Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed...

Area of Science:

• Genomics and Structural Biology
• Computational Biology and Bioinformatics
• Precision Medicine

Background:

• Accurate interpretation of genetic variants is essential for translating personal genomics into precision medicine.
• Genetic variants impacting protein features like active sites or interaction interfaces can cause diseases.
• Millions of genetic variants and thousands of protein structures are available in public databases.

Purpose of the Study:

• To accelerate the integration of genetic variant data and 3D protein structures.
• To identify community-driven approaches for advancing this integration beyond single-laboratory efforts.
• To propose a framework for collaborative progress in variant effect prediction.

Main Methods:

• Convened a two-day Gene Variation to 3D (GVto3D) workshop.
• Reviewed current advances and identified unmet needs in integrating genetic and structural data.
• Proposed a framework including standard formats, common ontologies, and a common API.

Main Results:

• Identified key areas for community collaboration in integrating genetic variation and 3D protein structure data.
• Proposed a framework to promote interoperability and collaborative development of variant effect prediction methods.
• Highlighted the need for standard formats, common ontologies, and a Tool Registry.

Conclusions:

• A collaborative framework is essential to overcome challenges in integrating genetic variants with 3D protein structures.
• Standardization and interoperability will enable advanced variant effect prediction and drug development.
• Community efforts can significantly advance the use of structural information for interpreting genetic variations in disease.