| Title: | Elucidating the genetic basis of type 1 diabetes |
| Lead Institution: | Jerusalem Multidisciplinary College |
| Principal investigator: | Dr Danny Zeevi |
Application 74655
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About
Type 1 Diabetes affects over 20 million people worldwide, and it has severe and costly implications on the health of patients. A better understanding of its genetic basis is crucial for its early diagnosis and treatment. Twin and family studies show Type 1 Diabetes to be a highly heritable disease. Yet, despite intensive research, most of the genetic basis of Type 1 Diabetes is still unknown. It has been suggested that rare genetic variants account for much of the risk of developing Type 1 Diabetes. However, identification of such rare variants requires large cohorts, whole-genome sequencing data, and detailed phenotypes in order to identify sub-types of the disease that are caused by different genetic variants. The UK Biobank data includes all of these features. We intend to use the UK Biobank data to achieve the following aims: 1) Identify novel and rare variants that are associated with the risk of developing Type 1 Diabetes. We will use the phenotype records and biomarkers data to identify Type 1 Diabetes patients, compare their genomes to healthy controls, and identify genetic variants that are associated with the disease. 2) Validate in the UK Biobank cohort genetic variants that were identified as associated with Type 1 Diabetes in other cohorts. 3) Develop and improve methods for calculating the risk for developing Type 1 Diabetes from genetic data. The project duration will be 36 months. Our research findings may open new therapeutic avenues that lead to developing novel treatments for Type 1 Diabetes. Identifying novel rare variants with large effects on the risk of developing Type 1 Diabetes could allow for tailoring specific treatments for patients and developing new treatments based on the genes involved. Identifying such variants could also be used to improve the estimation of the risk of developing Type 1 Diabetes in siblings and children of Type 1 Diabetes patients, thus aiding in decisions on the frequency and type of monitoring of such individuals.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 16628 | TinkerHap - A Novel Read-Based Phasing Algorithm with Integrated Multi-Method Support for Enhanced Accuracy | Uri Hartmann (+4) | 2025 | GigaScience |
Enabling scientific discoveries that improve human health