Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
St George's University Hospitals NHS Foundation Trust, London, UK.
J Med Genet. 2024 Sep 24;61(10):983-991. doi: 10.1136/jmg-2024-110034.
The 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework specifies that case-control observations can be scored as 'strong' evidence (PS4) towards pathogenicity.
We developed the PS4-likelihood ratio calculator (PS4-LRCalc) for quantitative evidence assignment based on the observed variant frequencies in cases and controls. Binomial likelihoods are computed for two models, each defined by prespecified OR thresholds. Model 1 represents the hypothesis of association between variant and phenotype (eg, OR≥5) and model 2 represents the hypothesis of non-association (eg, OR≤1).
PS4-LRCalc enables continuous quantitation of evidence for variant classification expressed as a likelihood ratio (LR), which can be log-converted into log LR (evidence points). Using PS4-LRCalc, observed data can be used to quantify evidence towards either pathogenicity or benignity. Variants can also be evaluated against models of different penetrance. The approach is applicable to balanced data sets generated for more common phenotypes and smaller data sets more typical in very rare disease variant evaluation.
PS4-LRCalc enables flexible evidence quantitation on a continuous scale for observed case-control data. The converted LR is amenable to incorporation into the now widely used 2018 updated Bayesian ACMG/AMP framework.
2015 年美国医学遗传学学院/分子病理学协会(ACMG/AMP)变异分类框架规定,病例对照观察可被评为致病性的“强”证据(PS4)。
我们开发了 PS4-似然比计算器(PS4-LRCalc),用于基于病例和对照中观察到的变异频率进行定量证据赋值。为两个模型计算二项式似然,每个模型都由预设的 OR 阈值定义。模型 1 代表变异与表型之间的关联假设(例如,OR≥5),模型 2 代表非关联假设(例如,OR≤1)。
PS4-LRCalc 能够连续定量变异分类的证据,表现为似然比(LR),可以对数转换为对数 LR(证据点)。使用 PS4-LRCalc,可以使用观察数据来量化对致病性或良性的证据。还可以针对不同外显率的模型评估变体。该方法适用于为更常见的表型生成的平衡数据集和在罕见疾病变体评估中更常见的较小数据集。
PS4-LRCalc 能够对病例对照观察数据进行连续尺度的灵活证据定量。转换后的 LR 可纳入现在广泛使用的 2018 年更新的贝叶斯 ACMG/AMP 框架。