How genes and aging shape Alzheimer’s and related dementias using medical records and DNA banks
Statistical Framework for Unraveling Age-Dependent Genetic Landscape of Alzheimer's Disease and Related Dementias: Harnessing Large-Scale EHR and DNA-Biobank Integration
We are building tools to find age-related genetic factors linked to Alzheimer’s and related dementias using DNA and electronic health records from many people.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Columbia University Health Sciences NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-11295482 on NIH RePORTER |
What this research studies
This project will analyze DNA and electronic health records from large biobanks to learn how genetic factors change with age and relate to Alzheimer’s and related dementias. Researchers will create a comprehensive set of age-dependent health measures and use a new network-based method to map genetic links across symptoms and coexisting conditions over time. The team will apply deep-learning imputation to handle missing information in records and include diverse populations to improve general relevance. The goal is to reveal genetic pathways that interact with aging and suggest targets for earlier detection or future therapies.
Who could benefit from this research
Good fit: Ideal candidates are older adults or people with Alzheimer’s or related dementias who have linked DNA data and longitudinal electronic health records in a biobank or healthcare system.
Not a fit: People without linked genetic and health-record data, younger individuals without age-related symptoms, or those whose memory problems stem from non‑AD causes may not directly benefit.
Why it matters
Potential benefit: Could help identify age-linked genetic risk patterns that enable earlier detection, better risk prediction, and new targets for treatments.
How similar studies have performed: Large biobank and genome-wide studies have identified many Alzheimer-related genes, but combining age-dependent networks across diverse EMRs with deep-learning imputation is a newer approach with limited precedents.
Where this research is happening
New York, United States
- Columbia University Health Sciences — New York, United States (Active)
Researchers
- Principal investigator: Wei, Ying — Columbia University Health Sciences
- Study coordinator: Wei, Ying
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.