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

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Dawood, Moez, Fayer, Shawn, Pendyala, Sriram, Post, Mason, Kalra, Divya, Patterson, Karynne, Venner, Eric, Muffley, Lara A. +10 more·2024·Genome Medicine
DOI 10.1186/s13073-024-01392-7PMC PMC11616159
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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.

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  • Many computational variant effect predictors (VEPs) have been described, with variable diagnostic performance.

    “Many computational variant effect predictors (VEPs) have been described, with variable diagnostic performance.”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [30]

    The Discussion names several commonly used computational predictors—BayesDel no-AF, aGVGD, and REVEL—supporting the claim that multiple predictors exist. The Results section on computational predictor evidence codes reports different contributions to VUS reclassification across ancestry groups, but does not compare the predictors’ diagnostic performance. No page numbers are available in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Problems with VEPs include biased performance in populations underrepresented in sequencing data.

    “Problems with VEPs include overfitting towards existing clinical annotations (29), biased performance in populations underrepresented in sequencing data (30), and variable predictive performance across genes due to inadequate calibration of scores (31).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [30]

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Authors
Dawood, M., Fayer, S., Pendyala, S., Post, M., Kalra, D., Patterson, K., Venner, E., Muffley, L. A., Fowler, D. M., Rubin, A. F., Posey, J. E., Plon, S. E., Lupski, J. R., Gibbs, R. A., Starita, L. M., Robles-Espinoza, C. D., Coyote-Maestas, W., & Gallego Romero, I.
Affiliations
  • Baylor College of Medicine (US)
  • University of Washington (US)
  • Brotman Baty Institute (US)
  • The University of Melbourne (AU)
Corresponding author
Dawood, Moez
ORCID
Venue
Genome Medicine
Publisher
Springer Science and Business Media LLC
Published
2024-12-03
Volume
16
Issue
1
Pages
143-143
ISSN
1756-994X (ISSN-L)
DOI
10.1186/s13073-024-01392-7
PMC
PMC11616159
OpenAlex
W4404963314
Cited by
31
References
5
Citations by year
2026: 17 · 2025: 13 · 2024: 1
FWCI
7.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome Medicine (publishedVersion, cc-by-nc-nd)
  • medRxiv (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion)
  • Digital Commons-TMC (Texas Medical Center) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Minerva Access (University of Melbourne) (submittedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc-nd)
Funders
  • Cancer Prevention and Research Institute of Texas
  • Melanoma Research Alliance (825924)
  • University of Washington
  • Johns Hopkins University
  • Wellcome Trust (227228/Z/23/Z)
  • European Commission (HORIZON2020, 810645)
  • State Government of Victoria
  • National Institutes of Health (OT2OD002751)
  • Medical Research Council
  • Australian Research Council (DP200101552)
  • National Health and Medical Research Council (2020501)
  • European Regional Development Fund (MOBEC008)
  • National Human Genome Research Institute (U24HG009649, RM1HG010461, UM1HG011969, U01HG011758)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords
MAVE, variants of uncertain significance, genetic ancestry, non-European ancestry, BRCA1, TP53, PTEN, clinical significance, allele frequency, health equity, All of Us

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