Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.
Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.
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DOI：
10.1186/s12881-015-0176-z
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发表时间：
2015-05-13
影响因子：
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通讯作者：
Love DR
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文献类型：
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作者：
Leong IU;Stuckey A;Lai D;Skinner JR;Love DR
长QT综合征（LQTS）是一种常染色体显性遗传病，易致恶性心律失常猝死。基因检测鉴定出许多不确定致病性的错义单核苷酸变异。确定遗传致病性是进行家族级联筛查的必要前提。许多实验室单独或联合使用计算机预测工具，或使用metaserver来预测致病性；然而，它们在LQTS背景下的准确性是未知的。我们在分析LQTS 1-3基因变异时评估了5个计算机程序和2个metaserserver的准确性。计算机工具SIFT， polyphen2， PROVEAN， snp & go和SNAP，单独或所有可能的组合，以及元服务器Meta-SNP和PredictSNP，对312个KCNQ1， KCNH2和SCN5A基因变异进行了测试，这些基因变异以前被体外或共分离研究表征为“致病”（283）或“良性”（29）。计算准确性、敏感性、特异性和马修斯相关系数（MCC），以确定每个LQTS基因的最佳组合，以及所有基因组合时的最佳组合。检测KCNQ1的最佳组合是PROVEAN、SNPs&GO和SIFT（准确性92.7%，灵敏度93.1%，特异性100%，MCC 0.70）。KCNH2的硅工具的最佳组合是SIFT和provan或provan， SNPs&GO和SIFT。两种组合在准确性（91.1%）、敏感性（91.5%）、特异性（87.5%）和MCC（0.62）方面得分相同。在SCN5A病例中，SNAP和PROVEAN提供了最佳组合（准确性81.4%，敏感性86.9%，特异性50.0%,MCC 0.32）。3个LQT基因组合时，SIFT、PROVEAN和SNAP的组合效果最佳（准确性82.7%，敏感性83.0%，特异性80.0%,MCC 0.44）。两个元服务器的性能都优于单个的计算机工具；然而，它们的表现并不比最好的硅工具组合更好。具有最佳性能的计算机工具的组合取决于基因。本文报道的计算机工具在评估KCNQ1和KCNH2基因变异方面可能有一定价值，但当分析应用于SCN5A基因变异时，应谨慎。本文的在线版本（doi:10.1186/s12881-015-0176-z）包含补充材料，可供授权用户使用。
Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1–3 gene variants. The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either “pathogenic” (283) or “benign” (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined. The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools. The combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants. The online version of this article (doi:10.1186/s12881-015-0176-z) contains supplementary material, which is available to authorized users.