| Title: | Whole genome and exome sequencing to implicate genetic variants in rare dementia syndromes |
| Lead Institution: | University of Florida |
| Principal investigator: |
Application 95030
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About
Alzheimer's disease is known as one of the most common causes of dementia. However, many other diseases and conditions can also lead to dementia, such as frontotemporal dementia and posterior cortical atrophy. These rarer conditions include difficulties with memory, language, movement, and behavioral changes. Accurately diagnosing a rare dementia can be challenging and requires enormous time and expense investment, raising great difficulties for individuals and family transitions. Recent advances and decreased costs in next-generation sequencing enable us to investigate genetic causes that are potentially associated with rare dementia syndromes. In this project, we propose to develop computational workflows to identify common and novel variants in rare dementia syndromes using both genomic data, including Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) data, and non-genomic data (e.g., phenotypic and image data) from multiple sources including UK Biobank. In the experimental design, we will perform a series of genic and genome-wide association tests to highlight the candidate genes and variants responsible for the rare dementia syndromes. Furthermore, rare dementia disease-related polygenic risk score will be calculated regarding the candidate genes and variants. This study aims to systematically assess the genetic causes in patients with rare dementia syndromes and prioritize genes and variants that are responsible for the diseases. The expected timeframe of this project is 36 months. The public health impacts are (1) the discovery of novel variants that are associated with rare dementia syndromes, (2) the potential to facilitate the accurate diagnosis on rare dementia and uncover molecular mechanisms underlying the pathology, and (3) the development of specific rare dementia treatments and benefits to the minority groups that would help reduce health disparity.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15269 | An Interpretable Population Graph Network to Identify Rapid Progression of Alzheimer's Disease Using UK Biobank. | Weimin Meng (+7) | 2025 | AMIA Annual Symposium Proceedings |
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