Variant Interpretation: Functional Assays to the Rescue.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Interpreting genetic variants is crucial for clinical genetics.
• Traditional methods like case-control studies are limited by variant rarity.
• Current computational predictions often lack the required clinical confidence.

Purpose of the Study:

• To discuss multiplex assays of variant effect (MAVEs) as a solution for variant interpretation.
• To highlight MAVE's ability to measure functional consequences of numerous variants.
• To propose combining MAVE data with machine learning for accurate pathogenicity predictions.

Main Methods:

• Utilizing multiplex assays of variant effect (MAVEs) to assess variant function.
• Measuring molecular and cellular phenotypes across disease-relevant genetic loci.
• Applying machine learning algorithms to large-scale functional data.

Main Results:

• MAVEs enable the assessment of all possible variants within specific genetic regions.
• Generation of comprehensive functional data for a wide range of variants.
• Development of "lookup tables" for high-confidence pathogenicity predictions.

Conclusions:

• MAVEs offer a scalable and efficient method for functional variant interpretation.
• Integrating MAVE data with computational approaches can overcome current limitations.
• A coordinated effort in MAVE data generation and analysis is essential for the "variant-interpretation crisis".