Development and validation of a Trans-Ancestry polygenic risk score for Type 1 Diabetes
Notice bibliographique
Résumé
Abstract Objectives The high heritability of type 1 diabetes has enabled the development of polygenic risk scores (PRS) as disease risk screening tools. PRS can identify individuals at the highest genetic risk in a population, who can benefit from autoantibody and metabolic surveillance, to avoid ketoacidosis at diagnosis and access preventive therapies. However, PRS for type 1 diabetes developed from European data perform less well in non-European ancestries. We aimed to develop a PRS with comparable performance among different ancestries. Methods Using a the PRS-CSx method, and data from large European, East-Asian, African-American and Hispanic type 1 diabetes GWAS (N total_cases =29,469), we developed a trans-ancestry PRS (TA-PS), combining a non- HLA component incorporating over a million variants, with the HLA component of a published European PRS (GRS2x). We tested the performance of the PRS using AUROC, sensitivity and specificity in a multi-ancestry T1D case-control cohort (N total = 4,657; N non-European =556) from Montreal, Canada. We validated our results in two independent T1D case-control cohorts (CHOP-CAG and GRACE) and two population-based cohorts (All of Us and UK Biobank). Results In our multi-ancestry Montreal-based cohort, TA-PS showed an AUROC of 0.89 which was significantly higher from the AUROC of 0.85 of GRS2x. At a 90th percentile cut-off, in African-Americans, the sensitivity of GRS2x was 0.32, compared to 0.56 in Europeans. For TA-PS, we obtained overall better sensitivities, ranging from 0.71 in Europeans to 0.77 in South Asians. TA-PS demonstrated slightly lower albeit acceptable specificity compared to that of GRS2x (> 0.83 across all ancestries). These results were validated in the four independent cohorts. Conclusion We developed a trans-ancestry PRS that outperformed the European-based GRS2x. Importantly, TA-PS provides a comparable prediction in various ancestries, which supports its use in population-wide screening programs. Research in context What is already known about this subject? - Polygenic risk scores (PRS) for type 1 diabetes are primarily developed using data from individuals of European ancestry. - The widely used, European-based GRS2 score shows reduced performance in non-European populations, particularly among individuals of African descent. - There are concerns regarding the equity of genetic risk prediction in population-based screening programs for T1D. What is the key question? - Can a trans-ancestry PRS provide accurate and equitable type 1 diabetes risk prediction across ancestries? What are the new findings? - A new trans-ancestry score, TA-PS, was developed by integrating an optimized non- HLA PRS to GRS2. - Compared to GRS2, in multi ancestry case-control and population-based cohorts, TA-PS improves sensitivity across all ancestry groups while maintaining high specificity. How might this impact on clinical practice in the foreseeable future? - TA-PS could provide equitable genetic risk stratification in population-wide screening programs for type 1 diabetes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».