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Updated: Jan 16, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Spectrum of genetic variants detected in children tested for long QT syndrome
Aurelija Kemezyte1, Raimonda Meskiene2, Algirdas Utkus2
1Faculty of Medicine, Vilnius University, M. K. Ciurlionio st. 21, Vilnius, 03101, Lithuania.
Background:
Long QT syndrome (LQTS) is one of the most common inherited cardiac arrhythmias associated with sudden cardiac death worldwide. Despite the widespread implementation of next-generation sequencing, its diagnostic value remains limited due to challenges in interpreting the clinical significance of the identified variants. Since LQTS is rare and underreported in Baltic region, studies on small populations are valuable for expanding current knowledge on this rare cardiac channelopathy. Our aim was to evaluate the diagnostic yield of genetic testing and clinical manifestations of the disease in paediatric patients assessed for LQTS in Lithuania.
Results:
The phenotypic spectrum of LQTS among the cohort was notably heterogeneous, with more than half of the asymptomatic patients at the time of genetic testing. The overall diagnostic yield was 22%. The majority of pathogenic or likely pathogenic variants were detected in the KCNQ1 gene, with 17% of these categorised as high-risk for arrhythmic events. Individuals harbouring pathogenic or likely pathogenic variants showed significantly prolonged corrected QT (QTc) intervals in comparison to those without such variants.
Conclusions:
Less stringent referral criteria may reduce the diagnostic yield of genetic testing for LQTS. In our cohort, 17% of patients with 1 type LQTS had genetic variants located in regions associated with elevated arrhythmic risk. This knowledge should be considered as part of their individualised care plans. Furthermore, the identification of multiple variants of uncertain significance highlights the ongoing need for enhanced interpretive frameworks to integrate complex genetic findings into routine clinical decision-making.
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