Genome-wide family-tree methods to improve genetic research in diverse populations
A genome-wide genealogical framework for statistical and population genetic analysis
This project creates genome-wide genetic family trees to help reveal how genes affect health for people from diverse and admixed ancestries, such as African American and Asian communities.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Southern California NIH-funded |
| Lab location | 1 site (Los Angeles, UNITED STATES) |
| Project ID | NIH-11135599 on NIH RePORTER |
What this research studies
The team will build whole-genome 'family trees' (ancestral recombination graphs) from existing human genetic data to capture hidden relatedness across the genome. They will develop statistical tools that use these trees to produce better estimates of how much genes contribute to traits and diseases and to compare genetic effects across populations. The methods are tailored to work with common genotyping data and to reduce bias when studying admixed groups like African American individuals. Most work is computational and relies on already-collected genetic and health datasets rather than testing new medicines or procedures.
Who could benefit from this research
Good fit: People with diverse or admixed ancestry (for example African American or Asian ancestry) who can share genetic and health data would be the best candidates to contribute to related studies.
Not a fit: Because the project develops analysis methods rather than testing treatments, people seeking immediate clinical therapies are unlikely to receive direct benefit.
Why it matters
Potential benefit: If successful, this work could make genetic risk estimates and research findings more accurate and fair for people from underrepresented ancestries.
How similar studies have performed: Existing relatedness and heritability methods have helped genetics research in homogeneous populations, but applying full genome-wide ancestral recombination graphs to diverse and admixed groups is a novel and emerging approach with promising recent advances.
Where this research is happening
Los Angeles, UNITED STATES
- University of Southern California — Los Angeles, United States (Active)
Researchers
- Principal investigator: Chiang, Charleston — University of Southern California
- Study coordinator: Chiang, Charleston
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.