ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants
ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants
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DOI：
10.1016/j.ajhg.2018.08.005
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发表时间：
2018-10-04
影响因子：
9.8
通讯作者：
Hocking, Toby Dylan
中科院分区：
文献类型：
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作者：
Alirezaie, Najmeh;Kernohan, Kristin D.;Hocking, Toby Dylan
高通量DNA测序的进展使人类基因组中变异的发现发生了革命性的变化；然而，解释这些变异的表型效应仍然是一个挑战。虽然有几种计算方法可以预测不同的影响，但它们的准确性有限，需要进一步改进。在这里，我们介绍ClinPred，一个有效的工具，用于识别与疾病相关的非同义变体。我们的预测器结合了两种机器学习算法，这两种算法使用现有的致病性分数，值得注意的是，它受益于包括来自gnomAD数据库的正常群体等位基因频率作为输入特征。我们方法的另一个主要优势是使用ClinVar作为训练集。ClinVar是一个快速增长的数据库，可以选择有信心地注释的致病变异。与其他方法相比，ClinPred在预测致病性方面表现出更高的准确性，获得了最高的曲线下面积(AUC)分数，并在不同的测试数据集中提高了特异性和敏感性。根据其他各种指标，它也获得了最好的性能。此外，ClinPred在疾病类型(癌症或罕见疾病)和机制(功能获得或丧失)方面的表现仍然强劲。重要的是，我们观察到，增加等位基因频率作为预测特征-而不是设置固定的等位基因频率截止-提高了预测的性能。我们为外显子中所有可能的人类错义变体提供预先计算的ClinPred评分，以便于社区使用。
Advances in high-throughput DNA sequencing have revolutionized the discovery of variants in the human genome; however, interpreting the phenotypic effects of those variants is still a challenge. While several computational approaches to predict variant impact are available, their accuracy is limited and further improvement is needed. Here, we introduce ClinPred, an efficient tool for identifying disease-relevant nonsynonymous variants. Our predictor incorporates two machine learning algorithms that use existing pathogenicity scores and, notably, benefits from inclusion of normal population allele frequency from the gnomAD database as an input feature. Another major strength of our approach is the use of ClinVar-a rapidly growing database that allows selection of confidently annotated disease-causing variants-as a training set. Compared to other methods, ClinPred showed superior accuracy for predicting pathogenicity, achieving the highest area under the curve (AUC) score and increasing both the specificity and sensitivity in different test datasets. It also obtained the best performance according to various other metrics. Moreover, ClinPred performance remained robust with respect to disease type (cancer or rare disease) and mechanism (gain or loss of function). Importantly, we observed that adding allele frequency as a predictive feature-as opposed to setting fixed allele frequency cutoffs-boosts the performance of prediction. We provide pre-computed ClinPred scores for all possible human missense variants in the exome to facilitate its use by the community.