Key result
Paralogue annotation identifies novel disease-causing variants with ~99% positive predictive value.
Why the study?
Distinguishing disease-causing genetic variants from rare benign variants is a principal challenge in clinical genetics due to rapid variant identification exceeding functional characterization capacity.
Effect estimate: PPV 98.7%
Reclassifies >1/3 novel VUS as likely pathogenic in BrS/CPVT cohorts (PPV 98.7%); supports method utility but leaves routine adoption open pending validation.
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.
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Walsh et al. (2013) studied Brugada syndrome and catecholaminergic polymorphic ventricular tachycardia (n=2,266). Paralogue annotation was evaluated on Categorization of novel non-synonymous variants as likely disease causing (PPV 98.7%). Paralogue annotation categorized over one third of novel non-synonymous variants as likely disease-causing with a positive predictive value of 98.7%, identifying >500 new disease loci.