The combination of SIFT, PROVEAN, and SNAP provided the best predictive performance for classifying long QT syndrome gene mutations, achieving 82.7% accuracy and an MCC of 0.44.
Do in silico prediction tools accurately classify long QT syndrome gene mutations compared to functional characterization?
In silico prediction tools can aid in assessing the pathogenicity of KCNQ1 and KCNH2 variants in Long QT syndrome, but their accuracy is gene-dependent and poor for SCN5A variants.
Effect estimate: MCC 0.44
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 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.
Leong et al. (Tue,) conducted a other in Long QT syndrome gene mutations (n=312). In silico prediction tools (SIFT, PROVEAN, SNAP) vs. Functional characterisation (ground truth) was evaluated on Predictive accuracy for all three LQT genes combined (MCC 0.44). The combination of SIFT, PROVEAN, and SNAP provided the best predictive performance for classifying long QT syndrome gene mutations, achieving 82.7% accuracy and an MCC of 0.44.