Using AI to understand how genes affect health across diverse populations
Machine Learning for Decoding the Genetic Architecture of Complex Traits and Diseases Across Populations
This project uses artificial intelligence on large genetic and protein datasets to find which genes and cell types influence traits and diseases for people from many ancestral backgrounds.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11260622 on NIH RePORTER |
What this research studies
You would be helping researchers who apply AI and advanced statistics to large-scale genetic and protein data to pinpoint which genes and cell types drive differences in traits and disease. They combine biobank-scale genetic studies, protein-level measurements (pQTLs), and single-cell technologies, and use computational deconvolution to create cell-type-specific protein profiles. The team also builds individualized genetic risk profiles with machine learning and works to make findings reliable across diverse ancestral groups. The overall aim is to reveal biological mechanisms and possible treatment targets that better reflect people from many backgrounds.
Who could benefit from this research
Good fit: People from diverse ancestral backgrounds who can contribute genetic data or blood-based samples, or whose de-identified biobank data can be used, would be most relevant to this work.
Not a fit: Patients whose conditions are driven mainly by non-genetic causes or who cannot or will not share genetic or sample data are unlikely to see direct benefits from this project.
Why it matters
Potential benefit: If successful, this work could produce better genetic risk predictions and identify new biological targets that lead to more effective and inclusive treatments.
How similar studies have performed: Previous studies combining GWAS, pQTL mapping, and machine learning have produced useful candidate genes and risk scores, but integrating cell-type proteomics with broad population diversity at this scale is still relatively new.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Tang, Hua — Stanford University
- Study coordinator: Tang, Hua
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.