247   248 , Tien-Yin Wong  86   87   91 , Tobias Bergler  9 , Ton J Rabelink  40   249 , Tõnu Esko  48   250 , Toomas Haller  48 , Unnur Thorsteinsdottir  42   100 , Uwe Völker  6   251 , Valencia Hui Xian Foo  86 , Veikko Salomaa  187 , Veronique Vitart  77 , Vilmantas Giedraitis  252 , Vilmundur Gudnason  42   148 , Vincent W V Jaddoe  120   136 , Wei Huang  253   254 , Weihua Zhang  35   137 , Wen Bin Wei  255 , Wieland Kiess  25   56   57 , Winfried März  126   256   257 , Wolfgang Koenig  196   258   259 , Wolfgang Lieb  260 , Xin Gao  61 , Xueling Sim  22 , Ya Xing Wang  142 , Yechiel Friedlander  261 , Yih-Chung Tham  86 , Yoichiro Kamatani  191   262 , Yukinori Okada  191   263   264 , Yuri Milaneschi  69 , Zhi Yu  15   82   265 ; Lifelines cohort study; DiscovEHR/MyCode study; VA Million Veteran Program; Klaus J Stark  8 , Kari Stefansson  42   100 , Carsten A Böger  7   9   66 , Adriana M Hung  11   80 , Florian Kronenberg  266 , Anna Köttgen  15   16 , Cristian Pattaro  32 , Iris M Heid  267
Collaborators, Affiliations
Meta-Analysis

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Thomas W Winkler et al. Commun Biol. .

Abstract

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

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Conflict of interest statement

GlaxoSmithKline and Merck & Co employed A.Y.C. Janssen Pharmaceuticals and GlaxoSmithKline employed D.M.W. K.B.S., L.M.Y.-A. and M.A.L. are full-time employees of GlaxoSmithKline. M.S. receives funding from Pfizer Inc. for a project not related to this research. J.Ä. reports personal fees from AstraZeneca, Boehringer Ingelheim and Novartis, outside of the submitted work. D.F.G., H.H., K.S., P.S., G.S. and U.T. are employees of deCODE/Amgen Inc. Kevin Ho received support by Fresenius Medical Care North America. M.K. is employed with Synlab Holding Deutschland GmbH. W.K. reports consulting fees from AstraZeneca, Novartis, Pfizer, The Medicines Company, DalCor, Kowa, Amgen, Corvidia, Daiichi-Sankyo, Genentech, Novo Nordisk, Esperion, OMEICOS, LIB Therapeutics, speaker honoraria from Amgen, AstraZeneca, Novartis, Berlin-Chemie, Sanofi, and Bristol-Myers Squibb, and grants and non-financial support from Abbott, Roche Diagnostics, Beckmann, and Singulex, outside the submitted work. C.L. received Grants/ Research Support from Bayer Ag/ Novo Nordisk, Husband works for Vertex. As of January 2020, A.M. is an employee of Genentech, and a holder of Roche stock. W.M. is employed with Synlab Holding Deutschland GmbH. D.O.M.-K. is a partime research physician at Metabolon, Inc. M.A.N. was supported by a consulting contract between Data Tecnica International LLC and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA and consults for a number of small biotech and pharma. M.L.O. received grant support from GlaxoSmithKline during conduct of the study and received support from Novartis, Merck, Amgen, and AstraZeneca. L.S.P. has served on Scientific Advisory Boards for Janssen, and has or had research support from Merck, Pfizer, Eli Lilly, Novo Nordisk, Sanofi, PhaseBio, Roche, Abbvie, Vascular Pharmaceuticals, Janssen, Glaxo SmithKline, and the Cystic Fibrosis Foundation. He is also a cofounder, Officer and Board member and stockholder for a company, Diasyst, Inc., which markets software aimed to help improve diabetes management. A.I.P. and D.F.R. are employees of Merck Sharp Dohme Corp. Bruce.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. P.R. received fees to his institution for research support from AstraZeneca and Novo Nordisk; for steering group participation from AstraZeneca, Gilead, Novo Nordisk, and Bayer; for lectures from Bayer, Eli Lilly and Novo Nordisk; and for advisory boards from Sanofi and Boehringer Ingelheim outside of this work. V.S. has received a modest honorarium from Sanofi for consulting. He also has ongoing research collaboration with Bayer Ltd. (all outside of the present study). L.W. received institutional grants from GlaxoSmithKline, AstraZeneca, BMS, Boehringer-Ingelheim, Pfizer, MSD and Roche Diagnostics. H.W. has received grant support paid to the institution and fees for serving on Steering Committees of the ODYSSEY trial from Sanofi and Regeneron Pharmaceuticals, the ISCHEMIA and the MINT studies from the National Institutes of Health, the STRENGTH trial from Omthera Pharmaceuticals, the HEART-FID study from American Regent, the DAL-GENE study from DalCor Pharma UK Inc., the AEGIS-II study from CSL Behring, the SCORED and SOLOIST-WHF from Sanofi Aventis Australia Pty. Ltd., and the CLEAR OUTCOMES study from Esperion Therapeutics. M.P. is partly funded by the study FinnGen (www.finngen.fi), which is jointly funded by a Finnish Governmental agency Business Finland and thirteen international pharmaceutical companies: Abbvie, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Genentech, a member of the Roche Group, GlaxoSmithKline (GSK), Janssen, Maze Therapeutics, MSD (the tradename of Merck & Co., Inc, Kenilworth, NJ USA), Novartis, Pfizer and Sanofi. C.C.K. is an Editorial Board Member for Communications Biology, but was not involved in the editorial review of, nor the decision to publish this article. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Data and analysis workflow.
a Overview on datasets and meta-analyses. b Approaches to identify DM/noDM-differences. c Approaches to identify novel eGFR loci and suggestive DM/noDM-differences. d Genetic risk score (GRS) analyses in HUNT.
Fig. 2
Fig. 2. Seven eGFR loci with differential effects by diabetes status.
We searched for DM/noDM-differential genetic associations on eGFR using the difference test approach and the overall+difference approach in combined stage 1 + 2 (CKDGen, UKB, MVP, MGI, and HUNT; nDM = 178,691; total nnoDM = 1,296,113). Seven difference loci were identified. a Shown are difference test P-values over chromosomal base position (Manhattan plot) highlighting the six loci identified by the difference test approach (red, PDiff < 5 × 10−8) and the one locus identified by the overall+difference test approach (orange, 610 variants with stage 1 POverall < 5 × 10−8, PDiff < 0.05/610 = 8.2 × 10−5). Loci are annotated by the name(s) of the nearest gene(s); asterix indicates loci that were also identified by the discovery+replication design (Table 1, Supplementary Fig. 1). b Shown is a comparison of DM-/noDM-specific eGFR-effect sizes for the seven identified difference lead variants based on combined stage 1 + 2 data. Effect sizes are aligned to the eGFR-decreasing alleles in noDM except for CSRNP1 (aligned to eGFR-decreasing allele in DM). Error bars reflect 95% confidence intervals of the estimated genetic effect.
Fig. 3
Fig. 3. Accounting for potential DM-/noDM-differences identified 32 novel eGFR loci.
We searched for novel loci associated with eGFR allowing for DM-/noDM-difference using the joint test or DM-/noDM-stratified tests approaches in combined stage 1 + 2 (nDM = 178,691; total nnoDM = 1,296,113). We found 32 novel genome-wide significant eGFR loci (P < 5 × 10−8, >500 kB distant of known eGFR loci compared to previous work,): 30 by joint, 17 by noDM-only and 2 by DM-only test. a Shown are P-values for eGFR based on joint, noDM-only, and DM-only test over chromosomal position. Highlighted in red are loci with suggestive DM/noDM-difference (PDiff < 0.05/34; corrected for 34 independent variants across 32 loci), blue for loci identified by joint and noDM-only test (15 loci), and purple for loci that were only identified by joint test (upper panel) or noDM-only test (middle panel). Loci were annotated by nearest genes if PDiff < 0.10 or if they were also identified by the discovery+replication design (the latter also indicated by asterix, Table 2). b Shown is a comparison of DM-/noDM-specific eGFR-effect sizes for the 32 novel eGFR locus lead variants. Highlighted in red are the locus names of loci with suggestive DM/noDM-difference (PDiff < 0.05/34; corrected for 34 independent variants across 32 loci). Effect sizes are aligned to the eGFR-decreasing alleles in noDM. Error bars reflect 95% confidence intervals of the estimated genetic effect.
Fig. 4
Fig. 4. The variants associated with eGFR in the CUBN locus differ from those associated with urinary albumin-to-creatinine ratio.
Shown are P-values for associations at the wider (top) and more narrow (bottom) CUBN locus region for a eGFR (joint test P-values, nDM = 178,691 and nnoDM = 1,296,113) and b urinary albumin-to-creatinine ratio (UACR; P-values from ref. , n = 564,257). Lead variant for eGFR is rs11254238; color codes variants’ correlation r2 to rs11254238 in all panels.
Fig. 5
Fig. 5. DM-only eGFR GWAS identified 29 loci, including 27 novel for eGFR in DM.
Shown are eGFR association P-values in individuals with DM over chromosomal position in combined stage (nDM = 178,691). This DM-specific analysis identified 29 independent eGFR-associated loci in DM. Compared to known DKD loci, (i.e., association with eGFR or CKD in type 1 and/or type 2 DM individuals) and known overall eGFR loci,, 2 loci are novel for eGFR overall and novel for DKD (red), 24 are novel for DKD but known for eGFR (orange), and 3 are known DKD and known eGFR loci (purple).
Fig. 6
Fig. 6. Gene prioritization highlights six genes at loci with established DM/noDM-difference.
Shown are gene prioritization results for the seven loci with established difference (Table 1, Supplementary Fig. 8). We highlighted six genes based on association-driving variants (PPA > 5%) that were deleteriously protein-relevant or expression-modulating, genes that were known as human kidney monogenes (OMIM or ref. with subsequent manual curation) and in addition SLC22A2 due to its known link to metformin response.
Fig. 7
Fig. 7. Gene prioritization highlights 12 genes at novel eGFR loci.
Shown are gene prioritization results for the 32 novel eGFR loci (Table 2, Supplementary Fig. 9): a for the four novel eGFR loci with suggestive difference, and b for the 28 other novel eGFR loci. We highlighted 12 genes based on association-driving variants (PPA > 5%) that were deleteriously protein-relevant or expression-modulating or genes that were known as human kidney monogenes (OMIM or ref. with subsequent manual curation).
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