Expanding genetic models to better understand how genes influence traits
Official title: Theory and infrastructure to expand the universe of statistical genetic models
Funded at Carnegie-Mellon University · NIH-11430640 · Data updated 2026-11-12
A research program aims to improve genetic analysis so biology can be studied separately from social and environmental factors, helping doctors and patients understand complex traits more clearly.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Carnegie-Mellon University NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | August 1, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $405,381 |
| NIH project number | 1R35GM166209-01 |
| Project ID | NIH-11430640 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are individuals whose genetic and trait data are analyzed in large biobank or family-based studies and who may benefit from more accurate genetic interpretations.
✕Less likely to help
Patients not involved in genetic research or not using genetic findings for clinical decision-making may see no direct benefit.
What this research studies
This project will develop new statistical tools to separate true biological signals from social and environmental influences in genetic studies. It will create multigenerational theories and corrected estimators for key genetic measures to make findings more reliable across diverse cohorts. A public benchmarking suite with datasets and leaderboards will help researchers test and compare approaches. The work will build infrastructure that supports scalable and portable genetic analyses across different populations and study designs.
Why it matters
Potential benefit: If successful, the methods could yield clearer insights into how genetics influences traits, potentially guiding better risk prediction and personalized medicine.
How similar studies have gone: Similar statistical-genetics work has shown that population structure and non-biological factors can bias results, and robust methods have improved interpretation.
Where this research is happening
-
Carnegie-Mellon University
Pittsburgh, United States Active
Researchers
- Principal investigator: Border, Richard — Carnegie-Mellon University
- Study coordinator: Border, Richard
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.