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

摘要: Purpose We evaluated the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines internal consistency compatibility with Bayesian statistical reasoning. Methods The ACMG/AMP criteria were translated into a naive classifier, assuming four levels evidence exponentially scaled odds pathogenicity. tested this framework range prior probabilities Results modeled using biologically plausible assumptions. Most combining compatible. One likely pathogenic combination was mathematically equivalent to one actually pathogenic. combinations that include against pathogenicity, showing our approach scored some as or would designate uncertain significance (VUS). Conclusion By transforming framework, we provide mathematical foundation what qualitative heuristic. Only 2 18 existing inconsistent overall framework. Mixed benign could yield pathogenic, benign, VUS result. This quantitative validates adopted by ACMG/AMP, provides opportunities further refine categories rules, supports efforts automate components assessments.