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Type 1 diabetes (T1D) is a complex disease and more than 100 genetic loci influencing T1D risk have been identified to date. Discovering the genes and biological mechanisms through which these variants impact T1D requires the integration of multiple data types. We have created the T1D Knowledge Portal (T1DKP; type1diabetesgenetics.org) to help researchers utilize genetic and functional genomic data to generate hypotheses about the genes involved in T1D and its complications. The T1DKP aggregates genetic association data from the T1D community along with relevant functional genomics (e.g., accessible chromatin, 3D conformation, gene expression, gene perturbation) data, which are loaded via a sister resource, the Common Metabolic Diseases Genome Atlas (cmdga.org) . Using the HuGeAMP software platform developed for the Common Metabolic Diseases Knowledge Portal (CMDKP; cmdkp.org) within the Accelerating Medicines Partnership for Common Metabolic Diseases, the T1DKP integrates data by applying bioinformatic methods such as meta-analysis of genetic association results to generate “bottom-line” p-values, enrichment of genetic association within tissue-specific genomic annotations, and more. Interactive visualizations allow the user to explore results and to perform custom, on-the-fly analyses. The T1DKP also includes expert-curated lists of predicted effector genes of T1D loci, displaying the supporting evidence behind each prediction. Since the T1DKP is nested within and built upon the same framework as the CMDKP, users may navigate seamlessly to see results relevant to other metabolic disorders. In total the T1DKP project aims to accelerate our understanding and treatment of T1D by providing decision support for researchers as they prioritize loci, variants, and genes for experimental study. Disclosure M.C.Costanzo: Other Relationship; Pfizer Inc. P.V.Kudtarkar: None. U.Nayak: None. S.Onengut-gumuscu: None. Y.Sun: None. S.S.Rich: None. J.Flannick: None. K.J.Gaulton: Consultant; Genentech, Inc., Stock/Shareholder; Neurocrine Biosciences, Inc., Vertex Pharmaceuticals Incorporated. N.Burtt: n/a. Funding National Institutes of Health (5UM1DK105554-07)

DNAme is associated with past HbA1c in DCCT/EDIC but these associations were examined when there was separation in HbA1c distribution by trial intervention arm. Thus, we performed an epigenome-wide association study (EWAS) of DNAme and HbA1c in a T1D cohort without intervention or risk factor exclusion to identify loci with DNAme associated with concurrent and future HbA1c over 28 years. Whole blood DNAme was assayed via Illumina EPIC beadchip in the Pittsburgh Epidemiology of Diabetes Complications (EDC) study of childhood onset ( Disclosure R.G.Miller: Stock/Shareholder; Becton, Dickinson and Company. J.Mychaleckyj: None. S.Onengut-gumuscu: None. T.J.Orchard: None. T.Costacou: None. Funding American Diabetes Association (1-19-JDF-109) ; NIH/NIDDK (R01-DK034818) , Rossi Memorial Fund

DNA methylation (DNAme) has been associated with risk of future CAD in the general population and is a potential target for pharmacological intervention. In T1D, DNAme has been associated with microvascular complications but its association with CAD has not been examined in this high risk population. Thus, we performed an epigenome-wide association study (EWAS) of DNAme and 28-year major CAD (CAD death, nonfatal myocardial infarction) incidence in an observational T1D cohort. Whole blood DNAme was assayed via Illumina EPIC beadchip in the Pittsburgh Epidemiology of Diabetes Complications (EDC) study of childhood onset ( Disclosure R.G.Miller: Stock/Shareholder; Becton, Dickinson and Company. J.Mychaleckyj: None. S.Onengut-gumuscu: None. T.J.Orchard: None. T.Costacou: None. Funding American Diabetes Association (1-19-JDF-109) ; NIH/NIDDK R01-DK034818, Rossi Memorial Fund

Modeling the course of C-peptide decline in autoantibody (Ab) positive individuals is important for type 1 diabetes (T1D) prediction and implementation of T1D prevention trials. Single nucleotide polymorphisms in TCF7L2, JAZF1, SLC30A8, INS, PTPRK, G6CP2, CLEC16, PTPN22 and HLA genes are associated with persistent C-peptide at or after T1D diagnosis. We sought to determine whether 12 SNPs in these genes and the T1D genetic risk score-2 (GRS2) can predict C-peptide trajectory before diagnosis of T1D. We studied Ab positive at-risk participants in the TrialNet Pathway to Prevention Study who had ImmunoChip data (N=1217, age at initial screen (mean±SD) 16.1±12.7 years, 51.5% female, 81.0% non-Hispanic white) . Over a mean follow up of 3.4±2. years, 255 (21.0%) developed multiple Ab and 336 (27.6%) developed clinical T1D. We analyzed the influence of these 12 SNPs and the T1D GRS2 on C-peptide AUC during progression from single to multiple Abs, from single Ab to clinical T1D, and from multiple Abs to clinical T1D. Analyses were adjusted for baseline C-peptide AUC, age, glucose AUC, BMI Z-score and HbA1c; the presence of high-risk HLA haplotypes (DR3 and DR4-DQ8) ; and the first 3 principal components. The type 2 diabetes (T2D) -associated TCF7L2 rs7901695 and rs4506565 SNPs were significantly associated with higher C-peptide AUC during progression from multiple Abs to T1D (p In conclusion, T2D-associated SNPs in the TCF7L2 gene and lower T1D GRS2 predict higher C-peptide particularly in progression from multiple Abs to clinical T1D. Disclosure T.M.Triolo: None. M.J.Redondo: Advisory Panel; Provention Bio, Inc. H.M.Parikh: None. M.Tosur: Advisory Panel; Provention Bio, Inc. P.Gottlieb: Advisory Panel; Janssen Research & Development, LLC, ViaCyte, Inc., Other Relationship; IM Therapeutics, Research Support; Caladrius Biosciences, Inc., Immune Tolerance Network, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Precigen, Inc., Tolerion, Inc. R.A.Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. S.Onengut-gumuscu: None. J.Krischer: None. S.S.Rich: None. A.Steck: None. Funding NIH K12 DK094712NIH RDK121843NIH RDK124395

Development of islet autoimmunity (IA) and type 1 diabetes (T1D) are associated with high-risk HLA class II loci as well as non-HLA genes. Monozygotic (MZ) twins have a high rate of concordance to T1D progression after the first twin develops T1D, especially for those diagnosed at a young age. We analyzed 52 T1D-associated non-HLA SNPs from ImmunoChip data in 159 MZ twins from the Twin Family Study: 79 twin probands with T1D and their 80 unaffected cotwins/triplets (including one set of triplets). Subjects are enrolled into the Twin Study when the proband is diagnosed with T1D and cotwins are followed longitudinally for the development of IA and/or T1D. In the cotwins, we analyzed the association between each of the non-HLA SNPs and IA after adjusting for HLA DR3/4*0302. In the twin probands with T1D, we used a linear regression model to evaluate the association of non-HLA SNPs with diabetes age at onset after adjusting for HLA DR3/4*0302. Median (IQR) age of diagnosis of the twin probands was 9.9 (5.4-13.9) years and age of last visit for the cotwins was 17.5 (11.2-26.7) years. Of the 80 cotwins, 41 (51.3%) developed IA and 15 (18.8%) were diagnosed with T1D. After adjusting for HLA DR3/4*0302, SNPs in CTLA4 (rs3087243), IL2 (rs4505848), IKZF1 (rs62447205), and INS (rs7111341) were found to be associated with the development of IA in cotwin subjects (all p Disclosure T. M. Triolo: None. F. Dong: None. H. C. Broncucia: None. S. Onengut-gumuscu: None. A. Steck: None. S. S. Rich: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (K12DK094712)

Background: Type 1 diabetes (T1D) genetic risk score (GRS) and islet autoantibody (AAb) specificity and titers influence T1D risk; however, these factors collected at baseline in cross sectional studies have not been effectively incorporated in the current models for prediction of progression along pre-clinical stages of T1D. Our aim was to develop an updated model of T1D prediction for research and clinical practice. Methods: AAb-positive relatives of individuals with T1D (n=1,233, 52% female, 88% European ancestry, age mean 15 yrs [SD 12.2]) from the TrialNet Pathway to Prevention were followed for a median of 3.1 years (IQR 1.1 -7.1). T1D developed in 498/1,233 (40.4%). T1D GRS, AAb number, AAb combination, AAb titer, age and sex were evaluated on the combined T1D prediction model, estimating its performance using 3-fold cross-validation (10x repeated). Results: Machine learning (survival random forest) and traditional methods (Cox models, extended Cox model) performed equivalently as measured by 5 yr horizon time-dependent AUC ROC (respectively 0.74, 0.74, 0.72), on calibration by integrated Brier score (respectively 0.15, 0.15, 0.16), and by calibration plots. AAb combinations with specific variables predicted T1D. In multivariate analysis, T1D GRS was a significant predictor of T1D in individuals positive for GADA, IAA or both. For any autoantibody combination including IA2A, the IA2A titer was predictive while the T1D GRS was not. Variable importance analyses showed that T1D was predicted by IA2A titer level, followed by T1D GRS and combinations of specific AAbs. Conclusion: We modeled individual estimates of T1D risk with T1D GRS, AAb characteristics, age, and sex. T1D prediction improved by adding T1D GRS and AAb characteristics to other variables, and was similar using machine learning or traditional models. T1D GRS was a significant predictor only in the absence of IA2A. In the presence of IA2A, IA2A titer was the most influential factor. Disclosure L. A. Ferrat: None. M. J. Redondo: Advisory Panel; Self; Provention Bio, Inc. A. Steck: None. H. M. Parikh: None. L. You: None. S. Onengut-gumuscu: None. P. Gottlieb: Advisory Panel; Self; Janssen Research & Development, LLC, Tolerion, Inc., Viacyte, Inc., Other Relationship; Self; ImmunoMolecular Therapeutics, Inc., Research Support; Self; Caladrius Biosciences, Inc., Immune Tolerance Network, Mercia Pharma Inc., National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Tolerion, Inc. S. S. Rich: None. J. Krischer: None. R. A. Oram: Consultant; Self; Janssen Research & Development, LLC. Funding National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK121843-01); JDRF (3-SRA-2019-827-S-B)

The risk for autoimmunity and subsequently type 1 diabetes is 10-fold higher in children with a first-degree family history of type 1 diabetes (FDR children) than in children in the general population (GP children). We analyzed children with high-risk HLA genotypes (n = 4,573) in the longitudinal TEDDY birth cohort to determine how much of the divergent risk is attributable to genetic enrichment in affected families. Enrichment for susceptible genotypes of multiple type 1 diabetes–associated genes and a novel risk gene, BTNL2, was identified in FDR children compared with GP children. After correction for genetic enrichment, the risks in the FDR and GP children converged but were not identical for multiple islet autoantibodies (hazard ratio [HR] 2.26 [95% CI 1.6–3.02]) and for diabetes (HR 2.92 [95% CI 2.05–4.16]). Convergence varied depending upon the degree of genetic susceptibility. Risks were similar in the highest genetic susceptibility group for multiple islet autoantibodies (14.3% vs .12.7%) and diabetes (4.8% vs. 4.1%) and were up to 5.8-fold divergent for children in the lowest genetic susceptibility group, decreasing incrementally in GP children but not in FDR children. These findings suggest that additional factors enriched within affected families preferentially increase the risk of autoimmunity and type 1 diabetes in lower genetic susceptibility strata.

Background: Type 1 diabetes (T1D) is determined by unknown environmental factors in genetically susceptible individuals. The design of most genome wide association studies of T1D involves comparison of cases (with varying duration of disease) with controls; hence, the genetic variants associated with T1D reflect current disease, ignoring variants that play a key role in the initiation of islet autoimmunity and the progression of subclinical disease. In this study, we focus on the genetic contribution to progression of islet autoimmunity using whole-genome sequencing in a collection of participants of the Diabetes AutoImmunity Study in the Young (DAISY). Methods: A total of 160 persistent islet autoantibody positive DAISY subjects were sequenced, including 87 subjects who progressed to T1D and 73 “non-progressors” over a mean of 11 years since seroconversion. Following whole genome sequence alignment, single nucleotide polymorphisms (SNPs) and small insertion/deletions (indels) were identified (∼6.8 million variants), and statistical analyses performed. Results: Genes with multiple SNPs associated with progression to T1D in subjects with islet autoimmunity (P < 8.5x10-8) did not overlap with known T1D risk loci; instead, four novel regions were identified - 1q21.3 (MRPS21-PRPF3), 2p25.2 (NRIR), 3q22.1 (COL6A6), and 20p12.1 (TASP1). Functional mapping of the associated SNPs within these risk loci indicates pathways critical for response to viral infections and response to interferon signaling contribute to progression to T1D once islet autoimmunity is initiated. Discussion: This study presents evidence that different genetic pathways may contribute to progression of islet autoimmunity from those identified once disease is established and support the need for follow-up studies to understand genetic risk factors that modulate progression of subclinical disease. Disclosure S. Onengut-Gumuscu: None. U. Paila: None. W. Chen: None. A. Ratan: None. Z. Zhu: None. A. Steck: None. B.I. Frohnert: None. K. Waugh: None. B. Webb-Robertson: None. J.M. Norris: None. L. Lange: None. M. Rewers: None. S.S. Rich: None.

Motivation: Twin studies estimate 50% of type 1 diabetes (T1D) risk is genetic. Genetic association has implicated T cells as central to the disease process. Thus, we generated chromatin accessibility profiles from CD4+ T cells isolated from 135 T1D cases and 144 controls and investigated whether (1) there are patterns of chromatin accessibility that differ from T1D patients compared to healthy controls; (2) established T1D risk variants influence chromatin accessibility. Methods: Frozen PBMCs were provided by the Type 1 Diabetes Genetics Consortium (T1DGC). CD4+ T cells were isolated using bead purification and Assay for Transposase Accessible Chromatin (ATAC-seq) was performed. ATAC-seq samples were processed using the PEPATAC pipeline and R packages edgeR and limm. Results: We tested for differences in accessibility between individuals with and without T1D adjusting for sex, age, and experimental variability. We identified 516 chromatin regions peaks with differential accessibility by T1D status (2 with increased and 514 with decreased accessibility at FDR Conclusion: These results provide hypotheses about molecular mechanisms of T1D in CD4+ T cells. Disclosure C.C. Robertson: None. W. Chen: None. S. Onengut-Gumuscu: None. P.J. Concannon: Stock/Shareholder; Self; Amgen. Other Relationship; Self; 10x Genomics. S.S. Rich: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (DK111906)

We examined the association between plasma 25-hydroxyvitamin D [25(OH)D] concentration and islet autoimmunity (IA) and whether vitamin D gene polymorphisms modify the effect of 25(OH)D on IA risk. We followed 8,676 children at increased genetic risk of type 1 diabetes at six sites in the U.S. and Europe. We defined IA as positivity for at least one autoantibody (GADA, IAA, or IA-2A) on two or more visits. We conducted a risk set sampled nested case-control study of 376 IA case subjects and up to 3 control subjects per case subject. 25(OH)D concentration was measured on all samples prior to, and including, the first IA positive visit. Nine polymorphisms in VDR, CYP24A, CYP27B1, GC, and RXRA were analyzed as effect modifiers of 25(OH)D. Adjusting for HLA-DR-DQ and ancestry, higher childhood 25(OH)D was associated with lower IA risk (odds ratio = 0.93 for a 5 nmol/L difference; 95% CI 0.89, 0.97). Moreover, this association was modified by VDR rs7975232 (interaction P = 0.0072), where increased childhood 25(OH)D was associated with a decreasing IA risk based upon number of minor alleles: 0 (1.00; 0.93, 1.07), 1 (0.92; 0.89, 0.96), and 2 (0.86; 0.80, 0.92). Vitamin D and VDR may have a combined role in IA development in children at increased genetic risk for type 1 diabetes.

The MC4R gene (18q21.32) plays a role in regulating appetite and energy homeostasis and is consistently linked with obesity. A variant near MC4R (rs12970134) has also been associated with IR and type 2 diabetes, independent of BMI. We examined the association of rs12970134 with HbA1c, IR, and BMI in the Pittsburgh Epidemiology of Diabetes Complications study, a prospective T1D cohort (mean age 27, T1D duration 18 years at baseline, 1986-88). In 422 EDC participants of European ancestry with available DNA, we assessed associations between 16 published IR SNPs, including rs12970134, and longitudinal measures of HbA1c, estimated glucose disposal rate (eGDR, an inverse measure of IR), and BMI over 18 years of follow-up (1986-88 to 2004-06). General additive mixed models were adjusted for sex, T1D duration, and principal components of ancestry. The rs12970134 SNP was associated with longitudinal HbA1c (β=0.33, p=5x10-5, see Figure) and eGDR (β=-0.31, p=0.008) trajectories, but not BMI (β=0.21, p=0.38). The associations remained significant after adjusting for BMI and insulin dose. There was no evidence for a SNP x time interaction (p>0.25 for both); all groups showed a similar fall in HbA1c after publication of the DCCT results. Thus, the rs12970134 SNP near MC4R is associated with glycemic control and insulin resistance, despite no association with BMI, in this T1D cohort. Disclosure R.G. Miller: None. T. Costacou: None. S. Onengut-Gumuscu: None. W. Chen: None. S.S. Rich: None. T.J. Orchard: Consultant; Self; Boehringer Ingelheim International GmbH. Funding National Institutes of Health; Rossi Memorial Fund; JDRF

Many SNPs in multiple loci are associated with IR in the general population. IR itself is associated with increased complication and mortality risk in T1D. We thus assessed whether a genetic risk score derived from IR SNPs (IR_GRS) is associated with mortality in the T1D prospective Pittsburgh Epidemiology of Diabetes Complications (EDC) Study. In 422 participants of European ancestry, with available DNA, a weighted IR_GRS based on 16 published IR-associated SNPs was calculated. First, for each SNP, the effect size of the association between each SNP and estimated glucose disposal rate (eGDR, an inverse measure of insulin resistance) was estimated using a general additive linear model. This effect size was then multiplied by the risk allele count to obtain the IR-weighted count. The IR_GRS was calculated as the sum of the 16 weighted counts. All deaths were classified by a physician committee yielding underlying and contributing causes. IR_GRS was related to 25-year total, any mention of cardiovascular disease (CVD), and any end-stage renal disease (ESRD) mortality in Cox models adjusted for sex, principal components for ancestry, and T1D duration and baseline (1986-88) clinical risk factors, including HbA1c, lipids, blood pressure, albumin excretion rate (AER), estimated glomerular filtration rate, and white blood cell count. IR_GRS was associated with increased risk of mortality outcomes: total (99 deaths, p=0.003), any CVD (71 deaths, p=0.01), and any ESRD (49 deaths, p=0.01). Adjustment for clinical risk factors attenuated these associations: total mortality (p=0.04), CVD mortality (p=0.17), ESRD mortality (p=0.10), largely as a result of adjustment for log-transformed AER, which was significantly associated with the IR_GRS (p=0.0003). These results, while needing validation in other cohorts, suggest that an IR_GRS may help to identify individuals with T1D who are at increased risk of mortality and vascular damage (i.e., AER) due to IR. Disclosure R.G. Miller: None. T. Costacou: None. S. Onengut-Gumuscu: None. W. Chen: None. S.S. Rich: None. T.J. Orchard: Consultant; Self; Boehringer Ingelheim International GmbH. Funding National Institutes of Health; Rossi Memorial Fund; JDRF

Despite finding more than 40 risk loci for type 1 diabetes (T1D), the causative variants and genes remain largely unknown. Here, we sought to identify rare deleterious variants of moderate-to-large effects contributing to T1D. We deeply sequenced 301 protein-coding genes located in 49 previously reported T1D risk loci in 70 T1D cases of European ancestry. These cases were selected from putatively high-risk families that had three or more siblings diagnosed with T1D at early ages. A cluster of rare deleterious variants in PTPN22 was identified, including two novel frameshift mutations (ss538819444 and rs371865329) and two missense variants (rs74163663 and rs56048322). Genotyping in 3,609 T1D families showed that rs56048322 was significantly associated with T1D and that this association was independent of the T1D-associated common variant rs2476601. The risk allele at rs56048322 affects splicing of PTPN22, resulting in the production of two alternative PTPN22 transcripts and a novel isoform of LYP (the protein encoded by PTPN22). This isoform competes with the wild-type LYP for binding to CSK and results in hyporesponsiveness of CD4+ T cells to antigen stimulation in T1D subjects. These findings demonstrate that in addition to common variants, rare deleterious variants in PTPN22 exist and can affect T1D risk.

Genetic studies of type 1 diabetes (T1D) have identified more than 40 risk loci, yet the causative genes in each locus is widely unknown. Here, we investigated a candidate gene in the 10q23.31 risk locus—FAD-dependent amine oxidase (RNLS). Variants in RNLS gene are associated with T1D risk in both European and African ancestry populations, indicating common genetic contributions to T1D. Currently, our understanding of the function of RNLS is very limited and primarily within the context of a few human diseases, most notably hypertension, kidney disease, cardiovascular disease, and cancer. In previous studies a positive RNLS-STAT3 feedback loop has been suggested. With consideration to these reports, and the importance of JAK-STAT signalling in immune-mediated diseases we evaluated if RNLS functions in activation of STAT3. An IFN-β time course was conducted to understand if overexpression of RNLS affects STAT3 levels and phosphorylation levels of STAT3. While we observed no difference in steady-state levels of STAT3 in cells overexpressing RNLS, we did observe increased levels of phospho-STAT3 upon IFN-β stimulation (p: 0.0051). In addition, to understand the function of RNLS in the immune system, we used RNAseq to compare transcriptomes of Jurkat cells expressing endogenous RNLS vs. ones overexpressing RNLS. RNAseq analysis identified 390 genes that were differentially expressed, by more than two-fold, of which 74.1 (289/390) of genes were up-regulated and 25.9 (101/390) were down-regulated in cells overexpressing RNLS. Tissue specific gene network analysis revealed that genes that were up-regulated mainly functioned in pathways related to chromosome segregation and cell division while genes that were down-regulated by RNLS were involved in IFN and NF-kappa B signaling pathways. Overall our results established a role for RNLS in cell-cycle related pathways and immune signaling pathways, providing new mechanistic insights of the role of RNLS in T1D. Disclosure S. Onengut-Gumuscu: None. N. Vogler: None. M. Faidas: None. R.R. Pickin: None. E. Gersz: None. S.S. Rich: None.

Single nucleotide polymorphisms (SNPs) located in the chromosomal region 16p13.13 have been previously associated with risk for several autoimmune diseases, including type 1 diabetes. To identify and localize specific risk variants for type 1 diabetes in this region and understand the mechanism of their action, we resequenced a 455-kb region in type 1 diabetic patients and unaffected control subjects, identifying 93 novel variants. A panel of 939 SNPs that included 46 of these novel variants was genotyped in 3,070 multiplex families with type 1 diabetes. Forty-eight SNPs, all located in CLEC16A, provided a statistically significant association (P < 5.32 × 10−5) with disease, with rs34306440 being most significantly associated (P = 5.74 × 10−6). The panel of SNPs used for fine mapping was also tested for association with transcript levels for each of the four genes in the region in B lymphoblastoid cell lines. Significant associations were observed only for transcript levels of DEXI, a gene with unknown function. We examined the relationship between the odds ratio for type 1 diabetes and the magnitude of the effect of DEXI transcript levels for each SNP in the region. Among SNPs significantly associated with type 1 diabetes, the common allele conferred an increased risk for disease and corresponded to lower DEXI expression. Our results suggest that the primary mechanism by which genetic variation at CLEC16A contributes to the risk for type 1 diabetes is through reduced expression of DEXI.

OBJECTIVE— The Type 1 Diabetes Genetics Consortium (T1DGC) has assembled and genotyped a large collection of multiplex families for the purpose of mapping genomic regions linked to type 1 diabetes. In the current study, we tested for evidence of loci associated with type 1 diabetes utilizing genome-wide linkage scan data and family-based association methods. RESEARCH DESIGN AND METHODS— A total of 2,496 multiplex families with type 1 diabetes were genotyped with a panel of 6,090 single nucleotide polymorphisms (SNPs). Evidence of association to disease was evaluated by the pedigree disequilibrium test. Significant results were followed up by genotyping and analyses in two independent sets of samples: 2,214 parent-affected child trio families and a panel of 7,721 case and 9,679 control subjects. RESULTS— Three of the SNPs most strongly associated with type 1 diabetes localized to previously identified type 1 diabetes risk loci: INS, IFIH1, and KIAA0350. A fourth strongly associated SNP, rs876498 (P = 1.0 × 10−4), occurred in the sixth intron of the UBASH3A locus at chromosome 21q22.3. Support for this disease association was obtained in two additional independent sample sets: families with type 1 diabetes (odds ratio [OR] 1.06 [95% CI 1.00–1.11]; P = 0.023) and case and control subjects (1.14 [1.09–1.19]; P = 7.5 × 10−8). CONCLUSIONS— The T1DGC 6K SNP scan and follow-up studies reported here confirm previously reported type 1 diabetes associations at INS, IFIH1, and KIAA0350 and identify an additional disease association on chromosome 21q22.3 in the UBASH3A locus (OR 1.10 [95% CI 1.07–1.13]; P = 4.4 × 10−12). This gene and its flanking regions are now validated targets for further resequencing, genotyping, and functional studies in type 1 diabetes.

A recent addition to the list of widely confirmed type 1 diabetes risk loci is the PTPN22 gene encoding a lymphoid-specific phosphatase (Lyp). However, evidence supporting a role for PTPN22 in type 1 diabetes derives entirely from the study of just one coding single nucleotide polymorphism, 1858C/T. In the current study, the haplotype structure of the PTPN22 region was determined, and individual haplotypes were tested for association with type 1 diabetes in family-based tests. The 1858T risk allele occurred on only a single haplotype that was strongly associated with type 1 diabetes (P = 7.9 × 10−5). After controlling for the effects of this allele, two other haplotypes were observed to be weakly associated with type 1 diabetes (P < 0.05). Sequencing of the coding region of PTPN22 on these haplotypes revealed a novel variant (2250G/C) predicted to result in a nonsynonymous amino acid substitution. Analysis of PTPN22 transcripts from a subject heterozygous for this variant indicated that it interfered with normal mRNA splicing, resulting in a premature termination codon after exon 17. These results support the conclusion that the 1858C/T allele is the major risk variant for type 1 diabetes in the PTPN22 locus, but they suggest that additional infrequent coding variants at PTPN22 may also contribute to type 1 diabetes risk.