Genetic association and machine learning improve the prediction of type 1 diabetes risk
Carolyn McGrail, Timothy J Sears, Emily N Griffin, Alexandra L Ghaben, Patrick Smadbeck, Jason Flannick, Parul Kudtarkar, Hannah Carter, Kyle Gaulton · 2026
Read Genetic association and machine learning improve the prediction of type 1 diabetes risk by Carolyn McGrail, Timothy J Sears, Emily N Griffin, Alexandra L Ghaben, Patrick Smadbeck, Jason Flannick, Parul Kudtarkar, Hannah Carter, Kyle Gaulton (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13175890.txt.
Description
Carolyn McGrail and co-authors improve in 2026 prediction of type one diabetes risk by joining gene scans with machine learning. The disease has a strong inherited part, and better risk scores can aid care and research. Past scores left room to grow, above all for complex risk profiles.
Gene scans in large groups of European ancestry found one hundred sixty risk signals. A learning model trained on those signals beat older scores in Europeans and held up in African American groups. Gains were largest for people with fewer high-risk gene forms. The model also found many cross-links between gene regions and split patients into four onset groups.
Catalog record
| CREATOR | Carolyn McGrail, Timothy J Sears, Emily N Griffin, Alexandra L Ghaben, Patrick Smadbeck, Jason Flannick, Parul Kudtarkar, Hannah Carter, Kyle Gaulton |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 63 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |