Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Eric E. Schadt and Pek Y. Lum

Genetics, Rosetta, Seattle, WA 98109

Corresponding Author: eric_schadt{at}merck.com

Common human diseases like obesity, diabetes, and atherosclerosis are the result of multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. The identification of susceptibility genes for these diseases using genetic and functional genomic technologies is accelerating, and the expectation over the next several years is that a significant number of genes will be identified for common human diseases (perhaps in the hundreds or even thousands). However, the identification of single genes for disease has only limited utility, given an understanding of how disease (or any complex phenotype for that matter) originates in a complex system will not follow from single gene analyses. Further, the identification of single genes for disease will not necessarily 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 more generally limit the overall utility of such discoveries. A number of highly integrative approaches aimed at reconstructing gene networks that are predictive of disease and associated complex traits have recently been developed and applied. Here we review a number of network reconstruction approaches that involve integrating genetic, gene expression, and clinical phenotype data to elucidate networks underlying disease. The interaction and causal networks that can be reconstructed from these types of data provide a richer context in which to interpret associations between genes and disease. Further, reconstruction of gene networks can lead to defining pathways underlying disease more objectively, and, as a result, lead to the identification of more robust points for therapeutic intervention as well as disease biomarkers.

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