Abstract
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.
Highlights
Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors
Notable examples are TCF7L2, a major disease gene for common forms of type 2 diabetes [1, 2]; INSIG2, a major obesity gene potentially explaining 4% of lifetime body mass index (BMI) in the human population [3]; CFH, one of the more striking discoveries for age-related macular degeneration, where a number of sequence variations in complement factor H have been found to be strongly associated with this disease in a number of human studies [4,5,6,7,8]; and ALOX5, a gene identified in human and mouse populations that predisposes to a number of diseaserelated traits, including atherosclerosis [9, 10], hyperlipidemia-dependent aortic aneurysm [11], and obesity and bone phenotypes [12]
After motivating the need to take a systems biology approach to dissecting complex disease traits, we review a number of approaches that have been recently developed to elucidate gene networks associated with complex traits such as common human diseases
Summary
Networks provide a convenient framework for representing high-dimensional data in which relationships among the many variables making up such data are the key to understanding the properties that emerge from the complex systems they represent. 2604 Journal of Lipid Research Volume 47, 2006 metabolism This module is enriched for genes comprising a previously published network involving an Insig subnetwork derived from the same cross, where Insig was mapped as a susceptibility gene for cholesterol levels as well as other metabolic traits, including obesity (Fig. 2A) [33]. Genes in the network are associated with cholesterol metabolism (light blue nodes) as well as lipid synthesis (green nodes) This example highlights that the identification of key modules in the network involves known biological pathways and is linked to disease traits, representing the utility of coexpression networks, with respect to the dissection of complex disease traits. The highly correlated, highly connected structures within a network module, as well as the weak links that tie these different structures together, highlight potential new strategies for exploring how to target such networks associated with a given disease in order to treat the disease effectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
