Cited by 595Genomics and Rare DiseasesBRCA gene mutations in cancerGenetic Associations and Epidemiology

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Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

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Tavtigian, Sean V., Greenblatt, Marc S., Harrison, Steven M., Nussbaum, Robert L., Prabhu, Snehit A., Boucher, Kenneth M., Biesecker, Leslie G.·2018·Genetics in Medicine

DOI 10.1038/gim.2017.210PMC PMC6336098

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Abstract

Purpose We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines for internal consistency and compatibility with Bayesian statistical reasoning.Methods The ACMG/AMP criteria were translated into a naive Bayesian classifier, assuming four levels of evidence and exponentially scaled odds of pathogenicity. We tested this framework with a range of prior probabilities and odds of pathogenicity.Results We modeled the ACMG/AMP guidelines using biologically plausible assumptions. Most ACMG/AMP combining criteria were compatible. One ACMG/AMP likely pathogenic combination was mathematically equivalent to pathogenic and one ACMG/AMP pathogenic combination was actually likely pathogenic. We modeled combinations that include evidence for and against pathogenicity, showing that our approach scored some combinations as pathogenic or likely pathogenic that ACMG/AMP would designate as variant of uncertain significance (VUS).Conclusion By transforming the ACMG/AMP guidelines into a Bayesian framework, we provide a mathematical foundation for what was a qualitative heuristic. Only 2 of the 18 existing ACMG/AMP evidence combinations were mathematically inconsistent with the overall framework. Mixed combinations of pathogenic and benign evidence could yield a likely pathogenic, likely benign, or VUS result. This quantitative framework validates the approach adopted by the ACMG/AMP, provides opportunities to further refine evidence categories and combining rules, and supports efforts to automate components of variant pathogenicity assessments.

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Authors: Tavtigian, S. V., Greenblatt, M. S., Harrison, S. M., Nussbaum, R. L., Prabhu, S. A., Boucher, K. M., & Biesecker, L. G.
Affiliations: University of Utah (US)
University of Vermont (US)
Harvard University (US)
Invitae (United States) (US)
Stanford University (US)
National Institutes of Health (US)
Corresponding author: Tavtigian, Sean V.
ORCID: Tavtigian, Sean V.
Greenblatt, Marc S.
Harrison, Steven M.
Nussbaum, Robert L.
Prabhu, Snehit A.
Boucher, Kenneth M.
Biesecker, Leslie G.
Venue: Genetics in Medicine
Publisher: Elsevier BV
Published: 2018-09
Volume: 20
Issue: 9
Pages: 1054-1060
ISSN: 1098-3600 (ISSN-L)
DOI: 10.1038/gim.2017.210
PMC: PMC6336098
OpenAlex: W2781534540
Cited by: 590
References: 10
Citations by year: 2026: 113 · 2025: 81 · 2024: 77 · 2023: 72 · 2022: 84 · 2021: 57 · 2020: 48 · 2019: 37 · 2018: 18
FWCI: 24.53
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 1236 d)
Locations: Genetics in Medicine (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: University of Connecticut
Johns Hopkins University
Wellcome Trust
Huntsman Cancer Institute (P30CA042014)
Invitae
University of Cambridge
National Institutes of Health
University of North Carolina at Chapel Hill
School of Medicine
National Human Genome Research Institute (HG200388 03, U41HG009650, U41HG006834, U01HG007436, HG200387, HG200388, U01HG007437, U41HG009649, ZIAHG200388, U24HG009650, ZIAHG200387)
National Cancer Institute (R01CA164944, R01CA121245, P30CA042014)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: variant pathogenicity assessment, Bayesian classification, naive Bayesian classifier, variants of uncertain significance, combining rules, prior probability
MeSH terms: Bayes Theorem, Genetic Testing, Humans, Software, Genetic Variation, Genome, Human, Sequence Analysis, DNA, Computational Biology, Genomics, High-Throughput Nucleotide Sequencing

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Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework

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