Search

BackgroundHeterogeneity in the rate of β-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of complementary multi-omics data obtained after the diagnosis of T1D may provide mechanistic insight into the diverse rates of disease progression.MethodsWe collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in β-cell mass measured as fasting C-peptide.ResultsTwo molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signaling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signaling events that were inversely associated with rapid decline in β-cell function. The second signature was related to translation and viral infection were inversely associated with change in β-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid β-cell decline.ConclusionFeatures that differ between individuals with slow and rapid decline in β-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies, as well as offering biomarkers of therapeutic effect.FundingThis work is funded by the Innovative Medicine Initiative 2 Joint Undertaking (IMI2 JU) under grant agreement N° 115797 (INNODIA) and N° 945268 (INNODIA HARVEST). This Joint Undertaking receives support from the Union’s Horizon 2020 research and innovation program and ‘EFPIA’, ‘JDRF’ and ‘The Leona M. and Harry B. Helmsley Charitable Trust’.

BACKGROUND: Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes. METHODS: Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations. FINDINGS: We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression. INTERPRETATION: There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes. FUNDING: A full list of funding bodies can be found under Acknowledgments. ispartof: EBIOMEDICINE vol:92 ispartof: location:Netherlands status: published

Background: Development of disease-modifying therapy for type 1 diabetes (T1D) is hampered by the limited understanding of pathogenesis, heterogeneity, lack of disease biomarkers and stratifiers. Using the INNODIA consortium pan-European infrastructure to collect prospective clinical data from newly diagnosed subjects combined with multi-omics data, we mined integrated datasets using deep learning to identify novel relationships that could transform the disease monitoring landscape. Method: Samples collected Results: Clustering identified five age-independent subgroups with significantly different progression patterns (p Discussion: The preliminary results suggest that the VAE can learn meaningful progression patterns from integrated clinical and multi-omics data, which holds great promise to uncover important data structures not readily discovered when analyzing single data types. Disclosure C. Brorsson: Employee; Spouse/Partner; Novo Nordisk A/S. J. Almagro armenteros: None. G. Mazzoni: Employee; Self; Novo Nordisk A/S. S. Kaur: None. A. M. Schulte: None. C. Mathieu: Advisory Panel; Self; Novo Nordisk, Sanofi, Merck Sharp and Dohme Ltd., Eli Lilly and Company, Novartis, AstraZeneca, Boehringer Ingelheim, Roche, Medtronic, ActoBio Therapeutics, Pfizer, Insulet and Zealand Pharma, Research Support; Self; Medtronic, Novo Nordisk, Sanofi and ActoBio Therapeutics, Speaker’s Bureau; Self; Novo Nordisk, Sanofi, Eli Lilly and Company, Boehringer Ingelheim, AstraZeneca and Novartis. S. Brunak: Board Member; Self; Intomics A/S, Proscion A/S, Stock/Shareholder; Self; Hoba Therapeutics Aps, Novo Nordisk A/S. On behalf of the innodia consortium: n/a. Funding INNODIA (115797)

Genetic variants identified by genome-wide association studies can contribute to disease risk by altering the production and abundance of mRNA, proteins and other molecules. However, the interplay between molecular intermediaries that define the pathway from genetic variation to disease is not well understood. Here, we evaluated the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3,029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant was associated with multiple molecular phenotypes over multiple genomic regions. We find varying proportions of shared genetic regulation across phenotypes, highest between expression and proteins (66.6%). We were able to recapitulate a substantial proportion of gene expression genetic regulation in a diverse set of 44 tissues, with a median of 88% shared associations for blood expression and 22.3% for plasma proteins. Finally, the genetic and molecular associations were represented in networks including 2,828 known GWAS variants. One sub-network shows the trans relationship between rs149007767 and RTEN, and identifies GRB10 and IKZF1 as candidates mediating genes. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants across different molecular phenotypes.

Type 2 diabetes (T2D) is a complex chronic disease characterized by considerable phenotypic heterogeneity. In this study, we applied a reverse graph embedding method to routinely collected data from 23,137 Scottish patients with newly diagnosed diabetes to visualize this heterogeneity and used partitioned diabetes polygenic risk scores to gain insight into the underlying biological processes. Overlaying risk of progression to outcomes of insulin requirement, chronic kidney disease, referable diabetic retinopathy and major adverse cardiovascular events, we show how these risks differ by patient phenotype. For example, patients at risk of retinopathy are phenotypically different from those at risk of cardiovascular events. We replicated our findings in the UK Biobank and the ADOPT clinical trial, also showing that the pattern of diabetes drug monotherapy response differs for different drugs. Overall, our analysis highlights how, in a European population, underlying phenotypic variation drives T2D onset and affects subsequent diabetes outcomes and drug response, demonstrating the need to incorporate these factors into personalized treatment approaches for the management of T2D.

Activation of the transcription factor nuclear factor kappa B (NFkB) contributes to β-cell death in type 1 diabetes (T1D). Genome-wide association studies have identified the gene TNF-induced protein 3 (TNFAIP3), encoding for the zinc finger protein A20, as a susceptibility locus for T1D. A20 restricts NF-κB signaling and has strong antiapoptotic activities in β-cells. Although the role of A20 on NF-κB inhibition is well characterized, its other antiapoptotic functions are largely unknown. By studying INS-1E cells and rat dispersed islet cells knocked down or overexpressing A20 and islets isolated from the β-cell-specific A20 knockout mice, we presently demonstrate that A20 has broader effects in β-cells that are not restricted to inhibition of NF-κB. These involves, suppression of the proapoptotic mitogen-activated protein kinase c-Jun N-terminal kinase (JNK), activation of survival signaling via v-akt murine thymoma viral oncogene homolog (Akt) and consequently inhibition of the intrinsic apoptotic pathway. Finally, in a cohort of T1D children, we observed that the risk allele of the rs2327832 single nucleotide polymorphism of TNFAIP3 predicted lower C-peptide and higher hemoglobin A1c (HbA1c) levels 12 months after disease onset, indicating reduced residual β-cell function and impaired glycemic control. In conclusion, our results indicate a critical role for A20 in the regulation of β-cell survival and unveil novel mechanisms by which A20 controls β-cell fate. Moreover, we identify the single nucleotide polymorphism rs2327832 of TNFAIP3 as a possible prognostic marker for diabetes outcome in children with T1D.

Over 40 susceptibility loci have been identified for type 1 diabetes (T1D). Little is known about how these variants modify disease risk and progression. Here, we combined in vitro and in vivo experiments with clinical studies to determine how genetic variation of the candidate gene cathepsin H (CTSH) affects disease mechanisms and progression in T1D. The T allele of rs3825932 was associated with lower CTSH expression in human lymphoblastoid cell lines and pancreatic tissue. Proinflammatory cytokines decreased the expression of CTSH in human islets and primary rat β-cells, and overexpression of CTSH protected insulin-secreting cells against cytokine-induced apoptosis. Mechanistic studies indicated that CTSH exerts its antiapoptotic effects through decreased JNK and p38 signaling and reduced expression of the proapoptotic factors Bim, DP5, and c-Myc. CTSH overexpression also up-regulated Ins2 expression and increased insulin secretion. Additionally, islets from Ctsh−/− mice contained less insulin than islets from WT mice. Importantly, the TT genotype was associated with higher daily insulin dose and faster disease progression in newly diagnosed T1D patients, indicating agreement between the experimental and clinical data. In line with these observations, healthy human subjects carrying the T allele have lower β-cell function, which was evaluated by glucose tolerance testing. The data provide strong evidence that CTSH is an important regulator of β-cell function during progression of T1D and reinforce the concept that candidate genes for T1D may affect disease progression by modulating survival and function of pancreatic β-cells, the target cells of the autoimmune assault. Published version

Background Vitamin D, certain single nucleotide polymorphisms (SNPs) in the vitamin D-receptor (VDR) gene and vitamin D metabolism genes have been associated with type 1 diabetes (T1D). Objective We wanted to examine if the most widely studied SNPs in genes important for production, transport, and action of vitamin D were associated with T1D or to circulating levels of vitamin D 25-hydroxyvitamin D [25(OH)D] in a juvenile Danish population. Methods We genotyped eight SNPs in five vitamin D metabolism genes in 1467 trios. 25(OH)D status were analyzed in 1803 children (907 patients and 896 siblings). Results We did not demonstrate association with T1D for SNPs in the following genes: CYP27B1, VDR, GC, CYP2R1, DHCR7, and CYP24A1. Though, variants in the GC gene were significantly associated with 25(OH)D levels in the joint model. Conclusion Some of the most examined SNPs in vitamin D metabolism genes were not confirmed to be associated with T1D, though 25(OH) levels were associated with variants in the GC gene.

SummaryType 1 diabetes is caused by autoimmune-mediated β cell destruction leading to insulin deficiency. The histone deacetylase SIRT1 plays an essential role in modulating several age-related diseases. Here we describe a family carrying a mutation in the SIRT1 gene, in which all five affected members developed an autoimmune disorder: four developed type 1 diabetes, and one developed ulcerative colitis. Initially, a 26-year-old man was diagnosed with the typical features of type 1 diabetes, including lean body mass, autoantibodies, T cell reactivity to β cell antigens, and a rapid dependence on insulin. Direct and exome sequencing identified the presence of a T-to-C exchange in exon 1 of SIRT1, corresponding to a leucine-to-proline mutation at residue 107. Expression of SIRT1-L107P in insulin-producing cells resulted in overproduction of nitric oxide, cytokines, and chemokines. These observations identify a role for SIRT1 in human autoimmunity and unveil a monogenic form of type 1 diabetes.

We hypothesised that the correlation between autoantibody specificity for the ZnT8 Arg325Trp isoforms and the type 2 diabetes-associated rs13266634 may affect beta-cell function at type 1 diabetes (T ID) onset. To study this, we tested 482 newly diagnosed diabetic probands and 478 healthy siblings from the Danish population-based T1D registry for autoantibodies to ZnT8 (ZnT8A) in addition to GAD65 and IA-2. The prevalence and titres of autoantibodies were correlated with genotypes for rs13266634 and HLA-DQB1, age at diagnosis (AAD) and insulin dose-adjusted HbA1c (IDAA1c), as a proxy for residual beta-cell function. We replicated the correlation between rs13266634 genotypes and specificity for the ZnT8-Argenine (ZnT8R) and ZnT8-Tryptophan (ZnT8W) isoforms previously reported. ZnT8A overlapped substantially with autoantibodies to glutamate decarboxylase 65 (GADA) and IA-2 (IA-2A) and correlated significantly with IA-2A prevalence (p < 2e-16). No effect on IDAA1c was demonstrated for ZnT8A or rs13266634. We found a correlation between ZnT8R positivity and HLA-DQB1*0302 genotypes (p = 0.016), which has not been shown previously. Furthermore, significantly lower ZnT8R and GADA prevalence and titres was found among probands with AAD < 5 years (prevalence: p = 0.004 and p = 0.0001; titres: p = 0.002 and p = 0.001, respectively). The same trend was observed for IA-2A and ZnT8W; however, the difference was non-significant. Our study confirms ZnT8 as a major target for autoantibodies at disease onset in our Danish T1D cohort of children and adolescents, and we have further characterised the relationship between autoantibody specificity for the ZnT8 Arg325Trp epitopes and rs13266634 in relation to established autoantibodies, AAD, measures of beta-cell function and HLA-DQB1 genotypes in T1D.

Udgivelsesdato: 2009-Mar AIMS/HYPOTHESIS: The suppressor of cytokine signalling 1 (SOCS1) is a natural inhibitor of cytokine and insulin signalling pathways and may also play a role in obesity. In addition, SOCS1 is considered a candidate gene in the pathogenesis of both type 1 diabetes (T1D) and type 2 diabetes (T2D). The objective was to perform mutation analysis of SOCS1 and to test the identified variations for association to T2D-related quantitative traits, T2D or T1D. METHODS: Mutation scanning was performed by direct sequencing in 27 white Danish subjects. Genotyping was carried out by TaqMan allelic discrimination. A total of more than 8100 individuals were genotyped. RESULTS: Eight variations were identified in the 5' untranslated region (UTR) region. Two of these had allele frequencies below 1% and were not further examined. The six other variants were analysed in groups of T1D families (n = 1461 subjects) and T2D patients (n = 1430), glucose tolerant first-degree relatives of T2D patients (n = 212) and normal glucose tolerant (NGT) subjects. The rs33977706 polymorphism (-820G > T) was associated with a lower body mass index (BMI) (p = 0.004). In a second study (n = 4625 NGT subjects), significant associations of both the rs33977706 and the rs243330 (-1656G > A) variants to obesity were found (p = 0.047 and p = 0.015) respectively. The rs33977706 affected both binding of a nuclear protein to and the transcriptional activity of the SOCS1 promoter, indicating a relationship between this polymorphism and gene regulation. CONCLUSIONS/INTERPRETATION: This study demonstrates that functional variations in the SOCS1 promoter may associate with alterations in BMI in the general white population.

Enteroviruses have been suggested as triggers of type 1 diabetes (T1D). We aimed to assess whether established T1D susceptibility single nucleotide polymorphisms (SNPs) and candidate SNPs in innate immune genes were associated with the frequency of enterovirus infection in otherwise healthy children. Fifty-six established T1D SNPs and 97 other candidate immunity SNPs were typed in 419 children carrying the T1D high-risk genotype, HLA-DR4-DQ8/DR3-DQ2 genotype, and 373 children without this genotype. Enteroviral RNA was detected using real-time polymerase chain reaction, with primers detecting essentially all enterovirus serotypes, in 7,393 longitudinal stool samples collected monthly (age range 3-36 months). The most significant association was with two T1D SNPs, rs12150079 (ZPBP2/ORMDL3/GSDMB region) (enterovirus frequency: AA 7.3%, AG 8.7%, GG 9.7%, RR = 0.86, overall p = 1.87E-02) and rs229541 (C1QTNF6/SSTR3/RAC2) (enterovirus frequency: CC 7.8%, CT 9.7%, TT 9.4%, RR = 1.13, overall p = 3.6E-02), followed by TLR8 (rs2407992) (p = 3.8E-02), TLR3 (1914926) (p = 4.9E-02), and two other T1D SNPs (IFIH1 rs3747517, p = 4.9E-02 and PTPN22, rs2476601, p = 5.3E-02). However, the quantile-quantile plot of p-values with confidence intervals for all 153 SNPs did not reveal clear evidence for rejection of the complete null hypothesis. Among a number of SNPs in candidate genes, we found no evidence for strong associations with enterovirus presence in stool samples from Norwegian children.

BACKGROUND: The incidence of type 1 diabetes mellitus (T1DM) is increasing globally, and as a consequence, more patients are affected by microvascular complications such as diabetic retinopathy (DR). The aim of this study was to elucidate possible associations between diabetes-related single-nucleotide polymorphisms (SNP) and the development of DR.METHODS: Three hundred and thirty-nine patients with T1DM from the Danish Cohort of Pediatric Diabetes 1987 (DCPD1987) went through an ophthalmic examination in 1995; 185 of these were reexamined in 2011. The development of DR was assessed by comparison of overall DR level between baseline and follow-up in the worst eye at baseline. Patients were graded on a modified version of the Early Treatment Diabetic Retinopathy Study (ETDRS) scale, and 20 SNPs were genotyped in 130 of the 185 patients.RESULTS: We found the CTSH/rs3825932 variant (C > T) was associated with reduced risk of progression to proliferative diabetic retinopathy (PDR) (OR [95 % CI] = 0.20 [0.07-0.56], p = 2.4 × 10(-3), padjust = 0.048) and ERBB3/rs2292239 variant (G > T) associated with increased risk of two-step progression (OR [95 % CI] = 2.76 [1.31-5.80], p = 7.5 × 10(-3), padjust = 0.15). The associations were independent of other known risk factors, such as HbA1c, sex, and diastolic blood pressure.CONCLUSION: In conclusion, CTSH/rs3825932 and ERBB3/rs2292239 SNPs were associated with reduced risk of progression to PDR and two-step progression of DR on the ETDRS scale accordingly. The variant CTSH remained statistically significant after adjusting for multiple testing. Our results suggest an overlap between genetic variants that confer risk of T1DM and progression of DR.

In type 1 diabetes (T1D), the insulin-producing β cells are destroyed by an immune-mediated process leading to complete insulin deficiency. There is a strong genetic component in T1D. Genes located in the human leukocyte antigen (HLA) region are the most important genetic determinants of disease, but more than 40 additional loci are known to significantly affect T1D risk. Since most of the currently known genetic candidates have annotated immune cell functions, it is generally considered that most of the genetic susceptibility in T1D is caused by variation in genes affecting immune cell function. Recent studies, however, indicate that most T1D candidate genes are expressed in human islets suggesting that the functions of the genes are not restricted to immune cells, but also play roles in the islets and possibly the β cells. Several candidates change expression levels within the islets following exposure to proinflammatory cytokines highlighting that these genes may be involved in the response of β cells to immune attack. In this review, the compiling evidence that many of the candidate genes are expressed in islets and β cells will be presented. Further, we perform the first systematic human islet expression analysis of all genes located in 50 T1D-associated GWAS loci using a published RNA sequencing dataset. We find that 336 out of 857 genes are expressed in human islets and that many of these interact in protein networks. Finally, the potential pathogenetic roles of some candidate genes will be discussed.

Vitamin D, certain single nucleotide polymorphisms (SNPs) in the vitamin D-receptor (VDR) gene and vitamin D metabolism genes have been associated with type 1 diabetes (T1D).We wanted to examine if the most widely studied SNPs in genes important for production, transport, and action of vitamin D were associated with T1D or to circulating levels of vitamin D 25-hydroxyvitamin D [25(OH)D] in a juvenile Danish population.We genotyped eight SNPs in five vitamin D metabolism genes in 1467 trios. 25(OH)D status were analyzed in 1803 children (907 patients and 896 siblings).We did not demonstrate association with T1D for SNPs in the following genes: CYP27B1, VDR, GC, CYP2R1, DHCR7, and CYP24A1. Though, variants in the GC gene were significantly associated with 25(OH)D levels in the joint model.Some of the most examined SNPs in vitamin D metabolism genes were not confirmed to be associated with T1D, though 25(OH) levels were associated with variants in the GC gene.

Genome-wide association studies (GWAS) have heralded a new era in susceptibility locus discovery in complex diseases. For type 1 diabetes, >40 susceptibility loci have been discovered. However, GWAS do not inevitably lead to identification of the gene or genes in a given locus associated with disease, and they do not typically inform the broader context in which the disease genes operate. Here, we integrated type 1 diabetes GWAS data with protein-protein interactions to construct biological networks of relevance for disease. A total of 17 networks were identified. To prioritize and substantiate these networks, we performed expressional profiling in human pancreatic islets exposed to proinflammatory cytokines. Three networks were significantly enriched for cytokine-regulated genes and, thus, likely to play an important role for type 1 diabetes in pancreatic islets. Eight of the regulated genes (CD83, IFNGR1, IL17RD, TRAF3IP2, IL27RA, PLCG2, MYO1B, and CXCR7) in these networks also harbored single nucleotide polymorphisms nominally associated with type 1 diabetes. Finally, the expression and cytokine regulation of these new candidate genes were confirmed in insulin-secreting INS-1 β-cells. Our results provide novel insight to the mechanisms behind type 1 diabetes pathogenesis and, thus, may provide the basis for the design of novel treatment strategies.

Type 1 diabetes (T1D) is a complex disease characterized by the loss of insulin-secreting β-cells. Although the disease has a strong genetic component, and several loci are known to increase T1D susceptibility risk, only few causal genes have currently been identified. To identify disease-causing genes in T1D, we performed an in silico “phenome–interactome analysis” on a genome-wide linkage scan dataset. This method prioritizes candidates according to their physical interactions at the protein level with other proteins involved in diabetes. A total of 11 genes were predicted to be likely disease genes in T1D, including the INS gene. An unexpected top-scoring candidate gene was huntingtin-interacting protein (HIP)-14/ZDHHC17 . Immunohistochemical analysis of pancreatic sections demonstrated that HIP14 is almost exclusively expressed in insulin-positive cells in islets of Langerhans. RNAi knockdown experiments established that HIP14 is an antiapoptotic protein required for β-cell survival and glucose-stimulated insulin secretion. Proinflammatory cytokines (IL-1β and IFN-γ) that mediate β-cell dysfunction in T1D down-regulated HIP14 expression in insulin-secreting INS-1 cells and in isolated rat and human islets. Overexpression of HIP14 was associated with a decrease in IL-1β–induced NF-κB activity and protection against IL-1β–mediated apoptosis. Our study demonstrates that the current network biology approach is a valid method to identify genes of importance for T1D and may therefore embody the basis for more rational and targeted therapeutic approaches.

Genetic susceptibility is considered an important factor for the development and progression to diabetic nephropathy (DN), and for more than 20 years researchers have tried tounravel the genetic determinants of the disease. It is now clear that the pathogenesis of DN is most likely multifactorial and attributed to several genetic and environmental risk factors. Several candidate genes have been shown to be associated with the disease, but the results have not been consistent and most of the genes conferring risk to DN remain to be identified. In addition, studies have suggested that there might be differences in susceptibility loci and/or alleles between diverse populations. Recent developments in genotyping technology and increased information on the human genome have facilitated genome-wide association scans (GWAS) for investigating novel disease susceptibility across the entire human genome. The few GWAS performed for DN so far in combination with improved understanding of the human genome have identified novel risk loci and emphasized the importance of performing detailed genetic studies across diverse ethnic populations to fully unravel the genetic susceptibility to DN.

Genetic susceptibility is considered an important factor for the development and progression to diabetic nephropathy (DN), and for more than 20 years researchers have tried tounravel the genetic determinants of the disease. It is now clear that the pathogenesis of DN is most likely multifactorial and attributed to several genetic and environmental risk factors. Several candidate genes have been shown to be associated with the disease, but the results have not been consistent and most of the genes conferring risk to DN remain to be identified. In addition, studies have suggested that there might be differences in susceptibility loci and/or alleles between diverse populations. Recent developments in genotyping technology and increased information on the human genome have facilitated genome-wide association scans (GWAS) for investigating novel disease susceptibility across the entire human genome. The few GWAS performed for DN so far in combination with improved understanding of the human genome have identified novel risk loci and emphasized the importance of performing detailed genetic studies across diverse ethnic populations to fully unravel the genetic susceptibility to DN.

To reassess earlier suggested type I diabetes (T1D) associations of the insulin receptor substrate 1 (IRS1) and the paired domain 4 gene (PAX4) genes, the Type I Diabetes Genetics Consortium (T1DGC) evaluated single-nucleotide polymorphisms (SNPs) covering the two genomic regions. Sixteen SNPs were evaluated for IRS1 and 10 for PAX4. Both genes are biological candidate genes for T1D. Genotyping was performed in 2300 T1D families on both Illumina and Sequenom genotyping platforms. Data quality and concordance between the platforms were assessed for each SNP. Transmission disequilibrium testing neither show T1D association of SNPs in the two genes, nor did haplotype analysis. In conclusion, the earlier suggested associations of IRS1 and PAX4 to T1D were not supported, suggesting that they may have been false positive results. This highlights the importance of thorough quality control, selection of tagging SNPs, more than one genotyping platform in high throughput studies, and sufficient power to draw solid conclusions in genetic studies of human complex diseases.

Proteins contributing to a complex disease are often members of the same functional pathways. Elucidation of such pathways may provide increased knowledge about functional mechanisms underlying disease. By combining genetic interactions in Type 1 Diabetes (T1D) with protein interaction data we have previously identified sets of genes, likely to represent distinct cellular pathways involved in T1D risk. Here we evaluate the candidate genes involved in these putative interaction networks not only at the single gene level, but also in the context of the networks of which they form an integral part. mRNA expression levels for each gene were evaluated and profiling was performed by measuring and comparing constitutive expression in human islets versus cytokine-stimulated expression levels, and for lymphocytes by comparing expression levels among controls and T1D individuals. We identified differential regulation of several genes. In one of the networks four out of nine genes showed significant down regulation in human pancreatic islets after cytokine exposure supporting our prediction that the interaction network as a whole is a risk factor. In addition, we measured the enrichment of T1D associated SNPs in each of the four interaction networks to evaluate evidence of significant association at network level. This method provided additional support, in an independent data set, that two of the interaction networks could be involved in T1D and highlights the following processes as risk factors: oxidative stress, regulation of transcription and apoptosis. To understand biological systems, integration of genetic and functional information is necessary, and the current study has used this approach to improve understanding of T1D and the underlying biological mechanisms.

Genome-wide association scans in type 2 diabetes (T2D) have identified a risk variant, rs13266634 (Arg325Trp), in SLC30A8 on chromosome 8. SLC30A8 encodes a beta-cell specific zinc-ion transporter and rs13266634 has been shown to affect insulin secretion. Recently, autoantibodies for Slc30A8 with high predictive value were demonstrated in individuals with type 1 diabetes (T1D), making this gene an interesting T1D candidate gene. We genotyped rs13266634 in 3008 cases and controls and 246 families from Denmark. Association to T1D could not be demonstrated.