Integrating global gene expression analysis and genetics.

The transcriptome is defined as the collection of all RNAs produced in a cell or tissue at a defined time in development and is one of many stages that make up a biological system. It is also one of the most important; providing the critical link in the flow of information between genes and disease. Therefore, identifying gene expression changes that are reacting to or causing disease promises to significantly enhance our understanding of common disorders. However, only recently has the technology, in the form of DNA microarrays, been in place to quantitate gene expression levels on a genome-wide scale. DNA microarrays are small chips that contain arrays of DNA sequences and are capable of simultaneously quantifying the expression of thousands of genes. When applied to samples representing diseased and normal states, microarray-based expression profiling can identify differentially expressed genes that may play a role in the disease or predict progression or severity. Additionally, the integration of genetics and gene expression promises to aid in uncovering common genetic variations that control a particular disease. In animal models, this approach has already been used to identify genes correlated with disease, prioritized candidates, model causal interactions between genes and traits, and generate gene coexpression networks; all of which have shed light on novel disease mechanisms. In this chapter, we provide an overview of DNA microarray technologies and discuss ways in which microarray expression data can be combined with more traditional experimental approaches to dissect the genetic basis of disease.