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Abraham G, Bhalala OG, de Bakker PI, et al. Towards a molecular systems model of coronary artery disease. Curr Cardiol Rep. 2014;16(6):488doi: 10.1007/s11886-014-0488-1.
Abraham, G., Bhalala, O. G., de Bakker, P. I., Ripatti, S., & Inouye, M. (2014). Towards a molecular systems model of coronary artery disease. Current cardiology reports, 16(6), 488. https://doi.org/10.1007/s11886-014-0488-1
Abraham, Gad, et al. "Towards a molecular systems model of coronary artery disease." Current cardiology reports vol. 16,6 (2014): 488. doi: https://doi.org/10.1007/s11886-014-0488-1
Abraham G, Bhalala OG, de Bakker PI, Ripatti S, Inouye M. Towards a molecular systems model of coronary artery disease. Curr Cardiol Rep. 2014;16(6):488. doi: 10.1007/s11886-014-0488-1. PMID: 24743898; PMCID: PMC4050311.
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Curr Cardiol Rep. 2014;16(6):488. doi: 10.1007/s11886-014-0488-1.

Towards a molecular systems model of coronary artery disease.

Current cardiology reports

Gad Abraham, Oneil G Bhalala, Paul I W de Bakker, Samuli Ripatti, Michael Inouye

Affiliations

  1. Medical Systems Biology, Department of Pathology and Department of Microbiology & Immunology, The University of Melbourne, Parkville, Victoria, 3010, Australia.

PMID: 24743898 PMCID: PMC4050311 DOI: 10.1007/s11886-014-0488-1

Abstract

Coronary artery disease (CAD) is a complex disease driven by myriad interactions of genetics and environmental factors. Traditionally, studies have analyzed only 1 disease factor at a time, providing useful but limited understanding of the underlying etiology. Recent advances in cost-effective and high-throughput technologies, such as single nucleotide polymorphism (SNP) genotyping, exome/genome/RNA sequencing, gene expression microarrays, and metabolomics assays have enabled the collection of millions of data points in many thousands of individuals. In order to make sense of such 'omics' data, effective analytical methods are needed. We review and highlight some of the main results in this area, focusing on integrative approaches that consider multiple modalities simultaneously. Such analyses have the potential to uncover the genetic basis of CAD, produce genomic risk scores (GRS) for disease prediction, disentangle the complex interactions underlying disease, and predict response to treatment.

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