The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines
Open accessAbstract
Recently, we demonstrated that the qualitative American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) guidelines for evaluation of Mendelian disease gene variants are fundamentally compatible with a quantitative Bayesian formulation. Here, we show that the underlying ACMG/AMP "strength of evidence categories" can be abstracted into a point system. These points are proportional to Log(odds), are additive, and produce a system that recapitulates the Bayesian formulation of the ACMG/AMP guidelines. The strengths of this system are its simplicity and that the connection between point values and odds of pathogenicity allows empirical calibration of the strength of evidence for individual data types. Weaknesses include that a narrow range of prior probabilities is locked in and that the Bayesian nature of the system is inapparent. We conclude that a points-based system has the practical attribute of user-friendliness and can be useful so long as the underlying Bayesian principles are acknowledged.
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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
Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework
Tavtigian, S. V., Greenblatt, M. S., Harrison, S. M., Nussbaum, R. L., Prabhu, S. A., Boucher, K. M., & Biesecker, L. G. (2018) · Genetics in Medicine · doi:10.1038/gim.2017.210 · Cited by 590
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
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Cited by
Pejaver, V., Byrne, A. B., Feng, B.-J., Pagel, K. A., Mooney, S. D., Karchin, R., O’Donnell-Luria, A., Harrison, S. M., Tavtigian, S. V., Greenblatt, M. S., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.10.013 · Cited by 589
Self-citationHorak, P., Griffith, M., Danos, A. M., Pitel, B. A., Madhavan, S., Liu, X., Chow, C., Williams, H., Carmody, L., Barrow-Laing, L., et al. (2022) · Genetics in Medicine · doi:10.1016/j.gim.2022.01.001 · Cited by 257
Genome-wide prediction of disease variants with a deep protein language model
Brandes, N., Goldman, G., Wang, C. H., Ye, C. J., & Ntranos, V. (2022) · doi:10.1101/2022.08.25.505311 · Cited by 24
Metadata
- Authors
- Tavtigian, S. V., Harrison, S. M., Boucher, K. M., & Biesecker, L. G.
- Venue
- Human Mutation
- Publisher
- Wiley
- Published
- 2020-08-30
- Volume
- 41
- Issue
- 10
- Pages
- 1734-1737
- ISSN
- 1059-7794 (ISSN-L)
- 1098-1004
- OpenAlex
- W3045929934
- Cited by
- 358
- References
- 3
- FWCI
- 10.99
- Type
- journal article
- Language
- en
- Open access
- bronze
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