Linking Genetic Variants to Real-Life Health Effects with Smart AI
Official title: Linking Variants to Multi-scale Phenotypes via a Synthesis of Subnetwork Inference and Deep Learning
Funded at University of Wisconsin-Madison · NIH-11534375 · Data updated 2026-11-12
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Wisconsin-Madison NIH-funded |
| Lab location | 1 site (Madison, United States) |
| Project start | September 1, 2021 |
| Project end | May 31, 2027 |
| Funding year | 2026 |
| Award (this year) | $578,217 |
| NIH project number | 3U01HG012039-05S1 |
| Project ID | NIH-11534375 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with genetic diseases or suspected genetic disorders who undergo genomic testing may benefit from improved interpretation of results.
✕Less likely to help
This work is primarily computational; direct patient benefits may come after validation in clinical settings.
What this research studies
This project aims to build a trainable system that links genetic variants to their effects on how genes work and how cells respond, using both known biology and learned patterns. It combines subnetworks of cellular pathways with machine learning to model genotype-phenotype relationships across different contexts. The team will use active learning to pick the most informative experiments for follow-up by a broader consortium. A new statistical framework will help clarify how variants influence diseases, from common conditions to rare genetic disorders.
Why it matters
Potential benefit: If successful, the work could improve the accuracy of genetic diagnoses and guide personalized treatment decisions based on a person’s unique genetic makeup.
How similar studies have gone: Previous efforts combining pathway information with data-driven models have shown promise in predicting variant effects, but integration across scales remains challenging.
Where this research is happening
-
University of Wisconsin-Madison
Madison, United States Active
Researchers
- Principal investigator: Craven, Mark W. — University of Wisconsin-Madison
- Study coordinator: Craven, Mark W.
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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.