Case–control cohort

We reasoned that our target risk of 10% could only be achieved by applying our multi-locus genetic scores in individuals who had the highest-risk HLA genotypes. We obtained data for controls from the UK Biobank (https://www.ukbiobank.ac.uk/) [20] and data for controls and cases from the WTCCC [21], and calculated the Winkler and Oram scores in 4,371 non-diabetic individuals who were heterozygous for HLA DR3-DQA1*0501-DQB1*0201 and DR4-DQA1*030X-DQB1*0302 (HLA DR3/DR4-DQ8) or who were homozygous for HLA DR4-DQA1*030X-DQB1*0302 (HLA DR4-DQ8/DR4-DQ8) (controls) and 781 patients with type 1 diabetes who had 1 of these 2 genotypes (cases). UK Biobank participants were aged 40 to 69 years, and the WTCCC patients were all aged <50 years when sampled.

TEDDY cohort

TEDDY is a prospective cohort study conducted at 3 centers in the US (Colorado, Georgia/Florida, and Washington) and 3 centers in Europe (Finland, Germany, and Sweden) [2,17]. Between 1 September 2004 and 28 February 2010, a total of 421,047 newborn children were screened for high-risk HLA genotypes for type 1 diabetes [22]. HLA genotype screening was conducted as previously described [22]. The families of children with type 1 diabetes risk HLA genotypes were invited to participate in the follow-up study in which blood samples were obtained every 3 months for the first 4 years and biannually thereafter for the analysis of islet autoantibodies (glutamic acid decarboxylase antibody [GADA], insulinoma antigen-2 antibody [IA-2A], and insulin autoantibodies [IAAs]). The HLA genotypes were confirmed by the central HLA Reference Laboratory at Roche Molecular Systems (Oakland, CA) for enrolled participants. The present report includes TEDDY children with the HLA DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype, without a first-degree relative with type 1 diabetes, if at least 1 blood sample was obtained after birth (Fig 1). This included 4,543 participants (2,278 [50.1%] girls). At analysis (follow-up to 31 May 2016), the median age of these children was 6.7 years (interquartile range, 2.5 to 8.6 years). Written informed consent was obtained for all study participants from a parent or primary caretaker for genetic screening and to participate in the prospective follow-up. The study was approved by local institutional review boards and is monitored by an external advisory board established by the US National Institutes of Health.

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Fig 1. Flow diagram of the TEDDY study participants included in this analysis.

https://doi.org/10.1371/journal.pmed.1002548.g001

TEDDY study outcomes

Islet autoantibodies (IAAs, GADA, and IA-2A) were measured by radiobinding assays every 3 months for the first 4 years and biannually thereafter. In the US, autoantibodies were measured at the Barbara Davis Center for Childhood Diabetes at the University of Colorado Denver reference laboratory. In Europe, autoantibodies were measured at the University of Bristol, the UK reference laboratory. All radiobinding assays were performed as previously described [2,23]. Samples positive for islet autoantibodies were retested at the second reference laboratory for confirmation. The outcome of islet autoantibody positivity was defined as a positive result at both reference laboratories (confirmed) and the presence of islet autoantibodies (GADA, IA-2A, or IAAs) on 2 or more consecutive visits (persistent). The date of seroconversion to islet autoantibodies (time to first autoantibody) was defined as the date of drawing the first of the 2 consecutive positive samples. The presence of persistent multiple islet autoantibodies was defined as the presence of at least 2 persistent and confirmed islet autoantibodies. The date of persistent multiple islet autoantibodies was defined as the date of drawing the first sample for which the second persistent and confirmed islet autoantibody was detected.

Children with positive islet autoantibodies that were due to maternal IgG transmission were not considered to be positive for that autoantibody unless the child had a negative sample before the first positive sample or the autoantibody persisted beyond 18 months of age [2].

Diabetes was diagnosed according to American Diabetes Association criteria [24].

Single nucleotide polymorphism typing

In the TEDDY study, single nucleotide polymorphisms (SNPs) of immune-related genes were genotyped using the Illumina ImmunoChip [25]. For SNPs rs11755527 (BACH2) and rs689 (INS), which were not available on the immunochip, the SNPs rs3757247 (BACH2) and rs1004446 (INS) were used (S1 Table). No proxy SNPs were available for rs917997 (IL18RAP).

Genetic scores

Genetic scores were determined as described by Winkler et al. [15], without including the intercept value from the logistic regression, and as described by Oram et al. [16]. The Winkler score was originally derived from the Type 1 Diabetes Genetics Consortium case–control dataset, and the Oram score was originally based on the odds ratios available on ImmunoBase (http://www.t1dbase.org/). The genetic score of each individual was derived from weighted values given to the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype plus a weighted value assigned to each susceptible allele of non-HLA SNPs for the Winkler score and HLA class I and non-HLA SNPs for the Oram score (S1 Table). A total of 39/40 non-HLA class II SNPs used in the Winkler score and 26/28 non-HLA class II SNPs used in the Oram score were available to calculate the genetic score in the TEDDY children, while 35/40 and 26/28 SNPs were available for the case–control cohort. For both scores, the HLA DR-DQ genotype weights were added to the weighted risks for each SNP according to the child’s number of risk alleles (0, 1, or 2) for each SNP (S1 Table). Additionally, since the Winkler and Oram scores were derived from partially overlapping genetic loci and each had distinct features, a merged genetic score was derived using the information for all available SNPs contained in the Winkler and Oram scores and was calculated for the TEDDY children (S1 Table). For simplicity, when SNPs overlapped in the Winkler and Oram scores, the mean weight of each SNP in the Winkler and Oram scores was used in the merged score, and for SNPs that were unique in the Winkler or the Oram score, the weight used in the original score was used for the merged score. Exceptions were for 2 SNPs (rs2069763 and rs3825932) that had a negative weight in the Winkler score but a positive weight in the Oram score, where the original Oram score weight was used to calculate the merged score.

Statistical analyses

An analysis plan was submitted to the TEDDY data coordinating center and approved by the TEDDY steering committee prior to compiling and analyzing the data (S1 Appendix). The merged score was added to this once both the Winkler and Oram scores were found to stratify risk. The Cox analysis and specificity analysis prescribed in the analysis plan were no longer considered to be sufficiently informative to include in the final analysis. The analysis was extended to include type 1 diabetes risk during revision of the manuscript.

For TEDDY children, the cumulative risks of developing islet autoantibodies, multiple islet autoantibodies, and diabetes were estimated using the Kaplan–Meier method and were compared between risk groups using the log-rank test. The risks of islet autoantibodies, multiple islet autoantibodies, and diabetes were calculated for increasing thresholds of the Winkler, Oram, and merged genetic scores. Analyses were also performed after stratification by HLA genotype, geographic location (US, Europe), and sex. The sensitivity of the genetic scores was assessed by calculating the proportion of children who developed islet autoantibodies, multiple islet autoantibodies, and diabetes whose genetic score was above the threshold value. Spearman’s correlation coefficient was used to assess whether the autoantibody risk by age 6 years or diabetes risk by age 10 years—and sensitivity for cases that developed by age 6 years or by age 10 years—changed with increasing score thresholds. The proportion of children in the general population who would be expected to have a genetic score above the threshold was calculated based on the frequency of children with the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype (2.9%) identified in the screening phase of the TEDDY study [22].

For the case–control dataset, we calculated the proportions of non-diabetic controls and cases of type 1 diabetes whose genetic score exceeded the thresholds, with score increments of 0.1. The sensitivity of the genetic scores was assessed by calculating the proportion of cases within the cohort who had a score above the threshold. The empirical risk was calculated as the ratio of the proportion of cases to the proportion of controls above the threshold multiplied by the assumed background risk of 5% for individuals with the DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype [11].

The distribution of genetic scores was compared among groups defined by islet autoantibody outcome, geographic location (US, Europe), or sex using the Mann–Whitney U test.

All analyses were performed using R 3.3.2 software (R Foundation for Statistical Computing, Vienna, Austria), IBM SPSS version 22.0 (IBM, Armonk, NY), and SAS 9.4 (SAS Institute, Cary, NC).

The datasets generated and analyzed during the current study are available in the NIDDK Central Repository at https://www.niddkrepository.org/studies/teddy. TEDDY immunochip (SNP) data that support the findings of this study have been deposited in NCBI’s Database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001037.v1.p1.

Genetic scores in the case–control population

The Winkler and Oram genetic scores in the WTCCC HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 cases were increased as compared to the UK Biobank HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 controls (P < 0.001; S1 Fig). Using the Winkler score, the calculated actual risk reached 10% above a threshold of 11.72, corresponding to a sensitivity of 58.7% (95% CI 55.2%–62.2%) for the patients who had the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype. Using the Oram score, an actual risk of 10% was reached above a score threshold of 11.67, corresponding to a sensitivity of 36.6% (95% CI 33.2%–40.0%; S1 Fig).

Having verified both scores for type 1 diabetes risk stratification in the case–control dataset, we reasoned that a composite score that included all the features from the Winkler and Oram scores would be justified. We therefore developed a merged genetic score that represented the average weighted values of loci, genotypes, and alleles common to the Winkler and Oram scores, and the original weighted values for loci and alleles that were unique to 1 of the scores (S1 Table). Using the prospectively followed TEDDY cohort, we then asked how well the Winkler, Oram, and merged scores could stratify the risk for pre-symptomatic type 1 diabetes.

Baseline risk for islet autoantibodies and diabetes in TEDDY children with HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype without family history of type 1 diabetes

Seroconversion to islet autoantibodies occurred in 386 children (8.5%) (166 [43.0%] girls), and 4,157 children (91.5%) remained islet autoantibody negative (2,112 [50.8%] girls). Of the 386 children with islet autoantibodies, 241 children (62.4%) developed multiple islet autoantibodies (102 [42.3%] girls; 81 [33.6%] from US). A total of 107 (2.3%) developed diabetes by age 10 years (47 [43.9%] girls). The cumulative risk for developing islet autoantibodies was 9.2% (95% CI 8.2%–10.1%; Fig 2A), of developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) by age 6 years was 5.8% (95% CI 5.0%–6.6%; Fig 2B), and of developing diabetes by age 10 years was 3.7% (95% CI 3.0%–4.4%; Fig 2C).

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Fig 2. Cumulative risks of 1 or more islet autoantibody, multiple islet autoantibody, and type 1 diabetes in TEDDY children with the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype.

The cumulative risk for 1 or more islet autoantibodies (A), multiple islet autoantibodies (B), and type 1 diabetes (C) for TEDDY children (y-axis) is shown relative to the age of the children (x-axis) and was calculated using the Kaplan–Meier method. The shaded area represents the 95% confidence interval of the cumulative risk. The numbers at risk indicate the number of children included in the analysis at each age.

https://doi.org/10.1371/journal.pmed.1002548.g002

Genetic scores in TEDDY children

We examined whether Winkler, Oram, and merged genetic scores were increased in children who developed islet autoantibodies. The genetic scores were calculated in 3,498 (1,471 US) children who had material for additional genetic analysis. The median follow-up in these children was 7.39 years. For each of the Winkler, Oram, and merged scores, the score was greater in children who developed islet autoantibodies by 6 years of age as compared to children who remained islet autoantibody negative (P < 0.001; Fig 3A and S2 Fig). The median merged score was 14.3 (IQR, 13.6–14.9) in children who developed islet autoantibodies versus 13.7 (IQR, 13.1–14.4) in children who remained islet autoantibody negative. The genetic scores were also slightly greater in European children (median merged score, 13.8; IQR, 13.1–14.5) than in US children (13.7; IQR, 13.1–14.4; P = 0.003; Fig 3B and S2 Fig). The frequencies of minor alleles differed between the US and European children for 7 of 43 SNPs (Bonferroni-corrected P of 0.05/43 = 0.0012; S2 Table). Scores were not different between boys and girls (P = 0.69; Fig 3C and S2 Fig).

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Fig 3. Merged genetic score in TEDDY children according to their islet autoantibody outcome, geographic location, and sex.

Islet autoantibody outcome (A); geographic location (B); sex (C). Red horizontal lines indicate the median genetic score value in each group.

https://doi.org/10.1371/journal.pmed.1002548.g003

Risk for islet autoantibodies and diabetes according to the genetic scores

We next asked if and how much the genetic scores could stratify risk in TEDDY children without a family history of type 1 diabetes. To address this, the cumulative risk for developing islet autoantibodies and for diabetes was compared between HLA DR3/DR4-DQ8 and DR4-DQ8/DR4-DQ8 children who were in the upper quartile, middle 2 quartiles, and lower quartile of the merged genetic score (Fig 4). The cumulative risk for developing islet autoantibodies by 6 years of age was 16.0% (95% CI 13.3%–18.6%) among children with a merged genetic score of >14.4, representing the upper quartile, compared with 6.9% (95% CI 5.9%–8.0%) in children with a score of ≤14.4 (P < 0.001). The cumulative risk for developing multiple islet autoantibodies by 6 years of age was 11.0% (95% CI 8.7%–13.3%) in children with a score of >14.4, compared with 4.1% (95% CI 3.3%–4.9%) in children with a score of ≤14.4 (P < 0.001). The cumulative risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%–9.9%) in children with a score of >14.4, compared with 2.7% (95% CI 1.9%–3.6%) in children with a score of ≤14.4 (P < 0.001). The risks were also stratified by the Winkler and Oram scores (P < 0.001; S3 Fig). However, the merged genetic score performed better than both the Winkler and Oram scores in identifying the HLA DR3/DR4-DQ8 and HLA DR4-DQ8/DR4-DQ8 children who developed multiple islet autoantibodies (S4 Fig).

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Fig 4. Cumulative risks of 1 or more islet autoantibody, multiple islet autoantibody, and type 1 diabetes development in TEDDY children with the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype stratified by their merged score.

The cumulative risk of developing 1 or more islet autoantibodies (A), multiple islet autoantibodies (B), and type 1 diabetes (C) (y-axis) is shown relative to age in years (x-axis) and was calculated using the Kaplan–Meier method. Curves are shown for children with genetic scores in the upper (orange line), lower (green line), and 2 middle (blue line) quartiles. The shaded areas represent the 95% confidence interval of the cumulative risk. The numbers at risk indicate the number of children included in the analysis at each age.

https://doi.org/10.1371/journal.pmed.1002548.g004

The merged genetic score stratified the risk for islet and multiple islet autoantibodies and for diabetes both in children who had the HLA DR3/DR4-DQ8 genotype and in children who had the HLA DR4-DQ8/DR4-DQ8 genotype (S5 Fig). The risks of islet autoantibodies, multiple islet autoantibodies, and diabetes in children with a merged genetic score of >14.4 were not significantly different between US and European children (P = 0.16, P = 0.97, and P = 0.96, respectively; S6 Fig), but autoantibody risks were higher in boys than in girls (P = 0.001 for 1 or more islet autoantibodies and P = 0.01 for multiple islet autoantibodies; S6 Fig).

Sensitivity versus risk for islet autoantibodies and diabetes according to the merged genetic score

Islet autoantibody and diabetes risk appeared incremental with increasing merged genetic score. Since the efficiency of a test is measured by both sensitivity and positive predictive value (risk), we examined the relationship between sensitivity and risk at increasing thresholds for the genetic score (Fig 5; S3–S5 Tables). As predicted, the cumulative risk for developing islet autoantibodies or multiple islet autoantibodies by age 6 years and diabetes by age 10 years increased (P < 0.001) and the sensitivity decreased (P < 0.001) with each increment in the genetic score threshold by the 5th percentile of the cohort. The risk for multiple islet autoantibodies reached a maximum of 13.2% (95% CI 9.2%–17.1%) above a threshold of >15.1, which identified 38 of the 173 children who developed multiple islet autoantibodies by age 6 years, corresponding to a sensitivity of 22.0% (95% CI 16.4%–28.7%). Above a threshold of 14.4, representing the upper 25% of merged genetic score values and where risk for multiple islet autoantibodies was 11.0% (95% CI 8.7%–13.3%), 82 of the 173 children who developed multiple islet autoantibodies by age 6 years were identified (sensitivity, 47.4%; 95% CI 40.1%–54.8%). In a population where the prevalence of HLA DR3/DR4-DQ8 and HLA DR4-DQ8/DR4-DQ8 genotypes is similar to that of the TEDDY cohort (2.9%), using the threshold of 14.4 would identify <1% of all newborns without a prior family history of type 1 diabetes.

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Fig 5. Cumulative risks and the proportion of cases identified for 1 or more islet autoantibodies, multiple islet autoantibodies, and type 1 diabetes in TEDDY children with the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype according to increasing thresholds of the merged genetic score.

Cumulative risk for developing islet autoantibodies by age 6 years and diabetes by age 10 years (A) and the proportion of cases positive for islet autoantibodies by age 6 years and diabetes by age 10 years (sensitivity; B) in TEDDY children with the HLA DR3/DR4-DQ8 or DR4-DQ8/DR4-DQ8 genotype stratified by their merged genetic score. The risk and sensitivity are shown for each increment in the genetic score by the 5th percentile of scores in the TEDDY children, ranging from >12.1 (the 5th percentile of children) to >15.4 (the 95th percentile of children). The risk and sensitivity are shown for the development of 1 or more islet autoantibodies (left panels), multiple islet autoantibodies (middle panels), and type 1 diabetes (right panels). Error bars indicate 95% confidence intervals.

https://doi.org/10.1371/journal.pmed.1002548.g005

Members of the TEDDY Study Group

Colorado clinical center: Marian Rewers, MD, PhD, PI^{1,4,5,6,10,11}, Kimberly Bautista¹², Judith Baxter^9,10,12,15, Ruth Bedoy², Daniel Felipe-Morales, Kimberly Driscoll, PhD⁹, Brigitte I. Frohnert, MD^2,14, Marisa Gallant, MD¹³, Patricia Gesualdo^2,6,12,14,15, Michelle Hoffman^12,13,14, Rachel Karban¹², Edwin Liu, MD¹³, Jill Norris, PhD^2,3,12, Adela Samper-Imaz, Andrea Steck, MD^3,14, Kathleen Waugh^6,7,12,15, Hali Wright¹². Barbara Davis Center for Childhood Diabetes, University of Colorado Denver.

Finland clinical center: Jorma Toppari, MD, PhD, PI^{¥^1,4,11,14}, Olli G. Simell, MD, PhD^{¥^1,4,11,13}, Annika Adamsson, PhD^{^12}, Suvi Ahonen*^±§, Heikki Hyöty, MD, PhD*^±6, Jorma Ilonen, MD, PhD^¥¶3, Sanna Jokipuu^{^}, Tiina Kallio^{^}, Leena Karlsson^{^}, Miia Kähönen^μ¤, Mikael Knip, MD, PhD*^±5, Lea Kovanen*^±§, Mirva Koreasalo*^±§2, Kalle Kurppa, MD, PhD*^±13, Tiina Latva-aho^μ¤, Maria Lönnrot, MD, PhD*^±6, Elina Mäntymäki^{^}, Katja Multasuo^μ¤, Juha Mykkänen, PhD^¥3, Tiina Niininen^±*¹², Sari Niinistö^±§2, Mia Nyblom*^±, Petra Rajala^{^}, Jenna Rautanen^±§, Anne Riikonen*^±§, Mika Riikonen^{^}, Minna Romo^{^}, Juulia Rönkä^μ¤, Jenni Rouhiainen^{^}, Tuula Simell, PhD, Ville Simell^{^¥13}, Maija Sjöberg^{¥^12,14}, Aino Stenius^μ¤12, Maria Leppänen^{^}, Sini Vainionpää^{^}, Eeva Varjonen^{¥^12}, Riitta Veijola, MD, PhD^μ¤14, Suvi M. Virtanen, MD, PhD*^±§2, Mari Vähä-Mäkilä^{^}, Mari Åkerlund*^±§, Katri Lindfors, PhD*¹³. ^¥University of Turku; *University of Tampere; ^μUniversity of Oulu; ^{^}Turku University Hospital, Hospital District of Southwest Finland; ^±Tampere University Hospital; ^¤Oulu University Hospital; ^§National Institute for Health and Welfare, Finland; ^¶University of Kuopio.

Georgia/Florida clinical center: Jin-Xiong She, PhD, PI^1,3,4,11, Desmond Schatz, MD*^4,5,7,8, Diane Hopkins¹², Leigh Steed^12,13,14,15, Jamie Thomas*^6,12, Janey Adams*¹², Katherine Silvis², Michael Haller, MD*¹⁴, Melissa Gardiner, Richard McIndoe, PhD, Ashok Sharma, Joshua Williams, Gabriela Young, Stephen W. Anderson, MD^{^}, Laura Jacobsen, MD*¹⁴. Center for Biotechnology and Genomic Medicine, Augusta University; *University of Florida; ^{^}Pediatric Endocrine Associates, Atlanta.

Germany clinical center: Anette G. Ziegler, MD, PI^1,3,4,11, Andreas Beyerlein, PhD², Ezio Bonifacio PhD*⁵, Michael Hummel, MD¹³, Sandra Hummel, PhD², Kristina Foterek^¥2, Nicole Janz, Mathilde Kersting, PhD^¥2, Annette Knopff⁷, Sibylle Koletzko, MD^¶13, Claudia Peplow¹², Roswith Roth, PhD⁹, Marlon Scholz, Joanna Stock^9,12,14, Katharina Warncke, MD¹⁴, Lorena Wendel, Christiane Winkler, PhD^2,12,15. Forschergruppe Diabetes e.V. at Helmholtz Zentrum München; Institute of Diabetes Research, Helmholtz Zentrum München; Technical University of Munich; *Center for Regenerative Therapies, Technische Universität Dresden; ^¶Department of Gastroenterology, Dr. von Hauner Children’s Hospital, Ludwig-Maximillian University of Munich; ^¥Research Institute for Child Nutrition, Dortmund.

Sweden clinical center: Åke Lernmark, PhD, PI^{1,3,4,5,6,8,10,11,15}, Daniel Agardh, MD, PhD¹³, Carin Andrén Aronsson^2,12,13, Maria Ask, Jenny Bremer, Ulla-Marie Carlsson, Corrado Cilio, PhD, MD⁵, Emelie Ericson-Hallström, Lina Fransson, Thomas Gard, Joanna Gerardsson, Rasmus Bennet, Monica Hansen, Gertie Hansson, Susanne Hyberg, Fredrik Johansen, Berglind Jonsdottir, MD, Helena Elding Larsson, MD, PhD^6,14, Marielle Lindström, Markus Lundgren, MD¹⁴, Maria Månsson-Martinez, Maria Markan, Jessica Melin¹², Zeliha Mestan, Karin Ottosson, Kobra Rahmati, Anita Ramelius, Falastin Salami, Sara Sibthorpe, Birgitta Sjöberg, Ulrica Swartling, PhD^9,12, Evelyn Tekum Amboh, Carina Törn, PhD^3,15, Anne Wallin, Åsa Wimar^12,14, Sofie Åberg. Lund University.

Washington clinical center: William A. Hagopian, MD, PhD, PI^{1,3,4,5,6,7,11,13,14}, Michael Killian^6,7,12,13, Claire Cowen Crouch^12,14,15, Jennifer Skidmore², Josephine Carson, Maria Dalzell, Kayleen Dunson, Rachel Hervey, Corbin Johnson, Rachel Lyons, Arlene Meyer, Denise Mulenga, Alexander Tarr, Morgan Uland, John Willis. Pacific Northwest Diabetes Research Institute.

Pennsylvania satellite center: Dorothy Becker, MD, Margaret Franciscus, MaryEllen Dalmagro-Elias Smith², Ashi Daftary, MD, Mary Beth Klein, Chrystal Yates. Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh Medical Center.

Data coordinating center: Jeffrey P. Krischer, PhD, PI^1,4,5,10,11, Michael Abbondondolo, Sarah Austin-Gonzalez, Maryouri Avendano, Sandra Baethke, Rasheedah Brown^12,15, Brant Burkhardt, PhD^5,6, Martha Butterworth², Joanna Clasen, David Cuthbertson, Christopher Eberhard, Steven Fiske⁹, Dena Garcia, Jennifer Garmeson, Veena Gowda, Kathleen Heyman, Francisco Perez Laras, Hye-Seung Lee, PhD^1,2,13,15, Shu Liu, Xiang Liu, PhD^2,3,9,14, Kristian Lynch, PhD^5,6,9,15, Jamie Malloy, Cristina McCarthy^12,15, Steven Meulemans, Hemang Parikh, PhD³, Chris Shaffer, Laura Smith, PhD^9,12, Susan Smith^12,15, Noah Sulman, PhD, Roy Tamura, PhD^1,2,13, Ulla Uusitalo, PhD^2,15, Kendra Vehik, PhD^4,5,6,14,15, Ponni Vijayakandipan, Keith Wood, Jimin Yang, PhD, RD^2,15. Past staff: Lori Ballard, David Hadley, PhD, Wendy McLeod. University of South Florida.

Autoantibody reference laboratories: Liping Yu, MD^{^5}, Dongmei Miao, MD^{^}, Polly Bingley, MD, FRCP*⁵, Alistair Williams*, Kyla Chandler*, Saba Rokni*, Claire Williams*, Rebecca Wyatt*, Gifty George*, Sian Grace*. ^{^}Barbara Davis Center for Childhood Diabetes, University of Colorado Denver; *School of Clinical Sciences, University of Bristol.

HLA reference laboratory: Henry Erlich, PhD³, Steven J. Mack, PhD, Anna Lisa Fear. Center for Genetics, Children’s Hospital Oakland Research Institute.

Repository: Sandra Ke, Niveen Mulholland, PhD. NIDDK Biosample Repository at Fisher BioServices.

SNP laboratory: Stephen S. Rich, PhD³, Wei-Min Chen, PhD³, Suna Onengut-Gumuscu, PhD³, Emily Farber, Rebecca Roche Pickin, PhD, Jordan Davis, Dan Gallo, Jessica Bonnie, Paul Campolieto. Center for Public Health Genomics, University of Virginia.

Project scientist: Beena Akolkar, PhD^{1,3,4,5,6,7,10,11}. National Institute of Diabetes and Digestive and Kidney Diseases.

Other contributors: Kasia Bourcier, PhD⁵, National Institute of Allergy and Infectious Diseases. Thomas Briese, PhD^6,15, Columbia University. Suzanne Bennett Johnson, PhD^9,12, Florida State University. Eric Triplett, PhD⁶, University of Florida.

Committees: ¹Ancillary Studies, ²Diet, ³Genetics, ⁴Human Subjects/Publicity/Publications, ⁵Immune Markers, ⁶Infectious Agents, ⁷Laboratory Implementation, ⁸Maternal Studies, ⁹Psychosocial, ¹⁰Quality Assurance, ¹¹Steering, ¹²Study Coordinators, ¹³Celiac Disease, ¹⁴Clinical Implementation, ¹⁵Quality Assurance Subcommittee on Data Quality.