# Systems Biology Analysis of Cardiac Electrical Activity and Arrhythmias.

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2020 · $476,969

## Abstract

Cardiac arrhythmias are a leading cause of morbidity and mortality in the United States. Abnormalities in
heart rate, cardiac conduction (PR and QRS) and repolarization (QT) measured on the ECG predispose to the
clinically important cardiac arrhythmias of atrial fibrillation (AF) and ventricular fibrillation (VF) / sudden cardiac
death (SCD). We examine the genomic basis of these ECG endophenotypes in order to deconstruct
arrhythmias into more proximate traits and discrete components, allowing us to better understand underlying
mechanisms, provide insight into arrhythmia generation, and help target development of novel therapies.
 The molecular architecture of cardiac electrical activity and arrhythmias is not fully understood, but
likely involves genomic, epigenomic, and environmental influences. Over the past 10 years, we have
identified numerous common loci associated with cardiac electrical activity and arrhythmias, yet these
common variants account for only a portion of the heritability of electrophysiologic and arrhythmic
phenotypes. The agnostic examination of genotype-phenotype associations employed in genome-
wide association studies (GWAS) does not incorporate knowledge of functional genomic regions or
important biologic relationships. Additionally, we currently lack an understanding of the molecular
mechanisms connecting mostly intergenic and intronic GWAS signals to phenotype. We therefore
hypothesize that a systems biology approach integrating genetic sequence variation with omic data
(epigenomic, transcriptomic, and proteomic data) will uncover novel associations and elucidate
biologic mechanisms associated with arrhythmia-related phenotypes. We further hypothesize that
examining the simultaneous association between sequence variation and multiple cardiac
electrophysiologic phenotypes will help uncover additional novel mechanisms associated with cardiac
electrical activity and arrhythmias.
 TOPMed's combination of rich phenotype data, with whole genome sequence (WGS),
epigenomic, transcriptomic, and proteomic data, provides a unique opportunity to more
comprehensively explore these hypotheses. We leverage sequence, omic, and phenotype data from
multiple cohort studies to efficiently and cost-effectively examine and dissect association of omic
factors with cardiac electrophysiology and arrhythmia risk. Our application is an ambitious yet
eminently feasible effort that integrates clinical, genetic, and systems biology expertise. We aim to
discover associations using omics data (Aims 1 and 2) and elucidate specific genes and biologic
pathways underlying these associations (Aims 3 and 4). Our ultimate goal is to identify pathways,
genes, and genetic variation that are clinically relevant, and therefore potentially the target of new
therapies, diagnostics, or risk predictions.

## Key facts

- **NIH application ID:** 9921462
- **Project number:** 5R01HL141989-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Dan E Arking
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $476,969
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9921462

## Citation

> US National Institutes of Health, RePORTER application 9921462, Systems Biology Analysis of Cardiac Electrical Activity and Arrhythmias. (5R01HL141989-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9921462. Licensed CC0.

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