ArticleNature genetics2026
Genetic association and machine learning improve the prediction of type 1 diabetes risk.
Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Novel type 1 diabetes polygenic scores improve identification of type 1 diabetes in diverse populations.medRxiv : the preprint server for health sciences · 2026Article
- Genetic association stratified by HLA-DR3 and HLA-DR4 status reveals heterogeneity in pathways of progression to type 1 diabetes.Diabetologia · 2026Article
- Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.Frontiers in genetics · 2026Article
- Single cell and spatial characterization of the human pancreas reveals drivers of beta cell dysfunction in cystic fibrosis.bioRxiv : the preprint server for biology · 2025Article
- Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.Nature reviews. Endocrinology · 2025Review
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9 authors.
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Abstract
Type 1 diabetes (T1D) has a large genetic component, and expanded genetic studies of T1D can enhance biological and therapeutic discovery and improve risk prediction. Here we performed genome-wide genetic association and fine-mapping analyses in 20,355 T1D and 797,363 nondiabetic individuals of European ancestry and in 10,107 T1D and 19,639 nondiabetic individuals at the MHC locus, which identified 160 risk signals. We trained a machine learning model, T1GRS, to predict T1D using genetic risk, which improved classification in Europeans and performed similarly in African Americans, compared to previous scores. T1GRS particularly improved prediction in T1D, with fewer high-risk HLA haplotypes and more complex risk profiles, and revealed 154 nonlinear interactions between MHC and non-MHC loci. Finally, we identified four genetic subclusters based on T1GRS features with significant differences in age of onset and diabetic complications. Overall, improved genetic discovery and prediction will have wide clinical, therapeutic and research applications for T1D.
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