Understanding how genetic variations affect gene expression and disease
Official title: Integrating the impacts of genetic variation with massively parallel mRNA and protein barcoding
Funded at University of Washington · NIH-11194039 · Data updated 2026-11-12
This study is looking at how changes in our genes can affect our health and help us understand diseases better, so that patients can get more personalized treatments based on their unique genetic makeup.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | September 25, 2024 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $636,648 |
| NIH project number | 1R56HG013312-01A1 |
| Project ID | NIH-11194039 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with unexplained genetic conditions or those who have undergone genetic testing showing variants of uncertain significance.
✕Less likely to help
Patients with well-characterized genetic conditions that already have established treatment protocols may not benefit from this research.
What this research studies
This research investigates how different genetic variations can influence gene expression and contribute to disease. By using advanced techniques like CRISPR and machine learning, the study aims to create models that can predict the effects of these variations on molecular functions. Patients may benefit from a better understanding of their genetic makeup and how it relates to their health, potentially leading to more personalized treatment options. The research combines synthetic biology with large-scale data analysis to improve the interpretation of genetic variants.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of how genetic variations affect health, enabling personalized medicine approaches for patients.
How similar studies have gone: Previous research has shown promise in using machine learning and synthetic biology to predict the effects of genetic variants, indicating that this approach could be effective.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Nivala, Jeffrey Matthew — University of Washington
- Study coordinator: Nivala, Jeffrey Matthew
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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.