Using multiplexed functional data to reduce variant classification inequities in underrepresented populations.

Area of Science:

• Genomics
• Clinical Genetics
• Bioinformatics

Background:

• Multiplexed Assays of Variant Effects (MAVEs) generate saturation-style functional data for all single variants in a gene.
• MAVEs hold potential to resolve variant classification disparities, particularly for Variants of Uncertain Significance (VUS) across diverse populations.

Purpose of the Study:

• To investigate genetic variant classification disparities between European-like and non-European-like genetic ancestries.
• To assess the utility of MAVE data in reclassifying Variants of Uncertain Significance (VUS) and mitigating ancestry-based disparities.

Main Methods:

• Analyzed clinical significance classifications in over 400,000 individuals from the All of Us and Genome Aggregation Database.
• Incorporated clinically calibrated MAVE data into automated variant reclassification rules for BRCA1, TP53, and PTEN genes.
• Utilized orthogonal statistical approaches and analyzed evidence code impacts for different ancestry groups.

Main Results:

• Identified a higher prevalence of VUS in non-European-like genetic ancestry groups across multiple medical specialties.
• Observed increased rates of Benign/Likely Benign variants in non-European-like ancestry and Pathogenic/Likely Pathogenic variants in European-like ancestry.
• Demonstrated that MAVE data significantly reclassified VUS in non-European-like ancestry individuals, effectively compensating for the observed disparity.

Conclusions:

• Prioritizing the generation of saturation-style MAVE data is essential for reducing VUS disparities.
• Equitable training data derived from MAVE studies will improve the performance of future computational predictors for variant classification.