Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels.
Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels.
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DOI：
10.1186/s12920-023-01454-6
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发表时间：
2023-02-28
影响因子：
2.7
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中科院分区：
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在解释遗传变异时，使用电子致病性预测作为证据被广泛接受，作为标准变异分类指南的一部分。虽然已经开发和评估了许多算法来对错义变体进行分类，但帧内插入/删除(Indels)的研究要少得多。我们使用来自gnomAD v3.1(次要等位基因频率为1-5%)、ClinVar和解密发育障碍研究的数据，创建了一个包含3,964个小(<； 100个碱基)INDELs的数据集，预测会导致框架内氨基酸的插入或缺失。我们使用这个数据集评估了九种致病性预测工具的性能：CDAD、CAPICE、FATHMM-INDELL、MutPred-INDEL、MutationTaster2021、PROVEAN、SIFT-INDELL、VEST-INDELL和VVP。我们的数据集包括来自gnomAD(n = 809)、ClinVar(n = 2882)和ddd(n = 273)的2224个良性/可能良性和1740个致病/可能致病变异。我们能够在所有工具中为91%的变体生成分数，根据公布的推荐阈值，ROC曲线(AUC)下的面积为0.81-0.96。为了避免因将我们的数据集包括在工具的训练数据中而造成的偏差，我们还评估了gnomAD或ClinVar中不存在的DDD变体(70种致病和81种良性)。使用这个子集，所有工具的AUC大大降低到0.64-0.87。然而，有几个工具的执行情况类似，vest-indel的AUC最高，分别为0.93(全数据集)和0.87(DDD子集)。为预测框内INDELs的致病性而设计的算法执行得足够好，以类似于错义预测工具的方式辅助临床变体分类。网上版载有补充材料，可在10.1186/s12920-023-01454-6查阅。
The use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied. We created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1–5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP. Our dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81–0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools’ training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64–0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset). Algorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools. The online version contains supplementary material available at 10.1186/s12920-023-01454-6.
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影响因子：
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McLaren W;Gil L;Hunt SE;Riat HS;Ritchie GR;Thormann A;Flicek P;Cunningham F
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