评估用于对癫痫相关基因错义变异进行准确致病性分类的新型硅学工具。

IF 6.6 1区 医学 Q1 CLINICAL NEUROLOGY
Epilepsia Pub Date : 2024-10-23 DOI:10.1111/epi.18155
Ludovica Montanucci, Tobias Brünger, Christian M Boßelmann, Alina Ivaniuk, Eduardo Pérez-Palma, Samden Lhatoo, Costin Leu, Dennis Lal
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引用次数: 0

摘要

目的:确定癫痫患者临床基因检测中错义变异的致病性对于指导个性化治疗至关重要。然而,由于大多数错义变异仍被归类为意义不确定的变异(VUS),而且许多计算工具可能会提供相互矛盾的预测结果,因此实现明确的致病性分类仍具有挑战性。在此,我们旨在评估最先进的计算工具在癫痫相关基因错义变异致病性预测方面的性能。这将有助于选择最合适的工具,并对其在临床环境中的应用进行严格评估:我们在三个精心策划的数据集上评估了九种针对癫痫相关基因错义变异的硅学致病性预测工具的性能。前两个数据集包括去年上传到ClinVar的癫痫相关基因中的错义变异,因此不属于九种工具中任何一种的训练集。这两个数据集基于两个不同的癫痫相关基因列表,分别包含约 700 个和约 250 个错义变异。第三个数据集包括癫痫相关基因中的约 400 个错义变异,其功能效应已通过实验确定,因此在此用于推断致病性。这三个数据集代表了盲测试集和独立测试集的最佳近似值:结果:在评估的九种工具中,AlphaMissense(曲线下面积[AUC]:.93、.88和.95)和REVEL(AUC:.93、.88和.93)显示出最佳的分类性能,在分类变异的数量上也优于其他工具:我们展示了哪些最新开发的预测工具在癫痫相关基因方面表现更佳,因此应将其纳入美国医学遗传学和基因组学学院/分子病理学协会(AGMC/AMP)的变异分类流程。应将利用新开发或更新的工具定期重新评估基因检测结果纳入标准临床实践,以提高诊断率,更好地为精准医疗提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating novel in silico tools for accurate pathogenicity classification in epilepsy-associated genetic missense variants.

Objective: Determining the pathogenicity of missense variants in clinical genetic tests for individuals with epilepsy is crucial for guiding personalized treatment. However, achieving a definitive pathogenic classification remains challenging, with most missense variants still classified as variants of uncertain significance (VUS) and with the availability of many computational tools which may provide conflicting predictions. Here, we aim to evaluate the performance of state-of-the-art computational tools in pathogenicity prediction of missense variants in epilepsy-associated genes. This will assist in selecting the most appropriate tool and critically assess their use in clinical setting.

Methods: We assessed the performance of nine in silico pathogenicity prediction tools for missense variants in epilepsy-associated genes on three carefully curated data sets. The first two data sets comprise missense variants in epilepsy associated genes that have been uploaded to ClinVar in the last year and were, therefore, not part of the training set of any of the nine considered tools. These two data sets are based on two different lists of epilepsy-associated genes and comprise ~700 and ~ 250 missense variants, respectively. The third data set includes ~400 missense variants within epilepsy-associated genes for which the functional effects have been determined experimentally and are therefore used here to infer pathogenicity. These three data sets represent the best available approximation to blind and independent test sets.

Results: Among the nine assessed tools, AlphaMissense (area under the curve [AUC]: .93, .88, and .95) and REVEL (AUC: .93, .88, and .93) showed the best classification performance, also outperforming other tools in the number of classified variants.

Significance: We show which recently developed prediction tools achieve higher performance in epilepsy-associated genes and should be integrated, therefore, into the American College of Medical Genetics and Genomics/Association of Molecular Pathology (AGMC/AMP) variant classification process. Periodic reevaluation of genetic test results with newly developed or updated tools should be incorporated into standard clinical practice to improve diagnostic yield and better inform precision medicine.

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来源期刊
Epilepsia
Epilepsia 医学-临床神经学
CiteScore
10.90
自引率
10.70%
发文量
319
审稿时长
2-4 weeks
期刊介绍: Epilepsia is the leading, authoritative source for innovative clinical and basic science research for all aspects of epilepsy and seizures. In addition, Epilepsia publishes critical reviews, opinion pieces, and guidelines that foster understanding and aim to improve the diagnosis and treatment of people with seizures and epilepsy.
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