Amber M Luckett, Richard A Oram, Aaron J Deutsch, Hector I Ortega, Diane P Fraser, Kaavya Ashok, Alisa K Manning, Josep M Mercader, Manuel Rivas, Miriam S Udler, Michael N Weedon, Anna L Gloyn, Seth A Sharp
{"title":"Standardized Measurement of Type 1 Diabetes Polygenic Risk Across Multi-Ancestry Population Cohorts.","authors":"Amber M Luckett, Richard A Oram, Aaron J Deutsch, Hector I Ortega, Diane P Fraser, Kaavya Ashok, Alisa K Manning, Josep M Mercader, Manuel Rivas, Miriam S Udler, Michael N Weedon, Anna L Gloyn, Seth A Sharp","doi":"10.1101/2025.01.16.25320691","DOIUrl":null,"url":null,"abstract":"<p><p>Type 1 diabetes (T1D) polygenic risk scores (PRS) are effective tools for discriminating T1D from other diabetes types and predicting T1D risk, with applications in screening and intervention trials. A previously published T1D Genetic Risk Score 2 (GRS2) is widely adopted, but challenges in standardization and accessibility have hindered broader clinical and research utility. To address this, we introduce GRS2x, a standardized and cross-compatible method for accurate T1D PRS calculation, demonstrating genotyping and reference panel independent performance across diverse datasets. GRS2x as a unified approach facilitates accessible and portable measurement of T1D polygenic risk.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759247/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2025.01.16.25320691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Type 1 diabetes (T1D) polygenic risk scores (PRS) are effective tools for discriminating T1D from other diabetes types and predicting T1D risk, with applications in screening and intervention trials. A previously published T1D Genetic Risk Score 2 (GRS2) is widely adopted, but challenges in standardization and accessibility have hindered broader clinical and research utility. To address this, we introduce GRS2x, a standardized and cross-compatible method for accurate T1D PRS calculation, demonstrating genotyping and reference panel independent performance across diverse datasets. GRS2x as a unified approach facilitates accessible and portable measurement of T1D polygenic risk.