ArticleGenetic epidemiology2023
Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes.
Article in Genetic epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study.PloS one · 2026Article
- Polygenic score from MODY genes is associated with type 1 diabetes and disease characteristics.Acta diabetologica · 2025Article
- Polygenic risk score for type 2 diabetes shows context-dependent effects across populations.Nature communications · 2025Article
- Integrating Polygenic Risk Scores (PRS) for Personalized Diabetes Care: Advancing Clinical Practice with Tailored Pharmacological Approaches.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2025Review
- Validation of GenProb-T1D and its clinical utility for differentiating types of diabetes in a biobank from a US healthcare system.Journal of diabetes investigation · 2025Article
- Genome-Wide Association Study with Three Control Cohorts of Japanese Patients with Esotropia and Exotropia of Comitant Strabismus and Idiopathic Superior Oblique Muscle Palsy.International journal of molecular sciences · 2024Article
- Recent advances in polygenic scores: translation, equitability, methods and FAIR tools.Genome medicine · 2024Review
- Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Polygenic risk scores (PRS) quantify the genetic liability to disease and are calculated using an individual's genotype profile and disease-specific genome-wide association study (GWAS) summary statistics. Type 1 (T1D) and type 2 (T2D) diabetes both are determined in part by genetic loci. Correctly differentiating between types of diabetes is crucial for accurate diagnosis and treatment. PRS have the potential to address possible misclassification of T1D and T2D. Here we evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC] = 0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC = 0.792) and separation in MGI (AUC = 0.686). In contrast, the best T2D model did not discriminate between T1D and T2D cases (AUC = 0.527). Our analysis suggests that a T1D PRS model based on independent single nucleotide polymorphisms may help differentiate between T1D, T2D, and controls in individuals of European genetic ancestry.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.