Genetic Analysis of Retinopathy in Type 1 Diabetes

Sayed Mohsen Hosseini

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Institute of Medical Science University of Toronto

Sayed Mohsen Hosseini

Doctor of Philosophy

Institute of Medical Science University of Toronto

2014 Abstract

Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Several lines of evidence suggest a genetic contribution to the risk of DR; however, no genetic variant has shown convincing association with DR in genome-wide association studies

(GWAS).

To identify common polymorphisms associated with DR, meta-GWAS were performed in three type 1 diabetes cohorts of White subjects: Diabetes Complications and Control

Trial (DCCT, n=1304), Wisconsin Epidemiologic Study of Diabetic Retinopathy

(WESDR, n=603) and Renin-Angiotensin System Study (RASS, n=239). Severe (SDR) and mild (MDR) retinopathy outcomes were defined based on repeated fundus photographs in each study graded for retinopathy severity on the Early Treatment

Diabetic Retinopathy Study (ETDRS) scale. Multivariable models accounted for glycemia (measured by A1C), diabetes duration and other relevant covariates in the association analyses of additive genotypes with SDR and MDR. Fixed-effects meta- analysis was used to combine the results of GWAS performed separately in WESDR,

RASS and subgroups of DCCT, defined by cohort and treatment group. Top association signals were prioritized for replication, based on previous supporting knowledge from the literature, followed by replication in three independent white T1D studies: Genesis-GeneDiab (n=502), Steno (n=936) and FinnDiane (n=2194).

No SNP reached genome-wide significance in survival meta-GWAS for SDR. In a case- control meta-GWAS, however, SNPs in DPP10 showed close to genome-wide significant association with SDR. Although, this association could not be replicated in three other studies (P>0.05), the direction of effect remained consistent in all but one of the examined populations. Among the top hits for SDR short of genome-wide significance, SNPs near NLPR3 and AKR1E2 were replicated, after accounting for multiple testing. These signals and other top signals in the meta-GWAS of SDR generally fall in proximity to strong functional candidate genes.

In survival and case-control meta-GWAS for MDR, no SNP reached genome-wide significance. Consistently, our estimation of common additive heritability suggests a stronger genetic component for SDR compared to MDR.

iii

Acknowledgments

First and foremost, I am grateful to all the patients who, despite their pain and suffering, selflessly took the time and effort to participate in long term studies of

DCCT/EDIC, WESDR and RASS. You inspire us, teach us and help us be useful. I really hope that the results of my research be a step in the right direction and translate to useful measures to help patients suffering from diabetes in the future.

I am truly grateful to my mentor, Dr. Andrew Paterson, for his unwavering support and great patience over years, giving me the opportunity to start and complete this work. Your direction and understanding have been indispensable.

I am thankful to members of Paterson and Bull lab, past and present, for their assistance and friendships: Daryl Waggott, Charlie “Zhijian” Chen, Enqing Shen. I would also like to thank everyone from our “Friday meetings” for stimulating discussions, great insights and support, in particular Drs. Shelley Bull, Andrew

Boright, Lei Sun and Angelo Canty. Thanks to Dr. Karen Eny for reviewing introduction of the thesis and providing helpful suggestions. I also thank Dr. Jerald

Lawless for his instrumental advice for developing time-to event models.

I am thankful to the members of my advisory committee, Dr. Thomas Hudson and Dr.

George Fantus, for their input, guidance and advice during my PhD work.

My doctorate study would not have been possible without graduate scholarships and research assistantships from the Hospital for Sick Children, Vision Science Research

Program, Peterborough K.M. Hunter Foundation, University of Toronto and Juvenile

Diabetes Research Foundation Canada. Travel awards from Banting and Best Diabetes

Centre have certainly enriched my learning experience.

On a personal note, I am thankful to my parents, brother and sisters for their consistent faith in me and for their continued love, support and encouragement. You are the reason I keep going on. I am grateful to my friends, especially Afshin, Vahid and

Vahideh, who stood by my side in difficult times. Finally, I am thankful to the ones who hurt me; you helped me improve, made me stronger and reminded me that:

“Au milieu de l'hiver, j'ai découvert en moi un invincible été.”

List of Abbreviations

A1C Glycated hemoglobin ACACB acetyl-CoA carboxylase beta ACE Angiotensin I Converting Enzyme ACN9 ACN9 homolog ACO1 aconitase 1 ACP6 acid phosphatase 6, lysophosphatidic ACR Albumin Creatinine Ratio ADCK4 aarF domain containing kinase 4 AER Albumin Excretion Ratio AFF3 AF4/FMR2 family, member 3 AGE Advanced Glycation End product AGER Advanced Glycosylation End product-specific Receptor AGT angiotensinogen AKR1B1 aldo-keto reductase family 1, member B1 AKR1E2 aldo-keto reductase family 1, member E2 ARHGAP22 Rho GTPase activating protein 22 ARL4C ADP-ribosylation factor-like 4C ASAP2 ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 ASB3 ankyrin repeat and SOCS box containing 3 BBS5 Bardet-Biedl syndrome 5 bFGF basic Fibroblast Growth Factor BMI Body Mass Index BP Blood Pressure BRB Blood-Retina Barrier CACNA1E calcium channel, voltage-dependent, R type, alpha 1E subunit CCBP2 chemokine binding protein 2 CCNE1 cyclin E1 CD300A CD300a molecule CHN2 chimerin 2 CI Confidence Interval CLOGLOG Complementary Log Log COMMD6 COMM domain containing 6 COX7A2 cytochrome c oxidase subunit VIIa polypeptide 2 CPNE4 copine IV CPVL carboxypeptidase, vitellogenic-like CSME Clinically Significant Macular Edema

CVD Cardiovascular Disease DAG diacylglycerol DBC1 deleted in bladder cancer 1 DBP Diastolic Blood Pressure DCCT Diabetes Complications and Control Trial DDX5 DEAD (Asp-Glu-Ala-Asp) box helicase 5 DM Diabetes Mellitus DME Diabetes Macular Edema DN Diabetic Nephropathy DPP10 dipeptidyl-peptidase 10 DR Diabetic Retinopathy dur Duration of Diabetes DZ Di-Zygotic EDIC Epidemiology of Diabetes Interventions and Complications EFNB2 ephrin B2 EFNB2 ephrin B2 EPO Erythropoietin ESRD End Stage Renal Disease ETDRS Early Treatment Diabetic Retinopathy Study FAM107B family with sequence similarity 107, member B FAM198A family with sequence similarity 198, member A FDR False Discovery Rate FGFR1 fibroblast growth factor receptor 1 FinnDiane Finnish Diabetic Nephropathy Study FSTL5 follistatin-like 5 FTO fat mass and obesity associated GAPDH glyceraldehyde-3-phosphate dehydrogenase GBE1 glucan (1,4-alpha-), branching enzyme 1 GeneDiab Génétique de la Néphropathie Diabétique GFR Glomerular Filteration Rate GH Growth Hormone GJA5 gap junction protein, alpha 5 GoKinD Genetics of Kidney in Diabetes (Study) GUSBP10 glucuronidase, beta pseudogene 10 GWAS Genome-Wide Association Study HAND2 heart and neural crest derivatives expressed transcript 2

HbA1c Glycated hemoglobin HDL High Density Lipoprotein HGF Hepatocyte Growth Factor

vii

HMGB1 high mobility group box 1 HR Hazard Ratio HS6ST3 heparan sulfate 6-O-sulfotransferase 3 HWE Hardy-Weinberg Equilibrium IBD Identity-By-Descent IBS Identity by State ICAM1 intercellular adhesion molecule 1 ICC Intra-Class Correlation IGF-1 Insulin-like Growth Factor 1 IGSF21 immunoglobin superfamily, member 21 IRMA Intra-Retinal Microvascular Abnormalities IRS2 insulin receptor substrate 2 IRX4 iroquois homeobox 4 ITGA2 integrin, alpha 2 KCNIP4 Kv channel interacting protein 4 KCNN2 potassium intermediate/small conductance calcium-activated channel, subfamily N, member 2 KLF12 Kruppel-like factor 12 LD Linkage Disequilibrium LDL Low Density Lipoprotein LINC00426 long intergenic non-protein coding RNA 426 LINC00460 long intergenic non-protein coding RNA 460 LINC00523 long intergenic non-protein coding RNA 523 LINC01118 long intergenic non-protein coding RNA 1118 LMO7 LIM domain 7 LOC643441 uncharacterized LOC643441 LOXHD1 lipoxygenase homology domains 1 LPA lysophosphatidic acid LRP2 low density lipoprotein receptor-related protein 2 LUZP2 leucine zipper protein 2 MA MicroAneurysm MAF Minor Allele Frequency MAGI3 membrane associated guanylate kinase, WW and PDZ domain containing 3 MDR Mild Diabetic Retinopathy MDS Multidimensional Scaling MHC Major Histocompatibiltiy Complex MIR3924 microRNA 3924 MKI67 marker of proliferation Ki-67 MTHFR methylenetetrahydrofolate reductase

viii

MTUS1 microtubule associated tumor suppressor 1 MVCD MicroVascular Complications of Diabetes MYLIP myosin regulatory light chain interacting protein MYSM1 Myb-like, SWIRM and MPN domains 1 MZ Mono-Zygotic N6AMT2 N-6 adenine-specific DNA methyltransferase 2 NF - κB Nuclear Factor - kappa B NLRP3 NLR family, pyrin domain containing 3 NOS3 nitric oxide synthase 3 NPDR Non-Proliferative Diabetic Retinopathy ODF1 outer dense fiber of sperm tails 1 OR Odds Ratio OR4K17 olfactory receptor, family 4, subfamily K, member 17 PARP2 poly (ADP-ribose) polymerase 2 PC Principal Component PCA Principal Component Analysis PCSK2 proprotein convertase subtilisin/kexin type 2 PDR Proliferative Diabetic Retinopathy PECAM1 platelet/endothelial cell adhesion molecule 1 PH Proportional Hazard PKC Protein Kinase C PLXDC2 plexin domain containing 2 PPARG peroxisome proliferator-activated receptor gamma PPP1R12B protein phosphatase 1, regulatory subunit 12B PTPRS protein tyrosine phosphatase, receptor type, S PVT1 Pvt1 oncogene RAGE receptor for advanced glycation end products RASS Renin Angiotensin System Study RFWD2 ring finger and WD repeat domain 2 RNF5P1 ring finger protein 5, E3 ubiquitin protein ligase pseudogene 1 RNFT2 ring finger protein, transmembrane 2 ROS1 c-ros oncogene 1 , receptor tyrosine kinase RPPH1 ribonuclease P RNA component H1 RREB1 ras responsive element binding protein 1 SBP Systolic Blood Pressure SDR Severe Diabetic Retinopathy SDR9C7 short chain dehydrogenase/reductase family 9C, member 7 SELP selectin P SERPINE1 serpin peptidase inhibitor, clade E, member 1

SFDR Stratified False Discovery Rate SH3BP4 SH3-domain binding protein 4 SIT1 signaling threshold regulating transmembrane adaptor 1 SLC14A2 solute carrier family 14 (urea transporter), member 2 SLC26A1 solute carrier family 26 (anion exchanger), member 1 SNP Single Nucleotide Polymorphism SOCS cytokine inducible SH2-containing protein SOD2 SuperOxide Dismutase 2, mitochondrial SPTLC3 serine palmitoyltransferase, long chain base subunit 3 STMND1 stathmin domain containing 1 SUV39H2 suppressor of variegation 3-9 homolog 2 T1D Type 1 Diabetes T2D Type 2 Diabetes TAC1 tachykinin, precursor 1 TAS trait associated SNP TBC1D4 TBC1 domain family, member 4 TBCD tubulin folding cofactor D TENM4 teneurin transmembrane protein 4 TLR4 toll-like receptor 4 TMEM30A transmembrane protein 30A TTC5 tetratricopeptide repeat domain 5 UCHL3 ubiquitin carboxyl-terminal esterase L3 USP2-AS1 USP2 antisense RNA 1 UTR untranslated region VB Venous Beading VEGF / VEGFA Vascular Endothelial Growth Factor VTDR Vision-Threatening Diabetic Retinopathy WESDR Wisconsin Epidemiologic Study of Diabetic Retinopathy ZMIZ1 zinc finger, MIZ-type containing 1 ZNF696 zinc finger protein 696 ZNF750 zinc finger protein 750

Table of Contents

Acknowledgments ...... iv

List of Abbreviations ...... vi

Table of Contents ...... xi

List of Tables ...... xvii

List of Figures ...... xxi

List of Appendices ...... xxiii

1. INTRODUCTION ...... 1

1.1 Clinical Features of Diabetic Retinopathy ...... 2

1.1.1 Natural history of DR ...... 2

1.1.2 Assessment ...... 3

1.1.3 Prevention and treatment ...... 6

1.2 Epidemiology of Diabetic Retinopathy in Type 1 Diabetes ...... 8

1.2.1 Health care impact ...... 8

1.2.2 Prevalence of diabetic retinopathy ...... 10

1.2.3 Incidence of diabetic retinopathy ...... 11

1.2.4 Risk factors for diabetic retinopathy ...... 13

1.2.5 Relationship between diabetic retinopathy and nephropathy ...... 20

1.3 Genetics of Diabetic Retinopathy ...... 22

1.3.1 Evidence for a genetic contribution to DR ...... 22

1.3.2 Linkage studies of diabetic retinopathy ...... 30

1.3.3 Candidate gene association studies of diabetic retinopathy ...... 33

1.3.4 Genome-wide association studies of diabetic retinopathy ...... 36

1.4 Pathogenesis of Diabetic Retinopathy ...... 43

1.4.1 Blood-retinal barrier impairment ...... 43

1.4.2 Impaired autoregulation of retinal blood flow ...... 44

1.4.3 Sorbitol accumulation ...... 44

1.4.4 Advanced glycation end products (AGEs) ...... 46

1.4.5 Protein Kinase C activation ...... 47

1.4.6 Retinal microthrombosis ...... 48

1.4.7 Angiogenic Factors ...... 48

1.5 Summary ...... 49

2. RESEARCH AIMS...... 50

2.1 General Aim...... 51

2.2 Specific Objectives ...... 51

3. METHODS ...... 53

3.1 Study Populations and Measurement of Phenotypes ...... 54

3.1.1 The Diabetes Control and Complications Trial - Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) ...... 54

3.1.2 The Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) ...... 56

3.1.3 Renin Angiotensin System Study (RASS) ...... 57

3.1.4 Grading of retinopathy severity ...... 59

3.1.5 Calculating weighted mean A1C ...... 60

3.2 Genotyping and Quality Control ...... 60

3.2.1 Sample quality ...... 61

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3.2.2 Marker quality ...... 65

3.3 Genotype Imputation ...... 66

3.4 Phenotype Modeling ...... 67

3.5 Genome-Wide Association Testing ...... 68

3.6 Meta-analysis ...... 69

3.7 Prioritizing SNPs from GWAS for Replication ...... 69

3.8 Replication ...... 73

3.8.1 The Finnish Diabetic Nephropathy (FinnDiane) Study ...... 73

3.8.2 The Steno Clinic Study ...... 74

3.8.3 GeneDiab / Genesis Studies ...... 74

4. RESULTS: META-GWAS OF SEVERE DIABETIC RETINOPATHY ...... 75

4.1 Characteristics of Study Populations ...... 76

4.2 Definition of Severe Diabetic Retinopathy Phenotype ...... 78

4.2.1 Defining time to SDR outcome ...... 80

4.3 Association of Baseline Risk Factors With Time-to SDR in DCCT/EDIC ...... 81

4.4 Development of Time to Event Models for SDR ...... 82

4.5 Identification of Loci Associated with Time-to SDR ...... 84

4.5.1 GWAS of time-to SDR in separate cohorts...... 84

4.5.2 Meta-GWAS of time-to SDR ...... 89

4.6 Case – Control Association Meta-Analysis of SDR ...... 93

4.6.1 Regression models for SDR ...... 93

4.6.2 Case-control GWAS of SDR separately by study ...... 94

4.6.3 Case-control meta-GWAS of SDR ...... 96

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4.7 Closer Look at Regions Associated with SDR ...... 100

4.7.1 2q14.1 locus containing DPP10 ...... 100

4.8 Estimation of Statistical Power ...... 107

5. RESULTS: PRIORITIZATION AND REPLICATION ...... 109

5.1 Prioritizing SDR meta-GWAS results for replication ...... 110

5.1.1 SNP function ...... 110

5.1.2 Candidate gene association studies of DR ...... 113

5.1.3 Genome-wide linkage studies of diabetic retinopathy ...... 115

5.1.4 Proteome studies of patients with DR ...... 117

5.1.5 Proteome and transcriptome studies of DR animal models ...... 120

5.1.6 Previous GWAS results ...... 121

5.1.7 Combining prior knowledge to prioritize meta-GWAS results ...... 121

5.2 Replication of top ranking SNPs ...... 125

5.3 Replication of Previous GWAS Hits for Diabetic Retinopathy ...... 131

5.4 Association of GWAS Hits for Diabetic Nephropathy with SDR ...... 134

6. RESULTS: META-GWAS OF MILD DIABETIC RETINOPATHY; ESTIMATION OF HERITABILITY ...... 137

6.1 Definition of Alternative Phenotype ...... 138

6.1.1 Mild diabetic retinopathy phenotype ...... 138

6.1.2 Time-to MDR outcome...... 139

6.1.3 Baseline factors associated with the incidence of MDR ...... 140

6.2 Case – Control Association Study of MDR ...... 141

6.2.1 Covariate effects and case-control GWAS of MDR ...... 141

6.2.2 Case-control meta-GWAS of MDR ...... 144

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6.3 Time to Event Association Study of MDR ...... 147

6.3.1 Time-to event models for MDR ...... 147

6.3.2 GWAS of time to MDR ...... 154

6.3.3 Meta-GWAS of time to MDR ...... 155

6.4 Closer Look at the Top Association Signals ...... 159

6.5 Estimation of Common Additive Heritability ...... 163

7. DISCUSSION ...... 165

7.1 Purpose and Strength of Study ...... 166

7.1.1 High phenotyping quality ...... 166

7.1.2 Adjusting for known risk factors and independent covariates ...... 166

7.1.3 Frequent longitudinal measurement of phenotypes ...... 169

7.2 Survival Analysis of the Retinopathy Outcome ...... 169

7.3 Limitations of the Study ...... 171

7.3.1 Statistical power ...... 171

7.3.2 Inter-study heterogeneity ...... 171

7.3.3 Unavailability of suitable replication studies ...... 172

7.4 Association of DPP10 and SDR ...... 172

7.5 Association of EFNB2 (13q33.2) with Time-to SDR ...... 176

7.6 No Genome-Wide Significant Hit in the Meta-GWAS of Time-to SDR ...... 177

7.6.1 Proprotein Convertase Subtilisin/Kexin type 2 (PCSK2) ...... 178

7.6.2 Protein-Tyrosine Phosphatase Sigma (PTPRS) ...... 178

7.6.3 COX7A2 / TMEM30A ...... 179

7.6.4 Myosin Regulatory Light Chain-Interacting Protein (MYLIP)...... 180

7.6.5 EXOC2 / IRF4 ...... 181

7.7 No Genome-Wide Significant Locus Other than DPP10 in the Case-Control Meta-GWAS of SDR ...... 181

7.7.1 NLR family, pyrin domain containing 3 (NLRP3) ...... 181

7.7.2 Immunoglobulin superfamily member 21 (IGSF21) ...... 183

7.7.3 Membrane-associated guanylate kinase-related 3 (MAGI3) ...... 184

7.7.4 ACN9 / TAC1 ...... 184

7.7.5 Microtubule associated tumor suppressor 1 (MTUS1) ...... 185

7.7.6 Aconitase 1 (ACO1) ...... 185

7.7.7 Aldo-Keto Reductase family 1, member E2 (AKR1E2) ...... 186

7.7.8 17q24.1 locus ...... 186

7.8 Replication of a Previous GWAS Hit for DR Near PLXDC2 ...... 187

7.9 Lack of Evidence for Association Between DN Loci and DR ...... 188

7.10 No Genome-Wide Significant Hit in Case-Control Meta-GWAS of MDR ...... 188

7.11 Association of ACP6/GJA5 Locus with Time-to MDR ...... 190

7.12 No Genome-Wide Significant Hit in the Time-to MDR Meta-GWAS ...... 191

7.13 Incorporating Prior Knowledge Increases the Number of SNPs Passing FDR Threshold ...... 193

7.14 Lack of Replication in Independent Studies for Most Top Loci ...... 194

7.15 SDR Shows Stronger Common Heritability than MDR ...... 195

8. CONCLUSIONS ...... 196

9. FUTURE DIRECTIONS ...... 199

References ...... 204

Appendices ...... 256

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List of Tables

Table 1. International clinical diabetic retinopathy disease severity scale ...... 4

Table 2. UK National Screening Committee (NSC) grading system for diabetic retinopathy ...... 5

Table 3. Current recommended target levels of risk factors in patients with diabetes. ... 6

Table 4. Ethnic risk of diabetic retinopathy ...... 24

Table 5. Familial clustering of diabetic retinopathy in the relatives of diabetic probands

...... 27

Table 6. Correlation between the retinopathy severity grades of probands and their relatives ...... 28

Table 7. Heritability estimates of diabetic retinopathy ...... 30

Table 8. Summary of linkage studies of diabetic retinopathy ...... 31

Table 9. Summary of loci with evidence of linkage (LOD > 1) to diabetic retinopathy. 32

Table 10. Summary of genetic polymorphisms associated with diabetic retinopathy in published meta-analyses ...... 34

Table 11. Summary of genome-wide association studies of diabetic retinopathy ...... 40

Table 12. ETDRS Scale for diabetic retinopathy severity in single eye...... 58

Table 13. Diabetic retinopathy severity scale per individual in DCCT/EDIC, WESDR,

RASS...... 59

Table 14. Summary of sample quality control procedures ...... 64

Table 15. Search phrases used in the literature review to identify publications related to each evidence group ...... 72

Table 16. Characteristics of study subjects in DCCT/EDIC, WESDR and RASS studies 77

xvii

Table 17. Cumulative incidence of severe diabetic retinopathy (SDR) in study populations ...... 79

Table 18. Association of baseline covariates with the incidence of SDR in DCCT/EDIC81

Table 19. Formulation of models for severe diabetic retinopathy (SDR) ...... 83

Table 20. Covariate effects for time-to SDR model in WESDR ...... 84

Table 21. Covariate effects for time-to SDR model in DCCT/EDIC subgroups ...... 85

Table 22. SNPs associated with time to SDR at genome wide significance level in

GWAS of single studies (P < 5×10-8) ...... 87

Table 23. Meta-analysis results of time to SDR for top SNPs (P < 5×10-8) in each study 88

Table 24. Top results (P < 10-5) from meta-GWAS of time-to SDR ...... 92

Table 25. Model formulation for severe diabetic retinopathy case-control analysis at last visit ...... 93

Table 26. Covariate effects on severe retinopathy status at last visit in case-control analyses ...... 94

Table 27. Top results (P< 10-5) from case-control meta-analysis of SDR ...... 98

Table 28. Quality and genotype counts of rs12466846 in study populations...... 100

Table 29. Association of rs12466846 with SDR and the effect of BMI ...... 105

Table 30. Association of rs12466846 with diabetic complications in WESDR ...... 106

Table 31. Sample size calculation for replication of rs12466846 association with SDR 106

Table 32. Frequency table for the effect types...... 112

Table 33. Stratified FDR analysis based on SNP effect ...... 113

Table 34. List of candidate genes with evidence for association with diabetic retinopathy ...... 114

xviii

Table 35. SFDR analysis of time-to SDR meta-GWAS based on DR candidate genes .. 115

Table 36. SFDR analysis of time-to SDR meta-GWAS based on published linkage studies ...... 116

Table 37. SFDR analysis of time-to SDR meta-GWAS based on human proteome studies

...... 117

Table 38. Summary of proteome studies in patients with diabetic retinopathy...... 118

Table 39. Proteins over/under expressed in proteome studies of diabetic retinopathy patients ...... 119

Table 40. SFDR analysis of time-to SDR meta-GWAS based on animal proteome and transcriptome studies ...... 121

Table 41. Combined SFDR of time-to SDR meta-GWAS ...... 122

Table 42. Characteristics of replication studies ...... 125

Table 43. Top SFDR SNPs nominally (P<0.05) associated with SDR in replication studies in the same direction as discovery...... 127

Table 44. Association results of top SNPs (P <10-5) from the discovery case-control meta-

GWAS of SDR in replication studies...... 128

Table 45. Association results of top SNPs (P <10-5) from the discovery time to SDR meta-

GWAS in replication studies...... 130

Table 46. Association results of top loci (P < 10-5) from previous GWAS studies of DR in the current SDR meta-analysis...... 132

Table 47. Summary of genome-wide association studies of diabetic nephropathy ...... 135

Table 48. Variants showing nominal association in SDR meta-GWAS among SNPs previously associated with diabetic nephropathy...... 136

xix

Table 49. Cumulative incidence of mild diabetic retinopathy (MDR) in each study population ...... 138

Table 50. Association of covariates with incidence of MDR in DCCT/EDIC...... 141

Table 51. Covariate effects on mild retinopathy in multivariate case-control analyses 142

Table 52. Top results (P<10-5) from case-control association meta-analysis of MDR... 146

Table 53. Formulation of time to mild diabetic retinopathy (MDR) models ...... 149

Table 54. Covariate effects in multivariable models for time to mild diabetic retinopathy ...... 150

Table 55. Comparison of parameter estimates between the study populations for time to MDR in multivariable models ...... 153

Table 56. Genome-wide significant results from GWAS of time to MDR ...... 156

Table 57. Meta-analysis results for genome-wide significant hits in single studies ..... 156

Table 58. Top results (P <10-5) from meta-analysis of time to MDR ...... 158

Table 59. Estimation of phenotypic variance explained by common genetic polymorphism in WESDR and DCCT/EDIC ...... 164

List of Figures

Figure 1. Proposed hemodynamic model for the pathogenesis of diabetic retinopathy.

...... 45

Figure 2. Polyol pathway and its role in microvascular diabetic complications ...... 45

Figure 3. Summary of the analytical scheme in the current genetic study of diabetic retinopathy...... 52

Figure 4. Mean glycated hemoglobin (A1C) during DCCT and EDIC separately by cohort-treatment groups...... 78

Figure 5. Kaplan-Meier plots of time-to SDR in WESDR and DCCT/EDIC subgroups. 80

Figure 6. Quantile-quantile (Q-Q) plots for time to SDR GWAS...... 86

Figure 7. Q-Q plot for meta-GWAS of time to severe diabetic retinopathy...... 89

Figure 8. Distribution of Q statistic for heterogeneity from the SDR meta-analysis...... 90

Figure 9. Manhattan plot for meta-GWAS of time-to SDR...... 91

Figure 10. Q-Q plots for SDR case-control GWAS...... 95

Figure 11. Q-Q plot for case-control meta-GWAS of severe diabetic retinopathy in

WESDR and DCCT/EDIC...... 97

Figure 12. Manhattan plot for case-control meta-analysis of SDR at last visit...... 97

Figure 13. Regional association plots for top results (P <10-5) from meta-analysis of time-to SDR...... 102

Figure 14. Regional association plots for SDR meta-analyses at 2q14.1 (DPP10) locus.

...... 102

Figure 15. Regional association plots for top results (P <10-5) from case- control meta-

GWAS of SDR at last visit...... 104

xxi

Figure 16. Forest plots for the top SNP at 2q14.1 locus...... 104

Figure 17. Statistical power to detect genetic association at various allele frequencies.

...... 107

Figure 18. Number of SNPs passing FDR threshold (γ) at each FDR level up to 0.5. .. 123

Figure 19. Comparison of ranking of SDR meta-GWAS results in FDR vs SFDR analysis...... 124

Figure 20. Kaplan-Meier plots of time-to MDR in DCCT/EDIC subgroups, WESDR and

RASS...... 139

Figure 21. Quantile-quantile plot of case-control association analyses of MDR ...... 144

Figure 22. QQ-plot of MDR case-control meta-GWAS...... 145

Figure 23. Histogram of heterogeneity test P values in meta-GWAS of MDR status. .. 145

Figure 24. Manhattan plot for case-control meta-GWAS of MDR...... 147

Figure 25. Quantile-quantile (QQ) plots for time to MDR GWAS ...... 154

Figure 26. Meta-analysis of time to MDR in the DCCT/EDIC, WESDR and RASS...... 157

Figure 27. Manhattan plot of time to MDR meta-analysis ...... 159

Figure 28. Regional association plots for top results (P <10-5) from meta-GWAS of time- to MDR...... 161

Figure 29. Regional association plots for top results (P <10-5) from case- control meta-

GWAS of MDR...... 163

Figure 30. Possible relations of risk factors with the association of genetic factor (G) and disease...... 167

Figure 31. The effect of observation window on case-control analysis of a survival trait.

...... 170

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List of Appendices

Table S1. List of genes showing consistent dysregulation in transcriptome and proteome studies of DR animal models …………………………………………………. 257

xxiii

1. INTRODUCTION

1 2

1.1 Clinical Features of Diabetic Retinopathy

Diabetes mellitus (DM) refers to a range of metabolic disorders characterized by hyperglycemia. It is either caused by destruction of pancreatic β cells leading to insufficient insulin production, as is the case in type 1 diabetes (T1D) or cellular insensitivity to insulin as happens in type 2 diabetes (T2D). Long-term complications of diabetes mellitus affect various organs. They are traditionally classified into microvascular (retinopathy, nephropathy and neuropathy) and macrovascular

(coronary artery disease, stroke and peripheral arterial disease) based on the size of vasculature believed to be primarily affected in each pathology (Fowler 2008).

1.1.1 Natural history of DR

Early signs of diabetic retinopathy (DR) appear on average 4-7 years after diagnosis of type 1 diabetes; but may be present at diagnosis in type 2 diabetes which may have gone undiagnosed for a long period of time (Klein and Klein 1997). The earliest sign of

DR is the appearance of retinal microaneurysms which are non-specific asymptomatic protrusions in the walls of small retinal vessels. A common accompanying sign in early

DR are small retinal hemorrhages caused by rupture of microaneurysms. Over time the number and size of both lesions increase (Klein and Klein 1997).

Subsequent manifestations may be the development of soft and/or hard exudates

(Klein and Klein 1997). Soft exudates which are infarctions of the nerve fiber layer of the retina are seen as white fluffy patches in an ophthalmic exam. Hard exudates manifest as yellow patches that are deposits of lipids in the outer layer of the retina presumed to be due to leaking of retinal capillaries (Klein and Klein 1997). Various abnormalities of retinal blood vessels may be observed as the disease progresses including venous beading (VB) and intra-retinal microvascular abnormalities (IRMA)

(Klein and Klein 1997). VB are focal areas of venous dilation and thinning of the venous walls. IRMA are abnormally dilated retinal capillaries near areas of obliteration of the normal retinal microvasculature (Klein and Klein 1997).

In the severe stages of the disease, fragile blood vessels develop on the retina known as neovascularization. DR remains asymptomatic up until this late stage. Sudden vision loss, due to extensive pan-retinal or vitreal hemorrhage caused by rupture of the neovasculature, may be a first presenting symptom. Such bleeding could be: spontaneously absorbed or surgically evacuated or if left untreated may result in development of fibrous proliferations. Traction on the retina due to these fibrous bands may lead to retinal detachment and ultimate vision loss. (Klein and Klein 1997)

An alternative outcome of DR, more common in type 2 diabetes, is diabetic macular edema (DME) characterized by the accumulation of hard exudates and fluid resulting in the thickening of the macula. Macular edema can cause either mild or severe visual loss (Klein and Klein 1997).

1.1.2 Assessment

Considering the asymptomatic nature of DR, routine ophthalmologic exam is necessary for early detection and evaluation of the severity of the disease. Indirect or direct ophthalmoscopy can both be used to detect the presence and assess the severity of DR.

However, sensitivity and specificity of ophthalmoscopy is highly dependent on the skill level of the examiner (Hutchinson et al. 2000). Stereoscopic color fundus photography gives a stereoscopic image of the posterior pole of the eye and is more sensitive and specific in documenting the presence, severity and progression of DR

(Hutchinson et al. 2000). To assess the extent of retinal ischemia or before focal laser

4 photocoagulation therapy, fluorescein angiography is necessary (American Academy of Ophthalmology Retina Panel 2008).

To facilitate communication between healthcare providers, an international clinical disease severity scale for DR (Table 1) has been developed (Wilkinson et al. 2003) which is mainly based on the Early Treatment Diabetic Retinopathy Study (ETDRS) classification for DR (see section 3.1.4‎ for further details on ETDRS scale).

An older, less elaborate grading system is still commonly used in clinical practice in certain countries including the UK (The Royal College of Ophthalmologists 2012).

Table 2 summarizes the grading system used by the UK National Screening Committee

(NSC) with approximate ETDRS equivalence (The Royal College of Ophthalmologists

2012).

Table 1. International clinical diabetic retinopathy disease severity scale Severity Level Findings on Ophthalmoscopy Derivation from ETDRS Levels

No apparent No abnormalities Level 10: DR absent retinopathy Mild NPDR Microaneurysms only Level 20: Very mild NPDR

Moderate NPDR More than just microaneurysms but less Levels 35, 43, 47: moderate NPDR less than Severe NPDR than 4:2:1 * Severe NPDR Any of the following: Level 53A-E: severe to very severe  Extensive (>20) intraretinal NPDR, 4:2:1 rule * hemorrhages in each of 4 Q  Definite venous beading in 2+ Q  Prominent IRMA in 1+ Q AND no signs of PDR PDR One or more of the following: Levels 61, 65, 71, 75, 81, 85:  Neovascularization PDR, high-risk PDR, very severe or  Vitreous / preretinal hemorrhage advanced PDR

NPDR: non-proliferative diabetic retinopathy; PDR: proliferative diabetic retinopathy; Q: quadrant; IRMA: intraretinal microvascular abnormality * 4:2:1 rule requires the presence of severe hemorrhages in 4 quadrants, or venous beading in 2 quadrants, or IRMA in a single quadrant for severe NPDR. Table adapted from (Wilkinson et al. 2003) and (American Academy of Ophthalmology 2002).

Table 2. UK National Screening Committee (NSC) grading system for diabetic retinopathy NSC DR Grading Observations in Ophthalmoscopy Approximate ETDRS Level None (R0) No abnormalities Level 10

Background (R1)  Microaneurysm(s) Level 20, 35  Retinal hemorrhage(s) ± any exudate Pre-proliferative (R2)  Venous beading Level 43 to 53  Venous loop or reduplication  Intraretinal microvascular abnormality (IRMA)  Multiple deep, round or blot hemorrhages  Cotton wool spots (CWS) Proliferative (R3) • New vessels on disc (NVD) Level 61 or worse • New vessels elsewhere (NVE) • Pre-retinal or vitreous haemorrhage • Pre-retinal fibrosis ± tractional retinal detachment

Adapted from (The Royal College of Ophthalmologists 2012) and (Shotliff and Duncan 2006).

Considering the sight threatening nature of DR and the availability of acceptable treatment modalities to slow disease progression, screening protocols are established for DR. Current practice guidelines recommend an initial fundus examination within three to five years of T1D diagnosis and at diagnosis of T2D (American Academy of

Ophthalmology Retina Panel 2008). Frequency of the recommended follow-up retinal examination varies between 3 to 24 months and depends on the stage of DR and complicating conditions of the patient such as pregnancy (American Academy of

Ophthalmology Retina Panel 2008).

1.1.3 Prevention and treatment

1.1.3.1 Primary prevention

Primary prevention of DR is focused on changing modifiable risk factors (see 1.2.4‎ below) mainly by controlling blood glucose, blood pressure and serum lipid levels through appropriate pharmacological or non-medicinal interventions. Table 3 summarizes the current recommended target level for risk factors in patients with DM

(American Diabetes Association 2013; Canadian Diabetes Association Clinical Practice

Guidelines Expert Committee 2013). Nutritional changes, physical activity, weight management and smoking cessation are other recommended primary prevention measurements to reduce the risk of diabetes complications in general (Canadian

Diabetes Association Clinical Practice Guidelines Expert Committee 2013).

1.1.3.2 Secondary prevention and treatment

Early detection of DR through regular screening is essential in the prevention of blindness due to DR (see 1.1.2‎ above).

Table 3. Current recommended target levels of risk factors in patients with diabetes. Risk Factor Target Level Hyperglycemia  A1C < 7.0%  Fasting or preprandial plasma glucose 4.0 – 7.0 mmol/L  2-hour postprandial plasma glucose < 10.0 mmol/L Blood Pressure ≤ 130/80 mmHg * Hyperlipidemia  Triglyceride < 1.7 mmol/L  LDL cholesterol < 2.6 mmol/L  HDL cholesterol men: > 1.1 mmol/L women: > 1.3 mmol/L

* Target levels are for patients without complicating factors and not at increased risk. BP target is for patients with normal renal function. Based on current recommendations of (American Diabetes Association 2013) and (Canadian Diabetes Association Clinical Practice Guidelines Expert Committee 2013)

1.1.3.2.1 Laser photocoagulation

Several studies confirm long-term benefits of laser photocoagulation therapy in reducing visual loss in patients with DR (Boyd et al. 2013; Chew et al. 2003). Panretinal photocoagulation (scatter laser treatment) in patients with severe non-proliferative and proliferative DR reduces legal blindness by 90%. It aims to ablate ischemic areas in the peripheral retina and is believed to prevent neovascularization by reducing the induction of angiogenic factors (Early Treatment Diabetic Retinopathy Study Research

Group 1978). Focal or grid laser treatment to the macula for clinically significant macular edema (CSME) reduces the incidence of moderate visual loss by 50%. It is aimed at controlling vascular leakage by cauterizing leaky microaneurysms or grid burns in regions with diffusely compromised blood-retinal barrier (Early Treatment

Diabetic Retinopathy Study Research Group 1985).

1.1.3.2.2 Intraocular pharmacological treatment

Considering the primary role of vascular endothelial growth factor (VEGF) in the development of DR, anti-VEGF drugs are now widely used in the treatment of macular edema. Intravitreal injection of ranibizumab, bevacizumab and pegaptanib, three anti-

VEGF antibodies, showed success in randomized clinical trials by improving both visual acuity and central macular thickness when used in patients with diabetic macular edema (DME) (Boyd et al. 2013; Nicholson and Schachat 2010). Early studies in

PDR suggest that bevacizumab is helpful in decreasing leakage from neovascular lesions, vitreous hemorrhage or as adjunct therapy in vitrectomy cases (Nicholson and

Schachat 2010). However, Health Canada has thus far only approved ranibizumab for use in DME (Boyd et al. 2013).

Intravitreal corticosteroids have potent anti-inflammatory and possible anti-angiogenic effects and have been used as an alternative treatment in DME with comparable results

8 to ranibizumab. However, corticosteroids significantly increase the risk of glaucoma and cataract progression and are not FDA approved (Boyd et al. 2013).

The use of Protein Kinase C inhibitors, Aldose Reductase inhibitors and Growth

Hormone / Insulin like Growth Factor inhibitors in the treatment of DR, is not currently supported for clinical use (Mohamed et al. 2007).

1.1.3.2.3 Surgical treatment

Surgical vitrectomy (removal of vitreous hemorrhage and cutting fibrous bands causing retinal traction) is the last resort for patients with retinal detachment; advanced

PDR with non-clearing vitreous hemorrhage or fibrosis and traction; persistent DME cases with or without vitreous traction (Boyd et al. 2013; El-Asrar et al. 2009).

1.1.3.3 Tertiary prevention

Patients with vision loss due to DR need both visual and psychological rehabilitation to cope with their condition. These measures include, but are not restricted to, visual magnification, assistance to ensure continued monitoring and control of blood glucose, mobility aids and psychological counseling. Such measures are necessary even in patients with moderate visual loss to ensure maintaining independence and quality of life (Boyd et al. 2013; Klein and Klein 1997).

1.2 Epidemiology of Diabetic Retinopathy in Type 1 Diabetes

1.2.1 Health care impact

Diabetic retinopathy is the most common long term complication of diabetes, the leading cause of blindness among adults in the US (Centers for Disease Control and

Prevention 2011) and the fifth common cause (4.8%) of blindness worldwide (Resnikoff et al. 2004).

In 2006, an estimate of half a million Canadians lived with DR with 100,000 vision- threatening DR and 6,000 blind cases (Public Health Agency of Canada 2011). Using a combination of data sources including large scale Canadian surveys and other population-based studies, Cruess et al estimated that 30,000 Canadians suffer from DR- related vision loss in 2007 (Cruess et al. 2011). The National Health and Nutrition

Examination Survey (2005-2008) estimated a crude prevalence of 28.5% for DR and

4.4% for vision-threatening DR (VTDR) among diabetic US adults (> 40 years) (Zhang et al. 2010b). Earlier estimates, from type 1 diabetes studies, suggested 767,000 cases of any DR and 376,000 cases of VTDR due to T1D, corresponding to prevalence of 0.37% and 0.18% in the US general population (Roy et al. 2004).

The main cost of vision loss due to DR is the resulting loss of well-being and quality of life of affected patients. DR also imposes significant healthcare costs. Using a bottom- up estimation, health system expenditure due to DR-related vision loss added up to

$205.7 million in Canada in 2007 (Cruess et al. 2011). The estimate does not include non-health related costs of vision loss such as productivity loss, costs of care, aids, etc. which are estimated to double the costs of vision loss (Cruess et al. 2011).

With the rising prevalence of diabetes, the health impact of diabetic retinopathy is predicted to increase in the future. It is estimated that 4.64% and 1.02% of the US general population over 40 years of age will be affected by DR and vision-threatening

DR in 2020 (Kempen et al. 2004). By 2050, 16 and 3.4 million American adults (> 40 years) are predicted to have DR and vision-threatening DR, respectively, both triple the numbers in 2005 (Saaddine et al. 2008).

1.2.2 Prevalence of diabetic retinopathy

Although changes in diabetes care, especially tighter control of glycemia, have improved the prospect and prognosis of diabetic retinopathy over the past 25 years; DR still remains a major health burden.

The Wisconsin Epidemiological Study of Diabetic Retinopathy (WESDR) has been one of the largest population-based cohort studies of diabetic retinopathy using stereoscopic color fundus photography of seven standard fields of both eyes and standardized centralized grading of retinopathy severity as the gold standard for disease assessment (Klein et al. 1984a). At baseline examination of WESDR (1980-1982) the prevalence of any DR and proliferative DR in T1D participants was 70.7% and

22.7% respectively.

Since prevalence and severity of DR depend on diabetes duration; evaluation should take diabetes duration into account. At WESDR baseline, the prevalence of DR was

14% during the first 5 years of T1D with no severe cases. However, after 20 years of diabetes, 53% of patients had PDR and practically everyone (97%) showed some degree of retinopathy (Klein et al. 1984a). Among 1613 T1D subjects with diabetes duration less than 5 years screened for participation in Diabetes Complications and Control Trial

(DCCT) between 1983 and 1989, 54% showed evidence of DR by fundus photography

(44%) or fluorescein angiography (Malone et al. 2001). Considering the design of the

DCCT, as a volunteer hospital-based study, this estimate is likely less representative of the overall population.

More recent population-based studies generally provide lower estimates for DR prevalence, consistent with improvements in glycemic control over the past 25 years

(Downie et al. 2011; LeCaire et al. 2013). Comparison of fundus photographs at 20 years of diagnosis between the Wisconsin Diabetes Registry Study (WDRS) and WESDR shows a significant reduction in the prevalence of DR over the past two decades

(LeCaire et al. 2013). Both studies were conducted in the same geographic area.

However, after 20 years of diabetes, 18% of WDRS patients (2007-2011) had pre-proliferative or proliferative DR compared to 43% in WESDR (1980-1996). A less drastic reduction was observed in the prevalence of any DR: 92% (95%CI: 89-95) in

WDRS vs. 97% (95%CI: 96-99) in WESDR. Mean HbA1c has decreased in the same period (8.0% in WDRS vs. 9.3% in WESDR, P < 0.001) due to more intensive treatment

(93.4% using 3 or more daily injections or an insulin pump vs. 21.3%, P < 0.001). An

Australian study reported a similar downward trend in the prevalence of DR after 8.6 years of diabetes from 1990 to 2009 (53%, 38%, 23% and 12% for successive five year periods, P < 0.001) (Downie et al. 2011).

The focus of this review is the epidemiology of DR in T1D. Prevalence of DR in T2D seems to be lower with a crude prevalence estimate of 29% in US adults (Ding and

Wong 2012; Zhang et al. 2010b). A recent study of 12,000 Swedish patients, also, provides higher estimates of prevalence for both DR and vision-threatening DR in T1D patients compared to T2D (42% and 12% in T1D vs. 28% and 5% in T2D) (Heintz et al.

2010). However, information from WESDR suggests that the observed differences in prevalence of DR between T1D and T2D patients is mostly explained by differences in glycemic control and diabetes duration and not necessarily the type of diabetes (Klein et al. 1994).

1.2.3 Incidence of diabetic retinopathy

The Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) examined incidence of DR over a 25 year period (1981-2006) with up to 5 retinal photographs. The

25 year cumulative incidence of DR and PDR in T1D patients was 97% and 42% respectively, after accounting for competing risk of death (Klein et al. 2008). The estimated annual incidence rate of PDR decreased from 4% (1985-1991) to < 3% (1991-

1995) and further declined in the last 11 years to < 2% (1995-2006) (Klein et al. 1994;

Klein et al. 2008).

The Pittsburgh Epidemiology of Diabetes Complications (EDC) study, which prospectively followed 652 subjects with T1D for 12 years until 2000, estimated the annual incidence rate of PDR to be 1.59% (95%CI: 1.34-1.88) with diabetes duration < 20 years and 5.40% (95%CI: 4.00-7.12) in the group with diabetes duration of 20-30 years

(Pambianco et al. 2006).

The Liverpool Diabetic Eye Study (LDES) recruited 831 T1D patients during 1991-99 and followed them for 5 years with annual fundus photography (3 field non- stereoscopic photographs for each eye). Cumulative incidence of DR was 7.8% in year 1 to 36.8% in 5 years. Similarly, cumulative incidence of PDR increased from 0.3%

(95%CI: 0.0-0.9) in the first year to 3.9% (95%CI: 1.4-5.4) after 5 years (Younis et al.

2003).

At least two studies have reported declines in the incidence of severe DR over the past decades, consistent with observations from WESDR. The FinnDiane study reported a significant decline in the incidence of severe DR after 20 years of duration from 23%

(95%CI: 21-25) in < 1975 and 33% (95%CI: 30-35) in 1975-79 cohorts, to 18% (95%CI: 15-

21) in the 1980-84 cohort and 6.4% (95%CI: 4.0-8.7) in the ≥1985 cohort (Kyto et al. 2011).

Similarly, in the Linkoping Diabetes Complications Study, after 25 years of duration, the cumulative incidence of PDR was 47% (95%CI: 34-61) in the oldest cohort (1961-65) compared to 28% (95% CI: 15-40) in the 1966-70 cohort and 24% (95%CI: 12-36) in the

1971-75 cohort (Nordwall et al. 2004). Both studies defined cohorts based on the year of diabetes diagnosis and followed them for over 20 years.

1.2.4 Risk factors for diabetic retinopathy

1.2.4.1 Hyperglycemia

Diabetic complications are the result of organ damage initiated by diabetic hyperglycemia. However, it took decades for the medical community to reach a consensus on the significance of this risk factor and proper treatment plan in diabetes.

Glycosylated hemoglobin (HbA1c) was discovered as a biomarker of diabetes by an

Iranian scientist (Rahbar 1968). Since the 1980s, HbA1c has been established as a proxy for glycemic control during the 4 months prior to its measurement in patients with diabetes (Gallagher et al. 2009). Discovery and clinical use of HbA1c, as a measure to capture overall glycemic exposure in diabetes by infrequent blood tests, set the stage to investigate the effect of hyperglycemia on the risk of diabetic complications.

The primary evidence for strong association of HbA1c with incidence and progression of DR came from observational studies. Analysis of up to 25 years of follow-up data in

WESDR showed that baseline HbA1c is a strong independent risk factor for progression of DR, defined as a 2 step increase in ETDRS severity level (Hazard Ratio (HR): 1.32,

95%CI: 1.26-1.38, P <0.001) and incidence of PDR (HR: 1.38, 95%CI: 1.31-1.46, P <0.001)

(Klein et al. 2008). In separate multivariate analysis, an increase in HbA1c between baseline and 4 year follow-up visit was associated with a higher incidence of PDR over the next 21 years (HR: 1.18 per 1% change, 95%CI: 1.11-1.25, P <0.001) (Klein et al.

2008).

Subsequent randomized controlled clinical trials have established a cause-effect relationship between hyperglycemia and diabetic retinopathy. The Diabetes

Complications and Control Trial (DCCT) compared the effects of conventional diabetic treatment at the time and an intensive treatment aimed at normalizing HbA1c level on the development and progression of DR. Conventional therapy consisted of 1-2 daily injections of insulin without dose adjustment based on daily glucose monitoring; while

14 the intensive therapy administered insulin by multiple daily injections or an external pump and the dose was adjusted based on blood glucose monitoring at least 4 times a day. Participants were selected to be free of hypertension, hypercholesterolemia or severe complications at baseline. The study was terminated prematurely in 1993 (an average follow-up of 6.5 years) after conclusively showing the beneficial effects of intensive treatment in delaying or slowing diabetic complications (The DCCT Research

Group 1993). In the primary prevention cohort (no DR in the beginning of DCCT) intensive therapy reduced the risk of DR progression (3 step on ETDRS severity scale sustained for 6 month) by 78% (95%CI: 65-87, P <0.001). In the secondary intervention cohort (with mild to moderate non-proliferative DR at the study onset) intensive treatment reduced the risk of 3-step progression of DR by 65% (95%CI: 49-75, P <0.001) and risk of developing of vision-threating DR by 61% (95%CI: 38-75, P <0.04). The beneficial effect of intensive therapy increased over time, as the cumulative incidence of DR was similar in the two treatment groups during the first 3 years of DCCT, showing a trend of early worsening in the intensive group during first year (The DCCT

Research Group 1995a).

The main determinant of DR progression was the mean HbA1c during the trial, with similar risk gradients in both treatment groups: A 10% lower mean HbA1c was associated with a 42% lower risk of DR progression (95%CI: 32-51) in the conventional treatment group and a 41% lower risk (95%CI: 24-54) in the intensive treatment group, adjusting for other baseline risk factors. The risk gradient appeared to be consistent throughout the range of HbA1c in the study with no sign of a protective threshold below which the risk of progression was eliminated (The DCCT Research Group

1995b).

Long term follow-up of DCCT patients in the subsequent Epidemiology of Diabetes

Interventions and Complications (EDIC) study showed that beneficial effects of

15 intensive therapy in DCCT continued for many years after the trial ended, arguing for a metabolic memory in diabetes. Despite similarity of HbA1c in the former conventional and intensive therapy groups during EDIC, adults in the intensive treatment group of

DCCT continued to show lower risk (risk reduction: 57%, 95%CI: 43-67, P <0.0001) of further retinopathy progression (3 step) up to year 10 of EDIC (White et al. 2010). Since the publication of DCCT results, intensive treatment (aiming to keep HbA1c < 6.5-7%) has become the standard of health care in T1D (Canadian Diabetes Association Clinical

Practice Guidelines Expert Committee 2013).

The causal relationship of hyperglycemia and DR also applies to T2D. Clinical trials in

T2D patients confirm the benefits of intensive treatment, aimed at near normalization of glycemic level, on preventing and delaying DR (Patel et al. 2008; UKPDS Group

1998a).

1.2.4.2 Hypertension

Early reports of increased risk of DR in hypertensive patients date back to the 1930s

(Wagener et al. 1934). It is believed that increased retinal blood flow in hypertension induces vascular damage and increases the risk of DR (Kohner et al. 1995).

In T1D patients from WESDR, systolic and diastolic blood pressure and hypertension status at baseline were associated with 25 year cumulative incidence of PDR in univariate analyses. However, after accounting for the effect of glycemic exposure and other covariates in multivariate analyses, only systolic blood pressure at baseline remained a significant risk factor, both for PDR (HR: 1.14 per 10 mmHg, 95%CI: 1.04-

1.25, P = 0.005) and macular edema (HR: 1.15 per 10 mmHg, 95%CI: 1.04-1.26, P = 0.004)

(Klein et al. 2008, 2009). Similarly in subjects with T2D, the UKPDS reported a significant association between baseline or updated mean systolic blood pressure and

16 microvascular complications (PDR or renal failure), after adjusting for other risk factors

(Adler et al. 2000).

Clinical trials of blood pressure (BP) control suggest a cause-effect relationship between hypertension and DR. In the UKPDS, tight blood pressure control (< 150/85 mmHg) was protective against microvascular complications including DR compared to a less strict control (< 180/105 mmHg) in T2D patients (UKPDS Group 1998b). After a median of 7.5 years of follow-up, despite similar glycemic control, patients under tight BP control had a 34% reduction in the risk of DR progression by ≥ 2 steps (relative risk:

0.66, 99%CI: 0.50-0.89, P=0.001) (Matthews et al. 2004). The ACCORD eye study provided contradictory results: after 4 years of follow-up intensive treatment (targeting a systolic BP < 120 mmHg) did not prove more effective compared to standard blood pressure control (targeting a systolic BP < 140 mmHg) in delaying the progression of

DR by at least 3 steps on the ETDRS scale or development of PDR (adjusted OR: 1.23,

95%CI: 0.84-1.79; P=0.29). Nonetheless, the two treatment groups only showed a mean difference of 5.6 mmHg in their blood pressure, which may describe these results (The

ACCORD Study Group and ACCORD Eye Study Group 2010).

Reducing BP even in normotensive patients seems beneficial. In the EURODIAB

Controlled Trial of Lisinopril in Insulin-Dependent Diabetes Mellitus (EUCLID study), normoalbuminuric normotensive patients on lisinopril (an ACE inhibitor) showed reduced odds of either DR progression by 1-step or more (OR: 0.50, 95%CI: 0.28-0.89,

P=0.02) or developing PDR (OR: 0.18, 95%CI: 0.04-0.82, P=0.03) which remained significant after adjusting for HbA1c (P =0.06 and P =0.04 respectively) (Chaturvedi et al.

1998). Similarly, in the Appropriate Blood Pressure Control in Diabetes (ABCD) trial, less progression of DR (≥2 steps) was observed in normotensive (< 140/90 mmHg) T2D patients under intensive (10 mmHg below baseline DBP) compared to moderate (80-89 mmHg) BP control over five years of follow-up (34 vs. 46%, P=0.02) (Schrier et al. 2002).

There is also evidence that blocking the renin-angiotensin system may have beneficial effect in delaying progression of DR, not mediated through BP. In normotensive normoalbuminuric T1D patients in the Renin Angiotensin System Study (RASS, 1997-

2008) after 5 years of follow-up, patients receiving enalapril (OR=0.35, 95% CI: 0.14-

0.85, P=0.02) or losartan (OR=0.30, 95% CI: 0.12-0.73, P=0.008) showed significantly lower odds of DR progression by two or more steps on the ETDRS scale, irrespective of changes in blood pressure (Mauer et al. 2009).

1.2.4.3 Hyperlipidemia

Hard exudates, observed in DR, are depositions of lipoproteins in the outer retinal layer. Therefore, it is not surprising that hyperlipidemia is associated with hard exudates in epidemiologic studies. For example, in T1D patients from WESDR, serum total cholesterol was associated with presence of hard exudates (OR: 1.65 per 50 mg/dL,

95%CI: 1.24-2.18) (Klein et al. 1991a). However, after accounting for the effects of glycemia, blood pressure and other covariates, serum cholesterol was not a significant risk factor for incidence or progression of PDR or CSME (Klein et al. 1999b). In the

DCCT, while adjusting for HbA1c and other covariates, total cholesterol (P=0.001), LDL cholesterol (P=0.002), total/HDL ratio (P=0.0004) and triglyceride (P=0.006) levels were all significant predictors of hard exudate development (Miljanovic et al. 2004). LDL

(P=0.03) and total/HDL ratio (P=0.03) also remained significant risk factors for CSME incidence, but not PDR incidence or DR progression. A subsequent study, using nuclear magnetic resonance spectroscopy to determine lipid subclass profiles of 968

(out of 1441) DCCT/EDIC participants, showed that ETDRS retinopathy severity score at EDIC year 4 was marginally associated with concurrent triglyceride (P=0.06) and

HDL level (P<0.002) while accounting for the effect of all the other relevant covariates

(Lyons et al. 2004).

Experimental evidence for the beneficial effect of lipid lowering therapy in preventing or delaying DR and hard exudates is limited. A number of small or short-term pilot trials suggest beneficial effects of clofibrate (a discontinued fibrate) and statins in preventing or regressing hard exudates and macular edema (Chowdhury et al. 2002;

Valensi and Picard 2011). In the Fenofibrate Intervention and Event Lowering in

Diabetes (FIELD) study ~10,000 patients with T2D were followed for 5 years. Patients in the fenofibrate treatment group were less likely to require laser treatment for DR compared to placebo (HR: 0.69, 95%CI: 0.56-0.84, P=0.0002). Also, in a subset of ~1000 participants undergoing fundus photography, a composite endpoint of ≥2 step progression, macular edema or laser treatment was less likely in the fenofibrate group

(HR: 0.66, 95%CI: 0.47-0.94, P=0.02) (Keech et al. 2007). The Action to Control

Cardiovascular Risk in Diabetes (ACCORD) eye study compared the progression of DR over 4 years in 1593 dyslipidemic participants with T2D randomly assigned to simvastatin and placebo (to only lower LDL) or simvastatin plus fenofibrate (to also reduce triglyceride and increase HDL). 6.5% of patients receiving fenofibrate vs. 10.2% with placebo showed ≥3 step progression of DR (OR: 0.60, 95%CI: 0.42-0.87, P=0.006)

(Chew et al. 2010).

1.2.4.4 Other risk factors

Thus far, aside from glycemia, blood pressure and lipids, no other risk factor has shown conclusive causal relationship with DR. Yet, a few other tentative risk factors are worth mentioning:

Puberty: DR is infrequent before 13 years of age, irrespective of diabetes duration

(Klein et al. 1984a). Several studies have confirmed a drastic increase in the incidence and severity of DR after puberty (Frost-Larsen and Starup 1980; Klein et al. 1990b;

Murphy et al. 1990). Hormonal changes, such as the increase in GH, IGF-1 and sex

19 hormones, leading to increased insulin resistance have been proposed as possible explanations.

Smoking: Smoking causes tissue hypoxia, platelet adhesion and aggregation. Both phenomena may accelerate DR. However, the association of smoking with DR has been inconsistent in epidemiologic studies. In WESDR, neither smoking status nor pack- years smoked showed an association with incidence or progression of DR (Moss et al.

1996). However, in post-hoc analysis of the DCCT, tobacco use (pack-year) was significantly associated with both pre-study (P=0.03) and overall (P=0.001) progression rate of ETDRS per year of diabetes duration (Cundiff and Nigg 2005). A matched case- control study of 384 T1D subjects reported higher prevalence of PDR in smokers

(P<0.025) compared to non-smokers (Muhlhauser et al. 1986). Studies in T2D patients do not agree on a detrimental association between smoking and DR (Kostraba et al.

1991; Stratton et al. 2001).

Body Mass Index (BMI): There is evidence for an association between high BMI and risk of DR, especially in T1D. In the WESDR, higher BMI was an independent risk factor for DR progression (HR: 1.16 per 4 kg/m2, 95%CI: 1.07-1.26, P<0.001) and PDR incidence (HR: 1.21 per 4 kg/m2, 95%CI: 1.07-1.36, P=0.002) over 25 years (Klein et al.

2008). In the Diabetes Incidence Study in Sweden (DISS), higher BMI shortened time to

DR (HR: 1.11, 95%CI: 1.04-1.18, P=0.001) (Henricsson et al. 2003). In the recent Diabetes

Management Project (DMP) study of T1D and T2D subjects, after accounting for other major risk factors, DR (OR: 3.12, 95%CI: 1.20-8.16, P=0.02) and PDR (OR: 6.52, 95%CI:

1.49-28.6, P=0.01) were more prevalent in the obese patients (BMI >30 kg/m2) compared to those with a normal BMI (Dirani et al. 2011). However, several studies in T2D patients provided inconsistent (van Leiden et al. 2003) or contradictory results (Lim et al. 2010; Raman et al. 2010).

Physical activity: Physical activity improves glycemic, blood pressure and lipid control. Nonetheless, observational studies do not support a significant association between physical activity and risk of DR when accounting for other covariates (Klein and Klein 2013). It seems that beneficial effects of physical activity are mostly mediated through other known risk factors.

Female hormonal status: Neither oral contraceptives nor hormone replacement therapy seem to increase risk of DR in women (Klein et al. 1999a; Klein et al. 1990c).

However, pregnancy increases the risk of DR progression, even beyond its detrimental effect on glycemia and blood pressure control (Hemachandra et al. 1995; Klein et al.

1990a; Rasmussen et al. 2010). Increased serum IGF-1 has been proposed as a potential mediator of DR progression in pregnancy (Ringholm et al. 2011). Considering this increased risk, evaluation of retinopathy status has been recommended before conception and early in the first trimester with frequent follow-ups during pregnancy

(every 1-3 months depending on severity of DR) in diabetic patients (American

Academy of Ophthalmology Retina Panel 2008).

1.2.5 Relationship between diabetic retinopathy and nephropathy

Correlation of diabetic retinopathy (DR) and nephropathy (DN) is not unexpected considering shared risk factors such as hyperglycemia and hypertension. However, these two microvascular complications seem to be correlated beyond the effect of common known risk factors, implying shared yet unknown pathophysiological mechanisms between them.

Markers of early nephropathy, such as albumin excretion rate or proteinuria, are associated with DR. In a Danish study of T1D patients, prevalence of PDR and blindness increased with increasing albuminuria (P<0.01). At every level of diabetes

21 duration prevalence of PDR was higher in those with macroalbuminuria compared to the microalbuminuria group which in turn had a higher prevalence than normoalbuminuric patients (Parving et al. 1988). Similarly in cross-sectional analyses of the WESDR, after controlling for other factors, microalbuminuria was associated with

PDR (OR: 3.17, 95%CI: 1.76-5.71) (Cruickshanks et al. 1993). In longitudinal analyses of

WESDR, proteinuria at baseline was a significant independent predictor of cumulative incidence of PDR (HR: 1.83, 95%CI: 1.31-2.56, P<0.001) and macular edema (HR: 1.43,

95%CI: 0.99-2.08, P=0.056) over 25 years (Klein et al. 2008, 2009). Similar results were reported in other longitudinal studies (Gilbert et al. 1998; Lloyd et al. 1995). Studies in

T2D patients, also, confirm albuminuria as a significant predictor of PDR, CSME and retinopathy severity (Klein et al. 1988; Savage et al. 1996).

Conversely, retinopathy severity is a predictor of future diabetic nephropathy. In the

WESDR, retinopathy severity status at baseline was significantly associated with 4 year incidence of gross proteinuria (P<0.0005) even after accounting for the effect of other factors such as blood pressure and A1C (OR: 1.16, 95%CI: 1.08-1.25, P<0.0001) (Klein et al. 1991b). In another large cohort study, PDR at baseline was an independent predictor of nephropathy at the 25 year follow-up exam (OR: 2.98, 95%CI: 1.18-7.51, P=0.02)

(Karlberg et al. 2012). One should keep in mind that single or infrequent measurement of A1C may not fully capture long-term glycemic exposure. Therefore, the independence of these associations may be attenuated once fully accounting for the effect of glycemic exposure. In the general population, retinopathy status seem to predict future renal dysfunction irrespective of diabetes (Wong et al. 2004).

Despite a similar microvascular origin and shared risk factors, there is not a complete correlation between the two conditions. This suggests different sensitivity of each organ to the same level of risk factors and possibly genetic determinants that influence such organ specific susceptibility in each patient.

1.3 Genetics of Diabetic Retinopathy

1.3.1 Evidence for a genetic contribution to DR

Individual variation in developing diabetic complications, including retinopathy, is well known to clinicians (Borch-Johnsen et al. 1987). A large portion of this individual variation has been attributed to the quality of glycemic (metabolic) control and other known risk factors. Yet, there are certain patients who do not develop diabetic retinopathy despite poor glycemic control and others who develop retinopathy despite fair control (The DCCT Research Group 1993; The DCCT/EDIC Research Group et al.

2009). In the Joslin study of long term (>50 years) T1D survivors, a high proportion of participants had not developed PDR (43%), despite years of diabetes. Current or longitudinal glycemic control was unrelated to development of complications in the

Joslin study. In the PDR free group, almost all (96%) of the patients with no progression of retinopathy during the first 17 years of follow-up, did not show any worsening of DR thereafter (Sun et al. 2011a). The individual variation in developing diabetic retinopathy, unexplained by conventional risk factors, can denote a possible genetic determination.

This section will review other observations that provide further evidence for a genetic contribution to diabetic retinopathy:

1.3.1.1 Ethnic differences in developing diabetic retinopathy

Many studies have reported ethnic differences in the prevalence of retinopathy both in people without diabetes and with diabetes (Ojaimi et al. 2011; Sivaprasad et al. 2012).

Multiethnic studies that have adjusted for known risk factors of DR generally support an independent ethnic risk for developing DR, especially in T2D. Table 4 summarizes the more recent of these studies.

A recent analysis of 2804 White and 1008 Black US adults over 40 years of age from

National Health and Nutrition Examination Survey (NHANES: 2005-2008) showed a higher crude prevalence of retinopathy in black persons (13.1±1.1%) compared to whites (6.3±0.5%). While adjusting for the effects of age, gender, hypertension, body mass index, family history of diabetes, oral anti-diabetic medication and insulin use, it appeared that the probability of retinopathy began to rise at lower A1C levels in blacks

(A1C value of 5.5% in blacks vs. 6.5% in whites; P<0.05). Also the adjusted risk of retinopathy seemed to be higher in blacks compared to whites throughout the ranges of A1C tested (Tsugawa et al. 2013). The Veterans Affairs Diabetes Trial (VADT) examined 1283 patients with T2D and reported significantly higher prevalence of moderate non-proliferative diabetic retinopathy or worse (ETDRS score >40) in both

Hispanics (36%) and Blacks (29%) in comparison to non-Hispanic whites (22%) which could not be accounted for by risk factors such as age, duration of diabetes, A1C and blood pressure (Emanuele et al. 2005). Lanting et al reviewed studies of retinopathy in diabetes and concluded that after adjusting for traditional risk factors, risk of retinopathy in US Hispanics seemed to be higher (than whites) while evidence for higher risk in blacks was controversial (Lanting et al. 2005).

Due to lower prevalence of T1D in people of non-European ancestry, few studies have examined ethnic differences in the prevalence of DR in T1D. Moreover, published studies generally do not account for the effect of traditional risk factors. Based on a few available studies, racial differences in risk of retinopathy seem to be less pronounced in

T1D. Crude prevalence of both any DR and vision-threatening DR was similar among

594 black participants in the New Jersey 725 study (1993-1997) and 790 white participants in WESDR (1980-1982) (Roy et al. 2004). Although one should keep in mind that WESDR was an earlier study. Among 3323 T1D patients participating in the

DRIVE-UK study, some ethnic differences were observed without reaching statistical significance (Sivaprasad et al. 2012).

Table 4. Ethnic risk of diabetic retinopathy study diabetes sample phenotype comparison crude OR adjusted OR P Multivariate type size (95% CI) (95% CI) model T1D 1537 Caucasian 1247 any DR Asian vs. Caucasian 1.10 (0.74-1.63) 1.78 <0.05 African 117 referable DR* African vs. Caucasian 0.95 (0.49-1.84) 3.36 <0.05 age at diagnosis, Centre for Diabetes Asian 118 sex, and Endocrinology in diabetes duration, Johannesburg T2D 3978 Caucasian 2662 any DR African vs. Caucasian 2.00 (1.62-2.48) 1.9 HbA1c, (Thomas et al. 2013) African 580 Asian vs. Caucasian 1.83 (1.48-2.26) 1.74 hypertension, Asian 562 mixed vs. Caucasian 2.66 (1.88-3.76) 2.95 smoking Mixed 159 1006 white 396 any DR black vs. white 3.77 (1.47-9.69) age, sex, education, T1D and black 306 health insurance, HbA1c, duration of NHANES 2005-2008 T2D diabetes, insulin (Zhang et al. 2010b) Mexican American 197 use, SBP, DBP, BMI, smoking, history of other 107 CVD

T2D 1035 white 614 any DR South Asian vs. white 1.30 (0.99-1.71) 1.46 (1.06-2.02) 0.02 age at diagnosis, UK Asian Diabetes sex, Study – Retinopathy South Asian 421 STDR South Asian vs. white 1.38 (0.95-2.02) 1.44 (0.94-2.23) 0.09 diabetes duration, SBP, A1C, (Raymond et al. 2009) maculopathy South Asian vs. white 1.54 (1.00-2.38) 1.53 (0.94-2.49) 0.08 insulin treatment

Anglia P University mixed 500 South Asian 268 STDR South Asian vs. white 3.18 (2.02-5.01) age, duration of (Pardhan et al. 2004) white 232 diabetes

The SEARCH for T1D and 265 White 188 any DR non-white vs. white 2.05 (0.97-4.33) 0.06 diabetes type, diabetes in youth T2D (222+43) (176+12) diabetes duration, study – pilot (Mayer- other 77 HbA1c, gender, age, parental education Davis et al. 2012) (46+31) T2D 117 Australian 60 any DR Greek vs. Australian 0.32 (0.10-0.99) age, duration of Greek 47 diabetes, HbA1c, (Brazionis et al. 2010) SBP, DBP, ACR, total cholesterol, triglyceride level Washington T1D 312 white 215 PDR black vs. white 1.86 (0.93-3.70) 0.73 (0.30-1.78) University Diabetes retinopathy grade at baseline, glycemic Research and Training African-American 97 control, follow-up Center (Arfken et al. interval 1998) Referable DR: pre-proliferative and proliferative DR and exudative maculopathy; Asian: Asian Indian; STDR: sight threatening DR

24 25

Although with our current knowledge it is not possible to tease apart the effect of socioeconomic and racial inequalities on the observed ethnic differences in prevalence of DR, there is some evidence suggesting an authentic ethnic difference. In a recent study of 24,458 diabetic individuals participating in the diabetes retinal screening service in South London, ethnic differences in sight threatening DR remained significant after adjusting for regional deprivation rank (based on income, employment, health access, education, housing and crime rate), age, sex, diabetes duration and diabetes type (Gulliford et al. 2010). Analysis of 23,434 US residents participating in the Behavioral Risk Factor Surveillance System (BRFSS, 1995-2001) showed that, contrary to common assumptions, Blacks and Hispanics were engaged equally or more in preventive care measures compared to whites based on yearly doctor visit, eye examination or foot examination in the past year (Oladele and Barnett

2006).

Nonetheless, ethnic differences do exist in the risk of diabetic retinopathy, especially in

T2D, a portion of which is probably attributable to differences in diabetes care among ethnic groups. However, there are ethnic variations unexplained by traditional risk factors which could be due to genetic differences among the studied populations.

1.3.1.2 Twin studies of diabetic retinopathy

Twin studies are very useful in investigating genetic basis for a disease, as higher concordance in monozygotic (MZ) compared to dizygotic (DZ) twins argues for a genetic contribution. One of the first published studies reported “strikingly similar” progression and severity of retinopathy in identical twins concordant for diabetes

(Pyke and Tattersall 1973). In a subsequent report of 95 identical twins, 35 out of 37 pairs concordant for T2D showed similar levels of retinopathy including 14 out of 15

26 pairs with similar duration of diabetes. Among the MZ twins concordant for T1D, 21 out of 31 pairs showed similar degrees of retinopathy. The authors concluded that DR was under stronger genetic influence in T2D compared to T1D (Leslie and Pyke 1982).

In the Japan Diabetic Society study of diabetic twins (mixed T1D and T2D) 25 out of 30 examined MZ pairs were concordant for level of retinopathy (3-degree scale: none, simple, proliferative) and 4 of 5 discordant pairs could be explained by differences in duration of diabetes (Committe on Diabetic Twins - Japan Diabetes Society 1988).

Limited twin studies, thus far, support a genetic predisposition to diabetic retinopathy.

However, there remains a need for twin studies of diabetic retinopathy with larger sample sizes, comparing MZ and DZ twins while adjusting for confounders such as duration of diabetes. An obstacle for such studies in T1D is the low concordance rate for T1D in DZ twins (~5%), which makes T1D concordant pairs relatively rare. A possible solution for this problem is comparing the prevalence of DR between MZ twins and sib-pairs concordant for diabetes.

1.3.1.3 Familial clustering of diabetic retinopathy

Increased risk of a disease in the relatives of an affected individual can signify a possible genetic contribution. The DCCT family clustering study reported higher risk of developing severe retinopathy (OR=3.07, 95%CI: 1.21-7.77) in the relatives of patients with severe retinopathy (ETDRS ≥ 47, moderate NPDR or worse) in the secondary intervention cohort of DCCT, which persisted after adjusting for duration of diabetes in the relatives. In separate subanalyses, this increased risk appeared to be only significant in the conventional treatment group (Table 5). However, the risk of developing any DR (ETDRS ≥ 20) was not higher in the relatives of patients with retinopathy in either primary or secondary cohort (The DCCT Research Group 1997).

Subsequent studies both in T1D and T2D sibships (Table 5) with comparable or larger

Table 5. Familial clustering of diabetic retinopathy in the relatives of diabetic probands

Probands Relatives

study n diabetes relation n diabetes phenotype phenotype in subgroup OR-crude OR-adjusted reference type type in probands relatives (95%CI) (95%CI)

DCCT 114 T1D 1st degree 129 mixed severe DR* severe DR* intensive Rx 2.42 (0.72-8.09) 1.77 (0.41-7.55)a (The DCCT

Research 2ndary cohort conventional Rx 4.33 (1.01-18.60) 5.17 (1.22-21.91)a Group 1997)

combined 3.07 (1.21-7.77) 3.12 (1.12-8.76)a

any DR† any DR† intensive Rx 0.36 (0.12-1.07) 0.51 (0.10-2.74)b

conventional Rx 0.78 (0.24-2.55) 0.63 (0.10-4.04)c

combined 0.52 (0.23-1.14) 0.59 (0.21-1.64)c

Chennai city 322 T2D siblings 355 T2D DR‡ DR‡ 4.3 (2.4-7.8) 3.37 (1.56-7.29)d (Rema et al.

2002) DR‡ NPDR without 3.85 (1.89-7.84) maculopathy

DR‡ NPDR with 3.38 (1.35-8.43) maculopathy

Starr County 282 T2D siblings 374 T2D any DR§ any DR§ 0.67 (0.41-1.09) (Hallman et al.

2005) severe DR¶ severe DR¶ 1.72 (1.10-2.69) 1.71 (1.03-2.84)e

FinnDiane 168 T1D siblings 182 T1D PDR** PDR** 4.07 (2.06-8.07) 2.76 (1.25-6.11)f (Hietala et al.

2008)

Chongqing 167 T2D siblings 247 T2D DR†† DR†† 4.21 (P<0.0001) 5.57 (2.34-13.21)g (Zhang et al. 2010a) * severe DR: ETDRS score ≥47 or clinically significant macular edema or laser treatment † any DR: ETDRS score ≥20 ‡ DR defined as non-proliferative DR without maculopathy or worse, i.e. presence of microaneurysms and hard exudates, mild intraretinal hemorrhages in fewer than 4 quadrants § any DR: ETDRS score ≥15 ¶ severe DR: ETDRS score ≥43 ** PDR: proliferative retinopathy, ETDRS score ≥61 †† DR: defined as moderate non-proliferative DR (NPDR) or worse, i.e. more than just microaneurysms a. model adjusted for duration of the diabetes in the relative and treatment group (only combined analysis) b. model adjusted for the relative’s age, duration of diabetes and interaction of these two predictors c. model adjusted for the relative’s age, duration of diabetes, interaction of these two predictors and HbA1c level plus the treatment group in the combined analysis d. DR in the sibling ~ duration of diabetes + retinopathy in proband + HbA1c + systolic blood pressure + proteinuria (all covariates were measurements in the siblings) e. model adjusted for diabetes duration and HbA1c level f. model adjusted for mean arterial blood pressure, A1C, duration of diabetes and gender. g. model adjusted for diabetes duration, history of smoking and non-esterified fatty acid level (predictors with P<0.05 in multiple logistic regression)

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Table 6. Correlation between the retinopathy severity grades of probands and their relatives study outcome relation number number Correlation* 95%CI of of family families members DCCT log ETDRS all available 1st degree relatives 219 460 0.19* 0.07-0.31 parent-child 127 268 0.33 0.17-0.47 father-child 70 144 0.25 0.02-0.45 mother-child 58 125 0.39 0.17-0.58 sib-sib 103 216 0.06* 0.00-0.24 FinnDiane ETDRS sib-sib 169 0.37* 0.24-0.43

* Intraclass correlation is reported in marked cells. Unmarked cells report pairwise correlation coefficients.

Data from (The DCCT Research Group 1997) and (Hietala et al. 2008).

sample sizes, generally support familial clustering in the occurrence of DR, independent of other known risk factors, especially with more severe forms of DR

(Hallman et al. 2005; Hietala et al. 2008; Rema et al. 2002; Zhang et al. 2010a). Hietala et al also reported a strong correlation for time-to proliferative DR in 29 sib pairs concordant for this outcome (ICC=0.47, 95% CI: 0.14-0.71, P=0.004) (Hietala et al. 2008).

Retinopathy grades of probands and relatives in both DCCT and FinnDiane generally show statistically significant correlations (Table 6). There seem to be clustering of both the occurrence and severity of DR in families.

While significant familial clustering of DR in both T1D and T2D argues for a genetic influence in DR, the cross-sectional nature of these studies generally make them less efficient in accounting for the confounding effect of glycemic control. The results of such studies should therefore be interpreted cautiously.

1.3.1.4 Heritability of diabetic retinopathy

Heritability (H2) is an estimate of the strength of genetic effects for a trait or disease (Al-

Chalabi and Almasy 2009; Khoury et al. 1993). It is defined as the proportion of total

phenotypic variance of a trait in a population ( ) that can be attributed to genotypic

variance (allelic differences) among individuals ( ).

The genetic variance ( ) in itself is the sum of (at least) three distinct components:

The additive genetic variance ( ) is the more familiar cumulative effect of individual alleles on the trait. Dominance variance ( ) represents the non-additive effect from interaction of alleles at the same locus while epistatic variance ( ) represents the effect of interaction between alleles at distinct loci.

Most contemporary human family studies only focus on the additive genetic effect and define narrow-sense heritability (h2) as the proportion of overall phenotypic variance described by the total additive genetic effects, which is probably an underestimation of the true genetic effect in many cases:

Studies that have examined heritability of DR in diabetic sibships provide estimates ranging from 0.18 to 0.52, consistent with a moderate genetic contribution (Table 7). It is possible that differences in the estimates arise from different genetic contribution based on diabetes type or DR severity. Another source of error in heritability estimate may be due to study design limitations; for example, recruitment of sibships based on concordance for diabetic nephropathy is a source of an ascertainment bias in the FIND and possibly the FinnDiane study.

Table 7. Heritability estimates of diabetic retinopathy Diabetes N of N of sib study ethnicity phenotype heritability model reference type families pairs Pima DR severity Starr 0.18 Age + sex + (Looker et al. native T2D 211 725 score worse County (0.02-0.36)* diabetes duration 2007) Americans eye Ethnicity + nephropathy status + DR severity sex + age + (age)2 + (Arar et al. FIND mixed T2D 767 2183 0.27 score diabetes duration + 2008) (diabetes duration)2 + sex*age + HbA1c (Arar et al. FIND mixed T2D 2183 PDR liability 0.25 NA 2008) (Hietala et al. FinnDiane Caucasian T1D 362 PDR 0.52 ± 0.31† A1C+duration+BP 2008) FIND: The Family Investigation of Nephropathy and Diabetes; FinnDiane: The Finnish Diabetic Nephropathy Study * estimate (95% confidence interval) † estimate ± standard error

1.3.2 Linkage studies of diabetic retinopathy

Linkage studies identify disease (or phenotype) loci that cosegregate with nearby genetic markers at specific genomic regions. Before the current boom of genome wide association studies, for more than a decade, linkage studies were the main method available for genetic study of complex diseases. The established parametric linkage methods, based on the single major locus assumption, have been extremely successful in mapping loci for Mendelian disease. Yet, these methods are not usually suitable for the study of complex diseases, because: there is generally considerable genetic heterogeneity; complex diseases usually do not follow an established mode of inheritance and it is difficult to recruit large families with multiple affected members.

Alternative nonparametric methods have been developed that use the extent of allele sharing in nuclear families or sib pairs to detect linkage, with relative success in mapping complex disease loci (Botstein and Risch 2003). Nonetheless, linkage studies, in their usual sample sizes, are generally underpowered in detecting alleles with modest effect sizes (Risch and Merikangas 1996). Besides, relatively low cost of array- based genotyping, difficulty of recruiting related individuals, availability of large

31 epidemiologic studies with data from thousands of unrelated patients and higher resolution of association studies have shifted the focus to association studies over the past decade.

Table 8 summarizes linkage studies of diabetic retinopathy published thus far. All of the studies used the affected sib-pair design which compares the extent of identity-by- descent (IBD) allele sharing between affected pairs relative to what is expected by chance.

Table 9 summarizes the result of the published linkage studies of DR (Hallman et al.

2007; Imperatore et al. 1998; Looker et al. 2007). All linkage peaks with LOD greater than 1 are reported. With the exception of chromosome 1 locus (1p36), showing suggestive linkage (Lander and Kruglyak 1995) as well as replication, no other loci showed consistent linkage with DR in these studies (Table 9). Causal gene (variant) at the 1p36 locus for DR is yet to be identified. Aside from genetic heterogeneity and ethnic differences that may explain the inconsistency of these linkage studies; the strong effect of other risk factors such as diabetes duration should also be taken into

Table 8. Summary of linkage studies of diabetic retinopathy ethnicity diabetes N affected N analysis phenotype max reference type sib pairs marker LOD Pima native T2D 103 516 two point; multipoint DR 2 1.46 (Imperatore et al. 1998) American 1 linkage Pima native T2D 211 516 quantitative trait linkage ETDRS score 3 3.1 (Looker et al. 2007) American 1 Mexican- T2D 282 360 unconditional analysis any DR 4 2.47 (Hallman et al. 2007) American 74 unconditional analysis NPDR-S/PDR 5 1.4 282 OSA 6 any DR 4 4.47 74 OSA 6 NPDR-S/PDR 5 2.53

1 These two studies analyzed the same population. 2 DR defined by the presence of at least one microaneurysm, hemorrhage or proliferative DR (ETDRS score ≥15). 3 DR severity (ETDRS) score of worse eye adjusted for age, sex and diabetes duration was the trait. 4 any DR defined as presence of early non-proliferative DR or worse. 5 NPDR-S/PDR defined as presence of moderate-to-severe non-proliferative or proliferative DR. 6 OSA: ordered subset analysis based on mean age of diagnosis in the sib pairs.

Table 9. Summary of loci with evidence for linkage (LOD > 1) to diabetic retinopathy.

Ordered Subset Analysis2 chr peak (cM) peak marker phenotype1 LOD LOD direction families used study3 P 1 34.2 D1S3669 ETDRS score 3.01 L 1 45.3 GGAT2A07 Any DR 1.24 H 0.017 1 53.1 D1S1622 ETDRS score 1.01 L 2 260.6 AFM112yd4 NPDR-S/PDR 1.11 H 0.024 3 9.4 GATA22G12 NPDR-S/PDR 1.29 H 0.015 3 117 GATA68D03 NPDR-S/PDR 1.40 H 0.011 3 117 GATA68D03 Any DR 2.41 H 0.0009 3 188 D3S3053 - D3S2427 DR 1.36 I 5 11.2 GATA84E11 NPDR-S/PDR 0.15 2.53 LH 24/52 H 0.013 6 30.6 GATA29A01 NPDR-S/PDR 0.62 2.28 LH 21/52 H 0.041 7 33.1 GATA41G07 Any DR 1.02 H 0.030 9 89 D9S1120 - D9S910 DR 1.46 I 12 13.2 GATA49D12 Any DR 2.47 4.47 HL 73/177 H 0.018 12 15.5 GATA49D12 Any DR 2.47 H 0.0007 12 100.5 GATA85A04 NPDR-S/PDR 1.03 H 0.030 15 78.4 ATA28G05 Any DR 1.07 H 0.026 15 100.6 GATA73F01 Any DR 0.99 3.65 HL 38/177 H 0.03 15 108.3 GATA22F01 Any DR 1.16 H 0.021 18 99 GATA7E12 Any DR 0.06 1.90 LH 17/177 H 0.033 19 100.6 Mfd238 NPDR-S/PDR 0.28 2.21 LH 27/52 H 0.037 20 54.1 GATA42A03 Any DR 0.00 2.67 HL 29/177 H 0.0042

1 ETDRS score was used as the trait after adjusting by age, sex and diabetes duration; DR was defined as presence of at least one microaneurysm (ETDRS>15); any DR was defined as early non-proliferative DR or worse; NPDR-S/PDR: moderate-to-severe non- proliferative or proliferative DR. 2 ordered subset analysis was performed by ranking families based on the average age of diabetes diagnosis and running the analysis sequentially in fewer (more homogeneous) subset of families. Direction shows the subsetting strategy, i.e. higher to lower average age of diabetes diagnosis or vice versa. 3 L: Looker et al, 2007; H: Hallman et al, 2007; I: Imperatore et al, 1998

account as a potential confounder. Interestingly, the most convincing linkage signal was detected in the only study that accounted for the effect of diabetes duration

(Looker et al. 2007).

1.3.3 Candidate gene association studies of diabetic retinopathy

Candidate gene case-control association studies examine the differences in frequency of certain genetic variants, within (or close to) a candidate gene, between cases and controls. Association is implied when the frequency of such variant is significantly higher (or lower) in cases, conferring risk (or protection) for the disease under study.

These candidate genes are usually selected either based on their genetic position (for example genes close to a linkage peak for the disease) or more commonly based on some prior biochemical or pathophysiologic knowledge (inferences based on the gene’s in vitro or in vivo function or expression pattern).

Over the past two decades, tens of candidate gene association studies of DR have been published. The results of these studies have often been inconsistent. Most of these inconsistencies are likely due to differences in the definition of phenotype (DR), use of healthy vs. diabetic controls and potential confounding effect of unaccounted risk factors such as diabetes duration which could arise from an ascertainment bias.

Moreover, differences in ethnicity or diabetes type (1 vs. 2), winner’s curse, small sample sizes or false positive results may further explain the observed inconsistencies.

Yet there are several genes with evidence for association of specific variants with diabetic retinopathy in meta-analyses of published association studies (Table 10), mostly contributing modest risk or protection.

The association of 287 bp indel polymorphism (rs4646994) of angiotensin converting enzyme (ACE) with diabetic retinopathy has been controversial. Based on the published meta-analyses, the association seems more consistent with the more severe proliferative DR. The same variant has also been associated with diabetic nephropathy in several meta-analyses. Other examples of pleiotropy are the associations of superoxide dismutase 2 (SOD2) and aldose reductase (AKR1B1) with both diabetic

Table 10. Summary of genetic polymorphisms associated with diabetic retinopathy in published meta-analyses

number number gene diabetes variant rsID function of ethnicities comparison of cases / symbol type studies controls

T2D 17 Chinese DR vs DWR 1039/1185 T2D 6 Chinese PDR vs DWR 147/292 mixed 7 mixed DR vs DWR 1008/1002 ACE I/D rs4646994 intron mixed 3 Caucasian PDR vs NPDR 276/296 * mixed 18 mixed DR vs DWR 2342/2048 mixed 6 mixed PDR vs DWR 590/449 G1704T rs184003 intron T2D 4 mixed DR vs DWR 776/934 AGER -374T/A rs1800624 promoter T2D 4 mixed DR vs DWR 634/953 Gly82Ser rs2070600 missense T2D 4 mixed DR vs DWR 729/914 AGT rs4762 rs4762 missense mixed 2 mixed DR vs DWR 201/350 * T1D 3 mixed DR vs DWR 114/149 * rs759853 rs759853 promoter mixed 3 Brazilian PDR vs NPDR 130/183 * mixed 15 mixed DR vs DWR 747/432 * AKR1B1 mixed 3 mixed NPDR vs DWR 159/78 *

(CA)n repeat promoter mixed 6 mixed PDR vs DWR 214/120 * mixed 3 mixed PDR vs NPDR 156/67 * T1D 6 mixed DR vs DWR 153/224 * CHN2 rs39059 + rs39059 T2D 1 Chinese DR vs DWR 805/1017 ICAM1 rs13306430 rs13306430 missense T2D 2 East Asian DR vs DWR 150/89 * ITGA2 rs2910964 rs2910964 intron T2D 2 mixed DR vs DWR 258/137 * MTHFR C677T rs1801133 missense T2D 5 mixed DR vs DWR 435/620 MTHFR C677T rs1801133 missense T2D 5 mixed DR vs DWR 435/620 MTHFR 677C/T rs1801133 missense mixed 8 mixed DR vs DWR NA/NA 4b/a (27bp NOS3 rs3138808 intron T2D 8 mixed DR vs DWR 1475/1609 indel) PPARG Pro12Ala rs1801282 missense T2D 8 mixed DR vs DWR 2720/2450 SERPINE1 4G/5G rs1799768 promoter T2D 9 mixed DR vs DWR 1217/1459 SOD2 C47T rs4880 missense mixed 6 mixed DR vs DWR 696/644 SUV39H2 rs17353856 rs17353856 syn. T1D 1 Caucasian PDR vs DWR 2139/2326 VEGFA -634G>C rs2010963 5'-UTR T2D 9 mixed DR vs DWR 1525/1422 VEGFA rs2010963 rs2010963 5'-UTR T2D 3 mixed NPDR vs DWR 242/328 * VEGFA -634 C/G rs2010963 5'-UTR mixed 3 mixed NPDR vs DWR 229/255 number of studies: number of studies included in meta-analysis, in case of CHN2 and SUV39H2 a single study performed meta- analysis of several populations. OR: odds ratio; I2: Higgins and Thompson’s heterogeneity index; Phet: Cochran’s Q test of heterogeneity P-value; FEM: fixed-effects meta-analysis; REM: random-effects meta-analysis; NA: not available DR: diabetic retinopathy; DWR: diabetes without retinopathy; PDR: proliferative DR; NPDR: non-proliferative DR * Abhary et al reported the number of cases and controls with the effect allele instead of number of cases and controls

freq. freq. Best effect other effect effect reported OR (95% CI) P I2 Phet method reference allele allele allele allele genetic cases controls model

D I 0.50 0.47 dominant 1.41 (1.16-1.72) 30 0.117 FEM (Lu et al. 2012)

D I NA NA dominant 1.86 (1.18-2.93) 24 0.251 FEM (Lu et al. 2012)

D I NA NA dominant 0.91 (0.73-1.13) >0.9 FEM (Fujisawa et al. 1998)

D I 0.58 0.49 allelic 1.52 (1.00–2.32) 0.05 REM (Abhary et al. 2009)

D I 0.53 0.54 allelic 1.06 (0.97-1.16) 27 0.12 FEM (Zhou and Yang 2010)

D I NA NA dominant 1.37 (1.02-1.84) 0 0.52 FEM (Zhou and Yang 2010)

T G NA NA allelic 1.24 (1.01–1.51) 0.036 0 0.93 REM (Niu et al. 2012) A T 0.23 0.24 recessive 0.64 (0.42, 0.99) 0.04 0 0.55 FEM (Yuan and Liu 2012) A (Ser) G (Gly) 0.12 0.13 recessive 2.89 (1.49, 5.60) 0.002 36 0.2 FEM (Yuan and Liu 2012) C T 0.24 0.27 allelic 0.79 (0.64–0.99) 0.04 REM (Abhary et al. 2009)

G A 0.29 0.45 allelic 0.49 (0.36–0.68) <0.0001 REM (Abhary et al. 2009)

G A 0.34 0.42 allelic 0.73 (0.54-0.99) 0.04 REM (Abhary et al. 2009)

Z-2 0.3 0.13 allelic 2.33 (1.49-3.64) 2E-04 REM (Abhary et al. 2009)

Z-2 0.38 0.28 allelic 1.64 (1.14-2.35) 0.008 REM (Abhary et al. 2009)

Z-2 0.29 0.2 allelic 1.51 (1.16-1.97) 0.002 REM (Abhary et al. 2009)

Z-2 0.34 0.22 allelic 1.77 (1.26–2.48) 9E-04 REM (Abhary et al. 2009)

Z+2 0.16 0.23 allelic 0.58 (0.36-0.93) 0.02 REM (Abhary et al. 2009)

A G 0.63 0.58 additive 1.29 (1.12-1.47) 0.93 FEM (Hu et al. 2011)

G A 0.36 0.5 allelic 0.56 (0.39–0.81) 0.002 REM (Abhary et al. 2009)

A G 0.46 0.34 allelic 1.65 (1.26–2.15) 2E-04 REM (Abhary et al. 2009)

T C 0.43 0.37 T vs C 1.39 (1.05-1.83) 52 0.08 REM (Zintzaras et al. 2005)

T C 0.43 0.37 TT vs CC 1.82 (1.08, 3.05) 40 0.15 REM (Zintzaras et al. 2005)

T C NA NA allelic 1.32 (1.06-1.64) 0.011 35 0.16 REM (Niu and Qi 2012) a (del) b (ins) 0.18 0.23 allelic 0.85 (0.74–0.97) 0.014 36 0.14 FEM (Zhao et al. 2012)

G (Ala) C (Pro) 0.10 0.19 dominant 0.81 (0.68-0.98) 0.03 46 0.07 FEM (Ma et al. 2012) 4G 5G 0.51 0.48 additive 1.30 (1.04-1.64) 0.27 FEM (Zhang et al. 2013)

C T 0.36 0.38 recessive 0.71 (0.53-0.95) 72 FEM (Tian et al. 2011)

T C 0.11 0.13 allelic 0.77 3E-04 FEM (Syreeni et al. 2011)

G C 0.61 0.62 recessive 1.26 (1.02-1.55) 0.03 0.16 FEM (Qiu et al. 2013)

G C 0.53 0.65 allelic 0.62 (0.48–0.81) 5E-04 REM (Abhary et al. 2009)

C G 0.47 0.36 allelic 1.61 (1.23-2.10) 5E-04 0.38 FEM (Zhao and Zhao 2010)

36 retinopathy and nephropathy (Abhary et al. 2009; Mooyaart et al. 2011; Tian et al.

2011). Such pleiotropy is expected; since both these complications are microvascular pathologies and may have risk factors in common.

The most common concern about candidate gene association studies is the threat of population stratification. Population stratification happens when cases and controls differ in their ethnic background or any other factor that alters allele frequencies which may lead to spurious associations. This is especially concerning in candidate gene association studies where there is no internal check for population stratification.

Genome-wide association studies, although prone to the same threat, provide measures to detect and correct for population stratification.

1.3.4 Genome-wide association studies of diabetic retinopathy

1.3.4.1 Key concepts in genome-wide association studies

Over the past decade, advances in microarray based genotyping technologies have made genome-wide association studies (GWAS) a standard and powerful tool for investigating the genetics of complex diseases. These studies generally seek evidence for genetic association by testing the association of hundreds of thousands single nucleotide polymorphisms (SNPs) one-at-a-time with the disease or trait of interest.

The main paradigm underlying GWAS is the common disease – common variant hypothesis

(Reich and Lander 2001) which states that the risk for a common disease is influenced by (multiple) common genetic variants each with a relatively small effect size.

Nearby common variants generally occur on continuous stretches of the genome that have been preserved in the population over generations (due the relative paucity of meiotic recombination) known as linkage disequilibrium (LD) or haplotype blocks. Alleles on each block show strong correlation to one another or, in genetic terms, are in strong

LD. In other words, an allele of one SNP is correlated (and is inherited) with an allele of another SNP on the same LD block. The Human Haplotype Map (HapMap) project was an international effort to catalogue common variants and LD structure across the human genome by genotyping populations from various ethnic origins (Altshuler et al.

2010; Frazer et al. 2007). Results of the HapMap project have given us the power to effectively capture most of the common variation across the genome by only genotyping a subset of about half to a million SNPs (i.e. tag SNPs). As a result, GWAS usually detect indirect associations; i.e. the SNP associated with the disease in a GWAS is often not the causal (or functional) variant but a tag SNP in strong LD with it.

GWAS generally focus on single locus analysis by testing association between several hundred thousand SNPs with the disease or trait of interest, one SNP at a time. Due to the multitude of hypotheses being tested (up to millions of SNPs), the conventional criteria for rejecting null hypothesis and protecting against a false positive result

(α=0.05) is inappropriate. A simple approach to correct for multiple testing in a GWAS is Bonferroni correction that adjusts the α to the number of tests (k) conducted (α =

0.05/k). In a GWAS setting, many of the tested SNPs are correlated. Therefore, it is common sense to adjust the level of α by the effective number of independent genomic regions in the population under study rather than the number of SNPs being tested.

Two separate studies, investigating effective number of independent tests using different complementary approaches, have come up with genome-wide significance thresholds very close to 5×10-8 for two-sided tests in the Caucasian population

(Dudbridge and Gusnanto 2008; International HapMap Consortium 2005).

Special attention is necessary in defining phenotypes of interest in GWAS. Quantitative traits are preferred from a statistical point of view; since they improve the power to detect genetic associations. Quantitative disease risk factors or markers are well established for some disease which could be used as phenotype in a GWAS. Serum

lipid levels for cardiovascular disease or HbA1C for diabetes complications are good examples. In the absence of such quantitative markers, the researchers revert to case- control GWAS. The importance of establishing standardized criteria to define affected - unaffected status could not be overemphasized in case-control studies.

Cohort studies provide the possibility of evaluating the influence of genetic variants on the time to onset of a disease (survival analysis). Such survival GWAS is supposed to provide better statistical power by taking advantage of all the available phenotype data and can provide estimate of disease incidence. Survival GWAS are common in cancer genetics (Couch et al. 2013), but are equally well suited for any disease where an at-risk cohort is followed for an extended time period (Landers et al. 2009; Smith et al. 2010).

This is especially true for chronic disease such as diabetes where complication-free survival is of major clinical interest.

Similar to candidate gene association studies, GWAS are prone to population stratification (see 1.3.3‎ above). However, unlike candidate gene studies, the problem is more readily detectable in GWAS using quantile-quantile (QQ) plots of test statistics.

Besides population genetics methods using principle component analysis (or similar approaches) can detect ethnic outliers for exclusion and provide measures to adjust for ancestry effects in the data (Price et al. 2006).

Validation of any positive GWAS results requires replication in independent populations. A working group in NHGRI has outlined certain criteria for successful replication of genotype-phenotype associations (Chanock et al. 2007) which include: sufficient sample-size of replication study, identity of phenotypes, ethnic similarity with the original study and finding similar magnitude of effect and significance level in the same direction for the same SNP or a perfect proxy SNP using the genetic model used in the initial study.

1.3.4.2 Review of genome-wide association studies of diabetic retinopathy

Thus far, four GWAS of diabetic retinopathy have been published, mostly in patients with type 2 diabetes (T2D). Table 11 summarizes the characteristics of these studies.

The Candidate Gene Association Resource (CARe) study (Sobrin et al. 2011) and candidate gene association study in the New Jersey 725 (Roy et al. 2009), although not

GWAS, were large scale studies with considerable genomic coverage and therefore included in this review. All of these studies have small sample sizes, with 1000 cases at most. None of the published GWAS, aside from one (Huang et al. 2011), has reported any genome-wide significant results. Neither were the top association signals replicated in independent populations examined in these studies.

Despite having a high standard of phenotyping (ETDRS scores from 7 field fundus photographs), with a sample size of 289, the first GWAS of DR was underpowered to detect any genome-wide significant association signal (Fu et al. 2010). Neither did the authors try to replicate any of their top signals that passed nominal significance in independent studies. The study properly accounted for the effects of age, gender, diabetes duration and glycosylated hemoglobin in the analysis model.

A subsequent GWAS in Taiwanese population reported 6 significant SNPs (P < 10-7) from 5 genomic regions associated with DR in patients with T2D (Huang et al. 2011).

There are several concerns about these results. The authors did report significant differences in age, diabetes duration, systolic blood pressure and HbA1c between cases and controls; but failed to account for these important predictors in their initial analysis. Six separate genetic models were examined for each SNP which necessitates a more rigorous correction for multiple testing. Moreover, the top three SNPs in this

GWAS have low minor allele frequencies which lead to sparse cells in contingency tables. A serious concern of type I error inflation remains without the possibility to examine quantile-quantile plots (which were not provided). A logistic regression sub-

Table 11. Summary of genome-wide association studies of diabetic retinopathy Diabetes Discovery N Imput Smallest Phenotyping Comparison (case vs. control) Ethnicity Genotyping platform Reference type (cases/controls) -ation P-value

MS-NPDR and PDR (ETDRS: 43-85) vs. Mexican (Fu et al. T2D 286 (103/183) fundus photograph 7 field Affymetrix 100K HM3 1.8E-05 normal to early NPDR (ETDRS: 10-37) American 2010)

(Huang et T2D 749 (174/575) fundus exam by ophthalmologist NPDR or PDR vs. no-DR Taiwanese Illumina HumanHap550 no 3.0E-15† al. 2011) all: 973/1856 GoKinD: self-reported laser Rx GoKinD: Affymetrix 5.0 (Grassi et al. T1D No DN subjects*: PDR or DME vs. not Caucasian HM2 1.6E-07 EDIC: fundus photograph 7 field EDIC: Illumina 550K 2011) 281/1715 (Sheu et al. T2D 1007 (437/570) fundus exam by ophthalmologist PDR vs. no DR Taiwanese Illumina OmniExpress HM2 1.3E-07 2013)

any DR (ETDRS ≥14) vs. 1254 (222/1032) 1.1E-06 no DR (ETDRS <14) (Sobrin et T2D fundus photograph (1-2 field) Caucasian Illumina iSelect IBC no al. 2011) DR (ETDRS ≥30) vs. 1154 (122/1032) 5.3E-07 no DR (ETDRS <14)

Severe DR (ETDRS ≥53 or history of 437 (128/309) panretinal laser or vitrectomy) vs. 1.1E-04 Illumina GoldenGate not severe DR (ETDRS < 53) African (Roy et al. T1D fundus photograph 7 field 1536 SNPs located in no American 2009) DR progress (2 or more steps) vs. 193 candidate genes 337 9.1E-05 little or no change at 6 years follow-up

NPDR: non-proliferative DR; MS-NPDR: moderate-to-severe NPDR; PDR: proliferative DR; DME: diabetic macular edema; HM: HapMap

* Secondary analysis after removing subjects with diabetic nephropathy (see text).

† There are some concerns about this study (see text).

(Sobrin et al. 2011) and (Roy et al. 2009) were candidate gene association studies and not GWAS.

40 41

analysis for the top results, accounting for diabetes duration and HbA1c, showed significant odds ratios for 5 out of 7 tested loci. Although the authors did not report

P-values for the latter analysis, the SNPs seem to only meet nominal significance

(0.05 < P < 0.01). Again, there was no attempt to replicate the top results.

The largest published GWAS of DR combined two studies of T1D: Genetics of Kidney in Diabetes (GoKinD) and Epidemiology of Diabetes Interventions and Complications

(EDIC) (Grassi et al. 2011). Reliability of self-reported laser treatment, used for assigning disease status in GoKinD, was confirmed in a previous study (Grassi et al.

2009). Diabetes duration, HbA1c level and nephropathy prevalence differed significantly between cases and controls both in GoKinD and EDIC; yet the GWAS did not account for these risk factors. GoKinD was designed as a case-control study of diabetic nephropathy. Since the sampling rate for cases and controls from the population are different; a case-control study is not a random sample of the general population. It has been shown that analysis of a secondary phenotype in a case-control study suffers from inflation of type I error, low power and biased estimates; especially in situations like GoKinD when the primary (diabetic nephropathy) and secondary

(diabetic retinopathy) phenotypes are correlated (Lin and Zeng 2009). Grassi et al. performed a sub-analysis in nephropathy controls which partially addresses the above mentioned problem by sacrificing statistical power. Neither of the two analyses produced any genome-wide significant results. No attempt was made to replicate top results. However, a subsequent replication study failed to replicate any of the top results (Grassi et al. 2012).

The most recent GWAS of DR in Chinese T2D patients used extreme sampling to compare proliferative DR and no-DR patients with similar diabetes duration, HbA1c and body mass index (Sheu et al. 2013). No SNP showed association at a genome-wide

42 significance level. None of the top three loci which harbor functionally relevant genes, showed evidence for replication in a group of Hispanic T2D patients when comparing patients with and without DR. The replication sample, however, did match the discovery in neither ethnicity nor the evaluated phenotype.

Sobrin et al conducted a comprehensive genetic association study in 2691 T2D subjects

(Sobrin et al. 2011). Initially, 39 genes, previously associated with T2D, diabetic retinopathy or nephropathy, were examined. Several SNPs in SELP and FTO showed significant association after Bonferroni correction, which remained significant after adjusting for other DR risk factors including body mass index. Analysis of the remaining SNPs on the IBC microarray (~49k SNPs from 2000 candidate genes associated or postulated for cardiovascular, metabolic or inflammatory disease) showed significant association for several SNPs which remained significant after

Bonferroni correction. However, none of the associations from either of analyses could be replicated in several other Caucasian or non-European populations.

Roy et al. examined the association of 1536 SNPs in 193 candidate genes with severe

DR and DR progression in T1D patients from the New Jersey 725 study (Roy et al.

2009). Candidate genes were selected based on previous evidence for association with

DR or based on their function: genes involved in glucose transport or metabolism, angiogenesis, inflammation, neurotransmission, hypertension and retinal development. 66 SNPs showed nominal significance (p < 0.05) for association with severe DR. However, none of the SNPs would maintain significance after accounting for multiple testing. Roy et al. also tested the association between these SNPs and DR progression of two or more steps using longitudinal data at 6 year follow-up excluding individuals with severe DR at baseline. In the latter analysis 76 SNPs were nominally significant, none of which passing significance threshold after Bonferroni correction for

43 number of tests. No attempt was made to replicate these results due to unavailability of comparable data set (Roy et al. 2009).

1.4 Pathogenesis of Diabetic Retinopathy

Chronic hyperglycemia is the primary cause of diabetic retinopathy. The mechanisms through which hyperglycemia leads to retinal vascular injury are not well understood.

Retinal vascular disruption, nonetheless, is characterized by abnormal hemodynamics, disruptions in permeability and capillary occlusion or non-perfusion (Ciulla et al.

2003). In this section, a few proposed and investigated pathophysiologic mechanisms for DR are discussed.

1.4.1 Blood-retinal barrier impairment

Impairment of blood-retinal barrier (BRB) is one of the earliest phenomena in DR, present well before any apparent sign of DR in fundus examination (Cunha-Vaz et al.

1975). Extensive breakdown of BRB leads to accumulation of extracellular fluid in the macula, observed in DME (Ferris and Patz 1984). Leukostasis is the primary event triggering BRB permeability in DR. Changes in leukocyte properties in diabetes

(decreased deformability, increased adhesiveness and activation) leads to leukostasis in retinal vessels early in DR (Miyamoto and Ogura 1999). According to a working model proposed by Hafezi-Moghadam in his recent review, interaction of leukocytes via their

β2-integrins to endothelial ICAM-1 results in the release of azurocidin, a serine protease, increasing BRB permeability (Hafezi-Moghadam 2012). Interestingly, variations in ICAM1 and integrin genes have been associated with DR in candidate gene association studies (see Table 10) (Abhary et al. 2009). Compromise of BRB, is followed by transmigration of immune cells into the neural retina, resulting in

44 considerable damage to neurons as a result of inflammation. BRB permeability is further aggravated by other DR mediators such as VEGF.

1.4.2 Impaired autoregulation of retinal blood flow

Change in retinal hemodynamics is believed to be a key player in development and progression of DR. Diabetic hyperglycemia causes an early reduction in retinal perfusion, even prior to onset of DR, mediated through protein kinase C (PKC) activation and ion channel dysfunction in contractile mural cells of retinal microvessels

(Curtis and Gardiner 2012). This reduction in retinal blood flow probably contributes to leukostasis, vaso-occlusion and ischemic hypoxia. Over long term, with progression of

DR a shift from hypo- to hyperperfusion is observed. The shift is probably caused by impaired autoregulation of retinal blood flow and further contributes to progressive

DR changes via shear stress on retinal vessel endothelium, release of vasoactive compounds and vascular leakage. Figure 1 presents a unifying theme for hemodynamic changes contributing to DR adapted from a recent comprehensive review (Curtis and Gardiner 2012).

1.4.3 Sorbitol accumulation

Excess cellular glucose, not used for energy production, enters the polyol pathway through reduction to sorbitol and subsequent dehydrogenization to fructose (Figure 2).

Activation of polyol pathway, therefore, leads to accumulation of sorbitol, consumption of NADPH, increase in NAD+ and decrease in NADPH dependent metabolism (Brownlee 2001).

Figure 1. Proposed hemodynamic model for the pathogenesis of diabetic retinopathy. Adapted from (Curtis and Gardiner 2012)

Aldose Reductase Sorbitol Dehydrogenase Glucose Sorbitol Fructose

NADPH NADP+ NAD+ NADH

Osmotic Oxidative PKC activation Other effects stress effects

Figure 2. Polyol pathway and its role in microvascular diabetic complications

Glucose entrance into retinal endothelial (and certain other) cells is insulin independent and glucose insensitive (Rajah et al. 2001), making them prone to sorbitol accumulation. Free glucose entrance into retinal cells under the hyperglycemic state of diabetes leads to activation of the polyol pathway, accumulation of sorbitol in the cell and depletion of NAPDH (Brownlee 2001). Accumulation of sorbitol disrupts the osmotic balance resulting in cell damage. Retinal pericytes seem to be especially sensitive to this osmotic imbalance leading to their early apoptosis in diabetes which contributes to BRB impairment (Sato et al. 1999). More importantly, NADPH depletion decreases the regeneration of reduced glutathione (GSH) which induces and exacerbates oxidative stress (Chung et al. 2003; Obrosova 2005). Activation of the polyol pathway may also contribute to microvascular damage through other molecular mechanisms such as GAPDH inhibition or PKC activation (Brownlee 2001; Obrosova

2005).

Interestingly, polymorphisms in the promoter of the aldose reductase gene (AKR1B1) are associated with DR (Abhary et al. 2009). Despite two decades of unsuccessful studies, a recent trial suggests that epalrestat, an aldose reductase inhibitor, is effective in preventing the progression of diabetic retinopathy and nephropathy (Hotta et al.

2012).

1.4.4 Advanced glycation end products (AGEs)

A detrimental result of chronic hyperglycemia is the combination of excess glucose with free amino acids and tissue or serum proteins. This nonenzymatic reaction is initially reversible but over time becomes irreversible through a series of molecular rearrangements leading to production of advanced glycation end products (AGE)

(Brownlee 2001). Increased production and high serum concentration of AGE in

47 diabetes contributes to microvascular complications. AGEs possibly alter enzymatic activity, decrease ligand binding, modify half-lives and alter immunogenicity of proteins (Ahmed 2005). Besides, interaction of AGEs and their cellular receptor (RAGE) has an important role in diabetic complications. This interaction causes oxidative stress and activates nuclear factor – κB (NF- κB) pathway leading to overexpression of various pro-inflammatory cytokines, and cellular adhesion molecules (Stitt 2010). AGEs are toxic to pericytes and also upregulate RAGE in these cells, which may contribute to their apoptosis early in DR (Chibber et al. 1997; Tanaka et al. 2000). Besides, exposure of retinal cells to AGE leads to upregulation of IL-6 and VEGF, both in cell culture and in patients (Nakamura et al. 2003; Yamagishi et al. 2002).

As mentioned previously (see section 1.3.3‎ above), polymorphisms in the AGE receptor gene (AGER) have been consistently associated with diabetic retinopathy in several candidate gene studies (see Table 10) (Niu et al. 2012; Yuan and Liu 2012).

1.4.5 Protein Kinase C activation

Level of diacylglycerol (DAG) and activity of protein kinase C (PKC) increase in response to hyperglycemia. Exposure of vascular endothelium to oxidative stress also increases the PKC activity. PKC-β is the predominant isoform activated in vascular tissue. PKC-β activation leads to increased endothelial cell permeability and is a key player in VEGF signaling. PKC signaling may also be involved in the early decreased retinal blood flow and basement membrane thickening in diabetes (Das Evcimen and

King 2007). Ruboxistaurin, an isoform-specific inhibitor of PKC-β, has shown positive results in reducing vision loss due to DR and DME in double-blind clinical trials, but is not yet approved for clinical use (Aiello et al. 2006; Aiello et al. 2011).

1.4.6 Retinal microthrombosis

Postmortem studies in both animals and humans suggest increased prevalence of microthrombi in DR (Boeri et al. 2001). Increased leukocytic adhesion, which is one of the earliest phenomena in DR, may be the cause. Microthrombosis leads to both retinal capillary occlusion and leakage. The resulting retinal ischemia stimulates the release of angiogenic factors (Ciulla et al. 2003).

1.4.7 Angiogenic Factors

Retinal hypoxia in DR leads to the upregulation of several growth factors in an abortive attempt to revascularize the diseased tissue, leading to neovascularization. Vascular endothelial growth factor (VEGF) has been extensively investigated as the key angiogenic mediator in the retina (Ciulla et al. 2003). VEGF is produced by many retinal cell types in response to hypoxia. Retinal immunostaining and vitreal concentration of VEGF increase in proportion to DR severity and successful panretinal photocoagulation has been shown to reduce its intraocular levels (Aiello et al. 1994).

VEGF promotes both neovascular formation and vascular permeability. Currently, anti-VEGF antibodies are in clinical use as angiostatic therapies for DR and DME

(Nicholson and Schachat 2010).

Erythropoietin (EPO) is another growth factor participating in DR angiogenesis.

Similar to VEGF, EPO concentration increases in vitreous of PDR patients. In fact, EPO is more strongly correlated with DR severity than VEGF. EPO blockade inhibits retinal neovascularization in animal models (Katsura et al. 2005; Watanabe et al. 2005a). EPO elevation is also observed in the vitreous of patients with macular edema but without

PDR (Hernández et al. 2006).

Insulin-like growth factor (IGF-1), basic fibroblast growth factor (bFGF) and hepatocyte growth factor (HGF) are among other growth factors, playing a role in the pathogenesis of DR (Praidou et al. 2010).

1.5 Summary

Diabetic retinopathy (DR) is a significant health problem, both in terms of health care costs and the impact on well-being of patients. Despite progress in prevention and treatment, DR remains the number one cause of blindness among working adults in western countries.

The main risk factor for DR is glycemic exposure of the patient. Blood pressure and hyperlipidemia are other independent risk factors. However, even after accounting for the main risk factors (measured cross-sectionally), there is evidence for a genetic contribution to the risk of DR.

Aside from a few polymorphisms from candidate gene association studies which show consistent association with DR, genome-wide linkage and association studies have generally been unsuccessful in finding variants associated with diabetic retinopathy.

Small sample sizes, modest genetic effects and low resolution of phenotyping are among the possible reasons for this general lack of success.

Additional genetic studies of DR will be beneficial not only in elucidating the pathophysiology of DR but also in discovering new prevention and treatment modalities that could help patients with diabetes.

2. RESEARCH AIMS

50 51

2.1 General Aim

To identify common genetic polymorphisms from across the human genome that are associated with diabetic retinopathy in type 1 diabetes.

2.2 Specific Objectives

1- To identify common polymorphisms consistently associated with time-to severe

diabetic retinopathy in the T1D populations of DCCT/EDIC and WESDR.

2- To identify common polymorphisms consistently associated with severe

retinopathy status in the T1D populations of DCCT/EDIC and WESDR.

3- To identify common polymorphisms consistently associated with time-to mild

diabetic retinopathy in the T1D populations of DCCT/EDIC, WESDR and RASS.

4- To identify common polymorphisms consistently associated with mild

retinopathy status in the T1D populations of DCCT/EDIC, WESDR and RASS.

5- To prioritize the association results from objective (1) using prior

pathophysiologic and genetic knowledge.

6- Evaluating the association of top signals from objectives (1-4) in independent

diabetic populations.

Figure 3 gives an overview of the analytical scheme for investigating the genetic basis of DR in the current study.

Dividing discovery populations into homogenous subgroups DCCT/EDIC into subgroups based on cohort and treatment WESDR

Performing separate GWAS in each subgroup Primary analysis: time-to event GWAS Secondary analysis: case-control GWAS

Combining GWAS results of subgroups fixed effects meta-analysis

Prioritizing meta-GWAS SNPs for replication SNP p-value + prior pathophysiologic and genetic knowledge

Replication of high priority SNPs Combined analysis of replication studies Combined analysis of discovery + replication

Figure 3. Summary of the analytical scheme in the current genetic study of diabetic retinopathy.

3. METHODS

53 54

3.1 Study Populations and Measurement of Phenotypes

Three cohorts of T1D patients were investigated in the discovery phase of the current study. All the studies were conducted according to the tenants of the declaration of

Helsinki and were approved by the institutional review boards of participating institutions. All the participants provided written informed consent to participate in the study and receive randomized treatments and separately consented to the use of collected DNA for medical research (Al-Kateb et al. 2007; Klein et al. 2008; Mauer et al.

2009; The DCCT Research Group 1993).

3.1.1 The Diabetes Control and Complications Trial - Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC)

The Diabetes Control and Complications Trial (DCCT, 1982-1993) was a multi-center randomized clinical trial of intensive diabetes treatment effectiveness in preventing development and progression of microvascular complications of type 1 diabetes (The

DCCT Research Group 1993). Participants (N=1441) were insulin dependent (deficient in C-peptide secretion with basal plasma C-peptide ≤0.2 pmol/ml and stimulated plasma C-peptide ≤0.2 pmol/ml for patients with duration > 5 yrs), 13 to 39 years old and did not show hypertension, hypercholesterolemia, severe diabetic complications or other medical conditions at the eligibility visit (The DCCT Research Group 1986). They were recruited at 29 centers across USA and Canada between 1983 and 1989 (The

DCCT Research Group 1993).

Participants were classified into primary-prevention and secondary-intervention cohorts based on the duration of diabetes and presence of complications. Patients in primary-prevention cohort (n=726) had no retinopathy (based on 7-field stereoscopic fundus photography), T1D duration of 1-5 years and urinary albumin excretion of less than 40 mg per 24 hours (measured over 4 hours). In comparison, patients in the

55 secondary-intervention cohort (n=715) had very mild to moderate non-proliferative retinopathy, a diabetes duration of 1-15 years and an albumin excretion of less than 200 mg per 24 hours at the eligibility visit (The DCCT Research Group 1993).

In each center, primary and secondary cohorts were randomized to receive either conventional or intensive diabetes treatment. For conventional therapy, patients received one or two daily injections of mixed intermediate and rapid-acting insulin and were educated about diet and exercise. Daily self-monitoring of urine or blood glucose without routine daily adjustment of insulin dosage was the standard practice in conventional treatment. The goals of conventional therapy were to become free of glycosuria, hyperglycemia symptoms, and ketonuria, and to maintain normal growth, development and ideal body weight without experiencing severe or frequent hypoglycemic episodes. Patients in the conventional treatment group had follow up examinations every three months. Intensive therapy consisted of three or more daily injections of insulin or an external insulin pump, self-monitoring of blood glucose at least four times per day and adjusting the insulin dosage, dietary intake and exercise accordingly. The goal of intensive therapy was to achieve near normal blood glucose

(preprandial 70-120 mg/dl, postprandial <180 mg/dl, weekly 3 a.m. > 65 mg/dl and monthly HbA1c < 6.05%). Patients on intensive therapy had monthly follow-up visits

(The DCCT Research Group 1993).

The trial was stopped a year early in June 1993 after concluding that intensive therapy effectively and substantially delays the onset and slows the progression of microvascular complications in T1D patients (The DCCT Research Group 1993). All the patients on conventional therapy were advised to switch to intensive therapy and care of all the participants were returned to the regular medical care system. Nevertheless, most (96%) of the DCCT participants were followed in the subsequent Epidemiology of

Diabetes Interventions and Complications (EDIC, 1994-2006) study with the goal of investigating long term effects of the original DCCT treatments (White et al. 2008).

Ophthalmologic examination including visual acuity and seven-field stereoscopic fundus photography was undertaken at eligibility and every 6 months during DCCT and every 4 years during EDIC since randomization for each individual, plus EDIC years 4 and 10 for the entire cohort. Photography was performed by trained and certified technicians and all the photographs were graded in a central reading unit by trained graders using the Early Treatment Diabetic Retinopathy Study (ETDRS) scale.

Graders were unaware of treatment group assignment and HbA1c levels of the patients

(ETDRS Research Group 1991). Reliability of the grading was evaluated by comparing scores for the same 50 fundus photographs read by different graders at DCCT closeout and at each EDIC year which showed an overall inter-grader nominal score agreement

(κ) of 0.91 for ETDRS scores (range of 0.82-0.92) (White et al. 2008).

Repeated measurements of HbA1c level were obtained in a central laboratory at eligibility visit and quarterly (in the conventional treatment group) or monthly (in the intensive treatment group) during DCCT and yearly during EDIC. The HbA1c assay was stable over time (Steffes et al. 2005).

3.1.2 The Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR)

The Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR, 1979-2007) was an observational cohort study of diabetic complications. A sample of diabetic patients receiving primary care in an 11-county area in southern Wisconsin (Health Service

Area 1) consisting of 996 participants diagnosed with diabetes before 30 years of age

(assumed T1D) and 1370 diagnosed after 30 years of age (assumed T2D), were examined at baseline visit (1980-82) (Klein et al. 1984a, b). Follow up examinations of

57 the cohort were conducted approximately every 5 years in 1984-86, 1990-92, 1995-96,

2000-2001 and 2006-2007 (Klein et al. 2008). WESDR examinations were done in a large mobile van providing standardized examination conditions near patients’ residences.

Each examination included complete ophthalmologic examination, fundus photography and measurements of A1C, random blood glucose and a test for proteinuria. 7-field standard stereoscopic fundus photographs were obtained at all visits except the 5th (2000-01) and were graded centrally, similar to the DCCT/EDIC, using the ETDRS scale (Klein et al. 2008).

3.1.3 Renin Angiotensin System Study (RASS)

The Renin Angiotensin System Study (RASS, 1997-2008) was a randomized double- blind clinical trial to investigate the effectiveness of renin-angiotensin system inhibition in preventing or delaying development of histologic lesions of diabetic nephropathy.

285 T1D patients without hypertension (blood pressure <135/85 mm Hg and no antihypertensive medication) and albuminuria (AER <20 μg/min) and with normal renal function (GFR > 90 ml/min/1.73 m2 body surface area) were randomly assigned to receive losartan (angiotensin receptor blocker), enalapril (angiotensin-converting enzyme inhibitor) or placebo and were followed for 5 years. BP, AER and A1C were measured quarterly and GFR was assessed annually. Percutaneous renal biopsies were obtained before study randomization and after 5 years (90%) as the main renal outcome. Retinopathy status was followed by stereoscopic fundus photographs taken at baseline, 2 years (35%) and 5 years (82%) and scored similar to WESDR at a central reading unit using the ETDRS scale. Although, no significant difference was observed between the treatment groups and placebo in terms of changes in mesangial fractional volume per glomerulus over the 5 year period (p>0.2); patients receiving enalapril

(OR=0.35, 95% CI: 0.14-0.85, P=0.02) or losartan (OR=0.30, 95% CI: 0.12-0.73, P=0.008)

Table 12. ETDRS Scale for diabetic retinopathy severity in single eye. Early Treatment Diabetic Retinopathy Study scale definitions and comparison of the scoring used in DCCT/EDIC, WESDR and RASS studies (Klein et al. 2008; Mauer et al. 2009; The DCCT Research Group 1995a) Level Level Severity Definition (DCCT (WESDR /EDIC) , RASS) No retinopathy diabetic retinopathy absent 10 10 very mild NPDR MAs only 20

MAs only or H or soft exudates in the absence of MAs. 21

MAs and 1 or more of the following: venous loops ≥31 μm; questionable soft 31 exudate, IRMA or VB; H mild NPDR MAs plus hard exudates, cotton-wool spots, and/or mild H 35

MAs and 1 or more of the following: 37 hard exudate and soft exudate moderate NPDR MAs plus mild IRMA or moderate H 43

MAs and 1 or more of the following: H/MAs ≥ SP-1 in 4 or 5 fields; H/MAs ≥ SP-2A in 1 field; 43

and IRMA in 1 to 3 fields moderate NPDR More extensive IRMA, severe H, or VB in one quadrant only 47

MAs and 1 or more of the following: both IRMA and H/MA characteristics from level 43; 47 IRMA in 4 or 5 fields; H/MAs ≥ SP-2A in 2 or 3 fields; and VB in 1 field Severe H in four quadrants, or VB in at least two quadrants, or moderately severe NPDR 53 severe IRMA in at least one quadrant MAs and 1 or more of the following: any 2 or 3 characteristics from level 47; 53

H/MAs ≥ in SP-2A in 4 or 5 fields; IRMA ≥ SP-8A; VB in ≥2 fields Fibrous proliferations only 60 mild PDR NVE < 0.5 DA in one or more quadrants 61

No evidence of levels 60 or 65 but scars of photocoagulation either in “scatter” 61 or confluent patches, presumably directed at new vessels. moderate PDR NVE ≥ 0.5 DA in one or more quadrants or NVD < 0.25-0.33 DA 65

PDR less than DRS- NVE; NVD < SP-10A; or PRH or VH < 1 DA 65 HRC high risk PDR NVD ≥ 0.25-0.33 DA and/or VH 71-75

VH and/or PRH ≥ 1 DA; NVE ≥ ½ DA with VH and/or PRH; DRS-HRC 71 NVD < SP-10A with VH and/or PRH; and NVD ≥ SP-10A advanced PDR NVD ≥ SP-10A with VH and/or PRH 75 advanced PDR fundus partially obscured 81-85

macular obscured by VH and/or PRH; end-stage PDR retinal detachment at center of macula; phthisis bulbi; 85

and enucleation secondary to complications of DR

NPDR: non-proliferative diabetic retinopathy; MA: Microaneurysm; H: (Retinal) Hemorrhages; IRMA: intraretinal microvascular abnormalities; VB: venous beading; SP: Standard Photo; PDR: proliferative diabetic retinopathy; NVE: new vessels elsewhere; DA: disc area; NVD: new vessels on or within 1 disc diameter of optic disc; PRH: panretinal hemorrhage; VH: vitreous hemorrhage; DRS-HRC: Diabetic Retinopathy Study high-risk characteristics

59 showed significantly lower odds of two or more steps progression in retinopathy, independent of changes in blood pressure (Mauer et al. 2009). Interestingly, treatment doses were doubled half-way through the study due to availability of new data supporting beneficial effects of higher doses in reducing proteinuria (Mauer et al.

2009).

3.1.4 Grading of retinopathy severity

All the three studies used the ETDRS scale for scoring retinal photographs with minor variations summarized in Table 12 and Table 13.

Table 13. Diabetic retinopathy severity scale per individual in DCCT/EDIC, WESDR, RASS. WESDR and RASS used a 15 step severity scale, while DCCT/EDIC used a 23 step interim ETDRS scale for severity. See table 1 for description of eye level ETDRS scale (Klein et al. 2008; Mauer et al. 2009; The DCCT Research Group 1995a). WESDR and RASS DCCT/EDIC Level Level Step Step (worse eye/better eye) (worse eye/better eye) 1 10/10 10/10 1 2 21/<21 20/<20 2 3 21/21 20/20 3 4 31/<31

5 31/31 35/<35 4 6 37/<37 35/35 5 7 37/37

8 43/<43 43/<43 6 9 43/43 43/43 7 10 47/<47 47/<47 8 11 47/47 47/47 9 12 53/<53 53/<53 10 13 53/53 53/53 11 14 60+/<60+ 61+/<61 12-23 15 60+/60+

3.1.5 Calculating weighted mean A1C

An updated weighted mean A1C was calculated for each visit as the mean of A1C measurements from the first up to that visit. In calculating updated weighted means for EDIC, quadruple weight was given to yearly EDIC measurements compared to quarterly DCCT measurements. No weighting was applied for calculations in DCCT,

WESDR and RASS.

Considering the relative stability of A1C levels in EDIC, missing A1C values were imputed by carrying forward A1C values from the last available measurement. In

WESDR, A1C level shows a gradual decline over time from first to sixth visit. Missing

A1C values in WESDR were imputed by fitting a random effects model with visit as the random predictor and using an unstructured covariance structure. Due to low missing rate, no imputation of A1C values was done in DCCT and RASS.

3.2 Genotyping and Quality Control

All of the studies were genotyped using Illumina Infinium BeadChip assays (Steemers and Gunderson 2007): DCCT/EDIC samples were genotyped using the Illumina

Human 1M, WESDR and RASS samples using the Illumina HumanOmni1-Quad, following manufacturer’s protocols (Illumina® Inc., San Diego, CA, USA). Genotypes were called per study using Illumina’s proprietary GenCall algorithm implemented in

BEADSTUDIO/GENOMESTUDIO software (Illumina®). Genotype calls were exported to

PLINK v1.07 (publically available at: http://pngu.mgh.harvard.edu/purcell/plink/)

(Purcell et al. 2007) for standard quality control procedures (Turner et al. 2011).

3.2.1 Sample quality

3.2.1.1 Sex inconsistencies

Predicted sex of each individual based on X chromosome heterozygosity was compared to recorded sex using --check-sex option of PLINK. Discrepancies may arise from mix-ups in sample handling or X chromosome anomalies. All the samples showing discrepancy were excluded from further analysis.

3.2.1.2 Cryptic relatedness

Kinship coefficients were calculated for all possible sample pairs in each study using

--genome option of PLINK and a pruned set of about 100K SNPs based on pairwise linkage disequilibrium. To create this pruned set of SNPs --indep-pairwise option in

PLINK was used so that all SNPs within a window size of 1500 to have an r2 less than 0.2 sliding the window 150 marker at a time. Kinship coefficient ( ̂) is a genome-wide measure of identity by descent (IBD). Values of 0.5, 025 and 0.125 represent first, second and third-degree relatives respectively. All samples with ̂ ≥ 0.05 were further investigated for possible sample relatedness and only one individual in each related pair was kept in the analysis.

3.2.1.3 Sample contamination

Potential sample contamination was assessed by calculating mean heterozygosity across the autosomal genome for each individual using --het option in PLINK. Samples with high heterozygosity (more than three standard deviations above the population mean) were removed from further analysis.

3.2.1.4 Sample call rate

Genotyping call rate of each sample was calculated using --missing option in PLINK after removing markers with low genotyping efficiency (less than 99%). Low call rate of

62 a sample (individual) may indicate poor DNA quality. Such samples were excluded from further analysis.

3.2.1.5 Population stratification

Population stratification is a situation when a study population consists of multiple subgroups with systematic differences in their genetic ancestry. Population stratification may lead to spurious association if the subgroups also differ in the phenotype under investigation; in such cases, differences in genetic ancestry rather than a true association of allele with phenotype is the cause of the apparent association

(Cardon and Palmer 2003). We undertook several measures to decrease the possibility of population stratification.

As a first measure, to ensure homogeneity of the study population, only individuals who self-identified as “White” were included in the subsequent analyses. In general, self-reported ethnicity shows very high (near perfect) correspondence with genetically- inferred ancestry (Tang et al. 2005).

Principal components analysis (PCA) or multidimensional scaling (MDS) are the two popular methods for addressing population substructure in genome-wide association studies (Price et al. 2010). Both PCA and MDS summarize high-dimensional genetic data (genotypes for hundreds of thousands of SNPs) into a much smaller number of components or dimensions, whose patterns can be used to group population into subpopulations. MDS and PCA have similar efficacies in identifying population structure (Wang et al. 2009). For historical reasons, in the current study PCA was applied to DCCT/EDIC and MDS to WESDR and RASS. As expected, follow-up application of PCA to WESDR provided similar results to MDS.

For both methods, a subset of SNPs selected not to be in strong linkage disequilibrium

(LD) were analyzed. To create these sets, SNPs within regions of the genome showing

63 extended LD were first removed including: MHC region on chr6: 25-33.5 Mb, lactase region on chr2: 135.2-136.8 Mb and large inversion polymorphisms on chr8: 8-12 Mb, chr17: 40.9-42 Mb and other regions with known long-range LD in the European population: chr11: 45-57 Mb, chr5: 44-51.5 Mb (positions are based on NCBI build 36.1 of human genome) (Fellay et al. 2007; Novembre et al. 2008; Price et al. 2008). The remaining autosomal SNPs were then pruned using --indep-pairwise option in

PLINK so that no pair of SNPs in a window of 1500 markers would have an r2 greater than 0.2 sliding the window 150 marker at a time.

For MDS, the pruned set of SNPs was used to genetically cluster each study population and unrelated samples from phase 3 of HapMap study (Pemberton et al. 2010) based on pairwise identity by state (IBS) distance (--cluster option in PLINK).

Multidimensional scaling (MDS) on the N by N matrix of pairwise genome-wide IBS distances were then performed using --mds-plot option in PLINK. The first 5 dimensions were calculated and plotted against one another. Ethnically mixed individuals were identified as outlier spots that did not cluster with HapMap white populations (CEU: Utah residents with Northern and Western European ancestry from the CEPH collection, TSI: Tuscans in Italy) and were excluded from further analysis.

For PCA, methods for inferring population structure using eigenanalysis (Patterson et al. 2006) implemented in EIGENSOFT v3 (http://www.hsph.harvard.edu/faculty/alkes- price/software/) or GCTA v1.02 (http://www.complextraitgenomics.com/software/gcta/)

(Yang et al. 2011) software packages were used to calculate principle components that summarize genetic variation. Ethnically mixed individuals were identified based on inspection of plots of eigenvectors against one another and were excluded from subsequent analysis.

As another measure against potential population stratification, principle components were re-calculated in the final homogenous study population (after removing outliers

64 identified in the initial MDS or PCA). Association between the first three PCs and the outcome was then assessed using linear regression and any significant PC was included in the genome-wide association study of that outcome (Price et al. 2006).

3.2.1.6 Genotyping reproducibility

In DCCT/EDIC, genotypes of ~1500 SNPs were compared to genotypes of the same

SNPs generated in an earlier study using Illumina® GoldenGate array and TaqMan® assay (Al-Kateb et al. 2008). Individuals showing significant disagreement between the two genotypes for these SNPs were excluded from further analysis as potential sample mix-up.

Table 14 summarizes sample quality control procedures for each of the studies.

Table 14. Summary of sample quality control procedures Study Genotyping Samples Sample QC Minimum Samples Chip * genotyped call rate † analyzed DCCT/EDIC Huamn1M 1441 1- gender mismatch with typed X-linked markers 98.8% 1304 (n=3) 2- call rate < 0.95 (n=0) 3- genotype discrepancy with an earlier study (n=58) 4 autosomal heterozygosity > 0.32 (n=0) 5- cryptic relatedness (n=2) 6- self-reported ethnicity other than white (n=50) 7- outliers in PCA (n=24) WESDR HumanOmni1- 661 1- gender mismatch with typed X-linked markers 97.08% 603 Quad (n=9) 2- cryptic relatedness (n=24) 3- autosomal heterozygosity > 0.3 (n=5) 4- call rate < 0.95 (n=29) 5- self-reported ethnicity other than white (n=3) 6- outliers in MDS analysis (n=4) RASS HumanOmni1- 274 1- gender mismatch with typed X-linked markers 95.18% 261 Quad (n=1) 2- cryptic relatedness (n=2) 3- high autosomal heterozygosity (n=1) 4- call rate < 0.95 (n=0) 5- self-reported ethnicity other than white (n=6) 6- outliers in MDS analysis (n=5)

* All genotyping platforms are from Illumina (Illumina, San Diego, CA, USA). † Sample call rates calculated after excluding markers with call rate <0.99 and maf<0.01.

3.2.2 Marker quality

3.2.2.1 Sex associated SNPs

Autosomal SNPs showing significant association with gender (p <10-8) were excluded from further analyses. In general, these SNPs are mismapped or match to multiple locations in the genome.

3.2.2.2 Marker genotyping call rate

SNP assays that fail for a large number of samples are normally poor assays with low genotyping quality. Using --geno option in PLINK, all SNPs with a call rate <0.95 were removed from further analysis. For SNPs with a minor allele frequency less than 5% a more strict call rate threshold of 0.98 was used for filtering low quality markers.

3.2.2.3 Duplicate sample concordance

To evaluate genotyping reproducibility, 24 duplicate samples were included in

DCCT/EDIC genotyping experiment. These samples had an overall concordance rate of

99.9995% and no marker was excluded due to poor reproducibility.

3.2.2.4 Minor allele frequency

SNPs with a minor allele frequency (MAF) <1% were flagged in all the analyses. SNPs with low MAF tend to challenge the genotyping calling algorithms and may be miscalled. Genotyping errors and population stratification also makes these SNPs prone to spurious association. Finally, power to detect an association is generally limited with low MAF SNPs.

3.2.2.5 Hardy-Weinberg equilibrium

SNPs showing deviation from Hardy-Weinberg Equilibrium (HWE) may have genotyping errors. An exact Hardy-Weinberg test, as described by Wigginton et al, was performed for the SNPs using --hardy option of PLINK (Wigginton et al. 2005). SNPs

66 showing significant departure from HWE (p < 10-6) were flagged in subsequent analyses.

3.3 Genotype Imputation

To address the difference in genotyping platforms across studies and to increase the number of SNPs in association analyses, genotype imputation was performed. Hidden

Markov model-based algorithms, as implemented in IMPUTE v2 software

(http://mathgen.stats.ox.ac.uk/impute/impute_v2.html), were used to probabilistically infer genotypes for untyped SNPs (Howie et al. 2009). Phased autosomal chromosomes from HapMap 3 (Altshuler et al. 2010) release 2 and HapMap 2 release 24 were used as the reference panel for imputation. Using more than one population from HapMap as reference panel often improves imputation accuracy (Huang et al. 2009). Therefore, a multi-population reference panel, consisting of all populations in HapMap, was used for imputation, letting the software choose the best customized reference set for each individual. Initial pre-phasing of the study population was performed using SHAPEIT program (Delaneau et al. 2012) (http://www.shapeit.fr/) to produce best-guess haplotypes before imputing untyped genotypes into the estimated haplotypes using

IMPUTE2.

Although imputation methods generally seem robust to genotyping QC (Southam et al.

2011), only genotyped SNPs that passed stringent QC criteria (call rate >0.95 for SNPs with a MAF ≥5%, call rate >0.99 for SNPs with MAF<5%, HWE exact p >10-6, removal of all non-polymorphic SNPs) were used for imputation. Variant names and positions were compared between study populations and reference panel and updated if necessary to ensure consistency across input files.

Several measures were undertaken, to ensure consistency of strand convention between study and reference panel. First, alleles were compared between study population and reference panel to identify and flip strand for SNPs other than A/T,

C/G. Second, --flip-scan command in PLINK was employed to detect potential strand flips by using differential LD patterns between study and reference panel. This method is based on calculating signed correlation between each index SNP and a set of nearby

SNPs in study population and reference panel separately. Normally, the signed correlation should be consistent between the two groups (positive LD pairs).

Occasionally, the sign is different between the two groups (negative LD pair). A large number of negative LD pairs for an index SNP is a sign of strand flip (Purcell 2010).

Third, an initial imputation was done including a leave-one-out masking experiment, where the input genotype for a SNP is masked internally to infer a best-guess imputed genotype for that SNP. Concordance between the input (actual) genotype and best- guess imputed (inferred) genotype and the statistical information measure for the SNP

(a measure of imputation quality) were examined (scatter plot of concord_type0 vs info_type0). SNPs with a high info metric and low concordance were investigated as likely strand flips. The third step was repeated a few times if necessary to achieve the best possible concordance for all the SNPs. This approach seems to increase imputation accuracy especially for A/T and C/G SNPs with MAF close to 0.5 where the internal

-fix_strand_g flag in IMPUTE2 (based on using allele frequency) may lead to unnecessary strand flips between study and reference panel for such SNPs.

3.4 Phenotype Modeling

The overall strategy involved survival analysis for time-to retinopathy phenotypes with adjustment for the effects of age, gender, duration of diabetes and time-dependent

A1C measurements. Complementary log-log model for continuous-time processes

(CLOGLOG model) was used for survival analysis which can accommodate both left censoring (individuals with the event at the study onset) and interval censoring

(unequal intervals between visits). Separate parameter estimates were allowed for covariates at baseline and subsequent visits by including baseline by effect interactions in the model.

Formulations for covariates in the model were optimized empirically (using transformation or polynomials) to achieve the lowest possible inflation in type I error for the SNP effects. For this purpose, a subset of 5000 randomly selected SNPs were analyzed in each study to find the model with the lowest inflation in type I error based on lower fit statistics (Akaike’s information criterion and Schwarz criterion), lower genomic control lambda and conformity to a uniform distribution for p-values

(evaluated by Kolmogorov-Smirnov test). LOGISTIC procedure of SAS v9.2 (SAS Inc.,

Cary, NC, USA) and glm function in R v2.13 (http://www.r-project.org/) software were used for statistical analyses.

3.5 Genome-Wide Association Testing

Genome-wide association testing (GWAS) was done assuming an additive coding for the genotype and using dosages from genotype imputation for untyped SNPs in R v2.13 with glm function (cloglog link of binomial family) adjusting for covariate effects as described above. Parameter estimates and standard errors from the Wald test were reported. The analysis was performed separately in WESDR, RASS and each cohort- treatment subgroup of DCCT/EDIC. Only SNPs with post-imputation information measures (info) greater than 0.5 were included in the analyses. Genomic inflation factor

(λGC) was calculated for each study as described (Devlin and Roeder 1999).

3.6 Meta-analysis

Meta-analysis was performed at each SNP to combine the results of the GWAS performed separately by study (and by cohort/treatment groups in DCCT/EDIC).

Meta-analyses calculations were performed using METAL (publically available at: http://www.sph.umich.edu/csg/abecasis/Metal/) (Willer et al. 2010) or META V1.3

(publically available at: http://www.stats.ox.ac.uk/~jsliu/meta.html) applying sample size (number of events) or inverse-variance weighted methodology assuming fixed effects (Borenstein et al. 2010). The genomic control method (Devlin and Roeder 1999) was used to correct standard errors by the square root of genomic inflation factor (λGC).

Cochran’s Q and I2 were used to quantify between-study heterogeneity (Higgins et al.

2003). For SNPs that showed significant evidence for heterogeneity, random effects meta-analyses were performed using METASOFT v2.0.1 (Han and Eskin 2011) (available at: http://genetics.cs.ucla.edu/meta/index.html). Results with two-sided P < 5×10-8 were considered genome-wide significant (Dudbridge and Gusnanto 2008).

3.7 Prioritizing SNPs from GWAS for Replication

To prioritize SNPs from GWAS for replication the stratified false discovery rate (SFDR) method was used (Sun et al. 2006). SFDR was developed as a simple method to utilize auxiliary biological information for prioritizing SNPs in a GWAS into separate strata believed to be more or less likely to contain true associations (Sun et al. 2006). It has the advantage of incorporating different types of hypothesis and concurrent application of multiple hypotheses at the same time and seems to be robust to uninformative or misleading prior information (Yoo et al. 2010). SFDR has been successfully applied in a

GWAS setting to improve the detection rate of associations (Schork et al. 2013; Sun et al. 2012).

False discovery rate (FDR) is a less stringent (and more powerful) method for controlling type I error in multiple hypothesis testing compared to family wise error rate (FWER) procedures such as the Bonferroni correction. While in FWER procedures the main concern is to avoid false positives, FDR is aimed to maximize power while controlling the proportion of false positives; i.e. at a q value of 0.1, 10% of the results declared significant are expected to be false positives (Benjamini and Hochberg 1995).

Stratified false discovery rate (SFDR) categorizes SNPs into various strata based on some prior knowledge according to a biologically relevant hypothesis and applies the

FDR method to each strata separately to calculate SFDR (Sun et al. 2006). Strata were defined based on evidence from prior studies supporting a higher chance for a set of

SNPs or genes to be associated with diabetic retinopathy. Prior knowledge included:

e1. Function of SNP (non-synonymous; coding, promoter, 3’UTR, etc.)

e2. SNPs within reported genetic linkage peaks for diabetic retinopathy

e3. SNPs within genes reported to be associated with diabetic retinopathy in

candidate gene studies (only genes with evidence of association in meta-

analyses)

e4. SNPs reaching nominal significance (p <0.05) in published GWAS studies of

diabetic retinopathy and their proxy SNPs (r2 >0.8 in a 500 kb window)

e5. SNPs within genes coding proteins with modified level in the eye tissue or

serum of diabetic retinopathy patients compared to controls based on proteomic

studies

e6. SNPs within genes with modified expression level in the eye tissue or serum of

animal models of diabetic retinopathy based on transcriptome or proteome

studies

To determine the function of SNPs (e1), position and id of the SNP was lifted to hg19 and dbSNP build 137. Function of the SNPs were predicted by SnpEff software v3.1

(available at: http://snpeff.sourceforge.net/index.html) based on the GRCh37.68 annotation database.

Complete linkage results from the two published linkage studies of diabetic retinopathy (Hallman et al. 2007; Looker et al. 2007), kindly provided by the authors, were used to determine SNPs that fell within reported linkage peaks (e2). A genetic map for the markers in each study, matching physical and linkage positions for each marker, was compiled using STS marker tables of UCSC table browser (1 out of 373 and 12 out of 516 markers in Starr County and Pima Indian studies respectively could not be mapped in hg19 release of human genome). To determine SNPs within linkage peaks (defined as LOD >1 in unconditional linkage analysis and LOD >2.2 in ordered subset analysis) SFDR v1.6 software was used (publically available at: http://www.utstat.toronto.edu/sun/Software/SFDR/index.html). SFDR uses a simple linear interpolation to estimate a LOD score for each SNP based on LOD score values for surrounding linkage markers. Similar methods and LOD thresholds were used in the combined SFDR analysis. SNPs were classified into the ones that do not fall within a linkage peak, SNPs that fall in a linkage peak in either the Pima study or any of the analyses performed in the Starr County study (unconditional or either of the two OSA analyses) and SNPs that fall in linkage peaks in both Pima and Starr County (either of the analyses performed in Starr County study).

For categories e3, e5 and e6, a comprehensive list of genes in each category was compiled by reviewing all the available published literature on PubMed

(http://www.ncbi.nlm.nih.gov/pubmed/) as of January 7th, 2013. Table 15 presents the search terms for each query. Coordinates of genes in each list were extracted from refGene table of UCSC table browser. Position of each variant was compared to the extracted coordinates using a Perl script (courtesy of Omid Golban) or BEDTools v2.17

(available at: http://code.google.com/p/bedtools/); variants were considered within a

72 gene if they fell within the gene boundary or ±10kb of a gene ignoring its direction. In the case of mouse/rat genes, the orthologous human genes were identified using mammalian orthology reports available at the Mouse Genome Informatics website

(http://www.informatics.jax.org/orthology.shtml) or by query of the HomoloGene database at NCBI website (http://www.ncbi.nlm.nih.gov/sites/entrez?db=homologene).

Complete lists of SNPs reaching nominal significance (p<0.05) were kindly provided by the authors of three out of four published GWAS (Fu et al. 2010; Grassi et al. 2011;

Sobrin et al. 2011). Proxies for these SNPs were identified using SNAP server v2.2

(http://www.broadinstitute.org/mpg/snap/index.php) based on HapMap phase 2 and phase 3 data using pairwise LD of r2 > 0.8 and a window size of 500 kb (Johnson et al.

2008).

Table 15. Search phrases used in the literature review to identify publications related to each evidence group

Number Category Search Query of records

((((genetics OR genetic OR gene OR polymorphism OR SNP OR allele OR DR candidate genotype OR variant OR variation OR mutation)) AND ((diabetes OR 32 genes diabetic) AND retinopathy))) AND (meta-analysis) (((diabetes OR diabetic) AND retinopathy)) AND (((proteome OR DR proteome proteomic OR proteomics OR "protein profile")) OR (("Proteomics"[Mesh]) 52 OR "Proteome"[Mesh])) (("Transcriptome" OR "microarray analysis"[MeSH Terms] OR ("microarray"[All Fields] AND "analysis"[All Fields]) OR "microarray analysis"[All Fields] OR ("gene"[All Fields] AND "expression"[All Fields] DR AND "profiling"[All Fields]) OR "gene expression profiling"[All Fields] 59 transcriptome OR "gene expression profiling"[MeSH Terms] OR ("gene"[All Fields] AND "expression"[All Fields] AND "profiling"[All Fields]))) AND ((diabetes OR diabetic) AND retinopathy)

In the combined SFDR analysis SNP were categorized into 7 different strata based on supporting prior knowledge (see Chapter 5‎ ). SFDR was calculated using the software

SFDR v1.6 (Sun et al. 2006).

π0, the estimated proportion of tests when null hypothesis (H0) is true, was used as a measure of enrichment in SFDR strata. In general, smaller π0 was considered as an indication of enrichment in the respective stratum (Sun et al. 2006).

3.8 Replication

Three independent European studies of T1D patients were used as replication studies.

3.8.1 The Finnish Diabetic Nephropathy (FinnDiane) Study

The FinnDiane study was a nationwide multicenter study of T1D patients. A total of

2088 participants with diabetes onset before 40 years of age were analyzed. To avoid possible misclassification of T2D patients 4 patients with age of diabetes onset > 30 years who also showed fast (< 10 years) progression to severe DR were excluded from the analysis. Fundus photographs and/or ophthalmologic exam records were acquired retrospectively. All the available data for each patient was used to grade DR severity on ETDRS scale by an ophthalmologist who was unaware of patients’ demographic or diabetic complications status. Association analysis for the SNPs of interest was performed using a Cox proportional hazard model with follow-up time from diabetes onset to development of event or censorship. SDR and MDR outcome were defined based on ETDRS severity scale using definitions comparable to discovery cohorts.

HbA1c (single measurement) and age of onset were included as predictors in the model and the analysis was stratified by gender. Genotypes of some of the SNPs were imputed using HapMap2 as the reference population using MACH software.

3.8.2 The Steno Clinic Study

A large well characterized cohort of patients with T1D (total 900 patients) from the

Steno clinic in Denmark had baseline retinopathy status (nil, simplex, proliferative or blind) defined based on the review of ophthalmologic exam records. The samples were genotyped using Illumina OmniQuad chip and genotyped of additional SNPs were imputed using HapMap 2 as the reference. The analysis compared genotype frequencies of proliferative DR (case) with nil DR (control) using logistic regression with diabetes duration, age, sex and HbA1c and the first four PCs from principal component analysis as predictors in the model.

3.8.3 GeneDiab / Genesis Studies

The Génétique de la Néphropathie Diabétique (GENEDIAB) study was a cross- sectional study of T1D patients in France (Marre et al. 1997). The Genesis France-

Belgium Study was a prospective, family-based study of diabetic complications

(Hadjadj et al. 2004). A combined total of 502 patients with diabetic retinopathy status available through review medical records (background DR, severe non-proliferative

DR and proliferative DR) were analyzed. Details of genotyping, imputation and statistical analysis were similar to the Steno clinic study.

4. RESULTS: META-GWAS OF SEVERE DIABETIC RETINOPATHY

75 76

4.1 Characteristics of Study Populations

Patients with type 1 diabetes (T1D) participating in three cohort studies of diabetic 1 2 3 complications: DCCT/EDIC , WESDR and RASS were studied. Details of these studies including recruitment criteria, follow-up and study outcomes are described in

Methods (see Chapter 3). Table 16 summarizes characteristics of the study populations.

To account for possible between group heterogeneity, separate analyses were planned for DCCT/EDIC subgroups defined based on cohort and assigned treatment group (see

Methods). By design, patients in the primary prevention cohort of DCCT/EDIC had shorter durations of diabetes at baseline compared to the secondary intervention cohort

(1-5 yr vs. 1-15 yr). The intensive therapy group received more frequent insulin injections or used an insulin pump with the aim to achieve near normal blood glucose levels (see Methods). As a result, the intensive group had significantly lower A1C values compared to the conventional treatment group during DCCT (mean ± standard deviation of A1C during DCCT: 7.29±0.90 vs. 9.06±1.25, P=1.3E-143, independent samples t-test). Figure 4 shows mean A1C values during DCCT/EDIC. There was no significant difference in A1C levels between DCCT/EDIC subgroups at baseline (P=0.4,

Welch’s ANOVA).

Among these studies, WESDR has the longest follow-up duration while DCCT/EDIC has more frequent follow-up visits and measurements (fundus photographs). RASS has the shortest follow-up duration and fewest visits. At the first visit, there was no significant difference in age between WESDR and DCCT subjects; but RASS participants were significantly older than both (P <0.05, Tukey's range test).

1 The Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications

2 The Wisconsin Epidemiologic Study of Diabetic Retinopathy

3 Renin Angiotensin System Study

Table 16. Characteristics of study subjects in DCCT/EDIC, WESDR and RASS studies

DCCT / EDIC Characteristic Unit WESDR RASS 1-C 1-I 2-C 2-I whole

N* count 344 307 323 330 1304 627 239 Gender % male 54.94 49.51 53.87 54.55 53.3 49.76 46.86 Age at first visit yr 26.1±7.6 26.8±7.3 27.0±6.5 27.5±6.9 26.8±7.1 26.4±11.0 30.0±9.6 Duration of diabetes at first visit yr 2.6±1.3 2.6±1.3 8.6±3.7 8.8±3.8 5.7±4.2 12.2±8.6 11.3±4.7 A1C level at first visit % 8.82±1.68 8.80±1.66 8.83±1.50 8.97±1.44 8.86±1.57 9.93±1.88 8.54±1.54 Mean A1C† during study % 8.58±1.09 7.77±1.00 8.44±1.03 7.71±0.95 8.13±1.09 8.85±1.15 8.55±1.42 Follow-up duration yr 15.4±3.0 15.8±2.5 16.5±3.0 16.8±2.5 16.1±2.8 22.0±5.9 4.7±1.6 Number of fundus photography visits count 15.5±3.7 15.7±3.7 17.5±3.4 17.9±3.4 16.7±3.7 4.5±0.8 2.4±0.6 BMI at first visit Kg/m2 23.3±3.0 23.2±2.8 23.7±2.8 23.5±2.6 23.4±2.8 23.3±4.0 25.8±4.1 Mean BMI during study Kg/m2 25.0±2.9 25.9±3.3 25.1±2.8 26.0±3.4 25.5±3.1 25.6±4.0 28.4±10.2 SBP at first visit mmHg 114±12 113±11 116±11 114±12 114±12 120±16 120±12 Mean SBP during study mmHg 115±8 115±7 117±8 117±8 116±8 125±14 117±9 DBP at first visit mmHg 72±9 72±9 73±9 73±9 73±9 78±11 70±8 Mean DBP during study mmHg 74±5 74±5 75±5 75±5 74±5 76±8 68±6 Total cholesterol at first visit mg/dl 172±35 176±33 179±32 178±33 176±33 NA NA Mean total cholesterol during study mg/dl 182±29 183±27 185±27 183±27 183±27 188±36 NA Triglyceride at first visit mg/dl 78±59 76±43 87±43 87±46 82±49 NA NA Mean triglyceride during study mg/dl 85±40 82±38 90±39 87±42 86±40 NA NA HDL at first visit mg/dl 51±13 52±13 49±11 49±12 50±12 NA NA Mean HDL during study mg/dl 54±12 54±12 51±10 52±12 53±12 52±15 NA LDL at first visit mg/dl 105±30 109±29 113±29 111±29 110±29 NA NA Mean LDL during study mg/dl 111±26 112±24 116±23 114±23 113±24 NA NA Smoker at first visit % 18.02 18.24 20.12 19.09 18.87 21.21 NA

Numbers are mean ± standard deviation 1-C: primary prevention cohort on conventional therapy; 1-I: primary prevention cohort on intensive therapy; 2-C: secondary intervention cohort on conventional therapy; 2-I: secondary intervention cohort on intensive therapy * number of subjects with phenotype and genotype data after QC † see chapter 2 for details of calculation of updated weighted mean A1C 77 78

Figure 4. Mean glycated hemoglobin (A1C) during DCCT and EDIC separately by cohort- treatment groups. Points represent mean of quarterly measurements during DCCT and yearly measurements in EDIC.

No significant difference in the sex ratio existed between the three studies (P > 0.05,

Pearson's chi-squared test). Duration of diabetes at first visit was longest in RASS and

WESDR followed by the DCCT/EDIC secondary intervention cohort and was shortest in the DCCT/EDIC primary prevention cohort (P < 0.05 for all pairwise comparisons except for RASS vs. WESDR, Tukey’s range test). At the first visit, A1C level in RASS was significantly lower than DCCT/EDIC which in turn had significantly lower A1C levels compared to WESDR (P <0.001 for all pairwise comparisons, Tukey’s range test).

4.2 Definition of Severe Diabetic Retinopathy Phenotype

Severe diabetic retinopathy (SDR) was defined as the occurrence of severe non- proliferative diabetic retinopathy or worse or receiving panretinal photocoagulation

79 treatment (scatter laser). Severity of diabetic retinopathy was evaluated by repeated fundus photographs of the participants in all studies, graded using the ETDRS1 scale

(see Introduction and Methods). An ETDRS level above 53/<53 in DCCT/EDIC, WESDR and RASS or a history of panretinal photocoagulation therapy was considered as SDR.

This is equivalent to step ≥10 on the retinopathy severity scale per individual in the

DCCT/EDIC and step ≥12 in WESDR and RASS (see Methods). Presentation of SDR in retinal photographs is microaneurysms plus one or more of the followings: severe retinal hemorrhage in 4 or 5 fields, venous beading in at least two fields, moderately severe IRMA in at least one field (4-2-1 rule).

Table 17 summarizes the incidence of SDR outcomes in each study population at first and follow-up visits. Few individuals in RASS developed SDR during the study.

Therefore, this outcome was not analyzed in RASS. Also, to avoid sparseness in the analysis of SDR, the two treatment groups of primary cohort in DCCT/EDIC were analyzed together.

Table 17. Cumulative incidence of severe diabetic retinopathy (SDR) in study populations

DCCT/EDIC WESDR RASS 1-C 1-T 2-C 2-I whole 44 13 129 51 237 323 8 SDR (12.79) (4.23) (39.94) (15.45) (18.17) (53.57) (3.35) 300 294 194 279 1067 280 231 No SDR (87.21) (95.77) (60.06) (84.55) (81.83) (46.43) (96.65) 0 0 0 0 0 91 6 SDR at first visit (0) (0) (0) (0) (0) (15.09) (2.51)

Each cell shows incidence as number (%). 1-C: primary prevention cohort on conventional therapy; 1-I: primary prevention cohort on intensive therapy; 2-C: secondary intervention cohort on conventional therapy; 2-I: secondary intervention cohort on intensive therapy

1 Early Treatment of Diabetic Retinopathy Study

4.2.1 Defining time to SDR outcome

Time to SDR outcome was defined as time from first visit to the development of SDR

(see above) in each study. During the DCCT fundus photography was performed every

6 months, much more frequently than in EDIC or WESDR. To ensure persistence of the phenotype, during DCCT time-to event was defined based on the first sustained occurrence of SDR; i.e. SDR should have persisted for two consecutive visits to be counted as an event. Figure 5 shows survival plots for SDR outcome in each study separately by subgroups.

Figure 5. Kaplan-Meier plots of time-to SDR in WESDR and DCCT/EDIC subgroups. The two treatment groups of DCCT/EDIC primary cohort have been combined. RASS is not analyzed due to sparseness of events. Horizontal axis is time from baseline in years.

4.3 Association of Baseline Risk Factors With Time-to SDR in DCCT/EDIC

The DCCT/EDIC is the larger study with a larger number of covariates measured more frequently. Therefore, to identify main factors affecting time-to SDR, Cox proportional hazard models for this outcome were investigated in DCCT/EDIC with baseline covariates as predictors. All models were conditioned on cohort and treatment groups and the exact method was used for handling ties (Table 18).

Table 18 summarizes association of baseline covariates with time-to SDR. In the univariate analysis, duration of diabetes at baseline, A1C level at eligibility visit, triglyceride and total cholesterol levels were associated with time-to SDR, whereas age, gender, body mass index (BMI), mean blood pressure, HDL level, and smoking history were not significantly associated. The mean level of A1C during the study was also strongly associated with time-to SDR. In multivariate analysis, duration of diabetes at

Table 18. Association of baseline covariates with the incidence of SDR in DCCT/EDIC Univariate Multivariate* Risk Variable Unit HR 95% CI P HR 95% CI P

Age / 1 yr 0.99 0.97-1.00 0.11

Gender male vs. female 1.06 0.82-1.37 0.67

Diabetes duration / 1 yr 1.07 1.03-1.11 8.2E-04 1.14 1.09-1.19 4.9E-10 BMI / 1 Kg/m2 1.02 0.98-1.07 0.35

A1C at eligibility / 1% 1.48 1.38-1.58 3.9E-28 1.29 1.19-1.40 3.2E-09 Mean BP / 10 mmHg 1.05 0.90-1.22 0.56

Triglyceride / 10 mg/dl 1.03 1.01-1.05 0.005

Cholesterol / 10 mg/dl 1.05 1.01-1.09 0.008

HDL / 10 mg/dl 0.98 0.88-1.10 0.73

Mean A1C / 1% 2.11 1.89-2.35 4.6E-41 1.90 1.67-2.15 3.1E-23 Smoking history ever vs. never 0.93 0.71-1.22 0.60

HR: Hazard Ratio; CI: Confidence Interval; BP: Blood Pressure; HDL: High Density Lipoprotein Mean BP was calculated by adding a third of pulse pressure to diastolic blood pressure. All covariates were measured at baseline except for Mean A1C. * Model was built using stepwise selection. Variables not shown were not significant and did not stay in the multivariate model.

82 baseline, A1C at eligibility and the mean A1C during DCCT/EDIC remained strong predictors of time-to SDR.

4.4 Development of Time to Event Models for SDR

Follow-up visits were performed at scheduled intervals leading to a lot of ties in time- to SDR. Infrequent visits (months or years apart) also lead to interval censoring, when the exact time of the event is unknown; all that is known is that the event happened sometime between the two visits (which are years apart in WESDR). Moreover, about

15% of WESDR participants displayed SDR at the first visit. For these individuals the exact time of the event cannot be determined; all we know is that the SDR event happened some time prior to the first visit. This situation is referred to as left- censoring. Cox proportional hazard (Cox PH) model, the popular method for survival analysis excludes left-censored individuals from the analysis and is not well suited for handling interval-censoring. To address these problems, complementary log-log models for continuous time processes (CLOGLOG model for short) were used in the analysis of time-to SDR. Unlike Cox PH, CLOGLOG models can handle left censoring, tied event times and interval censoring. CLOGLOG models are also able to accommodate time-dependent covariates (Allison 2010).

Evaluation of DCCT/EDIC (see 4.3‎ above) showed that duration of diabetes and A1C

(measure of glycemia) are the major predictors of time-to SDR. Therefore CLOGLOG models for time-to SDR included duration of diabetes at first visit, mean updated A1C during study as a time-dependent covariate, age and sex as predictors. To account for unequal intervals between visits, duration between visits was included as a predictor.

The models allowed for different intercepts for each visit. To avoid sparseness of events, the two treatment groups of primary cohort of DCCT/EDIC were analyzed together. Subsequent visits in each subgroup of DCCT/EDIC were also collapsed if

83 necessary, to avoid cells with a count less than 5 in contingency tables. To account for left censoring in the WESDR, different parameter estimates were allowed for the first and subsequent visits.

Inflation of type I error was observed in initial GWAS of time-to SDR in WESDR and primary cohort of DCCT/EDIC, as departure of P-values from a null uniform distribution and elevation of genomic control lambda (λGC). We hypothesized that inflation of type I error was caused by inefficient modeling of large effects of diabetes duration and updated mean A1C. A series of polynomial transformations for predictors were examined empirically in a subset of about 5000 independent SNPs from across the genome to identify the best model with acceptable λGC. These models usually provided the best fit based on smaller Akaike information criterion (AIC).

Table 19 shows the formulation of predictors in the final models used in GWAS.

Table 20 and Table 21 summarize parameter estimates and P-values for predictors in the multivariable time-to SDR models in WESDR and DCCT/EDIC subgroups respectively. The main significant risk factors for time-to SDR are duration of diabetes and mean A1C which capture glycemic exposure over time. The effect of A1C seems to be strong and overall consistent between DCCT/EDIC subgroups. Duration of diabetes, however, has a stronger effect in the secondary cohort groups compared to the primary cohort, which is consistent with the shorter duration in the latter group. In WESDR,

Table 19. Formulation of models for severe diabetic retinopathy (SDR) Study Model WESDR * SDR ~ visit + interval + age + sex + dur + dur × ln(dur) + A1C DCCT/EDIC – 1° cohort SDR ~ visit + interval + age + sex + dur + dur2 + A1C + trx DCCT/EDIC - 2° cohort, conventional Rx SDR ~ visit + interval + age + sex + dur + A1C DCCT/EDIC - 2° cohort, intensive Rx SDR ~ visit + interval + age + sex + dur + A1C

* Two separate estimates were allowed for first and subsequent visits for predictors in the model for WESDR. visit: visit as factor; interval: interval between visits (yr); age: age at first visit (yr); sex: gender (M vs F) dur: duration of diabetes at first visit (yr); A1C: time-dependent updated mean A1C (%); trx: treatment group

Table 20. Covariate effects for time-to SDR model in WESDR

First visit Subsequent visits Parameter Unit β SE P β SE P Interval between visits yr NA NA NA 0.147 0.041 3.0E-04 Age at first visit yr -0.029 0.015 4.9E-02 -0.003 0.009 7.4E-01 Gender M vs F 0.944 0.220 1.7E-05 0.233 0.135 8.3E-02 Time-dependent updated mean A1C % 0.198 0.058 5.8E-04 0.676 0.056 5.0E-33 Diabetes duration at first visit (dur) yr 1.647 0.252 6.5E-11 0.528 0.101 1.6E-07 dur × ln(dur) yr × ln(yr) -0.371 0.061 1.6E-09 -0.133 0.028 2.2E-06 β: parameter estimate; SE: standard error

diabetes duration seems to be the strongest predictor of SDR at first visit. Its effect is non-linear (negative sign of the quadratic term) and diminishes, although remaining significant, in subsequent visits. A1C is a strong risk factor both at the first and subsequent visits. The stronger effect of A1C in subsequent visits compared to the first visit could be due to multiple measurements capturing glycemic exposure more effectively over time (compared to a single measurement at the first visit). Male gender increased the risk of SDR at first visit but the effect was not significant at subsequent visits.

4.5 Identification of Loci Associated with Time-to SDR

4.5.1 GWAS of time-to SDR in separate cohorts

Genome-wide association studies were performed in WESDR and DCCT/EDIC subgroups using an additive allele coding in a panel of SNPs imputed to HapMap phase 2 and 3 (see Methods). Figure 6 shows quantile-quantile plots for SNPs from these analyses and the corresponding λGC. No SNPs showed genome-wide significant association (P < 5×10-8) with time-to SDR in WESDR. A few SNPs met genome-wide significance threshold in the time-to SDR association analyses of DCCT/EDIC

85 subgroups (Table 22). SNPs on chromosome 13 fall within a haplotype block and are in complete LD. The imputation at this locus is driven by the three genotyped SNPs that have acceptable genotyping quality in DCCT/EDIC. Most of these SNPs (except rs4263029) have minor allele frequency below 5% and unlike Wald test, likelihood ratio test for their association does not reach genome-wide significance threshold. rs2125967 has low imputation quality (info=0.38) and examination of regional association plot suggests that the observed association may be spurious (plot not shown).

None of these SNPs was significantly associated with time-to SDR in any of the other populations (P > 0.05, Table 23). In fixed effect meta-analysis of all the studies, these

SNPs did not reach genome-wide significance threshold for association with time to

SDR. However, significant heterogeneity in the effect of these SNPs was observed between different studies. Conventional method for random effects meta-analysis by definition will not provide more significant results than fixed effects meta-analysis.

However, in random effects meta-analysis using an alternative method which does not

Table 21. Covariate effects for time-to SDR model in DCCT/EDIC subgroups

Secondary cohort Secondary cohort Primary cohort Conventional Rx Intensive Rx Parameter β SE P β SE P β SE P

Interval between visits 0.098 0.093 2.9E-01 0.084 0.110 4.5E-01 0.244 0.150 1.1E-01 (yr) Age at baseline (yr) 0.029 0.017 8.8E-02 0.001 0.013 9.1E-01 0.012 0.020 5.6E-01

Gender (M vs. F) 0.337 0.275 2.2E-01 0.065 0.183 7.2E-01 0.075 0.282 7.9E-01 Duration of diabetes at NA NA NA 0.124 0.027 3.2E-06 0.199 0.044 5.7E-06 baseline (yr) centered dur (yr) 0.013 0.121 9.1E-01 NA NA NA NA NA NA

(centered dur)2 0.090 0.027 7.4E-04 NA NA NA NA NA NA Time-dependent updated 1.232 0.117 4.5E-26 0.876 0.081 1.5E-27 1.160 0.142 2.4E-16 mean A1C (%) Treatment group (Int. vs. -0.071 0.339 8.4E-01 NA NA NA NA NA NA conv.) β: parameter estimate; SE: standard error; dur: duration of diabetes at baseline

WESDR (λGC=1.014) DCCT/EDIC - 1° cohort (λGC=0.981)

DCCT/EDIC - 2° cohort, conventional Rx (λGC=1.005) DCCT/EDIC - 2° cohort, intensive Rx (λGC=1.000)

Figure 6. Quantile-quantile (Q-Q) plots for time to SDR GWAS. The plots compare Wald P-values using an additive coding for all analyzed SNPs (represented with dots) to an expected distribution under the null (red line). SNPs with a minor allele frequency more than 1% (genotyped or imputed to HapMap 2 or 3) and passing imputation quality criteria (info > 0.3) were included. The gray zones are 95% concentration bands. Genomic control inflation factors for each population are presented above the corresponding QQ plot.

Table 22. SNPs associated with time to SDR at genome wide significance level in GWAS of single studies (P < 5×10-8)

Locus study SNP Chr Position Info A1/A2 Freq1 effect SE PWald PLRT Location Closest Gene Distance (kb) 4p15.31 1 rs2125967 4 21,200,131 0.39 A/G 0.015 3.63 0.62 5.4E-09 5.7E-05 intron KCNIP4 18q21.1 2-C rs4263029 18 42,360,178 1.00 T/C 0.720 -0.77 0.14 4.7E-08 1.4E-07 intron LOXHD1 2p25.1 2-I rs10173663 2 9,234,170 0.89 A/T 0.987 -2.90 0.53 3.6E-08 2.1E-05 intergenic ASAP2 -30.2 12q24.22 2-I rs10850711 12 115,684,295 0.83 T/G 0.980 -2.95 0.50 2.7E-09 1.9E-06 intron RNFT2 13q33.2 2-I rs16968395 13 105,738,738 0.97 T/G 0.988 -3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -88.2 2-I rs16968398 13 105,739,104 1.00 A/G 0.012 3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -87.8 2-I rs16968401 13 105,739,799 GT A/G 0.012 3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -87.1 2-I rs16968405 13 105,744,563 0.98 T/C 0.012 3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -82.3 2-I rs16968406 13 105,744,899 0.98 A/G 0.988 -3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -82.0 2-I rs16968409 13 105,752,197 1.00 T/C 0.988 -3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -74.7 2-I rs1477928 13 105,753,499 0.98 C/G 0.988 -3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -73.4 2-I rs16968412 13 105,756,285 0.98 T/C 0.988 -3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -70.6 2-I rs8002340 13 105,761,401 GT T/C 0.988 -3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -65.5 2-I rs16968418 13 105,761,653 0.98 T/C 0.988 -3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -65.3 2-I rs7322613 13 105,762,913 0.94 A/G 0.013 2.96 0.46 1.7E-10 2.5E-06 intergenic LINC00460 -64.0 2-I rs1351060 13 105,763,804 0.98 C/G 0.012 3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -63.1 2-I rs1351061 13 105,763,841 0.98 T/C 0.012 3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -63.1 2-I rs1351062 13 105,763,943 0.98 C/G 0.012 3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -63.0 2-I rs16968436 13 105,766,941 0.99 A/G 0.012 3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -60.0 2-I rs7990482 13 105,784,656 0.96 A/C 0.988 -3.00 0.46 8.3E-11 1.8E-06 intergenic LINC00460 -42.3 2-I rs16968446 13 105,786,684 GT A/G 0.988 -3.00 0.46 8.2E-11 1.8E-06 intergenic LINC00460 -40.2 2-I rs7988543 13 105,788,131 0.97 A/G 0.012 3.00 0.46 8.9E-11 1.9E-06 intergenic LINC00460 -38.8 18q12.3 2-I rs2253347 18 41,213,463 0.87 A/G 0.026 2.57 0.44 5.8E-09 6.9E-06 intron SLC14A2

1= primary cohort of DCCT/EDIC; 2-C: secondary cohort – conventional treatment group of DCCT/EDIC; 2-I: secondary cohort – intensive treatment group of DCCT/EDIC Info: imputation quality metric (GT=genotyped SNP); A1/A2: effect / other allele; Freq1: frequency of the A1 in the study; effect: estimate of effect of A1 in additive test;

SE: standard error of effect estimate; PWald: Wald test P-value; PLRT: likelihood ratio test P-value; Distance is given from the 5’ of the longest known transcript for the closest gene (+ means downstream, - is upstream to the transcription direction)

Table 23. Meta-analysis results of time to SDR for top SNPs (P < 5×10-8) in each study Fixed Effects Random Effects

SNP A1/A2 Freq1 PWESDR P1 P2-C P2-I Direction Effect SE PFE I2 Phet βRE SE PRE PRE2 rs2125967 A/G 0.014-0.017 0.35 5.3E-09 0.76 0.97 -++- 1.65 0.43 1.1E-04 85 1.6E-04 0.83 1.25 0.51 5.4E-07 rs4263029 T/C 0.72-0.763 0.87 0.54 4.7E-08 0.57 -+-- -0.20 0.07 4.2E-03 87 5.5E-05 -0.21 0.22 0.35 9.5E-06 rs10173663 A/T 0.987-0.991 0.82 0.20 0.90 3.7E-08 +--- -0.98 0.29 6.2E-04 85 1.5E-04 -0.96 0.76 0.21 3.9E-06 rs10850711 T/G 0.979-0.985 0.13 0.71 0.75 2.5E-09 --+- -0.90 0.23 1.1E-04 87 3.8E-05 -0.91 0.70 0.19 2.9E-07 rs16968395 T/G 0.988-0.994 0.76 0.92 0.81 8.4E-11 ---- -1.32 0.30 8.2E-06 87 5.3E-05 -0.93 0.85 0.28 1.6E-08 rs16968398 A/G 0.006-0.012 0.76 0.83 0.80 8.4E-11 ++++ 1.32 0.29 7.4E-06 87 5.7E-05 0.95 0.84 0.26 1.6E-08 rs16968401 A/G 0.006-0.012 0.76 0.83 0.80 8.4E-11 ++++ 1.32 0.29 7.4E-06 87 5.7E-05 0.95 0.84 0.26 1.6E-08 rs16968405 T/C 0.006-0.012 0.76 0.83 0.80 8.4E-11 ++++ 1.32 0.29 7.4E-06 87 5.7E-05 0.95 0.84 0.26 1.6E-08 rs16968406 A/G 0.988-0.994 0.76 0.83 0.80 8.4E-11 ---- -1.32 0.29 7.4E-06 87 5.7E-05 -0.95 0.84 0.26 1.6E-08 rs16968409 T/C 0.988-0.994 0.76 0.83 0.80 8.4E-11 ---- -1.32 0.29 7.4E-06 87 5.7E-05 -0.95 0.84 0.26 1.6E-08 rs1477928 C/G 0.988-0.994 0.79 0.90 0.80 8.4E-11 ---- -1.32 0.30 8.5E-06 87 5.2E-05 -0.93 0.85 0.28 1.6E-08 rs16968412 T/C 0.988-0.994 0.76 0.83 0.80 8.4E-11 ---- -1.32 0.29 7.4E-06 87 5.7E-05 -0.95 0.84 0.26 1.6E-08 rs8002340 T/C 0.988-0.994 0.76 0.83 0.80 8.4E-11 ---- -1.32 0.29 7.4E-06 87 5.7E-05 -0.95 0.84 0.26 1.6E-08 rs16968418 T/C 0.988-0.994 0.76 0.83 0.81 8.4E-11 ---- -1.32 0.30 7.4E-06 87 5.7E-05 -0.95 0.84 0.26 1.6E-08 rs7322613 A/G 0.008-0.013 0.41 0.93 0.82 1.8E-10 ++++ 1.25 0.28 8.4E-06 86 8.0E-05 0.96 0.79 0.22 2.7E-08 rs1351060 C/G 0.006-0.012 0.76 0.83 0.85 8.4E-11 ++++ 1.33 0.30 7.3E-06 87 5.9E-05 0.94 0.85 0.27 1.6E-08 rs1351061 T/C 0.006-0.012 0.76 0.83 0.85 8.4E-11 ++++ 1.33 0.30 7.3E-06 87 5.8E-05 0.94 0.85 0.27 1.6E-08 rs1351062 C/G 0.006-0.012 0.76 0.83 0.85 8.4E-11 ++++ 1.33 0.30 7.3E-06 87 5.9E-05 0.94 0.85 0.27 1.6E-08 rs16968436 A/G 0.006-0.012 0.76 0.83 0.99 8.4E-11 ++++ 1.36 0.30 7.3E-06 87 5.9E-05 0.92 0.87 0.29 1.6E-08 rs7990482 A/C 0.988-0.994 0.76 0.89 0.96 8.4E-11 --+- -1.36 0.30 8.2E-06 87 5.4E-05 -0.89 0.88 0.31 1.6E-08 rs16968446 A/G 0.988-0.994 0.76 0.83 0.86 8.4E-11 --+- -1.37 0.31 7.9E-06 87 5.4E-05 -0.88 0.89 0.32 1.5E-08 rs7988543 A/G 0.006-0.012 0.76 0.84 0.84 9.2E-11 ++-+ 1.37 0.31 8.7E-06 87 5.4E-05 0.88 0.89 0.33 1.7E-08 rs2253347 A/G 0.019-0.03 0.38 0.75 0.26 6.1E-09 --++ 0.45 0.19 1.8E-02 90 1.2E-06 0.65 0.66 0.32 1.5E-06

Results for top SNPs reaching genome-wide significance threshold in each study were combined using fixed effects and random effects meta-analysis. Chromosome and position for each SNP is presented in the previous table.

P-value of association test in each study and the direction of effect is presented in the following order: WESDR; primary cohort of DCCT/EDIC (1); secondary cohort – conventional treatment group of DCCT/EDIC (2-C); secondary cohort – intensive treatment group of DCCT/EDIC (2-I)

A1/A2: effect / other allele; Freq1: range of frequency of A1 in the studies; I2: heterogeneity index; Phet: P-value of Cochran’s heterogeneity Q test

Results of fixed effects (FE) and random effects (RE) meta-analysis are presented. RE2: alternative method of random-effects meta-analysis described by (Han and Eskin 2011)

88 89

assume heterogeneity under the null hypothesis (Han and Eskin 2011), SNPs at 13q33.2 locus remained associated with time to SDR at genome-wide significance threshold in the meta-GWAS.

4.5.2 Meta-GWAS of time-to SDR

Association results from WESDR and DCCT/EDIC subgroups were combined in a fixed effects meta-analysis weighing each group proportional to the number of SDR events and allele frequencies as described (Zhou et al. 2011) and adjusting the test statistics from each study by the respective genomic-control inflation factor (λGC, Figure 6).

Comparison of the meta-GWAS test statistics to those expected under the null using a

QQ-plot (Figure 7) did not show any general excess of significant associations (λGC =

0.989). A fixed effects inverse variance weighted meta-analysis produced very similar

Figure 7. Q-Q plot for meta-GWAS of time to severe diabetic retinopathy. The plot compares observed test statistics (dots) of the meta-analysis for all the SNPs with an average minor allele frequency more than 1% with the expected distribution under the null (red line).

Figure 8. Distribution of Q statistic for heterogeneity from the SDR meta-analysis. Cochran’s heterogeneity statistics follows a null distribution (red dotted line indicates a uniform distribution) with marked depletion of smaller (significant) values; which is consistent with the fixed-effects assumption.

results with similar inflation factor (λGC =0.995). Consistent with a fixed-effects assumption, Cochran’s Q statistic for heterogeneity (Cochran 1954) followed a null distribution with relative depletion of small P-values (λGC = 0.972, Figure 8).

Figure 9 and Table 24 show genome-wide and top results (P < 10-5) from the meta- analysis respectively. No SNP with minor allele frequency greater than 1% showed genome-wide significant association with time-to SDR in the meta-GWAS weighted by number of events (minimum P = 1×10-7). The top SNP (rs17765218), however, did reach genome-wide significance in the inverse variance weighted meta-analysis (P = 2.7×10-8).

False discovery rate for the SNP was short of a 0.05 threshold (FDR = 0.07).

Figure 9. Manhattan plot for meta-GWAS of time-to SDR. The results are from a fixed effects meta-analysis weighing each study by the number of events. All the SNPs (genotyped or imputed to HapMap 2 or 3) with a minor allele frequency ≥1% and imputation quality ≥0.3 are plotted. No SNP reached genome-wide significance threshold (red line). Blue line indicates suggestive association (P < 10-5).

Table 24. Top results (P < 10-5) from meta-GWAS of time-to SDR

Marker Information Fixed Effects Meta-Analysis

Imputation Quality Closest Distance Nevent Inverse Variance Heterogeneity Locus SNP Chr Position A1/A2 Freq1 WESDR DCCT Location Gene (kb) P Direction Effect SE P.value I2 Phet 2q14.1 rs1006342 2 116,025,663 A/G 0.885 0.998 GT intron DPP10 1.2E-05 ---- -0.40 0.09 9.0E-06 0 0.62

rs2008031 2 116,027,310 A/G 0.115 0.997 0.998 intron DPP10 1.2E-05 ++++ 0.40 0.09 8.8E-06 0 0.62

rs12466846 2 116,028,631 T/C 0.885 GT GT intron DPP10 1.1E-05 ---- -0.40 0.09 8.3E-06 0 0.62

5p15.33 rs17610525 5 1,999,575 C/G 0.969 0.855 0.818 intergenic IRX4 -63.7 2.0E-06 ---- -0.79 0.17 1.9E-06 37 0.19 6p25.3 rs9406005 6 720,915 A/G 0.728 GT 0.988 exon FLJ36084 7.8E-07 ++++ 0.36 0.07 8.9E-07 0 0.80

rs9406006 6 721,262 A/G 0.728 GT 0.987 exon FLJ36084 1.2E-06 ++++ 0.36 0.07 1.3E-06 0 0.85

rs13199591 6 724,944 T/C 0.272 0.981 GT intron FLJ36084 2.1E-06 ---- -0.35 0.07 2.5E-06 0 0.89

rs9406014 6 727,060 T/G 0.711 GT 0.962 intergenic FLJ36084 -1.8 3.3E-06 ++++ 0.34 0.07 3.4E-06 0 0.97

rs7743921 6 727,953 T/C 0.723 0.988 0.983 intergenic FLJ36084 -2.7 7.1E-07 ++++ 0.36 0.07 7.5E-07 0 0.85

rs1986345 6 730,010 C/G 0.278 0.974 0.975 intergenic FLJ36084 -4.8 4.1E-07 ---- -0.37 0.07 4.7E-07 0 0.83

6p22.3 rs12660370 6 16,100,126 A/G 0.978 0.972 0.943 intergenic MYLIP -137.2 7.4E-06 -+-- -0.94 0.19 5.6E-07 68 0.02 6p22.3 rs6905993 6 17,191,086 T/C 0.988 0.994 GT intergenic STMND1 -19.4 3.1E-06 ---? -1.10 0.24 3.0E-06 0 0.79 rs6926316 6 17,191,817 A/G 0.012 GT 0.971 intergenic STMND1 -18.7 2.9E-06 +++? 1.10 0.24 2.8E-06 0 0.78

6q14 rs9360898 6 76,010,425 T/G 0.786 GT GT promoter COX7A2 -0.1 4.8E-06 ---- -0.32 0.07 5.6E-06 0 0.97 7p11.2 rs4276631 7 57,279,775 T/C 0.011 0.793 0.826 intergenic GUSBP10 -28 7.9E-06 ++++ 1.33 0.30 7.5E-06 0 0.48 8q22.3 rs10096299 8 103,614,493 A/G 0.792 0.994 0.994 intergenic ODF1 -18.5 8.0E-06 -+-- -0.33 0.07 5.3E-06 53 0.09 rs7816359 8 103,615,743 A/G 0.208 0.994 0.995 intergenic ODF1 -17.3 8.1E-06 +-++ 0.33 0.07 5.3E-06 53 0.09

rs4496937 8 103,616,725 A/G 0.792 0.996 0.995 intergenic ODF1 -16.3 6.8E-06 -+-- -0.33 0.07 4.3E-06 54 0.09

19p13.3 rs17765218 19 5,308,682 T/C 0.984 0.783 0.829 intergenic PTPRS -16.9 1.0E-07 -+-- -1.38 0.25 2.7E-08 35 0.20 20p12.1 rs16999053 20 17,310,001 A/G 0.018 0.994 0.988 intron PCSK2 9.4E-06 ++++ 0.92 0.21 8.3E-06 0 0.77

A1/A2: effect / other allele; Freq1: Frequency of A1; SE: standard error; Nevent P: P value of meta-analysis weighted by the number of events; I2: heterogeneity index; Phet: Cochran’s Q Pvalue Distance is calculated from the nearest gene. (-) indicates upstream and (+) downstream relative to transcription direction. Direction of effects based on the sign of β are shown in the following order: WESDR; primary cohort, 2ndary cohort-conventional Rx and 2ndary cohort-intensive Rx of DCCT/EDIC ?: result not available; model for these SNPs showed separation due to low minor allele frequency in 2ndary cohort intensive treatment group of DCCT/EDIC. Only SNPs with mean minor allele frequency greater than 1% and imputation quality greater than 0.5 are shown.

92 93

4.6 Case – Control Association Meta-Analysis of SDR

4.6.1 Regression models for SDR

Since most of the available replication studies are cross-sectional in nature with a single fundus photograph, a case-control association analysis at last available visit was also undertaken in WESDR and DCCT/EDIC by subgroup. To accommodate for the effects of major known risk-factors, age, gender, duration of diabetes and mean updated A1C during the studies they were included as predictors in the case-control analyses. For each analysis, the covariate formulation providing the best fit was selected (Table 25).

Association with the first three principal components was also tested in all the groups and none were significant in either univariate or multivariate models.

Table 26 summarizes the effect of covariates in WESDR and DCCT/EDIC subgroups.

Consistent with time-to event models the major risk factor for severe retinopathy status at last visit are duration of diabetes and mean A1C level during studies, which capture glycemic exposure. Age and gender were not significantly associated with the outcome in any of the groups. The effect of diabetes duration was relatively consistent between

WESDR and DCCT/EDIC subgroups. Updated mean A1C showed the strongest effect in the primary cohort of DCCT/EDIC (shortest diabetes duration), followed by the intensive

Table 25. Model formulation for severe diabetic retinopathy case-control analysis at last visit Study Model

WESDR SDR ~ age + sex + dur + dur2 + A1C DCCT/EDIC – 1° cohort SDR ~ age + sex + dur + A1C + trx DCCT/EDIC - 2° cohort, conventional Rx SDR ~ age + sex + ln(dur) + A1C DCCT/EDIC - 2° cohort, intensive Rx SDR ~ age + sex + dur + A1C age: age at last available visit (yr); sex: gender (M vs F); dur: duration of diabetes at last available visit (yr); A1C: updated mean A1C during study until last visit (%); trx: treatment group (conventional vs. intensive)

Table 26. Covariate effects on severe retinopathy status at last visit in case-control analyses

WESDR 1° cohort 2° cohort - Conv. Rx 2° cohort - Int. Rx Risk Factor OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P

Age (yr) 0.98 (0.96-1.01) 0.14 1.03 (0.99-1.08) 0.13 1.01 (0.97-1.04) 0.81 0.97 (0.92-1.03) 0.30

Gender (M vs F) 1.34 (0.94-1.91) 0.11 1.47 (0.74-2.91) 0.27 1.29 (0.76-2.19) 0.35 1.09 (0.52-2.28) 0.83

Duration of 1.10 (1.06-1.14) 2.6E-08 1.25 (1.12-1.39) 6.2E-05 1.15 (1.08-1.23) 7.4E-06 1.15 (1.04-1.26) 4.4E-03 Diabetes (yr) Updated mean 2.53 (2.10-3.06) 5.0E-22 4.49 (3.16-6.38) 6.1E-17 2.83 (2.10-3.83) 1.0E-11 3.60 (2.40-5.38) 4.8E-10 A1C (%) Square term for 1.00 (0.998-1.00) 7.0E-05 NA NA NA NA NA NA duration (yr2) Treatment group NA NA 1.33 (0.60-2.95) 0.48 NA NA NA NA (Conv. Vs. Int.)

OR: odds ratio, CI: confidence interval

treatment group of secondary cohort (tight control of glycemia). Comparatively, the effect of updated A1C was smaller, yet very strong, in the two groups with worst diabetes control (WESDR) and the conventional treatment arm of the secondary cohort in DCCT/EDIC).

4.6.2 Case-control GWAS of SDR separately by study

Figure 10 shows the quantile-quantile plot for the case-control association analysis at last visit in each group. No SNPs reached genome-wide significance threshold in the analysis of individual studies, except for 5 SNPs (2 loci) in the primary cohort of

DCCT/EDIC, but these SNPs did not show significant association in the analysis of any of the other groups (P >0.05).

WESDR (λGC = 1.013) DCCT/EDIC - 1° cohort (λGC = 1.049)

DCCT/EDIC - 2° cohort, conventional Rx (λGC = 1.030) DCCT/EDIC - 2° cohort, intensive Rx (λGC = 1.067)

Figure 10. Q-Q plots for SDR case-control GWAS. The plots compare additive test for all analyzed SNPs (represented with dots), with a minor allele frequency more than 1% (genotyped or imputed to HapMap 2 or 3) and passing imputation quality criteria (info > 0.5), to an expected distribution under the null (red line). The gray zones are 95% concentration bands.

4.6.3 Case-control meta-GWAS of SDR

Association results for all genotyped and HapMap imputed SNPs (both phases 2 and 3) passing quality control criteria were combined using fixed effects meta-analysis, after correcting test statistics by the genomic control inflation factor in each study, weighting each study proportional to the number of cases and controls and considering allele frequencies (Zhou et al. 2011). The quantile-quantile plot for the meta-analysis (Figure

11) and a λGC of 1.001 do not indicate an inflated type I error. A single locus on 2q14.1 showed significant (P < 5×10-8) association with SDR in the meta-GWAS (Figure 12 and

Table 27) and the association remained at the border of genome-wide significant after adjusting the test statistics by study specific λGC (PGC =6.2×10-8). Consistent with the fixed effects assumption, Cochran’s heterogeneity statistics for the meta-analysis followed the null distribution (not shown).

Although the two analyses ask different questions, there is relative agreement between the results of time-to event and case-control meta-analyses (ρ = 0.55, P <10-16, Spearman rank correlation between P-values). 2q14 locus ranks among the top results (P <10-4) in both analyses. The time-to SDR analysis asks which genetic variants affect the risk of developing SDR over time, while the case-control analysis just compares allele frequencies between cases and controls at a single time-point (after adjusting for covariates), hence ignoring the trend over time. Ideally, given enough duration, true signals associated with time-to event should also show association in case-control analysis. Case-control analysis only uses the information from a single visit and is expected to be universally underpowered compared to survival analysis.

Figure 11. Q-Q plot for case-control meta-GWAS of severe diabetic retinopathy in WESDR and DCCT/EDIC. The plot compares observed test statistics (dots) for all the SNPs with an average minor allele frequency more than 1% with the expected distribution under the null (red line). Each study was weighted proportional to the number of cases and controls.

Figure 12. Manhattan plot for case-control meta-analysis of SDR at last visit. All the SNPs (genotyped or imputed to HapMap 2 or 3) with a minor allele frequency ≥1% and imputation quality ≥0.5 are plotted. No SNP reached genome-wide significance threshold (red line). Blue line indicates suggestive association (P < 10-5).

Table 27. Top results (P< 10-5) from case-control meta-analysis of SDR info Closest Distance

Locus SNP Chr Position A1/A2 Freq1 W D Location Gene (kb) P GC Direction I 2 Q Pval Effect SE P.value 1p36.13 rs9662272 1 18,257,170 T/C 0.066 0.81 GT intergenic IGSF21 -49.7 6.8E-06 ---- 0 0.41 -0.90 0.22 5.2E-06 1p13.2 rs736202 1 113,620,903 T/C 0.103 0.88 GT intergenic LOC643441 -70.5 4.9E-06 ---- 0 0.78 -0.76 0.17 3.7E-06 1q44 rs6426244 1 245,619,172 T/C 0.659 GT GT intergenic NLRP3 -26.9 8.8E-06 ---- 34 0.21 -0.43 0.10 6.6E-06 2q14.1 rs1402446 2 115,885,861 A/G 0.920 0.99 0.99 intron DPP10 7.9E-06 ---+ 13 0.33 -0.81 0.18 6.2E-06 rs843390 2 115,891,622 C/G 0.081 1.00 0.99 intron DPP10 6.5E-06 +++- 13 0.33 0.82 0.18 5.0E-06 rs843394 2 115,904,553 T/G 0.919 1.00 GT intron DPP10 9.7E-06 ---+ 14 0.32 -0.80 0.18 7.6E-06 rs17362720 2 115,970,570 A/G 0.081 GT GT intron DPP10 4.8E-06 +++- 10 0.34 0.82 0.18 3.7E-06 rs17044463 2 115,973,369 A/C 0.114 1.00 1.00 intron DPP10 1.8E-07 ++++ 0 0.42 0.78 0.15 1.3E-07 rs10193980 2 115,974,698 A/G 0.114 1.00 1.00 intron DPP10 1.8E-07 ++++ 0 0.42 0.78 0.15 1.3E-07 rs7579023 2 115,979,389 T/C 0.886 1.00 1.00 intron DPP10 1.8E-07 ---- 0 0.42 -0.78 0.15 1.3E-07 rs7566337 2 115,980,507 C/G 0.886 1.00 1.00 intron DPP10 1.8E-07 ---- 0 0.42 -0.78 0.15 1.3E-07 rs13432069 2 115,986,942 T/G 0.886 1.00 1.00 intron DPP10 1.8E-07 ---- 0 0.42 -0.78 0.15 1.3E-07 rs13432497 2 115,987,368 T/C 0.886 1.00 1.00 intron DPP10 1.8E-07 ---- 0 0.42 -0.78 0.15 1.3E-07 rs13429901 2 115,996,269 T/C 0.114 1.00 GT intron DPP10 1.8E-07 ++++ 0 0.42 0.78 0.15 1.3E-07 rs10210316 2 116,015,059 T/G 0.886 GT GT intron DPP10 1.8E-07 ---- 0 0.42 -0.78 0.15 1.3E-07 rs2421273 2 116,017,257 T/C 0.886 1.00 1.00 intron DPP10 1.5E-07 ---- 0 0.44 -0.78 0.15 1.1E-07 rs7563882 2 116,018,500 A/C 0.886 1.00 1.00 intron DPP10 1.4E-07 ---- 0 0.46 -0.78 0.15 1.0E-07 rs6756582 2 116,020,461 A/G 0.114 1.00 1.00 intron DPP10 1.1E-07 ++++ 0 0.47 0.79 0.15 7.5E-08 rs1006342 2 116,025,663 A/G 0.886 1.00 GT intron DPP10 6.9E-08 ---- 0 0.51 -0.80 0.15 4.8E-08 rs2008031 2 116,027,310 A/G 0.114 1.00 1.00 intron DPP10 6.7E-08 ++++ 0 0.51 0.80 0.15 4.7E-08 rs12466846 2 116,028,631 T/C 0.886 GT GT intron DPP10 6.2E-08 ---- 0 0.51 -0.80 0.15 4.3E-08 7q21.3 rs972493 7 96,817,121 A/G 0.203 1.00 1.00 intergenic ACN9 168.1 5.8E-06 ---- 7 0.36 -0.53 0.12 4.0E-06 rs7791063 7 96,818,264 A/G 0.796 1.00 1.00 intergenic ACN9 169.3 5.7E-06 ++++ 8 0.35 0.53 0.12 3.9E-06 rs7791647 7 96,818,399 A/G 0.203 0.99 GT intergenic ACN9 169.4 7.8E-06 ---- 13 0.33 -0.52 0.12 5.4E-06 rs7791690 7 96,818,622 A/G 0.797 1.00 1.00 intergenic ACN9 169.6 5.8E-06 ++++ 7 0.36 0.53 0.12 4.0E-06 rs7791868 7 96,818,795 A/G 0.797 1.00 1.00 intergenic ACN9 169.8 5.8E-06 ++++ 7 0.36 0.53 0.12 4.0E-06 rs10252518 7 96,819,286 A/G 0.196 0.97 0.98 intergenic ACN9 170.3 9.9E-06 ---- 14 0.32 -0.53 0.13 6.9E-06 rs10253157 7 96,820,017 C/G 0.796 1.00 1.00 intergenic ACN9 171.0 5.6E-06 ++++ 8 0.35 0.53 0.12 3.9E-06 8p22 rs2979775 8 17,626,417 A/G 0.690 GT GT intron MTUS1 3.0E-06 ++++ 0 0.44 0.47 0.10 2.1E-06 9p21.1 rs2375715 9 31,799,147 A/G 0.083 0.98 0.99 intergenic ACO1 -575.5 4.3E-06 ---- 0 0.61 -0.83 0.19 3.3E-06 rs1023047 9 31,800,090 A/G 0.916 1.00 GT intergenic ACO1 -574.5 3.5E-06 ++++ 0 0.61 0.83 0.19 2.6E-06 rs2770348 9 31,800,320 T/G 0.916 0.99 0.99 intergenic ACO1 -574.3 2.5E-06 ++++ 0 0.65 0.85 0.19 1.8E-06 rs2770349 9 31,800,530 A/G 0.916 GT 0.99 intergenic ACO1 -574.1 3.4E-06 ++++ 0 0.60 0.83 0.19 2.6E-06 rs1928090 9 31,801,628 T/C 0.916 GT 1.00 intergenic ACO1 -573 3.2E-06 ++++ 0 0.61 0.84 0.19 2.4E-06 rs2770352 9 31,802,426 T/G 0.085 0.99 0.99 intergenic ACO1 -572.2 2.6E-06 ---- 0 0.59 -0.85 0.19 2.0E-06 rs2770357 9 31,805,043 T/C 0.084 1.00 1.00 intergenic ACO1 -569.6 3.4E-06 ---- 0 0.60 -0.84 0.19 2.6E-06

info Closest Distance Locus SNP Chr Position A1/A2 Freq1 W D Location Gene (kb) PGC Direction I2 Q Pval Effect SE Locus rs2770358 9 31,805,136 A/G 0.084 1.00 1.00 intergenic ACO1 -569.5 3.4E-06 ---- 0 0.60 -0.84 0.19 2.6E-06 rs2770359 9 31,805,283 A/T 0.916 1.00 1.00 intergenic ACO1 -569.3 3.4E-06 ++++ 0 0.60 0.84 0.19 2.6E-06 rs1854360 9 31,805,528 A/G 0.084 1.00 1.00 intergenic ACO1 -569.1 3.4E-06 ---- 0 0.60 -0.83 0.19 2.6E-06 rs1328738 9 31,805,857 T/C 0.084 1.00 0.99 intergenic ACO1 -568.7 3.5E-06 ---- 0 0.59 -0.83 0.19 2.7E-06 rs2770360 9 31,806,010 A/G 0.916 1.00 1.00 intergenic ACO1 -568.6 3.4E-06 ++++ 0 0.60 0.84 0.19 2.6E-06 rs2770777 9 31,806,036 T/C 0.916 1.00 1.00 intergenic ACO1 -568.6 3.4E-06 ++++ 0 0.60 0.84 0.19 2.6E-06 rs2255573 9 31,806,381 A/T 0.084 1.00 1.00 intergenic ACO1 -568.2 3.4E-06 ---- 0 0.60 -0.83 0.19 2.6E-06 rs2255579 9 31,806,411 A/G 0.084 1.00 1.00 intergenic ACO1 -568.2 3.4E-06 ---- 0 0.60 -0.83 0.19 2.6E-06 rs928033 9 31,806,777 A/C 0.916 1.00 1.00 intergenic ACO1 -567.8 3.6E-06 ++++ 0 0.59 0.83 0.19 2.7E-06 rs928032 9 31,807,229 T/G 0.084 1.00 1.00 intergenic ACO1 -567.4 3.9E-06 ---- 0 0.58 -0.83 0.19 3.0E-06 rs928030 9 31,807,381 T/G 0.916 0.99 0.99 intergenic ACO1 -567.2 3.0E-06 ++++ 0 0.59 0.85 0.19 2.2E-06 rs1928094 9 31,807,597 T/C 0.084 1.00 1.00 intergenic ACO1 -567 4.3E-06 ---- 0 0.56 -0.83 0.19 3.3E-06 rs2770361 9 31,807,859 T/C 0.084 1.00 1.00 intergenic ACO1 -566.7 4.5E-06 ---- 0 0.55 -0.82 0.19 3.4E-06 rs780747 9 31,808,898 A/G 0.083 1.00 1.00 intergenic ACO1 -565.7 4.9E-06 ---- 0 0.55 -0.82 0.19 3.8E-06 rs780748 9 31,809,554 T/C 0.917 1.00 1.00 intergenic ACO1 -565 5.7E-06 ++++ 0 0.52 0.81 0.19 4.4E-06 rs780753 9 31,811,673 T/C 0.084 1.00 1.00 intergenic ACO1 -562.9 5.9E-06 ---- 0 0.52 -0.81 0.19 4.5E-06 rs780754 9 31,812,262 T/C 0.084 1.00 1.00 intergenic ACO1 -562.3 5.9E-06 ---- 0 0.52 -0.81 0.19 4.6E-06 rs17182894 9 31,813,519 A/G 0.081 0.97 0.99 intergenic ACO1 -561.1 9.4E-06 ---- 0 0.52 -0.81 0.19 7.3E-06 rs780757 9 31,815,468 A/G 0.084 GT GT intergenic ACO1 -559.1 6.0E-06 ---- 0 0.52 -0.81 0.19 4.6E-06 rs1576251 9 31,819,226 T/C 0.084 1.00 1.00 intergenic ACO1 -555.4 6.0E-06 ---- 0 0.52 -0.81 0.19 4.6E-06 rs2770787 9 31,819,395 A/G 0.084 1.00 1.00 intergenic ACO1 -555.2 6.0E-06 ---- 0 0.52 -0.81 0.19 4.6E-06 rs2770792 9 31,820,991 T/C 0.917 0.99 1.00 intergenic ACO1 -553.6 6.0E-06 ++++ 0 0.52 0.81 0.19 4.6E-06 rs2770793 9 31,821,013 A/G 0.083 0.98 1.00 intergenic ACO1 -553.6 7.1E-06 ---- 0 0.52 -0.81 0.19 5.4E-06 rs1328744 9 31,821,298 A/G 0.917 1.00 GT intergenic ACO1 -553.3 5.9E-06 ++++ 0 0.51 0.81 0.19 4.6E-06 rs1328745 9 31,821,840 A/G 0.917 0.99 1.00 intergenic ACO1 -552.8 6.0E-06 ++++ 0 0.52 0.81 0.19 4.6E-06 rs1328746 9 31,822,191 A/G 0.917 0.99 1.00 intergenic ACO1 -552.4 6.0E-06 ++++ 0 0.52 0.81 0.19 4.6E-06 rs2770794 9 31,822,992 A/C 0.917 0.99 1.00 intergenic ACO1 -551.6 6.0E-06 ++++ 0 0.52 0.81 0.19 4.7E-06 10p15.1 rs17133449 10 4,748,118 T/C 0.746 GT GT intergenic AKR1E2 -37.8 9.3E-06 ++++ 0 0.93 0.48 0.11 6.9E-06 17q24.1 rs10512502 17 59,925,471 T/C 0.058 GT 0.99 3'UTR DDX5 3.0E-06 ++++ 0 0.84 0.94 0.20 2.1E-06

A1/A2: effect/other allele; Freq1: frequency of A1; Imp. Q.: imputation quality metric in WESDR (W) and DCCT/EDIC (D); GT: genotyped SNP; I2: heterogeneity index; Q Pval: Cochran’s heterogeneity test P value; Effect: estimate of additive genetic coefficient; SE: standard error of Effect Sample size weighted meta-analysis results are reported both with (PGC) and without (P.value) adjusting each study’s test statistics by the genomic control inflation factors. Distance is calculated from the nearest gene. (-) indicates upstream and (+) downstream relative to transcription direction. Direction of effects based on the sign of β are shown in the following order: WESDR, primary cohort, 2ndary cohort-conventional Rx and 2ndary cohort-intensive Rx of DCCT/EDIC

99 100

4.7 Closer Look at Regions Associated with SDR

Figure 13, Figure 14 and Figure 15 show regional association plots for the top results from time-to event and case-control meta-GWAS. In the time-to event meta-GWAS, no

SNP other than the index showed small p-values (P < 10-2) for associations at 7p11.2 and 19p13.3. Index SNPs in these loci have minor allele frequencies less than 5% which may explain the paucity of tag SNPs with strong LD with the index SNP.

4.7.1 2q14.1 locus containing DPP10

2q14.1 was the only locus that reached genome-wide significance threshold in the case- control meta-GWAS. Top SNPs at this locus also had an association P <10-4 loci in the time to SDR meta-GWAS (Figure 14). The index SNP at this locus (rs12466846) was genotyped in both studies with high quality (Table 28) and showed consistent effects in all the studied populations (Figure 16). Several other intronic SNPs of DPP10 which are generally in strong linkage disequilibrium with rs12466846, also show evidence of association with SDR in both case-control and time-to event meta-GWAS (Figure 14). In both meta-analyses, WESDR provided the strongest evidence for association, consistent with highest number of SDR events/cases.

Table 28. Quality and genotype counts of rs12466846 in study populations. GenTrain Mean GenCall Missing Genotype HWE P-value Score Score Rate A1 A2 MAF Counts All Cases Controls WESDR 0.92 0.95 0.0000 C T 0.118 2/144/481 0.006 0.011 0.235 DCCT 0.88 0.92 0.0008 C T 0.112 19/253/1031 0.404 0.451 0.840

A1: minor allele, A2: other allele, MAF: Minor Allele Frequency, HWE: Hardy-Weinberg Equilibrium

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Figure 13. Regional association plots for top results (P <10-5) from meta-analysis of time-to SDR. Each dot represents a SNP plotted against its physical location on horizontal axis (positions based on Build 36 of Human Genome - hg18) and p-value on vertical axis. SNPs are color- coded based on the linkage disequilibrium with the top index SNP (see legend, grey: LD unknown). The LD is calculated based on CEU samples of HapMap 2. Genes in the region are shown underneath each plot and the blue line graphs the recombination rate (right axis).

Figure 14. Regional association plots for SDR meta-analyses at 2q14.1 (DPP10) locus. Panels show the results for case-control and time-to event meta-analyses. For details of plotting convention see caption of figure 12.

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Figure 15. Regional association plots for top results (P <10-5) from case- control meta-GWAS of SDR at last visit. SNPs (dots) are color-coded based on LD pattern in CEU HapMap2 samples (legend). See caption to Figure 12 for further details.

4.7.1.1 Association of rs12466846 with diabetic risk factors and complications

To ensure independence of genetic effect at this locus, the association of rs12466846 with various risk factors was further investigated in WESDR. Considering the stronger genetic effect, WESDR seemed more suitable for answering the question. Additive genotype of rs12466846 was not associated with age, gender, diabetes duration, A1C, systolic or diastolic blood pressure, all measured at the first visit; C-peptide and insulin like growth factor measured at the second visit; total cholesterol and HDL measured at the third visit and mean A1C from visit 1 to 6 (all P> 0.05). rs12466846 showed association with baseline BMI (Table 29). However, adding BMI as a predictor does not attenuate the association of the SNP with SDR in either bivariate or multivariate models (Table 29). There is no significant interaction between the SNP and BMI on

SDR, and the effect of the SNP on the risk of SDR seems to be mostly independent of

case-control time-to event

Figure 16. Forest plots for the top SNP at 2q14.1 locus. Squares represent effect estimate and corresponding 95% CI for each study. Diamonds show summary effect estimates from the meta-analysis. The area of each square is proportional to the weight of corresponding study in meta-analysis.

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Table 29. Association of rs12466846 with SDR and the effect of BMI Effect Standard P Estimate Error association of rs12466846 with BMI BMI at first visit ~ SNP 0.771 0.372 0.039 association of rs12466846 with SDR SDR ~ SNP 0.498 0.117 2.0E-05 SDR ~ SNP + BMI 0.471 0.117 6.0E-05 SDR ~ SNP + other covariates 0.431 0.122 3.9E-04 SDR ~ SNP + other covariates + BMI 0.411 0.123 8.1E-04

BMI: body mass index SNP: additive genotype of rs12466846 (allele C)

the effect on BMI. Table 30 summarizes the association between rs12466846 and other long-term diabetic complications in WESDR. rs12466846 was not associated with any diabetic complications other than retinopathy. Among retinopathy outcomes, this SNP showed the strongest association with SDR, the more severe form of retinopathy. There was a borderline significant association with clinically significant macular edema and no significant association with mild retinopathy.

4.7.1.2 Estimation of required sample size for replication

To estimate the required sample size to replicate the association of rs12466846 with

SDR with sufficient power (β=0.8, α=0.05) in a case-control study, three scenarios were investigated using the effect size estimates from the meta-GWAS and various case- control ratios (Table 31). To adjust for winner’s curse, sample size calculations were repeated using a bias reduced estimate from WESDR calculated by bootstrap based methods implemented in br2 software (Sun et al. 2011b). Based on these calculations, sample sizes between 1000 (1:1 case control ratio) to 6000 (1:25 case-control ratio) will be necessary to replicate the observed association at DPP10 in independent studies.

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Table 30. Association of rs12466846 with diabetic complications in WESDR Outcome* N HR or OR (95% CI) P Death 620 1.04 (0.66-1.65) 0.87 Hypoglycemia1 Frequency of hypoglycemic episodes 613 0.87 (0.62-1.21) 0.41 Ever vs. Never 613 1.18 (0.48-2.92) 0.72 Retinopathy Sever non-proliferative retinopathy2 627 1.64 (1.31-2.07) 2.3E-05 Mild non-proliferative retinopathy3 321 1.01 (0.76-1.35) 0.95 Clinically significant macular edema4 558 1.46 (1.01-2.09) 0.04 Neuropathy5 494 1.04 (0.78-1.40) 0.77 Nephropathy Severe Nephropathy6 489 1.26 (0.91-1.76) 0.17 estimated GFR < 60 7 473 1.41 (0.98-2.03) 0.06 Hypertension8 497 1.24 (0.94-1.65) 0.13 N: Number of individuals used in the analysis; HR: hazard ratio; OR: odds ratio * Unless specified otherwise, all the analyses were done using Cox proportional hazard method for survival data since visit one. The only predictor in the model was the additive genotype of C allele of rs12466846. 1 History of hypoglycemic episodes was analyzed at first visit using ordinal (frequency of episodes) and regular (ever vs. never) logistic regression. 2 Sever NPDR defined as ETDRS level above 53/<53 or panretinal photocoagulation (see text). 3 Mild NPDR defined as ETDRS level 31/<31 or worse. 47% of cohort showed the outcome at first visit and were not included in this analysis. 4 Development of clinically significant macular edema or focal photocoagulation treatment 5 Neuropathy defined as reporting peripheral neuropathy symptoms or a physician diagnosis of PN in history. 6 Severe nephropathy defined as overt proteinuria, dialysis or kidney transplantation. 7 GFR estimated based on serum creatinine level using MDRD formula. eGFR<60 is evidence of moderate chronic kidney disease (CKD). 8 Incidence of hypertension defined as systolic BP >160 or diastolic BP >90 mmHg or receiving antihypertensive medication.

Table 31. Sample size calculation for replication of rs12466846 association with SDR

population Number of Required Cases scenario case :control prevalence 95%CI for OR bias reduced of DR (1.66-2.98) * OR=1.48 †

1. similar to WESDR 1:1 0.3 65-297 500 2. similar to primary cohort of DCCT/EDIC 1:10 0.1 26-142 246 3. similar to RASS 1:25 0.05 23-130 228

All calculations are performed assuming an additive genetic effects and minor allele frequency of 0.11 for rs12466846. * 95% Confidence interval for odds ratio is from inverse variance fixed effects meta-analysis. † Estimated bias-reduced odds ratio in WESDR with application of BR-squared method.

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4.8 Estimation of Statistical Power

We investigated statistical power of the current study to discover various effect sizes using a range of minor (effect) allele frequencies. Figure 17A summarizes the statistical power of the current study (n=1907, control:case ratio = 2.7 for combined WESDR and

DCCT/EDIC sample for SDR) to detect genetic associations (α= 5×10-8) of various effect sizes (as indicated by odds ratio) in a range of allele frequencies. With this sample size we only have enough power (β = 0.8) to detect genetic associations with an odds ratio greater than 1.6 to 2.7 at effect allele frequencies of 0.5 to 0.05 respectively. Required sample sizes to achieve enough statistical power (β = 0.8) to detect genetic associations

(α= 5×10-8) are presented in Figure 17B at various effect sizes and allele frequencies

Figure 17. Statistical power to detect genetic association at various allele frequencies. (A) Estimation of statistical power of the current study (sample size and case-control proportion) to detect genetic association (α= 5×10-8) with various effect sizes (x-axis) at a range of allele frequencies. (B) Estimation of required sample size (assuming a case-control proportion similar to the current study) to detect genetic association with various effect sizes with enough power (β = 0.8, α= 5×10-8) in a range of allele frequencies.

108 assuming a case : control ratio similar to our combined sample. Based on our calculations, to detect a modest genetic effect size (OR = 1.25) with enough power

(β = 0.8, α= 5×10-8) a sample size of 2,200 to 11,000 for allele frequencies of 0.5 to 0.05 will be required. The current study is in general under powered to detect genetic associations with more modest effect sizes.

5. RESULTS: PRIORITIZATION AND REPLICATION

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5.1 Prioritizing SDR meta-GWAS results for replication

The conventional agnostic approach relies solely on the association P-values from a

GWAS or meta-GWAS to prioritize SNPs for replication. In this approach, the wealth of biological and pathophysiological knowledge available in the scientific literature about the phenotype/disease under study is ignored. In this study, a stratified false discovery rate (SFDR) method (Sun et al. 2006) was adopted to include prior knowledge, available in the literature, in prioritizing SDR meta-GWAS results (see section 3.7‎ ).

Several lines of prior knowledge were examined to stratify the SNPs including: SNP function, DR candidate genes, genome-wide linkage peaks for DR and genes dysregulated in DR based on proteome or transcriptome studies. Each of these stratifying approaches was investigated separately to come up with a general unified approach incorporating multiple lines of evidence to stratify SNPs into separate strata in a final combined SFDR analysis.

5.1.1 SNP function

SNPs have different functional effects depending on their location in the genome.

Current central dogma believes that the largest biological effect of a SNP is conveyed through modulation of a protein’s function or level. Nonsense mutations may completely abolish the function of the encoded protein; while intergenic SNPs in a gene desert may have little effect on the expression level of genes.

In an analysis of the published GWAS studies results, reviewing 151 studies reporting

531 trait associated SNPs (TAS), it was shown that non-synonymous sites, promoters (5 kb upstream of transcription start sites), 3’UTRs and introns (in descending order) were enriched and intergenic regions were depleted for TAS. After accounting for the

111 hitchhiking effect caused by LD with non-synonymous SNPs the order remained unchanged, although only promoter SNPs retained significance (Hindorff et al. 2009).

A more recent study, reviewing 2113 publications, investigated the impact of SNP location (relative to a gene) on disease association using both likelihood of disease association and effect size (odds ratio of disease risk). Nonsense SNPs had the highest likelihood of disease association (and OR), followed by non-synonymous and synonymous SNPs which had similar likelihoods and OR for association with disease .

SNPs in the 3’UTR, promoters and 5’UTRs ranked next, followed by SNPs near 3’ of a gene and intronic SNPs. Intergenic SNPs were the least likely to be disease associated with smallest OR (Chen et al. 2010).

Of the SNPs present in our meta-GWAS (either genotyped or from imputation to

HapMap 2 and 3 with minor allele frequency ≥1%) 2,562,690 were mapped to unique locations in GRCh37 / dbSNP137. Using annotation from release 68 of Ensembl

(GRCh37.68) which includes all the known transcripts for a gene, 8,288,860 effects were identified for the SNPs. Table 32 summarizes the effect types for all the SNPs. It should be noted that counts in this table are based on transcripts (and not genes); for example an intronic SNP will be counted multiple times if the gene has several transcript variants (explaining the unusually high count for intronic SNPs). A SNP could also have more than one effect type due to overlap between different genes.

The SNPs in the SDR meta-GWAS were categorized into 5 strata based on their effects.

The strata were chosen based on previous studies (see above) to reflect likelihood of disease association and effect size reported in the literature (Chen et al. 2010; Hindorff et al. 2009). It was hypothesized that SNPs in different strata have different likelihoods for disease association. Consistent with this hypothesize, there is a significant difference between P-value distribution in different strata (P=1.4×10-4, Kruskal–Wallis rank sum test).

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Table 32. Frequency table for the effect types Type (alphabetical order) Count Percent CODON_DELETION (deletion of one or more codons) 6 0% DOWNSTREAM (within 5kb of a gene in 3’ direction) 771,210 9.304% EXON (within a exon not classified elsewhere) 89,877 1.084% FRAME_SHIFT (insertion or deletion causing a frame shift) 18 0% INTERGENIC (in an intergenic region) 1,173,918 14.163% INTRAGENIC (the variant hits a gene, no transcript within the gene) 7,193 0.087% INTRON (in an intron) 5,374,299 64.838% NON_SYNONYMOUS_CODING (a codon producing a different amino acid) 45,757 0.552% NON_SYNONYMOUS_START (a start codon mutated to another start codon) 11 0% SPLICE_SITE_ACCEPTOR (two bases before exon start, except for first exon) 327 0.004% SPLICE_SITE_DONOR (two bases after the end of a coding exon) 431 0.005% START_GAINED (in 5’UTR producing an alternate start codon) 2,821 0.034% START_LOST (start codon mutated to a non-start codon) 98 0.001% STOP_GAINED (variant causes a stop codon) 296 0.004% STOP_LOST (stop codon mutated into a non-stop codon) 49 0.001% SYNONYMOUS_CODING (a codon producing the same amino acid) 45,672 0.551% SYNONYMOUS_STOP (stop codon mutated to another stop codon) 59 0.001% UPSTREAM (within 5kb upstream of a gene, i.e. promoter region) 691,467 8.342% UTR_3_PRIME (3’UTR region) 69,891 0.843% UTR_5_PRIME (5’ UTR region) 15,460 0.187%

Table 33 summarizes the results of SFDR analysis based on the SNP effect. Consistent with a relative enrichment, there is a small steady decline in π0 estimate (see section 3.7‎ for further description) from stratum 1 to 5. Although enrichment is only statistically significant in stratum 5 compared to the other strata. We also examined if stratification based on SNP effects changes the number of null hypothesis rejections (SNPs with q- value less than γ at each FDR level) in the SFDR analysis (green line) compared to FDR without stratification (yellow line) (Figure 18). SFDR analysis increases the number of null rejects at all FDR levels above 0.1. For example at an FDR level of 0.4, 17 SNPs pass the threshold in SFDR analysis compared to a single SNP in the FDR without stratification (Figure 18).

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Table 33. Stratified FDR analysis based on SNP effect

Stratum Effects Number of SNPs π0 1 INTERGENIC or not classified elsewhere 982,824 1.0000 2 INTRON 1,199,906 0.9995 DOWNSTREAM (5 kb of a gene) 3 UPSTREAM (5 kb of a gene) 336,905 0.9962 UTR_3_PRIME UTR_5_PRIME 4 EXON (except in stratum 5) 42,346 0.9926 INTRAGENIC START_GAINED 5 SPLICE_SITE_ACCEPTOR 709 0.9873 SPLICE_SITE_DONOR START_LOST FRAME_SHIFT STOP_GAINED STOP_LOST

5.1.2 Candidate gene association studies of DR

Tens of candidate gene association studies of DR have been published over the past two decades (see section 1.3.3‎ ). Many of these studies have shown inconsistent results.

The observed inconsistency may be due to differences in the definition of DR, use of healthy vs. diabetic controls, ethnic differences, diabetes type (1 vs. 2), winner’s curse, small sample sizes among other reasons (see section 1.3.3‎ ). To compile a list of candidate genes with evidence for association with DR, 25 publications containing meta-analyses of previous candidate gene association studies of DR (see section 3.7‎ ) and OMIM records for microvascular complications of diabetes (MVCD) were reviewed. Table 34 lists 26 genes with consistent evidence for association with DR in this review.

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Table 34. List of candidate genes with evidence for association with diabetic retinopathy Gene Symbol Gene Name Reference ACE angiotensin I converting enzyme (Lu et al. 2012; Zhou and Yang 2010) ADRB3 adrenergic receptor beta 3 (Abhary et al. 2009) (Kang et al. 2012; Niu et al. 2012; Yuan AGER advanced glycosylation end product specific receptor and Liu 2012) AGT angiotensinogen (Abhary et al. 2009) AGTR1* angiotensin II receptor, type 1 (Abhary et al. 2009) aldo-keto reductase family 1, member B1 AKR1B1 (Abhary et al. 2010b; Abhary et al. 2009) (aldose reductase) CHN2 chimerin 2 (Hu et al. 2011) CPVL carboxypeptidase, vitellogenic-like (Hu et al. 2011) MVCD2 (OMIM: #612623) - (Abhary et EPO Erythropoietin al. 2010a; Tong et al. 2008) fat mass and obesity associated FTO (Sobrin et al. 2011) (alpha-ketoglutarate-dependent dioxygenase) HFE hemochromatosis MVCD7 (OMIM: #612635) ICAM1 intercellular adhesion molecule 1 (Abhary et al. 2009) ITGA2 integrin, alpha 2 (Abhary et al. 2009) (Abhary et al. 2009; Niu and Qi 2012; MTHFR methylenetetrahydrofolate reductase Zintzaras et al. 2005) NOS3 nitric oxide synthase 3 (Zhao et al. 2012) NPY* neuropeptide Y (Abhary et al. 2009) PON1 paraoxonase 1 MVCD5 (OMIM: #603933) PON2† paraoxonase 2 (Abhary et al. 2009) PPARG peroxisome proliferator-activated receptor gamma (Ma et al. 2012) SELP selectin P (Sobrin et al. 2011) serpin peptidase inhibitor, clade E, member 1 SERPINE1 (Zhang et al. 2013) (nexin, plasminogen activator inhibitor type 1) serpin peptidase inhibitor, clade F SERPINF1‡ (Iizuka et al. 2007; Uthra et al. 2010) (pigment epithelium derived factor) (Tian et al. 2011) - MVCD6 (OMIM: SOD2 superoxide dismutase 2, mitochondrial #612634) suppressor of variegation 3-9 homolog 2 SUV39H2 (Syreeni et al. 2011) (histone H3-K9 methyltransferase 2) VDR* vitamin D receptor (Abhary et al. 2009) MVCD1 (OMIM: #603933) - (Abhary et VEGFA vascular endothelial growth factor A al. 2009; Zhao and Zhao 2010) MVCD: MICROVASCULAR COMPLICATIONS OF DIABETES * The effect was consistent between studies but did not reach significance in meta-analysis (0.05 < P < 0.1). † Meta-analysis was only significant in T1D subgroup (not in all diabetics or T2D). ‡ No published meta-analysis or OMIM record. However, several genetic association and many functional studies support PEDF as a candidate gene for DR.

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Table 35. SFDR analysis of time-to SDR meta-GWAS based on DR candidate genes

Stratum Definition Number of SNPs π0 2 in a candidate gene for DR ± 10kb 2,807 0.948 1 elsewhere 2,559,883 1.000

Disease or trait associated genes (loci) quite often contain multiple independent associated variants (Ke 2012). We, therefore, hypothesized that the candidate genes for

DR (and their surroundings) are more likely to contain polymorphisms associated with

SDR aside from the original associated polymorphism.

SNPs from the meta-GWAS were stratified into two groups: SNPs within 10kb of a candidate gene vs. the rest. A 10kb window was chosen based on the average extent of

LD in Caucasians which usually ranges from 6.5kb in the low LD region to 23.2 in the high LD regions (Shifman et al. 2003).

SNPs in the candidate gene stratum tend to have smaller P-values in the SDR meta-

GWAS (P=0.01, Mann-Whitney U test) and this stratum shows significant enrichment for SNPs with P≤0.05 (OR=1.21, P=0.017, Fisher’s exact test). Table 35 summarizes the result of SFDR analysis which is consistent with enrichment for smaller P-values in the candidate gene stratum.

5.1.3 Genome-wide linkage studies of diabetic retinopathy

Three genome-wide linkage studies of diabetic retinopathy have been published thus far (see section 1.3.2‎ ), two of them analyzing the same population. We hypothesized that SNPs within linkage peaks from these studies are more likely to be associated with

DR in our meta-GWAS.

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Table 36. SFDR analysis of time-to SDR meta-GWAS based on published linkage studies SFDR based on a single study: LOD threshold Number of SNPs in Stratification based on results of * for stratification linkage peaks π0 † Pima study 1 20,912 0.981 Starr County: unconditional linkage 1 59,141 0.988 Starr County: Ordered Subset Analysis (low to high) 2.2 18,340 0.952 Starr County: Ordered Subset Analysis (high to low) 2.2 58,851 0.988

SFDR based on combined results of both studies: Stratum Definition‡ Number of SNPs π0 3 SNPs in linkage peak in both studies 7,823 0.9495 2 SNPs in linkage peak in one study 88,853 0.9998 1 SNPs not in linkage peaks 2,466,014 1.0000 * Three separate analyses were performed in the Starr County study (see introduction and methods) † In all single-study SFDR analyses, π0 estimate for the low-priority group was equal to 1. ‡ For categorizing SNPs in the combined SFDR, the union of the three analyses of Starr County was intersected with the results of Pima study.

SFDR analysis based on the results of the two more recent linkage studies (Hallman et al. 2007; Looker et al. 2007) were performed (see section 3.7‎ for details of SFDR analysis). Consistent with our hypothesis, SFDR analyses based on single studies show enrichment for smaller P-values within linkage signals (Table 36). Combining the results of both linkage studies, only SNPs within the linkage peak on 1p36 reported in both studies show enrichment for smaller P-values (π0=0.95). This stratum is significantly enriched for SNPs with a P≤0.05 in the SDR meta-GWAS (OR=1.66,

P<0.0001 Fisher’s exact test). There is an obvious increase in the number of null hypothesis rejections using SFDR analysis based on the results of linkage studies

(Figure 18). For example at an FDR level of 0.4, 100 SNPs pass the threshold in SFDR based on the linkage results (blue line) while only a single SNP passes the threshold in

FDR analysis without stratification (yellow line).

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5.1.4 Proteome studies of patients with DR

Several studies have compared the proteome of vitreous (as well as other eye tissues or serum) obtained during surgery from patients with DR with similar tissue from controls without diabetic retinopathy. Given the close contact of the vitreous with the retina, it is generally assumed that proteins with modified levels of expression in the vitreal proteome of DR subjects might be involved in the pathophysiology of DR. We, therefore, hypothesized that proteins showing dysregulation (higher or lower levels) in the proteome studies of DR subjects are more likely to contain genetic variants involved in DR.

Reviewing 18 publications presenting detailed results of proteome studies in DR subjects (see section 3.7‎ ) provided a list of 353 dysregulated proteins. Table 38 summarizes features of the reviewed studies. Proteins supported by more than one study with the same direction of change or by at least two detection methods in the same study comprise a list of 56 with modified expression in DR (Table 39).

An SFDR analysis that categorizes the SNPs within 10 kb of genes encoding proteins dysregulated in DR into the higher priority stratum shows enrichment for smaller P- values in this stratum (Table 37). The SNPs in this stratum tend to have smaller P- values (P=0.03, Mann-Whitney U test). A smaller π0 estimate is consistent with enrichment for SNPs with P≤0.05 in the meta-GWAS of time-to SDR (Table 37).

Table 37. SFDR analysis of time-to SDR meta-GWAS based on human proteome studies

Stratum Definition Number of SNPs π0 2 in a gene dysregulated in proteome of DR patients ± 10kb 3,988 0.949 1 elsewhere 2,558,702 1.000

Table 38. Summary of proteome studies in patients with diabetic retinopathy Study Groups compared Tissue Detection method N (Butler et al. 2005) PDR (20) vs no-PDR (30) vs NVI (4) vs NDM (8) VH ELISA 1 (Chiang et al. 2012) DR (11) vs no-DR (11) AH MALDI-TOF, Western blot 11 (Gao et al. 2007) PDR (3) vs no-DR (4) vs NDM (5) VH nono-LC/MS/MS, Western blot 28 (Gao et al. 2008) PDR (7) vs no-DR (ETDRS <10, 4) vs NDM (6) VH nono-LC/MS/MS 65 (Garcia-Ramirez et al. 2007) PDR (8) vs NDM(10) VH DIGE, MALDI-TOF, Western blot 11 (Garcia-Ramirez et al. 2009) PDR (4) vs NPDR (4) vs NDM (8) VH DIGE, Western blot 1 (Kim et al. 2006) PDR (15) vs NDM (15) VH MALDI-TOF 8 (Kim et al. 2007) PDR (8+11) vs NDM (14) VH LC-MALDI-MS/MS, LC-ESI-MS/MS 262 (Liu et al. 2011) PDR (8) vs NPDR (8) vs no-DR (8) vs NDM (8) Serum MALDI-TOF 4 (Mukai et al. 2008) PDR (23) vs NDM (21) VH MALDI-TOF, Western blot 1 (Ogata et al. 2002) DR (29) vs NDM (14) VH ELISA 2 (Shitama et al. 2008) PDR (16) vs NPDR (12) vs RRD (10 SRF) vs PVR (10) VH & SRF MALDI-TOF MS 10 (Simo et al. 2002b) PDR (37) vs NDM (21) VH ELISA 2 (Simo et al. 2002a) PDR (14) vs NDM (16) VH radioimmunoassay 1 (Simo et al. 2008) PDR (4) vs NDM (8) VH DIGE, Western blot 2 (Takada et al. 2010) neovascular membrane PDR (13) vs LC-MS/MS 20 idiopathic epiretinal membrane (13) (Wang et al. 2012) PDR (10) vs NDM (10) VH MALDI-TOF, Western blot 28 (Watanabe et al. 2005b) PDR (41) vs NDM (18) VH ELISA 2 (Watanabe et al. 2005a) PDR (73) vs NDM (71) VH radioimmunoassay 2 (Yamane et al. 2003) PDR (33) vs NDM (26) VH MALDI-TOF, Western blot 2 20 Total 353

DR: diabetic retinopathy; PDR: proliferative DR; NPDR: non-proliferative DR; NDM: non-diabetic, NVI: iris neovascularization; RRD: rheumatogenous retinal detachment; SRF: subretinal fluid; PVR: proliferative vitreoretinopathy; VH: vitreous humor; AH: aqueous humour; ELISA: enzyme-linked immunosorbent assay; MALDI-TOF: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; LC/MS/MS: liquid chromatography tandem mass spectrometry; DIGE: Difference gel electrophoresis

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Table 39. Proteins over/under expressed in proteome studies of diabetic retinopathy patients Gene Gene Name References Symbol A1BG alpha-1-B glycoprotein (Gao et al. 2008; Shitama et al. 2008) ACTA2 actin, alpha 2, smooth muscle, aorta (Gao et al. 2008; Kim et al. 2007) AFM Afamin (Gao et al. 2008; Gao et al. 2007) AGRN Agrin (Kim et al. 2007; Takada et al. 2010) angiotensinogen AGT (Gao et al. 2008; Gao et al. 2007) (aka: serpin peptidase inhibitor, clade A, member 8) (Gao et al. 2008; Gao et al. 2007; Kim et al. 2006; AHSG alpha-2-HS-glycoprotein Liu et al. 2011) (Chiang et al. 2012; Gao et al. 2008; Gao et al. APOA1 apolipoprotein A-I 2007; Garcia-Ramirez et al. 2007; Liu et al. 2011; Shitama et al. 2008; Simo et al. 2008) APOA2 apolipoprotein A-II (Gao et al. 2008; Gao et al. 2007) (Gao et al. 2008; Gao et al. 2007; Kim et al. 2006; APOA4 apolipoprotein A-IV Shitama et al. 2008) APOB apolipoprotein B (including Ag(x) antigen) (Gao et al. 2007; Kim et al. 2007) APOC3 apolipoprotein C-III (Gao et al. 2008; Gao et al. 2007; Kim et al. 2007) (Garcia-Ramirez et al. 2007; Liu et al. 2011; Simo APOH apolipoprotein H (aka: beta-2-glycoprotein I) et al. 2008) (Gao et al. 2007; Garcia-Ramirez et al. 2007; AZGP1 alpha-2-glycoprotein 1, zinc-binding Shitama et al. 2008; Wang et al. 2012) (Gao et al. 2008; Gao et al. 2007; Garcia-Ramirez et C3 complement component 3 al. 2007; Kim et al. 2007; Shitama et al. 2008) C4A complement component 4A (aka: Rodgers blood group) (Gao et al. 2008; Kim et al. 2007) (Gao et al. 2008; Gao et al. 2007; Garcia-Ramirez et C9 complement component 9 al. 2007) CA1 carbonic anhydrase I (Gao et al. 2008; Gao et al. 2007) CA2 carbonic anhydrase II (Gao et al. 2007; Wang et al. 2012) CAT Catalase (Kim et al. 2007; Yamane et al. 2003) CFB complement factor B (Gao et al. 2008; Garcia-Ramirez et al. 2007) CFI complement factor I (Gao et al. 2008; Wang et al. 2012) CHI3L1 chitinase 3-like 1 (aka: cartilage glycoprotein-39) (Gao et al. 2008; Gao et al. 2007) CLU Clusterin (Wang et al. 2012) COL18A1 collagen, type XVIII, alpha 1 (Kim et al. 2007; Takada et al. 2010) CRYAB crystallin, alpha B (Kim et al. 2007; Wang et al. 2012) CRYGC crystallin, gamma C (Kim et al. 2007; Wang et al. 2012) chemokine (C-X-C motif) ligand 12 CXCL12 (Butler et al. 2005) (aka: stromal cell-derived factor 1) ENO2 enolase 2 (gamma, neuronal) (Wang et al. 2012; Yamane et al. 2003) F2 coagulation factor II (aka: thrombin) (Gao et al. 2008) FN1 fibronectin 1 (Kim et al. 2007; Takada et al. 2010) GAPDH glyceraldehyde-3-phosphate dehydrogenase (Gao et al. 2007; Kim et al. 2007; Wang et al. 2012) GC group-specific component (aka: vitamin D binding protein) (Gao et al. 2008; Shitama et al. 2008) GSN Gelsolin (Gao et al. 2008; Gao et al. 2007) HPX Hemopexin (Gao et al. 2008; Wang et al. 2012) KRT2 keratin 2 (Kim et al. 2007; Wang et al. 2012) KRT9 keratin 9 (Chiang et al. 2012; Wang et al. 2012) KRT10 keratin 10 (Chiang et al. 2012; Kim et al. 2007) LAMB2 laminin, beta 2 (aka: laminin S) (Kim et al. 2007; Takada et al. 2010)

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LAMC1 laminin, gamma 1 (Kim et al. 2007; Takada et al. 2010) MDH1 malate dehydrogenase 1, NAD (soluble) (Kim et al. 2007; Wang et al. 2012) NRXN3 neurexin 3 (Kim et al. 2007) ORM1 orosomucoid 1 (Gao et al. 2007; Liu et al. 2011) ORM2 orosomucoid 2 (Gao et al. 2008; Gao et al. 2007) PEBP1 phosphatidylethanolamine binding protein 1 (Gao et al. 2007; Kim et al. 2007; Wang et al. 2012) PGK1 phosphoglycerate kinase 1 (Gao et al. 2008; Kim et al. 2007) PODN Podocan (Chiang et al. 2012) POSTN periostin, osteoblast specific factor (Takada et al. 2010) PTGDS prostaglandin D2 synthase 21kDa (brain) (Kim et al. 2006; Wang et al. 2012) (Gao et al. 2008; Garcia-Ramirez et al. 2007; RBP3 retinol binding protein 3, interstitial Garcia-Ramirez et al. 2009)

serpin peptidase inhibitor, clade A member 1 (Gao et al. 2008; Gao et al. 2007; Kim et al. 2006; SERPINA1 (aka: alpha-1 antiproteinase, antitrypsin) Mukai et al. 2008; Shitama et al. 2008) serpin peptidase inhibitor, clade C , member 1 (aka: SERPINC1 (Gao et al. 2008; Kim et al. 2007) antithrombin) serpin peptidase inhibitor, clade F member 1 (Gao et al. 2008; Gao et al. 2007; Garcia-Ramirez et SERPINF1 al. 2007; Kim et al. 2006; Ogata et al. 2002; Wang (aka: alpha-2 antiplasmin, pigment epithelium derived factor) et al. 2012) SERPING1 serpin peptidase inhibitor, clade G , member 1 (aka: C1 inhibitor) (Gao et al. 2008; Gao et al. 2007) (Chiang et al. 2012; Gao et al. 2007; Shitama et al. TF Transferrin 2008) THBS1 thrombospondin 1 (Kim et al. 2007; Takada et al. 2010) (Ogata et al. 2002; Simo et al. 2008; Watanabe et VEGFA vascular endothelial growth factor A al. 2005a; Watanabe et al. 2005b)

5.1.5 Proteome and transcriptome studies of DR animal models

Several studies have compared the proteome and transcriptome of diabetic and non- diabetic animal models. Given the duration of experiment in these studies (8-12 weeks), the animals are expected to present early signs of retinopathy. 16 gene expression and 7 proteome articles were reviewed, reporting 471 and 191 dysregulated genes respectively (643 unique genes). 81 genes were reported in more than 1 study with the same direction of change (see appendix).

We questioned if this set of genes are enriched for smaller P-values in the meta-GWAS of time-to SDR. There was no significant difference between the distribution of P- values in this set of genes and the rest of the genome (P=0.62, Mann-Whitney U Test).

Consistently, SFDR analysis does not show enrichment in the stratum defined based on

121

Table 40. SFDR analysis of time-to SDR meta-GWAS based on animal proteome and transcriptome studies

Stratum Definition Number of SNPs π0 2 genes dysregulated in animal proteome or transcriptome studies ± 10kb 3,766 0.996 1 elsewhere 2,558,924 1.000

this gene set (Table 40). Decreasing size of the flanking region in defining gene boundaries (5kb instead of 10kb) did not change the results.

5.1.6 Previous GWAS results

SNPs passing nominal significance in previous GWAS studies have been described as resembling gold dust (Shi et al. 2011); since a (small) proportion of them are likely to contain true associations that did not reach genome-wide significance threshold due to low power. We tested if such SNPs or their proxies (P=0.05 and 0.001 cut offs) in three previous GWAS studies of diabetic retinopathy (Fu et al. 2010; Grassi et al. 2011; Sobrin et al. 2011) may be enriched for smaller P-values in our meta-GWAS of time-to SDR.

The distribution of P-value for those SNPs was not different than the rest of the genome (P>0.1 for all comparisons, Mann-Whitney U test) and SFDR analysis based on this criterion did not show enrichment in the previous GWAS stratum either (based on

π0).

5.1.7 Combining prior knowledge to prioritize meta-GWAS results

In our SFDR analyses, stratifying SNPs based on SNP function, DR linkage studies, candidate genes for DR and proteome studies of DR patients seem to be informative and provide enrichment (see above). However, stratification based on previous GWAS results or expression and proteome studies of animal models did not seem to provide

122 enrichment (non-informative hypotheses). We then proceeded to combine the informative sources of knowledge to prioritize SDR meta-GWAS results for replication.

The SFDR methodology was used to calculate a stratified FDR for each SNP based on its P-value (from meta-GWAS) and the collective prior knowledge for that SNP. The genes supported by candidate gene or proteome studies do not lie within the linked region and were categorized into a separate stratum. The rest of the genome (outside the linkage peak for DR and not within gene list depicted by candidate gene or proteome study) was stratified based on the SNP function.

Table 41 summarizes the results of the combined SFDR. Using combined SFDR increases the number of rejected null hypotheses over FDR (no stratification) or single

SFDR (Figure 18). The graph depicts the count of SNPs passing FDR threshold

(rejection of null hypothesis) at each FDR level. Combined SFDR has the best performance (highest number of null rejections) compared to single hypothesis SFDRs or FDR without stratification at all FDR levels above 0.1. For example, at an FDR level of 0.4, only a single SNP passes the threshold in FDR without stratification; 17 and 100

SNPs pass the threshold in SFDR based on SNP effect and linkage results respectively; while

Table 41. Combined SFDR of time-to SDR meta-GWAS

Stratum Definition Number of SNPs π0 3 linkage peak in both previous linkage studies for DR 7,823 0.9495 2 protein dysregulated in human proteome studies of DR patients or 6,638 0.9437 candidate gene for DR 1-5 nonsense, frameshift, splice site, start lost or stop lost 704 0.9858 1-4 missense, coding-synonymous, exon or start gained 41,744 0.9926 1-3 untranslated region or upstream 333,444 0.9964 1-2 intron or downstream 1,192,094 0.9999 1-1 intergenic 980,243 > 0.9999 SNPs in stratum 2 and 3 were not categorized by SNP function. Therefore strata 1, 2 and 3, as defined, are mutually exclusive.

123

Figure 18. Number of SNPs passing FDR threshold (γ) at each FDR level up to 0.5. Notice that y-axis is on logarithmic scale. The number of rejected null hypothesis (SNPs) increases drastically in combined SFDR analysis compared to the original FDR.

in the combined SFDR analysis 262 SNPs pass the 0.4 threshold.

Despite improvement of discovery rate (higher number of null hypothesis rejections) in

SFDR compared to FDR, no SNP reached the conventional FDR threshold of 0.05 in either analysis. Despite being in the lowest priority stratum (1-1), the top ranking SNP remained unchanged in both analysis (rs17765218 on chromosome 19) but its q-value improved from 0.25 in the FDR analysis to 0.09 in the SFDR analysis.

SFDR analysis which incorporates prior information in calculating q-values, changes the rank of SNPs drastically compared to the agnostic approach of FDR analysis.

124

Figure 19. Comparison of ranking of SDR meta-GWAS results in FDR vs. SFDR analysis. Panels summarize meta-GWAS results in the original FDR analysis (A) and combined SFDR analysis (B – see text). Each dot represents a SNP plotted against logarithm of rank (y-axis) and its physical position (x-axis). The top ranking SNP on chromosome 19 (y=0) remains unchanged.

125

Figure 19 compares the genome-wide ranking of SNPs in the original FDR analysis on the meta-GWAS results without stratification and the final SFDR analysis. Despite being in the lowest priority stratum (1-1), the top loci on chromosome 19 and 6 remained unchanged. However, in the SFDR analysis a new prominent locus appeared on 1p36 corresponding to the SNPs within the linkage peak for DR (see above). The top

SNP at this locus, rs2796345 (meta-GWAS P =1.35×10-4), moved from a rank of 396 in

FDR analysis (q=0.81) to 8 in SFDR analysis (q=0.29).

5.2 Replication of top ranking SNPs

In the combined SFDR analysis (see above) 518 SNPs had a q-value < 0.5 (50% false discovery rate). Adding the top SNPs (P< 10-5) in either case-control or time-to SDR meta-GWAS (Table 24 and Table 27) extends the list to 585 SNPs. These SNPs fall within 39 independent genomic loci. We investigated the association between these

SNPs and severe DR in three independent populations with type 1 diabetes. Table 42 summarizes the characteristics of these three studies. No longitudinal data was available in the replication studies. Association between additive genotype and SDR

Table 42. Characteristics of replication studies FinnDiane Genesis/GeneDiab Steno Clinic Total N 2194 502 936 Status definition ETDRS scores based Background / preproliferative / proliferative on review of DR status from review of medical records medical records Case / Control 1681 / 513 359 / 143 458 / 478 Age (yr) 35.8 (11.2) 42.7 (11.5) Sex (% Male) Cases 56.9 % 58.9 % Controls 42.9 % 43.4 % Diabetes Duration (yr) Cases 24.3 (9.1) 30 (9) Controls 22.6 (8.7) 26 (8) HbA1C (%) Cases 8.7 (1.5) 8.7 (1.6) Controls 8.2 (1.3) 8.4 (1.4)

Numbers are mean (standard deviation).

126 was investigated in the replication studies while adjusting for the effect of covariates age, gender, diabetes duration and HbA1c level.

None of the SNPs with q < 0.5 in the combined SFDR analysis showed significant association with SDR in the replication studies with the same direction after Bonferroni correction of association P-values (P < 0.0013 for 38 independent loci). Table 43 summarizes the association results for the SNPs that reached nominal significance in any of the replication studies.

Table 44 displays the association results of the top SNPs in the discovery case-control meta-GWAS in the replication studies. A single intergenic SNP, rs6426244, on the long arm of chromosome 1 was significantly associated with SDR in the replication studies.

The SNP showed nominally significant association with SDR in FinnDiane study (P =

0.04). Combined analysis of the three replication studies augmented the significance level for this SNP (P = 9.9×10-4, fixed effects inverse variance weighted meta-analysis of results in replication studies). The association in replication studies surpassed

Bonferroni corrected significance threshold (P < 0.0013, see above) and the direction of effect remained consistent in all discovery and replication groups tested. However, fixed effects meta-analysis of all discovery and replication studies did not reach genome-wide significance threshold (P = 6.0 × 10-7). No other SNP reached genome- wide significance for association in the combined meta-analysis of discovery and replication studies. Nonetheless, all of these SNPs showed strong evidence for heterogeneity of effect between the studies (Cochran’s heterogeneity test P-value < 0.05 and I2 > 50, Table 44).

We also investigated the association of top SNPs from the time-to SDR discovery meta-

GWAS (Table 9 in Chapter 3) in the replication studies. Due to the cross-sectional nature of replication studies only a case-control analysis was feasible. None of the top

Table 43. Top SFDR SNPs nominally (P<0.05) associated with SDR in replication studies in the same direction as discovery. ____Rank______P (D+W)______Rsq______P-value______Freq Range Heterogeneity 2 SNP Chr Position A1/A2 FDR SFDR Psurvival Pccassoc Fr+Dk FD Fr Dk FD comb PFisher Min Max Pmeta Direction I Q Pval rs4920378 1 17,782,088 A/G 8223 83 2.7E-03 6.7E-03 GT 0.417 0.19 0.14 0.48 0.03 1.2E-03 0.03 0.06 1.1E-03 +++++++ 0 0.95 rs561698 1 18,008,469 A/G 7175 68 2.3E-03 5.0E-02 GT 0.865 0.03 0.26 0.77 0.98 5.6E-02 0.50 0.61 4.5E-01 -???-++ 68 0.02 rs473648 1 18,017,685 T/C 41934 307 1.7E-02 1.1E-02 0.991 NA 0.03 0.28 NA 0.76 4.4E-02 0.47 0.51 5.3E-02 +++++-? 52 0.07 rs619993 1 18,022,917 T/C 61171 411 2.6E-02 1.5E-02 GT 0.956 0.04 0.28 0.72 0.94 1.6E-01 0.48 0.62 2.2E-01 -----++ 46 0.08 rs11800573 1 18,356,592 A/G 39598 294 1.0E-02 1.8E-02 GT 0.732 0.37 0.02 0.37 0.17 1.3E-02 0.12 0.13 1.3E-02 ++++++- 42 0.11 rs9726285 1 18,357,532 C/G 33369 256 1.1E-02 1.3E-02 0.903 0.693 0.36 0.01 0.36 0.16 1.3E-02 0.12 0.13 9.0E-03 +++-++- 44 0.10 rs12087005 1 19,710,519 A/G 76702 475 2.4E-02 1.2E-01 GT 1.000 0.32 0.02 0.26 0.91 2.4E-02 0.91 0.96 4.4E-01 ------+ 43 0.10 rs4130336 1 19,711,726 A/G 69764 453 2.2E-02 1.2E-01 0.995 0.996 0.32 0.02 0.26 0.93 2.4E-02 0.91 0.96 4.5E-01 ------+ 43 0.10 rs12088373 1 19,715,988 A/G 44487 319 1.4E-02 1.0E-01 0.988 0.996 0.31 0.02 0.27 0.49 2.0E-02 0.04 0.09 1.5E-01 ++++++- 28 0.22 rs12093766 1 19,716,907 T/C 41425 305 1.3E-02 1.1E-01 0.987 0.988 0.31 0.02 0.25 0.97 2.0E-02 0.04 0.09 4.8E-01 ++++++- 43 0.11 rs2281238 1 19,821,455 A/G 22382 178 5.8E-03 6.3E-03 1.000 0.720 0.25 0.04 0.63 0.16 6.4E-03 0.09 0.10 8.2E-03 ++++++- 30 0.20 rs2314763 1 20,031,964 A/T 11724 105 4.8E-03 2.3E-02 0.598 NA 0.04 0.44 NA 0.65 1.7E-02 0.16 0.18 4.7E-02 +++++-? 23 0.26 rs1796915 1 20,133,123 A/G 25210 200 1.4E-02 1.3E-01 0.993 0.999 0.04 0.89 0.72 0.27 1.2E-02 0.62 0.66 8.2E-02 ---+--- 31 0.19 rs4654990 1 20,146,267 C/G 25772 205 1.3E-02 1.2E-01 0.985 0.983 0.04 0.88 0.71 0.26 1.1E-02 0.62 0.66 7.1E-02 ---+--- 28 0.22 rs2315070 1 20,166,055 A/G 31605 246 1.4E-02 8.7E-02 1.000 1.000 0.04 0.36 0.32 0.69 1.9E-01 0.37 0.43 5.7E-01 +++++-- 36 0.15 rs2872821 1 20,166,147 T/C 31955 249 1.4E-02 8.2E-02 0.996 1.000 0.05 0.30 0.32 0.51 2.3E-01 0.36 0.43 7.8E-01 +++++-- 39 0.13 rs4655169 1 20,459,659 C/G 64150 436 2.6E-02 6.0E-01 0.985 0.900 0.68 0.02 0.78 0.37 5.6E-02 0.32 0.36 2.9E-01 -++++-+ 20 0.28 rs1041922 1 20,468,507 T/G 64497 437 1.9E-02 5.6E-01 GT 0.762 0.73 0.02 0.41 0.63 8.0E-02 0.58 0.67 4.4E-01 +----+- 29 0.21 rs7536273 1 20,608,637 C/G 16047 136 9.2E-03 3.0E-02 0.954 0.975 0.61 0.59 0.04 0.47 7.1E-02 0.65 0.71 7.6E-02 ----++- 5 0.39 rs1555797 1 20,635,483 T/C 2350 24 2.8E-04 3.6E-03 GT 0.642 0.41 0.02 0.69 0.20 6.2E-03 0.06 0.08 9.8E-03 ++++-++ 35 0.16 rs638722 1 23,570,204 T/C 60511 408 3.0E-02 5.0E-03 0.999 1.000 0.84 0.05 0.87 0.29 3.6E-02 0.80 0.83 2.6E-02 ++++++- 51 0.06 rs445381 1 25,237,942 A/C 63527 426 1.3E-02 1.5E-02 0.999 0.875 0.07 0.04 0.89 0.81 2.0E-01 0.85 0.87 1.2E-01 ----+-+ 57 0.03 rs389865 1 25,239,227 A/G 78347 483 1.8E-02 2.0E-02 0.999 0.991 0.07 0.04 0.83 0.85 2.4E-01 0.13 0.14 1.5E-01 ++++-+- 51 0.06 rs3845303 1 25,297,887 A/T 3037 29 5.6E-04 2.7E-03 0.491 0.512 0.37 0.03 0.15 0.93 1.1E-01 0.52 0.57 8.4E-02 ++++-+- 59 0.02 rs7514417 1 25,319,976 A/G 1763 19 7.5E-04 3.0E-03 0.998 0.996 0.32 0.03 0.25 0.85 8.9E-02 0.66 0.71 8.8E-02 ++++-+- 56 0.04 rs4560984 1 25,321,997 A/G 3661 33 1.7E-03 9.7E-03 0.999 0.997 0.27 0.04 0.25 0.83 1.4E-01 0.30 0.35 2.9E-01 ----+-+ 57 0.03 rs2753319 1 25,325,258 A/G 3876 36 1.8E-03 1.2E-02 GT 0.999 0.20 0.04 0.28 0.84 1.5E-01 0.30 0.36 3.0E-01 ----+-+ 57 0.03 rs803304 1 25,329,625 A/G 4212 37 2.0E-03 1.4E-02 GT NA 0.30 0.04 NA 0.23 1.7E-02 0.64 0.69 1.3E-02 ++++-+? 21 0.27 rs803307 1 25,331,538 A/G 4586 39 2.2E-03 1.5E-02 0.987 0.999 0.30 0.04 0.24 0.93 1.5E-01 0.64 0.70 1.8E-01 ++++-+- 49 0.07 rs2753320 1 25,332,019 T/C 2613 26 1.1E-03 9.3E-03 0.998 0.967 0.35 0.04 0.22 0.82 1.1E-01 0.65 0.70 3.0E-01 ++++-+- 56 0.03 rs6673342 1 25,333,665 T/C 3036 28 1.3E-03 5.9E-03 GT 0.999 0.39 0.03 0.23 0.86 1.0E-01 0.66 0.71 2.4E-01 ++++-+- 58 0.03 rs11804893 1 25,334,666 A/G 3248 30 1.5E-03 1.1E-02 GT 0.999 0.34 0.04 0.23 0.91 1.2E-01 0.65 0.70 1.6E-01 ++++-+- 50 0.06 rs12737245 1 25,336,088 A/C 3593 31 1.5E-03 3.9E-03 0.980 0.986 0.36 0.03 0.28 0.94 1.0E-01 0.30 0.35 1.9E-01 ----+-+ 59 0.02 rs12736320 1 25,336,223 C/G 1477 16 6.0E-04 3.7E-03 0.979 0.985 0.36 0.03 0.28 0.94 7.6E-02 0.30 0.36 1.8E-01 ----+-+ 58 0.03 rs12744222 1 25,336,438 A/T 2153 21 9.3E-04 2.9E-03 0.978 0.984 0.35 0.03 0.28 0.83 8.6E-02 0.64 0.70 8.4E-02 ++++-+- 55 0.04 rs12735622 1 25,338,568 T/C 2057 20 8.6E-04 2.7E-03 0.971 0.984 0.34 0.03 0.28 0.84 8.7E-02 0.29 0.36 8.5E-02 ----+-+ 55 0.04 rs9803723 1 25,339,216 T/C 6599 60 3.3E-03 1.2E-02 0.970 0.983 0.25 0.04 0.32 0.88 1.5E-01 0.30 0.37 1.6E-01 ----+-+ 50 0.06 rs9803789 1 25,340,833 C/G 7356 70 3.3E-03 7.6E-03 0.961 0.975 0.15 0.04 0.31 0.99 2.1E-01 0.64 0.71 1.6E-01 ++++-+- 55 0.04 rs6680580 1 25,341,026 C/G 8336 84 3.5E-03 7.9E-03 0.961 0.975 0.15 0.04 0.31 0.82 2.1E-01 0.29 0.36 2.8E-01 ----+-+ 58 0.03 rs12756888 1 25,342,988 A/G 13050 115 5.8E-03 1.3E-02 0.940 0.971 0.11 0.04 0.36 0.83 2.6E-01 0.29 0.36 3.0E-01 ----+-+ 56 0.03 rs7816359 8 103,615,743 A/G 19 230 5.3E-06 3.4E-04 0.995 0.996 0.98 0.46 0.03 0.06 2.4E-03 0.20 0.21 8.6E-04 +-+++-+ 58 0.03 rs4496937 8 103,616,725 A/G 15 229 4.3E-06 3.0E-04 0.998 0.998 1.00 0.45 0.03 0.45 2.4E-03 0.79 0.80 8.2E-03 -+---+- 58 0.03 rs1347322 8 103,617,738 A/G 28 235 7.0E-06 3.9E-04 GT 1.000 0.99 0.45 0.03 0.07 2.9E-03 0.20 0.21 1.0E-03 +-++--+ 57 0.03 rs16869510 8 103,618,431 T/C 27 234 6.8E-06 3.8E-04 1.000 0.999 0.99 0.45 0.03 0.46 2.9E-03 0.20 0.21 9.3E-03 +-++--+ 57 0.03

P (D+W): discovery meta-GWAS in WESDR and DCCT subgroups; Psurvival: survival meta-GWAS; Pccassoc: case-control meta-GWAS; Rsq: imputation quality; Fr: French Genesis/GeneDiab study; Dk: Steno clinic study; FD: FinnDiane; comb: combined results of replication studies; PFisher: Z-score combination of survival meta-GWAS in DCCT+WESDR and replication studies; Freq Range: range of A1 frequency in all studies; Pmeta: fixed effects meta-analysis between discovery and replication;

Direction: direction of effects in the following order: WESDR, primary cohort, 2ndary cohort – conv. Rx, 2ndary cohort – int. Rx; Genesis/GeneDiab, Steno, FinnDiane; I2: heterogeneity index; Q Pval: Cochran’s heterogeneity test P-value; ?: not available. 127

Table 44. Association results of top SNPs (P <10-5) from the discovery case-control meta-GWAS of SDR in replication studies. case-control meta-GWAS (D+W) Replication case-control meta-GWAS of discovery and replication studies _____Rsq______P-value______Freq1___ Heterogeneity SNP Chr Position A1/A2 Freq1 β SE P Fr+Dk FD Fr Dk FD comb Min Max β SE P Direction I2 Q-PVal rs9662272 1 18,257,170 T/C 0.07 -0.90 0.22 6.8E-06 0.670 0.993 0.91 0.21 0.90 0.56 0.05 0.07 -0.30 0.11 8.0E-03 ----+-+ 60 2.0E-02 rs736202 1 113,620,903 T/C 0.10 -0.76 0.17 4.9E-06 0.852 0.982 0.86 0.86 0.10 0.08 0.09 0.12 -0.27 0.08 4.1E-04 ----+-- 53 4.9E-02 rs6426244 1 245,619,172 T/C 0.66 -0.43 0.10 8.8E-06 GT 0.995 0.12 0.12 0.04 9.9E-04 0.64 0.69 -0.23 0.05 6.0E-07 ------42 1.1E-01 rs1402446 2 115,885,861 A/G 0.92 -0.81 0.18 7.9E-06 0.991 0.996 0.72 0.80 0.29 0.74 0.90 0.92 -0.21 0.09 1.6E-02 ---+++- 68 5.2E-03 rs843390 2 115,891,622 C/G 0.08 0.82 0.18 6.5E-06 0.999 0.999 0.72 0.76 0.29 0.51 0.08 0.10 0.20 0.08 1.2E-02 +++---+ 69 4.1E-03 rs843394 2 115,904,553 T/G 0.92 -0.80 0.18 9.7E-06 0.987 0.999 0.72 0.73 0.28 0.52 0.90 0.92 -0.19 0.08 1.4E-02 ---+++- 68 4.9E-03 rs17362720 2 115,970,570 A/G 0.08 0.82 0.18 4.8E-06 GT 0.874 0.86 0.79 0.63 0.86 0.08 0.12 0.17 0.08 3.0E-02 +++---+ 70 3.0E-03 rs17044463 2 115,973,369 A/C 0.11 0.78 0.15 1.8E-07 1.000 1.000 0.82 0.47 0.27 0.51 0.11 0.14 0.19 0.07 4.0E-03 +++++-+ 74 6.6E-04 rs10193980 2 115,974,698 A/G 0.11 0.78 0.15 1.8E-07 0.999 1.000 0.82 0.47 0.27 0.51 0.11 0.14 0.19 0.07 4.0E-03 +++++-+ 74 6.6E-04 rs7579023 2 115,979,389 T/C 0.89 -0.78 0.15 1.8E-07 0.998 1.000 0.82 0.47 0.27 0.51 0.86 0.89 -0.19 0.07 4.0E-03 -----+- 74 6.6E-04 rs7566337 2 115,980,507 C/G 0.89 -0.78 0.15 1.8E-07 0.998 1.000 0.82 0.47 0.27 0.75 0.86 0.89 -0.21 0.07 4.8E-03 -----+- 74 7.3E-04 rs13432069 2 115,986,942 T/G 0.89 -0.78 0.15 1.8E-07 0.997 1.000 0.82 0.47 0.27 0.51 0.86 0.89 -0.19 0.07 4.0E-03 -----+- 74 6.6E-04 rs13432497 2 115,987,368 T/C 0.89 -0.78 0.15 1.8E-07 0.997 1.000 0.82 0.47 0.27 0.51 0.86 0.89 -0.19 0.07 4.0E-03 -----+- 74 6.6E-04 rs13429901 2 115,996,269 T/C 0.11 0.78 0.15 1.8E-07 0.998 1.000 0.82 0.47 0.27 0.75 0.11 0.14 0.21 0.07 4.8E-03 +++++-+ 74 7.3E-04 rs10210316 2 116,015,059 T/G 0.89 -0.78 0.15 1.8E-07 GT 0.999 0.82 0.47 0.26 0.51 0.86 0.89 -0.19 0.07 3.9E-03 -----+- 74 6.6E-04 rs2421273 2 116,017,257 T/C 0.89 -0.78 0.15 1.5E-07 0.998 0.999 0.83 0.47 0.26 0.51 0.86 0.89 -0.19 0.07 3.7E-03 -----+- 75 6.4E-04 rs7563882 2 116,018,500 A/C 0.89 -0.78 0.15 1.4E-07 0.997 0.999 0.84 0.47 0.25 0.75 0.86 0.89 -0.21 0.07 4.5E-03 -----+- 74 7.0E-04 rs6756582 2 116,020,461 A/G 0.11 0.79 0.15 1.1E-07 0.996 0.998 0.85 0.47 0.25 0.49 0.11 0.14 0.20 0.07 3.2E-03 +++++-+ 75 5.5E-04 rs1006342 2 116,025,663 A/G 0.89 -0.80 0.15 6.9E-08 0.996 1.000 0.87 0.47 0.21 0.72 0.86 0.89 -0.22 0.07 3.2E-03 -----+- 75 5.5E-04 rs2008031 2 116,027,310 A/G 0.11 0.80 0.15 6.7E-08 0.997 1.000 0.88 0.47 0.21 0.43 0.11 0.14 0.20 0.07 2.2E-03 +++++-+ 75 4.8E-04 rs12466846 2 116,028,631 T/C 0.89 -0.80 0.15 6.2E-08 GT 1.000 0.90 0.47 0.21 0.44 0.86 0.89 -0.20 0.07 2.3E-03 -----+- 75 4.5E-04 rs972493 7 96,817,121 A/G 0.20 -0.53 0.12 5.8E-06 0.981 0.910 0.67 0.57 0.92 0.82 0.11 0.21 -0.14 0.07 3.2E-02 -----++ 67 5.4E-03 rs7791063 7 96,818,264 A/G 0.80 0.53 0.12 5.7E-06 0.985 0.934 0.67 0.57 0.92 0.82 0.79 0.89 0.14 0.07 3.1E-02 +++++-- 67 5.3E-03 rs7791647 7 96,818,399 A/G 0.20 -0.52 0.12 7.8E-06 0.985 0.936 0.68 0.57 0.93 0.82 0.11 0.21 -0.14 0.07 3.6E-02 -----++ 67 5.7E-03 rs7791690 7 96,818,622 A/G 0.80 0.53 0.12 5.8E-06 0.985 0.948 0.68 0.57 0.93 0.82 0.79 0.89 0.14 0.07 3.2E-02 +++++-- 67 5.4E-03 rs7791868 7 96,818,795 A/G 0.80 0.53 0.12 5.8E-06 0.986 0.950 0.68 0.57 0.93 0.82 0.79 0.89 0.14 0.07 3.2E-02 +++++-- 67 5.4E-03 rs10252518 7 96,819,286 A/G 0.20 -0.53 0.13 9.9E-06 0.988 0.934 0.68 0.57 0.93 0.82 0.11 0.20 -0.14 0.07 4.4E-02 -----++ 67 6.3E-03 rs10253157 7 96,820,017 C/G 0.80 0.53 0.12 5.6E-06 1.000 0.977 0.70 0.55 0.93 0.80 0.79 0.89 0.14 0.07 3.5E-02 +++++-- 68 4.9E-03 rs2979775 8 17,626,417 A/G 0.69 0.47 0.10 3.0E-06 GT NA 0.86 0.85 NA 0.95 0.68 0.69 0.21 0.07 2.1E-03 +++++-? 66 1.2E-02 rs2375715 9 31,799,147 A/G 0.08 -0.83 0.19 4.3E-06 1.000 1.000 0.04 0.57 0.60 0.98 0.07 0.10 -0.18 0.09 4.2E-02 ----+-- 72 1.6E-03 rs1023047 9 31,800,090 A/G 0.92 0.83 0.19 3.5E-06 1.000 1.000 0.04 0.57 0.60 0.93 0.90 0.93 0.17 0.08 4.3E-02 ++++-++ 72 1.3E-03 rs2770348 9 31,800,320 T/G 0.92 0.85 0.19 2.5E-06 1.000 1.000 0.08 0.64 0.60 1.00 0.90 0.93 0.19 0.09 3.6E-02 ++++-++ 71 2.0E-03 rs2770349 9 31,800,530 A/G 0.91 0.83 0.19 3.4E-06 GT 1.000 0.04 0.57 0.60 0.93 0.90 0.93 0.17 0.08 4.2E-02 ++++-++ 73 1.3E-03 rs1928090 9 31,801,628 T/C 0.91 0.84 0.19 3.2E-06 GT 1.000 0.04 0.57 0.60 0.99 0.90 0.93 0.19 0.09 3.8E-02 ++++-++ 72 1.4E-03 rs2770352 9 31,802,426 T/G 0.09 -0.85 0.19 2.6E-06 1.000 1.000 0.04 0.57 0.60 0.93 0.07 0.10 -0.17 0.08 3.9E-02 ----+-- 73 1.1E-03 rs2770357 9 31,805,043 T/C 0.09 -0.84 0.19 3.4E-06 1.000 1.000 0.04 0.57 0.60 0.93 0.07 0.10 -0.17 0.08 4.1E-02 ----+-- 73 1.3E-03 rs2770358 9 31,805,136 A/G 0.09 -0.84 0.19 3.4E-06 1.000 1.000 0.04 0.57 0.60 0.99 0.07 0.10 -0.18 0.09 3.8E-02 ----+-- 72 1.4E-03

128

case-control meta-GWAS (D+W) Replication case-control meta-GWAS of discovery and replication studies _____Rsq______P-value______Freq1___ Heterogeneity SNP Chr Position A1/A2 Freq1 β SE P Fr+Dk FD Fr Dk FD comb Min Max β SE P Direction I2 Q-PVal rs2770359 9 31,805,283 A/T 0.91 0.84 0.19 3.4E-06 1.000 1.000 0.04 0.57 0.60 0.93 0.90 0.93 0.17 0.08 4.1E-02 ++++-++ 73 1.3E-03 rs1854360 9 31,805,528 A/G 0.09 -0.83 0.19 3.4E-06 1.000 0.999 0.04 0.57 0.59 0.99 0.07 0.10 -0.19 0.09 3.8E-02 ----+-- 72 1.4E-03 rs1328738 9 31,805,857 T/C 0.09 -0.83 0.19 3.5E-06 1.000 0.999 0.04 0.57 0.59 0.92 0.07 0.10 -0.17 0.08 4.1E-02 ----+-- 72 1.3E-03 rs2770360 9 31,806,010 A/G 0.91 0.84 0.19 3.4E-06 1.000 0.999 0.04 0.57 0.59 0.92 0.90 0.93 0.17 0.08 4.0E-02 ++++-++ 73 1.3E-03 rs2770777 9 31,806,036 T/C 0.91 0.84 0.19 3.4E-06 1.000 0.999 0.04 0.57 0.58 1.00 0.90 0.93 0.19 0.09 3.7E-02 ++++-++ 72 1.5E-03 rs2255573 9 31,806,381 A/T 0.09 -0.83 0.19 3.4E-06 1.000 0.999 0.04 0.57 0.58 1.00 0.07 0.10 -0.19 0.09 3.7E-02 ----+-- 72 1.5E-03 rs2255579 9 31,806,411 A/G 0.09 -0.83 0.19 3.4E-06 0.987 0.999 0.04 0.58 0.58 0.99 0.07 0.10 -0.19 0.09 3.8E-02 ----+-- 72 1.4E-03 rs928033 9 31,806,777 A/C 0.91 0.83 0.19 3.6E-06 1.000 0.999 0.04 0.57 0.58 0.90 0.90 0.93 0.17 0.08 4.0E-02 ++++-++ 72 1.4E-03 rs928032 9 31,807,229 T/G 0.09 -0.83 0.19 3.9E-06 1.000 0.999 0.04 0.57 0.58 0.90 0.07 0.10 -0.17 0.08 4.1E-02 ----+-- 72 1.4E-03 rs928030 9 31,807,381 T/G 0.91 0.85 0.19 3.0E-06 1.000 1.000 0.08 0.64 0.57 0.98 0.90 0.93 0.19 0.09 3.5E-02 ++++-++ 71 2.1E-03 rs1928094 9 31,807,597 T/C 0.09 -0.83 0.19 4.3E-06 1.000 1.000 0.04 0.57 0.57 0.90 0.08 0.10 -0.17 0.08 4.1E-02 ----+-- 72 1.4E-03 rs2770361 9 31,807,859 T/C 0.09 -0.82 0.19 4.5E-06 1.000 1.000 0.04 0.58 0.57 0.90 0.08 0.10 -0.17 0.08 4.2E-02 ----+-- 72 1.5E-03 rs780747 9 31,808,898 A/G 0.08 -0.82 0.19 4.9E-06 0.999 1.000 0.08 0.66 0.57 0.99 0.07 0.10 -0.18 0.09 4.0E-02 ----+-- 70 2.5E-03 rs780748 9 31,809,554 T/C 0.92 0.81 0.19 5.7E-06 0.999 1.000 0.08 0.66 0.57 0.99 0.90 0.93 0.18 0.09 4.1E-02 ++++-++ 70 2.6E-03 rs780753 9 31,811,673 T/C 0.08 -0.81 0.19 5.9E-06 0.999 1.000 0.08 0.68 0.57 0.90 0.07 0.10 -0.17 0.08 4.4E-02 ----+-- 70 2.4E-03 rs780754 9 31,812,262 T/C 0.08 -0.81 0.19 5.9E-06 0.999 1.000 0.08 0.68 0.56 0.89 0.07 0.10 -0.17 0.08 4.4E-02 ----+-- 70 2.4E-03 rs17182894 9 31,813,519 A/G 0.08 -0.81 0.19 9.4E-06 0.999 1.000 0.08 0.69 0.56 1.00 0.07 0.10 -0.18 0.09 5.0E-02 ----+-- 69 3.3E-03 rs780757 9 31,815,468 A/G 0.08 -0.81 0.19 6.0E-06 GT 1.000 0.08 0.69 0.56 1.00 0.07 0.10 -0.18 0.09 4.2E-02 ----+-- 70 2.6E-03 rs1576251 9 31,819,226 T/C 0.08 -0.81 0.19 6.0E-06 0.988 1.000 0.08 0.69 0.56 0.89 0.07 0.10 -0.17 0.08 4.4E-02 ----+-- 70 2.5E-03 rs2770787 9 31,819,395 A/G 0.08 -0.81 0.19 6.0E-06 0.988 1.000 0.08 0.69 0.56 1.00 0.07 0.10 -0.18 0.09 4.2E-02 ----+-- 70 2.7E-03 rs2770792 9 31,820,991 T/C 0.92 0.81 0.19 6.0E-06 0.975 1.000 0.07 0.69 0.56 0.99 0.90 0.93 0.18 0.09 4.1E-02 ++++-++ 70 2.7E-03 rs2770793 9 31,821,013 A/G 0.08 -0.81 0.19 7.1E-06 0.974 1.000 0.08 0.69 0.56 0.99 0.07 0.10 -0.18 0.09 4.4E-02 ----+-- 70 2.9E-03 rs1328744 9 31,821,298 A/G 0.92 0.81 0.19 5.9E-06 0.973 1.000 0.07 0.69 0.56 0.89 0.90 0.93 0.17 0.08 4.3E-02 ++++-++ 70 2.5E-03 rs1328745 9 31,821,840 A/G 0.92 0.81 0.19 6.0E-06 0.972 0.998 0.07 0.69 0.56 0.89 0.90 0.93 0.17 0.08 4.3E-02 ++++-++ 70 2.5E-03 rs1328746 9 31,822,191 A/G 0.92 0.81 0.19 6.0E-06 0.971 0.996 0.07 0.69 0.56 0.89 0.90 0.93 0.17 0.08 4.3E-02 ++++-++ 70 2.5E-03 rs2770794 9 31,822,992 A/C 0.92 0.81 0.19 6.0E-06 0.969 0.993 0.07 0.69 0.56 0.89 0.90 0.93 0.17 0.08 4.3E-02 ++++-++ 70 2.5E-03 rs17133449 10 4,748,118 T/C 0.75 0.48 0.11 9.3E-06 GT 1.000 0.14 0.73 0.62 0.26 0.72 0.78 0.18 0.06 1.2E-03 +++++++ 50 6.4E-02 rs10512502 17 59,925,471 T/C 0.06 0.94 0.20 3.0E-06 GT 0.990 0.77 0.76 0.35 0.50 0.05 0.11 0.12 0.08 1.6E-01 ++++++- 74 7.1E-04

Association results in the discovery studies of WESDR and DCCT/EDIC (W+D), replication studies and a combined inverse-variance weighted fixed effects meta-analysis between discovery and replication studies are presented separately. β: estimate of coefficient for additive genotype of A1; SE: standard error of estimate; P: association P-value; Rsq: imputation quality; comb: combined results in replication studies Direction: Direction of effect of discovery and replication studies is presented in the following order: (1) WESDR (W) (2) DCCT/EDIC: primary cohort (3) DCCT/EDIC: secondary cohort – conventional treatment (4) DCCT/EDIC: secondary cohort – intensive treatment (5) French studies: Genesis / GeneDiab (Fr) (6) Steno Clinic in Denmark (Dk) (7) FinnDiane (FD) 129

Table 45. Association results of top SNPs (P <10-5) from the discovery time to SDR meta-GWAS in replication studies. Discovery meta-GWAS Replication Meta-GWAS of discovery and replication WESDR + DCCT/EDIC _____Rsq______P-value______Freq1___ Heterogeneity SNP Chr Position A1/A2 Psurvival Pccassoc Fr+Dk FD Fr Dk FD comb Min Max PFisher Pmeta Direction I2 Q Pval rs1006342 2 116,025,663 A/G 9.0E-06 6.9E-08 0.996 1.000 0.87 0.47 0.21 0.72 0.86 0.89 9.9E-03 3.2E-03 -----+- 75 5.5E-04 rs2008031 2 116,027,310 A/G 8.8E-06 6.7E-08 0.997 1.000 0.88 0.47 0.21 0.43 0.11 0.14 1.0E-02 2.2E-03 +++++-+ 75 4.8E-04 rs12466846 2 116,028,631 T/C 8.3E-06 6.2E-08 GT 1.000 0.90 0.47 0.21 0.44 0.86 0.89 1.0E-02 2.3E-03 -----+- 75 4.5E-04 rs17610525 5 1,999,575 C/G 1.9E-06 7.2E-03 0.563 0.733 0.12 0.32 0.14 0.02 0.94 0.98 6.9E-01 3.3E-01 ----+++ 61 0.02 rs9406005 6 720,915 A/G 8.9E-07 8.0E-04 GT 0.996 0.85 0.71 0.43 0.70 0.72 0.74 1.0E-02 5.2E-02 ++++--+ 36 0.16 rs9406006 6 721,262 A/G 1.3E-06 9.7E-04 GT 0.997 0.85 0.71 0.43 0.70 0.72 0.74 1.1E-02 5.5E-02 ++++--+ 33 0.18 rs13199591 6 724,944 T/C 2.5E-06 2.1E-03 0.986 1.000 0.94 0.69 0.45 0.70 0.26 0.28 1.3E-02 7.0E-02 ----++- 20 0.28 rs9406014 6 727,060 T/G 3.4E-06 2.4E-03 0.900 0.913 0.65 0.79 0.48 0.87 0.70 0.73 2.1E-02 8.2E-02 ++++--+ 18 0.30 rs7743921 6 727,953 T/C 7.5E-07 1.0E-03 0.947 0.934 0.82 0.67 0.56 0.93 0.71 0.74 1.5E-02 7.7E-02 ++++--+ 34 0.17 rs1986345 6 730,010 C/G 4.7E-07 9.9E-04 0.938 0.933 0.83 0.67 0.59 0.95 0.26 0.29 1.4E-02 8.3E-02 ----++- 36 0.16 rs12660370 6 16,100,126 A/G 5.6E-07 2.5E-02 0.972 NA 0.92 0.45 NA 0.62 0.97 0.99 1.2E-02 1.9E-01 -+---+? 42 0.13 rs6905993 6 17,191,086 T/C 3.0E-06 6.6E-02 0.990 0.998 0.71 0.87 0.40 0.27 0.98 1.00 2.5E-03 7.0E-02 ---+--- 0 0.89 rs6926316 6 17,191,817 A/G 2.8E-06 6.4E-02 GT 0.998 0.70 0.92 0.40 0.29 0.00 0.02 2.7E-03 7.4E-02 +++-+++ 0 0.88 rs9360898 6 76,010,425 T/G 5.6E-06 4.5E-05 GT 0.994 0.55 0.91 0.73 0.59 0.77 0.86 8.2E-02 6.5E-02 ----+++ 58 0.03 rs4276631 7 57,279,775 T/C 7.5E-06 4.1E-01 0.894 0.843 0.21 0.98 0.80 0.58 0.01 0.03 1.4E-01 9.2E-01 ++-+--- 42 0.11 rs10096299 8 103,614,493 A/G 5.3E-06 3.4E-04 0.993 0.996 0.97 0.46 0.03 0.43 0.79 0.80 2.3E-03 7.9E-03 -+---+- 57 0.03 rs7816359 8 103,615,743 A/G 5.3E-06 3.4E-04 0.995 0.996 0.98 0.46 0.03 0.06 0.20 0.21 2.4E-03 8.6E-04 +-+++-+ 58 0.03 rs4496937 8 103,616,725 A/G 4.3E-06 3.0E-04 0.998 0.998 1.00 0.45 0.03 0.45 0.79 0.80 2.4E-03 8.2E-03 -+---+- 58 0.03 rs17765218 19 5,308,682 T/C 2.7E-08 1.5E-02 0.904 NA 0.55 0.95 NA 0.96 0.98 1.00 4.4E-04 8.3E-02 -+-+-+? 0 0.55 rs16999053 20 17,310,001 A/G 8.3E-06 1.4E-02 0.980 0.965 0.50 0.99 0.91 0.73 0.01 0.03 8.7E-03 1.2E-01 +++++++ 0 0.45

SNPs in this table showed a P-value less than 1E-5 in the discovery survival meta-GWAS (Psurvival). Results for these SNPs in the discovery case-control meta-GWAS (Pccassoc) are provided. Quality of imputation (Rsq) and P-value for the SNPs in replication cohorts are provided. Fr: French Studies Genesis / GeneDiab; Dk: Steno clinic study in Denmark; FD: FinnDiane Freq1: range of frequency of A1 in replication and discovery studies. The results of discovery and replication studies are combined in two meta-analyses.

PFisher: Z-score combination of discovery survival meta-GWAS and replication case-control results. Pmeta: Fixed effects meta-analysis of case-control association results in all discovery (4) and replication (3) studies. Heterogeneity tests in the latter analysis are provided in I2 (heterogeneity index) and Q-Pval (Cochran’s heterogeneity test) columns. Direction of effect in discovery and replication studies is presented in the following order: (1) WESDR (W) (5) French studies: Genesis / GeneDiab (Fr) (2) DCCT/EDIC: primary cohort (6) Steno Clinic in Denmark (Dk) (3) DCCT/EDIC: secondary cohort – conventional treatment (7) FinnDiane (FD) (4) DCCT/EDIC: secondary cohort – intensive treatment

130 131

SNPs from the survival meta-GWAS of SDR showed significant association with SDR status in any of replication studies (Table 45).

The majority of SNPs in Table 44 and Table 45 showed significant heterogeneity in effect between different studies in the final combined meta-analysis of discovery and replication studies (Q Pval < 0.05 and I2 > 50). Therefore, we repeated the analysis using random effects meta-analysis as described by (Han and Eskin 2011). Most of the SNPs showed smaller P-values in the Han and Eskin’s random effects meta-analysis ; yet no

SNP reached genome-wide significance threshold (all P > 5×10-8).

5.3 Replication of Previous GWAS Hits for Diabetic Retinopathy

By the time we finished the current study, four case-control GWAS studies (see 1.3.4‎ ) and an extensive candidate gene association study of DR have been published (Fu et al.

2010; Grassi et al. 2011; Huang et al. 2011; Sheu et al. 2013; Sobrin et al. 2011). We investigated the association of top reported signals in these studies (P < 10-5) with SDR in our meta-GWAS (Table 46). Of 54 SNPs representing 11 loci with suggestive evidence for association with DR in previous studies (P < 10-5), only SNPs at a single locus on chromosome 10 showed significant association with SDR in the same direction in our meta-analysis which remained significant after Bonferroni correction for the number of loci tested (P < 0.0045). Interestingly, both SNPs at this locus showed the same direction of effect in WESDR and DCCT/EDIC subgroups with no evidence of heterogeneity of effect (I2 = 0). No other locus was replicated in our meta-GWAS (all P >

0.05).

Table 46. Association results of top loci (P < 10-5) from previous GWAS studies of DR in the current SDR meta-analysis. Original association Meta-analysis (DCCT/EDIC+WESDR)

SNP Chr Position A1/A2 OR P Reference Closest Genes MAF OR Pcase-control Psurvival Direction rs12092121 1 58,925,598 G/A 0.67 3.1E-07 MYSM1 0.425 1.06 0.26 0.39 -+ (Huang et al. 2011) rs2811893 1 58,934,736 C/T 0.67 3.1E-07 MYSM1 0.423 1.06 0.27 0.38 -+ rs1399634 2 169,952,853 A/T 1.50 2.0E-06 LRP2 - BBS5 0.206 1.14 0.12 0.22 ++ (Sheu et al. 2013) rs4668142 2 169,972,523 G/T 0.63 2.7E-06 LRP2 - BBS5 0.211 0.89 0.14 0.16 -- rs2380261 2 235,305,919 A/C 1.50 2.1E-06 ARL4C - SH3BP4 0.267 1.01 0.47 0.39 ++ (Sheu et al. 2013) rs1441605 2 235,312,105 C/G 0.67 4.6E-06 ARL4C - SH3BP4 0.265 0.98 0.44 0.38 -- rs6856425 4 966,918 C/T 3.83 5.3E-07 (Sobrin et al. 2011) SLC26A1 0.023 1.20 0.29 0.24 ++ rs4470583 4 162,470,382 A/G 1.16 4.3E-07 (Huang et al. 2011) FSTL5 0.007 1.19 0.41 0.40 ++ rs13163610 5 93,574,633 C/A 0.28 3.2E-15 KIAA0825 0.120 0.91 0.25 0.21 -- (Huang et al. 2011) rs17376456 5 93,583,458 G/A 0.28 3.0E-15 KIAA0825 0.119 0.91 0.26 0.22 -- rs1571942 10 20,582,640 G/A 0.60 3.5E-07 PLXDC2 0.152 0.71 0.003 0.015 -- (Huang et al. 2011) rs12219125 10 20,633,093 T/G 1.62 9.3E-09 PLXDC2 0.153 1.40 0.004 0.016 ++ rs4838605 10 49,369,963 T/C 0.63 1.9E-09 ARHGAP22 0.360 1.00 0.48 0.48 -+ rs11101355 10 49,393,043 C/T 0.61 8.9E-07 (Huang et al. 2011) ARHGAP22 0.311 1.12 0.13 0.07 -+ rs11101357 10 49,393,306 G/A 0.61 8.9E-07 ARHGAP22 0.311 1.12 0.13 0.07 -+ rs4462262 10 58,859,184 T/C 0.65 9.2E-08 (Huang et al. 2011) MIR3924 0.390 1.05 0.30 0.23 -+ rs9573545 13 74,929,967 A/G 1.70 2.6E-07 TBC1D4 0.025 1.29 0.21 0.48 ++ rs9573546 13 74,930,534 A/G 1.70 2.5E-07 TBC1D4 0.024 1.27 0.23 0.47 ++ rs9573553 13 74,937,329 A/G 1.70 1.6E-07 TBC1D4 0.048 0.98 0.47 0.38 +- rs9565164 13 74,937,377 C/T 1.70 1.3E-07 TBC1D4 0.049 0.98 0.46 0.41 +- rs9573555 13 74,938,319 C/T 1.60 2.5E-06 TBC1D4 0.113 1.04 0.39 0.27 ++ rs4883999 13 74,940,886 A/T 1.60 5.0E-06 TBC1D4 0.115 1.06 0.35 0.22 ++ rs7335576 13 74,941,319 G/A 1.60 4.9E-06 TBC1D4 0.114 1.05 0.37 0.24 ++ (Sheu et al. 2013) rs9565165 13 74,950,791 A/G 1.70 2.6E-07 TBC1D4 0.049 0.90 0.31 0.37 +- rs9318349 13 74,959,947 T/C 1.60 4.4E-06 UCHL3 0.236 1.14 0.12 0.08 ++ rs9565170 13 74,962,660 C/G 1.60 3.1E-06 UCHL3 0.056 0.84 0.20 0.30 +- rs9543956 13 74,996,661 T/G 1.60 1.9E-06 UCHL3 - LMO7 0.264 1.05 0.34 0.18 ++ rs9543957 13 74,997,805 T/A 1.60 2.0E-06 COMMD6 0.264 1.05 0.34 0.18 ++ rs9573577 13 75,001,790 T/C 1.60 2.5E-06 COMMD6 0.084 0.94 0.35 0.47 +- rs4643195 13 75,001,841 G/A 1.60 2.3E-06 COMMD6 0.266 1.03 0.40 0.21 ++

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Original association Meta-analysis (DCCT/EDIC + WESDR)

SNP Chr Position A1/A2 OR P Reference Closest Genes MAF OR Pcase-control Psurvival Direction rs9573578 13 75,002,957 A/G 1.60 2.3E-06 COMMD6 0.266 1.03 0.40 0.21 ++ rs4885308 13 75,004,494 C/G 1.60 2.7E-06 COMMD6 0.085 0.92 0.30 0.45 +- rs9543976 13 75,034,649 G/A 1.60 6.1E-07 UCHL3 0.134 1.02 0.44 0.36 ++ rs2328964 13 75,036,831 G/T 1.60 5.6E-07 UCHL3 0.125 1.00 0.50 0.37 ++ rs2296146 13 75,041,573 C/T 1.60 6.1E-07 UCHL3 0.125 1.00 0.49 0.38 ++ rs7982517 13 75,060,040 A/G 1.60 4.1E-07 UCHL3 0.155 0.91 0.23 0.18 +- rs3783028 13 75,063,283 T/C 1.60 4.0E-07 UCHL3 0.156 0.91 0.22 0.17 +- rs7317250 13 75,065,142 G/A 1.60 4.0E-07 UCHL3 0.156 0.90 0.21 0.17 +- rs2031236 13 75,067,778 A/G 1.60 3.9E-07 UCHL3 0.127 1.02 0.44 0.42 ++ rs6562915 13 75,068,887 G/T 1.60 4.0E-07 UCHL3 0.156 0.91 0.23 0.19 +- rs6562916 13 75,069,223 A/G 1.60 4.0E-07 UCHL3 0.156 0.91 0.22 0.17 +- rs4885322 13 75,069,332 A/G 1.60 4.0E-07 (Sheu et al. 2013) UCHL3 0.156 0.90 0.22 0.17 +- rs4885323 13 75,070,415 C/T 1.60 4.0E-07 UCHL3 0.156 0.91 0.22 0.17 +- rs9543990 13 75,070,435 G/T 1.60 4.0E-07 UCHL3 0.156 0.91 0.22 0.17 +- rs7996884 13 75,070,745 T/A 1.60 4.0E-07 UCHL3 0.156 0.91 0.22 0.17 +- rs7339146 13 75,071,720 A/G 1.60 4.2E-07 UCHL3 0.125 1.00 0.50 0.38 ++ rs2328963 13 75,073,527 C/T 1.60 4.4E-07 UCHL3 0.156 0.91 0.23 0.19 +- rs8192763 13 75,077,171 A/T 1.60 4.4E-07 UCHL3 0.126 1.00 0.49 0.39 ++ rs1535724 13 75,081,274 G/C 1.60 4.8E-07 COMMD6LMO7 0.125 1.01 0.49 0.40 ++ rs7324195 13 75,090,523 G/A 1.60 4.8E-07 COMMD6LMO7 0.156 0.91 0.23 0.19 +- rs9565177 13 75,093,404 G/T 1.60 4.7E-07 COMMD6LMO7 0.125 1.01 0.48 0.40 ++ rs3818355 13 75,094,868 C/T 1.60 4.7E-07 COMMD6LMO7 0.125 1.01 0.48 0.40 ++ rs7986566 13 75,095,932 C/T 1.60 3.8E-07 COMMD6LMO7 0.156 0.90 0.21 0.15 +- rs2038823 13 95,749,434 T/G 0.43 4.7E-11 (Huang et al. 2011) HS6ST3 0.126 1.18 0.13 0.43 -+

Chr: Chromosome; Position: physical position based on hg18; A1/A2: minor/major alleles; OR: odds ratio; P: the association p-value in the original GWAS; MAF: minor allele frequency;

Pcase-control: One-sided p-value in the case-control SDR meta-analysis; Psurvival: One-sided p-value in the time-to SDR meta-analysis; Direction: direction of effect is reported in the original and current study.

All the original studies reported in this table were conducted in patients with T2D.

(Huang et al. 2011) was a GWAS of comparing T2D patients with DR and without DR in the Taiwanese population. (Sheu et al. 2013) was a GWAS comparing patients with PDR and no-DR in the Chinese population. (Sobrin et al. 2011) compared patients with DR (ETDRS score ≥30) and no DR. Please refer to 1.3.4‎ for further details.

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5.4 Association of GWAS Hits for Diabetic Nephropathy with SDR

Considering possible pleiotropy of diabetic nephropathy (DN) and retinopathy

(see 1.2.5‎ ), we investigated whether loci associated with diabetic nephropathy in previous GWAS show any evidence for association with diabetic retinopathy in our meta-GWAS. Ten relevant records of previous diabetic nephropathy GWAS were found in a PubMed search (Table 47). We also included loci from candidate gene studies showing evidence for association with diabetic nephropathy in a meta-analysis

(Mooyaart et al. 2011) and promoter SNP of EPO previously associated with DN and

DR (Tong et al. 2008).

Of 189 SNPs previously associated with DN (or their proxies), representing 97 loci, 21

SNPs at 14 independent loci show nominal evidence (P < 0.05) for association with SDR in our case-control meta-GWAS (Table 48). Yet, after adjusting for the number of loci tested, no SNP maintains significance for association with SDR.

Table 47. Summary of genome-wide association studies of diabetic nephropathy

Diabetes minimum Number Reference Design case control Ethnicity / Study Genotyping Type P of loci pooled ESRD and T1D duration (Craig et al. 2009) T1D no DN duration >20 yrs White, GoKinD Illumina HumanHap 550 1.60E-05 16 case-control ≥10 yrs DN: persistent proteinuria (Greene et al. 2008) case-control T1D no DN duration >20 yrs White, GoKinD Affymetrix 10 K Xba 2.12E-05 1 for >10 yrs or ESRD pooled No macroalbuminuria & (Hanson et al. 2007) T2D ESRD Gila River Indian Affymetrix 100k 2.00E-06 1 case-control diabetes duration ≥10yrs 100K multiplex PCR- (Maeda et al. 2007) case-control T2D DR and overt DN DR but no DN Japanese 8.00E-06 1 invader assays 100K multiplex PCR- (Maeda et al. 2010) case-control T2D DR and overt DN DR but no DN Japanese 1.40E-06 4 invader assays ESRD and (McDonough et al. 2011) case-control T2D Healthy controls African American Affymetrix array 6.0 7.04E-07 20 diabetes duration >5 yrs DN: persistent proteinuria White, (McKnight et al. 2009) case-control T2D no DN duration >10 yrs Illumina Human NS12 2.00E-05 23 or ESRD UK GokinD

(Pezzolesi et al. 2009) case-control T1D DN: ESRD or Proteinuria no DN duration >15 yrs White, GoKinD Affymetrix 500K 5.00E-07 4

case-control T1D ESRD non-ESRD 2.04E-09 7

Various, imputation to (Sandholm et al. 2012) case-control T1D DN no DN White 2.14E-07 5 HM2

case-control T1D ESRD normoalbuminuria 3.27E-07 12

55K multiplex PCR- (Tanaka et al. 2003) case-control T2D DR and overt DN DR but no DN Japanese 2.00E-05 1 invader assays candidate (Tong et al. 2008) mixed ESRD and PDR No ESRD or PDR White single SNP genotyping 2.76E-11 1 gene

ESRD: End Stage Renal Disease; DN: diabetic nephropathy; DR: diabetic retinopathy

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Table 48. Variants showing nominal association in SDR meta-GWAS among SNPs previously associated with diabetic nephropathy. Original Association (DN) Current meta-GWAS of SDR Direction

SNP Chr Position Reference Closest Gene A1/A2 OR P Freq1 OR Direction Pcase-control Psurvival DR vs DN rs7607342 2 52,881,302 (Craig et al. 2009) ASB3 T/C 0.74 1.3E-03 0.47 0.84 ---- 0.029 0.056 -- rs12712065 2 100,127,537 (Sandholm et al. 2012) AFF3 C/G 1.20 8.4E-06 0.48 1.17 ++++ 0.050 0.042 ++ rs11723864 4 174,810,981 (Sandholm et al. 2012) HAND2 C/G 0.66 6.9E-07 0.92 0.66 -++- 0.012 0.011 -- rs2243380 6 117,831,072 (McKnight et al. 2009) ROS1 T/C 1.03 3.0E-03 0.08 0.73 ---- 0.033 0.058 -+ rs39059 7 29,221,995 (Pezzolesi et al. 2009) CPVL/CHN2 A/G 1.39 5.0E-06 0.63 0.78 ---- 0.004 0.007 -+ rs39075 7 29,243,217 CPVL/CHN2 A/G 0.70 6.5E-07 0.40 1.25 +-++ 0.008 0.019 +- rs10808565 8 129,076,594 (Hanson et al. 2007) PVT1 T/C 3.34 1.4E-03 0.34 0.77 ---- 0.003 0.043 -+ rs3815871 8 129,077,760 PVT1 C/G 0.36 1.1E-03 0.33 0.77 ---- 0.005 0.042 -- rs10087240 8 129,081,756 PVT1 T/C 0.39 7.2E-03 0.48 1.17 ++++ 0.045 0.269 +- rs2720662 8 129,132,203 PVT1 T/C 2.89 3.0E-06 0.24 0.82 -+-- 0.036 0.115 -+ rs13282135 8 144,443,906 (Sandholm et al. 2012) ZNF696 A/T 0.88 7.8E-03 0.55 1.18 ++++ 0.041 0.086 +- rs4545118 8 144,444,912 ZNF696 A/C 1.27 9.5E-06 0.45 0.85 ---- 0.041 0.085 -+ rs1749824 10 80,593,868 (Craig et al. 2009) ZMIZ1 A/C 0.68 8.1E-05 0.43 0.84 --+- 0.028 0.212 -- rs2268388 12 108,128,028 (Maeda et al. 2010) ACACB A/G 0.62 1.4E-06 0.17 0.80 ---+ 0.033 0.055 -- rs9552271 13 20,220,107 (McKnight et al. 2009) N6AMT2 T/C 0.85 2.0E-03 0.32 0.84 --+- 0.043 0.117 -- rs1411766 13 109,050,161 (Pezzolesi et al. 2009) IRS2 A/G 1.41 1.8E-06 0.37 1.21 +-++ 0.022 0.021 ++ rs17412858 13 109,050,609 IRS2 A/G 0.71 1.8E-06 0.63 0.82 -+-- 0.022 0.021 -- rs8005245 14 19,655,882 (McKnight et al. 2009) OR4K17 C/G 0.93 9.0E-03 0.42 1.21 ++++ 0.021 0.159 +- rs1018534 14 100,189,987 (Sandholm et al. 2012) LINC00523 T/G 0.84 9.1E-05 0.32 0.75 ---- 0.002 0.002 -- rs1467537 14 100,191,027 LINC00523 T/C 0.82 2.4E-04 0.30 0.75 ---- 0.003 0.006 -- rs36012476 19 45,897,900 (McKnight et al. 2009) ADCK4 C/G 0.27 7.0E-04 0.05 0.60 -??? 0.049 0.031 --

Chr: chromosome; Position: physical position based on hg18; A1/A2: effect/other allele; OR: odds ratio; P: original association P-value with diabetic nephropathy; Freq1: mean frequency of A1 in the current meta-GWAS; Direction: direction of effect in studies in the current study (WESDR, primary cohort, 2ndary cohort – conventional Rx, 2ndary cohort – intensive Rx of DCCT/EDIC); Pcase-control: one-sided p-value in case-control meta-GWAS of SDR; Psurvival: one-sided p-value in survival meta-GWAS of SDR; Association direction in the original study (diabetic nephropathy) and current study (diabetic retinopathy) is compared

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6. RESULTS: META-GWAS OF MILD DIABETIC RETINOPATHY; ESTIMATION OF HERITABILITY

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Our initial analysis of DR focused on the more severe form of the disease. With improvement in diabetes care, fewer people progress to SDR. For example, in RASS which is the most recent of our cohorts, only 3.35% of patients progress to SDR by the final visit. Therefore, to improve power, defining a milder DR outcome seemed relevant. Association of common genetic polymorphisms with this outcome is discussed in this chapter.

6.1 Definition of Alternative Phenotype

6.1.1 Mild diabetic retinopathy phenotype

Mild diabetic retinopathy (MDR) was defined as the occurrence of non-proliferative diabetic retinopathy or worse. This is equivalent to step ≥ 4 of retinopathy severity scale per individual or an ETDRS level above 35/<35 in the DCCT/EDIC and above

31/<31 in WESDR and RASS (see Chapter 2). In retinal photographs of patients with

MDR, microaneurysms plus at least one of the followings would be observed: venous loops or hard exudates or cotton-wool spots or soft exudate or mild retinal hemorrhage

Table 49. Cumulative incidence of mild diabetic retinopathy (MDR) in each study population DCCT/EDIC WESDR RASS 1-C 1-T 2-C 2-I whole MDR 263 154 307 277 1000 563 109 (event) (76.45) (50.16) (95.05) (83.94) (76.69) (93.37) (45.99)

No MDR 81 153 16 53 304 40 128 (censored) (23.55) (49.84) (4.95) (16.06) (23.31) (6.63) (54.01)

MDR at first visit 0 0 90 81 171 282 61 (left censored) (0) (0) (27.86) (24.55) (13.11) (46.77) (25.74)

Each cell shows incidence as count and (%).

1-C: primary prevention cohort on conventional therapy; 1-I: primary prevention cohort on intensive therapy;

2-C: secondary intervention cohort on conventional therapy; 2-I: secondary intervention cohort on intensive therapy

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Figure 20. Kaplan-Meier plots of time-to MDR in DCCT/EDIC subgroups, WESDR and RASS. Notice shorter duration of follow-up in RASS compared to the other two populations.

or questionable intra-retinal microvascular abnormalities (IRMA) or questionable venous beading. Table 49 summarizes the cumulative incidence of MDR in

DCCT/EDIC, WESDR and RASS. Figure 20 displays survival plots for this outcome in these studies.

6.1.2 Time-to MDR outcome

Time-to-MDR outcome was defined as time from first visit to the development of MDR

(see above). As previously described, fundus photography was performed frequently

140 in DCCT (every 6 months). Longitudinal follow-up of retinopathy status in DCCT shows that in half of the patients, MDR appears transiently and regresses in the photographs taken 6 months later in the subsequent follow-up visit. To ensure persistence and prevent transient changes from affecting definition of MDR phenotype, during DCCT, time-to event was defined based on the first sustained occurrence of

MDR, persisting for two consecutive visits. This definition still ignores any possible regression of DR which may have occurred in subsequent visits. During EDIC and in

WESDR follow-up fundus photographs were taken less frequently (roughly every 2 to

5 years respectively) and persistence criteria was not necessary in defining MDR phenotype. Figure 20 displays survival plots for MDR in each study separately by subgroups. Considerable left-censoring (outcome present at the outset of the study) was observed in all groups except primary cohort of DCCT/EDIC by definition.

6.1.3 Baseline factors associated with the incidence of MDR

We investigated baseline factors associated with the incidence of MDR during the

DCCT/EDIC using Cox Proportional Hazard models. Similar to SDR (see Chapter 4), the main baseline predictors of MDR were diabetes duration, A1C level at eligibility and mean A1C during the study. In univariate analysis, body mass index (BMI), total cholesterol and triglyceride levels also showed significant association with time-to

MDR; whereas age, gender and mean blood pressure were borderline significant.

Using stepwise selection, gender, baseline BMI, diabetes duration, A1C level at eligibility and mean A1C during DCCT/EDIC showed significant independent association with time-to MDR in a multivariate model. Table 50 summarizes association of baseline covariates with time-to MDR.

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Table 50. Association of covariates with incidence of MDR in DCCT/EDIC.

Univariate Multivariate* Risk Variable Unit HR 95% CI P HR 95% CI P

Age / 1 yr 0.99 0.98-1.00 0.018

Gender male vs. female 1.18 1.03-1.36 0.017 1.27 1.10-1.46 9.1E-04

Diabetes duration / 1 yr 1.08 1.06-1.11 4.7E-10 1.12 1.09-1.15 1.7E-18

BMI / 1 Kg/m2 1.05 1.02-1.07 7.2E-05 1.03 1.01-1.06 0.007

A1C at eligibility / 1% 1.25 1.20-1.31 9.7E-27 1.20 1.14-1.26 9.6E-14

Mean BP / 10 mmHg 1.08 1.00-1.17 0.04

Triglyceride / 10 mg/dL 1.03 1.01-1.04 9.4E-06

Cholesterol / 10 mg/dL 1.03 1.01-1.05 0.006

HDL / 10 mg/dL 0.95 0.90-1.01 0.09

Mean A1C / 1% 1.51 1.41-1.61 1.6E-34 1.40 1.30-1.51 2.1E-19

Smoking history ever vs. never 1.08 0.93-1.24 0.32

HR: Hazard Ratio; CI: Confidence Interval; BP: Blood Pressure; HDL: High Density Lipoprotein Mean BP was calculated by adding a third of pulse pressure to diastolic blood pressure. All covariates were measured at baseline except Mean A1C. * Model was built using stepwise selection. Variables that are not shown were not significant and did not stay in the multivariate model.

6.2 Case – Control Association Study of MDR

6.2.1 Covariate effects and case-control GWAS of MDR

An initial case-control GWAS was performed in each study populations. Given sufficient duration almost all patients with diabetes seem to develop MDR (Figure 20).

Considering the strong effect of duration on MDR and in order to increase inter-study homogeneity, case-control association analyses were performed at time points when diabetes duration was more similar in populations under study: first visit of the

WESDR (12.3±8.5 yr), DCCT closeout visit for the secondary cohort of DCCT/EDIC

(15.5±4.1 yr) and the latest EDIC follow-up visit for the primary cohort of DCCT/EDIC

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Table 51. Covariate effects on mild retinopathy in multivariate case-control analyses

DCCT/EDIC DCCT/EDIC 1° cohort – conv. Rx 1° cohort – int. Rx Predictors OR (95% CI) P OR (95% CI) P

Age (yr) 1.01 (0.97-1.04) 0.74 0.99 (0.96-1.03) 0.70 Gender (M vs F) 2.12 (1.27-3.54) 3.9E-03 1.85 (1.07-3.18) 0.03 Duration of Diabetes (yr) 1.08 (0.95-1.23) 0.25 1.12 (0.97-1.30) 0.13 Updated mean A1C (%) 2.70 (2.02-3.60) 1.3E-11 3.70 (2.56-5.34) 3.1E-12

Square term for duration (yr2) 0.99 (0.97-1.00) 0.04 0.99 (0.97-1.01) 0.31

DCCT DCCT 2° cohort – conv. Rx 2° cohort – int. Rx Predictors OR (95% CI) P OR (95% CI) P Age (yr) 0.98 (0.94-1.03) 0.51 1.02 (0.99-1.06) 0.25 Gender (M vs F) 2.80 (1.54-5.11) 7.8E-04 1.40 (0.86-2.28) 0.18 Duration of Diabetes (yr) 1.44 (1.26-1.64) 3.7E-08 1.31 (1.16-1.48) 2.0E-05 Updated mean A1C (%) 2.14 (1.64-2.78) 2.0E-08 2.57 (1.82-3.62) 7.4E-08

Square term for duration (yr2) 0.98 (0.97-1.00) 9.1E-03 0.99 (0.98-1.01) 0.26

WESDR RASS

Predictors OR (95% CI) P OR (95% CI) P Age (yr) 1.02 (0.99-1.05) 0.14 0.98 (0.95-1.01) 0.15 Gender (M vs F) 1.43 (0.92-2.20) 0.11 1.50 (0.79-2.86) 0.22 Duration of Diabetes (yr) 1.32 (1.25-1.38) 6.9E-27 1.25 (1.16-1.35) 2.8E-08 Updated mean A1C (%) 1.22 (1.09-1.38) 9.4E-04 2.27 (1.73-2.99) 3.8E-09

Square term for duration (yr2) 0.99 (0.99-0.99) 2.1E-15 NA NA

OR: odds ratio, CI: confidence interval The analysis was done at last follow-up visit in primary cohort of DCCT/EDIC and RASS, at visit 1 in WESDR and at DCCT closeout in secondary cohort of DCCT. All risk factors were measured at the analysis time-point. Updated mean A1C was calculated up to the analysis time point. NA: Squared term for duration was not significant and did not improve the fit, therefore was not included in the model in RASS.

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(18.2±3.2 yr). Major known risk-factors were included as predictors in the analyses, to accommodate for their effects: age, gender, duration of diabetes and mean updated

A1C during the studies (Table 51). Association with the first three principal components was tested in all the groups and was significant in none.

Table 51 summarizes the effect of covariates in the study populations. Consistent with time-to event models (see above and below); major risk factors for mild retinopathy status are duration of diabetes and mean A1C that collectively capture total glycemic exposure. Age was not a significant risk factor in any of the groups. Male gender was a risk factor but only remained significant in the multivariate model in three subgroups of DCCT/EDIC. The effect of diabetes duration was consistent between WESDR, RASS and DCCT/EDIC secondary cohort. Updated mean A1C showed the strongest effect in primary cohort of DCCT/EDIC (longest follow-up and the most measurements) and was weakest in WESDR (single A1C measurement at first visit). Interestingly, in the population with the strongest effect of mean A1C (primary cohort of DCCT/EDIC) diabetes duration was not a significant predictor in the multivariable model.

Using the above models separate case-control GWAS were performed in DCCT/EDIC subgroups, WESDR and RASS. Figure 21 shows the quantile-quantile plots for the case- control GWAS. No SNPs reached genome-wide significance threshold in the analysis of individual studies, except for a single SNP in RASS (rs12186063, MAF=0.06, P=1.96E-8).

However, this SNP did not show significant association with MDR status in any of the other populations (P > 0.05).

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DCCT/EDIC - 1° cohort, conv. Rx (λGC=1.029) DCCT/EDIC - 2° cohort, conv. Rx (λGC=1.039) WESDR (λGC=0.993)

DCCT/EDIC - 1° cohort, int. Rx (λGC=1.017) DCCT/EDIC - 2° cohort, int. Rx (λGC=1.027) RASS (λGC=1.036)

Figure 21. Quantile-quantile plot of case-control association analyses of MDR

6.2.2 Case-control meta-GWAS of MDR

Association results for all genotyped and HapMap imputed SNPs (both HM2 and

HM3) passing quality control criteria were meta-analyzed, after correction for the genomic control inflation factor in each study, weighing each study proportional to the number of cases and controls and considering allele frequencies (Zhou et al. 2011).

Quantile- quantile plot for meta-analysis (Figure 22) and a λGC of 0.986 do not indicate an inflated type I error. Consistent with the fixed effects assumption, Cochran’s heterogeneity statistics for the meta-analysis followed the null distribution showing a deficit in small P values (Figure 23).

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Figure 22. QQ-plot of MDR case-control meta-GWAS.

No SNP reached genome- wide significance threshold in the MDR case-control meta- analysis. Table 52 and Figure 24 summarize top (P < 10-5) and genome-wide results from case-control meta-GWAS of MDR. Among the top SNPs (Table 52), rs11808056 on

Figure 23. Histogram of heterogeneity test P values in meta-GWAS of MDR status. Cochran’s heterogeneity test P-values follows the null distribution (red dotted line indicates a uniform distribution) with depletion of smaller (significant) values.

Table 52. Top results (P<10-5) from case-control association meta-analysis of MDR Marker Information Meta-analysis Imputation Quality Distance Fixed Effects Heterogeneity Locus SNP Chr Position A1/A2 Freq1 _D_ _W_ _R_ Function Closest Gene (kb) Effect _SE_ P value Direction I2 Q Pval

1q32.1 rs11808056 1 200,623,214 A/G 0.010 0.43 0.45 0.30 intron PPP1R12B 1.78 0.76 6.5E-06 ++++++ 1 0.41

3p22.1 rs7616750 3 43,025,223 C/G 0.771 0.99 0.99 0.99 intron FAM198A -0.41 0.09 7.9E-06 ------0 0.77

rs17455760 3 43,025,750 A/G 0.771 0.99 G G intron FAM198A -0.41 0.09 8.4E-06 ------0 0.78

3p12.3 rs1583673 3 81,243,768 T/C 0.323 0.91 0.92 0.93 intergenic GBE1 +377.8 -0.37 0.08 8.2E-06 ------0 0.63

rs6806220 3 81,270,557 T/C 0.367 G G G intergenic GBE1 +351.0 -0.34 0.08 7.7E-06 ------0 0.52

8p11.23 rs2604496 8 38,573,174 T/C 0.385 G G G intergenic RNF5P1 +3.7 0.34 0.08 5.4E-06 ++++++ 3 0.40

10q26.2 rs7098357 10 130,235,759 A/G 0.499 0.97 0.96 0.96 intergenic MKI67 -421.3 -0.33 0.08 8.4E-06 ----+- 53 0.06

11q14.1 rs10501442 11 78,807,059 T/C 0.098 0.95 G G intron TENM4 0.60 0.14 3.3E-06 ++++++ 0 0.45

12q13.3 rs1846400 12 55,579,703 A/G 0.470 G G G intergenic SDR9C7 +23.5 0.34 0.08 6.3E-06 ++++++ 12 0.34

14q11.2 rs1953550 14 19,830,713 A/G 0.079 0.98 0.99 0.99 intron TTC5 0.66 0.15 6.4E-06 ++++++ 0 0.94

rs3093871 14 19,880,575 A/T 0.056 0.99 0.99 0.99 promoter RPPH1 -0.5 0.81 0.19 1.3E-06 ++++++ 21 0.28

rs3093926 14 19,892,892 A/G 0.056 G 0.99 0.98 missense PARP2 0.83 0.19 8.9E-07 ++++-+ 25 0.25

17q25.3 rs607544 17 78,362,049 C/G 0.835 0.97 0.98 0.98 intron TBCD -0.50 0.11 2.7E-06 -----+ 4 0.39

rs8074277 17 78,382,917 T/C 0.828 G G G missense ZNF750 -0.47 0.11 6.0E-06 -----+ 0 0.47

rs12450046 17 78,383,677 A/G 0.172 G G G 5'UTR ZNF750 0.47 0.11 4.0E-06 +++++- 0 0.42

19q13.12 rs12972195 19 42,444,345 T/C 0.845 G G G intergenic LOC284412 4.3 0.46 0.10 5.4E-06 ++++++ 0 0.78

Chr: chromosome; A1/A2: effect / alternate allele; Freq1: weighted mean frequency of A1; Effect: effect estimate for A1; SE: standard error of effect; I2: Higgins and Thompson heterogeneity index; Q Pval: Cochran’s test of heterogeneity P-value Imputation Quality is info metric reported by IMPUTE2 software; G: genotyped SNP; D: DCCT/EDIC, W: WESDR, R:RASS studies Distance is calculated from the nearest gene for the intergenic SNPs only. (-) indicates upstream and (+) downstream relative to transcription direction. Direction of effects based on the sign of β are shown in the following order: (1) WESDR (2) DCCT/EDIC: primary cohort-conventional Rx (3) DCCT/EDIC: primary cohort-intensive Rx (4) DCCT: 2ndary cohort-conventional Rx (5) DCCT: 2ndary cohort-intensive Rx (6) RASS

146 147

Figure 24. Manhattan plot for case-control meta-GWAS of MDR. All the SNPs (genotyped or imputed to HapMap 2 or 3) with a minor allele frequency ≥1% are plotted. No SNP reached genome-wide significance threshold (red line). Blue line indicates suggestive association (P < 10-5).

chromosome 1 has both a low minor allele frequency and imputation quality in the

DCCT/EDIC (MAF=0.01, info=0.43), WESDR (MAF=0.01, info=0.45) and RASS

(MAF=0.01, info=0.30); suggesting that the observed association at this SNP could be a false positive signal due to imprecision of genotype imputation and/or inflation in test statistics at low allele frequencies.

6.3 Time to Event Association Study of MDR

6.3.1 Time-to event models for MDR

Similar to the analysis of SDR (see Chapter 4), to address issues such as tied event times, interval and left censoring, complementary log-log models for continuous time

148 processes (CLOGLOG models) were used in the time to event analysis of MDR (Allison

2010).

Based on the results of the initial analysis in DCCT/EDIC (see 6.1.3‎ 6.1.3‎ above), the following covariates were included in CLOGLOG models for time to MDR: duration of diabetes at first visit, mean updated A1C and BMI during the study as time-dependent covariates, plus age and gender. To avoid sparseness of events in the DCCT/EDIC, visits were collapsed to provide a minimum of 6 events per visit in contingency tables.

To account for unequal intervals between visits, the time period between visits was included as a predictor in the model. The model allowed for different intercepts for each visit. CLOGLOG model assumptions including proportional hazards over time and independence of predictors were evaluated by examining the interaction between each covariates and visit as well as between different covariates.

Initial GWAS of time to MDR showed elevation of genomic control lambda indicating inflation of type I error (1.05 ≤ λGC ≤ 1.25 in all studied populations except DCCT/EDIC secondary cohort – intensive treatment group). Observed elevation of λGC was not due to population stratification: the first three principal components (PC) were not associated with time to MDR in any of the populations; inclusion of these PCs in the analysis did not resolve the elevation of λGC. Moreover, case-control analyses of MDR in the same populations did not display elevated λGC, arguing against population stratification as the cause of the observed inflation in type I error in time to event analysis of MDR.

We tested if the observed inflation in type I error was caused by inefficient modeling of time to MDR. Inefficient modeling may arise from interaction between predictors, non- linear effects or violation of proportional hazards assumption. To fix the problem, a series of models using various transformations and interactions between predictors

149

Table 53. Formulation of time to mild diabetic retinopathy (MDR) models Study Model

MDR ~ visit + intrvl + age + sex + ln(dur) + bmi + A1C + (dur)2 WESDR * + (dur)3 + ln(dur) × visit one + bmi × visit one + A1C× visit one

MDR ~ visit + intrvl + age + sex + dur + ln(bmi) + ln(A1C) + DCCT/EDIC - 1° cohort, conventional Rx dur × first visit †

MDR ~ visit + intrvl + age + sex + ln(dur) + ln(bmi) + ln(A1C) DCCT/EDIC - 1° cohort, intensive Rx + ln(dur) × early visit ‡ + age × ln(A1C) + sex × ln(A1C)

MDR ~ visit + intrvl + ln(age) + sex + ln(dur) + ln(bmi) + A1C DCCT/EDIC - 2° cohort, conventional Rx + ln(dur) × baseline § + A1C × baseline §

MDR ~ visit + intrvl + age + sex + dur + ln(bmi) + ln(A1C) + DCCT/EDIC - 2° cohort, intensive Rx dur × baseline § + ln(dur) × dur

MDR ~ visit + intrvl + age + sex + ln(dur) + ln(bmi) + ln(A1C) RASS + ln(dur) × visit one + sex × ln(A1C) + ln(bmi) × ln(A1C) visit: visit as factor; intrvl: interval between visits (yr); age: age at baseline (yr); sex: gender (M vs F); dur: duration of diabetes at baseline (yr); bmi: time-dependent BMI (kg/m2); A1C: time-dependent updated mean A1C (%) * In WESDR, separate estimates were allowed for the effects of duration, bmi and A1C at visit one and subsequent visits. † Two separate estimates were allowed for duration at first visit with event and subsequent visits. ‡ Two separate estimates were allowed for duration in early visits (≤ 20 semiannual visit) and later visits (after 20 semiannual visit). § Two separate estimates were allowed at baseline visit and subsequent visits.

were tested in a subset of ~5000 independent SNPs from across the genome. Best models were empirically selected based on the lowest λGC values and conformity of P- values with uniform distribution. The selected models were generally among the ones providing the best fit based on smaller Akaike information criterion (AIC) and Schwarz criterion (SC). Formulations of predictors in the final models are summarized in Table

53. These models were used in the subsequent GWAS. Models in different studies adjusted for similar covariates with modified formulations to obtain the best fit for each study.

Parameter estimates and significance level for covariates in the final time to MDR multivariate models in the DCCT/EDIC subgroups, WESDR and RASS are summarized

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Table 54. Covariate effects in multivariable models for time to mild diabetic retinopathy Parameter Unit Estimate SE P DCCT/EDIC - primary cohort, conventional treatment number of events = 263 Interval between visits yr 0.06 0.06 2.7E-01 Age at baseline yr 0.01 0.01 1.4E-01 Gender M vs F 0.18 0.13 1.7E-01 Body mass index [ln(BMI)] kg/m2 1.42 0.46 2.3E-03 Diabetes duration at baseline (dur) yr 0.00 0.05 9.5E-01 Time-dependent updated mean A1C [ln(A1C)] % 5.56 0.54 1.0E-24 dur × first visit 0.50 0.15 6.9E-04

DCCT/EDIC - primary cohort, intensive treatment number of events = 154 Interval between visits yr 0.23 0.07 1.4E-03 Age at baseline (age) yr -0.65 0.19 8.0E-04 Gender M vs F 5.48 2.95 6.4E-02 Body mass index [ln(BMI)] kg/m2 0.23 0.53 6.7E-01 Diabetes duration at baseline [ln(dur)] yr -0.18 0.20 3.7E-01 Time-dependent updated mean A1C [ln(A1C)] % 1.16 2.38 6.3E-01 ln(dur) × early visit * 1.66 0.39 1.8E-05 sex × ln(A1C) -2.43 1.42 8.7E-02 age × ln(A1C) 0.31 0.09 8.2E-04

DCCT/EDIC - secondary cohort, conventional treatment number of events = 306 Interval between visits yr 0.06 0.14 6.5E-01 Age at baseline [ln(age)] yr 0.71 0.23 1.8E-03 Gender M vs F 0.31 0.12 9.6E-03 Body mass index [ln(BMI)] kg/m2 1.61 0.51 1.7E-03 Diabetes duration at baseline [ln(dur)] yr 0.79 0.13 3.4E-09 Time-dependent updated mean A1C (A1C) % 0.47 0.06 3.5E-15 ln(dur) × baseline 1.48 0.38 1.1E-04

A1C × baseline -0.18 0.09 5.3E-02

DCCT/EDIC - secondary cohort, intensive treatment number of events = 277 Interval between visits yr 0.02 0.12 8.5E-01 Age at baseline yr 0.01 0.01 1.6E-01 Gender M vs F 0.08 0.12 5.2E-01 Body mass index [ln(BMI)] kg/m2 1.24 0.47 8.7E-03 Diabetes duration at baseline (dur) yr 0.63 0.21 2.2E-03 ln(dur) × dur -0.18 0.07 8.6E-03

Time-dependent updated mean A1C [ln(A1C)] % 2.51 0.50 4.2E-07 dur × baseline 0.23 0.05 4.0E-06

151

Parameter Unit Estimate SE P WESDR number of events = 563 Interval between visits yr 0.12 0.05 2.6E-02 Age at first visit yr 0.00 0.01 8.2E-01 Gender M vs F 0.32 0.09 6.8E-04 Diabetes duration at first visit [ln(dur)] yr 0.32 0.11 6.0E-03 Body mass index (BMI) kg/m2 0.06 0.02 6.4E-04 Time-dependent updated mean A1C (A1C) % 0.45 0.05 7.4E-18 Squared term for duration yr2 -0.01 0.00 3.8E-04 Cubic term for duration yr3 0.00 0.00 6.3E-02 ln(dur) × visit one 2.13 0.24 2.0E-18

BMI × visit one -0.06 0.03 2.4E-02

A1C × visit one -0.29 0.07 9.5E-06

RASS number of events = 109 Interval between visits yr 0.19 0.15 2.0E-01 Age at first visit yr 0.00 0.01 8.2E-01 Gender M vs F -6.22 3.02 3.9E-02 Body mass index [ln(bmi)] kg/m2 20.39 9.84 3.8E-02 Diabetes duration at first visit [ln(dur)] yr 0.91 0.30 2.5E-03 Time-dependent updated mean A1C [ln(A1C)] % 31.42 14.59 3.1E-02 ln(dur) × visit one 2.58 0.61 2.6E-05

sex × ln(A1C) 3.14 1.38 2.4E-02 ln(bmi) × ln(A1C) -8.72 4.47 5.1E-02

* Two separate estimates were allowed for duration in early visits (≤ 20 semiannual visit) and later visits (after 20 semiannual visit).

in Table 54. Diabetes duration, A1C and BMI are the main significant risk factors for time to MDR. Diabetes duration had a stronger effect at first visit compared to later visits in all the populations. In the intensive treatment group of DCCT/EDIC primary cohort, diabetes duration had a stronger effect in the earlier visits (up to the 10th year after randomization) relative to the later visits. There was evidence for nonlinearity of effect for diabetes duration, BMI and A1C in most populations. Significant interactions between A1C and other covariates were observed in RASS and in the intensive treatment group of DCCT/EDIC primary cohort.

152

To facilitate comparisons, Table 55 summarizes the effect of covariates from identical time to MDR models in these studies. Updated mean A1C was the most significant predictor of time to MDR in all studies. The effect of A1C was strongest in the primary cohort and less strong in the secondary cohort of DCCT/EDIC. Interestingly, diabetes duration showed an opposite pattern: diabetes duration had a stronger effect on time to MDR in the secondary cohort (longer duration at baseline) compared to primary cohort of DCCT/EDIC. It seems that A1C and diabetes duration, which collectively measure the cumulative glycemic exposure in an individual, may show variable degrees of effect depending on the duration of diabetes and tightness of glycemic control in the study. Diabetes duration generally showed a stronger effect in the first visit compared to later visits in all studies. In particular, in the primary cohort of

DCCT/EDIC, diabetes duration was only a significant predictor of time to MDR at the first visit and not at the subsequent visits.

Higher BMI was an independent risk factor for MDR incidence in all studies, except for the primary cohort – intensive treatment group of DCCT/EDIC, with similar magnitudes of effect. Male gender was a risk factor for MDR incidence, but it did not reach significance in two of the DCCT/EDIC subgroups. Age, in general, was not a significant predictor of MDR incidence. However, in the conventional treatment - secondary cohort group of DCCT/EDIC, age showed modest association with time to

MDR in opposite directions. The association in this group may have been driven by moderate correlation of age and diabetes duration.

Table 55. Comparison of parameter estimates between the study populations for time to MDR in multivariable models DCCT/EDIC - 1° cohort, conventional Rx DCCT/EDIC - 1° cohort, intensive Rx Parameter Unit Estimate 95% LCL 95% UCL P Estimate 95% LCL 95% UCL P interval between visits yr 0.06 -0.05 0.18 2.8E-01 0.23 0.09 0.37 1.5E-03 age at baseline yr 0.01 0.00 0.03 1.6E-01 0.00 -0.03 0.02 8.1E-01 gender M vs F 0.18 -0.07 0.43 1.6E-01 0.35 0.02 0.68 3.6E-02

Body Mass Index - ln(BMI) kg/m2 1.42 0.51 2.33 2.3E-03 0.40 -0.62 1.42 4.4E-01 diabetes duration - ln(dur) yr -0.04 -0.29 0.21 7.4E-01 0.17 -0.17 0.51 3.2E-01 updated mean A1C – ln(A1C) % 5.54 4.48 6.60 1.2E-24 6.72 5.29 8.15 2.5E-20 ln(dur) × visit one 1.37 0.36 2.37 7.6E-03 1.89 0.34 3.44 1.7E-02

DCCT/EDIC - 2° cohort, conventional Rx DCCT/EDIC - 2° cohort, intensive Rx Parameter Unit Estimate 95% LCL 95% UCL P Estimate 95% LCL 95% UCL P interval between visits yr 0.07 -0.20 0.34 6.2E-01 0.02 -0.22 0.27 8.5E-01 age at baseline yr 0.03 0.01 0.04 5.7E-03 0.01 0.00 0.03 1.6E-01 gender M vs F 0.32 0.08 0.56 8.2E-03 0.07 -0.17 0.31 5.5E-01

Body Mass Index - ln(BMI) kg/m2 1.61 0.61 2.61 1.6E-03 1.25 0.32 2.18 8.1E-03 diabetes duration - ln(dur) yr 0.74 0.48 0.99 1.6E-08 0.61 0.36 0.85 9.0E-07 updated mean A1C – ln(A1C) % 3.62 2.75 4.50 6.1E-16 2.52 1.55 3.49 3.2E-07 ln(dur) × baseline 1.61 0.87 2.36 2.3E-05 1.89 1.05 2.72 9.7E-06

WESDR RASS Parameter Unit Estimate 95% LCL 95% UCL P Estimate 95% LCL 95% UCL P interval between visits yr 0.11 0.01 0.21 2.5E-02 0.18 -0.11 0.47 2.2E-01 age at first visit yr -0.01 -0.03 0.00 2.1E-02 0.00 -0.02 0.02 7.3E-01 gender M vs F 0.31 0.13 0.50 7.8E-04 0.60 0.19 1.01 4.3E-03 Body Mass Index - ln(BMI) kg/m2 1.73 0.87 2.59 7.8E-05 1.20 0.01 2.39 4.8E-02 diabetes duration - ln(dur) yr 0.46 0.27 0.66 4.3E-06 0.83 0.24 1.43 5.8E-03 updated mean A1C – ln(A1C) % 4.22 3.25 5.19 1.8E-17 4.24 2.84 5.65 3.0E-09 ln(BMI) × visit one -1.14 -2.39 0.11 7.4E-02 ln(dur) × visit one 1.35 1.03 1.67 1.4E-16 2.45 1.27 3.63 5.0E-05 ln(A1C) × visit one -2.60 -3.84 -1.36 3.8E-05

95% LCL: 95% lower confidence limit; 95%UCL: 95% upper confidence limit; Interaction between BMI and visit and A1C and visit were only included in WESDR. 153 154

6.3.2 GWAS of time to MDR

Genome-wide association studies of time to MDR were performed in the WESDR,

RASS and DCCT/EDIC subgroups. The association between additive coding of a panel of SNPs imputed to HapMap phase 3 with time to MDR were tested one SNP at a time using the CLOGLOG models described in Table 53. Figure 25 displays quantile- quantile plots for SNPs in these analyses and the corresponding genomic control inflation factors (λGC).

DCCT/EDIC - 1° cohort, conv. Rx (λGC=1.062) DCCT/EDIC - 2° cohort, conv. Rx (λGC=1.052) WESDR (λGC=1.038)

DCCT/EDIC - 1° cohort, int. Rx (λGC=1.066) DCCT/EDIC - 2° cohort, int. Rx (λGC=1.033) RASS (λGC=1.078)

Figure 25. Quantile-quantile (QQ) plots for time to MDR GWAS The plots compare additive test for all analyzed SNPs (represented with dots) to an expected distribution under the null (red line). The test statistics were adjusted by the indicated genomic control inflation factor (λGC) in each panel. All genotyped or imputed SNPs (to HapMap 3) with a minor allele frequency greater than 1% and passing imputation quality metric threshold (info > 0.3) are plotted.

155

No SNPs showed genome-wide significant (P <5×10-8) association with time to MDR in the WESDR, RASS and secondary cohort of DCCT/EDIC. Two SNPs (2 loci) and 3 SNPs

(3 independent loci) were significantly (P <5×10-8) associated with time to MDR in the conventional and intensive treatment groups of primary cohort of DCCT/EDIC, respectively (Table 56). Among these SNPs, only rs10494246 was replicated in another population: this SNP was significantly associated with time to MDR in primary cohort

– intensive treatment group (P <5×10-8) and showed significant association (P = 0.015) in the primary cohort – conventional treatment group of DCCT/EDIC in the same direction (Table 57). However, in the fixed effects meta-analysis of all the populations

(DCCT/EDIC subgroups, WESDR and RASS), rs10494246 was not associated with time to MDR (P=4.2×10-4). Overall, for all these SNPs considerable heterogeneity in the effect was observed among studies (I2 > 80 and Cochran’s heterogeneity P ≤ 10-4, Table 57).

6.3.3 Meta-GWAS of time to MDR

None of the SNPs showed significant association with time to MDR in an inverse variance weighted random effects meta-analysis (Table 57).

Association results for time to MDR in these studies were combined in a fixed effects meta-analysis, after adjusting the test statistics by the study specific genomic control inflation factors, and weighting each study proportional to the number of events and considering allele frequencies (Zhou et al. 2011). Examination of QQ plots indicate that meta-analysis P values follow the null distribution without showing any general excess of significant associations (Figure 26, λGC =1.00). Consistent with a fixed-effects assumption, Cochran’s test of heterogeneity P values follow a null distribution with relative paucity of smaller values (Figure 26, λGC = 0.99). A fixed effect meta-analysis weighted by inverse variance produced very similar results (λGC = 1.00).

Table 56. Genome-wide significant results from GWAS of time to MDR DCCT/EDIC: primary cohort - conventional treatment Closest Distance SNP Chr Position A1 A2 Imputation Quality Type Location Gene (kb) Freq1 Effect SE P-value rs12121303 1 174,236,798 T C 1.00 G intron RFWD2 0.99 -3.00 0.48 8.9E-10 rs7029904 9 35,642,384 A C 0.91 I promoter SIT1 -1.5 0.02 1.91 0.34 4.6E-08

DCCT/EDIC: primary cohort - intensive treatment Closest Distance SNP Chr Position A1 A2 Imputation Quality Type Location Gene (kb) Freq1 Effect SE P-value rs10494246 1 145,614,928 T C 1.00 G intergenic ACP6 -5.7 0.97 -1.79 0.28 1.1E-09 rs7735190 5 113,383,346 T C 1.00 G intergenic KCNN2 -343.0 0.03 1.63 0.28 1.2E-08 rs12175343 6 6,806,233 T C 0.95 I intergenic RREB1 -246.6 0.02 2.61 0.46 3.2E-08

G: Genotyped; I: Imputed; Distance from the closest gene is calculated based on the longest transcript variant. – denotes upstream and + downstream relative to transcript direction. P-value: Wald test association P-value after adjusting for the study specific genomic control inflation factor

Table 57. Meta-analysis results for genome-wide significant hits in single studies

SNP A1 A2 Direction P1-C P1-I P2-C P2-I PW PR Freq1 Pfixed I2 Phet beta SE Prandom rs10494246 T C --+--+ 0.015 1.1E-09 0.528 0.482 0.571 0.547 0.95-0.97 4.2E-04 86 1.3E-06 -0.37 0.26 0.16 rs12121303 T C --+-+- 8.9E-10 0.185 0.686 0.560 0.726 0.134 0.99-0.99 1.4E-03 86 1.7E-06 -0.83 0.53 0.12 rs12175343 T C ++--+- 0.164 3.2E-08 0.453 0.455 0.686 0.581 0.01-0.02 3.4E-02 84 6.3E-06 0.39 0.41 0.34 rs7029904 A C +--++- 4.6E-08 0.703 0.986 0.923 0.087 0.523 0.02-0.03 1.8E-03 81 1.0E-04 0.36 0.33 0.28 rs7735190 T C ++-+-- 0.590 1.2E-08 0.076 0.304 0.195 0.926 0.02-0.03 1.5E-01 87 2.6E-07 0.21 0.31 0.49

Direction of effect for A1 is presented in the following order: 1-C: primary cohort - conventional therapy; 1-I: primary cohort - intensive therapy; 2-C: secondary cohort - conventional therapy; 2-I: secondary cohort - intensive therapy; W: WESDR; R:RASS Freq1: range of frequency of A1 in the study populations; Pfixed: P value of fixed-effects meta-analysis; I2: Heterogeneity index; Phet: Cochran’s Heterogeneity test Pvalue; beta & SE: effect of A1 and standard error from the random effects meta-analysis; Prandom: P value of random effects meta-analysis Cells with the original genome-wide significant hit are shaded in gray. Cells in bold font reached nominal significance.

156 157

A. fixed effects meta-analysis (λGC=1.00) B.

Pmeta-analysis C.

Pheterogeneity Figure 26. Meta-analysis of time to MDR in the DCCT/EDIC, WESDR and RASS. A) Quantile-quantile plot: the plot compares the observed P-values with the expected distribution under the null (red line). SNPs with an average minor allele frequency greater than 1% are plotted. B) Histogram of MDR meta-analysis P values. C) Histogram of Cochran’s test of heterogeneity P values. Red dotted lines represent a uniform distribution.

No SNP with a minor allele frequency greater than 1% reached genome-wide significance in the meta-GWAS of time to MDR. Figure 27 and Table 58 summarize genome-wide and top results (P < 10-5) from the meta-analysis respectively.

Table 58. Top results (P <10-5) from meta-analysis of time to MDR

Marker Information Fixed-effects Meta-analysis

Imput. Quality Distance Nevent Inverse Variance Heterogeneity

2 Locus SNP Chr Position A1/A2 Freq1 D W R Location Gene (kb) P Effect SE P Direction I Q Pval

1q25.3 rs12563405 1 180,001,411 A/G 0.01 0.82 0.54 0.59 intron CACNA1E 8.7E-07 0.96 0.20 1.8E-06 ++++++ 50 0.07

2p21 rs9967800 2 46,878,983 A/C 0.05 G 0.93 G intergenic LINC01118 -18.3 1.1E-05 0.37 0.08 8.9E-06 ++++++ 0 0.94

3q22.1 rs12107685 3 132,890,185 T/C 0.01 0.95 0.98 0.92 intron CPNE4 1.5E-05 0.82 0.18 4.5E-06 ++++++ 43 0.12

9q33.1 rs7470806 9 120,251,439 A/G 0.92 G 1.00 1.00 intergenic DBC1 +717.3 1.5E-05 -0.29 0.07 9.9E-06 -----+ 17 0.30

rs10984054 9 120,303,788 T/C 0.91 0.97 G G intergenic DBC1 +664.9 8.7E-06 -0.29 0.07 7.0E-06 -----+ 28 0.22

10p13 rs10906734 10 14,740,249 A/G 0.34 0.68 G G intron FAM107B 7.5E-06 0.20 0.04 6.5E-06 ++++++ 0 0.47

11p14.3 rs16914101 11 25,520,069 A/G 0.02 0.97 G G intergenic LUZP2 -459.3 1.5E-05 0.67 0.15 6.9E-06 +++-++ 61 0.03

rs11828943 11 25,540,930 T/C 0.98 1.00 0.99 1.00 intergenic LUZP2 -484.2 1.1E-05 -0.62 0.14 5.7E-06 ------57 0.04

11q23.3 rs12364734 11 118,893,782 T/C 0.06 0.77 G G intron USP2-AS1 2.5E-06 0.40 0.08 2.4E-06 ++++++ 0 0.81

13q12.3 rs2388538 13 29,860,752 A/G 0.68 0.95 G G intergenic LINC00426 -14.7 9.0E-06 0.18 0.04 8.9E-06 ++++++ 0 0.90

13q22.1 rs945695 13 73,607,822 T/C 0.99 NA G G promoter KLF12 -1.8 1.1E-05 -1.31 0.29 6.3E-06 -????- 86 0.01

17q25.1 rs12601174 17 69,968,976 A/C 0.25 0.96 G G intergenic CD300A -5.1 8.6E-06 0.20 0.04 6.4E-06 +++++- 56 0.04

19q12 rs918587 19 35,017,096 T/C 0.36 G G G intergenic CCNE1 +10 9.9E-06 -0.17 0.04 1.6E-05 ------17 0.30

20p12.1 rs1452317 20 12,608,400 T/C 0.02 NA G G intergenic SPTLC3 -329.2 4.3E-06 1.28 0.27 3.0E-06 +????+ 0 0.76

Chr: Chromosome; A1/A2: effect / other allele; Freq1: frequency of effect allele; I2: Higgins and Thompson heterogeneity index; Q Pval: Cochran’s test of heterogeneity P-value Imputation Quality is info metric reported by IMPUTE2 software; G: genotyped SNP; NA: neither imputed nor genotyped; D: DCCT/EDIC, W: WESDR, R: RASS studies

Fixed-effects meta-analysis was performed by two methods: weighing each study by number of events (Nevent P) or inverse variance (P). Distance is calculated from the nearest gene. (-) indicates upstream and (+) downstream relative to transcription direction. Direction of effects based on the sign of β are shown in the following order: (1) WESDR (2) DCCT/EDIC: primary cohort-conventional Rx (3) DCCT/EDIC: primary cohort-intensive Rx (4) DCCT: 2ndary cohort-conventional Rx (5) DCCT: 2ndary cohort-intensive Rx (6) RASS

158 159

Figure 27. Manhattan plot of time to MDR meta-analysis Genotyped and imputed SNPs (to HapMap3) with a minor allele frequency ≥1% and imputation quality ≥0.3 are plotted. Red and blue lines indicate genome-wide significance and suggestive (P < 10-5) association thresholds, respectively.

6.4 Closer Look at the Top Association Signals

Figure 28 and Figure 29 show regional association plots for the top signals in the survival and case-control meta-GWAS of MDR, respectively. As mentioned earlier, no

SNP reached genome-wide significance threshold for association in either analyses.

There was no overlap between the top results (P <10-5) in the two meta-GWAS.

However, two of the top signals (P <10-5) in the survival meta-GWAS, rs12563405 and rs10906734, also showed strong association (P <10-4) in the case-control meta-GWAS.

At three of the top signals in the time to MDR meta-analysis (3q22.1, 11q23.3 and

13q22.1), no SNP other than the index has a small association P-value (P <0.01). Two of these loci (3q22.1 and 13q22.1) have a minor allele frequency close to 1% which may contribute to the paucity of tag SNPs in strong LD with the index SNP.

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Figure 28. Regional association plots for top results (P <10-5) from meta-GWAS of time-to MDR. Each dot represents a SNP plotted against its physical location on horizontal axis (positions based on Build 36 of Human Genome - hg18) and p-value on vertical axis. SNPs are color- coded based on the linkage disequilibrium with the top index SNP (see legend, grey: LD information not available. The LD is calculated based on CEU samples of 1000 genome (June 2010 release) except for 11p14.3, 19q12 and 20p12.1 which is based on CEU individuals of HapMap 2. Genes in the region are shown underneath each plot and the blue line graphs recombination rate (right axis).

This is probably the case at the other two loci with MAF ≤2% (1q25.3 and 20p12.1) where few SNPs other than the index have a P <0.01. Regional association plots for the

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Figure 29. Regional association plots for top results (P <10-5) from case- control meta-GWAS of MDR. SNPs (dots) are color-coded based on LD pattern in CEU samples of 1000 genome (legend). See caption to Figure 28 for further details.

top signals in the case-control meta-GWAS of MDR usually contain multiple SNPs with

P < 0.05 in each region.

6.5 Estimation of Common Additive Heritability

We estimated the proportion of phenotypic variance explained by common genotyped

SNPs (480,500 autosomal SNPs with a minor allele frequency greater than 1% used to calculate genetic relation matrix) using restricted maximum likelihood (REML) analysis implemented in GCTA software. Table 59 summarizes the heritability estimates using this method in the combined DCCT/EDIC and WESDR samples. Heritability estimates for other phenotypes in the same sample are presented for comparison. The proportion of phenotypic variance explained by common genetic variance (common additive heritability) estimated using this method is close to heritability estimates for these phenotypes from previous family or twin based studies (Hamsten et al. 1986;

Silventoinen et al. 2003). Due to limited sample size these estimates suffer from large

164 standard errors. The estimate of heritability for SDR (0.24) is much larger than the estimate for MDR (< 0.001); although both estimates have large standard errors and confidence intervals include zero. This is consistent with the common belief that MDR is mostly determined by the quality of glycemic control while changes in SDR have a stronger genetic contribution. Interestingly inclusion of A1C in the model increased the heritability estimate for SDR (not shown).

Table 59. Estimation of phenotypic variance explained by common genetic polymorphism in WESDR and DCCT/EDIC Heritability Standard Phenotype P Model estimate error

SDR ~ age + sex + diabetes duration + SDR 0.24 0.31 0.2 squared (diabetes duration) + mean A1C

Mean A1C ~ age + sex + diabetes duration + Mean A1C 0.07 0.15 0.3 study/cohort/treatment indicator

MDR ~ age + sex + diabetes duration + MDR < 0.001 0.14 0.5 squared (diabetes duration) + mean A1C + A1C * study interaction

Height 0.84 0.14 1 × 10-8 Height ~ age + sex + study

Cholesterol 0.26 0.17 0.07 Cholesterol ~ age + sex

HDL 0.47 0.18 0.005 HDL ~ age + sex

Heritability estimate is the proportion of phenotypic variance explained by common genetic variance (480K common autosomal SNPs) estimated using restricted maximum likelihood method.

P: likelihood ratio for the genetic variance component

7. DISCUSSION

165 166

7.1 Purpose and Strength of Study

The current study is the first survival meta-analysis of diabetic retinopathy (DR), the most common microvascular complication of diabetes and a leading cause of blindness.

Several lines of evidence support a genetic contribution to DR (see Introduction). At the onset of the current study, no genome-wide association study (GWAS) was published for DR. Four case-control GWAS published since (see section 1.3.4.2‎ ) have generally been unsuccessful in identifying any genome-wide significant loci.

7.1.1 High phenotyping quality

Cohorts in the current study (WESDR, DCCT/EDIC and RASS) are very well- phenotyped; using 7 field stereoscopic fundus photographs which is the gold standard of DR measurement, with stringent quality control criteria and employing the same central reading unit within and between studies, thus minimizing measurement errors.

Only one of the previous GWAS studies in Pima Native Americans (n=286) used similar high quality phenotype data (Fu et al. 2010). The three other studies either used self-reported laser treatment (Grassi et al. 2011) or fundus examination by an ophthalmologist (Huang et al. 2011; Sheu et al. 2013). Such non-standardized phenotyping may lead to low inter-rater agreement and misspecification of phenotype which could diminish the power of GWAS (van der Sluis et al. 2010).

7.1.2 Adjusting for known risk factors and independent covariates

Diabetes duration and glycemic exposure (measured by A1C) largely determine the risk of development and progression of diabetic retinopathy. Existence of such strong risk factors complicates any genetic study of DR. In order to prevent any potential

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Confounder Mediator

G Disease G Disease

Moderator Independent Factor

G Disease G Disease

Figure 30. Possible relations of risk factors with the association of genetic factor (G) and disease.

confounding and increase statistical power, in the current study, we included known risk factors for DR (i.e. A1C, diabetes duration and BMI) and other covariates (age, and gender) as predictors in the regression model. Only one of previous case-control

GWAS for DR went through comparable measures to account for the effect of known

DR risk factors in their analysis (Fu et al. 2010).

Risk factors may simply be independent covariates or act as a mediator, confounder or modifier in the investigation of genetic determinants of a disease (Figure 30). In a genetic association study, we are mostly concerned about confounding, since it may lead to spurious associations. Population stratification is the best known example of confounding; this happens when a genetic polymorphism is associated with ethnicity which is a risk factor for the disease, leading to spurious association between the genetic polymorphism and the disease (see section 1.3.3‎ ).

In the case-control association study of DR, both diabetes duration and A1C (capturing glycemic exposure) may act as potential confounders if there is ascertainment bias

168 between cases and controls. A1C could also act as a mediator when a gene modulates the risk of DR by affecting glycemia. Finally, certain genes may only contribute to DR development in certain glycemic conditions (A1C as a modifier). The main approach to protect against a confounding variable is to include it in the statistical model. A common example is including principal components as covariates in the genetic analysis to protect against population stratification.

Adjusting for known risk factors (covariates) is not only a sound solution against possible confounding but also a mean to increase statistical power. It has also been shown that adjusting for independent non-confounding covariates (such as age, gender, etc.) increases the power by reducing the residual variance in linear models. In non-linear models such as proportional hazard survival models, adjusting for covariates may also improve the fit and increase the power (Hsieh and Lavori 2000). In case-control association studies, where samples are ascertained based on their disease status and disease prevalence is low in the population, including the covariates may have detrimental effect on statistical power. However, in situations such as DR when the disease is prevalent or when using random sampling of the population irrespective of case-control status (such as WESDR), it is often more powerful to include non- confounding predictive covariates (Pirinen et al. 2012).

Inclusion of a mediator as a predictor in the model will diminish (partial mediation) or abolish (full mediation) the association of the gene and the disease. In the genetic study of DR, A1C is the most probable mediator; as there are reports of genes such as

SORCS1 that affect the risk of DR through modulating glycemia (Paterson et al. 2010).

Several GWAS have investigated genetic determinants of A1C (Paterson et al. 2010;

Soranzo et al. 2010a). Considering our primary goal to identify independent genetic determinates of DR, analytical models in the current study adjusted for A1C.

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7.1.3 Frequent longitudinal measurement of phenotypes

All the cohorts in our GWAS have followed the participants over an extended period of time (average of 5 yr in RASS, 16 yr in DCCT/EDIC and 22 yr in WESDR) with frequent measurements of the retinopathy phenotype (average of 2.4, 4.5 and 16.7 fundus photographs in RASS, WESDR and DCCT/EDIC respectively) and predictive covariates such as A1C. Frequent measurement of phenotype will not only decrease measurement bias, but also capture the phenotypic complexity over time. Both of these would have a positive impact on the study power.

7.2 Survival Analysis of the Retinopathy Outcome

The current study is the first survival meta-GWAS of DR. As a survival trait, DR consists of two aspects: occurrence of the event and the follow-up time before the event or censorship. Cross-sectional analysis of a survival trait like DR, is not only expected to diminish the statistical power by using a fraction of the information available; but may also be incomplete and misleading by ignoring the time dependent nature of the outcome. Situations could be considered when distribution of follow-up time is different between two groups while the proportion of event is identical (Figure 31).

Including duration of exposure as a covariate in a case-control analysis does not completely address this issue, since the exact time of the event and hence the duration of exposure is undetermined in such analysis. Time to event (survival) analysis is therefore the most appropriate method for analysis of a survival trait such as DR. One limiting factor in survival analysis is potential heterogeneity of genetic or environmental effects over time; since a pivotal assumption of most common methods of survival analysis is proportionality of hazard over time.

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Figure 31. The effect of observation window on case-control analysis of a survival trait. Group 1 and 2 would show similar occurrence rate in a case-control analysis performed in scenario A. Case-control analysis in scenario B (after similar follow-up times) does not suffer from this issue.

Most common methods of survival analysis, such as Cox proportional hazard models, would exclude individuals with the event at the start of the study (left censoring). In order to include these individuals, in the current study another proportional hazard model known as complementary log-log model (cloglog) was used. Besides, using the cloglog method enabled us to take advantage of the whole range of data and handle interval censoring (individuals missing certain follow-up visits).

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7.3 Limitations of the Study

7.3.1 Statistical power

The main limitation of the current study is modest sample size. Our study (n=1907 and

2146, 27.2% and 52.2% cases, for SDR and MDR respectively) has a larger sample size compared to three of the previously published GWAS for DR (n=286 to 1007).

However, our modest sample size only provides reasonable power (β=0.8) to detect effects greater than an odds ratio of 1.6 to 2.75 depending on the allele frequency (0.5 to

0.05 respectively, see section 4.8‎ ) in a case-control GWAS of SDR. To detect more modest genetic effects much larger sample sizes are required; for example to detect an odds ratio of 1.2 in a case-control analysis sample sizes between 3200 and 16500 are required for a risk allele frequency of 0.5 and 0.05 respectively, assuming similar disease prevalence and case-control proportion to the current study (see 4.8‎ ). We had limited opportunity to recruit larger sample sizes; since few studies of DR were available with longitudinal follow-up and using comparable high quality phenotyping measures.

7.3.2 Inter-study heterogeneity

The three cohorts used in the current study showed differences in diabetes duration, glycemic control, follow-up duration and frequency of measurements. We addressed the differences by performing the GWAS analyses separately in each cohort and by including main predictive covariates such as diabetes duration and glycemic exposure as covariates in the models. Yet, fixed effects meta-analysis used to combine results of these analyses assumes homogeneity of genetic effect between different cohorts. In all our meta-analyses, the distribution of P-values for Cochran’s heterogeneity test follows a null distribution consistent with the fixed-effects assumption. However, significant

172 heterogeneity may exist for a subset of SNPs which may detrimentally affect the statistical power.

7.3.3 Unavailability of suitable replication studies

As mentioned earlier, few studies have used similar high quality phenotyping of DR patients and even fewer have longitudinally followed these patients over extended time periods. As a result for the replication of top signals, we were limited to studies with lower quality outcome measurements like ophthalmologic exam results retrieved from patients’ medical records. We tried to define similar outcomes, as much as possible, in the replication studies; however, without similar phenotyping it is not possible to ensure parity of phenotype definition in discovery and replication studies.

Moreover, due to the lack of longitudinal follow-up, survival analysis was not feasible in replication studies. Our replication effort was limited to case-control analyses.

7.4 Association of DPP10 and SDR

In our meta-GWAS for SDR, dipeptidyl-peptidase 10 (DPP10) on 2q14.1 was the only locus which showed association close to the genome-wide significance threshold in the case-control analysis (P=6.2×10-8 for rs12466846). SNPs at this locus also showed association with SDR in survival meta-GWAS without reaching genome-wide significance threshold (P=1.1×10-5). The associated SNPs had high genotyping or imputation quality. Although we were not able to replicate this association in the three replication samples, the direction of effect remained consistent between all of the investigated populations except one.

In WESDR, DPP10 locus was not associated with other diabetic complications. The association of DPP10 with SDR appears to be independent of other risk factors such as glycemic exposure and blood pressure. Top SNP at this locus showed modest

173 association with BMI in WESDR. However, adjusting for BMI did not significantly change the association between DPP10 and SDR. It seems that the effect of DPP10 on

SDR risk is not mediated through an effect on BMI.

Top SNPs at this locus are all located in the introns of DPP10, a gene spanning a 1.4 Mb interval on chromosome 2. Dipeptidyl-peptidase 10 belongs to a family of specialized serine proteases (Qi et al. 2003). DPP4, the most famous member of this family, is the target for a group of antihyperglycemic agents known as gliptins which are used in the treatment of type 2 diabetes. DPP4 rapidly inactivates incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) by its peptidase activity. By inhibition of DPP4, gliptins prolong and enhance the activity of endogenous GLP-1 and GIP which are important regulators of blood glucose via prandial stimulation of insulin secretion (Green et al. 2006). DPP10 is unlikely to have a similar role; as unlike DPP4, DPP10 lacks a critical serine residue at the peptidase site and does not show any protease activity in vitro (Chen et al. 2006; Qi et al. 2003).

Consistently, the index SNP at DPP10 locus was not associated with residual C-peptide level in the WESDR.

Initial linkage reports of asthma to the 2q14 region (Daniels et al. 1996; Hizawa et al.

1998; Koppelman et al. 2002; Wjst et al. 1999) was followed by association mapping of asthma to SNPs in the initial exons of DPP10 (Allen et al. 2003). Although an initial replication study failed to replicate the observed association of asthma and DPP10

(Hersh et al. 2007); the association has been replicated in several better powered candidate gene (Blakey et al. 2009; Michel et al. 2010; Zhou et al. 2009) and genome- wide association studies (Mathias et al. 2010; Wu et al. 2010). Significant association has been reported for rs10208402 in DPP10 and asthma related phenotypes: serum IgE level and peripheral blood eosinophil count (Gao et al. 2010). rs10208402 is not in strong LD with the SDR associated SNPs in our study (distance = 1.1 Mb, r2 < 0.01, Dʹ = -0.05 based

174 on the 1000 genome phase 1 CEU data). A recent resequencing study has also shown that rare variants in DPP10 contribute to asthma susceptibility (Torgerson et al. 2012).

Schade et al. showed that DPP10 is expressed in the bronchial epithelium of rats and induction of asthma upregulates its expression (Schade et al. 2008). Allen et al. proposed that DPP10 may retain some protease activity that could contribute to enzymatic destruction of cytokines and chemokines thus altering the inflammation process in asthma (Allen et al. 2003). This mechanism seems unlikely, since DPP10 does not show protease activity (Chen et al. 2006).

DPP10 isoforms are highly expressed in the brain, adrenal gland and pancreas (Allen et al. 2003; Chen et al. 2006). Retinal expression of DPP10 is confirmed in mouse (Qi et al.

2003). DPP10 is a type II transmembrane protein with a cytosolic N-terminus that varies between isoforms, a single transmembrane and an extracellular β-propeller domain (McNicholas et al. 2009). DPP10 is an auxiliary subunit in the macromolecular complex that forms Kv4 subfamily of voltage-gated potassium channels (Jerng et al.

2004; Li et al. 2006; McNicholas et al. 2009). Potassium channels with Kv4.2 and Kv4.3 pore forming subunits mediate most of the subthreshold A-type K+ current in CNS neurons hence regulating the frequency of the repetitive firing in these neurons

(McNicholas et al. 2009). DPP10 associated with these voltage-gated potassium channels (Kv4) acts as a modulator of their cell surface expression, subcellular localization and electrophysiological properties (Foeger et al. 2012; Zagha et al. 2005).

Matsuyoshi et al. showed that DPP10 is predominantly associated with Kv4.3 channel subunits restricted to small-sized neurons in rat dorsal ganglia contributing to A-type

K+ current in C-fiber somatic afferent neurons (Matsuyoshi et al. 2012). Considering the high expression of DPP10 in dorsal root ganglia, it has been proposed that DPP10 plays a role in asthma by changing the sensitivity of the neurons that trigger bronchial hyperreactivity (Ren et al. 2005).

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DPP10 has been associated with neuropsychiatric disorders: common variants downstream of DPP10 have been associated with bipolar disorder (Djurovic et al. 2010) and recurrent rare copy number variations (CNVs) in DPP10 show enrichment in autism spectrum disorder (Girirajan et al. 2013; Marshall et al. 2008). Interestingly, in a recent genome-wide search for human-specific cis-regulatory changes in prefrontal cortex (compared to chimpanzee and macaque), histone H3 trimethylated at lysine 4

(H3K4me3), an epigenetic marker of transcription start site (TSS), showed two human specific peaks within DPP10 less than 0.5 Mb apart (Shulha et al. 2012). These peaks showed selective methylation in neuronal tissue compared to non-neural tissue in human tissues and the authors were able to identify a novel antisense RNA,

LOC389023, responsible for regulatory roles of the identified peaks (Shulha et al. 2012).

These findings support a neurodevelopmental role for DPP10 possibly in synaptogenesis (Somel et al. 2013).

Without a good understanding of the role of DPP10 and Kv4 channels in the retina and under diabetic conditions, we can only speculate on possible mechanisms by which

DPP10 may contribute to the risk of diabetic retinopathy. Current evidence suggests that Kv4 channels may be involved. Kv channels are highly expressed in most vascular smooth muscle cells and are important in controlling vascular tone (Ko et al. 2010).

Hyperglycemia changes Kv channel function which may lead to diabetic vascular dysfunction (Bubolz et al. 2005; Ko et al. 2010; Rainbow et al. 2006). Since microvascular dysregulation is one of the mechanisms involved in diabetic retinopathy (see

Introduction); DPP10, as a Kv channel associated protein, may contribute to the risk of diabetic retinopathy by modulating the effect of hyperglycemia on retinal microvasculature.

Dysregulation of Kv4 channels seems to have a role in various diabetic complications.

For example, in rat model for T1D, a main electrophysiologic abnormality in diabetic

176 cardiomyopathy is downregulation of key cardiac K+ channel genes including Kv2.1,

Kv4.2 and Kv4.3 in cardiac myocytes as early as day 14 of diabetes which enhances arrhythmogenicity of diabetic heart (Qin et al. 2001). Expression of type-A current subunits (Kv1.4, Kv3.4, Kv4.2 and Kv4.3) is also reduced by half in the dorsal root ganglia of rat model for diabetic neuropathic pain (Cao et al. 2010). Patch-clamp experiments suggest Kv4 expression in Müller glial cells and K+ current in these cells shows a different pattern in high glucose conditions (Chavira-Suarez et al. 2011). Kv4 channels dysfunction in retinal Müller cells is another possible mechanism for DPP10 involvement in diabetic retinopathy.

7.5 Association of EFNB2 (13q33.2) with Time-to SDR

Several SNPs on 13q33.2 spanning a 49 kb region located 152 kb downstream of EFNB2 showed significant association with time to SDR in the intensive treatment group of secondary cohort of DCCT/EDIC. The SNPs did not show significant association with time-to SDR in the other discovery cohorts (P > 0.05); but the direction of the effect was the same in all the cohorts. In fixed effects meta-analysis the association of these SNPs with time to SDR was among the top hits but short of genome-wide significance threshold (10-6 < P < 10-5); however, there was evidence for significant heterogeneity of effect and in random-effects meta-analysis using Han’s methodology, the association passed this threshold (P < 5×10-8). Although the association was not replicated in

FinnDiane (P > 0.05), the direction of effect remained consistent in all the studied populations. These results suggest significant heterogeneity of effect for this locus, the association reached significance only in the intensive treatment group of secondary cohort of DCCT/EDIC which uniquely has long duration of diabetes and tight control of diabetes (lowest mean A1C among the studied populations).

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Ephrin B2 (EFNB2) is a transmembrane ligand and a member of Eph-Ephrin receptor family. EFNB2 is highly expressed in arterial endothelial cells (Gerety et al. 1999) and its interaction with Ephrin receptor B4 (EphB4) is a key regulator of angiogenesis during development and in tumors (Kuijper et al. 2007). Expression of EFNB2 increases during neoangiogenesis in the adults and seems to be under the regulation of VEGF and Notch signaling (Gale et al. 2001; Kuijper et al. 2007). EFNB2 is also critical in the recruitment and adhesion of vascular smooth muscle cells and pericytes to the vessel walls (Foo et al. 2006; Kuijper et al. 2007). This role seems particularly relevant in DR; considering that pericyte failure is an early event in the pathogenesis of DR (see section 1.4‎ ). Fundamental role of EFNB2 in angiogenesis, its regulation by VEGF (a well-known candidate gene for DR) and its contribution to pericyte stability, make it a strong candidate gene for diabetic retinopathy. Experiments on mouse model of oxygen-induced retinopathy, a well-established model of angioproliferative retinopathy, supports the role of both EphB4 and EfnB2 in retinal angiogenesis (Ehlken et al. 2011).

7.6 No Genome-Wide Significant Hit in the Meta-GWAS of Time-to SDR

In our fixed-effects meta-GWAS of discovery cohorts, no locus reached genome-wide significance threshold for association with time-to SDR. This was not unexpected considering the limited power of current study in detecting modest effect sizes

(see 7.3.1‎ above). However, most of the top signals merit mentioning; since they are located within or near genes with pathophysiologic relevance to diabetic complications.

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7.6.1 Proprotein Convertase Subtilisin/Kexin type 2 (PCSK2)

An intronic SNP in PCSK2, prohormone convertase 2 gene, on chromosome 20 showed strong association with time-to SDR (P < 10-5). Proprotein convertases are serine proteases that play a role in the activation of various proteins involved in critical cellular pathways. PCSK2 is stored in secretory granules in neural and endocrine tissue and is involved in the activation of several polypeptide prohormones including insulin, glucagon-like peptides, corticotropin, β-lipotropin and somatostatin (Artenstein and

Opal 2011). Homozygous Pcsk2 mutant mice show growth retardation and chronic hypoglycemia, consistent with a glucagon deficiency (Furuta et al. 1997). Variants in this gene have been associated with reduced insulin secretion (Jonsson et al. 2012).

Polymorphisms in PCSK2 have also been associated with type 2 diabetes via linkage mapping (Leak et al. 2007; Yoshida et al. 1995) and with myocardial infarction in candidate gene association studies (Fujimaki et al. 2010).

Aside from its role in glucose hemostasis, it is possible for PCSK2 to be involved in retinal angiogenesis by activating angiogenic factors. One possible factor is ANGPTL4.

Angiopoietin-related/angiopoietin-like proteins (ARPs/Angptls) are a group of angiogenic regulators known to be dysregulated under hypoxic conditions (Oike et al.

2004). Specifically ANGPTL4 is induced under high glucose conditions in RPE cells and shows potent angiogenic activity on retinal endothelial cells (Yokouchi et al. 2013).

There is some evidence that proprotein convertases may be involved in the activation of Angptls (Jin et al. 2007).

7.6.2 Protein-Tyrosine Phosphatase Sigma (PTPRS)

An intergenic SNP, 17 kb upstream of PTPRS, showed strong association with time-to

SDR in our meta-GWAS; the association reached genome wide significance in the inverse variance weighted fixed effects meta-analysis (P = 2.7×10-8). The observed association was not replicated; however, PTPRS is a particularly interesting candidate

179 gene considering its role in neuroendocrine development and glucose hemostasis. Ptprs knock-out mice show strong hypoglycemia characterized by diminished expression of growth hormone and prolactin together with hypoplastic pancreatic islets and reduced insulin production (Batt et al. 2002). Moreover, Ptprs-/- mice show hypersensitivity to exogenous insulin in insulin tolerance test (Chagnon et al. 2006). Other closely related protein tyrosine phosphatases, such as leukocyte antigen-related (LAR), are known to play important roles in the insulin signaling pathway (Cheng et al. 2002).

Polymorphisms in PTPRS have been associated with T2D (Långberg et al. 2007).

7.6.3 COX7A2 / TMEM30A rs9360898, in the promoter of cytochrome C oxidase subunit VIIa polypeptide 2

(COX7A2) and 9 kb downstream of transmembrane protein 30A (TMEM30A), showed strong association with time-to SDR (10-6 < P < 10-5) in our meta-GWAS. The association was not replicated.

COX7A2 encodes polypeptide 2 of subunit VIIa of cytochrome C oxidase. As the terminal rate-limiting component of mitochondrial respiratory chain, cytochrome C oxidase regulates mitochondrial respiration (Kadenbach et al. 2004). Cytochrome C oxidase is involved in apoptotic cell death induced by reactive oxygen species (as happens in diabetic retinopathy). Type 2 apoptosis is triggered by changes in mitochondrial integrity which leads to the release of cytochrome C oxidase into the cytosol and activation of procaspase-9 and caspase-9 (Kadenbach et al. 2004).

Transcriptome and genetic association studies support the role of COX7A2 in diabetes and its complications. Gene expression profiling studies suggest that COX7A2 is dysregulated in diabetes: it was markedly upregulated in the peripheral lymphocytes of patients with diabetic nephropathy or diabetes compared to controls or non-diabetic nephropathy (Christina et al. 2013) and it showed consistent downregulation in skeletal muscle biopsies from T2D patients vs. controls (Pollard et al. 2005). COX7A2 also

180 showed significant down-regulation in proliferative vitreoretinopathy compared to the normal retina (Hollborn et al. 2005). In a candidate gene association study, a polymorphism downstream of COX7A2 showed association with decreased glucose- stimulated insulin secretion; the association was replicated in an independent population (Olsson et al. 2011).

Transmembrane protein 30A, TMEM30A, is not well characterized. However, current evidence suggests possible relevance of this gene to diabetes. It shows strong selective expression in endocrine pancreas (Lindskog et al. 2012) and in proteome profiling,

Tmem30a showed marked reduction in the liver cell membranes of db/db mouse, a model for diabetes, compared to normal mouse (Kim et al. 2013).

7.6.4 Myosin Regulatory Light Chain-Interacting Protein (MYLIP)

An intergenic SNP 137 kb upstream of MYLIP showed strong association with time-to

SDR in our meta-GWAS. The other closest gene relative to the index SNP is a microRNA, MIR633, located 124 kb 5’ to the SNP. We did not manage to replicate the observed association. MYLIP is a cytoskeletal effector protein expressed in most human tissues (Olsson et al. 2000). MYLIP is an E3 ubiquitin ligase that triggers ubiquitination of LDL receptor (LDLR) targeting it for degradation and thereby affecting cholesterol hemostasis (Zelcer et al. 2009). Several SNPs in the vicinity of MYLIP, that are in linkage disequilibrium with our index SNP (D’=1), have been associated with serum

LDL cholesterol level in previous genome-wide association studies (Teslovich et al.

2010; Waterworth et al. 2010). The observed association at this locus is interesting; considering that hyperlipidemia is a risk factor for diabetic retinopathy (see

Introduction). Nonetheless, the index SNP associated with SDR was not associated with mean serum level of LDL in either DCCT or WESDR (P > 0.3).

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7.6.5 EXOC2 / IRF4

Intergenic SNPs upstream of EXOC2 and IRF4 genes showed association with time-to

SDR in our meta-GWAS (smallest P = 4.1×10-7). The association was not replicated in any of our replication studies (P > 0.05).

As a subunit of exocyst complex, EXOC2 is involved in vesicle trafficking (Kee et al.

1997). Exocyst complex seems to play a critical role in insulin-stimulated uptake of glucose transporter type 4 (GLUT-4) containing vesicles (Inoue et al. 2003).

Interestingly, a SNP in the vicinity of another member of exocyst complex (EXOC1) has shown association with severe diabetic retinopathy (Grassi et al. 2012).

Interferon regulatory factor 4 (IRF4) is a transcription factor essential for the development of several types of immune cells (Xu et al. 2012). Evidence from the study of animal models suggests a role for this gene in the development of T1D (Besin et al.

2011). In a GWAS scan for T1D, IRF4 was among the top hits without reaching the genome-wide significance threshold (Trucco 2008).

7.7 No Genome-Wide Significant Locus Other than DPP10 in the Case-Control Meta-GWAS of SDR

In the case-control meta-GWAS of SDR in WESDR and DCCT/EDIC by subgroup, no locus other than DPP10 (see 7.4‎ above) reached genome-wide significance threshold for association. Nevertheless, a few of the top loci (P < 10-5) in this meta-GWAS showed evidence for association in our replication studies. Besides, most of them contain strong candidate genes that will be discussed here.

7.7.1 NLR family, pyrin domain containing 3 (NLRP3)

An intergenic SNP 27 kb upstream of NLRP3 (aka. cryopyrin) showed association with

SDR in our meta-GWAS without reaching the genome-wide significance threshold

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(10-6 < P < 10-5). The association was significant in the meta-analysis of replication studies even after Bonferroni correction for the number of tests (P < 0.001). The index

SNP showed similar direction of effect in all the examined populations.

NLRP3 is a member of the inflammasome complex which serves as a platform for the activation of caspase-1, leading to the activation of IL-1β and IL-18 in the immune response (Shaw et al. 2011). Recent research suggests a role for the inflammasome in the pathogenesis of autoimmune and inflammatory diseases including both type 1 and type 2 diabetes (Pontillo et al. 2010; Shaw et al. 2011; Van Dyke et al. 2010; Zhou et al.

2010). Intronic SNPs of NLRP3 (not in strong LD with our index SNP) are significantly associated with C-reactive protein and fibrinogen, two inflammation markers, in genome-wide association studies (Dehghan et al. 2011; Dehghan et al. 2009).

Interestingly, both these inflammatory markers seem to be associated with DR (Azad et al. 2013; Fujisawa et al. 1999; Schram et al. 2005).

Chronic hyperglycemia and oxidative stress activate the inflammasome complex via the interaction of thioredoxin interacting protein (TXNIP) and NLRP3 (Schroder et al.

2010; Zhou et al. 2010). NLRP3 seems to be involved in the pathogenesis of diabetic retinopathy in particular; chronic hyperglycemia induces the expression of TXNIP and pro-inflammatory genes in the retina of diabetic rats and in cultured Müller cells, leading to the induction of IL-1β and NLRP3 inflammasome activation (Devi et al.

2012). Interestingly, recent culture studies suggest a role for NLRP3 inflammasome in diabetic nephropathy: ATP-P2X4 signaling mediates hyperglycemia induced activation of NLRP3 which in turn regulates the release of IL-1 family cytokines, leading to tubulointerstitial inflammation in diabetic nephropathy (Chen et al. 2013).

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7.7.2 Immunoglobulin superfamily member 21 (IGSF21)

An intergenic SNP (rs9662272) 50 kb upstream of IGSF21 shows suggestive evidence for association with SDR status (10-6 < P < 10-5) in our study. We did not manage to replicate the observed association in the three examined populations (all P > 0.05).

IGSF21 and the index SNP are located within a confirmed linkage peak for diabetes retinopathy (Hallman et al. 2007; Looker et al. 2007). An earlier GWAS of proliferative diabetic retinopathy in EDIC and GoKinD studies reported a small P-value for a SNP downstream of IGSF21 (rs3007729, P = 5×10-6) (Grassi et al. 2011). The SNPs reported by

Grassi et al, however, is not in strong linkage disequilibrium with our index SNP

(distance = 410 kb, r2 = 0.01, D’ = 0.23 based on genotype of CEU individuals in 1000 genomes).

Little is known about this member of the immunoglobulin superfamily. A comprehensive query of human interactome databases reported heat shock 27 kDa protein 1 (HSPB1), v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS), thymosin beta 4 X-linked (TMSB4X) and diacylglycerol kinase delta 130 kDa (DGKD) to be interacting with IGSF21 (Orii and Ganapathiraju 2012). These genes have functional relevance to diabetic complications. HSPB1 is an antiapoptotic protein with neuroprotective role in the retina (O’Reilly et al. 2010) which is upregulated in podocytes in response to high glucose (Sanchez-Nino et al. 2012) and in the retina of diabetic rats (Brucklacher et al. 2008). KRAS is an intracellular signal transducer involved in insulin and VEGF signaling pathways and angiogenesis (Ferrara et al.

2004). TMSB4X is involved in endothelial cell migration and angiogenesis (Kaur and

Mutus 2012) and was shown to be upregulated in proliferative diabetic retinopathy

(Wang et al. 2011). DGKD contributes to the hyperglycemia induced peripheral insulin resistance (Chibalin et al. 2008).

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7.7.3 Membrane-associated guanylate kinase-related 3 (MAGI3)

An intergenic SNP, 114 kb upstream of MAGI3 showed suggestive evidence for association in our case-control meta-GWAS for SDR (10-6 < P < 10-5). This association was not replicated in the three examined population (P > 0.05). The observed association is nonetheless interesting; since MAGI3 is a tight junction protein. Tight junctions play an essential role in the blood-retina barrier. Compromise of tight junctions is one of the important events in the pathophysiology of diabetic retinopathy

(Sawada 2013).

7.7.4 ACN9 / TAC1

Several intergenic SNPs on chromosome 7, ~169 kb downstream of ACN9 homolog gene (ACN9) and ~379 kb upstream of tachykinin precursor 1 (TAC1, aka. substance P) showed strong association with SDR in our meta-GWAS (10-6 < P < 10-5). The association was not significant (P > 0.05) in the examination of replication studies.

ACN9 is a mitochondrial intermembrane space protein involved in gluconeogenesis

(Dennis and McCammon 1999). Gluconeogenesis pathway is upregulated in T2D and contributes to hyperglycemia and insulin sensitivity (Rolo and Palmeira 2006). ACN9 also shows very strong expression in the retinal pigmented epithelium (Booij et al.

2009). However, at this stage there is no direct evidence for the role of this gene in diabetic retinopathy.

TAC1 gene encodes both substance P and neurokinin A (Krause et al. 1987). Substance

P is a potent vasculogenic factor which shows higher circulating level in patients with proliferative and non-proliferative DR compared to no-DR diabetic patients (Lee et al.

2006). Electron microscopy study reveals formation of openings between endothelial cells in response to substance P; which has been proposed as a vasoactive factor involved in paracellular flux contributing to the increase in vascular permeability in

DR (Antonetti et al. 1999).

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7.7.5 Microtubule associated tumor suppressor 1 (MTUS1)

An intronic SNP in MTUS1 showed strong association with SDR in our meta-GWAS without reaching genome-wide significance level (10-6 < P < 10-5). We did not manage to replicate the association in the three examined replication studies (P > 0.05).

MTUS1 is a G-protein coupled receptor interacting protein involved in transactivation of receptor tyrosine kinases and also a component of renin–angiotensin system (RAS) as the angiotensin-II type 2 receptor-interacting protein (aka ATIP) (Nouet et al. 2004).

Angiotensin-II type 2 receptor (AT2R) is expressed on vascular cells and mediates vascular effects of RAS such as vasodilation, anti-inflammatory and anti-fibrotic effects

(Nguyen Dinh Cat and Touyz 2011). Tissue RAS has long been implicated in the pathophysiology of DR (Ströder et al. 2010). Therefore, speculating a role for MTUS1 in the pathogenesis of DR seems reasonable. Interestingly, MTUS1 shows significant downregulation in Müller cell line under hypoxic conditions (Loewen, 2009).

7.7.6 Aconitase 1 (ACO1)

We observed strong association (10-6 < P < 10-5) with SDR in our discovery meta-GWAS for a group of SNPs on chromosome 9 about 0.5 Mb upstream of ACO1. Although the association was not replicated in three more populations that we examined, the direction of effect remained consistent in all but one of the seven (discovery and replication) populations. Aconitase 1 (ACO1) plays a central role in the cellular iron homeostasis (Hentze et al. 1989). ACO1 may be inactivated under oxidative stress in diabetes due to the oxidation of critical sulfhydryl groups (Zou et al. 2004). The observed association near ACO1 is particularly interesting in the light of recent evidence for the role of iron overload in the pathogenesis of DR (Ciudin et al. 2010).

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7.7.7 Aldo-Keto Reductase family 1, member E2 (AKR1E2)

An intergenic SNP 110 kb upstream of AKR1E2 showed strong association with SDR in our discovery meta-GWAS. The association showed similar direction in all the examined population. We managed to replicate the association in FinnDiane (P =

0.0037); but the association in FinnDiane was no longer significant after Bonferroni correction for the number of loci tested for replication.

ARK1E2 is a member of the aldo-reductase superfamily (Mindnich and Penning 2009).

ARK1E2 was initially reported to have testis specific expression (Azuma et al. 2004); however, evaluation of available ESTs shows strong expression in several tissues including the eye. Little is known about the exact function of ARK1E2; however, catalytic domain is conserved in aldo-keto reductases and they generally catalyze the reduction of aldehydes and ketones to alcohols (Mindnich and Penning 2009). The observed association is particularly interesting considering the well-established role of polyol (aldose reductase) pathway in the pathogenesis of diabetes complications

(Brownlee 2001) (also see section 1.4.3‎ ).

7.7.8 17q24.1 locus

A SNP in the 3’UTR of DDX5 showed evidence for association with SDR in our discovery meta-GWAS. The association was not replicated in the three replication studies (P > 0.05).

DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) acts as a transcriptional regulator with important roles in cell proliferation, migration and apoptosis (Fuller-Pace 2013).

Particularly, DDX5 plays a role in the Platelet-derived Growth Factor (PDGF) induced cell proliferation (Yang et al. 2007); an important observation considering the role of

PDGF in pericyte recruitment (Hammes et al. 2002) and its upregulation in diabetic retinopathy (Freyberger et al. 2000).

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The platelet/endothelial cell adhesion molecule 1 (PECAM1) is another gene in the region located 88 kb downstream to our index SNP. PECAM1 is a member of the immunoglobulin superfamily and a major constituent of endothelial cell intercellular junction, implicated in angiogenesis (Newman 1997). It is believed to act as a vascular adhesion molecule which initiates the contact of rolling leukocytes with intimal endothelium (in association with P and E -selectins) via interaction with leukocyte adhesion molecules (Romer et al. 1995). Leukostasis is one of the first events in the pathogenesis of DR (see section 1.4‎ ). Consistent with a role in retinal response to oxidative stress, Pecam1 is upregulated in the rat retina under hyperoxic conditions

(Ishikawa et al. 2010). PECAM1 also shows strong expression in the fibrovascular membrane of proliferative DR (Limb et al. 1996).

7.8 Replication of a Previous GWAS Hit for DR Near PLXDC2

Among 11 loci with suggestive evidence for association with DR from previous GWAS studies (P < 10-5), a single locus on chromosome 10 was replicated in the same direction in the current meta-GWAS of SDR after accounting for multiple testing (P-value of

0.0032 and 0.0036 for rs1571942 and rs12219125 respectively). The original association between these SNPs and DR was reported in a case-control GWAS of Taiwanese patients with T2D (P-value of 3.4×10-7 and 9.3×10-9 for rs1571942 and rs12219125 respectively) (Huang et al. 2011). The direction of the effect remained consistent with the original study in all the replication populations. The two SNPs are located 50 kb apart and are in strong LD (r2 = 0.87): rs1571942 is located in the last intron of PLXDC2,

Plexin domain-containing protein 2, while rs12219125 is located 24 kb downstream of this gene. PLXDC2, also referred to as tumor endothelial marker 7- related protein

(TEM7R), is implicated in neurogenesis and angiogenesis (Miller-Delaney et al. 2011).

Interestingly, a closely related gene, tumor endothelial marker 7 (TEM7), shows strong

188 overexpression in fibrovascular membranes from PDR, suggesting a role in proliferation and maintenance of neovascular endothelial cells (Yamaji et al. 2008).

We were not able to replicate any other loci reported in previous GWAS of DR.

Possible explanations for this general lack of replication include: small sample size, population differences, differences in the effect between type 1 and type 2 diabetes, differences in phenotype definitions and false positive results.

7.9 Lack of Evidence for Association Between DN Loci and DR

Among loci previously associated with diabetic nephropathy, after accounting for multiple testing, no locus showed statistically significant evidence for association with diabetic retinopathy in the current meta-GWAS. Nonetheless, a few loci showed nominal significance for association with DR. The strongest associations with SDR were observed on chromosome 14 near LINC00523 and for intronic SNPs of PVT1 (Pvt1 oncogene) on 8q24.21 and intronic SNPs of CHN2 (chimerin 2) on chromosome 7. The general lack of association between DN hits and DR may be attributed to small sample sizes, false positive hits, differences in populations or disease specificity of the loci (DN specific). Yet, one of the most probable explanations is potential confounding; as some of the previous DN GWAS required presence of a certain level of DR or a minimum duration of diabetes in the controls.

7.10 No Genome-Wide Significant Hit in Case-Control Meta-GWAS of MDR

In the case-control meta-GWAS in WESDR, DCCT/EDIC subgroups and RASS, no SNP showed genome-wide significant association with MDR status. Yet some of the SNPs

189 showing strong association were located within or near genes with functional relevance to diabetic retinopathy.

SNPs on 3p22.1, ~ 140 kb downstream of chemokine binding protein 2 (CCBP2), were among the top hits. CCPBP2 is involved in inflammation; as CCBP2 deficient mice showed persistence in TNF-dependent inflammation due to an excess concentration of residual chemokines (Jamieson et al. 2005). The association of SNPs on 3p12.3, 350 kb downstream of glycogen branching enzyme 1 (GBE1) with MDR is interesting; since

GBE1 is involved in glycogen synthesis and shows significant upregulation in cultured

Muller cells under hypoxic conditions (Loewen et al. 2009). Association of a SNP 129 kb upstream of fibroblast growth factor receptor 1 (FGFR1) on chromosome 8 is worth mentioning; since basic fibroblast growth factor (FGF2) is believed to be one of the angiogenic mediators involved in PDR (Qazi et al. 2009). Another top hit was a SNP on chromosome 10, 462 kb downstream of protein tyrosine phosphatase receptor type E

(PTPRE). Interestingly this protein tyrosine phosphatase seems to be regulated by

FGF2, a potent angiogenic and pro-inflammatory factor, in microvascular endothelial cells (Andres et al. 2009). A top hit on chromosome 12 is an intergenic SNP located between 17-beta hydroxysteroid dehydrogenase 6 (HSD17B6) and retinol dehydrogenase 16 (RDH16) genes, both members of the retinol dehydrogenase family.

The observed association is particularly interesting, considering the protective role of retinol dehydrogenase against lipid peroxidation, which contributes to the pathogenesis of diabetic retinopathy (Lee et al. 2008; Miao et al. 2013). The locus on

17q25.3 is near a SNP previously associated with glycated hemoglobin level (rs1046896,

P = 2×10-26) in a GWAS of mostly non-diabetic subjects (Soranzo et al. 2010b). Our top

SNPs at this locus are in linkage disequilibrium with the previously reported SNP

(distance = 83 kb, r2=0.08, D’=1 based on the genotypes of CEU individuals in 1000 genome project). However, neither rs1046896 nor our top MDR associated SNPs at this

190 locus (rs607544, rs8074277, rs12450046) show evidence for association with mean A1C in either WESDR or DCCT.

7.11 Association of ACP6/GJA5 Locus with Time-to MDR

A polymorphism in the promoter of acid phosphatase 6 gene (ACP6) showed genome- wide significant association with time-to MDR in the primary cohort - intensive treatment group of DCCT/EDIC (P = 1.1×10-9). The observed association was replicated with the same direction in the primary cohort– conventional treatment group of

DCCT/EDIC (P = 0.015). However, the effect of polymorphism on time-to MDR showed strong heterogeneity in the other studies (I2 = 86) and the overall fixed-effects meta- analysis did not reach genome-wide significance threshold for association at this locus

(P = 4.2×10-4).

Acid phosphatase 6 regulates the biosynthesis of mitochondrial lipids by specifically hydrolyzing lysophosphatidic acid (LPA) to monoacylglycerol (Hiroyama and

Takenawa 1999). ACP6 also shows protein tyrosine phosphatase activity (Hiroyama and Takenawa 1999), an interesting observation considering the role of protein tyrosine phosphatases in negative regulation of insulin signaling in the cell.

The index SNP at this locus is located 80 kb downstream of gap junction alpha 5 gene

(GJA5, aka. Connexin-40). GJA5 is gap junction protein specifically expressed in vascular endothelium. Intercellular communication through gap junctions is essential in angiogenesis, as Cx40/Cx37 double knock-out mice show vascular malformations

(Simon and McWhorter 2002). High glucose induces downregulation of Connexin-40 and 43 both in endothelial cell culture and in diabetic rats (Li and Roy 2009; Zheng et al. 2010). Connexin 40 is implicated in atherosclerosis by transmitting anti-adhesive

191 signals within the vascular endothelium and may contribute to diabetic retinopathy by a similar mechanism (Wright et al. 2012).

7.12 No Genome-Wide Significant Hit in the Time-to MDR Meta- GWAS

No SNP reached genome-wide level for significance in the meta-GWAS of time-to

MDR. Yet most of the top hits from time-to MDR meta-GWAS are located near genes with functional relevance to diabetic retinopathy and will be discussed here:

An intronic SNP in voltage-dependent R-type calcium channel subunit alpha-1E

(CACNA1E) was among the top hits in the time-to MDR meta-GWAS. Cacna1e null mice are heavier than wild-type and show hyperglycemia and reduced glucose-evoked insulin secretion (Jing et al. 2005; Matsuda et al. 2001). Polymorphisms in CACNA1E have been previously associated with type 2 diabetes and reduced insulin secretion

(Holmkvist et al. 2007). This gene showed significant downregulation in the retina of patients with proliferative vitreoretinopathy compared to controls (Hollborn et al.

2005).

Another top hit is located 35.5 kb downstream of suppressor of cytokine signaling 5

(SOCS5), a less studied member of suppressors of cytokine signaling (SOCS) family.

SOCS proteins contribute to diabetes and its complications (Suchy et al. 2013). In diabetic retina, they initially improve glucose metabolism and promote neuroprotection against the ongoing inflammation; while their persistent expression caused by chronic inflammation and stress can induce insulin resistance and impair cell survival (Liu et al. 2008). In fact, experimental knock-down of SOCS3 is reported to increase anti-apoptotic and decrease pro-apoptotic protein levels in the retina (Jiang et al. 2012).

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Intergenic SNPs ~700 kb downstream of toll-like receptor 4 (TLR4) were among the top hits in the time-to MDR meta-GWAS. TLR4 is an important mediator of innate non- specific immune response via a signaling pathway parallel to IL1R both through NF-κB signaling (Medzhitov et al. 1997). TLR4 shows significant upregulation and activation in the monocytes of T1D patients (Devaraj et al. 2008) and in the retina of diabetic rats

(Devi et al. 2012). A non-synonymous polymorphism in this gene has been associated with early onset DR in T2D patients in a previous candidate-gene study (Buraczynska et al. 2009).

An intergenic SNP 70 kb downstream of high mobility group box 1 (HMGB1, aka. amphoterin) was among the top associated loci with time-to MDR. HMGB1 is an proinflammatory cytokine and a proangiogenic factor which is expressed in endothelial cells and is actively released from the cells in response to angiogenic and inflammatory signals (van Beijnum et al. 2008). HMGB1 may signal through both the receptor for advanced glycation end products (RAGE) and toll-like receptors 2 and 4

(TLR4); HMGB1-RAGE-TLR4 constitute a tripod that trigger NF-κB activation

(Nogueira-Machado et al. 2011). Consistent with a role in DR, HMGB1 is specifically upregulated in the retina of patients with either proliferative diabetic retinopathy or proliferative vitreoretinopathy (Pachydaki et al. 2006).

Association of a polymorphism in the promoter of Krueppel-like factor 12 (KLF12), a transcription factor, with time-to MDR is interesting. In a study of the inflammatory transcriptional signature of T1D, KLF12 showed under-expression in the peripheral blood mononuclear cells of recently onset T1D patients compared to healthy controls

(Levy et al. 2012). An intronic SNP in KLF12 has been associated with susceptibility to rheumatoid arthritis, another autoimmune disease, in previous GWAS association studies (Julia et al. 2008).

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Another top signal for time-to MDR was 10 kb downstream of cyclin E1 (CCNE1), a cell cycle regulator speculated in angiogenesis (Walshe and D'Amore 2008). Studies on endothelial cells cultured under high glucose conditions, mimicking diabetes, show repression of CCNE1 and cdc25A driven by the upregulation of miR-503 (Caporali et al. 2011). An intergenic SNP in this region, not in strong LD with our index SNP

(distance = 246 kb, D’=0.1 based on genotypes of CEU individuals in 1000 genome), was among the top hits in a previous meta-GWAS of proliferative diabetic retinopathy status in EDIC/GoKinD (rs10403021, P = 2×10-6).

7.13 Incorporating Prior Knowledge Increases the Number of SNPs Passing FDR Threshold

Genome-wide association studies, in the traditional sense, are blind to the function of

SNPs or their location in the genome; SNPs are usually selected for replication in a hypothesis-free manner solely based on the respective association P-value. However, the wealth of expanding biological knowledge has recently sparked interests in hypothesis driven GWAS. This approach tries to incorporate the available biological information, based on a hypothesis, in prioritizing GWAS results for replication

(Cantor et al. 2010). Examples of successful application of such an approach are increasingly reported (Okada et al. 2012; Sun et al. 2012).

We used stratified false discovery rate (SFDR) approach to prioritize sub- genome-wide significance threshold SNPs in our time-to SDR meta-GWAS for replication. Both positional (known linkage peak for DR) and functional (candidate genes for DR, genes dys-regulated in the vitreous of DR patients) evidence was employed in our combined

SFDR analysis (see Chapter 6). The combined SFDR approach increased the number of

SNPs passing FDR threshold in the 0.1-0.5 range. The beneficial effect of SFDR on the number of SNPs passing FDR threshold was more pronounced at higher FDR cut offs

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(0.3 – 0.5); SFDR approach increased the number of SNPs passing an FDR threshold of

0.5 from 1 to over 500. This is consistent with the low power of our meta-GWAS in detecting modest genetic effects. In other words, SFDR proved useful in “pulling out” the sub-threshold SNPs supported by our prioritization hypotheses; yet, due to the general low power of current study, even after application of SFDR no SNP passed a conservative threshold of FDR < 0.05. The fact that our top hits in the initial meta-

GWAS remain the top ranking SNPs in SFDR, despite being in the lowest priority stratum, argues for the robustness of SFDR method.

After correcting for the number of loci tested, only a single SNP on chromosome 1 showed significant association with SDR in the replication studies, with consistent direction of effect in all the discovery and replication population examined. rs6426244 is located near the NLRP3 gene involved in inflammasome (see 7.7.1‎ above).

7.14 Lack of Replication in Independent Studies for Most Top Loci

Aside from two loci, the attempt to replicate the top hits from our SFDR analysis in three independent studies was generally unsuccessful. The lack of success could be attributed to several factors. First, with rather small sample sizes, most of the replication studies had limited power to detect small effect sizes. This is especially true since the effect estimates from our discovery cohorts may suffer from the winner’s curse. Second, replication studies were cross-sectional studies and a case-control

GWAS in these populations may suffer from diminished power to detect associations discovered in a survival GWAS. Third, unlike discovery cohorts, replication studies used lower resolution methods (fundus exam by ophthalmologist) for phenotyping diabetic retinopathy. This may lead to inaccuracies in defining the outcome variable and lead to diminished power for replicating the association.

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7.15 SDR Shows Stronger Common Heritability than MDR

Using combined WESDR and DCCT/EDIC population we estimated the proportion of phenotypic variance explained by common genotyped SNPs (heritability) for cross- sectional DR status: 0.24 for SDR, < 0.01 for MDR and 0.08 for A1C in T1D patients.

These estimates of heritability suffer from large standard errors due to the small sample size (n = 1907). Yet, they are still useful in providing some insight into the genetics of DR.

First, our estimate of heritability for SDR is consistent with estimates of additive heritability from sib-pair studies (see section 1.3.1.4‎ ) which range from 0.18 to 0.52 for both PDR liability and DR severity score (Arar et al. 2008; Hietala et al. 2008; Looker et al. 2007).

Second, low heritability estimate for MDR is consistent with the strong environmental effect of glycemic control on this phonotype. MDR seems to be mostly determined by glycemic level than genetic determinants. Longitudinal follow-up of patients in DCCT shows that patients may fluctuate in their MDR status in subsequent visits. There is a general belief that the underlying pathological process in DR continues despite waxing and waning of local signs. We required persistence over two consecutive visits for definition of MDR, which reduced the number of regressions in MDR drastically.

However, remaining instability of the phenotype points to a strong environmental influence and low heritability.

Third, inclusion of A1C increases the heritability estimate for SDR (0.24 vs 0.05). This is consistent with the notion that including important environmental risk factors will increase the power to detect genetic determinants by reducing the variance.

8. CONCLUSIONS

196 197

In this first survival meta-GWAS of diabetic retinopathy using high quality longitudinal measurements of the phenotype and risk factors, no SNP passed genome- wide significance threshold for association with either time-to SDR or time-to MDR in the fixed-effects meta-analysis. This was not unexpected, as with the current sample size, we only had limited power to detect modest effects. Nonetheless, several loci among the top hits (P < 10-5) contain genes with strong pathophysiological relevance to

DR; including PCSK2, PTPRS, COX7A2, MYLIP, EXOC2 for time-to SDR and

CACNA1E, SOCS5, TLR4, HMGB1, KLF12, CCNE1 for time-to MDR. However, after accounting for the number of loci tested, the observed associations did not replicate in three replication studies.

In the GWAS of single studies SNPs near EFNB2 showed genome-wide significant association with time-to SDR in the intensive treatment - secondary cohort of

DCCT/EDIC. There was significant heterogeneity in the effect at this locus between discovery studies and in random-effects meta-analysis using Han’s method the association passed the genome-wide significance threshold. Although we did not manage to replicate the association in FinnDiane, the direction of effect remained consistent.

In the GWAS of single studies, a polymorphism near GJA5 showed genome-wide significant association with time-to MDR in the primary cohort – intensive treatment group of DCCT/EDIC. The association did replicate in the primary cohort – conventional treatment group of DCCT/EDIC. However, there was significant heterogeneity of effect between studies and the fixed effect meta-analysis did not reach genome-wide significance threshold.

We repeated the meta-GWAS in the same studies at a single time point using a case- control definition. A single locus at DPP10 reached genome-wide significance threshold for association with SDR. However, the association was not replicated.

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Among the top hits for SDR, association of NLPR3 with SDR was replicated in the combined replication set, after correcting for multiple testing. Similarly the association at AKR1E2 was replicated in FinnDiane. Other top hits for SDR are located near IGFS1,

MAGI3, TAC1, MTUS1, ACO1; genes with pathophysiological role to DR. In the case- control meta-GWAS for MDR no SNP reached genome-wide significance threshold for association. Neither did any of the top hits replicate in three independent studies.

Using information from prior genome-wide linkage, candidate gene and proteome studies of DR, together with the function of SNPs to prioritize our meta-GWAS results for replication drastically changed the order of top hits and increased the number of

SNPs passing a liberal FDR threshold of 0.5 from 1 to over 500 SNPs. Yet, after adjusting for the number of loci tested, aside from the SNPs close to NLRP3, the observed association for any of the SNPs in this prioritized list did not replicate in the three replication cohorts. Differences in resolution and definition of phenotype between discovery and replication studies, cross-sectional nature of replication studies and limited power are the possible contributing factors for the lack of replication.

We used restricted maximum likelihood analysis to estimate phenotypic variance

(heritability) explained by 480K common autosomal SNPs in DCCT/EDIC and WESDR.

Although these estimates showed high standard errors due to the small sample size, the estimated heritability for SDR was consistent with previous sib-pair studies and considerably greater than the estimate for MDR; arguing for a stronger genetic contribution to SDR compared to MDR.

9. FUTURE DIRECTIONS

199 200

The current study further supports a genetic basis for SDR and provides evidence for association at several loci with pathophysiological relevance to diabetic retinopathy.

An immediate extension of this work is continued effort to replicate the top associations in additional studies.

Among GWAS for diabetic nephropathy, another microvascular complication of diabetes, one of the most successful results was observed in a sex-specific analysis with the SNP showing a significant effect and replication only in females (Sandholm et al.

2013). A similar sex-specific analysis may prove beneficial in the genetic study of DR.

Future meta-GWAS of more diabetic populations are necessary to reach enough sample sizes to detect associations at polymorphism with small effect sizes (n > 10000)

(Altshuler et al. 2008) (see section 4.8‎ for SDR power calculations). Although the ideal method for GWAS of DR is using survival analysis, such analysis requires repeated longitudinal follow-up of the patients. There are limited numbers of diabetic cohorts available with high quality repeated measurements of DR. Our work provides evidence that a case-control analysis adjusted for important risk factors including diabetes duration may be a reasonable compromise to increase sample sizes.

Phenotyping using high resolution methods such as stereoscopic fundus photographs will increase the accuracy and avoid misclassification of cases and controls, thus improving the statistical power. Yet, it will limit the number of participating studies.

Grassi et al have provided some evidence that self-reported laser treatment history may be a useful surrogate with enough sensitivity and specificity in defining proliferative DR phenotype (Grassi et al. 2009).

Another limiting factor in finding suitable replication studies is that many of the available populations are case-control studies designed to study other diabetic complications especially diabetic nephropathy. There is enough evidence that study of

201 a secondary phenotype (such as DR) in a case-control population recruited based on a correlated primary phenotype (such as diabetic nephropathy) may suffer from biased estimates and reduced statistical power. The problem could be circumvented using proper analytical methods (Lin and Zeng 2009).

Imputation using larger reference panels from population-scale sequencing projects such as 1000 genome or UK10K will increase the number of SNPs under investigation and improve the accuracy of imputation for low frequency variants (1-5% minor allele frequency). Other approaches in analyzing repeated eye level data of DR may be worthy explorations in future analyses. For example, linear mixed models may be used for the analysis of repeated ordinal data (Sheu 2002).

A less studied retinal complication of diabetes is macular edema. Macular thickness is highly heritable in healthy subjects (Chamberlain et al. 2006). There is some evidence for a genetic contribution to macular edema (Awata et al. 2005) and a need for GWAS studies investigating such contribution. Although diabetes is the most common cause of macular edema, maculopathy is not specific to diabetes and proper exclusion criteria should be applied in the genetic analysis of DME to ensure homogeneity of phenotype.

The work presented here, also provides further evidence for the usefulness of incorporating a priori knowledge in prioritizing GWAS hits. Modifications of the same strategy may be useful in the future genetic studies of diabetic complications and diabetic retinopathy in particular. There is unlimited opportunity to formulate smart hypotheses to stratify polymorphisms across the genome, in order to boost the findings of a GWAS. Recent data from the ENCODE project (Bernstein et al. 2012) and eQTL studies provide two great opportunities for prioritizing genetic variants based on their predicted function. Although, eQTL strategy is limited by the availability of data from a biologically relevant tissue.

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Diabetic retinopathy is part of the broader pathophysiology in diabetes that involves microvasculature. There is a noticeable lack of genetic studies that jointly model microvascular complications, i.e. diabetic retinopathy, nephropathy and neuropathy.

Methods for such studies have been in development in recent years. It is expected that joint analysis of microvascular complications may improve statistical power and help identify important associations and potential drug targets for prevention of such complications.

Investigation of gene by gene and gene by environment interactions are missing pieces in the puzzle of diabetic complications genetics. Among the known risk factors diabetes duration and glycemic control (measured by A1C) are particularly worth studying. It is quite likely that some genetic variations may have protective or predisposing effects above or below a certain glycemic level or before or after specific diabetes duration. There are already examples of other gene-environment interactions in diabetes (Andreassi 2009; Eriksson et al. 2002).

The focus of GWAS is limited to common inherited genetic variants. Studying rare variants using next-generation sequencing and gene-based exome chip data is an important step in acquiring a complete picture of DR genetics. Investigation of copy number variations and epigenetic effects on DR is another important step.

Transcriptome analysis using microarray or RNA-seq methods is another necessary step in the genetic study of DR, which is limited by the availability of suitable biological material.

Finally, some of the top loci from the current study harbor strong candidate genes.

Further efforts are necessary to fine map the association signal. Functional studies to elucidate the pathophysiological link between the observed associations, gene expression and diabetic complications are potential long-term endeavors, as some of these genes may provide useful therapeutic and preventive targets for DR in the future.

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Appendices

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Table S1. List of genes showing consistent dysregulation in transcriptome and proteome studies of DR animal models Gene Confirmation Study N of Gene Name Reference Symbol method type studies A2M alpha-2-macroglobulin QPCR R 3 (Bixler et al. 2011; Gerhardinger et al. 2009; VanGuilder et al. 2011) ACY1 aminoacylase 1 P 2 (Quin et al. 2007; VanGuilder et al. 2011)

ALDOA aldolase A, fructose-bisphosphate P 2 (Quin et al. 2007; VanGuilder et al. 2011)

ATP6V1B2 ATPase, H+ transporting, lysosomal 56/58kDa, V1 subunit B2 P 2 (Quin et al. 2007; VanGuilder et al. 2011) (Bixler et al. 2011; Brucklacher et al. 2008; Gerhardinger et al. 2009; C1S complement component 1, s subcomponent QPCR R 4 VanGuilder et al. 2011) CAPS2 calcyphosine 2 R & P 2 (Kirwin et al. 2011; VanGuilder et al. 2011)

(Brucklacher et al. 2008; Gerhardinger et al. 2009; VanGuilder et al. CARHSP1 calcium regulated heat stable protein 1, 24kDa QPCR R 4 2011) CCR5 chemokine (C-C motif) receptor 5 (gene/pseudogene) WB, QPCR R 2 (Bixler et al. 2011; Brucklacher et al. 2008) CD1D CD1d molecule R 2 (Gerhardinger et al. 2009; Kirwin et al. 2011)

CD40 CD40 molecule, TNF receptor superfamily member 5 QPCR R 2 (Chu et al. 2011; Kowluru and Chan 2010) CD44 CD44 molecule (Indian blood group) QPCR R 2 (Brucklacher et al. 2008; Gerhardinger et al. 2009) (Bixler et al. 2011; Brucklacher et al. 2008; Gerhardinger et al. 2009; CHI3L1 chitinase 3-like 1 (cartilage glycoprotein-39) QPCR R 4 VanGuilder et al. 2011) CP ceruloplasmin (ferroxidase) QPCR R 3 (Bixler et al. 2011; Gerhardinger et al. 2009; VanGuilder et al. 2011) (Fort et al. 2009; Ha et al. 2011; Li et al. 2008; VanGuilder et al. CRYAA crystallin, alpha A WB R & P 5 2011; Wang et al. 2007) (Fort et al. 2009; Ha et al. 2011; Li et al. 2008; Liu et al. 2007; Wang CRYAB crystallin, alpha B iTRAQ, WB R & P 6 et al. 2007) (Kirwin et al. 2011; Li et al. 2008; Liu et al. 2007; Quin et al. 2007; CRYBA1 crystallin, beta A1 R & P 5 Wang et al. 2007) CRYBA2 crystallin, beta A2 iTRAQ, WB R & P 3 (Fort et al. 2009; Ha et al. 2011) CRYBA4 crystallin, beta A4 iTRAQ, WB R & P 3 (Fort et al. 2009; Li et al. 2008) CRYBB1 crystallin, beta B1 iTRAQ, WB R & P 3 (Fort et al. 2009; Li et al. 2008) (Fort et al. 2009; Ha et al. 2011; Li et al. 2008; Quin et al. 2007; CRYBB2 crystallin, beta B2 R & P 5 VanGuilder et al. 2011) CRYBB3 crystallin, beta B3 iTRAQ R & P 2 (Fort et al. 2009) CRYGB crystallin, gamma B iTRAQ, WB R & P 4 (Fort et al. 2009; Ha et al. 2011; VanGuilder et al. 2011) CRYGC crystallin, gamma C R 2 (Kirwin et al. 2011; VanGuilder et al. 2011)

QPCR, (Bixler et al. 2011; Fort et al. 2009; Ha et al. 2011; VanGuilder et al. CRYGD crystallin, gamma D R & P 5 iTRAQ, WB 2011) CRYGS crystallin, gamma S R & P 3 (Gao et al. 2009; Kirwin et al. 2011; Li et al. 2008)

CYP26A1 cytochrome P450, family 26, subfamily A, polypeptide 1 R 2 (Bixler et al. 2011; VanGuilder et al. 2011) DAG1 dystroglycan 1 (dystrophin-associated glycoprotein 1) R 2 (Gerhardinger et al. 2009; Ha et al. 2011)

DCLK1 doublecortin-like kinase 1 QPCR R 2 (Brucklacher et al. 2008; Gerhardinger et al. 2009)

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DDAH2 dimethylarginine dimethylaminohydrolase 2 P 3 (Liu et al. 2007; Quin et al. 2007; Wang et al. 2007)

EDN2 endothelin 2 QPCR R 2 (Brucklacher et al. 2008; VanGuilder et al. 2011) ELL3 elongation factor R polymerase II-like 3 R 2 (Bixler et al. 2011; Kirwin et al. 2011)

ENO1 enolase 1, (alpha) P 3 (Li et al. 2008; Quin et al. 2007; Wang et al. 2007)

FABP7 fatty acid binding protein 7, brain QPCR R 2 (Bixler et al. 2011; Kirwin et al. 2011) FAS Fas cell surface death receptor QPCR R 2 (Gerhardinger et al. 2009; Kowluru and Chan 2010) FGF2 fibroblast growth factor 2 (basic) QPCR, WB R & P 2 (VanGuilder et al. 2011) GAPDH glyceraldehyde-3-phosphate dehydrogenase P 2 (Quin et al. 2007; Wang et al. 2007)

GBP2 guanylate binding protein 2, interferon-inducible QPCR R 2 (Brucklacher et al. 2008; VanGuilder et al. 2011) GFAP glial fibrillary acidic protein QPCR R 3 (Bixler et al. 2011; Gerhardinger et al. 2009; VanGuilder et al. 2011) GLTP glycolipid transfer protein R 2 (Gerhardinger et al. 2009; VanGuilder et al. 2011)

GLUL glutamate-ammonia ligase QPCR R & P 3 (Quin et al. 2007; Wang et al. 2007; Yu et al. 2009) guanine nucleotide binding protein (G protein), beta GNB1 R & P 3 (Ha et al. 2011; Li et al. 2008; VanGuilder et al. 2011) polypeptide 1 HBB hemoglobin, beta QPCR R 2 (Chu et al. 2011; Gerhardinger et al. 2009) HLA-C major histocompatibility complex, class I, C R 2 (Chu et al. 2011; VanGuilder et al. 2011)

HMOX1 heme oxygenase (decycling) 1 R 2 (Bixler et al. 2011; VanGuilder et al. 2011)

HP haptoglobin R & P 2 (Kirwin et al. 2011; VanGuilder et al. 2011)

HSPA5 heat shock 70kDa protein 5 (glucose-regulated protein, 78kDa) QPCR R & P 2 (Ha et al. 2011; VanGuilder et al. 2011) HSPA8 heat shock 70kDa protein 8 R & P 3 (Ha et al. 2011; Quin et al. 2007; VanGuilder et al. 2011)

HSPB1 heat shock 27kDa protein 1 QPCR R 3 (Bixler et al. 2011; Brucklacher et al. 2008; VanGuilder et al. 2011) ICAM1 intercellular adhesion molecule 1 QPCR R 2 (Bixler et al. 2011; Brucklacher et al. 2008) JAK3 Janus kinase 3 WB, QPCR R 2 (Bixler et al. 2011; Brucklacher et al. 2008) KCNE2 potassium voltage-gated channel, Isk-related family, member 2 QPCR R 2 (Bixler et al. 2011; Brucklacher et al. 2008) LAMB3 laminin, beta 3 R 2 (Gerhardinger et al. 2009; Kirwin et al. 2011)

LAP3 leucine aminopeptidase 3 P 2 (VanGuilder et al. 2011; Wang et al. 2007)

LCN2 lipocalin 2 R 3 (Bixler et al. 2011; Gerhardinger et al. 2009; VanGuilder et al. 2011)

LENEP lens epithelial protein R 2 (Bixler et al. 2011; VanGuilder et al. 2011)

(Bixler et al. 2011; Brucklacher et al. 2008; Gerhardinger et al. 2009; LGALS3 lectin, galactoside-binding, soluble, 3 QPCR R 4 VanGuilder et al. 2011) LITAF lipopolysaccharide-induced TNF factor QPCR R 2 (Bixler et al. 2011; Gerhardinger et al. 2009) MARCKS myristoylated alanine-rich protein kinase C substrate P 2 (Gao et al. 2009; VanGuilder et al. 2011)

MLF1 myeloid leukemia factor 1 R 2 (Gerhardinger et al. 2009; VanGuilder et al. 2011)

matrix metallopeptidase 2 (gelatinase A, 72kDa gelatinase, MMP2 QPCR R 2 (Ha et al. 2011; Kowluru and Kanwar 2009) 72kDa type IV collagenase) MT1A metallothionein 1A QPCR R 3 (Bixler et al. 2011; Kirwin et al. 2011; VanGuilder et al. 2011) NPPA natriuretic peptide A QPCR R 2 (Bixler et al. 2011; Brucklacher et al. 2008) natriuretic peptide receptor C/guanylate cyclase C NPR3 QPCR R 2 (Bixler et al. 2011; Brucklacher et al. 2008) (atrionatriuretic peptide receptor C)

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platelet-activating factor acetylhydrolase 1b, catalytic subunit 2 PAFAH1B2 P 2 (Quin et al. 2007; VanGuilder et al. 2011) (30kDa) PDHB pyruvate dehydrogenase (lipoamide) beta P 2 (Quin et al. 2007; VanGuilder et al. 2011)

PKM pyruvate kinase, muscle P 2 (Li et al. 2008; VanGuilder et al. 2011)

PRDX6 peroxiredoxin 6 P 2 (Liu et al. 2007; VanGuilder et al. 2011)

RGR retinal G protein coupled receptor QPCR R 2 (Bixler et al. 2011; Kirwin et al. 2011)

RSE4 ribonuclease, Rse A family, 4 R 2 (Bixler et al. 2011; Kirwin et al. 2011) serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, (Gerhardinger et al. 2009; Kirwin et al. 2011; VanGuilder et al. SERPINA3 R 3 antitrypsin), member 3 2011) (Bixler et al. 2011; Brucklacher et al. 2008; Gerhardinger et al. 2009; SERPING1 serpin peptidase inhibitor, clade G (C1 inhibitor), member 1 QPCR R 4 VanGuilder et al. 2011) signal transducer and activator of transcription 3 (acute-phase (Brucklacher et al. 2008; Gerhardinger et al. 2009; VanGuilder et al. STAT3 QPCR R 3 response factor) 2011) (Bixler et al. 2011; Brucklacher et al. 2008; Gerhardinger et al. 2009; TIMP1 TIMP metallopeptidase inhibitor 1 QPCR R 4 VanGuilder et al. 2011) TNFRSF12A tumor necrosis factor receptor superfamily, member 12A QPCR R 2 (Brucklacher et al. 2008; Kowluru and Chan 2010) TNFRSF1A tumor necrosis factor receptor superfamily, member 1A R 2 (Bixler et al. 2011; Kowluru and Chan 2010) TP63 tumor protein p63 R 2 (Gerhardinger et al. 2009; Kowluru and Chan 2010)

TPI1 triosephosphate isomerase 1 P 2 (Liu et al. 2007; Quin et al. 2007)

TUBA1A tubulin, alpha 1a R & P 2 (Ha et al. 2011; Quin et al. 2007)

TUBB2B tubulin, beta 2B class IIb P 3 (Quin et al. 2007; VanGuilder et al. 2011; Wang et al. 2007)

TXNIP thioredoxin interacting protein QPCR R 3 (Bixler et al. 2011; Gerhardinger et al. 2009; VanGuilder et al. 2011) (Brucklacher et al. 2008; Chu et al. 2011; Ha et al. 2011; McArthur VEGFA vascular endothelial growth factor A QPCR R 4 et al. 2011)

Confirmation method: QPCR = quantitative real-time PCR, WB = Western Blot, iTRAQ = Isobaric tag for relative and absolute quantitation

Study type: R=transcriptome study, P=proteome study

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