Genetics and social factors that affect cancer outcomes
Genetic & Social Determinants of Health: Center for Admixture Science and Technology
This project develops privacy-protecting ways to combine large genetic and health datasets so people with cancer or at risk can get clearer information about how genes and social factors influence outcomes.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11196207 on NIH RePORTER |
What this research studies
You would be part of efforts to link genetic and health information from large projects like All of Us and the Million Veterans Program without sharing your raw data outside local sites. The team is building privacy-protecting algorithms that let data stay in place while sites run joint analyses together. They plan to model the patchwork of local ancestry, include understudied genetic features like tandem repeats and the MHC region, and account for social and healthcare factors that affect results. The goal is to separate genetic drivers of cancer risk from non-genetic causes and help reduce disparities in care.
Who could benefit from this research
Good fit: Ideal candidates are people with cancer or at risk who are enrolled in, or willing to join, large U.S. cohorts like All of Us or the Million Veterans Program and who can share genetic and health information.
Not a fit: Patients who are not represented in these cohorts, lack genetic data, or expect immediate personal clinical results are unlikely to gain direct benefit from this research right away.
Why it matters
Potential benefit: If successful, this could lead to better detection of genetic cancer risks across diverse populations and more equitable, tailored care recommendations.
How similar studies have performed: Previous genome-wide studies and shared-data projects have found risk genes, but combining local ancestry, tandem repeats, and privacy-preserving federated analysis is a newer approach with only limited prior examples and promising early results.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: Ohno-Machado, Lucila — Yale University
- Study coordinator: Ohno-Machado, Lucila
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 below.