Inferring Networks for Diseases
An important focus of medical research is to identify individual disease genes that are potential diagnostic and/or therapeutic targets. The most common diseases, such as allergies and diabetes, are complex in the sense that they are caused by altered interactions between tens, hundreds, or even thousands, of genes. Whilst the majority of those genes each have small effects, their combined effects may be large. In addition, environmental factors play major roles in complex diseases. Moreover, different combinations of genes and environmental factors may give rise to diseases with similar manifestations. An important clinical implication here is that patients who appear to have the same disease may not respond equally to the same medication; indeed, some may even deteriorate or experience serious adverse side effects. If it were possible to determine the combinations of genes and environmental factors that give rise to different diseases, this information could be used to “personalize” medication. It is also possible that an improved understanding of these factors could lead to diagnostic and/or therapeutic strategies for the prevention of disease. Network-based analysis may provide a theoretical and methodological framework to harness the complexity of common diseases whereby, instead of focusing on individual genes (or their products), the scale could be changed to network modules of interacting and functionally related genes. Such modules could then be used to determine the main pathogenic mechanisms in complex diseases, and how those mechanisms vary between patients. This information might, in turn, be used to identify combinations of genes (or their products) suitable as diagnostic and/or therapeutic targets.