Development and standardization of an improved type 1 diabetes genetic risk score for use in newborn screening and incident diagnosis
Open accessAbstract
OBJECTIVE: Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the “T1D GRS2”) to better discriminate diabetes subtypes and to predict T1D in newborn screening studies. RESEARCH DESIGN AND METHODS: In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores. RESULTS: The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; P < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction. CONCLUSIONS: An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.
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HLA-DR-DQ typing can be used only to increase or decrease the probability of type 1 diabetes.
“Thus, HLA-DR-DQ typing can be used only to increase or decrease the probability of type 1 diabetes and cannot be recommended for routine clinical diagnosis or classification (388).”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [388]
The Introduction (page not provided) describes HLA DR-DQ haplotypes as important factors that increase or decrease type 1 diabetes risk, supporting the probability-related part of the claim. The paper does not say that HLA-DR-DQ typing is limited to that use or recommend against its routine use for clinical diagnosis or classification. (LLM, conf=0.84)
Experimental — accuracy not yet measuredIn individuals who are islet autoantibody positive, HLA-DR-DQ genotyping or Genetic Risk Score analysis will help define the risk of type 1 diabetes.
“In those individuals who are islet autoantibody positive, HLA-DR-DQ genotyping or an analysis of Genetic Risk Score (388,392) will help define the risk of type 1 diabetes.”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [388]
The Introduction identifies HLA-DR-DQ as a major contributor to type 1 diabetes risk and says genetic risk scores improve risk discrimination. The Conclusions report that a model combining autoantibody data, a genetic risk score, age, and a metabolic score predicted progression in at-risk relatives. The paper does not establish that HLA-DR-DQ genotyping or a genetic risk score alone defines risk specifically among autoantibody-positive individuals. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPolygenic risk scores alone already predict breast cancer, prostate cancer, and type 1 diabetes risk in European descent patients more accurately than current clinical models.
“For example, polygenic risk scores alone already predict breast cancer, prostate cancer, and type 1 diabetes risk in European descent patients more accurately than current clinical models2–4.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [4]
The Results and Conclusions (pages not provided) show that the type 1 diabetes genetic risk score predicts risk in cohorts of European descent and improves simulated newborn screening over HLA genotyping alone and earlier genetic scores. They do not show that the score alone outperforms current clinical models. This citation is assessed for the type 1 diabetes portion of the sentence, not the breast or prostate cancer portions supported by other citations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA genetic risk score may be used at birth to identify children with a particularly high genetic risk of development of islet autoimmunity or type 1 diabetes.
“A genetic risk score may be used at birth to identify children with a particularly high genetic risk of development of islet autoimmunity or type 1 diabetes (388,390,392).”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [388]
In a simulation using one such genetic risk score, the majority of those who would go on to develop type 1 diabetes, >77%, could be identified within just 10% of the general population.
“In a simulation using one such genetic risk score, the majority of those who would go on to develop type 1 diabetes, >77%, could be identified within just 10% of the general population, identifying a subset who may most benefit from autoantibody testing (81).”
From 2. Diagnosis and classification of diabetes: standards of care in diabetes-2025 (2025) · cites this paper as [81]
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- Authors
- Sharp, S. A., Rich, S. S., Wood, A. R., Jones, S. E., Beaumont, R. N., Harrison, J. W., Schneider, D. A., Locke, J. M., Tyrrell, J., Weedon, M. N., Hagopian, W. A., & Oram, R. A.
- Affiliations
- University of Exeter (GB)
- University of Virginia (US)
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.
- Pacific Northwest Research Institute (US)
- Corresponding author
- Hagopian, William A.
- Oram, Richard A.
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2019-01-11
- Volume
- 42
- Issue
- 2
- Pages
- 200-207
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC6341291
- OpenAlex
- W2909847496
- Semantic Scholar
- b67917cf6ed50c0cb955b563cf620bce6925e244
- Cited by
- 366
- References
- 36
- Citations by year
- 2026: 41 · 2025: 83 · 2024: 60 · 2023: 55 · 2022: 41 · 2021: 42 · 2020: 27 · 2019: 17
- FWCI
- 19.14
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Care (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Open Research Exeter (University of Exeter) (acceptedVersion)
- Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion)
- Funders
- Diabetes UK (17/0005757, 16/0005529)
- National Institute of Diabetes and Digestive and Kidney Diseases (U01-DK-063829, R01-DK-096926, 2T32-DK-00724)
- Wellcome Trust (WT097835MF)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- type 1 diabetes, polygenic risk score, HLA alleles, HLA haplotypes, newborn screening, early-onset type 1 diabetes
- MeSH terms
- Alleles, Diabetes Mellitus, Type 1, Female, Genetic Testing, United Kingdom, Haplotypes, HLA Antigens, Humans, Infant, Newborn, Male, Reference Standards, Research Design, Risk Factors, Incidence, Neonatal Screening, Case-Control Studies, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Quality Improvement
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