Literature DB >> 28988457

Re-evaluating pathogenicity of variants associated with the long QT syndrome.

Jonathan R Kaltman1, Frank Evans1, Yi-Ping Fu1.   

Abstract

INTRODUCTION: Genetic testing for congenital long QT syndrome (LQTS) has become common. Recent studies have shown that some variants labelled as pathogenic might be misclassified due to sparse case reports and relatively common allele frequencies (AF) in the general population. This study aims to evaluate the presence of LQTS-associated variants in the Genome Aggregation Database (gnomAD) population, and assess the functional impact of these variants. METHODS AND
RESULTS: Variants associated with LQTS from the Human Gene Mutation Database were extracted and matched to the gnomAD to evaluate population-based AF. We used MetaSVM to predict the function of LQTS variants. Allele distribution by protein topology in KCNQ1, KCNH2, and SCN5A was compared between gnomAD (n = 123,136) and a cohort of LQTS patients aggregated from eight published studies (n = 2,683). Among the 1,415 LQTS-associated single nucleotide variants in 30 genes, 347 (25%) are present in gnomAD; 24% of the 347 variants were predicted as functionally tolerated compared with 4% of variants not present in gnomAD (P < 0.001). Of the 347 pathogenic variants in gnomAD, seven (2%) had an AF of ≥ 0.001 and 65 (19%) variants had an AF of ≥ 0.0001. In KCNQ1, KCNH2, and SCN5A, allele distribution by protein functional region was significantly different with gnomAD alleles appearing less frequently in highly pathogenic domains than case alleles.
CONCLUSION: A significant number of LQTS variants have insufficient evidence for pathogenicity and relatively common AF in the general population. Caution should be used when ascribing pathogenicity to these variants. © Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Entities:  

Keywords:  Exome Aggregation Consortium; clinical genetics; exome; genetic variation; long QT syndrome

Mesh:

Substances:

Year:  2017        PMID: 28988457      PMCID: PMC5777879          DOI: 10.1111/jce.13355

Source DB:  PubMed          Journal:  J Cardiovasc Electrophysiol        ISSN: 1045-3873


  28 in total

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9.  Genetic analysis, in silico prediction, and family segregation in long QT syndrome.

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Journal:  Eur J Hum Genet       Date:  2014-03-26       Impact factor: 4.246

10.  Mutations in Danish patients with long QT syndrome and the identification of a large founder family with p.F29L in KCNH2.

Authors:  Michael Christiansen; Paula L Hedley; Juliane Theilade; Birgitte Stoevring; Trond P Leren; Ole Eschen; Karina M Sørensen; Anne Tybjærg-Hansen; Lilian B Ousager; Lisbeth N Pedersen; Ruth Frikke-Schmidt; Frederik H Aidt; Michael G Hansen; Jim Hansen; Poul E Bloch Thomsen; Egon Toft; Finn L Henriksen; Henning Bundgaard; Henrik K Jensen; Jørgen K Kanters
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