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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-09

AUTHORS

Sean V Tavtigian, Marc S Greenblatt, Steven M Harrison, Robert L Nussbaum, Snehit A Prabhu, Kenneth M Boucher, Leslie G Biesecker

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. More... »

PAGES

1054-1060

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/gim.2017.210

DOI

http://dx.doi.org/10.1038/gim.2017.210

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100162100

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29300386


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