Using genetic data and protein shapes to find DNA changes that matter
Integrating genomic data and protein structures to improve measures of selective constraint
This project combines huge genetic databases with 3D protein information to spot DNA changes that are more likely to cause disease for people with suspected inherited conditions.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-11296912 on NIH RePORTER |
What this research studies
You might benefit because researchers are combining genetic data from hundreds of thousands of people with 3D maps of proteins and knowledge about protein function to find which spots in our genes cannot change without harm. They will start with over 700,000 exomes and plan to include more than 3,000,000 by the end of the project, and will add information such as post-translational modification sites and where proteins touch each other. The team will also study how these signs of constraint appear across protein complexes and broader molecular networks built from proteomics data. The goal is to make it easier for doctors and genetic testing labs to interpret rare missense variants found in genetic tests.
Who could benefit from this research
Good fit: People with suspected inherited genetic disorders or those whose genetic tests show rare missense variants are the most likely to benefit from the insights produced by this research.
Not a fit: Patients with conditions that are not caused by genetic changes or who have not had genetic testing are unlikely to get direct benefit from this project's findings.
Why it matters
Potential benefit: If successful, this work could make genetic test results clearer by showing which specific DNA changes are likely disease-causing, improving diagnosis and genetic counseling.
How similar studies have performed: Previous work using gene-level constraint metrics and protein structure has improved variant interpretation, and this project expands those approaches to much larger datasets and added structural and functional data.
Where this research is happening
Boston, United States
- Massachusetts General Hospital — Boston, United States (Active)
Researchers
- Principal investigator: Samocha, Kaitlin Elisabeth — Massachusetts General Hospital
- Study coordinator: Samocha, Kaitlin Elisabeth
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.