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Monogenic diabetes, including maturity\u2010onset diabetes of the young (MODY) and mitochondrial diabetes, highlights the importance of genetic diagnosis for subtype\u2010specific care. For type 2 diabetes (T2D), genome\u2010wide association studies (GWAS) using large\u2010scale biobank resources have identified numerous risk loci across the genome. These studies have emphasized characteristic features of East Asian T2D, including onset at lower BMI and relatively greater contributions from beta\u2010cell dysfunction pathway. Genetic studies of gestational diabetes mellitus (GDM) have revealed both T2D\u2010related and predominantly gestational components. For type 1 diabetes (T1D), population\u2010specific HLA and non\u2010HLA immune\u2010related loci and possible links to infectious exposures may partly explain global variation in incidence, including lower rates in Asian populations. Polygenic risk scores (PRS) can support risk stratification for diabetes onset and complications, but their implementation in Asian populations is limited by reduced cross\u2010population transferability. Ancestry\u2010matched and trans\u2010ancestry approaches are therefore needed to improve prediction and reduce disparities. Subtype\u2010aware frameworks, including clinical clustering, pathway\u2010specific PRS, and BMI\u2010stratified PRS, may further improve mechanistic interpretation and prediction. Expanding genomic resources across East, Southeast, and South Asia and promoting cross\u2010biobank studies will be essential for improving diabetes genomic research, PRS validation, and equitable precision diabetes care.<\/jats:p>","DOI":"10.1111\/jdi.70409","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T06:30:15Z","timestamp":1785565815000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Genetic basis of Asian diabetes"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1197-0574","authenticated-orcid":false,"given":"Takafumi","family":"Ojima","sequence":"first","affiliation":[{"name":"Department of Genome Informatics, Graduate School of Medicine The University of Tokyo  Tokyo Japan"},{"name":"Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences  Yokohama Japan"},{"name":"Department of Statistical Genetics, Graduate School of Medicine The University of Osaka  Suita Japan"},{"name":"Graduate School of Medicine Tohoku University  Sendai Japan"},{"name":"Tohoku Medical Megabank Organization Tohoku University  Sendai Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4827-6404","authenticated-orcid":false,"given":"Toshimasa","family":"Yamauchi","sequence":"additional","affiliation":[{"name":"Department of Diabetes and Metabolic Diseases, Graduate School of Medicine The University of Tokyo  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"issue":"11","key":"e_1_2_16_2_1","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1007\/s00125-021-05639-5","article-title":"Do gene\u2013environment interactions have implications for the precision prevention of type 2 diabetes?","volume":"65","author":"S\u00f8rensen 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