There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Thanaphong Phongpreecha |
| Institution: | Stanford University |
| Department: | Pathology |
| Country: | |
| Proposed Analysis: | Within the genetics study of Alzheimer's disease (AD), previous studies have identified multiple genetic risks for the disease, including APOE, CD33, and more. Many rare variants have also been implicated, such as in PLCG2 and ABI3. However, these identifications are essentially from univariate analyses (excluding pleiotropic effects), ex. GWAS, and hence they remain only risk factors but do not have sufficient predictive power for diagnosis. Recently, studies have shown the usefulness of using multivariate machine learning (ML) models to identify new rare variants that can also predict abdominal aortic aneurysm (AAA) with good accuracy. Here, we hypothesize that there exist novel rare variants in whole genome sequence (WGS) that are identifiable through complex ML and capable of AD. diagnosis. |
| Additional Investigators |
