Improving genetic risk prediction using advanced tools for variant interpretation
Official title: Systematic integration of variant interpretation tools into genetic and genomic risk prediction
Funded at Icahn School of Medicine at Mount Sinai · NIH-11053528 · Data updated 2026-11-12
This study is working on improving how doctors predict the risks of genetic conditions by using smart computer tools to better understand if certain genetic changes are harmful or not, so they can make more accurate decisions for patients like you.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | March 11, 2024 |
| Project end | January 31, 2029 |
| Funding year | 2025 |
| Award (this year) | $601,403 |
| NIH project number | 5R01HG013350-02 |
| Project ID | NIH-11053528 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals undergoing genetic testing for rare diseases or those with a family history of genetic conditions.
✕Less likely to help
Patients without any genetic predisposition to diseases or those not undergoing genetic testing may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of genetic and genomic risk predictions by integrating advanced machine learning tools that assess whether genetic variants are harmful or benign. The project aims to develop methods that allow these tools to be effectively used in clinical settings, ensuring that rare genetic variants are accurately accounted for in risk assessments. By calibrating existing predictors and creating new risk scores, the research seeks to improve the clinical decision-making process for patients with genetic conditions.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of disease risk for patients based on their genetic information.
How similar studies have gone: Previous research has shown promise in using machine learning for genetic variant interpretation, indicating that this approach could lead to significant advancements in clinical applications.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Pejaver, Vikas Rao — Icahn School of Medicine at Mount Sinai
- Study coordinator: Pejaver, Vikas Rao
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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.