| Title: | Complementing rare disease studies with data from national cohort projects. |
| Lead Institution: | Queen's University Belfast |
| Principal investigator: | Dr Amy Jayne McKnight |
Application 63533
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About
Approximately six percent of the UK population are affected by a rare disease at some point in their lives. Rare diseases are often associated with a long diagnostic odyssey, significant annual healthcare costs, few effective treatment options, and major impacts on day-to-day living for patients and their families. An accurate diagnosis and early intervention may significantly improve patient outcomes. We plan to combine the rich resources available within UK Biobank with other population-based datasets to improve patient focused clinical care. Our group is keen to discover molecular features that influence rare diseases; we will take a multi-omic approach to improve diagnosis and identify molecular features that influence rare disease pathways. We are also keen to improve support for people with rare diseases, who often experience significant social isolation. UK Biobank provides a very rich resource to compare access to services, pain, activity, loneliness, and support experienced by people with a rare disease compared to common diseases, compared to a group of people of a similar age who are unaffected by these conditions. This study will not directly help UK Biobank participants, but will help deliver robust research to aid clinical decision making and diagnosis, with the hope this could lead to improved outcomes for people with rare diseases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 20179 | Detecting comorbidity patterns in rare disease patients with machine learning | Benjamin Mark Connor (+4) | 2026 | Frontiers in Epidemiology |
Enabling scientific discoveries that improve human health