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Journal of Lipid Research, Vol. 47, 2601-2613, December 2006
Copyright © 2006 by American Society for Biochemistry and Molecular Biology
Thematic Review |
Thematic review series: Systems Biology Approaches to Metabolic and Cardiovascular Disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes
Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, WA 98109
Published, JLR Papers in Press, October 1, 2006.
1 To whom correspondence should be addressed. e-mail: eric_schadt{at}merck.com
Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.
Supplementary key words systems biology • networks • genetical genomics
Abbreviations: BMI, body mass index; eQTL, expression quantitative trait loci; QTL, quantitative trait loci
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