Evidence map›Paper›PMID 39627863›Full record

ArticleGenome medicine2024

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 and 8 more

Abstract read
In one paragraph

Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. An integrated cardiometabolic genetic testing program in a predominantly Hispanic population within a community setting.Genetics in medicine : official journal of the American College of Medical Genetics · 2026
    Article
  5. Not Forgotten: Patient Experiences with Genetic Variant Reclassifications.medRxiv : the preprint server for health sciences · 2026
    Article
  6. Article
  7. Review
  8. Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026
    Review
  9. Review
  10. A scalable approach to resolving variants of uncertain significance.bioRxiv : the preprint server for biology · 2026
    Article
  11. Article
  12. Article
  13. Review
  14. MaveMD: A functional data resource for genomic medicine.medRxiv : the preprint server for health sciences · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Moez DawoodHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. Moez.Dawood@bcm.edu.
Shawn FayerBrotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Sriram PendyalaDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Mason PostBrotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Divya KalraHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
Karynne PattersonDepartment of Genome Sciences, University of Washington, Seattle, WA, USA.
Eric VennerHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
Lara A MuffleyBrotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Douglas M FowlerBrotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Alan F RubinBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Jennifer E PoseyDepartment of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Sharon E PlonHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
James R LupskiHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
Richard A GibbsHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
Lea M StaritaBrotman Baty Institute for Precision Medicine, Seattle, WA, USA.
Carla Daniela Robles-EspinozaLaboratorio Internacional de Investigación Sobre El Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Querétaro, Qro, Mexico.
Willow Coyote-MaestasDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA. willow.coyote-maestas@ucsf.edu.
Irene Gallego RomeroHuman Genomics and Evolution, St Vincent's Institute of Medical Research, Fitzroy, 3065, Australia. irene.gallego@svi.edu.au.ORCID 0000-0003-1613-8998

Funding

The Center for Actionable Variant Analysis; measuring variant function at scaleUM1HG011969 · NHGRI · UNIVERSITY OF WASHINGTON · PI Douglas M Fowler, Lea Starita · 2021 to 2026
$9.9M
NHGRI NIH HHS UM1 HG011969
6 · The paper itself

Abstract

backgroundMultiplexed 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).

methodsWe 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.

resultsUsing 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.

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

Indexed as

Genetic VariationBRCA1 ProteinDatabases, GeneticGene FrequencyHumansMinority GroupsPTEN PhosphohydrolaseTumor Suppressor Protein p53BRCA1 ProteinBRCA1 protein, humanPTEN PhosphohydrolasePTEN protein, humanTP53 protein, humanTumor Suppressor Protein p53All of UsBenignEquityGenetic ancestrygnomADInequityMAVEMissenseMultiplexed assay of variant effectsPathogenicVariants of uncertain significanceVUS

Identifiers

PMID39627863
PMCPMC11616159

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.