Cardiac arrhythmias are prevalent and are associated with substantial morbidity and healthcare utilization. In particular, supraventricular tachycardias and bradyarrhythmias are common causes of palpitations and syncope, and may lead to sudden death in some circumstances. Many treatments for these conditions are incompletely effective or associated with potential adverse effects. Despite the recognized public health importance of arrhythmias, there is a limited understanding of their mechanisms. Our overall goals are to identify the causes of arrhythmias and improve treatments for affected patients. The specific objective of this proposal is to leverage large-scale human genetic association studies to understand the mechanisms of both supraventricular tachycardias and bradyarrhythmias. The proposal is motivated by three key observations. First, monogenic forms of supraventricular tachycardias and bradyarrhythmias, familial aggregation of these arrhythmias, and preliminary data identifying common variation associated with these conditions all indicate that there is a substantial genetic basis for supraventricular tachycardias and bradyarrhythmias. Yet genome-wide association studies, a highly efficient method for understanding human disease, are lacking for these conditions. Second, we have substantial experience with collaborative genetic association analyses of arrhythmias, and have established the Arrhythmia GENetics (AGENT) neTwork, a multi-site consortium of investigators that will contribute samples for the proposed aims. Third, our team is comprised of experts in complex trait and arrhythmia genetics who have developed innovative methods to enable functional characterization of identified genetic loci. The applicant is an Early Stage Investigator with experience in arrhythmia genetics.
In Aims 1 and 2 of the current proposal, we will identify genetic susceptibility loci associated with supraventricular tachycardias and bradyarrhythmias by performing genome-wide association studies in well-characterized individuals.
In Aim 3 we will quantify the aggregate genetic contributions to supraventricular tachycardias and bradyarrhythmias, systematically assess the genetic architecture of these arrhythmias, and estimate the genetic correlation of arrhythmias and related phenotypes.
In Aim 4, we will move from association to mechanism by characterizing the electrophysiological phenotype of the top supraventricular tachycardia and bradyarrhythmia genes in stem cell-derived cardiomyocytes, a zebrafish model system, and by cellular electrophysiology. We anticipate that our multi-faceted approach will facilitate an improved understanding of the causes of arrhythmias. Such insights may lead to novel therapeutic approaches for patient management and a comprehensive understanding of cardiovascular biology relevant to the broader scientific community.
Cardiac arrhythmias impose a substantial public health burden, yet there is a limited understanding of the mechanisms leading to these conditions. Motivated by preliminary data, we seek to leverage large-scale human genetic analyses to understand the biological mechanisms of two leading classes of arrhythmias ? supraventricular tachycardias and bradyarrhythmias. The insights gained from the proposed aims will lead to a greater understanding of the causes of arrhythmias, which may lead to novel therapeutic approaches and a comprehensive understanding of cardiovascular biology relevant to the broader scientific community.
|Aragam, Krishna G; Chaffin, Mark; Levinson, Rebecca T et al. (2018) Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery. Circulation :|
|Khurshid, Shaan; Choi, Seung Hoan; Weng, Lu-Chen et al. (2018) Frequency of Cardiac Rhythm Abnormalities in a Half Million Adults. Circ Arrhythm Electrophysiol 11:e006273|
|Roselli, Carolina; Chaffin, Mark D; Weng, Lu-Chen et al. (2018) Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet 50:1225-1233|
|Khera, Amit V; Chaffin, Mark; Aragam, Krishna G et al. (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50:1219-1224|
|Lubitz, Steven A; Khurshid, Shaan; Weng, Lu-Chen et al. (2018) Predictors of oral anticoagulant non-prescription in patients with atrial fibrillation and elevated stroke risk. Am Heart J 200:24-31|
|Weng, Lu-Chen; Preis, Sarah R; Hulme, Olivia L et al. (2018) Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation. Circulation 137:1027-1038|