Lydia D. Hellwig PhD, ScM, CGC, Joaquin Villar PhD, FACMG, Clesson Turner MD
<p>Clinical genetic testing for hereditary cardiovascular diseases is recommended by many cardiovascular groups (Musunuru et al., <span>2020</span>; Wilde et al., <span>2022</span>). Genetic test results can be important for patient medical management and for the care for family members (Cirino et al., <span>2017</span>). Appropriate classification of genetic variants is a critical component of this process and ultimately impacts patient and family outcomes (Care et al., <span>2017</span>; Phillips et al., <span>2005</span>). The American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) created recommendations for the classification of pathogenicity of variants in genes associated with monogenic disease (Richards et al., <span>2015</span>). These recommendations include defining the criteria for evidence used in classification as well as providing a framework for weighing and combining different types of evidence for the classification. Despite this standardized approach to interpretation, analysis and appropriate classification of variants remain challenging across disease contexts in clinical genetics (McInnes et al., <span>2021</span>).</p><p>In this issue of <i>Annals of Noninvasive Electrocardiology</i>, Younis et al. (<span>2023</span>) report that computational genetic variant prediction tools could identify the majority of pathogenic variants in congenital long QT syndrome (LQTS) 1–3. The authors also found that the computational scores did not predict clinical outcomes.</p><p>While it is encouraging that the variant prediction tools correlated with pathogenicity in this study, it is also important to note that determination of variant pathogenicity includes multiple types of evidence, including variant prediction evidence. Importantly, computational in silico predictors alone should not be used to classify the pathogenicity of a variant, but can be used as one piece of evidence in the classification of a genetic variant. The ACMG/AMP recommendations specify that using computational predictors are “supporting” level of evidence for or against pathogenicity using criteria PP3 and BP4 (Care et al., <span>2017</span>). Supporting-level evidence must be combined with other more substantial lines of evidence to classify the variant. Furthermore, a recent manuscript by Pejaver et al. (<span>2022</span>) provided evidence for redefining how computational tools can be used to provide evidence for or against pathogenicity of variants using the Bayesian adaptation of the ACMG/AMP framework. This work showed that the tools can provide stronger than supporting evidence and the computational tools varied in their ability to reach these levels of evidence. These authors also pointed out that it is important to select a single tool to use for PP3/BP4 missense evidence to avoid biases in results selection.</p><p>In addition, although the terms continue to be used interchangeably in the literature, recently,
遗传性心血管疾病的临床基因检测被许多心血管群体推荐(Musunuru等人,2020;Wilde et al., 2022)。基因检测结果对于患者的医疗管理和家庭成员的护理可能很重要(Cirino等人,2017)。遗传变异的适当分类是这一过程的关键组成部分,并最终影响患者和家庭的结果(Care等人,2017;Phillips et al., 2005)。美国医学遗传学和基因组学学院和分子病理学协会(ACMG/AMP)为单基因疾病相关基因变异的致病性分类提出了建议(Richards等,2015)。这些建议包括确定用于分类的证据标准,以及为衡量和组合不同类型的分类证据提供一个框架。尽管有这种标准化的解释方法,但在临床遗传学的疾病背景下,变异的分析和适当分类仍然具有挑战性(McInnes等人,2021)。在这一期的《无创心电学年鉴》中,Younis等人(2023)报道,计算遗传变异预测工具可以识别先天性长QT综合征(LQTS)的大多数致病变异1-3。作者还发现,计算分数并不能预测临床结果。虽然本研究中变异预测工具与致病性相关是令人鼓舞的,但同样重要的是要注意,变异致病性的确定包括多种类型的证据,包括变异预测证据。重要的是,计算机计算机预测不应该单独用于分类变异的致病性,但可以用作遗传变异分类的一个证据。ACMG/AMP的建议明确指出,使用计算预测指标可以“支持”使用标准PP3和BP4支持或反对致病性的证据水平(Care等人,2017年)。支持级证据必须与其他更实质性的证据相结合,才能对变体进行分类。此外,Pejaver等人(2022)最近发表的一篇论文为重新定义计算工具如何使用ACMG/AMP框架的贝叶斯适应性来提供支持或反对变异致病性的证据提供了证据。这项工作表明,这些工具可以提供比支持性证据更有力的证据,而计算工具在达到这些证据水平的能力上各不相同。这些作者还指出,选择单一工具用于PP3/BP4错义证据以避免结果选择中的偏差是很重要的。此外,尽管这两个术语在文献中仍然可以互换使用,但最近,临床遗传学和基因组学界已经开始区分变体分类和变体解释这两个术语之间的差异。变异分类被定义为评估变异致病性的过程,而变异解释是指将基因检测结果与患者临床特征和家族史结合起来进行临床诊断的过程(Biesecker et al., 2018)。这些细微差别在该领域具有挑战性,在未来的临床基因组工作中,仔细定义和清楚地使用这些术语将继续是重要的。Younis等人(2023)还发现计算机计算机工具不能预测临床结果,并得出结论,需要进行变异位置/功能分析才能更准确地解释风险。在变异分类过程之外,计算机计算机工具的潜在用途,例如使用这些工具作为临床严重程度的指标,还不太清楚。准确评估LQTS和其他遗传性心血管疾病的风险解释仍然具有挑战性,因为可变的表达性和不完全外显性(Lankaputhra &Voskoboinik, 2021)。除了额外的位置/功能数据可能有助于澄清相关基因中具有致病性和可能致病性变异的个体之间的风险分层外,还可以使用进一步的基于人群和基因型优先的方法来进一步评估与变异解释相关的这些复杂问题(Wilczewski等人,2023)。所有作者(LDH、JV和CT)都参与了稿件的构思/解读、起草或修改,并对所提交的稿件提供最终批复。作者没有需要披露的利益冲突。这里表达的观点和主张是作者的观点和主张,并不反映卫生科学军警大学或国防部的官方政策或立场。这里的观点和主张是作者的观点和主张,并不反映亨利·M·史密斯的官方政策或立场。 杰克逊军事医学发展基金会或美国国家卫生研究院。
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