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. 2018 Jul;67(7):1414-1427.
doi: 10.2337/db17-0914. Epub 2018 Apr 27.

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

Natalie R van Zuydam  1   2 Emma Ahlqvist  3 Niina Sandholm  4   5   6 Harshal Deshmukh  7 N William Rayner  8   2   9 Moustafa Abdalla  8   2   10 Claes Ladenvall  3 Daniel Ziemek  11 Eric Fauman  12 Neil R Robertson  8   2 Paul M McKeigue  13 Erkka Valo  4   5   6 Carol Forsblom  4   5   6 Valma Harjutsalo  4   5   6   14 Finnish Diabetic Nephropathy Study (FinnDiane)Annalisa Perna  15 Erica Rurali  15 M Loredana Marcovecchio  16 Robert P Igo Jr  17 Rany M Salem  18 Norberto Perico  15 Maria Lajer  19 Annemari Käräjämäki  20   21 Minako Imamura  22   23   24 Michiaki Kubo  24 Atsushi Takahashi  25   26 Xueling Sim  27 Jianjun Liu  27   28   29 Rob M van Dam  27 Guozhi Jiang  30 Claudia H T Tam  30 Andrea O Y Luk  30   31   32 Heung Man Lee  30   31   32   33 Cadmon K P Lim  30 Cheuk Chun Szeto  30 Wing Yee So  30 Juliana C N Chan  30   31   32 Hong Kong Diabetes Registry Theme-based Research Scheme Project GroupSu Fen Ang  34 Rajkumar Dorajoo  28 Ling Wang  28 Tan Si Hua Clara  34 Amy-Jayne McKnight  35 Seamus Duffy  35 Warren 3 and Genetics of Kidneys in Diabetes (GoKinD) Study GroupMarcus G Pezzolesi  36 GENIE (GEnetics of Nephropathy an International Effort) ConsortiumMichel Marre  37 Beata Gyorgy  37 Samy Hadjadj  38   39   40 Linda T Hiraki  41 Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Research GroupTarunveer S Ahluwalia  19   42 Peter Almgren  43 Christina-Alexandra Schulz  43 Marju Orho-Melander  43 Allan Linneberg  44   45   46 Cramer Christensen  47 Daniel R Witte  48   49 Niels Grarup  42 Ivan Brandslund  50   51 Olle Melander  52 Andrew D Paterson  41 David Tregouet  37 Alexander P Maxwell  35 Su Chi Lim  53   54   55 Ronald C W Ma  30   31   32   33 E Shyong Tai  27   54   56 Shiro Maeda  22   23   24 Valeriya Lyssenko  3   57 Tiinamaija Tuomi  4   6   58   59 Andrzej S Krolewski  60 Stephen S Rich  61 Joel N Hirschhorn  62   63   64 Jose C Florez  63   65   66 David Dunger  16   67 Oluf Pedersen  42 Torben Hansen  42   68 Peter Rossing  19   42 Giuseppe Remuzzi  15   69   70 SUrrogate markers for Micro- and Macrovascular hard endpoints for Innovative diabetes Tools (SUMMIT) ConsortiumMary Julia Brosnan  71 Colin N A Palmer  72 Per-Henrik Groop  4   5   6   73 Helen M Colhoun  74 Leif C Groop  3   59 Mark I McCarthy  8   2   75
Collaborators, Affiliations

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

Natalie R van Zuydam et al. Diabetes. 2018 Jul.

Abstract

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined T1D+T2D GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 subjects with diabetes (18,582 with DKD). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, P = 4.5 × 10-8) associated with microalbuminuria in European T2D case subjects. However, no replication of this signal was observed in Asian subjects with T2D or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously reported DKD signals, except for those at UMOD and PRKAG2, both associated with estimated glomerular filtration rate. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk variant discovery for DKD.

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Figures

Figure 1
Figure 1
Eight DKD phenotypes were analyzed in subjects with T2D (blue boxes) and in a combined (green boxes) analysis of subjects with T2D or T1D (yellow box). N indicates the total sample count for either the all DKD (number of case subjects is given in parentheses) or the eGFR phenotype and may vary by variant as well as by DKD phenotype. Replication was sought for 164 loci and 47 loci from each analysis, respectively, in subjects of European and Asian ancestry with either T1D or T2D.
Figure 2
Figure 2
A: Manhattan plot of P values from the meta-analysis of allelic effects on early DKD in subjects with T2D of European descent. The red line represents genome-wide significance (P < 5 × 10−8) and the blue line suggestive significance (P < 1 × 10−6). The peak represented by rs9942471 (P = 4.5 × 10−8) near GABRR1 is highlighted in orange. B: A forest plot of allelic OR and imputation information scores (RSQ) from individual studies that contributed to the discovery and replication analyses of rs9942471 in microalbuminuria phenotype; rs9942471 genotypes were not available in Steno. C: A LocusZoom plot of the signal near GABRR1 led by rs9942471 that was associated with microalbuminuria in European subjects with T2D.
Figure 3
Figure 3
A heat map of GRS associations with DKD phenotypes in subjects with either T1D or T2D. A GRS for BMI was significant after correction for multiple testing, whereas other traits, including systolic blood pressure, were not associated with DKD phenotypes. adj., adjusted; FG, fasting glucose; In, insulin; IR, insulin resistance.

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