{
  "abstract": "Background: Many commonly used computational tools for variant effect prediction exhibit ancestry-related bias because they are trained on clinical or population datasets that under-represent global diversity, leading to uneven and sometimes unfair variant classification across ancestries. Multiplexed assays of variant effect (MAVEs) and population-free variant effect predictors (VEPs) instead offer alternatives that are unbiased with respect to human ancestry, providing classification evidence that generalises across populations.\n\nMethods: Here, we evaluate MAVE- and VEP-based classification across five cancer-associated genes with high-quality MAVE datasets, focusing primarily on three leading population-free VEPs: GEMME, EVE, and CPT-1.\n\nResults: We find that MAVEs are more conservative and decisive in classification, assigning fewer variants to the pathogenic category while yielding fewer indeterminate classifications when calibrated to ACMG/AMP guidelines. Although MAVEs show heightened sensitivity in functionally assayed regions, VEPs identify a broader range of pathogenic variants overall. By combining clinical evidence strengths from MAVEs and VEPs, we reclassify over 90% of variants of uncertain significance across the SG10K_Health and Mexico City Prospective Study reference datasets as at least likely benign or likely pathogenic. We further isolate variants where MAVE- and VEP-based classifications are discordant, highlighting method-specific limitations.\n\nConclusions: Together, these findings clarify the complementary strengths of experimental and computational classification approaches and provide a path to less biased and more equitable variant interpretation in clinical genomics, helping to mitigate disparities in diagnosis across ancestries.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13073-026-01799-4.",
  "abstract_shown": true,
  "affiliations": [
    "Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore",
    "MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK",
    "Center for Systems Biology Dresden, Dresden, Germany",
    "Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nikita Bora"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Mihaly Badonyi"
    },
    {
      "affs": [
        1
      ],
      "name": "Ankit K. Pathak"
    },
    {
      "affs": [
        3
      ],
      "name": "Rutharra Ghayadthri Manisekaran"
    },
    {
      "name": "SG10K_Health Consortium"
    },
    {
      "affs": [
        1
      ],
      "name": "Joseph A. Marsh"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Joanne Ngeow"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/039sqdn9hjw12.bib",
  "cited_by": 0,
  "doi": "10.1186/s13073-026-01799-4",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13737655/",
  "id": "039sqdn9hjw12",
  "issns": [
    "1756-994X"
  ],
  "landing_url": "https://doi.org/10.1186/s13073-026-01799-4",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "1311|Genomics and Rare Diseases"
    ]
  ],
  "openalex": "W7222884048",
  "pdf_url": null,
  "pmcid": "PMC13737655",
  "pmid": "42989408",
  "published": "2026-10-18",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/039sqdn9hjw12.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:47Z",
      "native_id": "PMC13737655",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s13073-026-01799-4"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1311|Genomics and Rare Diseases",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Combining MAVEs and computational predictors improves variant classification across ancestries in hereditary cancer genes",
  "title_text": "Combining MAVEs and computational predictors improves variant classification across ancestries in hereditary cancer genes",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:47Z",
  "url": "https://research.draconitic.com/paper/039sqdn9hjw12",
  "venue": "Genome Medicine",
  "versions": []
}