Combining multiplexed functional data to improve variant classification.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• The increasing number of variants of uncertain significance (VUS) in clinical databases necessitates scalable methods for their classification.
• Multiplexed assays of variant effect (MAVEs) offer a high-throughput approach to assess the functional consequences of numerous genetic variants simultaneously.
• Multiple MAVEs for a single gene may measure different functional impacts, requiring integrated analysis for comprehensive variant effect assessment.

Purpose of the Study:

• To provide a standardized framework for combining functional data from multiple MAVEs.
• To enhance the strength of evidence for clinical gene variant classification.
• To facilitate the reclassification of VUS and the discovery of novel pathogenicity mechanisms.

Main Methods:

• Development of a stepwise process for data curation, collection, and integration from multiple MAVEs.
• Generation and validation of computational models to combine diverse functional data.
• Application of the framework to integrate data from four MAVEs for the TP53 gene.

Main Results:

• Demonstrated successful integration of multiplexed functional data from multiple MAVEs for the TP53 gene.
• The integrated data provided a more complete understanding of variant effects.
• The approach has the potential to increase evidence strength for variant classification.

Conclusions:

• Combining data from multiple MAVEs is a viable strategy to address the VUS challenge.
• This integrated approach can significantly strengthen functional evidence for clinical variant classification.
• The methodology aids in reclassifying VUS and identifying novel disease-causing mechanisms.