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
Research article
1 Diagnostic Genetics, LabPlus, Auckland City Hospital, Auckland, New Zealand
2 Bioinformatics Institute, University of Auckland, Auckland, New Zealand
3 Green Lane Paediatric and Congenital Cardiac Services, Starship Children’s Hospital, Private Bag 92024, Auckland 1142, New Zealand
4 Cardiac Inherited Disease Group, Auckland City Hospital, Auckland, New Zealand
5 Department of Child Health, University of Auckland, Auckland, New Zealand
BMC Medical Genetics 2015, 16:34Â
doi:10.1186/s12881-015-0176-z
Published: 13 May 2015
Abstract (provisional)
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,
SNPsamp;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, SNPsamp;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, SNPsamp;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.