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Marquis-Nicholson R.,University of Melbourne | Prosser D.O.,Diagnostic Genetics | Love J.M.,Diagnostic Genetics | Zhang L.,Diagnostic Genetics | And 8 more authors.
Circulation: Cardiovascular Genetics | Year: 2014

Background: Large gene rearrangements, not detectable by standard molecular genetic sequencing techniques, are present in a minority of patients with long QT syndrome. We aimed to screen for large rearrangements in genes responsible for long QT syndrome as part of the molecular autopsy of a 36-year-old woman who died suddenly and had a negative autopsy. A retrospective analysis of an ECG identified a long QT interval, but sequencing of known LQT genes was uninformative. Methods and Results: Array comparative genomic hybridization was used to screen for deletions and duplications in 101 genes implicated in cardiac disorders and sudden death using a postmortem blood sample. A 542 kb deletion encompassing the entire KCNJ2 gene was identified in the decedent. The mother had electrocardiographic U-wave changes consistent with Andersen-Tawil syndrome and exaggerated by exercise but none of the characteristic noncardiac features. Fluorescence in situ hybridization confirmed the deletion in the decedent and established its presence in the mother. Conclusions: A novel application of array comparative genomic hybridization and fluorescence in situ hybridization has identified that long QT syndrome and sudden cardiac death may occur as a result of a deletion of an entire gene. The case also supports recent research suggesting that noncardiac features of Andersen-Tawil syndrome occur only with missense or minor gene rearrangements in the KCNJ2 gene, resulting in a dominant negative effect on Kir2.x channels. © 2014 American Heart Association, Inc.


Skinner J.R.,Cardiac Inherited Disease Group | Skinner J.R.,University of Auckland | Van Hare G.F.,University of Washington
Heart Rhythm | Year: 2014

For all of us working in the field of inherited heart conditions, our ultimate aim is the prevention of sudden cardiac death in young people in our communities. We share the passion and drive to this aim with our colleagues Saul et al,1 who write to advocate infant screening of infants for LQTS. Although Saul et al aimed to write an unbiased review of the subject, they present data that support screening while underrepresenting evidence against it. Their illustrative Figure 1 is arguably misleading, presenting a graph of freedom from any cardiac event in symptomatic individuals with familial LQTS. We know that 87% of deaths from LQTS occur in those who were previously symptomatic.2 This discussion, however, is not about symptomatic patients with LQTS; it is about the detection of presymptomatic individuals on a community level. Our aim is to present evidence that has led us to oppose the conclusions and suggestions of their article. Most pediatric cardiologists do not wish to see ECG screening in infancy,3 and we are among them. Saul et al state that there is sufficient evidence to propose ECG screening in infancy for LQTS. We disagree. We disagree with this view for a number of reasons:(1) The effectiveness of such a program has not been evaluated in terms of outcome.(2)The ECG is an unreliable diagnostic tool with unacceptable reproducibility, specificity, and sensitivity (3)The adverse effects of overdiagnosing or underdiagnosing LQTS in thousands of individuals have not been evaluated. (4)There are no definitive criterion standard by which LQTS can be excluded once the possibility is raised, and in particular genetic testing is not sensitive or specific enough to do so. (5)There is a paucity of normative ECG and genetic data for non-Whites. We propose what we believe is a more attractive alternative: the detection of LQTS in the community through an active multidisciplinary program to detect probands and screen family members, based around a clinical registry. This has already proven to be effective.4-8 If adequately resourced, this method will provide a quicker, more reliable, and more societally acceptable method to detect and manage families at risk, such that it might conceivably render population screening redundant. © 2014 Heart Rhythm Society. All rights reserved.


Leong I.U.S.,Diagnostic Genetics | Sucich J.,Diagnostic Genetics | Prosser D.O.,Diagnostic Genetics | Skinner J.R.,Greenlane Paediatric and Congenital Cardiac Service | And 7 more authors.
Upsala Journal of Medical Sciences | Year: 2015

Background. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a heritable cardiac disorder characterized by life-threatening ventricular tachycardia caused by exercise or acute emotional stress. The standard diagnostic screening involves Sanger-based sequencing of 45 of the 105 translated exons of the RYR2 gene, and copy number changes of a limited number of exons that are detected using multiplex ligation-dependent probe amplification (MLPA).Methods. In the current study, a previously validated bespoke array comparative genomic hybridization (aCGH) technique was used to detect copy number changes in the RYR2 gene in a 43-year-old woman clinically diagnosed with CPVT.Results. The CGH array detected a 1.1 kb deletion encompassing exon 3 of the RYR2 gene. This is the first report using the aCGH technique to screen for mutations causing CPVT.Conclusions. The aCGH method offers significant advantages over MLPA in genetic screening for heritable cardiac disorders. © 2015 Informa Healthcare.


Waddell-Smith K.E.,Green Lane Paediatric and Congenital Cardiac Services | Waddell-Smith K.E.,University of Auckland | Earle N.,University of Auckland | Skinner J.R.,Green Lane Paediatric and Congenital Cardiac Services | And 2 more authors.
Archives of Disease in Childhood | Year: 2015

Long QT syndrome is the most commonly recognised cause of sudden cardiac death in children. With a prevalence of 1 in 2000, family screening is identifying large numbers of hitherto asymptomatic gene carriers in the community, about a third of whom have a normal QT interval. The mainstay of treatment is long term uninterrupted beta blocker therapy, a treatment with many potential side effects. This article reviews the evidence and suggests a cohort who may, after assessment in a specialised cardiac-genetic clinic, be spared this treatment because of very low baseline risk. These are asymptomatic boys and prepubertal girls with a heart rate corrected QT interval persistently less than 470 ms who do not indulge in high risk activities (especially swimming) and do not have a missense mutation in the c-loop region of the KCNQ1 (long QT 1) gene. © 2015, BMJ Publishing Group. All rights reserved.


Leong I.U.S.,Diagnostic Genetics | Stuckey A.,University of Auckland | Lai D.,Green Lane Paediatric and Congenital Cardiac Services | Skinner J.R.,Green Lane Paediatric and Congenital Cardiac Services | And 3 more authors.
BMC Medical Genetics | Year: 2015

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. © 2015 Leong et al.; licensee BioMed Central.

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