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.

INTRODUCTION: The zebrafish (Danio rerio) has emerged as a popular model species. The rapid development of zebrafish embryos provides opportunities for investigation of genes essential for developmental processes, the human counterparts of which might be implicated in diseases. Understanding when and where genes are expressed can facilitate greater understanding of their function, and also allow the genes to be manipulated by gene knockdown in temporally and spatially specific manners. Quantitative real-time polymerase chain reaction (qRT-PCR) is widely applied in gene expression studies. This protocol presents techniques to optimize RNA isolation from zebrafish embryos; quality assessment and the use of multiple reference genes are also emphasized. The combined use of TRIzol extraction and column-based purification is strongly recommended, because the resulting RNA is of better quality than RNA isolated using either of those methods alone. The procedure can be performed in 2 d, with individual stages taking up to 15 h to complete.

Darier disease is a rare and severe autosomal dominant skin disease characterised by malodorous keratotic papules in seborrheic areas of the skin. Darier disease affects up to 1 in 30 000 people and is caused by mutations in the ATP2A2 gene, which encodes to the sarco/endoplasmic reticulum calcium-ATPase isoform 2 that pumps calcium into the endoplasmic reticulum. Although many ATP2A2 variants have been described, it is not known if genotype correlates with phenotype, which could be important for prognosis and treatment. This is the first study to use whole exome sequencing to screen the ATP2A2 gene in a cohort of 28 clinically diagnosed Darier disease patients. Twenty-one different disease causing variants were identified and 15 of these were novel. Sixteen of the 21 variants were predicted to be pathogenic using in silico prediction programs. There were seven missense, four intronic/splice-sites, three frameshifts, two in-frame deletions, four nonsense and one synonymous mutations. This study also found ten patients who harbour more than one ATP2A2 variant. The phenotype of the patient cohort was assessed by photography and by patient questionnaires. The genotype-phenotype association was examined for all variants in relation to the patient’s disease severity score, and no correlation could be established.