Key result
Large-scale genetic studies have identified over 100 loci associated with atrial fibrillation, advancing the potential use of polygenic risk scores for clinical risk prediction and targeted therapy.
Why the study?
Despite rapid discoveries of AF-related genetic variants, it remains difficult for clinicians to interpret the role of genetics in AF prediction, management, and clinical decision-making.
This review highlights the rapid acceleration in discovering AF-related genetic variants and discusses the potential for translating these genetic advances into clinical risk prediction and personalized management.
Genetic advances in AF support polygenic score exploration; leaves open clinical validation before risk prediction use.
Atrial fibrillation (AF) is the most common heart rhythm disorder worldwide and may have serious cardiovascular health consequences. AF is associated with increased risk of stroke, dementia, heart failure, and death. There are several known robust, clinical risk predictors for AF, such as male sex, increasing age, and hypertension; however, during the last couple of decades, a substantive genetic component has also been established. Over the last 10 years, the discovery of novel AF-related genetic variants has accelerated, increasing our understanding of mechanisms behind AF. Current studies are focusing on mapping the polygenic structure of AF, improving risk prediction, therapeutic development, and patient-specific management. Nevertheless, it is still difficult for clinicians to interpret the role of genetics in AF prediction and management. Here, we provide an overview of relevant topics within the genetics of AF and attempt to provide some guidance on how to interpret genetic advances and their implementation into clinical decision-making.
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Kalstø et al. (2019) conducted a review in Atrial Fibrillation. Genetic variants and polygenic risk scores was evaluated. Large-scale genetic studies have identified over 100 loci associated with atrial fibrillation, advancing the potential use of polygenic risk scores for clinical risk prediction and targeted therapy.
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