Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework.
Genet Med
; 20(9): 1054-1060, 2018 09.
Article
de En
| MEDLINE
| ID: mdl-29300386
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.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Théorème de Bayes
/
Analyse de séquence d'ADN
/
Biologie informatique
Type d'étude:
Guideline
/
Prognostic_studies
/
Qualitative_research
Limites:
Humans
Langue:
En
Journal:
Genet Med
Sujet du journal:
GENETICA MEDICA
Année:
2018
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique