Prioritizing genes for systematic variant effect mapping.

Area of Science:

• Genetics and Genomics
• Molecular Biology
• Clinical Diagnostics

Background:

• Most rare missense variants are classified as variants of uncertain significance (VUS) due to lack of functional data.
• Functional assays, like multiplexed assays of variant effect (MAVE), can generate variant effect maps but prioritization of targets has been suboptimal.
• There is a need to prioritize genes for MAVE to maximize clinical impact on variant interpretation.

Purpose of the Study:

• To develop and apply a systematic strategy for prioritizing genes for functional testing of missense variation.
• To guide the generation of variant effect maps towards proteins with the greatest clinical utility.
• To improve the interpretation of rare missense variants in clinical settings.

Main Methods:

• Mined databases of clinically interpreted variants (e.g., ClinVar) to identify genes with numerous missense VUS.
• Applied three sequential prioritization strategies: (i) number of unique missense VUS, (ii) movability- and reappearance-weighted impact scores, and (iii) difficulty-adjusted impact scores.
• Ranked genes based on these combined metrics to identify optimal targets for systematic functional testing.

Main Results:

• Developed a robust, multi-strategy approach to rank genes for functional variant effect mapping.
• The prioritization strategies effectively identify genes where functional testing would have the greatest impact on clinical variant interpretation.
• The methodology provides a framework for resource allocation in functional genomics studies.

Conclusions:

• This gene prioritization framework enables more impactful functional testing of missense variants.
• The approach aims to accelerate the classification of VUS, thereby improving genetic diagnostics.
• The source code is publicly available to facilitate broader application in the research community.