Tracing gene-to-gene cause-and-effect chains related to Alzheimer's
Estimation and inference in directed acyclic graphical models for biological networks
Building new computer and statistical tools that use genetic differences to map how genes may lead to Alzheimer's, to help researchers find causes and targets.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Minnesota NIH-funded |
| Lab location | 1 site (Minneapolis, United States) |
| Project ID | NIH-11304572 on NIH RePORTER |
What this research studies
This project will create statistical and computational methods that treat genetic differences (SNPs) as natural interventions to reveal cause-and-effect links among genes and Alzheimer's-related traits. The team will focus on very large genetic and molecular datasets where there are many more variables than people, and will handle problems like hidden factors and unreliable genetic markers. The methods aim to reconstruct directed gene networks that show which genetic changes likely drive changes in gene activity and disease risk. Researchers will test these tools using existing Alzheimer-related genetic and molecular data to improve how we identify causal genes and pathways.
Who could benefit from this research
Good fit: People with Alzheimer's disease or mild cognitive impairment, and those who have had genetic testing or agreed to share their genetic and biological data in research, would be most relevant to this work.
Not a fit: Patients without available genetic or molecular data or those looking for an immediate therapy are unlikely to receive direct clinical benefit from this methods-focused project in the near term.
Why it matters
Potential benefit: If successful, these tools could help pinpoint genetic causes of Alzheimer's and highlight new targets for diagnosis, prevention, or treatment development.
How similar studies have performed: Related genetic-causal methods like Mendelian randomization have linked genes to disease, but building high-dimensional directed gene networks that handle invalid instruments and hidden confounders is a newer and less-tested approach.
Where this research is happening
Minneapolis, United States
- University of Minnesota — Minneapolis, United States (Active)
Researchers
- Principal investigator: Shen, Xiaotong Tom — University of Minnesota
- Study coordinator: Shen, Xiaotong Tom
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.