Calibration and refinement of ACMG/AMP criteria for variant classification with BayesQuantify.

IF 3.7 2区 医学 Q2 GENETICS & HEREDITY
Sihan Liu, Xiaoshu Feng, Yang Wu, Fengxiao Bu
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引用次数: 0

Abstract

Background: Improving the precision and accuracy of variant classification in clinical genetic testing requires further specification and stratification of the American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) criteria. While the ClinGen Bayesian framework enables quantitative evidence calibration for selected criteria, standardised tools to optimise evidence thresholds and refine ACMG/AMP criteria remain underdeveloped.

Methods: To address this need, we developed BayesQuantify, an R package that provides a unified tool for quantifying evidence strength for the ACMG/AMP criteria based on the Bayesian framework. BayesQuantify accepts a variant classification file as input and automatically calculates the odds of pathogenicity for each evidence strength, incorporating a user-provided prior probability of pathogenicity. Through bootstrapping, BayesQuantify generates thresholds by aligning the 95% lower bound of positive likelihood ratio/local positive likelihood ratio with the odds of pathogenicity for different evidence strengths. Three independent datasets derived from ClinVar, HGMD and gnomAD were used to evaluate the utility of BayesQuantify.

Results: BayesQuantify supports the calibration of both categorical and continuous ACMG/AMP evidence. Specifically, we replicated the PP3/BP4 thresholds for four computational tools recommended by ClinGen. Our analysis also indicated that the PM2 criterion can reach 'supporting,' or 'moderate,' evidence, varying by prior probability. Importantly, we established thresholds for supporting, moderate and strong evidence for in-silico tools, thereby expanding the application of PP3/BP4 criteria for missense variants in the PTEN gene.

Conclusion: BayesQuantify is a user-friendly tool that enhances the flexibility and reproducibility of ACMG/AMP criteria refinement, thus improving the accuracy and consistency of variant classification. The package is freely available at https://github.com/liusihan/BayesQuantify.

基于BayesQuantify的ACMG/AMP变量分类标准的校准和改进。
背景:提高临床基因检测中变异分类的精确性和准确性,需要进一步规范和分层美国医学遗传学学院/分子病理学协会(ACMG/AMP)的标准。虽然ClinGen贝叶斯框架能够对选定的标准进行定量证据校准,但用于优化证据阈值和完善ACMG/AMP标准的标准化工具仍然不发达。方法:为了满足这一需求,我们开发了BayesQuantify,这是一个R软件包,它提供了一个统一的工具,用于基于贝叶斯框架量化ACMG/AMP标准的证据强度。BayesQuantify接受变体分类文件作为输入,并自动计算每个证据强度的致病性几率,结合用户提供的致病性先验概率。通过bootstrapping, BayesQuantify将不同证据强度的正似然比/局部正似然比的95%下界与致病性几率对齐,从而生成阈值。使用来自ClinVar、HGMD和gnomAD的三个独立数据集来评估BayesQuantify的效用。结果:BayesQuantify支持分类和连续ACMG/AMP证据的校准。具体来说,我们复制了ClinGen推荐的四种计算工具的PP3/BP4阈值。我们的分析还表明,PM2标准可以达到“支持”或“中等”证据,根据先验概率而变化。重要的是,我们为计算机工具建立了支持、适度和强有力证据的阈值,从而扩大了PP3/BP4标准在PTEN基因错义变异中的应用。结论:BayesQuantify是一个用户友好的工具,增强了ACMG/AMP标准细化的灵活性和可重复性,从而提高了变异分类的准确性和一致性。该软件包可在https://github.com/liusihan/BayesQuantify免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Medical Genetics
Journal of Medical Genetics 医学-遗传学
CiteScore
7.60
自引率
2.50%
发文量
92
审稿时长
4-8 weeks
期刊介绍: Journal of Medical Genetics is a leading international peer-reviewed journal covering original research in human genetics, including reviews of and opinion on the latest developments. Articles cover the molecular basis of human disease including germline cancer genetics, clinical manifestations of genetic disorders, applications of molecular genetics to medical practice and the systematic evaluation of such applications worldwide.
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