“Cardiac arrhythmias are a leading cause of morbidity and mortality in the United States,” said Shojaie. “While molecular mechanisms of arrhythmias are not fully understood, they likely involve genomic, epigenomic, and environmental influences.”
Using statistical machine learning and systems biology approaches, the team aims to integrate genetic sequence variation with multiple omics data (epigenomics, transcriptomics, and proteomics) in order to uncover novel associations and clarify biologic mechanisms associated with arrhythmia-related phenotypes. The goal is to identify pathways, genes, and genetic variants that are clinically relevant and, therefore, are potential targets for new therapies, diagnostics, or risk predictions.
“Integrating these multiplex data pose many challenges, but also offer exciting opportunities for developing statistical machine learning and network analysis methods to glean insight into mechanisms of cardiac arrhythmias,” said Shojaie.
This R01 grant is in collaboration with Nona Sotoodehnia, MD, MPH, co-director UW Cardiovascular Health Research Unit, and Dan Eytan Arking, PhD, co-director Biological Mechanisms Core, Johns Hopkins University. The TOPMed program is sponsored by the National Heart, Lung and Blood Institute (NHLBI).