Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines.
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines.
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DOI：
10.1186/s13059-017-1353-5
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发表时间：
2017-11-28
期刊：
影响因子：
12.3
通讯作者：
Plon SE
中科院分区：
文献类型：
--
作者：
Ghosh R;Oak N;Plon SE
临床报告的美国医学遗传学学会和美国病理学家学会（ACMG/AMP）变异分类指南广泛用于诊断实验室的变异解释。ACMG/AMP指南建议在所有使用的计算机模拟算法之间的预测完全一致，而不指定算法的数量或类型。该建议的主观性导致临床实验室之间变异分类的不一致性，并阻止了变异的明确分类。使用来自ClinVar数据库的14，819个良性或致病性错义变体，我们比较了不同生物学和技术变量的数据集之间的25种算法的性能。不同算法组合之间的一致性存在很大差异，良性变异的一致性特别低。我们还确定了一个以前未报告的来源，错误的变异解释（假一致）一致的硅片预测是相反的证据提供的其他来源。我们确定了最近开发的具有高预测能力的算法，并且对疾病机制，基因约束和遗传模式等变量具有鲁棒性，尽管基于对临床遗传学文献（2011-2017）的回顾，性能较差的算法更常使用。我们的分析确定了具有独立于潜在疾病机制的高性能特征的算法。我们描述了一致性增加的算法组合，在使用ACMG/AMP指南评估临床相关变体期间，应改善计算机算法的使用。本文的在线版本（doi：10.1186/s13059-017-1353-5）包含补充材料，可供授权用户使用。
The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants. Using 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017). Our analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines. The online version of this article (doi:10.1186/s13059-017-1353-5) contains supplementary material, which is available to authorized users.
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