Using Multiplexed Functional Data to Reduce Variant Classification Inequities in Underrepresented Populations

Authors

Moez Dawood
Shawn Fayer
Sriram Pendyala
Mason Post
Divya Kalra
Karynne Patterson
Eric Venner
Lara A Muffley
Douglas M Fowler
Alan F Rubin
Jennifer E Posey
Sharon E Plon
James R Lupski
Richard A Gibbs
Lea M Starita
Carla Daniela Robles-Espinoza
Willow Coyote-Maestas
Irene Gallego Romero

Publication Date

12-3-2024

Journal

Genome Medicine

DOI

10.1186/s13073-024-01392-7

PMID

39627863

PMCID

PMC11616159

PubMedCentral® Posted Date

12-3-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Genetic Variation, BRCA1 Protein, Tumor Suppressor Protein p53, Databases, Genetic, Minority Groups, Gene Frequency, PTEN Phosphohydrolase, MAVE, Multiplexed assay of variant effects, Variants of uncertain significance, VUS, Pathogenic, Benign, Genetic ancestry, Equity, Inequity, All of Us, gnomAD, Missense

Abstract

BACKGROUND: Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS).

METHODS: We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN.

RESULTS: Using two orthogonal statistical approaches, we show a higher prevalence (p ≤ 5.95e - 06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation (p ≤ 2.5e - 05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were increased in individuals of European-like genetic ancestry (p ≤ 2.5e - 05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry (p = 9.1e - 03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency (p = 7.47e - 06) and computational predictor (p = 6.92e - 05) evidence codes for individuals of non-European-like genetic ancestry.

CONCLUSIONS: Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Comments

Associated Data