Improved genetic risk scoring algorithm for type 1 diabetes prediction. Issue 3 (19th January 2022)

• Record Type: Journal Article
• Authors: Qu, Hui‐Qi; Qu, Jingchun; Glessner, Joseph; Liu, Yichuan; Mentch, Frank; Chang, Xiao; March, Michael; Li, Jin; Roizen, Jeffrey D.; Connolly, John J.; Sleiman, Patrick; Hakonarson, Hakon
• Is Part Of: Pediatric diabetes. Volume 23:Issue 3(2022)
• Page Start: 320
• Page End: 323
• Publication Date: 2022-01-19
• Subjects: eMERGE -- genetic risk score -- PRS -- screening -- type 1 diabetes; Diabetes in children -- Periodicals; 616.462
• DOI: 10.1111/pedi.13310
• Languages: English
• ISSNs: 1399-543X

Abstract

Abstract: Background: Precise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta‐islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D‐GRS2) capable of predicting T1D risk in children of European ancestry. The T1D‐GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans‐ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities. Methods: Here, we describe a T1D‐GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia. Results: It demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population‐specific thresholds are needed for different populations. Conclusions: The study highlights the potential to further improve T1D‐GRS2 performance with the inclusion of additional genetic markers.