Evidence map›Paper›PMID 40791705›Full record

ArticlemedRxiv : the preprint server for health sciences2025

The impact of ancestry on performance of type 1 diabetes genetic risk scores: high discrimination performance is maintained in African ancestry populations, but population specific thresholds may improve risk prediction.

Steven Squires, Jean Claude Katte, Dana Dabelea, Catherine Pihoker, Jasmin Divers, Eugene Sobngwi, Moffat J Nyirenda, Raymond J Kreienkamp, Angela D Liese, Amy S Shah and 10 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

20 authors.

Steven SquiresClinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter.ORCID 0000-0003-3681-1342
Jean Claude KatteClinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter.
Dana DabeleaLifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center Colorado School of Public Health, University of Colorado Anschutz Medical Campus.
Catherine PihokerUniversity of Washington, Seattle, WA, USA.
Jasmin DiversDepartment of Foundations of Medicine, NYU Grossman Long Island School of Medicine.
Eugene SobngwiDepartment of Internal Medicine and Specialities, Faculty of Medicine and Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon.ORCID 0000-0001-5457-6572
Moffat J NyirendaNon-Communicable Diseases Theme, Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe, Uganda.
Raymond J KreienkampBoston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Angela D LieseArnold School of Public Health.
Amy S ShahCincinnati Children's Hospital Medical Center and The University of Cincinnati, Cincinnati OH, USA.
Lawrence DolanCincinnati Children's Hospital Medical Center and The University of Cincinnati, Cincinnati OH, USA.
Kristi ReynoldsDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA.
Maria J RedondoTexas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA.
William HagopianPacific Northwest Diabetes Research Institute, University of Washington.
Segun FatumoPrecision Healthcare University Research Institute (PHURI), Queen Mary University of London, London, UK.
Mesmin Y DehayemNational Obesity Centre and The Endocrinology and Metabolic Diseases Unit, Yaoundé Central Hospital, Yaoundé, Cameroon.
Andrew HattersleyClinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter.ORCID 0000-0001-5620-473X
Michael N WeedonClinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter.
Angus JonesClinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter.
Richard A OramClinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter.

Funding

Type 1 diabetes genetic risk scores for the diagnosis of diabetes type in children of diverse racial and ethnic backgroundR01DK124395 · NIDDK · BAYLOR COLLEGE OF MEDICINE · PI REDONDO, MARIA JOSE · 2021 to 2024
$2.6M
NIDDK NIH HHS R01 DK124395
6 · The paper itself

Abstract

objectiveGenetic risk scores (GRSs) for type 1 diabetes (T1D) may assist T1D classification and prediction but are often developed from European populations. To improve health outcomes, it is important to understand the performance and utility of GRSs in diverse ancestry populations. RESEARCH DESIGN AND

methodsWe assessed performance of three previously published T1D GRSs in differentiating people with and without Type 1 diabetes in African (with/without T1D=194/235), European (n=1109/125), and Hispanic (266/170) ancestry populations in the USA, and from Cameroon and Uganda (n=144/5001). The assessed GRSs were developed from European ancestry populations (GRS1, GRS2) and from an African ancestry population (AAGRS).

resultsThe discriminative power, as measured by the area under the receiver operating characteristic curve (AUC), for GRS2 and AAGRS were equivalent on the African ancestry populations, and both outperformed the GRS1: the AUCs produced by the GRS2, AAGRS and GRS1 on Uganda/Cameroon data were 0.882 (0.845-0.914), 0.874 (0.838-0.907) and 0.816 (0.772-0.857) respectively. GRS2 outperformed the AAGRS and GRS1 on Hispanic and European populations. The GRS2 distributions varied by population, with lower average scores for African populations. If the same GRS2 risk thresholds of 11.5 were set for European and African populations, the sensitivities were 0.91 and 0.53, respectively.

conclusionsThe GRS2 produced similar or improved discriminative power across the populations but the AAGRS matched performance on African ancestry participants with fewer single nucleotide polymorphisms. Varying GRS2 risk thresholds may be required for different populations due to the divergent distributions.

Identifiers

PMID40791705
PMCPMC12338948

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