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Abstract：
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.
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.
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).
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.
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.
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).
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.
摘要：
我们评估了美国医学遗传学和基因组学学院/分子病理学协会（ACMG/AMP）变异致病性指南的内部一致性和与贝叶斯统计推理的兼容性。
ACMG/AMP标准被转化为朴素贝叶斯分类器，假设四个水平的证据和指数缩放的致病性几率。我们用一系列的先验概率和致病性几率测试了这个框架。
我们使用生物学上合理的假设对ACMG/AMP指南进行建模。大多数ACMG/AMP组合标准是兼容的。一个ACMG/AMP可能的致病性组合在数学上等同于致病性，一个ACMG/AMP致病性组合实际上可能是致病性。我们对包括支持和反对致病性的证据的组合进行了建模，表明我们的方法将某些组合评分为致病性或可能致病性，ACMG/AMP将其指定为不确定意义（VUS）的变体。
通过将ACMG/AMP准则转换为贝叶斯框架，我们为什么是定性启发式提供了数学基础。现有的18种ACMG/AMP证据组合中只有2种在数学上与整体框架不一致。致病性和良性证据的混合组合可能会产生可能的致病性，可能是良性的，或VUS结果。这个定量框架验证了ACMG/AMP采用的方法，提供了进一步完善证据类别和组合规则的机会，并支持自动化变异致病性评估组件的努力。
ACMG/AMP标准被转化为朴素贝叶斯分类器，假设四个水平的证据和指数缩放的致病性几率。我们用一系列的先验概率和致病性几率测试了这个框架。
我们使用生物学上合理的假设对ACMG/AMP指南进行建模。大多数ACMG/AMP组合标准是兼容的。一个ACMG/AMP可能的致病性组合在数学上等同于致病性，一个ACMG/AMP致病性组合实际上可能是致病性。我们对包括支持和反对致病性的证据的组合进行了建模，表明我们的方法将某些组合评分为致病性或可能致病性，ACMG/AMP将其指定为不确定意义（VUS）的变体。
通过将ACMG/AMP准则转换为贝叶斯框架，我们为什么是定性启发式提供了数学基础。现有的18种ACMG/AMP证据组合中只有2种在数学上与整体框架不一致。致病性和良性证据的混合组合可能会产生可能的致病性，可能是良性的，或VUS结果。这个定量框架验证了ACMG/AMP采用的方法，提供了进一步完善证据类别和组合规则的机会，并支持自动化变异致病性评估组件的努力。
