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

BMC Medical Genetics

Donald R Love²  Jonathan R Skinner²  Daniel Lai¹  Alexander Stuckey⁴  Ivone US Leong³ 

[1] Green Lane Paediatric and Congenital Cardiac Services, Starship Children’s Hospital, Private Bag 92024, Auckland 1142, New Zealand;Department of Child Health, University of Auckland, Auckland, New Zealand;Diagnostic Genetics, LabPlus, Auckland City Hospital, Auckland, New Zealand;Bioinformatics Institute, University of Auckland, Auckland, New Zealand

关键词: In silico prediction tools;    Ion channels;    Genetics;    Long QT syndrome;   

Others  :  1210170
DOI  :  10.1186/s12881-015-0176-z

 received in 2014-11-03, accepted in 2015-04-22,  发布年份 2015

【 摘 要 】

Background

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.

Methods

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2015 Leong et al.; licensee BioMed Central.

【 图 表 】

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