Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to...

Area of Science:

• Genomics
• Molecular Biology
• Clinical Genetics

Background:

• Interpreting DNA sequence variants in clinical genetic testing is challenging, with many classified as variants of uncertain significance (VUSs).
• Functional data from academic literature is traditionally used to support variant classification.
• High-throughput functional assays, such as multiplex assays of variant effect (MAVEs), offer a scalable source of evidence for variant interpretation.

Purpose of the Study:

• To describe a framework for generating, validating, and incorporating MAVE data into clinical genetic testing.
• To develop a semi-quantitative method for variant classification using MAVE data.
• To enhance the clinical utility of functional genomics data in genetic testing laboratories.

Main Methods:

• Developed cellular evidence models using single-cell gene expression to assess DNA variation effects in 44 clinically relevant genes.
• Applied the framework to 22 genes with existing MAVE datasets.
• Integrated modeling data from a total of 24 genes into a semi-quantitative variant classification approach.

Main Results:

• Incorporated functional data from MAVE models into a variant classification method.
• Classified 4043 observed variants in over 57,000 individuals using MAVE-derived evidence.
• Demonstrated the feasibility of using high-throughput functional data for clinical variant interpretation.

Conclusions:

• Genetic testing laboratories can generate, analyze, and validate high-throughput functional data.
• Integrating MAVE data enables more definitive clinical variant classifications for patients.
• This approach addresses the challenge of VUS interpretation at scale in genetic testing.