Interpreting genetic risk scores using biological data and family history
Official title: Enhancing the Interpretability and Applicability of Polygenic Scores through Multi-Omics Integration and Analysis of Family-Based Studies
Funded at University of Virginia · NIH-11469065 · Data updated 2026-11-12
This project will help make genetic risk scores easier to understand and more useful for people and families affected by conditions like autism, cancer, and heart disease by combining genetic, biological, and family information.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Virginia NIH-funded |
| Lab location | 1 site (Charlottesville, United States) |
| Project start | September 19, 2024 |
| Project end | January 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $248,999 |
| NIH project number | 4R00HG013674-03 |
| Project ID | NIH-11469065 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are people and families who have contributed genetic and family-health data to cohort or family studies related to autism, cancers, or cardiovascular disease, or who are willing to provide such data or samples.
✕Less likely to help
People without genetic or family health data or those seeking immediate clinical treatments are unlikely to see direct personal benefits from this research in the short term.
What this research studies
From your perspective, researchers are breaking broad genetic risk scores into meaningful parts that map to specific biological signals and family patterns. They will use machine learning to connect groups of genetic variants with molecular data (such as gene activity and other 'omics') and build partitioned genetic scores that reflect different biological pathways. They will also create family-based statistical methods to estimate how several related risk factors influence a disease while correcting for biases like assortative mating and population differences. The work uses cohort and family-study data and aims to produce clearer explanations of genetic risk for conditions such as autism, cancer, and cardiovascular disease.
Why it matters
Potential benefit: If successful, this work could help clinicians and families understand which biological pathways drive a person's genetic risk and support more targeted prevention, monitoring, or research follow-up.
How similar studies have gone: While polygenic scores are already used to predict risk for some conditions, integrating multi-omics with family-based causal methods is relatively new and not yet widely validated.
Where this research is happening
-
University of Virginia
Charlottesville, United States Active
Researchers
- Principal investigator: Wang, Ziqiao — University of Virginia
- Study coordinator: Wang, Ziqiao
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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.