Evidence map›Paper›PMID 42587193›Full record

ReviewDiabetologia2026

Development and application of type 1 diabetes polygenic scores across diverse populations.

Despoina Manousaki, Dominika A Michalek, Hui-Qi Qu, Lauric A Ferrat, Aaron J Deutsch

Abstract readReview
PubMed Publisher
In one paragraph

Review in Diabetologia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Despoina Manousaki *Azrieli Research Center of the CHU Sainte-Justine University Hospital, Université de Montréal, Montréal, QC, Canada.ORCID http://orcid.org/0000-0002-4133-0618
Dominika A Michalek *Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA.ORCID http://orcid.org/0000-0002-0634-0319
Hui-Qi QuThe Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.ORCID http://orcid.org/0000-0001-9317-4488
Lauric A FerratDepartment of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0002-3166-9685
Aaron J DeutschDiabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. ajdeutsch@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-6750-5335

Funding

CIHR 202410EA6-543538-EDA-CFCF-281927Division of Diabetes, Endocrinology, and Metabolic Diseases K23 DK140643Division of Diabetes, Endocrinology, and Metabolic Diseases R01 DK122586JDRF 2-SRA-2022-1262-S-BJDRF 3-SRA-2025-1790-S-BLeona M. and Harry B. Helmsley Charitable Trust 2204-05134Santé Junior 2 Scholar
6 · The paper itself

Abstract

Type 1 diabetes is highly influenced by genetic risk factors, especially variation in the HLA genes. Polygenic scores can strongly predict type 1 diabetes risk by integrating variants across the genome. Most polygenic scores perform best in European-ancestry populations, due to low representation of other ancestry groups in existing studies, as well as substantial variation in HLA alleles across the globe. However, recent multi-ancestry genome-wide association studies have broadened our understanding of type 1 diabetes genetic risk, and advanced computational techniques can accurately capture HLA alleles at high resolution. This article will review novel approaches to develop type 1 diabetes polygenic scores that demonstrate high predictive power across diverse populations.

Indexed as

DiversityGenetic ancestryGenetic risk scoreGeneticsGWASHLAMulti-ancestryPolygenic scoreReviewType 1 diabetes

Identifiers

PMID42587193

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.