Common forms of metabolic and cardiovascular diseases involve the interplay of numerous genes as well as important environmental factors. Traditional biochemical and genetic approaches generally attempt to dissect these diseases one gene at a time, for example, by analysis of Mendelian forms or genetically engineered experimental organisms. But, it is also important to understand how the genes interact with each other and the environment, and how these interactions change in disease states. Technological advances, such as the development of expression arrays that allow quantification of all transcript levels in a cell or tissue, have made it feasible to globally monitor molecular phenotypes that underlie disease states. By applying statistical methods, relationships between DNA variation, gene expression patterns, and diseases can be modeled.