Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabete : a prospective study in children

RESEARCH ARTICLE Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children Ezio Bonifacio 1 , Andreas Beyerlein 2,3,4 , Markus Hippich 2,3,4 , Christiane Winkler 2,3,4 , Kendra Vehik 5 , Michael N. Weedon 6 , Michael Laimighofer 7 , Andrew T. Hattersley 6 , Jan Krumsiek 7 , Brigitte I. Frohnert 8 , Andrea K. Steck 8 , William A. Hagopian 9 , Jeffrey P. Krischer 5 ,Åke Lernmark 10 , Marian J. Rewers 8 , Jin-Xiong She 11 , Jorma Toppari 12,13 , Beena Akolkar 14 , Richard A. Oram 6,15,16 , Stephen S. Rich 17 , Anette-G. Ziegler 2,3,4 *, for the TEDDY Study Group ¶ 1DFG–Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universita ¨t Dresden, Dresden, Germany, 2Institute of Diabetes Research, Helmholtz Zentrum Mu¨nchen, Munich, Germany, 3Forschergruppe Diabetes, Technical University of Munich, Klinikum Rechts der Isar, Munich, Germany, 4Forschergruppe Diabetes e.V. at Helmholtz Zentrum Mu¨nchen, Munich, Germany, 5Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida, United States of America, 6Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, United Kingdom, 7Institute of Computational Biology, Helmholtz Zentrum Mu¨nchen, Munich, Germany, 8Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, Colorado, United States of America, 9Pacific Northwest Diabetes Research Institute, Seattle, Washington, United States of America, 10 Department of Clinical Sciences, Clinical Research Centre, Skåne University Hospital, Lund University, Malmo, Sweden, 11 Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, Georgia, United States of America, 12 Department of Pediatrics, Turku University Hospital, Turku, Finland, 13 Department of Physiology, University of Turku, Turku, Finland, 14 National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America, 15 Clinical Islet Transplant Program, University of Alberta, Edmonton, Alberta, Canada, 16 National Institute for Health Research, Exeter Clinical Research Facility, Exeter, United Kingdom, 17 Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America ¶ Membership of the TEDDY Study Group is provided in the Acknowledgments. *[email protected] Abstract Background Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes. Methods and findings The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 1 / 18 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Bonifacio E, Beyerlein A, Hippich M, Winkler C, Vehik K, Weedon MN, et al. (2018) Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children. PLoS Med 15(4): e1002548. https://doi.org/10.1371/journal. pmed.1002548 Academic Editor: Ronald C. W. Ma, Chinese University of Hong Kong, CHINA Received: November 14, 2017 Accepted: March 1, 2018 Published: April 3, 2018 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: The datasets generated and analyzed during the current study will be made 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. developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%–6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%–4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%–13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%–4.9%, P< 0.001) in children with a genetic score of 14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%–9.9%) in children with a merged 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). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%–54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%–60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case–control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations. Conclusions A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials. Author summary Why was this study done? • Prevention of childhood diseases such as type 1 diabetes is of medical importance. • Prevention of type 1 diabetes might be best achieved by intervention prior to the development of islet autoantibodies, which define a pre-symptomatic disease stage. • Early intervention requires tools such as measures of genetic risk that identify future cases. • Risk for type 1 diabetes in the absence of a family history is currently identified by HLA genotyping, with maximum identified risk reaching around 5%. • Genetic scores derived from multiple risk loci may improve risk stratification for presymptomatic type 1 diabetes. Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 2 / 18 Funding: This work was supported by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Juvenile Diabetes Research Foundation (JDRF), and Centers for Disease Control and Prevention (CDC). This work was supported in part by NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082), and by iMed–the Helmholtz Initiative on Personalized Medicine. EB is supported by the DFG Research Center and Cluster of Excellence - Center for Regenerative Therapies Dresden (FZ 111). BA from the NIDDK was involved in the design and conduct of the study as well as the review of the manuscript, and approval to submit the manuscript. Otherwise, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A patent has been applied for (EP17178396/LU100334) with the title "Method the risk to develop type 1 diabetes" by Helmholtz Zentrum Mu¨nchen Deutsches Forschungszentrum fu¨r Gesundheit und Umwelt (GmbH). EB, AGZ, CW and JK are one of the inventors. The patent includes the genetic score that is examined in the manuscript. RAO has a personal funding from Diabetes UK to study the biology of Type 1 diabetes (this includes a research grant to work on genetic risk scores in Type 1 diabetes). RAO has a UK Medical Research Council confidence in concept grant to turn a type 1 diabetes genetic risk score into a diagnostic test for clinical practice. AB, MH, KV, MNW, ML, ATH, BIF, AKS, WAH, JPK, AL, MJR, JXS, JT, BA, SSR have declared that no other competing interests exist. Abbreviations: GADA, glutamic acid decarboxylase antibody; HLA, human leukocyte antigen; IA-2A, insulinoma antigen-2 antibody; IAA, insulin autoantibody; TEDDY, The Environmental Determinants of Diabetes in the Young; WTCCC, Wellcome Trust Case Control Consortium. What did the researchers do and find? • Two previously proposed genetic scores for type 1 diabetes risk were calculated for over 3,000 children without a family history of type 1 diabetes but with 1 of the 2 highest-risk HLA genotypes (heterozygous DR3 and DR4-DQ8 or homozygous DR4-DQ8) participating in the TEDDY cohort study, which prospectively follows children from birth for the development of islet autoantibodies and diabetes. • We found that both of the genetic scores, and a merged genetic score that combined the features of both, stratified the risk for islet autoantibodies and diabetes in the children. • The upper quartile of the merged genetic score was associated with a >10% risk for the pre-symptomatic stage of multiple islet autoantibodies, and almost half the children who developed pre-symptomatic or symptomatic diabetes were identified by this score. What do these findings mean? • Combining genetic information from multiple risk loci can improve the prediction of diseases such as type 1 diabetes. • A genetic risk score model is proposed that could be used to recruit infants into early type 1 diabetes primary prevention trials. • The model provides a new paradigm for genetic screening and selection of at-risk infants that, together with family history and HLA genotyping, could identify up to 25% of future childhood cases of type 1 diabetes from less than 1% of newborns. Introduction Precision medicine typically relies on our ability to identify individuals with precise genetic elements that define a disease. These elements may be used not only to select optimal treatment modalities, but also to identify individuals who may benefit from preventative interventions. In pediatric disease, current studies seeking to elucidate disease etiology, as well as clinical trials aimed at prevention, rely on identifying and enrolling infants with increased risk [1–7]. The risk for diseases such as allergy, type 1 diabetes, and celiac disease is often assessed in terms of family history [1–3,7], which, at best, identifies 10% of children who subsequently develop the condition [7,8]. In type 1 diabetes, genotypes in the human leukocyte antigen (HLA) DRB1, DQA1, and DQB1 loci are sometimes used to identify at-risk infants from the general population [2,9,10]. Risk is 5% in children with the 2 highest-risk HLA genotypes (DR3 and DR4-DQ8 or homozygous for DR4-DQ8), and 40% of cases of childhood type 1 diabetes have 1 of these 2 genotypes [11]. Although the HLA loci are the strongest genetic risk markers for type 1 diabetes, many other regions of the genome also confer susceptibility to type 1 diabetes [12]. Therefore, it is conceivable that risk stratification could be improved if risk is calculated according to genetic information derived from multiple genetic susceptibility regions [13,14]. We previously applied logistic regression to the Type 1 Diabetes Genetics Consortium (T1DGC) case–control dataset and developed a weighted genetic score derived from HLA and 40 type 1 diabetes susceptibility loci (Winkler score) [15]. Independently, a genetic score Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 3 / 18 derived from HLA plus 25 susceptibility loci was developed in the UK using Wellcome Trust Case Control Consortium (WTCCC) data (Oram score) [16]. These studies suggested that the scores might improve our ability to predict and diagnose type 1 diabetes. Hence, genetic scores could become a new paradigm for stratifying type 1 diabetes risk and for recruitment into primary prevention trials, and provide a proof of principle for other diseases with multiple known genetic susceptibility markers. With this in mind, the 2 consortia joined efforts to determine how the 2 genetic scores and a merged score performed in a prospective study. The Environmental Determinants of Diabetes in the Young (TEDDY) study, a multicenter cohort study set in Germany, Finland, Sweden, and the US, has intensively followed several thousand HLA-selected children from birth for the development of islet autoantibodies and of diabetes [17]. The presence of 2 or more islet autoantibodies (multiple islet autoantibodies) in genetically at-risk children defines a pre-symptomatic stage of type 1 diabetes where progression to type 1 diabetes is around 80% over 10 years [18,19]. TEDDY offers the unique opportunity to test the multiple-locus genetic scores in a prospectively studied cohort of children who have high-risk HLA genotypes in the absence of family history of type 1 diabetes [2,17]. The objective of our analysis was to determine whether the genetic scores could identify infants from the general population who had at least a 10% risk for type 1 diabetes, a risk threshold that has been used for primary prevention trials and that has previously only been achievable in infants with a family history of type 1 diabetes [3]. Methods 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 nondiabetic 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 Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 4 / 18 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. 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 Fig 1. Flow diagram of the TEDDY study participants included in this analysis. https://doi.org/10.1371/journal.pmed.1002548.g001 Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 5 / 18 [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 Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 6 / 18 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 Utest. 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. Results 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 Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 7 / 18 (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). 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 (Bonferronicorrected Pof 0.05/43 = 0.0012; S2 Table). Scores were not different between boys and girls (P = 0.69; Fig 3C and S2 Fig). 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 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 and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 8 / 18 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). 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 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 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 Genetic scores and risk for type 1 diabetes PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002548 April 3, 2018 9 / 18 SNP laboratory: Stephen S. Rich, PhD 3 , Wei-Min Chen, PhD 3 , Suna Onengut-Gumuscu, PhD 3 , 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, PhD 5 , 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, PhD 6 , University of Florida. Committees: 1 Ancillary Studies, 2 Diet, 3 Genetics, 4 Human Subjects/Publicity/Publications, 5 Immune Markers, 6 Infectious Agents, 7 Laboratory Implementation, 8 Maternal Studies, 9 Psychosocial, 10 Quality Assurance, 11 Steering, 12 Study Coordinators, 13 Celiac Disease, 14 Clinical Implementation, 15 Quality Assurance Subcommittee on Data Quality. Author Contributions Conceptualization: Ezio Bonifacio, Markus Hippich, Christiane Winkler, Anette-G. Ziegler. Data curation: Ezio Bonifacio, Christiane Winkler, Kendra Vehik, Michael N. Weedon, Michael Laimighofer, Andrew T. Hattersley, Jan Krumsiek, Brigitte I. Frohnert, Andrea K. Steck, William A. Hagopian, Jeffrey P. Krischer, Åke Lernmark, Marian J. Rewers, JinXiong She, Jorma Toppari, Richard A. Oram, Stephen S. Rich, Anette-G. Ziegler. Formal analysis: Ezio Bonifacio, Andreas Beyerlein, Markus Hippich, Christiane Winkler, Kendra Vehik, Michael N. Weedon, Michael Laimighofer, Andrew T. Hattersley, Jan Krumsiek, Richard A. Oram, Anette-G. Ziegler. Funding acquisition: Åke Lernmark, Marian J. Rewers, Jin-Xiong She, Jorma Toppari, Beena Akolkar, Anette-G. Ziegler. Investigation: Ezio Bonifacio, Markus Hippich, Christiane Winkler, Michael N. Weedon, Andrew T. Hattersley, William A. Hagopian, Åke Lernmark, Marian J. Rewers, Jorma Toppari, Beena Akolkar, Richard A. Oram, Stephen S. Rich, Anette-G. Ziegler. Methodology: Ezio Bonifacio, Andrew T. Hattersley, Stephen S. Rich, Anette-G. Ziegler. Project administration: Christiane Winkler, William A. Hagopian, Jeffrey P. Krischer, Beena Akolkar. Resources: Anette-G. Ziegler. 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