How gain-of-function gene changes affect human diseases
Official title: Network-based Framework to Decode Novel Gain-of-Function Mutations and their Mechanistic Roles in General Human Diseases
Funded at Baylor Research Institute · NIH-11260564 · Data updated 2026-11-12
Researchers are using computer network models to figure out how certain gene changes called gain-of-function mutations can disrupt cellular processes and contribute to disease, aiming to help people with genetic conditions.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Baylor Research Institute NIH-funded |
| Lab location | 1 site (Dallas, United States) |
| Project start | March 1, 2020 |
| Project end | February 28, 2031 |
| Funding year | 2026 |
| Award (this year) | $441,375 |
| NIH project number | 2R35GM133658-06 |
| Project ID | NIH-11260564 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with unexplained genetic conditions, rare or familial disorders, or known gain-of-function mutations would be the most relevant candidates for related studies or data contribution.
✕Less likely to help
Patients whose conditions are not driven by genetic variants or who need immediate clinical treatment are less likely to benefit directly from this computational research.
What this research studies
This project builds computational and bioinformatic models that map how mutated proteins interact in cell networks. The team uses genetic and molecular data to predict which mutations change signaling pathways and gene regulation. These predictions can guide laboratory experiments and help explain why some people develop disease while others with similar genes do not. The focus is on decoding ‘‘gain-of-function’’ mutations that make proteins overly active or give them new activities.
Why it matters
Potential benefit: If successful, this work could help identify disease-causing mutations and point to better diagnoses and targets for new treatments.
How similar studies have gone: Related computational approaches have helped prioritize and annotate genetic variants, but accurately decoding gain-of-function mutations across diseases remains challenging and partly unproven.
Where this research is happening
-
Baylor Research Institute
Dallas, United States Active
Researchers
- Principal investigator: Yi, S. Stephen — Baylor Research Institute
- Study coordinator: Yi, S. Stephen
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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.