University of Edinburgh



Genome-wide association study of diabetic kidney disease highlights biology involved in renal basement membrane collagenRany M. Salem1*, Jennifer N. Todd2, 3, 4*, Niina Sandholm5, 6, 7*, Joanne B. Cole2, 3, 4*, Wei-Min Chen8, Darrel Andrews9, Marcus G. Pezzolesi10, Paul M. McKeigue11, Linda T. Hiraki12, Chengxiang Qiu13, Viji Nair14, Chen Di Liao12, Jing Jing Cao12, Erkka Valo5, 6, 7, Suna Onengut-Gumuscu8, Adam M. Smiles15, Stuart J. McGurnaghan16, Jani K. Haukka5, 6, 7, Valma Harjutsalo5, 6, 7,17, Eoin P. Brennan9, Natalie van Zuydam18, 19, Emma Ahlqvist20, Ross Doyle9, Tarunveer S. Ahluwalia21, Maria Lajer21, Maria F. Hughes9, Jihwan Park13, Jan Skupien15, Athina Spiliopoulou11, Andrew Liu22, Rajasree Menon14, 23, Carine M. Boustany-Kari24, Hyun M. Kang23, 25, Robert G. Nelson26, Ronald Klein27, Barbara E. Klein27, Kristine E. Lee27, Xiaoyu Gao28, Michael Mauer29, Silvia Maeastroni30, Maria Luiza Caramori29, Ian H. de Boer31, Rachel G. Miller32, Jingchuan Guo32, Andrew P. Boright12, David Tregouet33, 34, Beata Gyorgy33, 34, Janet K. Snell-Bergeon35, David M. Maahs36, Shelley B. Bull37, Angelo J. Canty38, Colin N.A. Palmer39, Lars Stechemesser40, Bernhard Paulweber40, Raimund Weitgasser41, Jelizaveta Sokolovska42, 43, Vita Rovīte44, Valdis Pīrāgs42, 43, Edita Prakapiene45, Lina Radzeviciene46, Rasa Verkauskiene46, Nicolae Mircea Panduru6, 47, Leif C. Groop48, 49, Mark I. McCarthy18, 19, 50, Harvest F. Gu51, 52, Anna M?llsten53, Henrik Falhammar54, 55, Kerstin Brismar54, 55, GENIE Consortium, DCCT/EDIC Research Group, SUMMIT Consortium, Finian Martin9, Peter Rossing21, 56, Tina Costacou32, Gianpaolo Zerbini30, Michel Marre57, 58, 59, Samy Hadjadj60, 61, Amy J. McKnight62, Carol Forsblom5, 6, Gareth McKay62, Catherine Godson9, A. Peter Maxwell62, Matthias Kretzler14, 23, Katalin Susztak13, Helen M. Colhoun16, Andrzej Krolewski15, Andrew D. Paterson12, Per-Henrik Groop5, 6, 7, 63, Stephen S. Rich8, Joel N. Hirschhorn2, 3, Jose C. Florez3, 4, 64, 65AFFILIATIONSFamily Medicine and Public Health, University of California San Diego, La Jolla, CA 92093 USA. Division of Endocrinology/ Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115 USA. Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA 02142 USA. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114 USA. Folkh?lsan Institute of Genetics, Folkh?lsan Research Center, Helsinki, Finland. Abdominal Center Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland. University of Virginia, 3812B OMS, Charlottesville, VA 22908 USA. Diabetes Complications Research Centre/ Conway Institute, School of Medicine and Medical Sciences, University College Dublin, Dublin 4 Ireland. University of Utah Division of Nephrology and Hypertension Diabetes and Metabolism Center, University of Utah, Salt Lake City, UT 84132 USA. Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, EH16 4TJ UK. The Hospital for Sick Children, Toronto, ON M5G 0A4 Canada. Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA. Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA. Joslin Diabetes Center, Boston, MA 02115 USA. Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU UK. The Chronic Disease Prevention Unit, National Institute for Health and Welfare, 00271, Helsinki, Finland. Wellcome Trust Centre for Human Genetics, University of Oxford, OX37BN, UK. Oxford Centre for Diabetes, Endocrinology and Metabolism, Oxford, OX37BN, UK. Lund University Diabetes Centre, Malm?, 20502, Sweden. Steno Diabetes Center Copenhagen, Niels Steensens Vej 2, DK 2820 Gentofte, Denmark. Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, 48109 USA. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA. Cardiometabolic Diseases Research, Boehringer Ingelheim, Ridgefield, CT 06877, USA. Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109 USA. Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Health, Phoenix, AZ 85014, USA. University of Wisconsin-Madison, Madison, WI 53726 USA. The George Washington University, Washington, DC 20052 USA. University of Minnesota, Minneapolis, MN 55454 USA. Complications of Diabetes Unit, Diabetes Research Institute (DRI), Division of Immunology, Transplantation and Infectious Diseases, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132 Milano, Italy. University of Washington, Seattle, WA 98195 USA. University of Pittsburgh Public Health, Pittsburgh, PA, 15213, USA. Sorbonne Université, UPMC Univ Paris 06, INSERM UMR_S 1166, Paris, France. ICAN Institute for Cardiometabolism and Nutrition, Paris, France. University of Colorado School of Medicine, Aurora, CO 80045 USA. Department of Pediatrics-Endocrinology, Stanford University, Stanford, California 94305 USA. The Lunenfeld-Tenenbaum Research Institute and University of Toronto, Toronto, ON M5G 0A4 Canada. Dept. of Mathematics & Statistics, McMaster University, Hamilton, Ontario, L8S 4K1 Canada. Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and Medical School, University of Dundee, DD19SY UK. First Department of Medicine, Paracelsus Medical University, Müllner Hauptstrasse 48, 5020 Salzburg, Austria. Department of Medicine, Diakonissen-Wehrle Hospital, Salzburg, Austria. University of Latvia, Rai?a 19, Riga, Latvia. Pauls Stradins University Hospital, Pilsonu 13, Riga, Latvia. Latvian Biomedical Research and Study Centre, Rātsupītes 1, Riga, Latvia. Medical Academy, Lithuanian University of Health Sciences, Kaunas, LT 44307 Lithuania. Institute of Endocrinology, Medical Academy, Lithuanian University of Health, Kaunas 44307 Lithuania Sciences. 2nd Clinical Department, Carol Davila University of Medicine and Pharmacy, Bucharest, 020021 Romania. Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malm?, 205 02 Sweden. Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, C329b, Finland. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, OX37BN, UK. Department of Clinical Science, Intervention and Technology, Karolinska University Hospital, Huddinge, Stockholm 14152 Sweden. School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 210009 China. Ume? University, Department of Clinical Sciences, Pediatrics, 90187 Ume?, Sweden. Rolf Luft Center for Diabetes Research and Endocrinology, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm 17176 Sweden. Department of Endocrinology, Diabetes and Metabolism, Karolinska University Hospital, Stockholm, 17176 Sweden. University of Copenhagen, Copenhagen, Denmark. Assistance Publique – H?pitaux de Paris (AP-HP), Bichat Hospital, DHU FIRE, Department of Diabetology, Endocrinology and Nutrition, Paris, France. Paris Diderot University, Sorbonne Paris Cité, UFR de Médecine, Paris, France; INSERM, UMRS 1138, Cordeliers Research Center, Paris, France. Fondation Ophtalmologique Adolphe de Rothschild, Paris, France. Centre Hospitalier Universitaire de Poitiers, Department of Endocrinology and Diabetology, Poitiers, France; INSERM CIC 1402, Poitiers, France. L’institut du thorax, INSERM, CNRS, CHU Nantes, Nantes, France. Centre for Public Health, Queens University of Belfast, Northern Ireland, BT7 1NN UK. Department of Diabetes, Central Clinical School, Monash University, Melbourne, Victoria, Australia. Diabetes Unit, Massachusetts General Hospital, Boston MA 02114 USA. Department of Medicine, Harvard Medical School, Boston MA 02115* These authors contributed equallyCorresponding author: Jose C. Florez jcflorez@mgh.harvard.eduLead author: Jose C. FlorezSIGNIFICANCE STATEMENT Diabetic kidney disease (DKD) is a devastating microvascular complication of both type 1 and type 2 diabetes. It has been shown to have a heritable component, but prior searches for the genetic determinants of this condition have had limited success. In this study, a new international genomics consortium (the Diabetic Nephropathy Collaborative Research Initiative, funded by the JDRF) has coalesced to assemble nearly 20,000 samples from participants with type 1 diabetes, with and without kidney disease. We report 16 new signals at genome-wide significance and begin to describe the genetic architecture of this globally relevant phenotype. Our top signal centers on a protective missense coding variant at COL4A3, which encodes an integral component of the glomerular basement membrane that when mutated causes Alport syndrome. ABSTRACT Background: Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. Methods: To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions based on albuminuria and renal function. Results: We identified 16 genome-wide significant 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) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD (BMP7) or renal biology (COLEC11 and DDR1). Conclusion: The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.KEYWORDSDiabetic kidney disease, genome-wide association study, type 1 diabetes, genetics, diabetic complications, COL4A3 INTRODUCTIONThe devastating diabetic complication of diabetic kidney disease (DKD) is the major cause of end-stage renal disease (ESRD) worldwidePEVuZE5vdGU+PENpdGUgRXhjbHVkZVllYXI9IjEiPjxBdXRob3I+Q2VudGVycyBmb3IgRGlzZWFz

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ADDIN EN.CITE.DATA 1,2. Current treatment strategies at best slow the progression of DKD, and do not halt or reverse the disease. Although improved glycemic control influences the rate of diabetic complications, a large portion of the variation in DKD susceptibility remains unexplained: one third of people with type 1 diabetes (T1D) develop DKD despite adequate glycemic control, while others maintain normal renal function despite long-term severe chronic hyperglycemia ADDIN EN.CITE <EndNote><Cite><Author>Krolewski</Author><Year>1996</Year><RecNum>16</RecNum><DisplayText><style face="superscript">3</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1336686827">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Krolewski, M.</author><author>Eggers, P.W.</author><author>Warram, J.H.</author></authors></contributors><titles><title>Magnitude of end-stage renal disease in IDDM: a 35 year follow-up study</title><secondary-title>Kidney Int</secondary-title></titles><periodical><full-title>Kidney Int</full-title></periodical><pages>2041-2046</pages><volume>50</volume><number>6</number><dates><year>1996</year></dates><urls></urls></record></Cite></EndNote>3. Though DKD demonstrates both familial clusteringPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYXJqdXRzYWxvPC9BdXRob3I+PFllYXI+MjAwNDwvWWVh

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ADDIN EN.CITE.DATA 7, the specific genetic factors influencing DKD risk remain largely unknown. Recent genome-wide association studies (GWAS) have only identified a handful of loci for DKD, albuminuria, or estimated glomerular filtration rate (eGFR) in individuals with diabetesZW5jZXMsIFVuaXZlcnNpdHkgb2YgQ29wZW5oYWdlbiwgQ29wZW5oYWdlbiwgRGVubWFyay4mI3hE

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ADDIN EN.CITE.DATA 7-13. Potential reasons for the limited success include small sample sizes, modest genetic effects, and lack of consistency of phenotype definitions and statistical analyses across studies. Through collaboration within the JDRF Diabetes Nephropathy Collaborative Research Initiative (DNCRI), we adopted three approaches to improve our ability to find new genetic risk factors for DKD: 1) assembling a large collection of T1D cohorts with harmonized DKD phenotypes, 2) creating a comprehensive set of detailed DKD definitions, and 3) augmenting genotype data with low frequency and exome array variants. METHODS Cohorts and Phenotype Definitions. The GWAS meta-analysis included up to 19,406 patients with T1D of European origin from 17 cohorts (for study list and details see Table S1). All participants gave informed consent and all studies were approved by ethics committees from participating institutions. We defined a total of 10 different case-control outcomes to cover the different aspects of renal complications, using both albuminuria and eGFR (Figure 1). Five comparisons (“All vs. ctrl”, “Micro”, “DN”, “Macro”, and “ESRD vs. macro”) were based on albuminuria, measured by albumin excretion rate (AER) from overnight or 24-h urine collection, or by albumin creatinine ratio (ACR). Two out of three consecutive collections were required (when available) to classify the renal status of subjects as either normoalbuminuria, microalbuminuria, macroalbuminuria, or ESRD; for detailed thresholds, see Table S9. Controls with normal AER were required to have a minimum diabetes duration of 15 years; subjects with microalbuminuria/macroalbuminuria/ESRD were required to have minimum diabetes duration of 5/10/10 years, respectively, to exclude renal complications of non-diabetic origins. Two comparisons (“ESRD vs. ctrl” and “ESRD vs. non-ESRD”) were based on presence of ESRD as defined by eGFR<15 mL/min or dialysis or renal transplant. Two phenotypes (“CKD” and “CKD extreme”) were defined based on eGFR estimated by the CKD-EPI formula: controls had eGFR ≥60ml/min/1.73m2 for both phenotypes, and ≥15 years of diabetes duration; cases had eGFR <60ml/min/1.73m2 for the “CKD” phenotype, and eGFR <15 ml/min/1.73m2 or dialysis or renal transplant for the “CKD extreme” phenotype, and ≥10 years of diabetes duration. For the “CKD-DN” phenotype that combined both albuminuria and eGFR data, controls were required to have both eGFR ≥60ml/min/1.73m2 and normoalbuminuria; cases had both eGFR <45ml/min/1.73m2 and micro- or macroalbuminuria, or ESRD. GWAS Genotyping, Quality Control and Imputation. All study samples underwent genotyping, quality control (QC) and imputation centrally at the University of Virginia. In brief, samples were genotyped on the HumanCore BeadChip array (Illumina, San Diego, CA, USA), which contains ~250,000 genome-wide tag SNPs and over 200,000 exome-focused variants. All samples were passed through a stringent QC protocol. Following initial genotype calling with Illumina software, all samples were re-called with zCall, a calling algorithm specifically designed for rare SNPs from arrays. Variant orientation and position were aligned to hg19 (Genome Reference Consortium Human Build 37, GRCh37). Variant names were updated using 1000 Genomes as a reference. The data were then filtered for low quality variants (e.g. call rates <95% and excessive deviation from Hardy-Weinberg equilibrium) and samples (e.g. call rates <98%, gender mismatch, extreme heterozygosity). Principal Component Analysis (PCA) was performed separately for each cohort in order to empirically detect and exclude outliers with evidence of non-European ancestry (see supplement for full QC details, and Figure S1 for trait specific Manhattan and QQ plots). Genotypes were expanded to a total of approximately 49 million by imputation, using the minimac imputation toolPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ib3dpZTwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 14,15 and 1,000 Genomes Project (phase 3v5) as a reference.GWAS Analysis. A genome-wide association analysis was performed for each of the case-control definitions under an additive genetic model, adjusting for age, sex, diabetes duration, study site (where applicable) and principal components. We conducted a second set of analyses adjusting for BMI and HbA1c which we refer to as our fully adjusted covariate model. Allele dosages were used to account for imputation uncertainty. Inverse-variance fixed effects meta-analysis was performed using METAL and the following filters: INFO score >0.3, minor allele count >10 in both cases and controls, and presence of variant in at least two cohorts (Manhattan and QQ plots each trait and covariate model presented in Figure S1). The X chromosome was similarly analyzed for males and females both separately and in a combined analysis, with the exception of using hard call genotypes in place of allele dosages. ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2005</Year><RecNum>20</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>20</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542677109">20</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Ji, L.</author></authors></contributors><auth-address>The Ministry of Education (MOE) Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, People&apos;s Republic of China.</auth-address><titles><title>Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix</title><secondary-title>Heredity (Edinb)</secondary-title></titles><periodical><full-title>Heredity (Edinb)</full-title></periodical><pages>221-7</pages><volume>95</volume><number>3</number><edition>2005/08/04</edition><keywords><keyword>*Data Interpretation, Statistical</keyword><keyword>*Genetic Predisposition to Disease</keyword><keyword>Genetic Testing/*methods</keyword><keyword>Genetics, Medical/*methods</keyword><keyword>*Models, Genetic</keyword><keyword>*Research Design</keyword></keywords><dates><year>2005</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0018-067X (Print)&#xD;0018-067X (Linking)</isbn><accession-num>16077740</accession-num><urls><related-urls><url> We estimated the percentage of variance explained for all genome-wide significant SNPs across all disease definitions using the McKelvey-Zavoina ADDIN EN.CITE <EndNote><Cite><Author>McKelvey</Author><Year>1975</Year><RecNum>515</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>515</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1559260861">515</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McKelvey, Richard D.</author><author>Zavoina, William</author></authors></contributors><titles><title>A statistical model for the analysis of ordinal level dependent variables</title><secondary-title>The Journal of Mathematical Sociology</secondary-title></titles><periodical><full-title>The Journal of Mathematical Sociology</full-title></periodical><pages>103-120</pages><volume>4</volume><number>1</number><dates><year>1975</year><pub-dates><date>1975/01/01</date></pub-dates></dates><publisher>Routledge</publisher><isbn>0022-250X</isbn><urls><related-urls><url> pseudo-R2 statistic predicting continuous latent variables underlying binary outcomes. Glomerular basement membrane measurement in Renin-Angiotensin System Study (RASS). In brief, RASS was a double-blind placebo-controlled randomized trial of enalapril and losartan on renal pathology among 285 normoalbuminuric, normotensive subjects with T1D and normal or increased measured glomerular filtration rate (>90 ml/min/1.73m2).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NYXVlcjwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 18. All RASS participants contributed DNA for genotyping. In silico replication in SUMMIT consortium. The SUMMIT consortium included up to 5,193 European Ancestry subjects with type 2 diabetes (T2D), with and without kidney disease. In silico replication was performed on previously published GWAS on DKD with harmonized trait definitions for seven of our primary T1D analyses: “DN”, “Micro”, “Macro”, “ESRD”, “ESRD vs. non-ESRD”, “CKD”, and “CKD-DN” under an additive model, adjusting for age, gender and duration of diabetes.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gWnV5ZGFtPC9BdXRob3I+PFllYXI+MjAxODwvWWVh

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ADDIN EN.CITE.DATA 13RNAseq and microarray profiling of human kidney samples from the Pima cohort. Kidney biopsy samples from the Pima Indian cohort were manually micro-dissected into 119 glomerular and 100 tubule-interstitial tissues to generate gene expression profiles ADDIN EN.CITE <EndNote><Cite><Author>Cohen</Author><Year>2002</Year><RecNum>38</RecNum><DisplayText><style face="superscript">19</style></DisplayText><record><rec-number>38</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542679994">38</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cohen, C. D.</author><author>Frach, K.</author><author>Schlondorff, D.</author><author>Kretzler, M.</author></authors></contributors><auth-address>Medizinische Poliklinik, Ludwig-Maximilians-University of Munich, Munich, Germany.</auth-address><titles><title>Quantitative gene expression analysis in renal biopsies: a novel protocol for a high-throughput multicenter application</title><secondary-title>Kidney Int</secondary-title></titles><periodical><full-title>Kidney Int</full-title></periodical><pages>133-40</pages><volume>61</volume><number>1</number><edition>2002/01/12</edition><keywords><keyword>Biopsy/*methods</keyword><keyword>DNA, Complementary</keyword><keyword>Diagnostic Techniques, Urological</keyword><keyword>Dissection/methods</keyword><keyword>Gene Expression</keyword><keyword>Humans</keyword><keyword>Kidney/*pathology</keyword><keyword>Kidney Diseases/*pathology</keyword><keyword>RNA, Messenger/analysis/isolation &amp; purification</keyword><keyword>Reproducibility of Results</keyword><keyword>*Reverse Transcriptase Polymerase Chain Reaction/standards</keyword><keyword>Tissue Preservation</keyword></keywords><dates><year>2002</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0085-2538 (Print)&#xD;0085-2538 (Linking)</isbn><accession-num>11786093</accession-num><urls><related-urls><url>. Expression profiling in the Pima Indian cohort kidney biopsies was carried out using Affymetrix GeneChip Human Genome U133 Array and U133Plus2 Array, as reported previously, and Affymetrix Human Gene ST Genechip 2.1PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CZXJ0aGllcjwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+

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ADDIN EN.CITE.DATA 20,21, and on RNA-seq (Illumina). The libraries were prepared using the ClonTech SMARTSeq v4 Ultra Low Input polyA selection kit. Samples were sequenced on a HiSeq 4000, single end, 75bp. Mapping to human reference genome GRCh38.7 was performed with STAR 2.5.2b (). For annotation and quantification of mapping results we used cufflinks, cuffquant and cuffnorm in version 2.2.1 (). After mapping and quantification, PCA and Hierarchical Clustering was used to identify outliers and reiterated until no more outliers could be identified.RNA-sequencing and cis-eQTL analysis in human kidney samples from University of Pennsylvania cohort. Human kidney samples were obtained from surgical nephrectomies for a total of 455 subjects with pathological data and were manually microdissected under a microscope in RNAlater for glomerular and tubular compartments (433 tubule and 335 glomerulus samples). The local renal pathologist performed an unbiased review of the tissue section by scoring multiple parameters, and RNA were prepared using RNAeasy mini columns (Qiagen, Valencia, CA) according to manufacturer's instructions. Whole kidneyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LbzwvQXV0aG9yPjxZZWFyPjIwMTc8L1llYXI+PFJlY051

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ADDIN EN.CITE.DATA 23 eQTL analyses have been described previously. Tubular and glomerular eQTL data sets were generated by 121 samples of tubules and 119 samples of glomeruli, respectivelyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5RaXU8L0F1dGhvcj48WWVhcj4yMDE4PC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA 24. The single cell sequencing libraries were sequenced on an Illumina HiSeq with 2x150 paired-end kit. The sequencing reads were demultiplexed, aligned to the mouse genome (mm10) and processed to generate gene-cell data matrix using Cell Ranger 1.3 ()PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXJrPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 24.Genomic features of human kidney. Human kidney-specific chromatin immunoprecipitation followed by sequencing (ChIP-seq) data can be found at GEO: GSM621634, GSM670025, GSM621648, GSM772811, GSM621651, GSM1112806, GSM621638. Different histone markers were combined into chromatin states using ChromHMM ADDIN EN.CITE <EndNote><Cite><Author>Bernstein</Author><Year>2010</Year><RecNum>525</RecNum><DisplayText><style face="superscript">25</style></DisplayText><record><rec-number>525</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1560792968">525</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bernstein, B. E.</author><author>Stamatoyannopoulos, J. A.</author><author>Costello, J. F.</author><author>Ren, B.</author><author>Milosavljevic, A.</author><author>Meissner, A.</author><author>Kellis, M.</author><author>Marra, M. A.</author><author>Beaudet, A. L.</author><author>Ecker, J. R.</author><author>Farnham, P. J.</author><author>Hirst, M.</author><author>Lander, E. S.</author><author>Mikkelsen, T. S.</author><author>Thomson, J. A.</author></authors></contributors><auth-address>Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. Bernstein.Bradley@mgh.harvard.edu</auth-address><titles><title>The NIH Roadmap Epigenomics Mapping Consortium</title><secondary-title>Nat Biotechnol</secondary-title></titles><periodical><full-title>Nat Biotechnol</full-title></periodical><pages>1045-8</pages><volume>28</volume><number>10</number><edition>2010/10/15</edition><keywords><keyword>*Cooperative Behavior</keyword><keyword>*Epigenomics</keyword><keyword>Genome, Human/genetics</keyword><keyword>Humans</keyword><keyword>*National Institutes of Health (U.S.)</keyword><keyword>Reference Standards</keyword><keyword>Stem Cells/metabolism</keyword><keyword>United States</keyword></keywords><dates><year>2010</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1546-1696 (Electronic)&#xD;1087-0156 (Linking)</isbn><accession-num>20944595</accession-num><urls><related-urls><url> and gene set analysis. PASCAL and MAGMA (v1.06) gene and pathway scores were conducted on all 20 sets of GWAS summary statistics using default pathway libraries from BioCarta, REACTOME, and KEGG. MAGENTA (vs2, July 2011) pathway analysis included 4725 pathways with a minimum of five genes within the gene set for the 10 standard adjustment models. We conducted DEPICT individually on all 20 sets of GWAS summary statistics with P<10-5 and additional pooled analyses using genome-wide minimum P-values from: 1) All 20 analyses (10 phenotypes and 2 covariate models) and 2) 16 analyses of the 8 most related phenotypes which excluded ESRD vs Macro and Micro. Transcriptome-wide association study (TWAS): TWAS of kidney glomeruli and tubuli was performed using MetaXcan with default parameters,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYXJiZWlyYTwvQXV0aG9yPjxZZWFyPjIwMTg8L1llYXI+

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ADDIN EN.CITE.DATA 23RESULTSPhenotypic comparisonsWe investigated a broad spectrum of DKD definitions based on albuminuria and renal function criteria, defining a total of 10 different case-control comparisons to cover the different aspects of disease progression (Figure 1). Seven comparisons were based on albuminuria and/or ESRD (including diabetic nephropathy [DN], defined as either macroalbuminuria or ESRD); two were defined based on eGFR (used to classify severity of chronic kidney disease [CKD]); and one combined both albuminuria and eGFR data (“CKD-DN”). Each phenotypic definition was analyzed separately in GWAS; to account for the 10 definitions each analyzed under two covariate adjustment models, we estimated ADDIN EN.CITE <EndNote><Cite><Author>Li</Author><Year>2005</Year><RecNum>20</RecNum><DisplayText><style face="superscript">16</style></DisplayText><record><rec-number>20</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542677109">20</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Li, J.</author><author>Ji, L.</author></authors></contributors><auth-address>The Ministry of Education (MOE) Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, People&apos;s Republic of China.</auth-address><titles><title>Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix</title><secondary-title>Heredity (Edinb)</secondary-title></titles><periodical><full-title>Heredity (Edinb)</full-title></periodical><pages>221-7</pages><volume>95</volume><number>3</number><edition>2005/08/04</edition><keywords><keyword>*Data Interpretation, Statistical</keyword><keyword>*Genetic Predisposition to Disease</keyword><keyword>Genetic Testing/*methods</keyword><keyword>Genetics, Medical/*methods</keyword><keyword>*Models, Genetic</keyword><keyword>*Research Design</keyword></keywords><dates><year>2005</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0018-067X (Print)&#xD;0018-067X (Linking)</isbn><accession-num>16077740</accession-num><urls><related-urls><url> the total effectively independent tests as 7.4, allowing us to compute a more conservative study-wide significance threshold (P<6.76×10-9), based on genome-wide significance (P<5×10-8) and Bonferroni correction for 7.4 effective genome-wide association results highlight COL4A3GWAS meta-analysis included association results for up to 19,406 individuals with T1D of European descent from 17 cohorts for the 10 case-control definitions (Table S1). We identified 16 novel independent loci that achieved genome-wide significance (P<5×10-8) in either the minimal or fully adjusted models, in which four lead SNPs also surpassed our more conservative study-wide significance threshold (Table 1; Figure 2, Manhattan plot; Figures S2a-p, regional association and forest plots). None of the loci reaching genome-wide significance have been previously identified in GWAS or candidate gene studies for DKD or closely related traits. All SNPs with minor allele frequency greater than 1% explain 2.5% and 3.0% of the total variance (McKelvey-Zavoina ADDIN EN.CITE <EndNote><Cite><Author>McKelvey</Author><Year>1975</Year><RecNum>515</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>515</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1559260861">515</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>McKelvey, Richard D.</author><author>Zavoina, William</author></authors></contributors><titles><title>A statistical model for the analysis of ordinal level dependent variables</title><secondary-title>The Journal of Mathematical Sociology</secondary-title></titles><periodical><full-title>The Journal of Mathematical Sociology</full-title></periodical><pages>103-120</pages><volume>4</volume><number>1</number><dates><year>1975</year><pub-dates><date>1975/01/01</date></pub-dates></dates><publisher>Routledge</publisher><isbn>0022-250X</isbn><urls><related-urls><url> pseudo-R2) of DN after adjusting for covariates in the minimal and full covariate models, respectively (Table S12). The strongest signal was rs55703767 (minor allele frequency [MAF]=0.21), a common missense variant (G>T; Asp326Tyr) in exon 17 of COL4A3. This SNP was associated with protection from DN (odds ratio [OR]=0.79, P=5.34×10-12), any albuminuria (OR=0.84, P=3.88×10-10), the combined CKD-DN phenotype (OR =0.77, P=5.30×10-9), and macroalbuminuria (OR=0.79, P=9.28×10-9). Interestingly, we found that rs55703767 in COL4A3 was more strongly associated in men (OR=0.73, P=1.29×10-11) than in women (OR=0.85, P=1.39×10-3; Phet=1.58×10-2). COL4A3 encodes the alpha 3 chain of collagen type IV, a major structural component of the GBM ADDIN EN.CITE <EndNote><Cite><Author>Yagame</Author><Year>1995</Year><RecNum>17</RecNum><DisplayText><style face="superscript">27</style></DisplayText><record><rec-number>17</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542676909">17</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Yagame, M.</author><author>Kim, Y.</author><author>Zhu, D.</author><author>Suzuki, D.</author><author>Eguchi, K.</author><author>Nomoto, Y.</author><author>Sakai, H.</author><author>Groppoli, T.</author><author>Steffes, M. W.</author><author>Mauer, S. M.</author></authors></contributors><auth-address>Department of Internal Medicine, School of Medicine, Tokai University, Kanagawa, Japan.</auth-address><titles><title>Differential distribution of type IV collagen chains in patients with diabetic nephropathy in non-insulin-dependent diabetes mellitus</title><secondary-title>Nephron</secondary-title></titles><periodical><full-title>Nephron</full-title></periodical><pages>42-8</pages><volume>70</volume><number>1</number><edition>1995/01/01</edition><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Biopsy</keyword><keyword>Collagen/*metabolism</keyword><keyword>Diabetes Mellitus, Type 2/complications/*metabolism/pathology</keyword><keyword>Diabetic Nephropathies/etiology/*metabolism/pathology</keyword><keyword>Extracellular Matrix/*metabolism/ultrastructure</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Immunohistochemistry</keyword><keyword>Male</keyword><keyword>Microscopy, Electron</keyword><keyword>Microscopy, Fluorescence</keyword><keyword>Middle Aged</keyword></keywords><dates><year>1995</year></dates><isbn>1660-8151 (Print)&#xD;1660-8151 (Linking)</isbn><accession-num>7617116</accession-num><urls><related-urls><url>. COL4A3 variation and kidney phenotypesIn persons with T1D and normoalbuminuria, GBM width predicts progression to proteinuria and ESRD independently of glycated hemoglobin (HbA1c) ADDIN EN.CITE <EndNote><Cite><Author>Caramori</Author><Year>2013</Year><RecNum>78</RecNum><DisplayText><style face="superscript">28</style></DisplayText><record><rec-number>78</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542682697">78</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Caramori, M. L.</author><author>Parks, A.</author><author>Mauer, M.</author></authors></contributors><auth-address>Department of Medicine, University of Minnesota, Minneapolis, Minnesota, USA. caram001@umn.edu</auth-address><titles><title>Renal lesions predict progression of diabetic nephropathy in type 1 diabetes</title><secondary-title>J Am Soc Nephrol</secondary-title></titles><periodical><full-title>J Am Soc Nephrol</full-title><abbr-1>Journal of the American Society of Nephrology : JASN</abbr-1></periodical><pages>1175-81</pages><volume>24</volume><number>7</number><edition>2013/05/21</edition><keywords><keyword>Adult</keyword><keyword>Albuminuria/pathology</keyword><keyword>Biopsy</keyword><keyword>Diabetes Mellitus, Type 1/*pathology/physiopathology</keyword><keyword>Diabetic Nephropathies/*pathology/physiopathology</keyword><keyword>*Disease Progression</keyword><keyword>Female</keyword><keyword>Follow-Up Studies</keyword><keyword>Glomerular Basement Membrane/pathology</keyword><keyword>Humans</keyword><keyword>Kidney/*pathology</keyword><keyword>Kidney Glomerulus/pathology</keyword><keyword>Logistic Models</keyword><keyword>Male</keyword><keyword>Survival Rate</keyword></keywords><dates><year>2013</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>1533-3450 (Electronic)&#xD;1046-6673 (Linking)</isbn><accession-num>23687360</accession-num><urls><related-urls><url>. 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ADDIN EN.CITE.DATA 18 participants with T1D and normal AER, eGFR (>90 ml/min/1.73 m2) and blood pressure, who had biopsy and genetic data (Table S2). The DKD-protective minor T allele was associated with 19.7 nm lower GBM width (standard error (SE) 8.2 nm, P=0.02), with the lowest mean GBM width among TT homozygotes (Figure 3; Table S3), after adjusting for age, sex, and diabetes duration, and without detectable interactions with T1D duration or mean HbA1c. Thus, the protective T allele carriers had thinner GBM prior to any renal complications. We did not detect any eQTL association between rs55703767 and COL4A3 expression in mouse glomeruli or in human tissues, and thus assume that the variant affects the COL4A3 structure rather than expression levels. Nevertheless in a Pima Indian cohort of 97 subjects with DKD with morphometric and expression data from renal biopsies, COL4A3 expression was negatively correlated with the GBM surface density (filtration surface density) (β=-0.27, P=0.02), which is associated with eGFR in DKD in both T1D and T2DPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GdWZhYTwvQXV0aG9yPjxZZWFyPjIwMTY8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 29,30. Furthermore, in 335 micro-dissected human glomerulus samples, expression of COL4A3 was negatively correlated with glomerulosclerosis, potentially reflecting podocyte depletion in sclerotic glomeruli (corr=0.16, P=4.8×10-3; Figure S3). COL4A3 expression in glomeruli, but not in tubules, was also nominally correlated with eGFR (corr 0.108, P=0.047; Figure S3).Evidence for hyperglycemia specificityHyperglylcemia promotes the development of diabetic complications. If a genetic variant exerts a stronger effect in the setting of hyperglycemia, 1) it might not be detected in general CKD, 2) it may be detected whether hyperglycemia is conferred by T1D or T2D, 3) its effect may be stronger at higher glycemic strata, and 4) interventions that reduce glycemia may attenuate the association signal. COL4A3 rs55703767 was not associated with eGFR in a general population sample of 110,517 mainly non-diabetic participants of European ancestryPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Hb3Jza2k8L0F1dGhvcj48WWVhcj4yMDE3PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 31 (Table S4). However, in a smaller cohort of 5,190 participants with T2D and DKD phenotypes in the SUrrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools (SUMMIT) consortium, we detected a directionally consistent suggestive association of COL4A3 rs55703767 with DN (2-tailed P=0.08; Table S5). We further stratified the association analyses by HbA1c in the Finnish Diabetic Nephropathy (FinnDiane) Study, a T1D cohort study with extensive longitudinal phenotypic data ADDIN EN.CITE <EndNote><Cite><Author>Thorn</Author><Year>2005</Year><RecNum>46</RecNum><DisplayText><style face="superscript">32</style></DisplayText><record><rec-number>46</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1336701055">46</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Thorn, Lena M.</author><author>Forsblom, Carol</author><author>Fagerudd, Johan</author><author>Thomas, Merlin C.</author><author>Pettersson-Fernholm, Kim</author><author>Saraheimo, Markku</author><author>Waden, Johan</author><author>Ronnback, Mats</author><author>Rosengard-Barlund, Milla</author><author>Bjorkesten, Clas-Goran af</author><author>Taskinen, Marja-Riitta</author><author>Groop, Per-Henrik</author><author>on behalf of the FinnDiane Study Group,</author></authors></contributors><titles><title>Metabolic syndrome in type 1 diabetes: Association with diabetic nephropathy and glycemic control (the FinnDiane study)</title><secondary-title>Diabetes Care</secondary-title><alt-title>Diabetes Care</alt-title></titles><periodical><full-title>Diabetes Care</full-title><abbr-1>Diabetes Care</abbr-1></periodical><alt-periodical><full-title>Diabetes Care</full-title><abbr-1>Diabetes Care</abbr-1></alt-periodical><pages>2019-2024</pages><volume>28</volume><number>8</number><dates><year>2005</year><pub-dates><date>August 1, 2005</date></pub-dates></dates><urls><related-urls><url>. Based on the time-weighted mean of all available HbA1c measurements for each individual, 1,344 individuals had mean HbA1c <7.5% (58 mmol/mol), and 2,977 with mean HbA1c ≥7.5%. COL4A3 rs55703767 was nominally significant (P<0.05) only in individuals with HbA1c ≥7.5% (Figure 4, Table S6, Figure S4). However, the interaction between HbA1c and COL4A3 rs55703767 was not significant (P=0.83). Upon further examination, the genetic effect was diminished also in the highest HbA1c quartile (>9.3%), as the environmental effect of HbA1c seems to overwhelm any potential genetic effects at COL4A3 (test of heterogeneity non-significant). In a similar setting of individuals with T2D from the GoDARTS study (N=3226)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Nb3JyaXM8L0F1dGhvcj48WWVhcj4xOTk3PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 13,33, no difference was observed for COL4A3 rs55703767 between HbA1c strata below or above 7.5% (Figure S5). We performed a similar HbA1c stratified analysis in the Diabetes Control and Complications Trial (DCCT), whose participants, all with T1D, continue to be followed in the Epidemiology of Diabetes Interventions and Complications (EDIC)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5OYXRoYW48L0F1dGhvcj48WWVhcj4yMDE0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 34,35. In DCCT-EDIC the effect of COL4A3 rs55703767 was stronger among those recruited in the secondary cohort (mild retinopathy and longer diabetes duration at baseline) who were originally randomized to conventional treatment and therefore had higher HbA1c than the intensive treatment group (Table S7). Taken together, these independent lines of evidence strongly suggest that the COL4A3 variant effect on DKD risk is amplified by poor glycemic control.Other association signalsA comprehensive list of all loci that achieved genome-wide significance from either the minimal or the fully adjusted covariate models is reported in Table 1. The fewer covariates required for the minimal model results in improved statistical power due to fewer subjects with missing data, whereas the fully adjusted model allows the identification of associations potentially mediated by covariates. Comparison of the adjustment models revealed strong consistency between the two models (Figure S6). Table 1 is stratified into two sets of loci: common and/or known kidney biology loci (top half, n=8) and uncommon and no known kidney biology loci (bottom half, n=8). We focus on loci in the top half of the table, common and/or in/near genes with relevant kidney biology. Two other genome-wide significant signals were near genes encoding proteins related to collagen. Variant rs12615970 (MAF=0.13), located 53 kb downstream of COLEC11, was associated with CKD (OR=1.31, P=9.43×10-9), and rs116772905 (MAF=0.011) in exon 14 of DDR1 was associated with microalbuminuria (OR=3.78, P=4.40×10-8). rs116772905 is in perfect linkage disequilibrium with rs118124843, the lead association with microalbuminuria for this locus under the full adjustment model (taking into account both BMI and HbA1c), located 29 kb downstream of DDR1 (OR=3.97, P=3.37×10-8). COLEC11 encodes a collectin protein containing both a collagen-like domain and a carbohydrate recognition domain for binding sugars, and DDR1 encodes the discoidin domain-containing receptor 1, which binds collagens including type IV collagen.In addition to COL4A3 rs55703767, three other low-frequency variants achieved study-wide significance (P<6.76×10-9), each associated with microalbuminuria: rs142823282 (MAF=0.017), 22 kb upstream of TAMM41 encoding a mitochondrial translocator assembly and maintenance proteinPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CbHVuc29tPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48

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ADDIN EN.CITE.DATA 38 (OR=6.75, P=2.67×10-9), and rs145681168 (MAF=0.014), in intron 3 of two transcripts of HAND2 antisense RNA 1 (HAND2-AS1; OR=5.53, P=5.40×10-9) and 50 kb upstream of HAND2, encoding a heart and neural crest derivatives transcription factor. Two additional common variants achieved genome-wide significance: rs551191707 (MAF=0.122) in PRNCR1 associated with ESRD when compared with macroalbuminuria (OR=1.70, P=4.39×10-8) and rs61983410 (MAF=0.213) in an intergenic region on chromosome 14 associated with microalbuminuria (full model OR=0.78, P=3.06×10-8). The remaining seven variants associated with features of DKD had lower allele frequencies (four with 0.01≤MAF≤0.05 and four with MAF<0.01) and did not achieve study-wide significance. As we had done for COL4A3 rs55703767, we tested whether the associations of the 15 other variants were amplified by hyperglycemia. None of the 15 variants were significantly associated with eGFR in the general population (Table S4). In the smaller SUMMIT T2D cohortPEVuZE5vdGU+PENpdGU+PEF1dGhvcj52YW4gWnV5ZGFtPC9BdXRob3I+PFllYXI+MjAxODwvWWVh

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ADDIN EN.CITE.DATA 13 we were able to interrogate seven loci with comparable trait definitions. The odds ratios were directionally consistent in six of them (binomial sign test: P=0.0625, Table S5). In FinnDiane seven of the remaining 15 loci were observed with sufficient frequency (minor allele counts >10) to allow subgroup analysis. Two additional SNPs (rs149641852 in SNCAIP and rs12615970 near COLEC11) were nominally significant (P<0.05) only in individuals with HbA1c ≥7.5%, however the genotype × HbA1c interaction term was non-significant (Table S6, Figure S4).Variants previously associated with DKD We investigated the effect of variants previously associated at genome-wide significance with renal complications in individuals with diabetesQS4mI3hEO1ZhbmRlcmJpbHQgVW5pdmVyc2l0eSBTY2hvb2wgb2YgTWVkaWNpbmUsIDQ0OCBFc2tp

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ADDIN EN.CITE.DATA 8-13,39. Across the ten sub-phenotypes in our meta-analysis, we found evidence of association for seven of nine examined loci (P<0.05, Figure S7): We replicated two loci that were previously discovered without overlapping individuals with the current study: SCAF8/CNKSR3 rs12523822, originally associated with DKD (P=6.8×10-4 for “All vs ctrl”) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5JeWVuZ2FyPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48

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dGU+AG==

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ADDIN EN.CITE.DATA 31. Associations at the AFF3, RGMA-MCTP2, and ERBB4 loci, identified in the GEnetics of Nephropathy—an International Effort (GENIE) consortiumPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TYW5kaG9sbTwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+

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ADDIN EN.CITE.DATA 12, comprised of a subset of studies included in this current effort, remained associated with DKD, though the associations were attenuated in this larger dataset (RGMA-MCTP2 rs12437854 P=2.97×10-5; AFF3 rs7583877 P=5.97×10-4; ERBB4 rs7588550 P=3.53×10-5; Figure S8). Associations were also observed at the CDCA7/SP3 (rs4972593, P=0.020 for “CKD-DN”, originally for ESRD exclusively in women ADDIN EN.CITE <EndNote><Cite><Author>Sandholm</Author><Year>2013</Year><RecNum>410</RecNum><DisplayText><style face="superscript">11</style></DisplayText><record><rec-number>410</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1381116555">410</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sandholm, Niina</author><author>McKnight, Amy Jayne</author><author>Salem, Rany M.</author><author>Brennan, Eoin P.</author><author>Forsblom, Carol</author><author>Harjutsalo, Valma</author><author>M?kinen, Ville-Petteri</author><author>McKay, Gareth J.</author><author>Sadlier, Denise M.</author><author>Williams, Winfred W.</author><author>Martin, Finian</author><author>Panduru, Nicolae Mircea</author><author>Tarnow, Lise</author><author>Tuomilehto, Jaakko</author><author>Tryggvason, Karl</author><author>Zerbini, Gianpaolo</author><author>Comeau, Mary E.</author><author>Langefeld, Carl D.</author><author>for the FIND Consortium</author><author>Godson, Catherine</author><author>Hirschhorn, Joel N.</author><author>Maxwell, Alexander P.</author><author>Florez, Jose C.</author><author>Groop, Per-Henrik</author></authors></contributors><titles><title>Chromosome 2q31.1 Associates with ESRD in Women with Type 1 Diabetes</title><secondary-title>Journal of the American Society of Nephrology</secondary-title></titles><periodical><full-title>Journal of the American Society of Nephrology</full-title></periodical><pages>1537-1543</pages><volume>24</volume><number>10</number><dates><year>2013</year><pub-dates><date>October 1, 2013</date></pub-dates></dates><urls><related-urls><url>) and GLRA3 (rs1564939, P=0.016 for “CKD extremes”, originally for AERPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TYW5kaG9sbTwvQXV0aG9yPjxZZWFyPjIwMTQ8L1llYXI+

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ADDIN EN.CITE.DATA 10,39), but these analyses also include individuals that overlap with the original studies. Apart from the UMOD locus, none of the 63 loci associated with eGFR in the general populationPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Hb3Jza2k8L0F1dGhvcj48WWVhcj4yMDE3PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 31 were associated with DKD after correction for multiple testing (P<7.0×10-4, Figure S9).Gene and gene set analysisWe conducted gene-level analyses by employing two methods that aggregate SNP summary statistics over a gene region while accounting for linkage disequilibrium, MAGMA and PASCALPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYW1wYXJ0ZXI8L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFy

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ADDIN EN.CITE.DATA 40,41. MAGMA identified five genes at a Bonferroni-corrected threshold (P<0.05/18,222 genes tested = 2.74×10-6): the collagen gene COL20A1 associated with “CKD extreme” (full model P=5.77×10-7) and “ESRD vs. non-ESRD” (full model P=9.53×10-7), SLC46A2 associated with “All vs. ctrl” (P=7.38×10-7), SFXN4 associated with “Macro” (full model P=1.65×10-7), GLT6D1 associated with “ESRD vs. macro” (P=1.49×10-6), and SNX30 associated with “All vs ctrl” (P=2.49×10-6) (Table S8). Although PASCAL did not identify any significant gene level associations, the five MAGMA-identified genes had P<5.0×10-4 in PASCAL (Table S9). Both SFXN4 and CBX8 have been reported to be differentially methylated in patients with diabetes with and without nephropathyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CZWxsPC9BdXRob3I+PFllYXI+MjAxMDwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 42,43. Additionally, we used MAGMA, PASCAL, DEPICT, and MAGENTA to conduct gene-set analysis in our GWAS dataset. The four methods identified 12 significantly enriched gene sets (Table S10). One gene set, “negative regulators of RIG-I MDA5 signaling” was identified in two different pathway analyses (MAGMA and PASCAL) of our fully adjusted GWAS of ESRD vs. Macro. Several additional related and overlapping gene sets were identified, including “RIGI MDA5 mediated induction of IFN alpha beta pathways”, “TRAF3 dependent IRF activation pathway”, and “TRAF6 mediated IRF activation” (PASCAL) and “activated TLR4 signaling” (MAGENTA). RIG-I, MDA5 and the toll-like receptor TLR4 are members of the innate immune response system that respond to both cellular injury and infectionPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Mb288L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA 46,47. Specifically, the TLR4 receptor and several of its ligands and downstream cytokines display differential levels of expression in DKD renal tubules vs. normal kidneys and vs. non-diabetic kidney disease controlsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MaW48L0F1dGhvcj48WWVhcj4yMDEyPC9ZZWFyPjxSZWNO

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ADDIN EN.CITE.DATA 48, and TLR4 knockout mice are protected from DKD and display marked reductions in interstitial collagen deposition in the kidneyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NYTwvQXV0aG9yPjxZZWFyPjIwMTQ8L1llYXI+PFJlY051

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ADDIN EN.CITE.DATA 49. Other pathways of interest include “other lipid, fatty acid and steroid metabolism”, “nitric oxide signaling in the cardiovascular system”, and “Tumor necrosis factor (TNF) family member”, with both nitric oxide and TNF-? implicated in DKD ADDIN EN.CITE <EndNote><Cite><Author>Colhoun</Author><Year>2018</Year><RecNum>79</RecNum><DisplayText><style face="superscript">50</style></DisplayText><record><rec-number>79</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542682808">79</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Colhoun, H. M.</author><author>Marcovecchio, M. L.</author></authors></contributors><auth-address>MRC Institute of Genetics &amp; Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK. Helen.colhoun@igmm.ed.ac.uk.&#xD;Department of Paediatrics, University of Cambridge, Cambridge, UK.</auth-address><titles><title>Biomarkers of diabetic kidney disease</title><secondary-title>Diabetologia</secondary-title></titles><periodical><full-title>Diabetologia</full-title></periodical><pages>996-1011</pages><volume>61</volume><number>5</number><edition>2018/03/10</edition><keywords><keyword>*Biomarker</keyword><keyword>*Diabetic kidney disease</keyword><keyword>*Epidemiology</keyword><keyword>*Nephropathy</keyword><keyword>*Review</keyword></keywords><dates><year>2018</year><pub-dates><date>May</date></pub-dates></dates><isbn>1432-0428 (Electronic)&#xD;0012-186X (Linking)</isbn><accession-num>29520581</accession-num><urls><related-urls><url>

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ADDIN EN.CITE.DATA 23 from subjects of European descent without any evidence of renal disease (Figure S10). No findings in this data set achieved significance after correction for multiple testing. In the GTEx and eQTLgen datasets, COL4A3 rs55703767 had a significant eQTL (P=5.63×10-38) with the MFF gene in blood, but is most likely due to modest LD with other nearby strong eQTLs in the region. rs118124843 near DDR1 and VARS2 had multiple significant eQTLs in blood besides VARS2 (P=1.71×10-5; Table S11). Interestingly, rs142823282 near TAMM41 was a cis-eQTL for PPARG (P=4.60×10-7), a transcription factor regulating adipocyte development, glucose and lipid metabolism; PPARγ agonists have been suggested to prevent DKDPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGFzbWFuPC9BdXRob3I+PFllYXI+MjAxMjwvWWVhcj48

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ADDIN EN.CITE.DATA 53. To ascertain the potential functional role of our top non-coding signals, we mined ChIP-seq data derived from healthy adult human kidney samples ADDIN EN.CITE <EndNote><Cite><Author>Bernstein</Author><Year>2010</Year><RecNum>525</RecNum><DisplayText><style face="superscript">25</style></DisplayText><record><rec-number>525</rec-number><foreign-keys><key app="EN" db-id="dzr5svr2kwt2d5ez9epx9px6xs5059v9zxav" timestamp="1560792968">525</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Bernstein, B. E.</author><author>Stamatoyannopoulos, J. A.</author><author>Costello, J. F.</author><author>Ren, B.</author><author>Milosavljevic, A.</author><author>Meissner, A.</author><author>Kellis, M.</author><author>Marra, M. A.</author><author>Beaudet, A. L.</author><author>Ecker, J. R.</author><author>Farnham, P. J.</author><author>Hirst, M.</author><author>Lander, E. S.</author><author>Mikkelsen, T. S.</author><author>Thomson, J. A.</author></authors></contributors><auth-address>Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA. Bernstein.Bradley@mgh.harvard.edu</auth-address><titles><title>The NIH Roadmap Epigenomics Mapping Consortium</title><secondary-title>Nat Biotechnol</secondary-title></titles><periodical><full-title>Nat Biotechnol</full-title></periodical><pages>1045-8</pages><volume>28</volume><number>10</number><edition>2010/10/15</edition><keywords><keyword>*Cooperative Behavior</keyword><keyword>*Epigenomics</keyword><keyword>Genome, Human/genetics</keyword><keyword>Humans</keyword><keyword>*National Institutes of Health (U.S.)</keyword><keyword>Reference Standards</keyword><keyword>Stem Cells/metabolism</keyword><keyword>United States</keyword></keywords><dates><year>2010</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1546-1696 (Electronic)&#xD;1087-0156 (Linking)</isbn><accession-num>20944595</accession-num><urls><related-urls><url>. SNP rs142823282 near TAMM41 was located close to kidney histone marks H3K27ac, H3K9ac, H3K4me1, and H3K4me3, suggesting that this is an active regulator of TAMM41 or another nearby gene (Figure S11). Interestingly, in recent work we have shown that DNA methylation profiles in participants with T1D with/without kidney disease show the greatest differences in methylation sites near TAMM41 ADDIN EN.CITE <EndNote><Cite><Author>Swan</Author><Year>2015</Year><RecNum>46</RecNum><DisplayText><style face="superscript">54</style></DisplayText><record><rec-number>46</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542680748">46</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Swan, E. J.</author><author>Maxwell, A. P.</author><author>McKnight, A. J.</author></authors></contributors><auth-address>Centre for Public Health, Queen&apos;s University of Belfast.&#xD;Regional Nephrology Unit, Belfast City Hospital, Belfast, UK.</auth-address><titles><title>Distinct methylation patterns in genes that affect mitochondrial function are associated with kidney disease in blood-derived DNA from individuals with Type 1 diabetes</title><secondary-title>Diabet Med</secondary-title></titles><periodical><full-title>Diabet Med</full-title></periodical><pages>1110-5</pages><volume>32</volume><number>8</number><edition>2015/04/09</edition><keywords><keyword>Case-Control Studies</keyword><keyword>DNA Methylation</keyword><keyword>DNA, Mitochondrial/*metabolism</keyword><keyword>Diabetes Mellitus, Type 1/*complications</keyword><keyword>Diabetic Nephropathies/etiology/*genetics</keyword><keyword>Epigenesis, Genetic</keyword><keyword>Genes, Mitochondrial</keyword><keyword>Humans</keyword><keyword>Kidney Failure, Chronic/etiology/*genetics</keyword><keyword>Mitochondria/*genetics/metabolism</keyword><keyword>Renal Insufficiency, Chronic/etiology/genetics</keyword></keywords><dates><year>2015</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1464-5491 (Electronic)&#xD;0742-3071 (Linking)</isbn><accession-num>25850930</accession-num><urls><related-urls><url>. To establish whether the expression of our top genes shows enrichment in a specific kidney cell type, we queried an expression dataset of ~50,000 single cells obtained from mouse kidneysPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXJrPC9BdXRob3I+PFllYXI+MjAxODwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 23 While none of the genes survived correction for multiple testing, analysis suggested 18 genes with differential expression in cases and controls with P<1×10-4, including the NPNT, PRRC2C, and VPS33B genes (Table S12). NPNT encodes for nephronectin, an extra-cellular matrix protein on GBM. Knocking out NPNT or decreasing NPNT expression levels have been shown to induce podocyte injury related to GBM.PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5NdWxsZXItRGVpbGU8L0F1dGhvcj48WWVhcj4yMDE3PC9Z

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Tm90ZT5=

ADDIN EN.CITE.DATA 55 On the contrary, TWAS predicted higher NPNT expression within DN cases vs. normal AER. In line with our TWAS finding, NPNT is significantly upregulated in glomeruli of diabetic nephropathy mouse model vs. non-diabetic mouse (p=6.4x10-4, fold change 1.3, in top 2%, accessed through ).PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ib2RnaW48L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 58DISCUSSIONOur genome-wide analysis of 19,406 participants with T1D identified 16 genome-wide significant loci associated with DKD, four of which remained significant after a conservative correction for multiple testing. Four of the 16 genome-wide significant signals are in or near genes with known or suggestive biology related to renal function/collagen (COL4A3, BMP7, COLEC11, and DDR1), but this is the first time that naturally occurring variation (MAF > 1%) in these loci has been associated with DKD. Our most significant signal was a protective missense variant in COL4A3, rs55703767, reaching both genome-wide and study-wide significance with multiple definitions of DKD. Moreover, this variant demonstrated a significant association with GBM width such that protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. COL4A3, with COL4A4 and COL4A5, make up the so-called “novel chains” of type IV collagen ADDIN EN.CITE <EndNote><Cite><Author>Kleppel</Author><Year>1992</Year><RecNum>48</RecNum><DisplayText><style face="superscript">59</style></DisplayText><record><rec-number>48</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542680859">48</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kleppel, M. M.</author><author>Fan, W.</author><author>Cheong, H. I.</author><author>Michael, A. F.</author></authors></contributors><auth-address>Department of Pediatrics, University of Minnesota Medical School, Minneapolis 55455.</auth-address><titles><title>Evidence for separate networks of classical and novel basement membrane collagen. Characterization of alpha 3(IV)-alport antigen heterodimer</title><secondary-title>J Biol Chem</secondary-title></titles><periodical><full-title>J Biol Chem</full-title></periodical><pages>4137-42</pages><volume>267</volume><number>6</number><edition>1992/02/25</edition><keywords><keyword>Animals</keyword><keyword>Basement Membrane/*chemistry</keyword><keyword>Blotting, Western</keyword><keyword>Cattle</keyword><keyword>Collagen/*chemistry</keyword><keyword>Descemet Membrane/chemistry</keyword><keyword>Electrophoresis, Gel, Two-Dimensional</keyword><keyword>Humans</keyword><keyword>Kidney Glomerulus/chemistry</keyword><keyword>Lens, Crystalline/chemistry</keyword><keyword>Nephritis, Hereditary/*immunology</keyword><keyword>Precipitin Tests</keyword></keywords><dates><year>1992</year><pub-dates><date>Feb 25</date></pub-dates></dates><isbn>0021-9258 (Print)&#xD;0021-9258 (Linking)</isbn><accession-num>1740456</accession-num><urls><related-urls><url>, which together play both structural and signaling roles in the GBM. Specifically, COL4A3 is known to bind a number of molecules including integrins, heparin and heparin sulfate proteoglycans, and other components of the GBM such as laminin and nidogen. These interactions mediate the contact between cells and the underlying collagen IV basement membrane, and regulate various processes essential to embryonic development and normal physiology including cell adhesion, proliferation, survival and differentiation. Dysregulation of these interactions has been implicated in several pathological conditions including CKD ADDIN EN.CITE <EndNote><Cite><Author>Khoshnoodi</Author><Year>2008</Year><RecNum>49</RecNum><DisplayText><style face="superscript">60</style></DisplayText><record><rec-number>49</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542680931">49</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Khoshnoodi, J.</author><author>Pedchenko, V.</author><author>Hudson, B. G.</author></authors></contributors><auth-address>Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, USA. jamshid.khoshnoodi@vanderbilt.edu</auth-address><titles><title>Mammalian collagen IV</title><secondary-title>Microsc Res Tech</secondary-title></titles><periodical><full-title>Microsc Res Tech</full-title></periodical><pages>357-70</pages><volume>71</volume><number>5</number><edition>2008/01/26</edition><keywords><keyword>Animals</keyword><keyword>Anti-Glomerular Basement Membrane Disease/genetics/metabolism</keyword><keyword>Chromosomes, Human</keyword><keyword>Collagen Type IV/*chemistry/genetics/*metabolism</keyword><keyword>Gene Expression Regulation, Developmental</keyword><keyword>Genetic Diseases, Inborn/genetics/metabolism</keyword><keyword>Humans</keyword><keyword>Integrins/metabolism</keyword><keyword>Mammals/embryology/genetics/*metabolism</keyword><keyword>Multigene Family</keyword><keyword>Nephritis, Hereditary/genetics/metabolism</keyword><keyword>Protein Conformation</keyword><keyword>Protein Processing, Post-Translational</keyword></keywords><dates><year>2008</year><pub-dates><date>May</date></pub-dates></dates><isbn>1059-910X (Print)&#xD;1059-910X (Linking)</isbn><accession-num>18219669</accession-num><urls><related-urls><url>. 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ADDIN EN.CITE.DATA 62 Of note, the common rs55703767 (COL4A3 Asp326Tyr) variant, protecting from DKD, was also associated with thinner GBM in individuals with diabetes but without renal complications, a feature that seems to be beneficial in the context of diabetes. The rs55703767 SNP is predicted to alter the third amino acid of the canonical triple-helical domain sequence of Glycine (G)-X-Y (where X and Y are often proline (P) and hydroxyproline (Y), respectively) from G-E-D (D=Aspartic) to G-E-YPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5QYXJraW48L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 63, potentially impacting the structure of the collagen complex. In addition, a recent studyPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HdWFuPC9BdXRob3I+PFllYXI+MjAxNjwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 64 of candidate genes involved in renal structure reported rs34505188 in COL4A3 (not in linkage disequilibrium with rs55703767, r2=0.0006) to be associated with ESRD in African Americans with T2D (MAF=2%, OR=1.55, P=5×10-4). Together with the trend towards association we have seen in SUMMIT and the glycemic interaction we have reported here, these findings suggest variation in COL4A3 may be associated with DKD in T2D as well.Given its association as a protective SNP, we can speculate that the rs55703767 variant may confer tensile strength or flexibility to the GBM, which may be of particular relevance in the glomerular hypertension associated with DKD. Alternatively, COL4A3 may regulate the rates of production and/or turnover of other GBM components, affecting GBM width changes in diabetes. How these effects might confer protection in a manner dependent on ambient glucose concentrations is unknown. Future mechanistic studies will be required to determine the precise role of this variant in DKD; elucidation of its interaction with glycemia in providing protection might be relevant to other molecules implicated in diabetic complications.In keeping with the collagen pathway, the synonymous exonic variant rs118124843, which reached genome-wide significance for the “Micro” phenotype, is located near DDR1, the gene encoding the discoidin domain-containing receptor 1. Based on chromatin conformation interaction data from Capture HiC Plotter (CHiCP), ADDIN EN.CITE <EndNote><Cite><Author>Schofield</Author><Year>2016</Year><RecNum>93</RecNum><DisplayText><style face="superscript">65</style></DisplayText><record><rec-number>93</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1544468653">93</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Schofield, E. C.</author><author>Carver, T.</author><author>Achuthan, P.</author><author>Freire-Pritchett, P.</author><author>Spivakov, M.</author><author>Todd, J. A.</author><author>Burren, O. S.</author></authors></contributors><auth-address>JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK.&#xD;Nuclear Dynamics Programme, The Babraham Institute, Cambridge CB22 3AT, UK.</auth-address><titles><title>CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets</title><secondary-title>Bioinformatics</secondary-title></titles><periodical><full-title>Bioinformatics</full-title></periodical><pages>2511-3</pages><volume>32</volume><number>16</number><edition>2016/05/07</edition><keywords><keyword>Chromosomes</keyword><keyword>Computer Graphics</keyword><keyword>Genome</keyword><keyword>Genomics</keyword><keyword>*Internet</keyword><keyword>*Promoter Regions, Genetic</keyword><keyword>*Regulatory Elements, Transcriptional</keyword><keyword>*Software</keyword></keywords><dates><year>2016</year><pub-dates><date>Aug 15</date></pub-dates></dates><isbn>1367-4811 (Electronic)&#xD;1367-4803 (Linking)</isbn><accession-num>27153610</accession-num><urls><related-urls><url> the rs118124843 containing fragment interacts with six gene promoter regions, including DDR1, suggesting that the variant regulates DDR1 expression across multiple tissues (Table S11). 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ADDIN EN.CITE.DATA 69. Thus, through its role in collagen binding DDR1 has been suggested as a possible therapeutic target for kidney diseasePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Hcm9zczwvQXV0aG9yPjxZZWFyPjIwMTA8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 69. The association of rs12615970, an intronic variant on chromosome 2 near the COLEC11 gene, met genome-wide significance for the CKD phenotype, as well as nominal significance for multiple albuminuria-based traits. The rs12615970 containing fragment was found to interact with COLEC11, ALLC, and ADI1 transcription start sites in chromatin conformation data on GM12878 cell line (Table S11)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TY2hvZmllbGQ8L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFy

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ADDIN EN.CITE.DATA 72,73. Mice with kidney-specific deficiency of COLEC11 are protected against ischemia-induced tubule injury due to reduced complement deposition ADDIN EN.CITE <EndNote><Cite><Author>Farrar</Author><Year>2016</Year><RecNum>64</RecNum><DisplayText><style face="superscript">74</style></DisplayText><record><rec-number>64</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542681853">64</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Farrar, C. A.</author><author>Tran, D.</author><author>Li, K.</author><author>Wu, W.</author><author>Peng, Q.</author><author>Schwaeble, W.</author><author>Zhou, W.</author><author>Sacks, S. H.</author></authors></contributors><titles><title>Collectin-11 detects stress-induced L-fucose pattern to trigger renal epithelial injury</title><secondary-title>J Clin Invest</secondary-title></titles><periodical><full-title>J Clin Invest</full-title></periodical><pages>1911-25</pages><volume>126</volume><number>5</number><edition>2016/04/19</edition><keywords><keyword>Acute Kidney Injury/chemically induced/genetics/*metabolism/pathology</keyword><keyword>Animals</keyword><keyword>Collectins/genetics/*metabolism</keyword><keyword>Complement Pathway, Mannose-Binding Lectin/*drug effects/genetics</keyword><keyword>Complement System Proteins/genetics/*metabolism</keyword><keyword>Fucose/*toxicity</keyword><keyword>Kidney Tubules, Proximal/*injuries/*metabolism/pathology</keyword><keyword>Mannose-Binding Protein-Associated Serine Proteases/genetics/metabolism</keyword><keyword>Mice</keyword><keyword>Mice, Knockout</keyword></keywords><dates><year>2016</year><pub-dates><date>May 2</date></pub-dates></dates><isbn>1558-8238 (Electronic)&#xD;0021-9738 (Linking)</isbn><accession-num>27088797</accession-num><urls><related-urls><url>, and mutations in COLEC11 have been identified in families with 3MC syndrome, a series of rare autosomal recessive disorders resulting in birth defects and abnormal development, including kidney abnormalitiesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Sb29yeWNrPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48

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ADDIN EN.CITE.DATA 75.The intronic variant rs144434404, associated at study-wide significance with the microalbuminuria phenotype, resides within the bone morphogenetic protein 7 (BMP7) gene. BMP7 encodes a secreted ligand of the transforming growth factor-beta superfamily of proteins. Developmental processes are regulated by the BMP family of glycosylated extracellular matrix molecules, via serine/threonine kinase receptors and canonical Smad pathway signaling. Coordinated regulation of both BMP and BMP-antagonist expression is essential for developing tissues, and changes in the levels of either BMP or BMP-antagonists can contribute to disease progression such as fibrosis and cancer ADDIN EN.CITE <EndNote><Cite><Author>Walsh</Author><Year>2010</Year><RecNum>53</RecNum><DisplayText><style face="superscript">76</style></DisplayText><record><rec-number>53</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542681216">53</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Walsh, D. W.</author><author>Godson, C.</author><author>Brazil, D. P.</author><author>Martin, F.</author></authors></contributors><auth-address>School of Medicine and Medical Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland. david.walsh@ucd.ie</auth-address><titles><title>Extracellular BMP-antagonist regulation in development and disease: tied up in knots</title><secondary-title>Trends Cell Biol</secondary-title></titles><periodical><full-title>Trends Cell Biol</full-title></periodical><pages>244-56</pages><volume>20</volume><number>5</number><edition>2010/03/02</edition><keywords><keyword>Animals</keyword><keyword>Bone Development</keyword><keyword>Bone Morphogenetic Proteins/*antagonists &amp; inhibitors/chemistry/*metabolism</keyword><keyword>Carrier Proteins/chemistry/*metabolism</keyword><keyword>Humans</keyword><keyword>Models, Molecular</keyword><keyword>Neoplasms/metabolism/pathology</keyword><keyword>Protein Binding</keyword><keyword>Protein Structure, Tertiary</keyword><keyword>*Signal Transduction</keyword></keywords><dates><year>2010</year><pub-dates><date>May</date></pub-dates></dates><isbn>1879-3088 (Electronic)&#xD;0962-8924 (Linking)</isbn><accession-num>20188563</accession-num><urls><related-urls><url>. BMP7 is required for renal morphogenesis, and Bmp7 knockout mice die soon after birth due to reduced ureteric bud branchingPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5aZWlzYmVyZzwvQXV0aG9yPjxZZWFyPjIwMDU8L1llYXI+

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ADDIN EN.CITE.DATA 38. More recently, we have identified a mechanism through which BMP7 orchestrates renal protection through Akt inhibition and highlights Akt inhibitors as potential anti-fibrotic therapeutics ADDIN EN.CITE <EndNote><Cite><Author>Higgins</Author><Year>2017</Year><RecNum>57</RecNum><DisplayText><style face="superscript">80</style></DisplayText><record><rec-number>57</rec-number><foreign-keys><key app="EN" db-id="wapv0a9tqarpwze5z0t59f5hz0trw25zfp2a" timestamp="1542681456">57</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Higgins, D. F.</author><author>Ewart, L. M.</author><author>Masterson, E.</author><author>Tennant, S.</author><author>Grebnev, G.</author><author>Prunotto, M.</author><author>Pomposiello, S.</author><author>Conde-Knape, K.</author><author>Martin, F. M.</author><author>Godson, C.</author></authors></contributors><auth-address>UCD Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland. Electronic address: debra.higgins@ucd.ie.&#xD;UCD Diabetes Complications Research Centre, School of Medicine, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland.&#xD;F. Hoffmann-La Roche, Dept of Metabolic and Vascular Disease, CH4070 Basel, Switzerland.</auth-address><titles><title>BMP7-induced-Pten inhibits Akt and prevents renal fibrosis</title><secondary-title>Biochim Biophys Acta Mol Basis Dis</secondary-title></titles><periodical><full-title>Biochim Biophys Acta Mol Basis Dis</full-title></periodical><pages>3095-3104</pages><volume>1863</volume><number>12</number><edition>2017/09/20</edition><keywords><keyword>*Akt</keyword><keyword>*Anti-fibrotic</keyword><keyword>*Fibrosis</keyword><keyword>*Hypoxia</keyword><keyword>*pten</keyword><keyword>*TGFbeta(1)</keyword></keywords><dates><year>2017</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>0925-4439 (Print)&#xD;0925-4439 (Linking)</isbn><accession-num>28923783</accession-num><urls><related-urls><url>. It is also noteworthy that the BMP7 antagonist grem-1 is implicated in DKDPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Sb3hidXJnaDwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+

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ADDIN EN.CITE.DATA 81-83 and gremlin has been implicated as a biomarker of kidney disease ADDIN EN.CITE <EndNote><Cite><Author>Afkarian</Author><Year>2014</Year><RecNum>5723</RecNum><DisplayText><style face="superscript">84</style></DisplayText><record><rec-number>5723</rec-number><foreign-keys><key app="EN" db-id="9axfrvwd4rteemetv9jvewpbdfd00w5pve00" timestamp="1543070669">5723</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Afkarian, M.</author><author>Hirsch, I. B.</author><author>Tuttle, K. R.</author><author>Greenbaum, C.</author><author>Himmelfarb, J.</author><author>de Boer, I. H.</author></authors></contributors><auth-address>Kidney Research Institute and Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington.</auth-address><titles><title>Urinary excretion of RAS, BMP, and WNT pathway components in diabetic kidney disease</title><secondary-title>Physiol Rep</secondary-title></titles><periodical><full-title>Physiol Rep</full-title></periodical><pages>e12010</pages><volume>2</volume><number>5</number><edition>2014/05/06</edition><keywords><keyword>BMP pathway</keyword><keyword>WNT pathway</keyword><keyword>diabetic kidney disease</keyword><keyword>pathophysiology</keyword><keyword>renin-angiotensin system</keyword></keywords><dates><year>2014</year></dates><isbn>2051-817X (Print)&#xD;2051-817X (Linking)</isbn><accession-num>24793984</accession-num><urls><related-urls><url>. Strengths of this analysis include the large sample size, triple that of the previous largest GWAS; the uniform genotyping and quality control procedures; standardized imputation for all studies (1,000 Genomes reference panel); the inclusion of exome array content; the exploration of multiple standardized phenotype definitions of DKD; and supportive data from various sources of human kidney samples. Several of the loci identified have known correlations with kidney biology, suggesting that these are likely true associations with DKD. However, we acknowledge a number of limitations. First, nine variants have low MAF and were driven by only two cohorts, indicating that further validation will be required to increase confidence in these associations. Second, seven variants were significantly associated with microalbuminuria only, a trait shown to be less heritable in previous studies. We included these loci to maximize comprehensiveness in reporting novel DKD associations. Replication in independent samples and functional confirmation is required to validate all of these loci. Even though the gene-level, gene set and pathway analyses had limited power, these analyses identified several additional potential DKD loci and pathways, some with relevance to kidney biology, that require further follow-up. Finally, while we included only controls with a minimum diabetes duration of 15 years, we cannot fully rule out that some of the controls would progress to DKD in the future, as the improvements in diabetes treatment in the last decades have postponed the onset of complications. We also excluded cases with short diabetes duration to avoid renal complications that might be due to other causes. These phenotypic definitions were meant to overcome the limitation that in clinical practice kidney biopsies are rarely taken from individuals with diabetes to verify the diagnosis. As for any late onset disease, these challenges in phenotypic definition may have reduced our power to detect additional associations. We note, however, that this relatively small degree of contamination would lead to loss of power and increased type II error rather than false positive findings; therefore, it does not undermine the robustness of the associations reported here.Diabetic complications are unquestionably driven by hyperglycemia and partially prevented by improved glycemic control in both T1D and T2D, but there has been doubt over what contribution, if any, inherited factors contribute to disease risk. In line with previous genetic studies, this study with a markedly expanded sample size identified several loci strongly associated with DKD risk. These findings suggest that larger studies, aided by novel analyses and including T2D, will continue to enhance our understanding of the complex pathogenesis of DKD, paving the way for molecularly targeted preventive or therapeutic interventions. AUTHOR CONTRIBUTIONSConceptualization, C.G., A.P.M., P-H.G., J.N.H, and J.C.F.; Formal Analysis, R.M.S., J.B.C., N.S., E.V., C-D.L., J-J.C., M.G.P., A.M.S., C.Q., V.N., J.P., A.L., R.M., H.K., R.K., B.E.K., A.J.C., A.D.P., W-M.C., N.R.v-Z., and S.O-G.; Investigation M.G.P., A.M.S., J.S. C-X.Q., V.N., J.K.H, A.J.M, J.K.S-B., D.M.M., A.P.M., A.D.P., C.F., E.A., T.A., M.L., C.M.B-K., H.M.K., S.M., D.T., B.G., S.B.B., C.N.A.P., L.S., B.P., R.W., V.R., V.P., E.P., L.R., R.V., N.M.P., L.C.G., M.I.M., H.F.G., A.M., H.F., K.B., P.R., T.C., G.Z., M.M., S.H., C.F., W-M.C., and S.O-G; Resources A.M.S., V.H., A.J.M, R.G.N., M.L.C., M.M, X.G., J.K.S-B., D.M.M., J.G., R.G.M., A.P.M., A.D.P., W-M.C., and S.O-G; Data Curation L.T.H., C-D. L., J-J.C., A.M.S., V.H., J.S., A.J.M., R.K., B.E.K., K.E.L., X.G., M.M., M.L.C., J.K.S-B., D.M.M., J.G., R.G.M., A.P.M., W-M.C., and S.O-G; Writing – Original Draft R.M.S., J.N.T., N.S., J.B.C., E.P.B., D.A., R.D., M.F.H., L.T.H., A.D.P., and C.G.; Writing – Review & Editing R.M.S., J.N.T., N.S., J.B.C., E.P.B., D.A., R.D., M.F.H., L.T.H., A.D.P., C.G., C.Q., J.P., J.S., M.M., M-L.C., I.H.dB., F.M., A.J.M., G.M., A.P.M., K.S., M.I.M., L.G., S.S.R., C.F., J.N.H., and J.C.F.; Visualization R.M.S, J.N.T., N.S., J.B.C., M.P.G., C.Q., C-D.L., J-J.C., J.P., J.S., R.K., B.E.K., K.E.L., A.J.M., G.M., A.P.M., and A.D.P.; Project Administration J.N.T.; Supervision A.D.P., A.K., P-H.G., C.G., A.P.M., H.M.C., S.S.R., M.K., K.S., J.N.H, and J.C.F.; Funding Acquisition A.D.P., A.K., J.C.F., and J.N.H. All authors approved the final version of the manuscript.ACKNOWLEDGEMENTSThis study was supported by a grant from the JDRF (17-2013-7) and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01 DK081923 and R01 DK105154. R.M.S was supported in part by JDRF # 3-APF-2014-111-A-N, and National Heart, Lung and Blood Institute (NHLBI) R00 HL122515. J.N.T. was supported by NIDDK K12-DK094721. N.S. received funds from the European Foundation for the Study of Diabetes (EFSD) Young Investigator Research Award funds and Academy of Finland (299200). C.G., D.A., E.B., E.M., M.H. and R.D. are supported by SFI-HRB US Ireland Research Partnership SFI15/US/B3130. R.G.N. was supported in part by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. The FinnDiane study was funded by JDRF (17-2013-7), Folkh?lsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Liv och H?lsa Foundation, Helsinki University Central Hospital Research Funds (EVO), the Novo Nordisk Foundation, and Academy of Finland (275614). The EDC study was supported by NIDDK grant DK34818 and by the Rossi Memorial Fund. The WESDR study was funded by National Eye Institute EY016379. The Sweden study was supported by Family Erling-Persson (K. B.) and Stig and Gunborg Westman foundations (H.F.G.). AusDiane acknowledges Dr. Bernhard Baumgartner from Department of Medicine, Diakonissen-Wehrle Hospital, Salzburg, Austria. LatDiane acknowledges the following doctors and researchers: A.Bogdanova, D. Grikmane, I.Care, A.Fjodorova, R.Graudi?a, A.Valtere, D.Teterovska, I.Dzīvīte-Kri?āne, I.Kirilova, U.Lauga-Tūri?a, K.Geldnere, D.Seisuma, N.Fokina, S.?teina, L.Jaunozola, I.Balcere, U.Gaili?a, E.Menise,?S.Broka,?L.Akmene, N. Kap?a, J. Nagaiceva, A. Pētersons, A. Lejnieks, I. Konrāde, I. Salna, A. Dekante, A. Grāmatniece, A. Sali?a, A. Silda, V. Me?e?ko, V. Mihejeva, K. Kudrjavceva, I. Marksa, M. Cirse, D. Zeme, S. Kalva-Vaivode, J. Klovi??, L. Nikitina-Za?e, S. Skrebinska, Z. Dzērve, R. Mallons. LitDiane acknowledges Dr. Jurate Lasiene, Prof. Dzilda Velickiene, Dr. Vladimiras Petrenko and Nurses from the Department of Endocrinology, Hospital of Lithuanian University of Health Sciences. RomDiane acknowledges the following doctors and researchers: M. Anghel, DM Cheta, BA Cimpoca, D. Cimponeriu, CN Cozma, N. Dandu, D. Dobrin, I Duta, AM Frentescu, C. Ionescu-Tirgoviste, R. Ionica, R. Lichiardopol, AL Oprea, NM Panduru , A. Pop, G. Pop, R. Radescu, S. Radu, M. Robu, C. Serafinceanu, M. Stavarachi, O Tudose from the ”N.C Paulescu” National Institute for Diabetes Nutrition and Metabolic Diseases from Bucharest, and M. Bacu, D. Clenciu, C. Graunteanu, M. Ioana, E. Mota and M. Mota from Craiova Emergency County Hospital.SUMMIT Consortium acknowledgements: This work was supported by the following funders: IMI (SUMMIT 115006); Wellcome Trust 098381, 090532, 106310; NIH R01-MH101814; and JDRF 2-SRA-2014-276-Q-R. SDR: Thanks to Maria Sterner and Malin Neptin for GWAS genotyping in the Scannia Diabetes Registry. Grants: Swedish Research Council, ERC-Adv res grant 269045-GENE TARGET T2D, Academy of Finland grantas 263401 and 267882, Sigrid JUselius Foundation. FinnDiane: We thank M. Parkkonen, A. Sandelin, A-R. Salonen, T. Soppela and J. Tuomikangas for skillful laboratory assistance in the FinnDiane study. We also thank all the subjects who participated in the FinnDiane study and gratefully acknowledge all the physicians and nurses at each centre involved in the recruitment of participants (Table S12). The FinnDiane Study was supported by grants from the Folkh?lsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Liv och H?lsa Foundation, Helsinki University Central Hospital Research Funds (EVO), the Novo Nordisk Foundation, European Foundation for the Study of Diabetes (EFSD) Young Investigator Research Award funds, and the Academy of Finland (275614 and 299200). GoDARTS: We are grateful to all the participants in this study, the general practitioners, the Scottish School of Primary Care for their help in recruiting the participants, and to the whole team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. The study complies with the Declaration of Helsinki. We acknowledge the support of the Health Informatics Centre, University of Dundee for managing and supplying the anonymised data and NHS Tayside, the original data owner. The Wellcome Trust United Kingdom Type 2 Diabetes Case Control Collection (GoDARTS) was funded by The Wellcome Trust (072960/Z/03/Z, 084726/Z/08/Z, 084727/Z/08/Z, 085475/Z/08/Z, 085475/B/08/Z) and as part of the EU IMI-SUMMIT program.We acknowledge the following conflicts of interest: P-H.G. has received investigator-initiated research grants from Eli Lilly and Roche, is an advisory board member for AbbVie, AstraZeneca, Boehringer Ingelheim, Cebix, Eli Lilly, Janssen, Medscape, Merck Sharp & Dohme, Novartis, Novo Nordisk and Sanofi; and has received lecture fees from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Elo Water, Genzyme, Merck Sharp & Dohme, Medscape, Novo Nordisk and Sanofi. M.I.M. is a Wellcome Investigator and an NIHR Senior Investigator. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. He serves on advisory panels for Pfizer, NovoNordisk, and Zoe Global; has received honoraria from Merck, Pfizer, NovoNordisk and Eli Lilly; has stock options in Zoe Global; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. P.R. has received consultancy and/or speaking fees (to his institution) from AbbVie, Astellas, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, MSD, Novo Nordisk and Sanofi Aventis and research grants from AstraZeneca and Novo Nordisk, and shares in Novo Nordisk.DATA AND SOFTWARE AVAILABILITYGWAS summary statistics for all ten DKD phenotypes and two adjustment models are available for download at the AMP-Type 2 Diabetes Knowledge Portal (), under “JDRF Diabetic Nephropathy Collaborative Research Initiative GWAS” datasets. Individual level genotype data cannot be shared for all cohorts due to restrictions set by study consents and by EU and national regulations, individual genotype data. Table of Contents (Supplemental Results)Table S1. Cohorts contributing to analyses.3Table S2. Characteristics of RASS participants. Categorical variables display counts and percentage. Continuous values are mean ± standard deviation.3Table S3. Multivariate analysis of association between rs55703767 and GBM width4Table S4: Look-up of the lead loci in GWAS on eGFR in the general population (Gorski et al., 2017).15Table S5: Look-up of the lead loci in GWAS in the SUMMIT consortium (van Zuydam et al., 2018).26Table S6: Association at lead loci stratified by HbA1c <7.5%.7Table S7. Association of rs55703767 with DN in DCCT/EDIC subgroups.8Table S8. Significant (P<0.05/18,222 genes tested = 2.74 × 10-6) gene level associations with diabetic kidney disease in MAGMA.9Table S9. Top nominally significant gene level associations (P < 1.0 x 10-5) with diabetic kidney disease in PASCAL.9Table S10: Significant gene set and pathway analysis results.10Table S11. eQTL associations and chromatin conformation interactions for the lead SNPs.11Table S12: Transcriptome-wide association analysis (TWAS) results with p<1×10-416Table S13: Pseudo-R2 of all SNPs across all GWAS as calculated by the McKelvey and Zavoina method.17Table S14(S13). Physicians and nurses at health care centers participating in the collection of FinnDiane patients.18Table S14 (S15): Members of the SUMMIT consortium.22Figure S1. Manhattan and QQ Plots for each case-control definition and covariate model (minimal and full)32Figure S2. Regional chromosomal location plots and forest plots by cohort of newly discovered DKD associations52Figure S3 (S2). Correlation of expression of COL4A3 with degree of fibrosis and eGFR in microdissected kidney samples.60Figure S4 (S3). Genotype – phenotype associations at the lead loci when stratified by mean HbA1c <7.5% in the FinnDiane study.61Figure S5 (S4): Genotype – phenotype associations at the lead rs55703767 (COL4A3) locus when stratified by mean HbA1c <7.5% in up to 3226 individuals with type 2 diabetes (T2D) from the GoDARTS.62Figure S6 (S6). Fishplots comparing significance and directionality between minimal and fully adjusted models for each of the 10 phenotype definitions.63Figure S7 (S5): Association at previously reported loci (p<5×10-8) for renal complications in individuals with diabetes.65Figure S8 (S7): Forest plots of the associations at the previously reported lead loci from the GENIE consortium with largely overlapping studies.66Figure S9 (S7): Meta-analysis results for the loci that have previously been associated with DKD, or with eGFR or AER in the general population67Figure S10 (S8). Expression of quantitative trait loci (eQTL) analysis in microdissected tubule samples.69Figure S11 (S9). 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IHG-2, a mesangial cell gene induced by high glucose, is human gremlin. Regulation by extracellular glucose concentration, cyclic mechanical strain, and transforming growth factor-beta1. J Biol Chem 275, 9901-4 (2000).84.Afkarian, M. et al. Urinary excretion of RAS, BMP, and WNT pathway components in diabetic kidney disease. Physiol Rep 2, e12010 (2014).TABLES Table 1. Loci associated with DKD at study-wide (P<6.76×10-9, bold) and genome-wide (P<5×10-8) significance. 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, or noncoding 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; OR, odds ratio; min, minimally adjusted covariate model; full, fully adjusted covariate model.SNPChr:posEffect alleleOther alleleEAFNotable gene(s)PhenotypeORminP-valueminORfullP-valuefullrs557037672:228121101TG0.206COL4A3(M, B, N)DN0.795.34×10-120.788.19×10-11??????All vs. ctrl0.833.88×10-100.849.68×10-9??????CKD+DN0.775.30×10-90.763.77×10-8??????Macro0.789.28×10-90.779.38×10-9rs126159702:3745215GA0.133COLEC11 (B)ALLC (N, G)CKD0.769.43×10-90.771.60×10-7rs1428232823:11910635GA0.011TAMM41(N, B)Micro6.738.32×10-109.181.13×10-11rs1456811684:174500806GA0.014HAND2-AS1 (N, G, B)?Micro5.532.06×10-77.475.40×10-9rs1181248436:30887465TC0.011DDR1 (B)VARS2 (G)Micro3.794.42×10-83.993.37×10-8rs772730767:99728546TC0.008MBLAC1(N, B)Micro9.161.04×10-87.10 2.28×10-7rs5511917078:128100029CAC0.122PRNCR1(N)ESRD vs. macro1.704.39×10-81.713.15×10-6rs6198341014:26004712TC0.213STXBP6(N)Micro0.799.84×10-80.783.06×10-8rs14443440420:55837263TC0.011BMP7(N, G, B)Micro6.782.67×10-96.664.65×10-9???????????rs1150611733:926345AT0.014LINC01266(N)ESRD vs. ctrl9.404.07×10-88.344.08×10-5rs1162160593:36566312AC0.016STAC(N, G)ESRD vs. non-ESRD8.731.37×10-811.781.41×10-4rs1914496394:71358776AT0.005MUC7(N)DN32.421.32×10-832.472.09×10-8rs1496418525:121774582TG0.012SNCAIP(N, G)CKD extreme9.011.37×10-8------rs18393729411:16937846GT0.007PLEKHA7(N, G)Micro17.221.65×10-823.622.10×10-6rs11355420614:73740250AG0.012PAPLN(N, G)Macro4.605.39×10-710.428.46×10-9rs18529910918:1811108TC0.007intergenicCKD20.751.28×10-844.754.99×10-7 ................
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