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

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

doi:10.1186/s13073-024-01392-7

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

Genome Med. 2024 Dec 3;16(1):143. doi: 10.1186/s13073-024-01392-7.

Authors

Moez Dawood^{1

2

3}, Shawn Fayer^{4

5}, Sriram Pendyala^{5

6}, Mason Post⁴, Divya Kalra⁷, Karynne Patterson⁵, Eric Venner^{7

8}, Lara A Muffley^{4

5}, Douglas M Fowler^{4

5

9}, Alan F Rubin^{10

11}, Jennifer E Posey⁸, Sharon E Plon^{7

8}, James R Lupski^{7

8

12

13}, Richard A Gibbs^{7

8}, Lea M Starita^{4

5}, Carla Daniela Robles-Espinoza^{14

15}, Willow Coyote-Maestas^{16

17}, Irene Gallego Romero^{18

19

20

21}

Affiliations

¹ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. [email protected].
² Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. [email protected].
³ Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA. [email protected].
⁴ Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
⁵ Department of Genome Sciences, University of Washington, Seattle, WA, USA.
⁶ Medical Scientist Training Program, University of Washington, Seattle, WA, USA.
⁷ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
⁸ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
⁹ Department of Bioengineering, University of Washington, Seattle, WA, USA.
¹⁰ Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
¹¹ Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia.
¹² Texas Children's Hospital, Houston, TX, USA.
¹³ Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
¹⁴ Laboratorio Internacional de Investigación Sobre El Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Qro, Mexico.
¹⁵ CASM, Wellcome Sanger Institute, Hinxton, UK.
¹⁶ Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA. [email protected].
¹⁷ Quantitative Biosciences Institute, University of California, San Francisco, USA. [email protected].
¹⁸ Human Genomics and Evolution, St Vincent's Institute of Medical Research, Fitzroy, 3065, Australia. [email protected].
¹⁹ School of BioSciences and Melbourne Integrative Genomics, The University of Melbourne, Royal Parade, Parkville, 3010, Australia. [email protected].
²⁰ Center for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Riia 23B, 51010, Tartu, Estonia. [email protected].
²¹ Mary MacKillop Institute for Health Research, Australian Catholic University, Fitzroy, Australia. [email protected].

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.

Keywords: All of Us; Benign; Equity; Genetic ancestry; Inequity; MAVE; Missense; Multiplexed assay of variant effects; Pathogenic; VUS; Variants of uncertain significance; gnomAD.

MeSH terms

BRCA1 Protein / genetics
Databases, Genetic
Gene Frequency
Genetic Variation*
Humans
Minority Groups
PTEN Phosphohydrolase
Tumor Suppressor Protein p53 / genetics

Substances

BRCA1 Protein
Tumor Suppressor Protein p53
PTEN protein, human
BRCA1 protein, human
TP53 protein, human
PTEN Phosphohydrolase