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Updated: Jun 9, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

LQTS gene LOVD database.

Tao Zhang¹, Arthur Moss, Peikuan Cong

¹James D. Watson Institute of Genome Sciences, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China.

|September 3, 2010

Summary

This summary is machine-generated.

This study details a comprehensive online database of genetic variants associated with Long QT Syndrome (LQTS), a disorder causing arrhythmias and sudden death. The database aids researchers and clinicians in understanding LQTS genetic causes.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

Published on: March 6, 2019

Area of Science:

Genetics
Cardiology
Bioinformatics

Background:

Long QT Syndrome (LQTS) is a genetic disorder linked to cardiac arrhythmias and sudden death.
LQTS is primarily caused by mutations in cardiac ion channel genes, with recent discoveries implicating additional genes.

Purpose of the Study:

To establish an online database for comprehensive information on genetic variants in LQTS-associated genes.
To facilitate research and clinical practice by centralizing variant data and enabling user submissions.

Main Methods:

Creation of an online database (http://www.genomed.org/LOVD/introduction.html) to store and present LQTS-associated gene variant information.
Inclusion of variants from published literature and submission of novel findings, such as a potential pathogenic mutation in the KCNH2 gene.

Main Results:

As of February 2010, the database contained 1738 unique variants across 12 genes.
Categorization of variants: 950 pathogenic, 265 possibly pathogenic, 131 unknown/unclassified, and 292 with no known pathogenicity.
Identification of a potential novel pathogenic mutation in the KCNH2 splice site in ten Chinese families.

Conclusions:

The developed LQTS database serves as a valuable resource for researchers and clinicians.
The database supports the ongoing study and management of Long QT Syndrome by providing accessible genetic variant data.