Improving genetic risk prediction for diverse populations
Official title: Federated and transfer learning methods for cross-ancestry and cross-phenotype integration of genomic datasets
Funded at Harvard School of Public Health · NIH-11000258 · Data updated 2026-11-12
This study is working on new ways to use genetic information from different populations, especially those of African ancestry, to make better predictions about disease risks, so that everyone can get more personalized healthcare based on their unique genetics.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Harvard School of Public Health NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | December 20, 2022 |
| Project end | November 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $377,754 |
| NIH project number | 5R01GM148494-03 |
| Project ID | NIH-11000258 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals from non-European ancestries, particularly those of African descent, who may benefit from enhanced genetic risk prediction tools.
✕Less likely to help
Patients who are not of African ancestry or those who do not have a genetic predisposition to the conditions being studied may not receive direct benefits from this research.
What this research studies
This research focuses on developing innovative methods to integrate genomic data from diverse populations, particularly those of African ancestry, to enhance the accuracy of genetic risk predictions for various diseases. By utilizing advanced techniques like transfer learning and federated learning, the project aims to overcome the limitations of existing genetic studies that predominantly involve European populations. The goal is to create a more equitable approach to genetic risk assessment that considers the unique genetic architectures of under-represented groups. Patients may benefit from improved risk stratification and personalized healthcare recommendations based on their genetic background.
Why it matters
Potential benefit: If successful, this research could lead to more accurate genetic risk assessments for patients from diverse ancestries, ultimately improving health outcomes.
How similar studies have gone: Other research has shown promise in using advanced data integration methods for improving health outcomes in diverse populations, indicating that this approach has potential for success.
Where this research is happening
-
Harvard School of Public Health
Boston, United States Active
Researchers
- Principal investigator: Duan, Rui — Harvard School of Public Health
- Study coordinator: Duan, Rui
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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.