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Atrial Fibrillation Genomics: Discovery and Translation.
Yoo, David H; Bodmer, Rolf; Ocorr, Karen; Larson, Christopher J; Colas, Alexandre R; Muse, Evan D.
  • Yoo DH; Scripps Clinic, La Jolla, CA, 92037, USA.
  • Bodmer R; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
  • Ocorr K; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
  • Larson CJ; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
  • Colas AR; Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 92037, USA.
  • Muse ED; Scripps Clinic, La Jolla, CA, 92037, USA. emuse@scripps.edu.
Curr Cardiol Rep ; 23(11): 164, 2021 10 01.
Article en En | MEDLINE | ID: mdl-34599422
PURPOSE OF REVIEW: Our understanding of the fundamental cellular and molecular factors leading to atrial fibrillation (AF) remains stagnant despite significant advancement in ablation and device technologies. Diagnosis and prevention strategies fall behind that of treatment, but expanding knowledge in AF genetics holds the potential to drive progress. We aim to review how an understanding of the genetic contributions to AF can guide an approach to individualized risk stratification and novel avenues in drug discovery. RECENT FINDINGS: Rare familial forms of AF identified monogenic contributions to the development of AF. Genome-wide association studies (GWAS) further identified single-nucleotide polymorphisms (SNPs) suggesting polygenic and multiplex nature of this common disease. Polygenic risk scores accounting for the multitude of associated SNPs that each confer mildly elevated risk have been developed to translate genetic information into clinical practice, though shortcomings remain. Additionally, novel laboratory methods have been empowered by recent genetic findings to enhance drug discovery efforts. AF is increasingly recognized as a disease with a significant genetic component. With expanding sequencing technologies and accessibility, polygenic risk scores can help identify high risk individuals. Advancement in digital health tools, artificial intelligence and machine learning based on standard electrocardiograms, and genomic driven drug discovery may be integrated to deliver a sophisticated level of precision medicine in this modern era of emphasis on prevention. Randomized, prospective studies to demonstrate clinical benefits of these available tools are needed to validate this approach.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Fibrilación Atrial Tipo de estudio: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Fibrilación Atrial Tipo de estudio: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article