Novel type 1 diabetes polygenic scores improve identification of type 1 diabetes in diverse populations

Abstract

Most polygenic scores (PS) for type 1 diabetes were developed using European (EUR) ancestry datasets, limiting performance in non-European (non-EUR) populations. We evaluated novel type 1 diabetes PS across diverse populations to assess whether newer models improve prediction in underrepresented populations. Seven type 1 diabetes PS (T1D GRS2, T1D GRS2', T1D GRSHLA, T1GRS, TA-PS, TA-PS (S), T1D MAPS) were evaluated in All of Us and Mass General Brigham Biobank. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). T1D GRS2 and T1D MAPS showed the highest AUC in All of Us, while other scores demonstrated comparatively lower performance. These highest-performing scores were then evaluated in a meta-analysis across three additional biobanks (Genomic Health Initiative at Endeavor Health, Penn Medicine BioBank, Geisinger MyCode). Among 2,782 individuals with type 1 diabetes and 546,577 controls, T1D MAPS showed comparable performance to T1D GRS2 in EUR populations but significantly improved discrimination in non-EUR populations (meta-analysis {Delta}AUC=0.049, p=1.7x10-7). Overall, T1D MAPS improves prediction in non-EUR populations, highlighting the importance of multi-ancestry approaches for equitable genetic risk prediction. These findings could inform precision medicine in diverse populations.

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BibTeX RIS

BibTeX

@misc{nam2026novel,
  title = {{Novel type 1 diabetes polygenic scores improve identification of type 1 diabetes in diverse populations}},
  author = {S. Nam and R. J. Kreienkamp and J. Li and R. Mandla and H. Tran and K. Taylor and M. Vora and A. Huerta-Chagoya and J. Wei and A. J. Mulford and A. R. Sanders and J. Xu and L. K. Billings and B. Pasaniuc and D. J. Carey and U. L. Mirshahi and J. C. Florez and A. K. Manning and J. M. Mercader and M. S. Udler and A. J. Deutsch},
  howpublished = {medRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.04.26361335},
  url = {https://www.medrxiv.org/content/10.64898/2026.09.04.26361335v1},
  note = {CC BY-NC},
}

RIS

TY  - UNPB
TI  - Novel type 1 diabetes polygenic scores improve identification of type 1 diabetes in diverse populations
AU  - S. Nam
AU  - R. J. Kreienkamp
AU  - J. Li
AU  - R. Mandla
AU  - H. Tran
AU  - K. Taylor
AU  - M. Vora
AU  - A. Huerta-Chagoya
AU  - J. Wei
AU  - A. J. Mulford
AU  - A. R. Sanders
AU  - J. Xu
AU  - L. K. Billings
AU  - B. Pasaniuc
AU  - D. J. Carey
AU  - U. L. Mirshahi
AU  - J. C. Florez
AU  - A. K. Manning
AU  - J. M. Mercader
AU  - M. S. Udler
AU  - A. J. Deutsch
PY  - 2026
DA  - 2026/09/04
PB  - medRxiv
DO  - 10.64898/2026.09.04.26361335
UR  - https://www.medrxiv.org/content/10.64898/2026.09.04.26361335v1
AB  - Most polygenic scores (PS) for type 1 diabetes were developed using European (EUR) ancestry datasets, limiting performance in non-European (non-EUR) populations. We evaluated novel type 1 diabetes PS across diverse populations to assess whether newer models improve prediction in underrepresented populations. Seven type 1 diabetes PS (T1D GRS2, T1D GRS2', T1D GRSHLA, T1GRS, TA-PS, TA-PS (S), T1D MAPS) were evaluated in All of Us and Mass General Brigham Biobank. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). T1D GRS2 and T1D MAPS showed the highest AUC in All of Us, while other scores demonstrated comparatively lower performance. These highest-performing scores were then evaluated in a meta-analysis across three additional biobanks (Genomic Health Initiative at Endeavor Health, Penn Medicine BioBank, Geisinger MyCode). Among 2,782 individuals with type 1 diabetes and 546,577 controls, T1D MAPS showed comparable performance to T1D GRS2 in EUR populations but significantly improved discrimination in non-EUR populations (meta-analysis {Delta}AUC=0.049, p=1.7x10-7). Overall, T1D MAPS improves prediction in non-EUR populations, highlighting the importance of multi-ancestry approaches for equitable genetic risk prediction. These findings could inform precision medicine in diverse populations.
ER  -

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