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Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen

Rany M. Salem ,1 Jennifer N. Todd,2,3,4 Niina Sandholm ,5,6,7 Joanne B. Cole ,2,3,4 Wei-Min Chen,8 Darrell Andrews,9 Marcus G. Pezzolesi,10 Paul M. McKeigue,11 Linda T. Hiraki,12 Chengxiang Qiu,13 Viji Nair,14 Chen Di Liao,12 Jing Jing Cao,12 Erkka Valo ,5,6,7 Suna Onengut-Gumuscu,8 Adam M. Smiles,15 Stuart J. McGurnaghan,16 Jani K. Haukka,5,6,7 Valma Harjutsalo,5,6,7,17 Eoin P. Brennan,9 Natalie van Zuydam,18,19 Emma Ahlqvist,20 Ross Doyle,9 Tarunveer S. Ahluwalia ,21 Maria Lajer,21 Maria F. Hughes,9 Jihwan Park,13 Jan Skupien,15 Athina Spiliopoulou,11 Andrew Liu,22 Rajasree Menon,14,23 Carine M. Boustany-Kari,24 Hyun M. Kang,23,25 Robert G. Nelson,26 Ronald Klein,27 Barbara E. Klein,27 Kristine E. Lee ,27 Xiaoyu Gao,28 Michael Mauer,29 Silvia Maestroni,30 Maria Luiza Caramori,29 Ian H. de Boer ,31 Rachel G. Miller,32 Jingchuan Guo ,32 Andrew P. Boright,12 David Tregouet,33,34 Beata Gyorgy,33,34 Janet K. Snell-Bergeon,35 David M. Maahs,36 Shelley B. Bull ,37 Angelo J. Canty,38 Colin N.A. Palmer,39 Lars Stechemesser,40 Bernhard Paulweber,40 Raimund Weitgasser,40,41 Jelizaveta Sokolovska,42 Vita Rovıte,43 Valdis Pırags, 42,44 Edita Prakapiene,45 Lina Radzeviciene,46 Rasa Verkauskiene,46 Nicolae Mircea Panduru,6,47 Leif C. Groop,20,48 Mark I. McCarthy,18,19,49,50 Harvest F. Gu,51,52 Anna Möllsten,53 Henrik Falhammar,54,55 Kerstin Brismar,54,55 Finian Martin,9 Peter Rossing,21,56 Tina Costacou ,32 Gianpaolo Zerbini,30 Michel Marre,57,58,59,60 Samy Hadjadj,61,62,63 Amy J. McKnight,64 Carol Forsblom,5,6,7 Gareth McKay,64 Catherine Godson,9 A. Peter Maxwell ,64 Matthias Kretzler,14,23 Katalin Susztak ,13 Helen M. Colhoun,16 Andrzej Krolewski,15 Andrew D. Paterson,12 Per-Henrik Groop,5,6,7,65 Stephen S. Rich,8 Joel N. Hirschhorn,2,3 Jose C. Florez,3,4,66,67 and SUMMIT Consortium, DCCT/EDIC Research Group, GENIE Consortium

Due to the number of contributing authors, the affiliations are listed at the end of this article.

ABSTRACT Background Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown. Methods To identify genetic variants predisposing to diabetic kidney disease, we performed genome- wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collab- orative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function. Results Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inheri- ted nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant

2000 ISSN : 1046-6673/3010-2000 JASN 30: 2000–2016, 2019 www.jasn.org CLINICAL RESEARCH association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1). Conclusions The 16 diabetic kidney disease–associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

JASN 30: 2000–2016, 2019. doi: https://doi.org/10.1681/ASN.2019030218

The devastating diabetic complication of diabetic kidney dis- Significance Statement ease (DKD) is the major cause of worldwide.1,2 Current treat- ment strategies at best slow the progression of DKD, and do Although studies show that diabetic kidney disease has a heritable not halt or reverse the disease. Although improved glycemic component,searchesforthegeneticdeterminantsofthiscomplication control influences the rate of diabetic complications, a large of diabetes have had limited success. In this study, a new international genomics consortium, the JDRF funded Diabetic Nephropathy Col- portion of the variation in DKD susceptibility remains unex- laborative Research Initiative, assembled nearly 20,000 samples from plained: one third of people with type 1 diabetes (T1D) de- participants with type 1 diabetes, with and without kidney disease. velop DKD despite adequate glycemic control, whereas others The authors found 16 new diabetic kidney disease–associated loci at maintain normal renal function despite long-term severe genome-wide significance. The strongest signal centers on a pro- COL4A3 chronic hyperglycemia.3 tective missense coding variant at , a gene that encodes a component of the glomerular basement membrane that, when 4–6 Though DKD demonstrates both familial clustering and mutated, causes the progressive inherited nephropathy Alport single nucleotide polymorphism (SNP) heritability,7 the spe- syndrome. These GWAS-identified risk loci may provide insights into cific genetic factors influencing DKD risk remain largely un- the pathogenesis of diabetic kidney disease and help identify po- known. Recent genome-wide association studies (GWAS) tential biologic targets for prevention and treatment. have only identified a handful of loci for DKD, albuminuria, 7–13 or eGFR in individuals with diabetes. Potential reasons for committees from participating institutions. We defined a total the limited success include small sample sizes, modest genetic of ten different case-control outcomes to cover the different fi effects, and lack of consistency of phenotype de nitions and aspects of renal complications, using both albuminuria and statistical analyses across studies. Through collaboration eGFR (Figure 1). Five comparisons (“All versus control (ctrl),” within the JDRF Diabetes Nephropathy Collaborative Re- “Micro,”“diabetic nephropathy [DN],”“Macro,” and “ESKD search Initiative, we adopted three approaches to improve versus macro”) were on the basis of albuminuria, measured by our ability to find new genetic risk factors for DKD: (1) albumin excretion rate (AER) from overnight or 24-hour assembling a large collection of T1D cohorts with harmonized urine collection, or by albumin-to-creatinine ratio. Two out DKD phenotypes, (2) creating a comprehensive set of detailed of three consecutive collections were required (when avail- DKD definitions, and (3) augmenting genotype data with low able) to classify the renal status of patients as either normoal- frequency and exome array variants. buminuria, microalbuminuria, macroalbuminuria, or ESKD; for detailed thresholds, see Figure 1. Controls with normal AER were required to have a minimum diabetes duration of 15 years; METHODS participants with microalbuminuria/macroalbuminuria/ Cohorts and Phenotype Definitions ESKD were required to have minimum diabetes duration of The GWAS meta-analysis included up to 19,406 patients with 5, 10, and 10 years, respectively, to exclude renal complications “ ” T1D of European origin from 17 cohorts (for study list and of nondiabetic origins. Two comparisons ( ESKD versus ctrl “ ” details see Supplemental Table 1). All participants gave in- and ESKD versus non-ESKD ) were on the basis of presence fi , formed consent and all studies were approved by ethics of ESKD as de ned by eGFR 15 ml/min or dialysis or renal transplant. Two phenotypes (“CKD” and “CKD extreme”)were defined on the basis of eGFR estimated by the CKD Epidemi- Received March 1, 2019. Accepted July 8, 2019. ology Collaboration formula: controls had eGFR$60 ml/min Published online ahead of print. Publication date available at www.jasn.org. per 1.73 m2 for both phenotypes, and $15 years of diabetes R.M.S., J.N.T., N.S., and J.B.C. contributed equally to this work. duration; cases had eGFR,60 ml/min per 1.73 m2 for the Lead author: Jose C. Florez. CKD phenotype, and eGFR,15 ml/min per 1.73 m2 or dialysis $ Correspondence: Dr. Jose C. Florez, Simches Research Building - CPZN 5.250, or renal transplant for the CKD extreme phenotype, and 10 Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts, years of diabetes duration. For the “CKD-DN” phenotype fl 02114. Email: jc [email protected] that combined both albuminuria and eGFR data, controls 2 Copyright © 2019 by the American Society of Nephrology were required to have both eGFR$60 ml/min per 1.73 m and

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Normal Micro- Macro- ESKD AER albuminuria albuminuria

DN 12,076 4948

Micro 12,113 2447

Macro 12,124 2751

All vs. ctrl 12,053 7247

ESKD vs. macro 2725 2187 Albuminuria/ESKD based ESKD vs. ctrl 12,101 2187 ESKD vs. 17,219 2187 non-ESKD eGFR

eGFR eGFR eGFR ≥60 ESKD 45-59 15-44

CKD 14,838 4266

CKD-extreme 14,993 2235

CKD-DN 11,766 2897 Comb. eGFR based

Figure 1. Phenotypic analysis of DKD. Schematic diagram of outcomes analyzed in this study. Numbers indicate the total number of cases (darker gray) and controls (lighter gray) included in the meta-analyses for each phenotype. ESKD defined as eGFR,15 ml/min per 1.73 m2 or undergoing dialysis or having renal transplant. normoalbuminuria; cases had both eGFR,45 ml/min per ancestry (see Supplemental Material for full QC details, 1.73 m2 and micro- or macroalbuminuria, or ESKD. and Supplemental Figure 1 for trait-specific Manhattan and QQ plots). Genotypes were expanded to a total of ap- GWAS Genotyping, Quality Control, and Imputation proximately 49 million by imputation, using the minimac All study samples underwent genotyping, quality control (QC), imputation tool14,15 and 1000 Genomes Project (phase 3v5) and imputation centrally at the University of Virginia. In as a reference. brief, samples were genotyped on the HumanCore BeadChip array (Illumina, San Diego, CA), which contains approximately GWAS Analysis 250,000 genome-wide tag SNPs and .200,000 exome-focused A genome-wide association analysis was performed for each of variants. All samples were passed through a stringent QC the case-control definitions under an additive genetic model, protocol. Following initial genotype calling with Illumina adjusting for age, sex, diabetes duration, study site (where software, all samples were recalled with zCall, a calling algo- applicable), and principal components. We conducted a sec- rithm specifically designed for rare SNPs from arrays. Variant ond set of analyses, adjusting for body mass index and glycated orientation and position were aligned to hg19 (Genome Ref- hemoglobin (HbA1c), which we refer to as our fully adjusted erence Consortium Human Build 37, GRCh37). Variant covariate model. Allele dosages were used to account for nameswereupdatedusing1000Genomesasareference. imputation uncertainty. Inverse-variance fixed effects meta- The data were then filtered for low-quality variants (e.g., call analysis was performed using METAL and the following fil- rates ,95% and excessive deviation from Hardy–Weinberg ters: INFO score .0.3, minor allele count .10 in both cases equilibrium) and samples (e.g., call rates ,98%, sex mis- and controls, and presence of variant in at least two cohorts match, extreme heterozygosity). Principal component analy- (Manhattan and QQ plots each trait and covariate model pre- sis was performed separately for each cohort to empirically sented in Supplemental Figure 1). The X chromosome was detect and exclude outliers with evidence of non-European similarly analyzed for men and women both separately and

2002 JASN JASN 30: 2000–2016, 2019 www.jasn.org CLINICAL RESEARCH in a combined analysis, with the exception of using hard call RNA-Sequencing and cis Expression Quantitative Trait genotypes in place of allele dosages.16 We estimated the per- Loci Analysis in Human Kidney Samples from University centage of variance explained for all genome-wide significant of Pennsylvania Cohort SNPs across all disease definitions using the McKelvey and Human kidney samples were obtained from surgical nephrec- Zavoina17 pseudo-R2 statistic predicting continuous latent tomies for a total of 455 participants with pathologic data and variables underlying binary outcomes. weremanually microdissectedunder amicroscopein RNAlater for glomerular and tubular compartments (433 tubule and 335 Glomerular Basement Membrane Measurement in glomerulus samples). The local renal pathologist performed Renin-Angiotensin System Study an unbiased review of the tissue section by scoring multiple In brief, the renin-angiotensin system study (RASS) was a parameters, and RNA were prepared using RNAeasy mini col- double-blind, placebo-controlled, randomized trial of enalap- umns (Qiagen, Valencia, CA) according to manufacturer’s ril and losartan on renal pathology among 285 normoalbumi- instructions. nuric, normotensive participants with T1D and normal or Whole kidney,22 tubularm and glomerular23 expression increased measured GFR (.90 ml/min per 1.73 m2).18 Par- quantitative trait loci (eQTL) analyses have been described ticipants were followed for 5 years with percutaneous kidney previously. Tubular and glomerular eQTL data sets were gen- biopsy completed prior to randomization and at 5 years. erated by 121 samples of tubules and 119 samples of glomeruli, Structural parameters measured by electron microscopy on respectively.23 The cis window was defined as 1 Mb up- and biopsy included glomerular basement membrane (GBM) downstream of the transcriptional start site (61Mb).The width, measured by the electron microscopic orthogonal whole kidney cis-eQTL(furtherreferredtoasjusteQTL) intercept method.18 All RASS participants contributed DNA data set was generated from 96 human samples obtained for genotyping. from The Cancer Genome Atlas (TCGA) through the TCGA Data portal.22 In Silico Replication in SUMMIT Consortium The Surrogate Markers for Micro- and Macro-vascular Hard Mouse Kidney Single-Cell RNA-Sequencing Endpoints for Innovative Diabetes Tools (SUMMIT) consor- Kidneys were harvested from 4- to 8-week-old male mice with tium included up to 5193 European Ancestry participants with C57BL/6 background and dissociated into single-cell suspen- type 2 diabetes (T2D), with and without kidney disease. In sion as described in our previous study.24 The single-cell se- silico replication was performed on previously published quencing libraries were sequenced on an Illumina HiSeq with fi GWAS on DKD with harmonized trait de nitions for seven 23150 paired-end kit. The sequencing reads were demulti- “ ”“ ”“ ” of our primary T1D analyses: DN, Micro, Macro, plexed, aligned to the mouse genome (mm10) and processed “ ”“ ”“ ” “ ” ESKD, ESKD versus non-ESKD, CKD, and CKD-DN to generate gene-cell data matrix using Cell Ranger 1.3 (http:// under an additive model, adjusting for age, sex, and duration 10xgenomics.com).24 of diabetes.13

RNA-Sequencing and Microarray Profiling of Human Genomic Features of Human Kidney fi Kidney Samples from the Pima Cohort Human kidney-speci c chromatin immunoprecipitation se- Kidney biopsy samples from the Pima Indian cohort were quencing data can be found at Gene Expression Omnibus under manually microdissected into 119 glomerular and 100 tu- accession numbers GSM621634, GSM670025, GSM621648, bule-interstitial tissues to generate gene expression pro- GSM772811, GSM621651, GSM1112806, and GSM621638. files.19 Expression profiling in the Pima Indian cohort kidney Different histone markers were combined into chromatin states 25 biopsies was carried out using Affymetrix GeneChip Human using ChromHMM. Genome U133 Array and U133Plus2 Array, as reported pre- viously, and Affymetrix Human Gene ST GeneChip 2.1,20,21 Gene and Gene Set Analysis and on RNA-seq (Illumina). The libraries were prepared PASCAL and MAGMA (v1.06) gene and pathway scores were using the ClonTech SMARTSeq v4 Ultra Low Input polyA conducted on all 20 sets of GWAS summary statistics using selection kit. Samples were sequenced on a HiSeq4000 using default pathway libraries from BioCarta, REACTOME, and single-end, 75 bp reads. Mapping to human reference ge- KEGG. MAGENTA (vs2, July 2011) pathway analysis included nome GRCh38.7 was performed with STAR 2.5.2b (https:// 4725 pathways with a minimum of five genes within the gene github.com/alexdobin/STAR). For annotation and quantifi- set for the ten standard adjustment models. We conducted cation of mapping results we used cufflinks, cuffquant, and DEPICT individually on all 20 sets of GWAS summary statis- 2 cuffnorm in version 2.2.1 (https://cole-trapnell-lab.github.io/ tics with P,10 5 and additional pooled analyses using cufflinks/). After mapping and quantification, principal genome-wide minimum P values from all 20 analyses (ten component analysis and hierarchical clustering was used phenotypes and two covariate models) and 16 analyses of the to identify outliers and reiterated until no more outliers could eight most related phenotypes, which excluded ESKD versus be identified. Macro and Micro.

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2 Transcriptome-Wide Association Study (P,6.76310 9), on the basis of genome-wide significance 2 A transcriptome-wide association study (TWAS) of kidney (P,5310 8) and Bonferroni correction for 7.4 effective tests. glomeruli and tubules was performed using MetaXcan with default parameters,26 on the basis of eQTL data for human Top Genome-Wide Association Results Highlight glomerular and tubular cells.23 COL4A3 GWAS meta-analysis included association results for up to 19,406 individuals with T1D of European descent from 17 RESULTS cohorts for the ten case-control definitions (Supplemental Table 1). We identified 16 novel independent loci that achieved 2 Phenotypic Comparisons genome-wide significance (P,5310 8) in either the minimal We investigated a broad spectrum of DKD definitions on the or fully adjusted models, in which four lead SNPs also sur- basis of albuminuria and renal function criteria, definingatotal passed our more conservative study-wide significance thresh- of ten different case-control comparisons to cover the different old (Figure 2, Manhattan plot; Table 1; Supplemental Figure 2, aspects of disease progression (Figure 1). Seven comparisons A–P, regional association and forest plots). None of the loci were on the basis of albuminuria and/or ESKD (including DN, reaching genome-wide significance have been previously defined as either macroalbuminuria or ESKD), two were de- identified in GWAS or candidate gene studies for DKD or fined on the basis of eGFR (used to classify severity of CKD), closely related traits. All SNPs with minor allele frequency and one combined both albuminuria and eGFR data (CKD- (MAF) .1% explain 2.5% and 3.0% of the total variance DN). Each phenotypic definition was analyzed separately in (McKelvey and Zavoina17 pseudo-R2) of DN after adjusting GWAS; to account for the ten definitions each analyzed under for covariates in the minimal and full covariate models, respec- two covariate adjustment models, we estimated16 the total tively (Supplemental Table 2). effectively independent tests as 7.4, allowing us to compute The strongest signal was rs55703767 (MAF=0.21), a com- a more conservative study-wide significance threshold mon missense variant (G.T; Asp326Tyr) in exon 17 of

12 DN COL4A3 Macro ESKD vs. ctrl TAMM41 ESKD vs. non-ESKD ESKD vs. macro Micro 10 CKD CKD extreme

BMP7 6.76 × 10-9 COLEC11 HAND2-AS1 PAPLN 8 STAC MUC7 SNCAIP MBLAC1 PLEKHA7 18p11 5.00 × 10-8 LINCO1266 DDR1 PRNCR1 STXBP6

6 (p–value) 10 –log

4

2

0 6 8 9 1 2 3 4 5 7 11 12 13 14 15 16 17 18 19 20 21 22 10 Chromosomal Location

Figure 2. Genome-wide association testing of all ten phenotypic comparisons. Multiphenotype Manhattan plot shows lowest P value at each marker for each of the ten phenotypic comparisons, under the standard and fully-adjusted model. Significance of SNPs

(2log10[P value], y axis) is plotted against genomic location (x axis). Loci surpassing genome-wide significance (red line) and/or study- wide significance (blue line) are colored by phenotype.

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2 2 Table 1. Loci associated with DKD at study-wide (P,6.76310 9, see bolding) and genome-wide (P,5310 8) significance Effect Other SNP Chr:pos EAF Notable Gene(s) Phenotype OR P Value OR P Value Allele Allele min min full full Common Variants – 2 rs55703767 2:228121101 T G 0.206 COL4A3 (M, B, N) DN 0.79 5.34310 12 0.78 8.19310 11 2 2 All versus ctrl 0.83 3.88310 10 0.84 9.68310 9 2 2 CKD+DN 0.77 5.30310 9 0.76 3.77310 8 2 2 Macro 0.78 9.28310 9 0.77 9.38310 9 2 2 rs12615970 2:3745215 G A 0.133 COLEC11 (B) CKD 0.76 9.43310 9 0.77 1.60310 7 ALLC (N, G) 2 2 rs142823282 3:11910635 G A 0.011 TAMM41 (N, B) Micro 6.73 8.32310 10 9.18 1.13310 11 2 2 rs145681168 4:174500806 G A 0.014 HAND2-AS1 Micro 5.53 2.06310 7 7.47 5.40310 9 (N, G, B) 2 2 rs118124843 6:30887465 T C 0.011 DDR1 (B) Micro 3.79 4.42310 8 3.99 3.37310 8 VARS2 (G) 2 2 rs551191707 8:128100029 CA C 0.122 PRNCR1 (N) ESKD versus 1.70 4.39310 8 1.71 3.15310 6 macro 2 2 rs61983410 14:26004712 T C 0.213 STXBP6 (N) Micro 0.79 9.84310 8 0.78 3.06310 8 2 2 rs144434404 20:55837263 T C 0.011 BMP7 (N, G, B) Micro 6.78 2.67310 9 6.66 4.65310 9

Uncommon Variants 2 2 rs115061173 3:926345 A T 0.014 LINC01266 (N) ESKD versus 9.40 4.07310 8 8.34 4.08310 5 ctrl 2 2 rs116216059 3:36566312 A C 0.016 STAC (N, G) ESKD versus 8.73 1.37310 8 11.78 1.41310 4 non-ESKD 2 2 rs191449639 4:71358776 A T 0.005 MUC7 (N) DN 32.42 1.32310 8 32.47 2.09310 8 2 rs149641852 5:121774582 T G 0.012 SNCAIP (N, G) CKD 9.01 1.37310 8 —— extreme 2 2 rs77273076 7:99728546 T C 0.008 MBLAC1 (N) Micro 9.16 1.04310 8 7.10 2.28310 7 2 2 rs183937294 11:16937846 G T 0.007 PLEKHA7 (N, G) Micro 17.22 1.65310 8 23.62 2.10310 6 2 2 rs113554206 14:73740250 A G 0.012 PAPLN (N, G) Macro 4.60 5.39310 7 10.42 8.46310 9 2 2 rs185299109 18:1811108 T C 0.007 intergenic CKD 20.75 1.28310 8 44.75 4.99310 7 Common variants and/or genes with relevant kidney biology are reported in the top half of the table. Uncommon variants (MAF,2%) with no known relevant kidney biology are reported in the bottom half of the table. Genes are annotated as follows: missense variant in the indicated gene (M); intronic, synonymous,ornoncoding variant in the indicated gene (G); gene nearest to lead variant (N); gene has relevant kidney (B). Chr, chromosome; pos, position; EAF, effect allele frequency; min, minimally adjusted covariate model; full, fully adjusted covariate model.

COL4A3. This SNP was associated with protection from (Supplemental Table 3). The DKD-protective minor T allele DN (odds ratio [OR], 0.79; 95% Confidence Interval was associated with 19.7 nm lower GBM width (SEM 8.2 nm; 2 [95% CI], 0.73 to 0.84; P=5.34310 12), any albuminuria P=0.02), with the lowest mean GBM width among TT homo- 2 (OR, 0.83; 95% CI, 0.79 to 0.88; P=3.88310 10), the zygotes (Figure 3, Supplemental Table 4), after adjusting for combined CKD-DN phenotype (OR, 0.77; 95% CI, 0.71 age, sex, and diabetes duration, and without detectable interac- 2 to 0.84; P=5.30310 9), and macroalbuminuria (OR, 0.78; tions with T1D duration or mean HbA1c. Thus, the protective 2 95% CI, 0.72 to 0.85; P=9.28310 9). Interestingly, we T allele carriers had thinner GBM before any renal complications. found that rs55703767 in COL4A3 was more strongly as- We did not detect any eQTL association between sociated in men (OR, 0.73; 95% CI, 0.66 to 0.80; rs55703767 and COL4A3 expression in mouse glomeruli or 2 P=1.29310 11) than in women (OR, 0.85; 95% CI, 0.76 to in human tissues, and thus assume that the variant affects 23 22 0.94; P=1.39310 ; Phet=1.58310 ). COL4A3 encodes the a3chain the COL4A3 structure rather than expression levels. Never- of collagen type IV, a major structural component of the GBM.27 theless in a Pima Indian cohort of 97 participants with DKD with morphometric and expression data from renal COL4A3 Variation and Kidney Phenotypes biopsies, COL4A3 expression was negatively correlated In persons with T1D and normoalbuminuria, GBM width with the GBM surface density (filtration surface density) predicts progression to proteinuria and ESKD independently (b=20.27; P=0.02), which is associated with eGFR in of glycated hemoglobin (HbA1c).28 We examined the influ- DKD in both T1D and T2D.29,30 Furthermore, in 335 micro- ence of the COL4A3 variant on GBM width in 253 RASS18 dissected human glomerulus samples, expression of participants with T1D and normal AER, eGFR (.90 ml/min COL4A3 was negatively correlated with glomerulosclerosis, per 1.73 m2), and BP, who had biopsy and genetic data potentially reflecting podocyte depletion in sclerotic glomeruli

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400 Sex Male 300 Female

200

100

0

-100

-200 Adjusted residuals of within-person mean GBM width

GG GT TT rs55703767 genotypes

Figure 3. Adjusted residuals of GBM width by rs55703767 genotype and sex. Box and whisker plot of residuals of mean GBM width after adjusting for age, sex, and diabetes duration, stratified by GG, GT, or TT genotype at rs55703767, with overlay of individual data points for both women (pink) and men (blue).

2 (correlation=0.16; P=4.8310 3; Supplemental Figure 3). participants with T2D and DKD phenotypes in the SUMMIT COL4A3 expression in glomeruli, but not in tubules, was also consortium, we detected a directionally consistent suggestive nominally correlated with eGFR (correlation=0.108; P=0.05; association of COL4A3 rs55703767 with DN (two-tailed Supplemental Figure 3). P=0.08; Supplemental Table 6). We further stratified the association analyses by HbA1c in Evidence for Hyperglycemia Specificity the Finnish Diabetic Nephropathy (FinnDiane) Study, a T1D Hyperglycemia promotes the development of diabetic compli- cohort study with extensive longitudinal phenotypic data.32 cations. If a genetic variant exerts a stronger effect in the setting On the basis of the time-weighted mean of all available HbA1c of hyperglycemia, (1) it might not be detected in general CKD, measurements for each individual, 1344 individuals had mean (2) it may be detected whether hyperglycemia is conferred by HbA1c ,7.5% (58 mmol/mol), and 2977 with mean HbA1c T1D or T2D, (3) its effect may be stronger at higher glycemic $7.5%. COL4A3 rs55703767 was nominally significant strata, and (4) interventions that reduce glycemia may atten- (P,0.05) only in individuals with HbA1c $7.5% (Figure 4, uate the association signal. COL4A3 rs55703767 was not as- Supplemental Figure 4, Supplemental Table 7). However, the sociated with eGFR in a general population sample of 110,517 interaction between HbA1c and COL4A3 rs55703767 was not mainly nondiabetic participants of European ancestry31 (Sup- significant (P=0.83). Upon further examination, the genetic plemental Table 5). However, in a smaller cohort of 5190 effect was diminished also in the highest HbA1c quartile

P P Pheno P HbA1c<7.5 HbA1>7.5

All vs. ctrl 0.007 0.87 0.0018

DN 0.0022 0.659 0.001

Macro 0.06 0.663 0.0066

CKD-DN 0.0117 0.973 0.0079

0.60 0.80 1.0 1.2 1.4 OR (95% CI)

HbA1c < 7.5% HbA1c > 7.5%

Figure 4. Association at rs55703767 (COL4A3)stratified by HbA1c below or above 7.5%, for the phenotypes reaching genome-wide significance in the combined meta-analysis. Analysis included 1344 individuals with time-weighted mean HbA1c ,7.5% (58 mmol/mol), and 2977 with mean HbA1c $7.5% from the FinnDiane study; the individuals had median 19 HbA1c measurements (range 1–129).

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(.9.3%), as the environmental effect of HbA1c seems to over- translocator assembly and maintenance protein36,37 (OR, 2 whelm any potential genetic effects at COL4A3 (test of hetero- 6.75; 95% CI, 3.66 to 12.38; P=1.13310 11); rs144434404 geneity nonsignificant). In a similar setting of individuals with (MAF=0.011), in intron 1 of BMP7 encoding the bone mor- T2D from the Genetics of Diabetes Audit and Research in phogenetic protein 7 previously implicated in DKD38 (OR, 2 Tayside Scotland (GoDARTS) study (n=3226),13,33 no differ- 6.75; 95% CI, 3.61 to 12.74; P=2.67310 9); and rs145681168 ence was observed for COL4A3 rs55703767 between HbA1c (MAF=0.014), in intron 3 of two transcripts of HAND2 an- strata below or above 7.5% (Supplemental Figure 5). We tisense RNA 1 (HAND2-AS1; OR, 5.53; 95% CI, 2.90 to 10.53; 2 performed a similar HbA1c stratified analysis in the Diabetes P=5.40310 9)and50kbupstreamofHAND2,encodinga Control and Complications Trial (DCCT), whose participants, heart and neural crest derivatives transcription factor. all with T1D, continue to be followed in the Epidemiology of Two additional common variants achieved genome-wide Diabetes Interventions and Complications (EDIC).34,35 In significance: rs551191707 (MAF=0.122) in PRNCR1 associ- DCCT-EDIC the effect of COL4A3 rs55703767 was stronger ated with ESKD when compared with macroalbuminuria 2 among those recruited in the secondary cohort (mild retinop- (OR, 1.70; 95% CI, 1.40 to 2.04; P=4.39310 8); and athy and longer diabetes duration at baseline) who were orig- rs61983410 (MAF=0.213) in an intergenic region on chromo- inally randomized to conventional treatment and therefore some 14 associated with microalbuminuria (full model OR, 2 had higher HbA1c than the intensive treatment group (Sup- 0.78; 95% CI, 0.71 to 0.85; P=3.06310 8). The remaining plemental Table 8). Taken together, these independent lines of eight variants associated with features of DKD had lower allele evidence strongly suggest that the COL4A3 variant effect on frequencies (four with 0.01#MAF#0.05 and four with DKD risk is amplified by poor glycemic control. MAF,0.01) and did not achieve study-wide significance. As we had done for COL4A3 rs55703767, we tested whether Other Association Signals the associations of the 15 other variants were amplified by A comprehensive list of all loci that achieved genome-wide hyperglycemia. None of the 15 variants were significantly as- significance from either the minimal or the fully adjusted co- sociated with eGFR in the general population (Supplemental variate models is reported in Table 1. The fewer covariates Table 5). In the smaller SUMMIT T2D cohort13 we were able required for the minimal model results in improved statistical to interrogate seven loci with comparable trait definitions. power because of fewer participants with missing data, The ORs were directionally consistent in six of them (bino- whereas the fully adjusted model allows the identification of mial sign test: P=0.06; Supplemental Table 6). In FinnDiane associations potentially mediated by covariates. Comparison seven of the remaining 15 loci were observed with sufficient of the adjustment models revealed strong consistency between frequency (minor allele counts .10) to allow subgroup anal- the two models (Supplemental Figure 6). Table 1 is stratified ysis. Two additional SNPs (rs149641852 in SNCAIP and into two sets of loci: common and/or known kidney biology rs12615970 near COLEC11) were nominally significant loci (top half, n=7) and uncommon and no known kidney (P,0.05) only in individuals with HbA1c $7.5%; however, biology loci (bottom half, n=9). We focus on loci in the top the genotype-by-HbA1c interaction term was nonsignificant half of the table, common and/or in/near genes with relevant (Supplemental Figure 4, Supplemental Table 7). kidney biology. Two other genome-wide significant signals were near genes Variants Previously associated with DKD encoding proteins related to collagen. Variant rs12615970 We investigated the effect of variants previously associated at (MAF=0.13), located 53 kb downstream of COLEC11,was genome-wide significance with renal complications in individ- associated with CKD (OR, 1.31; 95% CI, 1.19 to 1.45; uals with diabetes.8–13,39 Across the ten subphenotypes in our 2 P=9.43310 9), and rs116772905 (MAF=0.011) in exon 14 meta-analysis, we found evidence of association for seven of of DDR1 was associated with microalbuminuria (OR, 3.78; nine examined loci (P,0.05; Supplemental Figure 7): We rep- 2 95% CI, 2.35 to 6.11; P=4.40310 8). rs116772905 is in per- licated two loci that were previously discovered without over- fect linkage disequilibrium with rs118124843, the lead associ- lapping individuals with the current study: SCAF8/CNKSR3 2 ation with microalbuminuria for this locus under the full rs12523822, originally associated with DKD (P=6.8310 4 adjustment model (taking into account both body mass index for “All versus ctrl”)8;andUMOD rs77924615, originally as- and HbA1c), located 29 kb downstream of DDR1 (OR, 3.97; sociated with eGFR in both individuals with and without di- 2 2 95% CI, 2.44 to 6.52; P=3.37310 8). COLEC11 encodes a abetes (P=5.2310 4 for “CKD”).31 Associations at the AFF3, collectin protein containing both a collagen-like domain RGMA-MCTP2,andERBB4 loci, identified in the Genetics and a carbohydrate recognition domain for binding sugars, of Nephropathy–an International Effort consortium12 com- and DDR1 encodes the discoidin domain-containing receptor prising a subset of studies included in this current effort, re- 1, which binds collagens including type IV collagen. mained associated with DKD, although the associations were In addition to COL4A3 rs55703767, three other low-frequency attenuated in this larger dataset (RGMA-MCTP2 rs12437854 2 2 2 variants achieved study-wide significance (P,6.76310 9), each P=2.97310 5; AFF3 rs7583877 P=5.97310 4; ERBB4 2 associated with microalbuminuria: rs142823282 (MAF=0.017), rs7588550 P=3.53310 5; Supplemental Figure 8). Associa- 22 kb upstream of TAMM41 encoding a mitochondrial tions were also observed at the CDCA7/SP3 (rs4972593,

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P=0.02 for “CKD-DN,” originally for ESKD exclusively in Expression and Epigenetic Analyses women11)andGLRA3 (rs1564939, P=0.02 for “CKD ex- We interrogated gene expression datasets in relevant tissues tremes,” originally for AER10,39), but these analyses also include to determine whether our top signals underlie eQTL. We first individuals that overlap with the original studies. Apart from analyzed genotype and RNA-sequencing gene expression data the UMOD locus, none of the 63 loci associated with eGFR from 96 whole human kidney cortical samples22 and micro- in the general population31 were associated with DKD after dissected human kidney samples (121 tubule and 119 glo- 2 correction for multiple testing (P,7.0310 4; Supplemental merular samples)23 from participants of European descent Figure 9). without any evidence of renal disease (Supplemental Figure 10). No findings in this data set achieved significance after correction Gene and Gene Set Analysis for multiple testing. In the GTEx and eQTLgen datasets, 2 We conducted gene-level analyses by using two methods COL4A3 rs55703767 had a significant eQTL (P=5.63310 38) that aggregate SNP summary statistics over a gene region with the MFF gene in blood, but is most likely due to modest while accounting for linkage disequilibrium: MAGMA and LD with other nearby strong eQTLs in the region. rs118124843 PASCAL.40,41 MAGMA identified five genes at a Bonferroni- near DDR1 and VARS2 had multiple significant eQTLs in blood 2 2 corrected threshold (P,0.05/18,222 genes tested =2.74310 6): besides VARS2 (P=1.71310 5; Supplemental Table 12). Inter- the collagen gene COL20A1 associated with “CKD extreme” estingly, rs142823282 near TAMM41 was a cis-eQTL for PPARG 2 2 (full model P=5.77310 7)and“ESKD versus non-ESKD” (P=4.60310 7), a transcription factor regulating adipocyte 2 (full model P=9.53310 7), SLC46A2 associated with “All development and glucose and lipid metabolism; PPARg ago- 2 versus ctrl” (P=7.38310 7), SFXN4 associated with “Macro” nists have been suggested to prevent DKD.53 2 (full model P=1.65310 7), GLT6D1 associated with “ESKD To ascertain the potential functional role of our top non- 2 versus macro” (P=1.49310 6), and SNX30 associated with coding signals, we mined chromatin immunoprecipitation 2 “All versus ctrl” (P=2.49310 6) (Supplemental Table 9). Al- sequencing data derived from healthy adult human kidney though PASCAL did not identify any significant gene-level samples.25 SNP rs142823282 near TAMM41 was located close associations, the five MAGMA-identified genes had to kidney histone marks H3K27ac, H3K9ac, H3K4me1, and 2 P,5.0310 4 in PASCAL (Supplemental Table 10). Both H3K4me3, suggesting that this is an active regulator of SFXN4 and CBX8 have been reported to be differentially TAMM41 or another nearby gene (Supplemental Figure 11). methylated in patients with diabetes with and without ne- Interestingly, in recent work we have shown that DNA meth- phropathy.42,43 ylation profiles in participants with T1D with/without kidney Additionally, we used MAGMA, PASCAL, DEPICT, and disease show the greatest differences in methylation sites near MAGENTA to conduct gene-set analysis in our GWAS dataset. TAMM41.54 The four methods identified 12 significantly enriched gene sets To establish whether the expression of our top genes shows (Supplemental Table 11). One gene set, “negative regulators of enrichment in a specific kidney cell type, we queried an ex- RIG-I MDA5 signaling” was identified in two different path- pression dataset of approximately 50,000 single cells obtained way analyses (MAGMA and PASCAL) of our fully adjusted from mouse kidneys.24 Expression was detected for six genes GWAS of ESKD versus Macro. Several additional related and in the mouse kidney atlas: three (COL4A3, SNCAIP,andBMP7) overlapping gene sets were identified, including “RIGI MDA5 were almost exclusively expressed in podocytes (Figure 5), mediated induction of IFN alpha beta pathways,”“TRAF3 de- supporting the significant role for podocytes in DKD. pendent IRF activation pathway,” and “TRAF6 mediated Gene expression levels in kidneys in cases versus controls IRF activation” (PASCAL) and “activated TLR4 signaling” were predicted with TWAS on the basis of the GWAS summary (MAGENTA). RIG-I, MDA5 and the toll-like receptor TLR4 statistics and eQTL data of kidney glomeruli and tubuli.23 are members of the innate immune response system that re- While none of the genes survived correction for multiple test- spond to both cellular injury and infection44,45 and transduce ing, analysis suggested 18 genes with differential expression 2 highly intertwined signaling cascades. These include the sig- in cases and controls with P,1310 4, including the NPNT, naling molecules TRAF3 and TRAF6, which induce expres- PRRC2C,andVPS33B genes (Supplemental Table 13). NPNT sion of type 1 IFN and proinflammatory cytokines implicated encodes for nephronectin, an extracellular matrix protein in the progression of DKD.46,47 Specifically, the TLR4 receptor on GBM. Knocking out NPNT or decreasing NPNT expres- and several of its ligands and downstream cytokines display sion levels have been shown to induce podocyte injury related differential levels of expression in DKD renal tubules versus to GBM.55 On the contrary, TWAS predicted higher NPNT normal kidneys and versus non-DKD controls,48 and TLR4 expression within DN cases versus normal AER. In line with knockout mice are protected from DKD and display marked our TWAS finding, NPNT is significantly upregulated in glo- reductions in interstitial collagen deposition in the kidney.49 meruli of DN mouse model versus nondiabetic mouse 2 Other pathways of interest include “other lipid, fatty acid and (P=6.4310 4; fold change 1.3, in top 2%, accessed through steroid metabolism,”“nitric oxide signaling in the cardiovas- www.neproseq.org).56 Furthermore, a variant near PRRC2C was cular system,” and “TNF family member,” with both nitric recently associated with albuminuria in the UK Biobank,57 and oxide and TNF-a implicated in DKD.50,51,52 rare mutations in VPS33B gene cause arthrogryposis, renal

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nCells % Endo Podo PT LOH DCT CD-PC CD-IC Fib Macro Neutro B lymph T lymph NK 1,001 2.29 Endo COL4A3 78 0.18 Podo

SNCAIP 26,482 60.54 PT 1,581 3.61 LOH BMP7 8,544 19.53 DCT 870 1.99 CD-PC PLEKHA7 1,729 3.95 CD-IC VARS2 549 1.26 Fib 228 0.52 Macro DDR1 74 0.17 Neutro 235 0.54 B lymph Z-score 1,308 2.99 T lymph 313 0.72 NK -1.5 0 1.5

Figure 5. Single-cell RNA-sequencing in mouse kidney shows COL4A3, SNCAIP,andBMP7 are specifically expressed in podocytes. Mean expression values of the genes were calculated in each cluster. The color scheme is on the basis of z-score distribution; the map shows genes with z-score .2. In the heatmap, each row represents one gene and each column is a single cell type. Percentages of assigned cell types are summarized in the right panel. CD-IC, collecting duct intercalated cell; CD-PC, collecting duct principal cell; CD-Trans, collecting duct transitional cell; DCT, distal convoluted tubule; Endo, containing endothelial, vascular, and descending loop of Henle; Fib, fibroblast; LOH, ascending loop of Henle; Lymph, lymphocyte; Macro, macrophage; Neutro, neutrophil; NK, natural killer cell; Podo, podocyte; PT, proximal tubule. dysfunction, and cholestasis-1 syndrome involving proximal– and other components of the GBM, such as laminin and ni- tubular dysfunction and usually death by 1 year of age.58 dogen. These interactions mediate the contact between cells and the underlying collagen IV basement membrane, and reg- ulate various processes essential to embryonic development DISCUSSION and normal physiology, including cell adhesion, proliferation, survival, and differentiation. Dysregulation of these interac- Our genome-wide analysis of 19,406 participants with T1D tions has been implicated in several pathologic conditions, identified 16 genome-wide significant loci associated with including CKD.60 DKD, four of which remained significant after a conservative Mutations in COL4A3 are responsible for the autosomal correction for multiple testing. Four of the 16 genome-wide recessive form of Alport syndrome, a progressive inherited significant signals are in or near genes with known or suggestive nephropathy, as well as benign familial hematuria, character- biology related to renal function/collagen (COL4A3, BMP7, ized by thin (or variable width) GBM, and thought to be a COLEC11,andDDR1), but this is the firsttimethatnaturally milder form of Alport syndrome.61 Furthermore, mutations occurring variation (MAF.1%) in these loci has been associ- in COL4A3 have also been identified in patients with FSGS, ated with DKD. Our most significant signal was a protective often leading to proteinuria and renal failure. Some of these missense variant in COL4A3, rs55703767, reaching both patients with FSGS presented with segmental GBM thin- genome-wide and study-wide significance with multiple def- ning.62 Of note, the common rs55703767 (COL4A3 As- initions of DKD. Moreover, this variant demonstrated a sig- p326Tyr) variant, protecting from DKD, was also associated nificant association with GBM width such that protective with thinner GBM in individuals with diabetes but without allele carriers had thinner GBM before any signs of kidney renal complications, a feature that seems to be beneficial in disease, and its effect was dependent on glycemia. the context of diabetes. The rs55703767 SNP is predicted to COL4A3, with COL4A4 and COL4A5, make up the so- alter the third amino acid of the canonical triple-helical do- called “novel chains” of type IV collagen,59 which together main sequence of Glycine (G)-X-Y (where X and Y are often play both structural and signaling roles in the GBM. Specifi- proline [P] and hydroxyproline [Y], respectively) from G-E-D cally, COL4A3 is known to bind a number of molecules in- (D=Aspartic) to G-E-Y,63 potentially affecting the structure cluding integrins, heparin, and heparin sulfate proteoglycans, of the collagen complex. In addition, a recent study64 of

JASN 30: 2000–2016, 2019 GWAS Loci and Diabetic Kidney Disease 2009 CLINICAL RESEARCH www.jasn.org candidate genes involved in renal structure reported of rare autosomal recessive disorders resulting in birth defects rs34505188 in COL4A3 (not in linkage disequilibrium with and abnormal development, including kidney abnormalities.75 rs55703767, r2,0.01) to be associated with ESKD in black The intronic variant rs144434404, associated at study-wide Americans with T2D (MAF=2%; OR, 1.55; 95% CI, 1.22 to significance with the microalbuminuria phenotype, resides 2 1.97; P=5310 4). Together with the trend toward association within the bone morphogenetic protein 7 (BMP7)gene. we have seen in SUMMITand the glycemic interaction we have BMP7 encodes a secreted ligand of the TGF-b superfamily reported here, these findings suggest variation in COL4A3 may of proteins. Developmental processes are regulated by the be associated with DKD in T2D as well. BMP family of glycosylated extracellular matrix molecules Given its association as a protective SNP, we can speculate via serine/threonine kinase receptors and canonical Smad that the rs55703767 variant may confer tensile strength or pathway signaling. Coordinated regulation of both BMP and flexibility to the GBM, which may be of particular relevance BMP-antagonist expression is essential for developing tissues, in the glomerular hypertension associated with DKD. Alter- and changes in the levels of either BMP or BMP-antagonists natively, COL4A3 may regulate the rates of production and/or can contribute to disease progression such as fibrosis and can- turnover of other GBM components, affecting GBM width cer.76 BMP7 is required for renal morphogenesis, and Bmp7 changes in diabetes. How these effects might confer protection knockout mice die soon after birth, because of reduced ure- in a manner dependent on ambient glucose concentrations teric bud branching.77–79 Maintenance of Bmp7 expression in is unknown. Future mechanistic studies will be required to glomerular podocytes and proximal tubules of diabetic mice determine the precise role of this variant in DKD; elucidation prevents podocyte loss and reduces overall diabetic renal in- of its interaction with glycemia in providing protection might jury.38 More recently, we have identified a mechanism through be relevant to other molecules implicated in diabetic which BMP7 orchestrates renal protection through Akt inhi- complications. bition, and highlights Akt inhibitors as potential antifibrotic In keeping with the collagen pathway, the synonymous ex- therapeutics.80 It is also noteworthy that the BMP7 antagonist onic variant rs118124843, which reached genome-wide signif- grem-1 is implicated in DKD,81–83 and gremlin has been im- icance for the “Micro” phenotype, is located near DDR1,the plicated as a biomarker of kidney disease.84 gene encoding the discoidin domain-containing receptor 1. Strengths of this analysis include the large sample size, triple On the basis of chromatin conformation interaction data that of the previous largest GWAS; the uniform genotyping from Capture HiC Plotter,65 the rs118124843 containing frag- and QC procedures; standardized imputation for all studies ment interacts with six gene promoter regions, including (1000 Genomes reference panel); the inclusion of exome array DDR1, suggesting that the variant regulates DDR1 expression content; the exploration of multiple standardized pheno- across multiple tissues (Supplemental Table 12). DDR1 is a type definitions of DKD; and supportive data from various collagen receptor66 shown to bind type IV collagen,67 and is sources of human kidney samples. Several of the loci identi- highly expressed in kidneys, particularly upon renal injury.68 fied have known correlations with kidney biology, suggesting Upon renal injury, Ddr1-deficient mice display lower levels of that these are likely true associations with DKD. However, we collagen,69 decreased proteinuria, and an increased survival acknowledge a number of limitations. First, nine variants have rate compared with wild-type controls,70 with Ddr1/Col4a3 low MAF and were driven by only two cohorts, indicating that double-knockout mice displaying protection from progressive further validation will be required to increase confidence in renal fibrosis and prolonged lifespan compared with Col4a3 these associations. Second, seven variants were significantly knockout mice alone.69 Thus, through its role in collagen associated with microalbuminuria only, a trait shown to be binding, DDR1 has been suggested as a possible therapeutic less heritable in previous studies. We included these loci to target for kidney disease.69 maximize comprehensiveness in reporting novel DKD associ- The association of rs12615970, an intronic variant on chro- ations. Replication in independent samples and functional mosome 2 near the COLEC11 gene, met genome-wide signif- confirmation is required to validate all of these loci. Although icance for the CKD phenotype, as well as nominal significance the gene-level, gene set, and pathway analyses had limited for multiple albuminuria-based traits. The rs12615970 con- power, these analyses identified several additional potential taining fragment was found to interact with COLEC11, ALLC, DKD loci and pathways, some with relevance to kidney biology, and ADI1 transcription start sites in chromatin conformation that require further follow-up. Finally, although we included data on GM12878 cell line (Supplemental Table 12).65,71 Col- only controls with a minimum diabetes duration of 15 years, lectin-11 is an innate immune factor synthesized by multiple we cannot fully rule out that some of the controls would prog- cell types, including renal epithelial cells with a role in pattern ress to DKD in the future, as the improvements in diabetes recognition and host defense against invasive pathogens, treatment in the past decades have postponed the onset of through binding to fructose and mannose sugar moieties.72,73 complications. We also excluded cases with short diabetes du- Mice with kidney-specificdeficiency of COLEC11 are protected ration to avoid renal complications that might be due to other against ischemia-induced tubule injury because of their re- causes. These phenotypic definitions were meant to overcome duced complement deposition,74 and mutations in COLEC11 the limitation that in clinical practice kidney biopsy specimens have been identified in families with 3MC syndrome, a series are rarely taken from individuals with diabetes to verify the

2010 JASN JASN 30: 2000–2016, 2019 www.jasn.org CLINICAL RESEARCH diagnosis. As for any late-onset disease, these challenges in R. Graudinxa, A. Valtere, D. Teterovska, I. Dzıvıte-Krišane, I. Kirilova, phenotypic definition may have reduced our power to detect U. Lauga-Turin xa, K. Geldnere, D. Seisuma, N. Fokina, S. Steina, additional associations. We note, however, that this relatively L. Jaunozola, I. Balcere, U. Gailiša, E. Menise, S. Broka, L. Akmene, small degree of contamination would lead to loss of power and N. Kapļa, J. Nagaiceva, A. Petersons, A. Lejnieks, I. Konrade, I. Salna, increased type 2 error rather than false positive findings; there- A. Dekante, A. Gramatniece, A. Salinxa, A. Silda, V. Mešecko, V.Mihejeva, fore, it does not undermine the robustness of the associations K. Kudrjavceva, I. Marksa, M. Cirse, D. Zeme, S. Kalva-Vaivode, reported here. J. Klovinxš, L. Nikitina-Zakxe, S. Skrebinska, Z. Dzerve, and R. Mallons. Diabetic complications are unquestionably driven by hy- LitDiane acknowledges Dr. Jurate Lasiene, Prof. Dzilda Velickiene, perglycemia and partially prevented by improved glycemic Dr. Vladimiras Petrenko and nurses from the Department of Endo- control in both T1D and T2D, but there has been doubt crinology, Hospital of Lithuanian University of Health Sciences. over what contribution, if any, inherited factors contribute RomDiane acknowledges the following doctors and researchers: to disease risk. In line with previous genetic studies, this study M. Anghel, DM Cheta, BA Cimpoca, D. Cimponeriu, CN Cozma, with a markedly expanded sample size identified several loci N. Dandu, D. Dobrin, I Duta, AM Frentescu, C. Ionescu-Tirgoviste, strongly associated with DKD risk. These findings suggest that R. Ionica, R. Lichiardopol, AL Oprea, NM Panduru, A. Pop, G. Pop, larger studies, aided by novel analyses and including T2D, will R. Radescu, S. Radu, M. Robu, C. Serafinceanu, M. Stavarachi, continue to enhance our understanding of the complex path- O Tudose from the N.C Paulescu National Institute for Diabetes ogenesis of DKD, paving the way for molecularly targeted pre- Nutrition and Metabolic Diseases from Bucharest; and M. Bacu, ventive or therapeutic interventions. D. Clenciu, C. Graunteanu, M. Ioana, E. Mota, and M. Mota from Craiova Emergency County Hospital. SDR: Thanks to Maria Sterner and Malin Neptin for GWAS genotyping in the Scannia Diabetes ACKNOWLEDGMENTS Registry. The FinnDiane study thanks M. Parkkonen, A. Sandelin, A-R. Salonen, T. Soppela, and J. Tuomikangas for skillful laboratory Conceptualization: Godson, Maxwell, Groop, Hirschhorn, and assistance in the FinnDiane study. We also thank all the participants Florez; Formal analysis: Salem, Cole, Sandholm, Valo, Di Liao, Cao, of the FinnDiane study and gratefully acknowledge all the physicians Pezzolesi, Smiles, Qiu, Nair, Park, Liu, Menon, Kang, R. Klein, and nurses at each centre involved in the recruitment of participants B.E. Klein, Canty, Paterson, Chen, van Zuydam, and Onengut- (Supplemental Table 14). GoDARTS: We are grateful to all the par- Gumuscu; Investigation: Pezzolesi, Smiles, Skupien Qiu, Nair, Haukka, ticipants in this study, the general practitioners, the Scottish School McKnight, Snell-Bergeon, Maahs, Maxwell, Paterson, Forsblom, of Primary Care for their help in recruiting the participants, and to Ahlqvist, Ahluwalia, Lajer, Boustany-Kari, Kang, Maeastroni, the whole team, which includes interviewers, computer and labora- Tregouet, Gyorgy, Bull, Palmer, Stechemesser, Paulweber, Weitgasser, tory technicians, clerical workers, research scientists, volunteers, Rovıte, Pırags, Prakapiene, Radzeviciene, Verkauskiene, Panduru, managers, receptionists, and nurses. The study complies with the Groop, McCarthy, Gu, Möllsten, Falhammar, Brismar, Rossing, Declaration of Helsinki. We acknowledge the support of the Health Costacou, Zerbini, Marre, Hadjadj, Forsblom, Chen, and Onengut- Informatics Centre, University of Dundee for managing and supply- Gumuscu; Resources: Smiles, Harjutsalo, McKnight, Nelson, ing the anonymised data and NHS Tayside, the original data owner. Caramori, Mauer, Gao, Snell-Bergeon, Maahs, Guo, Miller, Maxwell, For a full list of SUMMIT consortium members, see Supplemental Paterson, Chen, and Onengut-Gumuscu; Data curation: Hiraki, Di Table 15. Liao, Cao, Smiles, Harjutsalo, Skupien, McKnight, R. Klein, B.E. Klein, The views expressed in this article are those of the author(s) and not Lee, Gao, Mauer, Caramori, Snell-Bergeon, Maahs, Guo, Miller, necessarily those of the NHS, the NIHR, or the Department of Health. Maxwell, Chen, and Onengut-Gumuscu; Writing, original draft: Data and Software Availability Salem, Todd, Sandholm, Cole, Brennan, Andrews, Doyle, Hughes, GWAS summary statistics for all ten DKD phenotypes and two Hiraki, Paterson, and Godson; Writing, review and editing: Salem, adjustment models are available for download at the AMP-Type 2 Todd, Sandholm, Cole, Brennan, Andrews, Doyle, Hughes, Hiraki, Diabetes Knowledge Portal (http://www.type2diabetesgenetics.org/ Paterson, Godson, Qiu, Park, Skupien, Mauer, Caramori, de Boer, informational/data), under “JDRF Diabetic Nephropathy Collabo- Martin, McKnight, McKay, Maxwell, Susztak, McCarthy, Groop, Rich, rative Research Initiative GWAS” datasets. Individual-level genotype Forsblom, Hirschhorn, and Florez; Visualization: Salem, Todd, data cannot be shared for all cohorts because of restrictions set by Sandholm, Cole, Pezzolesi, Qiu., Di Liao, Cao, Park, Skupien, R. Klein, study consents and by European Union and national regulations re- B.E. Klein, Lee, McKnight, McKay, Maxwell, and Paterson; Project administration: Todd; Supervision: Paterson, Krolewski, Groop, garding individual genotype data. Godson, Maxwell, Colhoun, Rich, Kretzler, Susztak, Hirschhorn, and Florez; Funding acquisition: Paterson, Krolewski, Florez, and Hirschhorn. All authors approved the final version of the manuscript. DISCLOSURES AusDiane acknowledges Dr. Bernhard Baumgartner from De- We acknowledge the following conflicts of interest: Groop has received in- partment of Medicine, Diakonissen-Wehrle Hospital, Salzburg, vestigator-initiated research grants from Eli Lilly and Roche, is an advisory Austria. LatDiane acknowledges the following doctors and re- board member for AbbVie, AstraZeneca, Boehringer Ingelheim, Cebix, Eli searchers: A. Bogdanova, D. Grikmane, I. Care, A. Fjodorova, Lilly, Janssen, Medscape, Merck Sharp & Dohme, Novartis, Novo Nordisk

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and Sanofi, and has received lecture fees from AstraZeneca, Boehringer In- work. Dr. Hiraki reports grants from Juvenile Diabetes Research Foundation gelheim, Eli Lilly, Elo Water, Genzyme, Merck Sharp & Dohme, Medscape, (JDRF), during the conduct of the study. Dr. Rossing reports grants and other Novo Nordisk and Sanofi. McCarthy is a Wellcome Investigator and an NIHR from Astra Zeneca, other from Boehringer Ingelheim, other from Bayer, Senior Investigator. He serves on advisory panels for Pfizer, NovoNordisk, and grants and other from Novo Nordisk, other from MSD, other from Eli Lilly, Zoe Global, has received honoraria from Merck, Pfizer, NovoNordisk and other from astellas, other from Abbvie, other from Mundi Pharma, other from Eli Lilly, has stock options in Zoe Global, and has received research funding Sanofi, other from Gilead, outside the submitted work. from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. Rossing has received consultancy and/or speaking fees (to his institution) from AbbVie, FUNDING Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Eli fi Lilly, MSD, Novo Nordisk and Sano Aventis and research grants from As- This study was supported by a grant from the JDRF (17-2013-7) and Na- traZeneca and Novo Nordisk, and shares in Novo Nordisk. tional Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Dr. Onengut-Gumuscu reports grants from JDRF, during the conduct of grants R01 DK081923 and R01 DK105154. Salem was supported in part by the study. Dr. Caramori reports grants from Bayer Pharmaceuticals, personal JDRF grant 3-APF-2014-111-A-N, and National Heart, Lung and Blood fees from Elcelix, outside the submitted work. Dr. de Boer reports personal Institute grant R00 HL122515. Todd was supported by NIDDK grant K12- fi fees from Boehringer-Ingelheim, personal fees from Ironwood, non- nancial DK094721. Sandholm received funds from the European Foundation for the fi support from Medtronic, non- nancial support from Abbott, outside the sub- Study of Diabetes (EFSD) Young Investigator Research Award funds and Acad- mitted work. Dr. Snell-Bergeon reports grants from NIH, grants from JDRF, emy of Finland (299200). Godson, Andrews, Brennan, Martin, Hughes, and during the conduct of the study; other from GlaxoSmithKline, outside the Doyle are supported by Science Foundation Ireland - Health Research Board submitted work. Dr. Weitgasser reports personal fees from Abbott, personal (SFI-HRB) US Ireland Research Partnership SFI15/US/B3130. Nelson was fees from Astra Zeneca, personal fees from Boehringer-Ingelheim, grants supported in part by the Intramural Research Program of the NIDDK. The and personal fees from Eli Lilly, personal fees from MSD, grants and personal FinnDiane study was funded by JDRF (17-2013-7), Folkhälsan Research fees from NovoNordisk, personal fees from Dexcom, personal fees from Ro- Foundation, the Wilhelm and Else Stockmann Foundation, the Liv och Hälsa che, grants and personal fees from Sanofi, personal fees from Servier, personal Foundation, Helsinki University Central Hospital Research Funds (EVO), the fees from Takeda, personal fees from Spar, outside the submitted work. Novo Nordisk Foundation (NNF OC0013659), and Academy of Finland Dr. McCarthy reports grants, personal fees and other from Pfizer, grants, (275614 and 316664). The Pittsburgh Epidemiology of Diabetes Complica- personal fees and other from Merck, grants, personal fees and other from tions Study (EDC) study was supported by NIDDK grant DK34818 and by Novo Nordisk, grants from Takeda, grants from Servier, grants from Sanofi the Rossi Memorial Fund. The Wisconsin Epidemiologic Study of Dia- Aventis, grants from Boehringer Ingelheim, grants from Astra Zeneca, grants betic Retinopathy (WESDR) study was funded by National Eye Institute from Janssen, grants, personal fees and other from Eli Lilly, grants from Abb- grant EY016379. The Sweden study was supported by Family Erling-Persson vie, grants from Roche, during the conduct of the study; grants, personal fees (Brismar) and Stig and Gunborg Westman foundations (Gu). SUMMIT Con- and other from Pfizer, grants, personal fees and other from Eli Lilly, grants, sortium: this work was supported by Innovative Medicines Initiative (IMI) personal fees and other from Merck, other from Zoe Global, other from Gen- (SUMMIT 115006); Wellcome Trust grants 098381, 090532, and 106310; entech (wef June 2019), outside the submitted work. Dr. Costacou reports National Institutes of Health grant R01-MH101814; and JDRF grant 2-SRA- grants from National Institutes of Health (NIH), during the conduct of the 2014-276-Q-R; and other grants from Swedish Research Council, European study. Dr. McKnight reports grants from Northern Ireland Public Health Research Council Advanced (ERC-Adv) research grant 269045-GENE Agency (Research and Development Division) and Medical Research Council TARGET T2D, Academy of Finland grants 263401 and 267882, and Sigrid as part of the USA-Ireland-Northern Ireland research partnership and De- JUselius Foundation. The George M. O’Brien Michigan Kidney Translational partment for the Economy NI 15/IA/3152 during the conduct of the study. Core Center, funded by NIH/NIDDK grant 2P30-DK-081943 for the bioinfor- Dr. Kretzler reports grants from NIH, non-financial support from Universityof matics support. The Wellcome Trust United Kingdom Type 2 Diabetes Case Michigan, during the conduct of the study; grants from JDRF, grants from Control Collection (GoDARTS) was funded by the Wellcome Trust (072960/Z/ Astra-Zeneca, grants from NovoNordisc, grants from Eli Lilly, grants from 03/Z, 084726/Z/08/Z, 084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as Gilead, grants from Goldfinch Bio, grants from Merck, grants from Janssen, part of the European Union’s IMI-SUMMIT program. grants from Boehringer-Ingelheim, grants from Elpidera, grants from Euro- pean Union Innovative Medicine Innitiative, outside the submitted work; In addition, Dr. Kretzler has a patent Biomarkers for CKD progression issued. SUPPLEMENTAL MATERIAL Dr. Susztak reports grants from GSK, Regeneron, Boehringer, Gilead, Bayer, Lilly, Merck, outside the submitted work. Dr. Colhoun reports grants from This article contains the following supplemental material online at AStraZeneca LP, other from Bayer, grants and other from Eli Lilly and Com- pany, other from Novartis Pharmaceuticals, grants and other from Regeneron, http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2019030218/-/ grants from Pfizer Inc., other from Roche Pharmaceuticals, grants and other DCSupplemental. from Sanofi Aventis, grants and personal fees from Novo Nordisk, outside the Supplemental Table 1. Cohorts contributing to analyses. submitted work. Dr. Groop reports personal fees from AbbVie, personal fees Supplemental Table 2. Pseudo-R2 of all SNPs across all GWAS as from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees calculated by the McKelvey and Zavoina method. from Eli Lilly, personal fees from Elo Water, personal fees from Janssen, per- Supplemental Table 3. Characteristics of RASS participants. Cat- sonal fees from Medscape, personal fees from Mundipharma, personal fees from MSD, personal fees from Novartis, personal fees from Novo Nordisk, egorical variables display counts and percentage. Continuous values personal fees from Sanofi, outside the submitted work. Dr. Marre reports are mean6SD. personal fees from Novo-Nordisk, personal fees from Servier, personal fees Supplemental Table 4. Multivariate analysis of association between from Merck, outside the submitted work; and Marre is president of the Fon- rs55703767 and GBM width. dation Francophone pour la Recherche sur le Diabète, a French, non for profit fi Supplemental Table 5. Look-up of the lead loci in GWAS on eGFR institution. This institution is supported nancially by the following compa- 31 nies: Novo-Nordisk, Merck, Sanofi, Eli Lilly, Astra-Zeneca, Roche, Abbott. in the general population (Gorski et al. ). Dr. Hirschhorn reports other from Camp4 Therapeutics, outside the submit- Supplemental Table 6. Look-up of the lead loci in GWAS in the ted work. Dr. Florez reports personal fees from Janssen, outside the submitted SUMMIT consortium (van Zuydam et al.13).

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Supplemental Table 7. Association at lead loci stratified by 4. Harjutsalo V, Katoh S, Sarti C, Tajima N, Tuomilehto J: Population- HbA1c,7.5%. based assessment of familial clustering of diabetic nephropathy in type – Supplemental Table 8. Association of rs55703767 with DN in 1diabetes.Diabetes 53: 2449 2454, 2004 5. Quinn M, Angelico MC, Warram JH, Krolewski AS: Familial factors DCCT/EDIC subgroups. determine the development of diabetic nephropathy in patients with Supplemental Table 9. Significant (P,0.05/18,222 genes IDDM. Diabetologia 39: 940–945, 1996 tested =2.74310–6) gene level associations with DKD in MAGMA. 6. Seaquist ER, Goetz FC, Rich S, Barbosa J: Familial clustering of diabetic Supplemental Table 10. Top nominally significant gene-level as- kidney disease. Evidence for genetic susceptibility to diabetic ne- – sociations (P,1.0310–5) with DKD in PASCAL. phropathy. NEnglJMed320: 1161 1165, 1989 fi 7. Sandholm N, Van Zuydam N, Ahlqvist E, Juliusdottir T, Deshmukh HA, Supplemental Table 11. Signi cant gene set and pathway analysis Rayner NW, et al.: The FinnDiane Study Group; The DCCT/EDIC Study results. Group; GENIE Consortium; SUMMIT Consortium: The genetic land- Supplemental Table 12. eQTL associations and chromatin con- scape of renal complications in type 1 diabetes. JAmSocNephrol28: formation interactions for the lead SNPs. 557–574, 2017 8. Iyengar SK, Sedor JR, Freedman BI, Kao WH, Kretzler M, Keller BJ, Supplemental Table 13. TWAS results with P,1310–4. et al.: Family Investigation of Nephropathy and Diabetes (FIND): Ge- Supplemental Table 14. Physicians and nurses at health care nome-wide association and trans-ethnic meta-analysis for advanced centers participating in the collection of FinnDiane patients. diabetic kidney disease: Family investigation of nephropathy and di- Supplemental Table 15. Members of the SUMMIT consortium. abetes (FIND). PLoS Genet 11: e1005352, 2015 Supplemental Figure 1. Manhattan and QQ plots for each case- 9. Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, et al.: ICBP Consortium; AGEN Consortium; CARDIOGRAM; CHARGe-Heart Fail- control definition and covariate model (minimal and full). ure Group; ECHOGen Consortium: Genetic associations at 53 loci Supplemental Figure 2. Regional chromosomal location plots and highlight cell types and biological pathways relevant for kidney func- forest plots by cohort of newly discovered DKD associations. tion. Nat Commun 7: 10023, 2016 Supplemental Figure 3 (S2). Correlation of expression of COL4A3 10. Sandholm N, Forsblom C, Mäkinen VP, McKnight AJ, Osterholm AM, with degree of fibrosis and eGFR in microdissected kidney samples. He B, et al.: SUMMIT Consortium; FinnDiane Study Group: Genome- wide association study of urinary albumin excretion rate in patients with Supplemental Figure 4 (S3). Genotype–phenotype associations at type 1 diabetes. Diabetologia 57: 1143–1153, 2014 fi , the lead loci when strati ed by mean HbA1c 7.5% in the FinnDiane 11. Sandholm N, McKnight AJ, Salem RM, Brennan EP, Forsblom C, study. Harjutsalo V, et al.: FIND Consortium; FinnDiane Study Group and the Supplemental Figure 5 (S4): Genotype–phenotype associations at GENIE Consortium: Chromosome 2q31.1 associates with ESRD in – the lead rs55703767 (COL4A3) locus when stratified by mean HbA1c womenwithtype1diabetes.JAmSocNephrol24: 1537 1543, 2013 12. Sandholm N, Salem RM, McKnight AJ, Brennan EP, Forsblom C, ,7.5% in up to 3226 individuals with T2D from the GoDARTS. Isakova T, et al.: DCCT/EDIC Research Group: New susceptibility loci fi Supplemental Figure 6 (S6). Fishplots comparing signi cance and associated with kidney disease in type 1 diabetes. PLoS Genet 8: directionality between minimal and fully adjusted models for each of e1002921, 2012 the ten phenotype definitions. 13. van Zuydam NR, Ahlqvist E, Sandholm N, Deshmukh H, Rayner NW, Supplemental Figure 7 (S5). Association at previously reported loci Abdalla M, et al.: Finnish Diabetic Nephropathy Study (FinnDiane); Hong Kong Diabetes Registry Theme-based Research Scheme Project (P,5310–8) for renal complications in individuals with diabetes. Group; Warren 3 and Genetics of Kidneys in Diabetes (GoKinD) Study Supplemental Figure 8 (S7). Forest plots of the associations at the Group; GENIE (GEnetics of Nephropathy an International Effort) Con- previously reported lead loci from the GENIE consortium with largely sortium; Diabetes Control and Complications Trial (DCCT)/Epidemi- overlapping studies. ology of Diabetes Interventions and Complications (EDIC) Research Supplemental Figure 9 (S7). Meta-analysis results for the loci that Group; SUrrogate markers for Micro- and Macrovascular hard end- points for Innovative diabetes Tools (SUMMIT) Consortium: A genome- have previously been associated with DKD, or with eGFRor AER in the wide association study of diabetic kidney disease in subjects with type 2 general population. diabetes. Diabetes 67: 1414–1427, 2018 Supplemental Figure 10 (S8). eQTL analysis in microdissected 14. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR: Fast tubule samples. and accurate genotype imputation in genome-wide association studies – Supplemental Figure 11 (S9). Functional annotation of TAMM41. through pre-phasing. Nat Genet 44: 955 959, 2012 15. Fuchsberger C, Abecasis GR, Hinds DA: minimac2: Faster genotype Supplemental Appendix 1. Cohort descriptions and references. imputation. Bioinformatics 31: 782–784, 2015 16. Li J, Ji L: Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. 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Hansen S, Selman L, Palaniyar N, Ziegler K, Brandt J, Kliem A, et al.: Collectin 11 (CL-11, CL-K1) is a MASP-1/3-associated plasma collectin See related editorial, “Biologic Underpinnings of Type 1 Diabetic Kidney with microbial-binding activity. J Immunol 185: 6096–6104, 2010 Disease,” on pages 1782–1783.

AFFILIATIONS

1Department of Family Medicine and Public Health, University of California San Diego, La Jolla, California; 2Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts; 3Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts; 4Center for Genomic Medicine and 66Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts; 5Folkhälsan Research Center, Folkhälsan Institute of Genetics, Helsinki, Finland; 6Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; 7Research Program for Clinical and Molecular Metabolism, Faculty of Medicine and 48Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; 8Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, Virginia; 9Diabetes Complications Research Centre, Conway Institute, School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland; 10Division of Nephrology and Hypertension, Diabetes and Metabolism Center, University of Utah, Salt Lake City, Utah; 11Usher Institute of Population Health Sciences and Informatics and 16Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom; 12The Hospital for Sick Children, Toronto, Ontario, Canada; 13Departments of Medicine and Genetics, University of Pennsylvania, Philadelphia, Pennsylvania; 14Division of Nephrology, Department of Internal Medicine and 23Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; 15Joslin Diabetes Center, Boston, Massachusetts; 17The Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland; 18Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK; 19Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; 20Department of Genomics, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden; 21Steno Diabetes Center Copenhagen, Gentofte, Denmark; 22Department of Biostatistics and 25Center for

JASN 30: 2000–2016, 2019 GWAS Loci and Diabetic Kidney Disease 2015 CLINICAL RESEARCH www.jasn.org

Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan; 24Cardiometabolic Diseases Research, Boehringer Ingelheim, Ridgefield, Connecticut; 26Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona; 27University of Wisconsin-Madison, Madison, Wisconsin; 28The George Washington University, Washington, DC; 29University of Minnesota, Minneapolis, Minnesota; 30Complications of Diabetes Unit, Division of Immunology, Transplantation and Infectious Diseases, Diabetes Research Institute, IRCCS San Raffaele Scientific Institute, Milano, Italy; 31University of Washington, Seattle, Washington; 32University of Pittsburgh Public Health, Pittsburgh, Pennsylvania; 33INSERM UMR_S 1166, Sorbonne Université, UPMC Univ Paris 06, Paris, France; 34ICAN Institute for Cardiometabolism and Nutrition, Paris, France; 35University of Colorado School of Medicine, Aurora, Colorado; 36Department of Pediatrics-Endocrinology, Stanford University, Stanford, California; 37The Lunenfeld- Tanenbaum Research Institute, University of Toronto, Toronto, Ontario, Canada; 38Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada; 39Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK; 40First Department of Medicine, Paracelsus Medical University, Salzburg, Austria; 41Department of Medicine, Diakonissen-Wehrle Hospital, Salzburg, Austria; 42University of Latvia, Riga, Latvia; 44Pauls Stradins University Hospital, Riga, Latvia; 43Latvian Biomedical Research and Study Centre, Riga, Latvia; 45Medical Academy and 46Institute of Endocrinology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania; 472nd Clinical Department, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; 49Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK; 50Genentech, 1 DNA Way, South San Francisco, California; 51Department of Clinical Science, Intervention and Technology and 55Department of Endocrinology, Diabetes and Metabolism, Karolinska University Hospital, Stockholm, Sweden; 52School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; 53Division of Pediatrics, Department of Clinical Sciences, Umeå University, Umeå, Sweden; 54Department of Molecular Medicine and Surgery, Rolf Luft Center for Diabetes Research and Endocrinology, Karolinska Institutet, Stockholm, Sweden; 56University of Copenhagen, Copenhagen, Denmark; 57Department of Diabetology, Endocrinology and Nutrition, Bichat Hospital, DHU FIRE, Assistance Publique–Hôpitaux de Paris, Paris, France; 58UFR de Médecine, Paris Diderot University, Sorbonne Paris Cité, Paris, France; 59INSERM UMRS 1138, Cordeliers Research Center, Paris, France; 60Fondation Ophtalmologique Adolphe de Rothschild, Paris, France; 61Department of Endocrinology and Diabetology, Centre Hospitalier Universitaire de Poitiers, Poitiers, France; 62INSERM CIC 1402, Poitiers, France; 63L’institut du thorax, INSERM, CNRS, CHU Nantes, Nantes, France; 64Centre for Public Health, Queens University of Belfast, Northern Ireland, UK; 65Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia; and 67Department of Medicine, Harvard Medical School, Boston, Massachusetts

2016 JASN JASN 30: 2000–2016, 2019