| Title: | Type 1 diabetes fine-mapping using related traits and meta-analyses with existing T1D data |
| Lead Institution: | University of Oxford |
| Principal investigator: | Professor John Todd |
Application 31295
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About
We have developed a method to jointly analyse multiple correlated traits to identify the most likely causal DNA variants associated with both traits simultaneously. The wealth of medical information offered by the UK Biobank makes it the perfect platform to implement our method, applying it initially to type 1 diabetes (T1D) and blood biomarkers and other traits we expect to correlate with T1D status (lymphocyte count, birth weight, Body Mass Index, thyroid disease etc.). By identifying the genetic variants that regulate both the disease risk and a relevant, related, trait, we may be able to point to the mechanism that variants act through to alter T1D risk. We would also like to combine UK Biobank data with DNA data from other individuals either with or without T1D to increase our chances of detecting differences in DNA sequences between individuals with T1D and those without the disease. By doing this, we will enhance understanding of the genetic underpinning of the disease. New susceptibility genes will be discovered which may point towards new therapeutic targets.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14572 | Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow-Up Studies | Daniel J. M. Crouch (+13) | 2025 | Genetic Epidemiology |
Enabling scientific discoveries that improve human health