Improving genetic risk prediction for underrepresented groups
Official title: Improving PGS Prediction for Underrepresented Groups Through Transfer Learning
Funded at Henry Ford Health + Michigan State University Health Sciences · NIH-10983102 · Data updated 2026-11-12
This study is working to make genetic risk predictions more accurate for people from underrepresented backgrounds, like African Americans, so they can get better insights into their health risks based on their unique genetics.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Henry Ford Health + Michigan State University Health Sciences NIH-funded |
| Lab location | 1 site (East Lansing, United States) |
| Project start | September 19, 2024 |
| Project end | July 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $478,037 |
| NIH project number | 1R01HG013794-01 |
| Project ID | NIH-10983102 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals of African American descent who are interested in understanding their genetic risk for various diseases.
✕Less likely to help
Patients of European descent may not receive direct benefits from this research as it focuses on improving predictions for underrepresented groups.
What this research studies
This research aims to enhance the accuracy of genetic risk predictions, known as Polygenic Scores (PGS), specifically for individuals from underrepresented ancestry groups, such as African Americans. By utilizing a technique called Transfer Learning, the researchers will develop new algorithms that leverage existing data to improve predictions for these populations. The study will involve creating and testing advanced statistical models that can better account for genetic variations in non-European individuals, ultimately aiming to provide more reliable health risk assessments. Patients may benefit from more accurate predictions of disease risk based on their genetic background.
Why it matters
Potential benefit: If successful, this research could lead to more accurate health risk assessments for patients from underrepresented groups, improving personalized medicine.
How similar studies have gone: Other research has shown promise in using Transfer Learning to improve predictive models in genomics, indicating that this approach could be effective.
Where this research is happening
-
Henry Ford Health + Michigan State University Health Sciences
East Lansing, United States Active
Researchers
- Principal investigator: De Los Campos, Gustavo — Henry Ford Health + Michigan State University Health Sciences
- Study coordinator: De Los Campos, Gustavo
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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.