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J Med Genet. 2014 Jan;51(1):35-44. doi: 10.1136/jmedgenet-2013-101917. Epub 2013 Oct 17.

Paralogue annotation identifies novel pathogenic variants in patients with Brugada syndrome and catecholaminergic polymorphic ventricular tachycardia.

Author information

1
NIHR Royal Brompton Cardiovascular Biomedical Research Unit, Royal Brompton and Harefield NHS Trust, London, UK.

Abstract

BACKGROUND:

Distinguishing genetic variants that cause disease from variants that are rare but benign is one of the principal challenges in contemporary clinical genetics, particularly as variants are identified at a pace exceeding the capacity of researchers to characterise them functionally.

METHODS:

We previously developed a novel method, called paralogue annotation, which accurately and specifically identifies disease-causing missense variants by transferring disease-causing annotations across families of related proteins. Here we refine our approach, and apply it to novel variants found in 2266 patients across two large cohorts with inherited sudden death syndromes, namely catecholaminergic polymorphic ventricular tachycardia (CPVT) or Brugada syndrome (BrS).

RESULTS:

Over one third of the novel non-synonymous variants found in these studies, which would otherwise be reported in a clinical diagnostics setting as 'variants of unknown significance', are categorised by our method as likely disease causing (positive predictive value 98.7%). This identified more than 500 new disease loci for BrS and CPVT.

CONCLUSIONS:

Our methodology is widely transferable across all human disease genes, with an estimated 150 000 potentially informative annotations in more than 1800 genes. We have developed a web resource that allows researchers and clinicians to annotate variants found in individuals with inherited arrhythmias, comprising a referenced compendium of known missense variants in these genes together with a user-friendly implementation of our approach. This tool will facilitate the interpretation of many novel variants that might otherwise remain unclassified.

KEYWORDS:

Cardiovascular Medicine; Clinical Genetics; Congenital Heart Disease; Genetics

PMID:
24136861
PMCID:
PMC3888601
DOI:
10.1136/jmedgenet-2013-101917
[Indexed for MEDLINE]
Free PMC Article

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