Advanced AI to find genetic causes and protections for Alzheimer's disease
Causal and integrative deep learning for Alzheimer's disease genetics
Researchers are using new deep-learning methods on large human genetic and brain datasets to uncover genes and biological factors that raise or lower the risk of Alzheimer's disease for people with or at risk of Alzheimer's.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Minnesota NIH-funded |
| Lab location | 1 site (Minneapolis, United States) |
| Project ID | NIH-11164569 on NIH RePORTER |
What this research studies
From a patient's perspective, the team will combine large public human datasets—like GWAS results, whole-genome sequencing, other omics, and brain imaging—to look for genetic and biological factors tied to Alzheimer's. They will build and train deep neural-network methods that expand current transcriptome-based approaches and instrumental-variable techniques to better separate likely causal genes from misleading signals. Most of the work is computational using existing human data rather than enrolling new patients, though findings could guide later studies that ask people to donate samples or join clinical trials. The aim is to produce clearer targets and pathways that researchers can follow up with diagnostic tests or treatments.
Who could benefit from this research
Good fit: Ideal candidates for related future efforts would be people with Alzheimer's disease or those at high risk who are willing to contribute genetic, omics, or brain imaging data to research.
Not a fit: People without available genetic or brain imaging data, or those whose cognitive problems are clearly due to non-Alzheimer causes, may not benefit directly from this work.
Why it matters
Potential benefit: If successful, this work could reveal genes and biological pathways that cause or protect against Alzheimer's, guiding development of new diagnostics, biomarkers, or therapies.
How similar studies have performed: Previous GWAS and TWAS approaches have identified many genetic links to Alzheimer's but often cannot prove causality, so this deep-learning extension is promising but not yet proven in patients.
Where this research is happening
Minneapolis, United States
- University of Minnesota — Minneapolis, United States (Active)
Researchers
- Principal investigator: Pan, Wei — University of Minnesota
- Study coordinator: Pan, Wei
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.