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Loss-of-Function FLNC Variants Are Associated With Arrhythmogenic Cardiomyopathy Phenotypes When Identified Through

Eric D Carruth1, Maria Qureshi2, Amro Alsaid2

  • 1Department of Translational Data Science and Informatics (E.D.C., H.L.K., B.K.F., C.M.H.), Geisinger, Danville, PA.

Circulation. Genomic and Precision Medicine
|June 14, 2022
PubMed

Insights

Loss-of-function variants in the FLNC gene are linked to increased risks of arrhythmogenic cardiomyopathy and related heart conditions. Genomic screening for FLNC variants can identify actionable genetic conditions.

Area of Science:

  • Genetics
  • Cardiology
  • Genomic Medicine

Background:

  • The FLNC gene is implicated in arrhythmogenic cardiomyopathy, an actionable genetic condition.
  • The association between FLNC loss-of-function (LOF) variants and disease phenotypes in unselected populations remains unclear.

Purpose of the Study:

  • To investigate the association between FLNC loss-of-function (LOF) variants and arrhythmogenic cardiomyopathy-associated phenotypes in a large, unselected clinical cohort.

Main Methods:

  • Identified rare FLNC LOF variants in 171,948 individuals using exome sequencing linked to health records.
  • Analyzed associations between FLNC LOF variants and cardiomyopathy phenotypes, including diagnoses and cardiac evaluations.

Main Results:

  • Sixty individuals (0.03%) carried FLNC LOF variants, showing significantly increased odds for dilated cardiomyopathy (OR, 4.9), supraventricular tachycardia (OR, 3.2), and left-dominant arrhythmogenic cardiomyopathy (OR, 4.2).
  • Reduced left ventricular ejection fraction was observed in individuals with FLNC LOF variants (52% vs. 57%).
  • At least 9% of individuals with FLNC LOF variants exhibited evidence of penetrant disease.

Conclusions:

  • FLNC LOF variants are associated with an increased risk of ventricular dysfunction and arrhythmia in the general population.
  • Genomic screening for FLNC variants is supported for identifying actionable secondary genetic findings.
Abstract

Keywords:
cardiomyopathyelectronic health recordsgenotypemagnetic resonance imagingphenotype

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