Aaron J Deutsch, Andrew S Bell, Dominika A Michalek, Adam B Burkholder, Stella Nam, Raymond J Kreienkamp, Seth A Sharp, Alicia Huerta-Chagoya, Ravi Mandla, Ruth Nanjala, Yang Luo, Richard A Oram, Jose C Florez, Suna Onengut-Gumuscu, Stephen S Rich, Alison A Motsinger-Reif, Alisa K Manning, Josep M Mercader, Miriam S Udler
{"title":"1型糖尿病多祖先多基因评分的建立和验证。","authors":"Aaron J Deutsch, Andrew S Bell, Dominika A Michalek, Adam B Burkholder, Stella Nam, Raymond J Kreienkamp, Seth A Sharp, Alicia Huerta-Chagoya, Ravi Mandla, Ruth Nanjala, Yang Luo, Richard A Oram, Jose C Florez, Suna Onengut-Gumuscu, Stephen S Rich, Alison A Motsinger-Reif, Alisa K Manning, Josep M Mercader, Miriam S Udler","doi":"10.1101/2025.06.20.25329522","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we developed a multi-ancestry polygenic score to accurately predict type 1 diabetes risk across diverse populations.</p><p><strong>Research design and methods: </strong>We used recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2<sub>EUR</sub> and T1D GRS<sub>AFR</sub>. We also developed an updated score (T1D MAPS2) that combines T1D GRS2<sub>EUR</sub> and T1D MAPS.</p><p><strong>Results: </strong>Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2<sub>EUR</sub> (0.82, <i>P</i> = 0.04) and T1D GRS<sub>AFR</sub> (0.82, <i>P</i> = 0.007). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2<sub>EUR</sub> (0.89 vs. 0.91, <i>P</i> = 0.02). However, T1D MAPS2 performed equivalently to T1D GRS2<sub>EUR</sub> in European ancestry (0.91 vs. 0.91, <i>P</i> = 0.45) while still performing better in non-European ancestry (0.90 vs. 0.82, <i>P</i> = 0.04).</p><p><strong>Conclusions: </strong>A novel polygenic score improves type 1 diabetes risk prediction in non-European ancestry while maintaining high predictive power in European ancestry. These findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204267/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of a Type 1 Diabetes Multi-Ancestry Polygenic Score.\",\"authors\":\"Aaron J Deutsch, Andrew S Bell, Dominika A Michalek, Adam B Burkholder, Stella Nam, Raymond J Kreienkamp, Seth A Sharp, Alicia Huerta-Chagoya, Ravi Mandla, Ruth Nanjala, Yang Luo, Richard A Oram, Jose C Florez, Suna Onengut-Gumuscu, Stephen S Rich, Alison A Motsinger-Reif, Alisa K Manning, Josep M Mercader, Miriam S Udler\",\"doi\":\"10.1101/2025.06.20.25329522\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we developed a multi-ancestry polygenic score to accurately predict type 1 diabetes risk across diverse populations.</p><p><strong>Research design and methods: </strong>We used recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2<sub>EUR</sub> and T1D GRS<sub>AFR</sub>. We also developed an updated score (T1D MAPS2) that combines T1D GRS2<sub>EUR</sub> and T1D MAPS.</p><p><strong>Results: </strong>Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2<sub>EUR</sub> (0.82, <i>P</i> = 0.04) and T1D GRS<sub>AFR</sub> (0.82, <i>P</i> = 0.007). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2<sub>EUR</sub> (0.89 vs. 0.91, <i>P</i> = 0.02). However, T1D MAPS2 performed equivalently to T1D GRS2<sub>EUR</sub> in European ancestry (0.91 vs. 0.91, <i>P</i> = 0.45) while still performing better in non-European ancestry (0.90 vs. 0.82, <i>P</i> = 0.04).</p><p><strong>Conclusions: </strong>A novel polygenic score improves type 1 diabetes risk prediction in non-European ancestry while maintaining high predictive power in European ancestry. These findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.</p>\",\"PeriodicalId\":94281,\"journal\":{\"name\":\"medRxiv : the preprint server for health sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204267/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv : the preprint server for health sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2025.06.20.25329522\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2025.06.20.25329522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Development and Validation of a Type 1 Diabetes Multi-Ancestry Polygenic Score.
Objective: Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we developed a multi-ancestry polygenic score to accurately predict type 1 diabetes risk across diverse populations.
Research design and methods: We used recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2EUR and T1D GRSAFR. We also developed an updated score (T1D MAPS2) that combines T1D GRS2EUR and T1D MAPS.
Results: Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2EUR (0.82, P = 0.04) and T1D GRSAFR (0.82, P = 0.007). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2EUR (0.89 vs. 0.91, P = 0.02). However, T1D MAPS2 performed equivalently to T1D GRS2EUR in European ancestry (0.91 vs. 0.91, P = 0.45) while still performing better in non-European ancestry (0.90 vs. 0.82, P = 0.04).
Conclusions: A novel polygenic score improves type 1 diabetes risk prediction in non-European ancestry while maintaining high predictive power in European ancestry. These findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.
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