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Rare Variants Associated with Arrhythmogenic Cardiomyopathy: Reclassification Five Years Later.

Marta Vallverdu-PratsMireia AlcaldeGeorgia Sarquella-BrugadaSergi CesarElena ArbeloAnna Fernandez-FalguerasMónica CollAlexandra Pérez-SerraMarta PuigmuléAnna IglesiasVictoria FiolCarles Ferrer-CostaBernat Del OlmoFerran PicóLaura LopezPaloma JordàAna García-ÁlvarezColoma Tirón de LlanoRocío ToroSimone GrassiAntonio OlivaJosep BrugadaRamon BrugadaÒscar Campuzano
Published in: Journal of personalized medicine (2021)
Genetic interpretation of rare variants associated with arrhythmogenic cardiomyopathy (ACM) is essential due to their diagnostic implications. New data may relabel previous variant classifications, but how often reanalysis is necessary remains undefined. Five years ago, 39 rare ACM-related variants were identified in patients with features of cardiomyopathy. These variants were classified following the American College of Medical Genetics and Genomics' guidelines. In the present study, we reevaluated these rare variants including novel available data. All cases carried one rare variant classified as being of ambiguous significance (82.05%) or likely pathogenic (17.95%) in 2016. In our comprehensive reanalysis, the classification of 30.77% of these variants changed, mainly due to updated global frequencies. As in 2016, nowadays most variants were classified as having an uncertain role (64.1%), but the proportion of variants with an uncertain role was significantly decreased (17.95%). The percentage of rare variants classified as potentially deleterious increased from 17.95% to 23.07%. Moreover, 83.33% of reclassified variants gained certainty. We propose that periodic genetic reanalysis of all rare variants associated with arrhythmogenic cardiomyopathy should be undertaken at least once every five years. Defining the roles of rare variants may help clinicians obtain a definite diagnosis.
Keyphrases
  • copy number
  • heart failure
  • healthcare
  • genome wide
  • machine learning
  • palliative care
  • deep learning
  • big data
  • gene expression
  • clinical practice
  • electronic health record
  • artificial intelligence
  • drug induced