| Title: | Using statistical genetics and artificial intelligence to predict risk, resilience and age-of-onset in Alzheimer's disease |
| Lead Institution: | Massachusetts General Hospital |
| Principal investigator: | Dr Dmitry Prokopenko |
Application 81874
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About
Alzheimer's disease (AD) is the most common form of dementia and has a substantial burden on patients, families, society and the healthcare system. The goal of this proposal is to apply our knowledge in genetics, statistics and artificial intelligence (AI) to identify and validate associated genetic factors using whole genome sequencing (WGS) data from UK Biobank. Those genetic factors coupled with other nongenetic risk factors will allow better evaluation and prediction of AD risk and age at onset. This research will contribute to knowledge about susceptibility to AD. We will obtain genetic data, population characteristics, as well as other characteristics and combine it with family-based and case-control AD WGS cohorts. We will identify genetic risk and protective factors which lead to cognitive decline and dementia or to resistance to dementia. Finally, we will identify sex-specific and other stratified genetic loci associated with AD and related phenotypes and use available information to better predict AD and its onset.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 14201 | Assessing polyomic risk to predict Alzheimer's disease using a machine learning model | Tiffany Ngai (+10) | 2024 | Alzheimer's & Dementia |
Enabling scientific discoveries that improve human health