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Availability of benign missense variant ‘truthsets’ for validation of functional assays: current status and a systematic approach

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Rowlands, Charlie F., Allen, Sophie, Garrett, Alice, Durkie, Miranda, Burghel, George J., Robinson, Rachel, Callaway, Alison, Field, Joanne +9 more·2025·The American Journal of Human Genetics
DOI 10.1016/j.ajhg.2025.08.014PMC PMC12696494

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Abstract

Multiplex assays of variant effect (MAVEs) provide promising new sources of functional evidence, potentially empowering improved classification of germline genomic variants, particularly rare missense variants, which are commonly assigned as variants of uncertain significance (VUSs). However, paradoxically, quantification of clinically applicable evidence strengths for MAVEs requires construction of "truthsets" comprising missense variants already robustly classified as pathogenic and benign. In this study, we demonstrate how benign truthset size is the primary driver of applicable functional evidence toward pathogenicity (PS3). We demonstrate, when using existing ClinVar classifications as a source of benign missense truthset variants, that only 19.8% (23/116) of established cancer susceptibility genes had a PS3 evidence strength of "strong" attainable when simulating validation for a hypothetical new MAVE (also applying favorable assumption of perfect concordance). We describe a systematic framework for benign truthset construction in which all possible missense variants in a gene of interest are concurrently assessed for assignation of (likely) benignity via established ACMG/AMP combination rules, including population frequency, in silico evidence codes, and case-control signal. We apply this framework to eight hereditary breast and ovarian cancer genes, demonstrating that systematically generated benign missense truthsets allow maximum application of PS3 at greater (or equivalent) strength-reaching "moderate" for CHEK2 and "strong" for the other seven genes-than those derived from ClinVar ≥2∗ classifications alone. We propose, given many genes have few existing benign-classified missense variants, that the application of this systematic framework to disease genes more broadly will be important for leveraging full value from MAVEs.

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  • A pragmatic solution to the problem has been proposed based on proactively generating benign variant lists based on factors such as population frequencies, disease rarity, and ACMG/AMP criteria.

    “A pragmatic solution to the problem has been proposed based on proactively generating benign variant lists based on factors such as population frequencies, disease rarity, and ACMG/AMP criteria (105).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [105]

    The Workflow for systematic generation of benign missense truthsets proposes a proactive approach using ACMG/AMP benignity criteria and population-frequency codes. The Assignation of putative pathogenicity and benignity flags section describes gene-phenotype-specific frequency thresholds, but the passages in our copy do not explicitly identify disease rarity as a factor in generating the lists. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Modelling based on ACMG/AMP criteria, as applied to cancer-related variants, has shown that truthsets of benign variants are much more limiting than those for convincing pathogenic variants.

    “Modelling based on ACMG/AMP criteria, as applied to cancer-related variants, has shown that truthsets of benign variants are much more limiting than those for convincing pathogenic variants (105).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [105]

  • Loss-of-function PPARG mutations give rise to familial partial lipodystrophy.

    “loss-of-function PPARG mutations give rise to familial partial lipodystrophy (105).”

    From Massively parallel functional genomic assays in endocrinology: from promise to delivery (2026) · cites this paper as [105]

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Authors
Rowlands, C. F., Allen, S., Garrett, A., Durkie, M., Burghel, G. J., Robinson, R., Callaway, A., Field, J., Frugtniet, B., Palmer-Smith, S., Grant, J., Pagan, J., McDevitt, T., Snape, K., Hanson, H., McVeigh, T., & Turnbull, C.
Affiliations
  • Institute of Cancer Research (GB)
  • Sheffield Children's NHS Foundation Trust (GB)
  • University of Manchester (GB)
  • Leeds Teaching Hospitals NHS Trust (GB)
  • University Hospital Southampton NHS Foundation Trust (GB)
  • Nottingham University Hospitals NHS Trust (GB)
  • St George’s University Hospitals NHS Foundation Trust (GB)
  • Cardiff and Vale University Health Board (GB)
  • NHS Greater Glasgow and Clyde (GB)
  • Western General Hospital (GB)
  • Children's Health Ireland at Crumlin (IE)
  • University of Exeter (GB)
  • Royal Marsden NHS Foundation Trust (GB)
Corresponding author
Turnbull, Clare
ORCID
Venue
The American Journal of Human Genetics
Publisher
Elsevier BV
Published
2025-10
Volume
112
Issue
10
Pages
2281-2294
ISSN
0002-9297 (ISSN-L)
DOI
10.1016/j.ajhg.2025.08.014
PMC
PMC12696494
OpenAlex
W4414057696
Cited by
6
References
4
Citations by year
2026: 5 · 2025: 1
FWCI
3.50
Type
journal article
Language
en
Open access
Licence
Locations
  • The American Journal of Human Genetics (publishedVersion)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion)
  • Royal Devon and Exeter Research Repository (Royal Devon and Exeter) (submittedVersion)
Funders
Cancer Research UK (EDDPGM-Nov22/100004, C61296/A27223, 76689)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
MAVE, variant interpretation, disease-associated genes, systematic framework
MeSH terms
Breast Neoplasms, Female, Genetic Testing, Humans, Germ-Line Mutation, Genetic Predisposition to Disease, Mutation, Missense

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