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Whole Blood Gene Expression and Atrial Fibrillation: The Framingham Heart Study

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2014

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Public Library of Science
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Lin, H., X. Yin, K. L. Lunetta, J. Dupuis, D. D. McManus, S. A. Lubitz, J. W. Magnani, et al. 2014. “Whole Blood Gene Expression and Atrial Fibrillation: The Framingham Heart Study.” PLoS ONE 9 (5): e96794. doi:10.1371/journal.pone.0096794. http://dx.doi.org/10.1371/journal.pone.0096794.

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Abstract

Background: Atrial fibrillation (AF) involves substantial electrophysiological, structural and contractile remodeling. We hypothesize that characterizing gene expression might uncover important pathways related to AF. Methods and Results: We performed genome-wide whole blood transcriptomic profiling (Affymetrix Human Exon 1.0 ST Array) of 2446 participants (mean age 66±9 years, 55% women) from the Offspring cohort of Framingham Heart Study. The study included 177 participants with prevalent AF, 143 with incident AF during up to 7 years follow up, and 2126 participants with no AF. We identified seven genes statistically significantly up-regulated with prevalent AF. The most significant gene, PBX1 (P = 2.8×10−7), plays an important role in cardiovascular development. We integrated differential gene expression with gene-gene interaction information to identify several signaling pathways possibly involved in AF-related transcriptional regulation. We did not detect any statistically significant transcriptomic associations with incident AF. Conclusion: We examined associations of gene expression with AF in a large community-based cohort. Our study revealed several genes and signaling pathways that are potentially involved in AF-related transcriptional regulation.

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Biology and Life Sciences, Cell Biology, Signal Transduction, Cell Signaling, Computational Biology, Genome Analysis, Genome-Wide Association Studies, Transcriptome Analysis, Gene Regulatory Networks, Genetics, Gene Expression, Genetics of Disease, Genomics, Mutation, Medicine and Health Sciences, Cardiology, Cardiovascular Diseases, Arrhythmia, Epidemiology, Cardiovascular Disease Epidemiology, Genetic Epidemiology, Clinical Research Design, Cohort Studies

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