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

Sean V Tavtigian1, Marc S Greenblatt2, Steven M Harrison3

  • 1Department of Oncological Sciences and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA. sean.tavtigian@hci.utah.edu.

Genetics in Medicine : Official Journal of the American College of Medical Genetics
|January 5, 2018
PubMed
Summary
This summary is machine-generated.

The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines were evaluated using Bayesian statistics. Most guidelines are compatible, providing a mathematical foundation for variant classification.

Keywords:
Bayesian frameworkmedical geneticsunclassified variantsvariant classificationvariants of uncertain significance

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Area of Science:

  • Genomic medicine
  • Molecular pathology
  • Bioinformatics

Background:

  • The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) established guidelines for variant pathogenicity classification.
  • These guidelines provide a framework for interpreting genetic variants but lack a formal mathematical basis.

Purpose of the Study:

  • To evaluate the internal consistency of the ACMG/AMP variant pathogenicity guidelines.
  • To assess the compatibility of ACMG/AMP criteria with Bayesian statistical reasoning.
  • To provide a mathematical foundation for the ACMG/AMP guidelines.

Main Methods:

  • Translated ACMG/AMP criteria into a naive Bayesian classifier.
  • Assumed four levels of evidence and exponentially scaled odds of pathogenicity.
  • Tested the framework with various prior probabilities and odds of pathogenicity.

Main Results:

  • Most ACMG/AMP combining criteria were compatible with the Bayesian framework.
  • Identified minor inconsistencies in specific ACMG/AMP evidence combinations.
  • Demonstrated that mixed evidence (pathogenic and benign) could result in classifications of pathogenic, likely pathogenic, or variant of uncertain significance (VUS).

Conclusions:

  • The Bayesian framework provides a mathematical foundation for the qualitative ACMG/AMP guidelines.
  • The quantitative framework validates the ACMG/AMP approach and suggests areas for refinement.
  • Supports automation of variant pathogenicity assessments.