Atrial fibrillation (AF) is the most common arrhythmia. It is expected to affect 6-12 million Americans by 2050. AF is a major public health burden, associated with a 5-fold increased stroke risk, doubling in dementia risk, tripling in heart failur risk and nearly 2-fold increase in mortality. The estimated excess cost of health care due to AF is nearly $26 billion annually. AF is known to be heritable, and in recent years, 3 genetic loci fo AF have been identified through genome-wide association studies. More recently, we completed a large meta-analysis of genome-wide studies and identified 6 novel loci for AF. We will build upon our prior work using a multidisciplinary approach that integrates targeted sequencing of all 9 AF GWAS loci, robust replication in the CHARGE AF Consortium, and model system based functional analyses. We will take advantage of two well- characterized cohorts with early-onset AF from the Framingham Heart Study and Massachusetts General Hospital. Specifically, we propose to: 1) Identify potentially causative genetic variants at the 9 published GWAS loci by analyzing extant targeted sequencing data of the top 5 loci for AF generated by the NIH Resequencing and Genotyping Service in 480 individuals with early-onset AF and 480 referents and by performing new targeted sequencing of 4 additional AF loci in the same subjects. 2) Replicate the top 400 AF variants with MAF>0.5% identified in our targeted sequencing projects in 5,776 independent AF cases and 9,229 referents, and replicate the rare and singleton SNPs (MAF<0.5%) in the top 3 genetic regions in 1,000 independent individuals with early-onset AF and 1,000 referents. 3) Functionally evaluate the newly identified genetic variants for AF by using cellular electrophysiology to characterize coding variation in ion channel and using a combination of zebra fish, mice, and cell-based assays to examine non-coding variants at the two most promising AF related loci. Our translational approach will facilitate a greater understanding of the molecular basis of this common and morbid arrhythmia. Identification of causative variants may enhance risk stratification, and will provide preventive and therapeutic targets for drug discovery in the broader scientific and pharmaceutical community.
Despite affecting ~3 million Americans, relatively little is known about the underlying mechanisms that lead to atrial fibrillation (AF). The goal of this application is to perform sequencing of 9 genomic regions associated with AF in individuals with early-onset atrial fibrillation to find the causative genetic variants associated with this arrhythmia. Identifying the causal variants will lead to better risk stratification, improved treatment, and potentially prevention of AF.
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