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Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework
Open accessAbstract
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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“This procedure yielded a prior probability of pathogenicity (prevalence in the data set) of 4.41%, higher than that estimated previously for an exome of a healthy individual19 using a similar procedure but lower than the prevalence of 10% assumed by Tavtigian et al.9 for clinical sequencing data.”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [9]
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“Under reasonable assumptions, we used a positive likelihood ratio of pathogenicity to quantify the strength of evidence that corresponds to a supporting level of evidence and established that, within this framework, the strength of evidence required for moderate, strong, and very strong rose exponentially.9”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [9]
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“We have previously modeled the ACMG/AMP rules for combining evidence and showed how they fit a probabilistic framework.9”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [9]
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“These fractions were smaller than the experience-based prior probability assumed by Tavtigian et al.9 (10%) and the prior probability that was estimated in this study using the gnomAD set (4.41%).”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as Tavtigian et al.9
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Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594
Plon, S. E., Eccles, D. M., Easton, D., Foulkes, W. D., Genuardi, M., Greenblatt, M. S., Hogervorst, F. B. L., Hoogerbrugge, N., Spurdle, A. B., Tavtigian, S. V., et al. (2008) · Human Mutation · doi:10.1002/humu.20880 · Cited by 912
Self-citationInSiGHT, O. B. O., Thompson, B. A., Spurdle, A. B., Plazzer, J., Greenblatt, M. S., Akagi, K., Al‐Mulla, F., Bapat, B. V., Bernstein, I., Capellá, G., et al. (2013) · Nature Genetics · doi:10.1038/ng.2854 · Cited by 468
Self-citationEaston, D. F., Deffenbaugh, A. M., Pruss, D., Frye, C., Wenstrup, R. J., Allen-Brady, K., Tavtigian, S. V., Monteiro, A. N. A., Iversen, E. S., Couch, F. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/521032 · Cited by 455
Self-citationGoldgar, D. E., Easton, D. F., Deffenbaugh, A. M., Monteiro, Á. N. A., Tavtigian, S. V., & Couch, F. J. (2004) · The American Journal of Human Genetics · doi:10.1086/424388 · Cited by 388
Self-citationTavtigian, S. V., Byrnes, G. B., Goldgar, D. E., & Thomas, A. (2008) · Human Mutation · doi:10.1002/humu.20896 · Cited by 233
Self-citationAnalysis of missense variation in human BRCA1 in the context of interspecific sequence variation
Abkevich, V., Zharkikh, A., Deffenbaugh, A. M., Frank, D., Chen, Y., Shattuck, D., Skolnick, M. H., Gutin, A., & Tavtigian, S. V. (2004) · Journal of Medical Genetics · doi:10.1136/jmg.2003.015867 · Cited by 157
Self-citationA Full-Likelihood Method for the Evaluation of Causality of Sequence Variants from Family Data
Thompson, D. J., Easton, D. F., & Goldgar, D. E. (2003) · The American Journal of Human Genetics · doi:10.1086/378100 · Cited by 141
Thompson, B. A., Goldgar, D. E., Paterson, C. A., Clendenning, M., Walters, R., Arnold, S., Parsons, M. T., Walsh, M. D., Gallinger, S., Haile, R. W., et al. (2012) · Human Mutation · doi:10.1002/humu.22213 · Cited by 95
Self-citationThompson, B. A., Greenblatt, M. S., Vallee, M. P., Herkert, J. C., Tessereau, C., Young, E. L., Adzhubey, I. A., Li, B., Bell, R., Feng, B., et al. (2013) · Human Mutation · doi:10.1002/humu.22214 · Cited by 95
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Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion
Tayoun, A. N. A., Pesaran, T. F., DiStefano, M. T., Oza, A. M., Rehm, H. L., Biesecker, L. G., Harrison, S. M., & ClinGen Sequence Variant Interpretation Working Group (ClinGen SVI) (2018) · Human Mutation · doi:10.1002/humu.23626 · Cited by 923
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Self-citationExpert specification of the ACMG/AMP variant interpretation guidelines for genetic hearing loss
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Metadata
- 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
- Venue
- Genetics in Medicine
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 20
- Issue
- 9
- Pages
- 1054-1060
- ISSN
- 1098-3600 (ISSN-L)
- 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
- 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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