Genetics of New-Onset Diabetes After Transplantation

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Genetics of New-Onset Diabetes after Transplantation

† † Jennifer A. McCaughan,* Amy Jayne McKnight,* and Alexander P. Maxwell*

*Nephrology Research Group, Queen’s University, Belfast, Northern Ireland; and †Regional Nephrology Unit, Belfast City Hospital, Belfast, Northern Ireland

ABSTRACT New-onset diabetes after transplantation is a common complication that reduces recipient survival. Research in renal transplant recipients has suggested that pancreatic b-cell dysfunction, as opposed to insulin resistance, may be the key pathologic process. In this study, clinical and genetic factors associated with new-onset diabetes after transplantation were identified in a white population. A joint analysis approach, with an initial genome-wide association study in a subset of cases followed by de novo genotyping in the complete case cohort, was implemented to identify single-nucleotide polymorphisms (SNPs) associated with the development of new-onset diabetes after transplantation. Clinical variables associated with the development of diabetes after renal transplantation included older recipient age, female sex, and percentage weight gain within 12 months of transplantation. The genome-wide association study identified 26 SNPs associated with new-onset diabetes after transplantation; this association was validated for eight SNPs (rs10484821, rs7533125, rs2861484, rs11580170, rs2020902, rs1836882, rs198372, and rs4394754) by de novo genotyping. These associations remained significant after multivariate adjustment for clinical variables. Seven of these SNPs are associated with genes implicated in b-cell apoptosis. These results corroborate recent clinical evidence implicating b-cell dysfunction in the pathophysiology of new- onset diabetes after transplantation and support the pursuit of therapeutic strategies to protect b cells in the post-transplant period.

J Am Soc Nephrol 25: 1037–1049, 2014. doi: 10.1681/ASN.2013040383

One-year graft survival after renal transplantation is black ethnicity, older recipient age, female sex, obesity, now excellent, exceeding 93% for organs donated immunosuppression, and viral infections.5,6,8,13,16,17 after brain death and 96% for those from living Until recently, the pathophysiology of NODAT donors.1–3 Technical advancements in surgery, im- was considered to be analogous to type 2 diabetes proved understanding of immunology, and inno- mellitus. Renal transplant recipients have increased vative developments in pharmacology have altered insulin resistance compared with transplant-naïve the landscape of renal transplantation. The goal of persons with normal renal function.18 In a nondiabetic preventing early graft loss has largely been achieved renal transplant population, the main determinants of and arguably the greatest challenge now is the insulin resistance are obesity and corticosteroid ther- avoidance of late graft failure. Although there has apy.19 Insulin resistance improves in renal transplant been a considerable improvement in 1-year renal recipients after successful transplantation20,21 and re- transplant survival, the rate of graft attrition after cipients have enhanced insulin sensitivity compared the ﬁrst year remains frustratingly constant.2,4 New-onset diabetes after transplantation (NODAT) is a common and serious disorder that Received April 17, 2013. Accepted October 25, 2013. 5–7 curtails recipient survival. NODAT is associated Published online ahead of print. Publication date available at with cardiovascular complications8–11 and develops www.jasn.org. – 12 in 2% 50% of renal transplant recipients. Approx- Correspondence: Dr. Jennifer A. McCaughan, Regional Ne- imately 50% of recipients with NODAT require phrology Unit, Belfast City Hospital, Lisburn Road, Belfast, insulin therapy.6–8,13–15 A number of clinical varia- Northern Ireland BT9 7AB. Email: [email protected] bles have been associated with NODAT, including Copyright © 2014 by the American Society of Nephrology

J Am Soc Nephrol 25: 1037–1049, 2014 ISSN : 1046-6673/2505-1037 1037 BASIC RESEARCH www.jasn.org with dialysis patients.22 At 1 year, there is no significant difference patients received prednisolone for at least 1 year after trans- in insulin resistance between renal transplant recipients with plantation. NODAT and those with normal glucose tolerance.18,23 Fur- In our study, the NODAT clinical phenotype was strictly thermore, insulin resistance indices before transplantation defined as a new requirement for oral hypoglycemic agents or and in the early post-transplant period do not predict NODAT insulin for management of hyperglycemia after transplantation. development.11 NODATstatus was available for 605 recipients; 58 of 605 recipients Pancreatic b-cell dysfunction may prove to be the main (9.6%) developed NODAT during the follow-up period. pathologic contributor to NODAT. In glucose clamp studies, a deficit in insulin secretion was common to renal Clinical Analyses transplant recipients with NODAT.18,20,21,24 There are a num- At 12 months, 529 adult renal transplant recipients had a ber of possible mechanisms for b-cell toxicity in renal trans- functioning graft; 57 of these patients developed NODAT plantation, including hyperglycemia,25 elevated free fatty during the follow-up period. acids,26 and the effect of immunosuppressive medication.27 The median graft survival was 10.4 years. During the follow- A recent proof-of-concept clinical trial demonstrated that ag- up period, there were 162 cases of death-censored graft failure. gressive management of post-transplant hyperglycemia with A further 159 recipients died with a functioning graft. Biopsy- insulin significantly reduced the 1-year incidence of NODAT.28 proven acute rejection (P,0.001), donor age (P=0.001), and This provides further evidence that post-transplant hypergly- CNI usage (P=0.004) were associated with death-censored cemia plays a key role in NODAT development. graft failure after 1 year. In this cohort, HLA mismatching This study investigates clinical andgeneticfactors associated did not significantly influence death-censored graft survival with NODAT in a relatively large, white renal transplant (P=0.85). This is likely to reflect the policy of favorable HLA population. Clinical variables were identified in a carefully matching at our center.29,30 phenotyped, ethnically homogeneous cohort. Initial exploratory analysis was conducted via a genome-wide association NODAT study (GWAS) in a subgroup of NODAT cases patients and Comparison of clinical variables between NODAT cases and controls to identify genetic variants associated with NODAT. controls is shown in Table 1. The median time to NODATwas De novo genotyping was then performed in a larger cohort of 100 months (interquartile range, 113 months). Clinical vari- NODAT patients and controls to validate the findings. ables associated with NODAT were age, female sex, and percentage weight change in the first year after transplantation. There was no association between CNI therapy and NODAT. RESULTS In multivariate analysis, the associations with recipient age, female sex, and percentage weight change in the first year Patient Cohort remained significant. The association between weight at trans- There were 707 first, deceased donor kidney transplants plantation and NODAT was also significant after adjustment performed at Belfast City Hospital (Belfast, UK) between for confounding (Table 2). May 1986 and May 2005. Over 99% of both recipients and donors were white; genetic analysis was restricted to those of Genetic Analyses recorded white ancestry. The average age of recipients was GWAS 37 years (range, 2–77 years) and the average age of donors was There were 561,233 single-nucleotide polymorphisms (SNPs) 42 years (range, 1–75 years). There were 439 male recipients genotyped in 256 individuals consisting of 26 NODAT patients (62.1%) and 428 male donors (59.1%). All recipients had their and 230 controls. After quality control, the average genotyping primary renal diagnosis classified according to the European rate was .99%. Twenty-six SNPs were provisionally associ- 2 Dialysis and Transplantation Association coding system. ated with NODAT (P,10 5); these SNPs were taken forward Diagnoses were categorized as glomerular disease (21%), for second-stage genotyping (Supplemental Table 1). pyelonephritis/interstitial nephritis (20%), autosomal dominant polycystic kidney disease (15%), diabetic nephropathy Reported NODAT SNPs (9%), other specified miscellaneous etiologies (22%), and A literature review identified 27 SNPs previously reported to be CKD not defined (13%). The median follow-up time was associated with NODAT with an additional 10 SNPs investi- 12.2 years (range, 0–26.0 years). gated for association (Table 3).31–45 Nine of these SNPs were There were changes to the routine post-transplant immu- genotyped in the GWAS and proxy SNPs were identified for 17 nosuppression during the study period. Before 1989, all SNPs that were not directly genotyped (r2.0.80). No associ- recipients received dual therapy with prednisolone and aza- ation was identified between these previously reported SNPs thioprine. Subsequently, calcineurin inhibitor (CNI)–based and NODAT in our population (Supplemental Table 2). Proxy maintenance therapy was introduced. Mycophenolate mofetil SNPs were unavailable for the remaining 11 reported SNPs. became available in 1998 and from this time, approximately Only one of these SNPs (rs1050450) had previously been as- 25% of patients had CNI-free maintenance regimens. All sociated with NODAT in a white population.

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Table 1. Comparison of clinical variables between NODAT Table 3. Reported SNPs investigated for association with cases and controls NODAT Variable NODAT (n=57) Control (n=370) P Value Gene SNP Population Reference Recipient age (yr) 49.1613.2 42.4614.0 0.001 ATF6 rs2340721a White Fougeray et al.31 Recipient sex CAPN10 rs5030952 White Kurzawski et al.32 Male 30 (53) 249 (67) CCL5 rs2107538 Korean Jeong et al.33 Female 27 (47) 121 (33) 0.04 rs2280789 Korean Jeong et al.33 Donor age (yr) 37.1614.8 37.6616.1 0.80 rs3817655 Korean Jeong et al.33 Donor sex ENPP1 rs1044498a Hispanic Yang et al34 Male 33 (58) 213 (58) GPX1 rs1050450 White Dutkiewicz et al.35 Female 24 (42) 157 (42) 1.0 HNF1A rs1169288a Hispanic Yang et al.34 Primary renal disease rs1800574a Hispanic Yang et al.34 GN 7 (12) 89 (24) 0.07 HNF4A rs2144908 Hispanic Yang et al.34 Interstitial/pyelonephritis 15 (26) 79 (21) 0.50 rs1884614 Hispanic Yang et al.34 ADPKD 16 (28) 60 (16) 0.05 rs1800961a Hispanic Yang et al.34 Other 10 (18) 84 (23) 0.40 IFNg rs2430561 White Babel et al.36 Unknown 9 (16) 58 (16) 0.70 IL-17E rs1124053 Korean Kim et al.37 Decade of transplantation IL-17RA rs2229151 Korean Kim et al.37 1986–1995 24 (42) 174 (47) rs4819554 Korean Kim et al.37 1996–2005 33 (58) 196 (53) 0.60 IL-17RB rs1043261 Korean Kim et al.37 HLA mismatch (n)2.161.1 2.261.1 0.60 rs1025689 Korean Kim et al.37 Acute rejection 9 (16) 48 (13) 0.90 IL-1B rs3136558 Korean Kim et al.37 Immunosuppression IL-2 rs2069762 Korean Kim et al.37 Calcineurin inhibitors 41 (72) 299 (81) 0.40 rs2069763 Korean Kim et al.37 Azathioprine 35 (61) 224 (61) 1.0 IL-4 rs2243250 Korean Kim et al.37 Mycophenolate mofetil 28 (49) 185 (50) 0.90 rs2070874 Korean Kim et al.37 mTOR inhibitor 7 (12) 34 (9) 0.60 IL-6 rs1800795 White Bamoulid et al.38 Weight at transplant (kg) 72.2613.3 68.5614.3 0.07 IL-7R rs1494558 Korean Kim et al.37 Weight change at 1 yra rs2172749 Korean Kim et al.37 Weight loss 9 (17) 71 (21) IRS1 rs1801278 Hispanic Yang et al.34 Weight gain,10% 11 (20) 122 (36) KCNJ11 rs5219 Spanish Tavira et al.39 Weight gain.10% 34 (63) 149 (43) 0.04b Hispanic Yang et al.34 Data are expressed as n (%) or mean6SD. ADPKD, autosomal dominant KCNQ1 rs2237895 Spanish Tavira et al.40 polycystic kidney disease; mTOR, mammalian target of rapamycin. NFATC4 rs10141896 Hispanic Chen et al.41 a Values for weight change at 1 year were available for 396 of 427 recipients. PPARG rs1801282a Hispanic Yang et al.34 bLinear by linear. PPARGC1 rs8192678a Hispanic Yang et al.34 SUR1 rs1799854a Hispanic Yang et al.34 Table 2. Multivariate logistic regression analysis for NODAT rs1801261a Hispanic Yang et al.34 OR SLC30A8 rs13266634 Korean Kang et al.42 Variable P Value (95% Confidence Interval) TCF7L2 rs7903146 White Kurzawski et al.43 a 44 Recipient agea 1.4 (1.1 to 1.8) 0.003 rs12255372 White Ghisdal et al. 45 Recipient sex 2.2 (1.2 to 4.3) 0.02 Korean Kang et al. a 34 Weight at transplantation 1.03 (1.01 to 1.05) 0.01 Hispanic Yang et al. 34 Weight change in first year 2.0 (1.3 to 3.1) 0.002 Hispanic Yang et al. a fi aPer decade. Nonsigni cant association.

Germany) technology was used to genotype 44 SNPs; TaqMan Linkage disequilibrium was assessed for all SNPs (Supple- (Applied Biosystems, Warrington, UK) was used to genotype mental Figures 1–50) and is displayed for the top-ranked SNPs six SNPs (Supplemental Figures 51–100, Supplemental Table on chromosomes 1 and 6 (Figures 1 and 2). 3). It was not possible to genotype three SNPs due to problems with primer design (Supplemental Table 4). The average gen- Second-Stage Genotyping otyping success rate was .99% and no SNP demonstrated de- De novo genotyping was undertaken in all NODAT patients viation from the Hardy–Weinberg equilibrium (HWE). and 383 controls. The top-ranked SNPs associated with In logistic regression analysis, eight SNPs were associated NODAT in the GWAS (n=26), previously published NODAT with NODAT (P#0.001). After adjustment for the clinical SNPs investigated in the GWAS (n=26), and the SNP with a re- variables known to be associated with NODAT (Table 2), ad- ported association in a white population for which a proxy was justed P values, adjusted odds ratios (ORs), and 95% conﬁ- unavailable (n=1) were genotyped. Sequenom iPLEX (Hamburg, dence intervals for ORs were calculated (Table 4). Using

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Figure 1. Regional association plots illustrating linkage disequilibrium between SNPs associated with NODAT. (A) On chromosome 6. (B) On chromosome 1.

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This is the first exploratory GWAS to be undertaken for NODAT. The secondary validation phase confirmed associations between the top-ranked loci; candidate genes, the top- ranked gene enrichment set, and the most significantly associated pathway are implicated in b-cell apoptosis (Tables 5 and 6, Supplemental Table 5).47–58 b-Cell dysfunction may be the primary pathologic process in NODAT18,20,21 and is discussed further in view of the genetic associations identified. Insulin resistance may also contribute to NODAT pathogenesis but this has not been conclusively proven.11,18,20,24,28

Glucose-Stimulated Insulin Secretion Hyperglycemia upregulates Fas receptor expression on the b cell surface (Figure 3); initially, this promotes cell proliferation and insulin secretion.63,64 The SNP rs4394754 is upstream from the 59-untranslated region of INPP5A. The inositol polyphosphate 5-phosphatases are implicated in insulin signaling and exocytosis and inhibit the proliferation of insulin-producing cells in vitro.56,57,65 Hyperglycemia stimulatesATP generation in mitochondria, causing membrane depolarization, calcium ion inﬂux, and increased insulin exocytosis.63,66

Glucotoxicity Hyperglycemia initiates a train of events that begins with Figure 2. Haplotype block on chromosome 6. enhanced insulin secretion but, unchecked, will result in b-cell apoptosis. After gene enrichment, the top-ranked gene set associated with NODAT was “response to stimulus.” b Cells demonstrate increased apoptosis and reduced proliferation Haploview,46 a single haplotype block was identiﬁed between after 2 days of exposure to moderately elevated glucose con- adjacent SNPs on chromosome 6 (Figure 2). Haplotype anal- centrations in vitro.67 Within 4 days, human b cells have al- ysis did not increase the strength of the association between most completely lost their secretory function.63 The period of these SNPs and NODAT (P=0.01). exposure to elevated glucose concentrations is more impor- tant than the degree of hyperglycemia.68,69 In the immediate Gene Enrichment and Pathway Analyses post-transplantation period, hyperglycemia is virtually uni- Gene enrichment and pathway analyses were undertaken for versal among renal transplant recipients; 87% of nondiabetic the top-ranked SNPs from the GWAS using Partek pathway recipients at the Mayo Clinic recorded a blood glucose$200 within Genomics Suite 6.6. The top hit was the “response to mg/dl during this period.28,70 In a recent clinical trial, all renal stimulus” gene set (P=0.01) (Supplemental Figure 101, Sup- transplant recipients recorded a blood glucose $140 mg/dl plemental Table 5). In pathway analysis, the P13K-AKT sig- during hospitalization.28 Factors contributing to post-transplant naling pathway had the highest enrichment score (P,0.001) hyperglycemia include the catecholamine stress response to sur- (Table 6, Supplemental Figure 102). gery,71,72 corticosteroid treatment,73 and restoration of normal renal degradation and excretion of insulin.74,75 Hyperglycemia in the immediate post-transplant period is DISCUSSION associated with a 4-fold increase in NODAT.10,76 Arecent proof-of-concept trial suggests that aggressive manage- NODAT is a common and serious complication of solid organ ment of hyperglycemia in the postoperative period can re- transplantation.5,6 The growing prevalence of obesity among duce the incidence of NODAT. Hecking et al. compared renal transplant recipients combined with improved access to insulin therapy when blood glucose exceeded 140 mg/dl transplantation for older patients is likely to increase the in- to standard diabetic treatmentinacohortofkidneytrans- cidence of NODAT.59–61 Therapeutic strategies for NODAT plant recipients. At 1 year, there were no cases of NODAT in have focused on altering immunosuppressive regimens with the treatment group compared with an incidence of 28% in limited success.13,17,62 It is unlikely that NODAT can be avoi- the control group.28 It is plausible that early management ded by tailoring immunosuppression without a detrimental of hyperglycemia with insulin prevents b-cell glucotoxicity effect on graft outcomes. and apoptosis.

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Table 4. Association results for de novo genotyped SNPs with NODAT OR SNP Gene Location MAF P Value P adj adj (95% Conﬁdence Interval) 2 rs10484821 ATP5F1P6 6:139868910 0.14 1.5310 7 0.000002 3.5 (2.1 to 5.8) rs7533125 DNAJC16 1:15883744 0.23 0.001 0.001 2.4 (1.5 to 3.6) rs2861484 CELA2B 1:15812665 0.20 0.001 0.0002 2.4 (1.5 to 3.7) rs11580170 AGMAT 1:15909744 0.26 ,0.00 0.0002 2.2 (1.4 to 3.4) rs2020902 CASP9 1:15834360 0.18 ,0.001 0.0003 2.3 (1.5 to 3.6) rs1836882 NOX4 11:89232161 0.12 0.001 0.001 2.7 (1.5 to 4.8) rs198372 NPPA 1:11909514 0.13 ,0.001 0.001 2.5 (1.5 to 4.2) rs4394754 INPP5A 10:134343062 0.28 0.001 0.001 2.1 (1.4 to 3.2) rs7145618 PPP2R5C 14:102329098 0.08 0.01 0.002 3.1 (1.5 to 6.4) rs17722392 KIDINS220 2:8940154 0.06 0.02 0.003 3.2 (1.5 to 6.9) rs2265919 SHPRH 6:146221753 0.42 0.002 0.003 2.0 (1.3 to 3.1) rs1783606 SHANK2 11:70576651 0.12 0.02 0.003 2.4 (1.3 to 4.1) rs2265477 SHPRH 6:146212338 0.44 0.01 0.01 1.8 (1.2 to 2.8) rs2069763a IL-2 4:123377482 0.28 0.01 0.01 0.48 (0.3 to 0.8) rs6903252 Intergenic 6:145922777 0.47 0.01 0.01 1.8 (1.2 to 2.8) rs2265917 SHPRH 6:146212285 0.42 0.01 0.01 1.8 (1.2 to 2.8) rs10899444 SHANK2 11:70606500 0.12 0.05 0.01 2.2 (1.2 to 3.9) rs16936667 PRDM14 8:70975726 0.14 0.02 0.01 2.2 (1.2 to 3.8) rs341497 DIAPH3 13:60429001 0.06 0.004 0.01 2.6 (1.3 to 5.2) rs1016429 GRIN3A 9:104402364 0.07 0.01 0.01 2.5 (1.2 to 5.0) rs10117679 GRIN3A 9:104378479 0.04 0.01 0.02 2.8 (1.2 to 6.4) rs1871184 ITGA1 5:52234323 0.15 0.01 0.02 1.9 (1.1 to 3.1) rs3212574 ITGA2 5:52366779 0.22 0.01 0.02 1.7 (1.1 to 2.7) rs2240747 ZNRF4 19:5456930 0.18 0.01 0.03 1.8 (1.1 to 3.1) rs6793265 ITPR1 3:4735533 0.12 0.08 0.07 1.7 (0.9 to 3.0) rs2070874a IL-4 5:132009710 0.13 0.10 0.10 0.56 (0.3 to 1.1) rs17657199 NDST1 5:149950246 0.05 0.10 0.10 2.2 (0.8 to 5.6) rs2069762a IL-2 4:123377980 0.33 0.10 0.10 1.4 (0.9 to 2.1) rs7903146a TCF7L2 10:114758349 0.31 0.20 0.20 1.4 (0.9 to 2.1) rs1043261a IL-17RB 3:53899276 0.07 0.20 0.20 0.54 (0.2 to 1.4) rs2280789a CCL5 17:34207003 0.11 0.30 0.20 0.61 (0.3 to 1.3) rs2172749a IL-7R 5:35855264 0.32 0.30 0.30 0.77 (0.5 to 1.2) rs1494558a IL-7R 5:35861068 0.32 0.40 0.30 0.77 (0.5 to 1.2) rs1800795a IL-6 7:22766645 0.39 0.60 0.30 1.3 (0.8 to 2.0) rs12255372a TCF7L2 10:114808902 0.30 0.30 0.30 1.3 (0.8 to 1.9) rs5219a KCNJ11 11:17409572 0.35 0.70 0.30 0.80 (0.5 to 1.3) rs2107538a CCL5 17:34207780 0.17 0.40 0.40 0.77 (0.4 to 1.4) rs1801282a PPARG 3:12393125 0.13 0.50 0.40 0.75 (0.4 to 1.5) rs3817655a CCL5 17:34199641 0.17 0.40 0.40 0.79 (0.4 to 1.4) rs1799854 ABCC8 11:17448704 0.43 0.30 0.50 1.2 (0.8 to 1.8) rs4819554a IL-17RA 22:17565035 0.22 0.50 0.50 0.83 (0.5 to 1.4) rs1169288a HNF1A 12:121416650 0.32 0.60 0.60 1.1 (0.7 to 1.8) rs2340721a ATF6 1:161849385 0.32 0.40 0.60 1.1 (0.7 to 1.7) rs1025689a IL-17RB 3:53883722 0.36 0.70 0.60 1.1 (0.7 to 1.8) rs1124053a IL-17E 14:22914819 0.26 0.50 0.70 1.1 (0.7 to 1.7) rs2144908a HNF4A 20:42985717 0.14 0.70 0.70 0.88 (0.4 to 1.8) rs8192678a PPARGC1A 4:23815662 0.34 0.40 0.70 0.93 (0.6 to 1.5) rs13266634a SLC30A8 8:118184783 0.31 0.60 0.70 1.1 (0.7 to 1.7) rs1044498a ENPP1 6:132172368 0.15 0.90 0.80 0.93 (0.5 to 1.7) rs1800961a HNF4A 20:43042364 0.03 0.99 NA NA (NA) NA, no rs1800961 minor alleles present in the NODAT cases. aSNPs previously reported to be associated with NODAT.

Hyperglycemia-induced b-cell apoptosis occurs via multi- IL-1b–converting enzyme inhibitory protein.63,64,77 In the ab- ple pathways (Figure 4). Prolonged hyperglycemia attenuates sence of Fas-associated protein with death domain–like IL-1b– the expression of Fas-associated protein with death domain–like converting enzyme inhibitory protein, Fas receptors induce

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Table 5. Candidate genes and association with diabetes for top-ranked SNPs Candidate Gene Other SNP Gene Association SNP Genesa Function Associations with Diabetes Mellitus rs10484821 ATP5F1P6b Mitochondrial pseudogene None None known rs7533125 DNAJC16, CASP9, Component of heat None Differential expression AGMAT, CELA2B, DDI2 shock protein 40 in pancreatic islets of type 2 patients with diabetes47 rs2861484 CELA2B, CASP9, Pancreatic elastase None None known CELA2A, DNAJC16, CTRC, EFHD2, AGMAT rs11580170 AGMAT, DNAJC16, Agmatine None Reduces apoptosis,48 increases CASP9, DDI2, CELA2B insulin secretion,49 and insulin sensitivity50 in rats rs2020902 CASP9, DNAJC16, CELA2B, Caspase 9 Non-Hodgkin’s Component of intrinsic CELA2A, AGMAT, CTRC, lymphoma apoptotic pathway EFHD2 in pancreatic b cells51 rs1836882 NOX4 Subunit of NADPH None Increases ROS generation, reduces oxidase complex insulin gene expression and increases b-cell apoptosis in mice and in vitro,52 blockade replenishes islet insulin stores in animal models53 rs198372 NPPA, NPPB, CLCN6, METHFR, Natriuretic peptide None Inhibits adipokine and cytokine KIAA2013, PLOD1 production,54 reduced natriuretic peptide levels are associated with the development of diabetes55 rs4394754 INPP5A Membrane associated None Reduces b-cell proliferation in vitro,56 type 1 inositol 1,4, stimulates calcium induced insulin 5-triphosphate signaling and exocytosis 5-phosphatase in mice,57 regulates hepatic gluconeogenesis in mice58 Where there is more than one candidate, the gene in closest proximity to the SNP was chosen (underlined). aCandidate genes within 100 kB of SNP recorded in order of proximity. bPseudogene.

P, Table 6. Top-ranked ( 0.05) gene pathways from GWAS results dysfunction.81 During prolonged hypergly- Enrichment Pathway Name Enrichment Score cemia, generation of reactive oxygen species P Value (ROS) by both mitochondria and the PI3K-Akt signaling pathway 8.0 ,0.001 NADPH pathway exceed the oxidative stress Glutamatergic synapse 6.8 0.001 threshold of the b cell.66,82,83 ATP5F1P6 Regulation of actin cytoskeleton 5.0 0.01 (rs10484821) is an inferred mitochondrial Phosphatidylinositol signaling system 4.8 0.01 ATP synthase pseudogene.84 NOX4 Extracellular matrix–receptor interaction 4.7 0.01 (rs1836882) encodes an enzyme of the Arrhythmogenic right ventricular cardiomyopathy 4.6 0.01 85 Hypertrophic cardiomyopathy 4.2 0.02 NADPH oxidase complex. In animal Small cell lung cancer 4.1 0.02 models of type 2 diabetes, NADPH oxidase Dilated cardiomyopathy 4.1 0.02 blockade reduces oxidative stress and re- Oocyte meiosis 4.0 0.02 plenishes insulin stores.53 Agmatine, a pro- Hematopoietic cell lineage 4.0 0.02 tein encoded by AGMAT (rs11580170), acts Dopaminergic synapse 3.7 0.03 as a ROS scavenger to protect the mito- P13K-Akt, phosphatidylinositol 3-kinase and protein kinase B. chondrial membrane.48 In animal models, agmatine also enhances insulin secretion.49 apoptosis via both caspase-dependent and independent path- Damaged mitochondria release cytochrome C and induce ap- ways.69,77–79 Protracted stimulation of IL-1b promotes apopto- optosis via the activation of caspase 977; rs2020902 is located in sis by downregulating other protective proteins within the the CASP9 gene.49 b cell.64,80 In addition, both Fas activation and IL-1b induce Sustained demands on the endoplasmic reticulum (ER) mitochondrial dysfunction77 and in vitro studies of b cells in during hyperglycemia result in cytoplasmic accumulation of NODAT have demonstrated a role for mitochondrial calcium,86,87 causing activation of the caspase cascade86 and

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(rs7533125) gene forms part of the heat shock protein 40 complex. NODAT is associated with elevated serum proinsulin concentrations15,20 and the exocytosis of this inactive precursor reﬂects ER stress.88

Lipotoxicity Body weight and, in this study, percentage weight gain in the first year after transplantation are associated with NODAT.5,6 Leptin is secreted by adipose tissues and is associated with both pretransplantation body fat and post-transplantation weight gain.89,90 Leptin enhances IL-1b production in pancreatic islets promoting apoptosis.91 Elevated leptin concentrations correlate with increased serum free fatty acids. These directly induce b-cell apoptosis by caspase activation,77 induction of ER stress,87 and ROS generation.66 Figure 3. Glucose-stimulated insulin secretion. (1) Hyperglycemia induces Fas re- The natriuretic peptides, encoded by NPPA ceptor expression on the b-cell surface. (2) Fas receptor expression and IL-1b pro- (rs198372), inhibit leptin secretion and low duction increase transcription of insulin genes and b-cell proliferation. (3) Mitochondrial natriuretic peptide levels are associated with generation of ATP is stimulated by hyperglycemia. (4) Insulin production in the ER is the development of diabetes mellitus.54,55 enhanced and increased insulin secretion restores normoglycemia. Immunosuppression The contribution of corticosteroids and CNIs to the development of NODAT is well recognized.17,92 Corticosteroids increase insulin resistance and promote b-cell apoptosis by exacerbating mitochondrial and ER stress.13,73,93,94 The calcineurin/nuclear factor of activated T cell pathway modulates insulin exocytosis and b-cell proliferation. In animal models and in vitro, calcineurin/nuclear factor of activated T-cell blockade reduces insulin exocytosis and b-cell mass.27,95,96 However, the evidence for an association between CNIs and NODAT is mixed; a recent meta-analysis found only a weak association between CNI therapy and NODAT.17 fi Figure 4. b-Cell glucotoxicity. (1) Prolonged hyperglycemia results in large numbers In this study, a statistically signi cant asso- of Fas receptors being expressed on the b-cell membrane. (2) Fas activation induces ciation was not demonstrated, which may b-cell apoptosis via both caspase-dependent and independent pathways. (3) Fas ac- reflect the low incidence of NODAT in our tivation and elevated IL-1b concentrations induce mitochondrial dysfunction. (4)Ex- population. Mycophenolate also induces cessive mitochondrial stimulation generates ROS that cause oxidative damage to the b-cell death by upregulation of the caspase b cell. (5) Damaged mitochondria release cytochrome C, which activates the caspase cascade and induction of ER stress.27,77 The cascade. (6) Reduced mitochondrial generation of ATP limits the production of insulin contribution of each of these immunosup- by the ER. (7) Prolonged insulin demands result in ER stress and intracytoplasmic pressants explains the disappointing results accumulation of calcium ions, which stimulates apoptosis. (8) There is inadequate of limiting one group in reducing NODAT insulin exocytosis to restore normoglycemia. incidence. mitochondrial damage.48 In b cells, ER stress upregulates heat Strengths and Limitations shock protein 40 expression; this correlates with increased This study identified clinical and genetic variables associated apoptosis.47 The transcription product of the DNAJC16 with NODAT in an ethnically homogeneous population. It is

1044 Journal of the American Society of Nephrology J Am Soc Nephrol 25: 1037–1049, 2014 www.jasn.org BASIC RESEARCH the first to utilize an exploratory GWAS with confirmation by MAFs of 10%, 20%, or 30% respectively and OR of 2.0 with de novo genotyping; all previous studies have focused on can- 95% confidence. The top-ranked SNPs associated with NODAT didate genes. The majority of SNPs previously reported to be are in biologically plausible pathways that influence -cell apo- associated with NODAT have been identified in a single pop- ptosis and corroborate current clinical evidence regarding ulation with failure of replication in other ethnic NODAT pathogenesis. The associated genes or their tran- groups.33,34,37 The TCF7L2 SNP rs7903146 is an exception to scription products have been associated with diabetes in vitro this; rs7903146 variants have been associated with NODAT in as well as in animal models and in longitudinal follow-up Korean45 (P=0.02), white European44 (P=0.002), and Polish studies. Caucasian43 (P=0.02) populations, although not all reports In conclusion, NODAT is a serious complication of solid demonstrate an association.34,97,98 This study had a comparable organ transplantation that is detrimental to recipient sur- cohort size and rs7903146 minor allele frequency to the other vival.5–7 Recent clinical studies suggest that b-cell toxicity, studies but did not find an association between rs7903146 and predominantly mediated by hyperglycemia, may be the pri- NODAT. This may reflect the use of an extreme NODAT phe- mary pathologic process in NODAT28,76 and that aggressive notype, which could prevent identification of genetic variants management of elevated serum glucose in the post-transplant associated with milder disease. It is also possible that there may period may prevent NODAT.28 This study is the first explor- be an interaction between rs7903146 and CNIs in the risk for atory GWAS with secondary validation for NODAT under- NODAT. Recipients were receiving CNIs in all three studies re- taken in a population of solid organ transplant recipients. porting an association, whereas 25% of this cohort had CNI-free SNPs associated with NODAT were associated with b-cell ap- immunosuppression. optotic pathways. This provides further evidence that b-cell dys- Strengths of this study include the availability of high- function and death are key components of NODAT pathogenesis. quality clinical follow-up data over 25 years, the identification and correction for clinical variables in the same population, the use of multiple genotyping technologies, and second-stage CONCISE METHODS typing of additional DNA samples to validate discovery findings. Stringent diagnostic criteria were used to militate Patient Cohort against phenotypic heterogeneity and to enrich the population All kidney transplant procedures in Northern Ireland are performed at for risk alleles. The reported prevalence of NODAT in renal the Belfast City Hospital. Consecutive renal transplant recipients who transplant recipients varies between 2% and 50%, reflecting received first, deceased donor kidney transplants between May 1986 differences in diagnostic criteria and reporting.12 Although the and May 2005 inclusive were included in this study. Donor and diagnostic criteria used in this study will have potentially un- recipient clinical variables, graft outcomes, and survival are pro- derestimated the NODAT incidence, we are confident that all spectively recorded in the Northern Ireland Kidney Transplant recipients who developed a “severe diabetic phenotype” were Database. Available data include donor age and sex, recipient age identified. The small number of NODAT cases is a weakness of and sex, primary renal disease, HLA match, immunosuppression, this study, resulting in the possibility that SNPs associated acute rejection episodes, weight, incidence of NODAT, allograft with a milder phenotype or that are less strongly associated survival, recipient survival, and cause of death. There were significant with NODAT may not have been identified. However, GWAS advances in transplantation during the study period. Toallow for this, have been successfully utilized to identify loci associated with the variable “decade of transplantation” was considered. The first extreme phenotypes in a number of complex diseases.99–102 decade terminated on December 31, 1995, with the second decade As a consequence of the small number of NODAT cases, commencing on January 1, 1996. Follow-up was continued until Au- these results did not reach the conventional threshold for gust 2012. genome-wide significance. As described, the stringent diagnostic criteria were used to generate phenotypic homogeneity NODAT with reduced case numbers as a consequence. A joint analysis NODAT was defined as a new requirement for oral hypoglycemic approach was implemented to maximize power in this study; therapy or insulin after kidney transplantation. Controls were renal SNPs of interest were identified in the exploratory GWAS transplant recipients who did not have diabetes mellitus at trans- and de novo genotyping was used in the complete cohort of plantation and did not develop a requirement for oral hypoglycemic NODAT patients to validate these findings. This study has therapy or insulin during the follow-up period. ,30% power to identify a risk allele, with a minor allele frequency (MAF) of 10% and an OR of 1.5 with 95% confidence; Clinical Analyses however, the strong effects observed confirm that the smaller All adults (aged$16 years) who received a first, deceased donor trans- sample size utilized was sufficient to identify risk alleles for plant between May 1986 and May 2005 inclusive and who had a NODAT. Larger studies are required to identify genetic var- functioning renal transplant at 1 year were included. One-year graft iants that confer a smaller effect on the risk of developing survival was a prerequisite to investigating the effect of weight gain in NODAT. The individuals genotyped for this study provided the first year on NODAT incidence. Clinical variables investigated for at least 60%, 80%, and 90% power to identify a risk allele at association with NODAT were recipient age, recipient sex, primary

J Am Soc Nephrol 25: 1037–1049, 2014 Genetics of NODAT 1045 BASIC RESEARCH www.jasn.org renal disease, donor age, donor sex, HLA mismatch, acute rejection, Gene Set Enrichment and Pathway Analyses immunosuppressive regimen, weight at transplantation, percent Gene set enrichment and pathway analysis were performed on the top weight gain in the first year after transplantation, and decade hits from the GWASusing Partek software (version 6.6; Partek, Inc., St. of transplantation. SPSS for Windows (version 21.0; SPSS, Inc., Louis, MO). For gene set enrichment and pathway analysis, Fisher’s Chicago, IL) was utilized for all analyses, with P values,0.05 con- exact test was utilized, gene sets were restricted to those with two or sidered significant. more genes included, and genes were analyzed according to the hg19 genome build. Genetic Analyses GWAS Ethics Statement DNA from individuals in the patient cohort was genotyped using the Ethical approval for this study was granted by the Office for Research Illumina 660K array as part of the Wellcome Trust Case Control Ethics Committees, Northern Ireland (http://www.hscbusiness.hscni. Consortium 3 Study into Renal Transplant Dysfunction (http://www. net/orecni.htm, 08/NIR03/79, 12/NI/0178). This study adhered to wtccc.org.uk/ccc3/). No sample was excluded for poor DNA quality, the Declaration of Istanbul. quantity, or poor genotype concordance with previous genotypes during a fingerprint evaluation stage. There were 26 NODAT patients and 230 controls successfully genotyped. Standard quality control ACKNOWLEDGMENTS was utilized with exclusion if the genotyping call rate was ,90%, if 2 MAF was ,1%, if there was deviation from HWE (P,10 6), or if the sample call rate was ,95%. Extreme heterozygosity and cryptic re- The authors acknowledge the assistance of Stuart Hacking in the fi latedness were not observed in this cohort. Known copy number preparation of gures for this article. The initial GWAS typing was variation and mitochondrial SNPs were excluded from analyses. generated by the United Kingdom and Ireland Renal Transplant These quality control steps and association analysis was performed Consortium, led by Professor Graham Lord, as part of the Wellcome 2 using PLINK.103 The level of statistical significance was set at P,10 5 Trust Case Control Consortium 3 Study into Renal Transplant Dys- considering the relatively small number of cases available; second- function (http://www.wtccc.org.uk/ccc3/projects/ccc3_rtd.html). stage genotyping was conducted for validation and supporting data This work was supported by the Northern Ireland Kidney Research for all SNPs of interest. Linkage disequilibrium was calculated and Fund. J.A.M. is a recipient of a Kidney Research UK Clinical Research results were plotted using Haploview46 with regional association plots Training Fellowship. generated in LocusZoom.104 In addition, there are 37 SNPs previously published as associated with NODAT. The association between these SNPs and NODAT was DISCLOSURES investigated in our GWAS cohort. Where reported SNPs were not None. genotyped in the GWAS, proxy SNPs were identified (r2.0.80) and association with NODAT was investigated. REFERENCES Second-Stage Genotyping 2 All SNPs associated with NODAT with a P value,10 5 from the 1. Hariharan S, Johnson CP, Bresnahan BA, Taranto SE, McIntosh MJ, GWAS were selected for further analysis. We included previously Stablein D: Improved graft survival after renal transplantation in the United States, 1988 to 1996. NEnglJMed342: 605–612, 2000 reported NODAT SNPs as well as a single SNP (rs1050450) that 2. 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J Am Soc Nephrol 25: 1037–1049, 2014 Genetics of NODAT 1049 Supplementary Material

Table 1: Top-ranked SNPs from GWAS association with NODAT (p ≤10-5)

SNP Gene Location OR 95%CI p rs7533125 DNAJC16 1:15883744 4.6 2.4 - 8.6 3.4x10-6 rs2020902 CASP9 1:15834360 4.0 2.2 - 7.2 4.5 x10-6 rs3212574 ITGA2 5:52366779 4.1 2.2 - 7.8 1.1 x10-5 rs2861484 CELA2B 1:15812665 4.2 2.2 - 8.1 1.3 x10-5 rs2265477 SHPRH 6:146212338 4.4 2.2 - 8.7 2.8 x10-5 rs11580170 AGMAT 1:15909744 3.7 2.0 - 6.8 2.9 x10-5 rs17657199 NDST1 5:149950246 8.8 3.2 - 24.8 3.3 x10-5 rs1783606 SHANK2 11:70576651 4.4 2.2 - 8.9 4.1 x10-5 rs6903252 intergenic 6:145922777 4.4 2.2 - 8.8 4.4 x10-5 rs1871184 ITGA1 5:52234323 4.3 2.1 - 8.7 4.4 x10-5 rs198372 NPPA 1:11909514 3.9 2.0 - 7.6 5.0 x10-5 rs2240747 ZNRF4 19:5456930 4.7 2.2 - 9.8 5.1 x10-5 rs4394754 INPP5A 10:134343062 3.6 1.9 - 6.8 5.2 x10-5 rs1836882 NOX4 11:89232161 4.3 2.1 - 8.6 5.3 x10-5 rs341497 DIAPH3 13:60429001 6.5 2.6 - 16.1 5.4 x10-5 rs10117679 GRIN3A 9:104378479 8.1 2.9 - 22.3 5.6 x10-5 rs16936667 PRDM14 8:70975726 4.3 2.1 - 8.6 5.9 x10-5 rs10899444 SHANK2 11:70606500 4.1 2.0 - 8.0 6.2 x10-5 rs2265917 SHPRH 6:146212285 3.9 2.0 - 7.6 6.3 x10-5 rs2265919 SHPRH 6:146221753 3.9 2.0 - 7.6 6.3 x10-5 rs1370602 NCKAP5 2:133688972 4.0 2.0 - 7.9 6.7 x10-5 rs10484821 ATP5F1P6 6:139868910 4.0 2.0 - 7.9 7.3 x10-5 rs17722392 KIDINS220 2:8940154 6.2 2.5 - 15.4 8.1 x10-5 rs1016429 GRIN3A 9:104402364 5.6 2.4 - 13.3 8.2 x10-5 rs6793265 ITPR1 3:4735533 4.2 2.0 - 8.7 9.1 x10-5 rs7145618 PPP2R5C 14:102329098 5.8 2.4 - 13.9 9.5 x10-5

Table 2: GWAS results for SNPs previously reported to be associated with NODAT

SNP Proxy SNP r2 OR 95% CI p rs8192678 NA NA 1.8 1.0 - 3.3 0.04 rs1169288 rs2650000 0.922 1.7 0.9 - 3.0 0.09 rs1799854 NA NA 1.6 0.9 - 2.8 0.1 rs1494558 NA NA 0.6 0.3 - 1.2 0.2 rs2172749 rs1494558 1 0.6 0.3 - 1.2 0.2 rs7903146 NA NA 1.5 0.8 - 2.7 0.2 rs5219 NA NA 0.7 0.4 - 1.3 0.2 rs1124053 rs3759614 0.81 1.5 0.8 - 2.9 0.3 rs2069763 rs4505848 1 0.7 0.3 - 1.4 0.3 rs2280789 rs2306630 1 0.5 0.2 - 1.8 0.3 rs13266634 NA NA 1.4 0.7 - 2.5 0.3 rs1025689 rs7627178 1 1.3 0.7 - 2.4 0.3 rs2430561 rs2069727 1 1.3 0.7 - 2.3 0.4 rs4819554 rs2041629 0.954 1.3 0.7 - 2.5 0.4 rs12255372 NA NA 1.3 0.7 - 2.4 0.4 rs1043261 NA NA 0.7 0.2 - 2.3 0.5 rs2144908 rs4812829 1 0.8 0.3 - 1.9 0.6 rs2069762 rs6835946 0.951 1.2 0.7 - 2.0 0.6 rs1801282 rs6802898 1 0.8 0.3 - 2.0 0.6 rs1044498 rs6926970 1 1.1 0.5 - 2.4 0.7 rs2340721 rs905594 0.925 1.1 0.6 - 2.0 0.8 rs3817655 rs2291299 1 0.9 0.4 - 2.0 0.8 rs2107538 rs2251660 0.858 1.1 0.5 - 2.5 0.8 rs2070874 rs2243290 1 0.9 0.4 - 2.2 0.8 rs1800795 rs1554606 0.868 0.9 0.5 - 1.7 0.8 rs1800961 NA NA - 0 1.0

Table 3: Association results for de novo genotyped SNPs with NODAT in the original discovery cohort and in the new cases from the second stage genotyping cohort

This table displays the odds ratios and p values for SNPs previously reported to be associated with

NODAT and newly identified associations from the discovery GWAS in the cohort typed as part of the

GWAS (ORdiscovery and pdiscovery) and in the new cases genotyped in the second stage genotyping

(ORnew and pnew). The overall odds ratios and p values from the second stage genotyping are recorded in Table 4. The failure of the p values to reach statistical significance in the new cases during second stage genotyping reflects the much smaller number of samples typed.

SNP Gene Location OR discovery p discovery OR new p new rs10484821 ATP5F1P6 6:139868910 4.394 1.87 X10-5 2.986 0.000188 rs7533125 DNAJC16 1:15883744 3.944 3.12 X10-6 1.289 0.3682 rs2861484 CELA2B 1:15812665 3.735 6.52 X10-6 1.37 0.2797 rs11580170 AGMAT 1:15909744 3.288 3.53 X10-5 1.426 0.1783 rs2020902 CASP9 1:15834360 3.596 6.63 X10-6 1.151 0.6567 rs1836882 NOX4 11:89232161 4.108 0.000115 1.817 0.117 rs198372 NPPA 1:11909514 3.95 3.87 X10-5 1.366 0.3878 rs4394754 INPP5A 10:134343062 3.325 4.03 X10-5 1.144 0.6188 rs7145618 PPP2R5C 14:102329098 4.699 0.000185 1.176 0.7448 rs17722392 KIDINS220 2:8940154 5.397 0.000127 0.8648 0.8174 rs2265919 SHPRH 6:146221753 4.171 3.06 X10-5 1.052 0.8584 rs1783606 SHANK2 11:70576651 4.355 2.74 X10-5 0.7621 0.5452 rs2265477 SHPRH 6:146212338 4.233 3.33 X10-5 0.9246 0.7738 rs2069763* IL-2 4:123377482 0.6453 0.2203 0.3988 0.01062 rs6903252 Intergenic 6:145922777 4.123 6.16 X10-5 0.9258 0.7766 rs2265917 SHPRH 6:146212285 3.666 9.73 X10-5 1.003 0.9905 rs10899444 SHANK2 11:70606500 4.279 3.50 X10-5 0.6258 0.3343 rs16936667 PRDM14 8:70975726 3.15 0.000606 0.9153 0.8542 rs341497 DIAPH3 13:60429001 4.556 0.000196 1.435 0.4422 rs1016429 GRIN3A 9:104402364 4.644 0.000121 0.7479 0.6309 rs10117679 GRIN3A 9:104378479 6.138 9.15 X10-5 1.006 0.993 rs1871184 ITGA1 5:52234323 2.84 0.000764 1.058 0.8777 rs3212574 ITGA2 5:52366779 3.345 4.97 X10-5 0.9083 0.7667 rs2240747 ZNRF4 19:5456930 2.98 0.001381 1.23 0.5472 rs6793265 ITPR1 3:4735533 3.62 0.000321 0.755 0.5355 rs2070874* IL-4 5:132009710 0.6676 0.4028 0.4972 0.143 rs17657199 NDST1 5:149950246 3.777 0.006313 N/A 0.9967 rs2069762* IL-2 4:123377980 1.337 0.3174 1.418 0.1674 rs7903146* TCF7L2 10:114758349 1.3 0.4097 1.331 0.2887 rs1043261* IL-17RB 3:53899276 0.4964 0.3419 0.5659 0.3498 rs2280789* CCL5 17:34207003 0.5451 0.3133 0.8338 0.679 rs2172749* IL-7R 5:35855264 0.671 0.2401 0.9203 0.7624 rs1494558* IL-7R 5:35861068 0.6747 0.2487 0.9281 0.7868 rs1800795* IL-6 7:22766645 1.092 0.7636 1.248 0.3803 rs12255372* TCF7L2 10:114808902 1.344 0.3307 1.118 0.6824 rs5219* KCNJ11 11:17409572 0.6043 0.1403 1.253 0.3902 rs2107538* CCL5 17:34207780 0.7982 0.5864 0.7686 0.4778 rs1801282* PPARG 3:12393125 0.7297 0.5144 0.9024 0.7936 rs3817655* CCL5 17:34199641 0.8007 0.5916 0.7711 0.4831 rs1799854 ABCC8 11:17448704 1.475 0.1899 0.9902 0.9695 rs4819554* IL-17RA 22:17565035 1.222 0.5391 0.5795 0.1248 rs1169288* HNF1A 12:121416650 1.511 0.1829 0.8952 0.704 rs2340721* ATF6 1:161849385 1.274 0.4272 1.226 0.4549 rs1025689* IL-17RB 3:53883722 1.423 0.2569 0.9165 0.7621 rs1124053* IL-17E 14:22914819 1.266 0.4435 0.9479 0.8514 rs2144908* HNF4A 20:42985717 1.004 0.9927 0.7388 0.5759 rs8192678* PPARGC1A 4:23815662 1.428 0.2349 0.5731 0.0643 rs13266634* SLC30A8 8:118184783 1.245 0.4851 1.026 0.9367 rs1044498* ENPP1 6:132172368 1.049 0.9078 0.9768 0.9486 rs1800961* HNF4A 20:43042364 N/A 0.9966 N/A 0.9965 N/A - no minor alleles present in NODAT cases

* - SNPs previously reported to be associated with NODAT Table 4: Information on SNPs failing second stage genotyping

SNP Problem Sequence rs1050450 multiple triplet CTGTTGCTCGTAGCTGCTGAAATTCCTCTCCGCCCTTGG repeats in primer GATTGCGCATGGAGGGCAAAATCCCGGTGACTCATAGA sequences AAATCTCCCTTGTTTGTGGTTAGAACGTTTCTCTCCTCCT CTTGACCCCGGGTTCTAGCTGCCCTTCTCTCCTGTAGGA GAACGCCAAGAACGAAGAGATTCTGAATTCCCTCAAGTA CGTCCGGCCTGGTGGTGGGTTCGAGCCCAACTTCATGC TCTTCGAGAAGTGCGAGGTGAACGGTGCGGGGGCGCA CCCTCTCTTCGCCTTCCTGCGGGAGGCCCTGCCAGCTC CCAGCGACGACGCCACCGCGCTTATGACCGACCCCAAG CTCATCACCTGGTCTCCGGTGTGTCGCAACGATGTTGCC TGGAACTTTGAGAAGTTCCTGGTGGGCCCTGACGGTGT GCCCCTACGCAGGTACAGCCGCCGCTTCCAGACCATTG ACATCGAGCCTGACATCGAAGCCCTGCTGTCTCAAGGG CNCAGCTGTGCCTAGGGCGCCCCTCCTACCCCGGCTGC TTGGCAGTTGCAGTGCTGCTGTCTCGGGGGGGTTTTCA TCTATGAGGGTGTTTCCTCTAAACCTACGAGGGAGGAAC ACCTGATCTTACAGAAAATACCACCTCGAGATGGGTGCT GGTCCTGTTGATCCCAGTCTCTGCCAGACCAAGGCGAG TTTCCCCACTAATAAAGTGCCGGGTGTCAGCAGAACTGT GTGTATGTCCTGTGTCATTGTCATTTGGGAATTCTTTTTC TTTTCTTTTTTTTTTTTTTTTTTTGAGACGGAGTTTTTTGCT CTATTGCCCAGGCTGGAGTGCAGTGGCGCAATCTAGGC TCACTGCAAGCTCCGCCTCCCGGGTTCACGCCATTCTC CTGCCTCAACCTCCTGAGTAGCTGGGACTACAGGTGCC TGCCACCACGCCCGGCTAATTTTTTGTATTTTTAGTAGAG ACAGGGTTTCACCGTGTTAGCCAGGATGGTATCGA rs2430561 inadequate known bases to generate EXTEND primer rs1370602 Heavily masked TGAACTTTTATAGCTGGAAGAAGGCACTGAGATCATTTTA region CAAGTAAGGAAGTTAGCCTGACCTGCCCAATGCTACACA GNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNCTATTAGTCTATTCGCCTGTT ATTAGGCCTATTANNNNNNNNNNNNNNNNNNNNNNNNN NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNN NNN

Table 5: Top ranked (p <0.005) gene enrichment sets

Function Type Enrichment Enrichment Score p-value receptor complex cellular component 10.7 0.00002 basal part of cell cellular component 10.5 0.00003 postsynaptic density cellular component 8.8 0.0002 intrinsic apoptotic signaling pathway biological process 8.2 0.0003 ionotropic glutamate receptor complex cellular component 7.9 0.0004 integrin complex cellular component 7.9 0.0004 positive regulation of neuron apoptotic process biological process 7.1 0.0009 protein serine/threonine phosphatase complex cellular component 7.1 0.0009 positive regulation of neuron death biological process 7.0 0.0009 collagen binding molecular function 6.9 0.001 intrinsic apoptotic signaling pathway in biological process 6.9 0.001 response to DNA damage regulation of apoptotic process biological process 6.7 0.001 regulation of programmed cell death biological process 6.7 0.001 apoptotic signaling pathway biological process 6.6 0.001 regulation of execution phase of apoptosis biological process 6.6 0.001 regulation of cell death biological process 6.5 0.001 cellular response to growth factor stimulus biological process 6.5 0.002 response to growth factor stimulus biological process 6.4 0.002 positive regulation of catalytic activity biological process 6.3 0.002 positive regulation of protein phosphorylation biological process 6.0 0.002 neurotrophin TRK receptor signaling pathway biological process 6.0 0.002 neurotrophin signaling pathway biological process 6.0 0.002 cellular response to radiation biological process 6.0 0.002 integrin-mediated signaling pathway biological process 5.9 0.003 positive regulation of phosphorylation biological process 5.9 0.003 homeostatic process biological process 5.9 0.003 spermidine biosynthetic process biological process 5.8 0.003 putrescine biosynthetic process biological process 5.8 0.003 oxygen sensor activity molecular function 5.8 0.003 apoptosome cellular component 5.8 0.003 PH domain binding molecular function 5.8 0.003 inositol-1,3,4,5-tetrakisphosphate 5- molecular function 5.8 0.003 phosphatase activity platelet dense tubular network cellular component 5.8 0.003 starch binding molecular function 5.8 0.003 cell-matrix adhesion biological process 5.8 0.003 macromolecular complex cellular component 5.8 0.003 cellular homeostasis biological process 5.7 0.003 tissue homeostasis biological process 5.7 0.003 GKAP/Homer scaffold activity molecular function 5.5 0.004 inositol-1,4,5-trisphosphate 5-phosphatase molecular function 5.5 0.004 activity inositol 1,4,5-trisphosphate-sensitive calcium- molecular function 5.5 0.004 release channel activity calcineurin complex cellular component 5.5 0.004 positive regulation of phosphorus metabolic biological process 5.5 0.004 process positive regulation of phosphate metabolic biological process 5.5 0.004 process positive regulation of molecular function biological process 5.5 0.004 amino acid binding molecular function 5.4 0.004 signal transduction in response to DNA damage biological process 5.4 0.004

Regional association plots highlighting the genomic context and local significance values for all SNPs selected for second stage genotyping, Initially the SNPs identified from the GWAS and secondly, those previously reported to be associated with NODAT. Each circle represents a SNP; with proximal SNPs to the index SNP color coded according to linkage disequilibrium based on HapMap Phase II CEU reference data (Plotted SNPs). Known functional SNPs are indicated with symbols other than filled circles. Blue lines indicate the recombination rate

(cM/Mb).

(i) SNPs associated with NODAT (p <10-5) from GWAS

Figure 1: rs198372

Figure 2: rs341497

Figure 3: rs1016429

Figure 4: rs1370602

Figure 5: rs1783606

Figure 6: rs1836882

Figure 7: rs1871184

Figure 8: rs2020902

Figure 9: rs2240747

Figure 10: rs2265917

Figure 11: rs2265919

Figure 12: rs2861484

Figure 13: rs3212574

Figure 14: rs4394754

Figure 15: rs6793265

Figure 16: rs6903252

Figure 17: rs7145618

Figure 18: rs10117679

Figure 19: rs104848

21 Figure 20: rs10899444

Figure 21: rs11580170

Figure 22: rs16936667

Figure 23: rs17657199

Figure 24: rs17722392

(ii) SNPs previously reported to be associated with NODAT

Figure 25: rs5219

Figure 26: rs1025689 (proxy rs7627178)

Figure 27: rs1043261

Figure 28: rs1044498 (proxy rs6926970)

Figure 29: rs1124053 (proxy rs3759614)

Figure 30: rs1169288 (proxy rs2650000)

Figure 31: rs1494558

Figure 32: rs1799854

Figure 33: rs1800795 (proxy rs1554606)

Figure 34: rs1800961

Figure 35: rs1801282 (proxy rs6802898)

Figure 36: rs2069762 (proxy rs6835946)

Figure 37: rs2069763 (proxy rs4505848)

Figure 38: rs2070874 (proxy rs2243290)

Figure 39: rs2107538 (proxy rs2251660)

Figure 40: rs2144908 (proxy rs4812829)

Figure 41: rs2172749 (proxy rs1494558)

Figure 42: rs2280789 (proxy rs2306630)

Figure 43: rs2340721 (proxy rs905594)

Figure 44: rs2430561 (proxy rs2069727)

Figure 45: rs3817655 (proxy rs2291299)

Figure 46: rs4819554 (proxy rs2041629)

Figure 47: rs7903146

Figure 48: rs8192678

Figure 49: rs12255372

Figure 50: rs13266634

Cluster plots for SNPs from second stage genotyping

Figure 51: rs5219

Figure 52: rs198372

Figure 53: rs341497

Figure 54: rs1016429

Figure 55: rs1025689

Figure 56: rs1043261

Figure 57: rs1044498

Figure 58: rs1124053

Figure 59: rs1169288

Figure 60: rs1494558

Figure 61: rs1783606

Figure 62: rs1799854

Figure 63: rs1800795

Figure 64: rs1800961 (Taqman)

Figure 65: rs1801282

Figure 66: rs1836882

Figure 67: rs1871184

Figure 68: rs2020902

Figure 69: rs2069762 (Taqman)

Figure 70: rs2069763 (Taqman)

Figure 71: rs2070874

Figure 72: rs2107538

Figure 73: rs2144908

Figure 74: rs2172749

Figure 75: rs2240747

Figure 76: rs2265477

Figure 77: rs2265917 (Taqman)

Figure 78: rs2265919

Figure 79: rs2280789

Figure 80: rs2340721

Figure 81: rs2861484

Figure 82: rs3212574

Figure 83: rs3817655

Figure 84: rs4394754

Figure 85: rs4819554

Figure 86: rs6793265

Figure 87: rs6903252

Figure 88: rs7145618

Figure 89: rs7533125

Figure 90: rs7903146 (Taqman)

Figure 91: rs8192678

Figure 92: rs10117679

Figure 93: rs10484821

Figure 94: rs10899444

Figure 95: rs11580170

Figure 96: rs12255372

Figure 97: rs13266634

Figure 98: rs16936667

Figure 99: rs17657199

Figure 100: rs17722392

Figure 101: Results of gene set enrichment performed using Fisher’s exact test, restricting gene sets to those with two or more genes included and analysing genes according to the hg19 genome build

p = 0.04

p = 0.03

p = 0.006

Figure 102: The P13K-AKT signaling pathway with coloured boxes identifying top ranked genes and pathways in the NODAT GWAS