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DNA Microarrays - Applications Of Microarray Analysis

chromosome clinical genes expression disease gene

Microarrays are new enough that their applications are still being developed. Microarray expression analysis can be used to help study complex, multigenic diseases such as Parkinson's disease (PD). The great challenge in understanding the genetics of such disorders is identifying susceptibility genes, which are genes that increase a person's risk of developing the disease. Frequently, the first step in discovering a susceptibility gene is linkage analysis. This technique can identify regions of a chromosome that harbor such a gene, but the regions that are identified are frequently very large, containing hundreds of genes. Screening through all of these genes individually is tremendously slow and labor-intensive. Expression analysis using microarrays can help prioritize these genes for further analysis by providing independent lines of evidence that specific genes are involved in the disease process.

Brain tissue can be collected through anatomical donations from patients with Parkinson's disease and from unaffected individuals, for example. Regions of the brain that are especially affected in Parkinson's patients can be compared to the same regions from unaffected individuals. Genes whose levels of expression vary can be identified. Hundreds or thousands of genes may be identified in this way, but they can then be compared to those that are found, through linkage analysis, to be linked to Parkinson's disease. There may be only tens of genes common to both groups. These genes can be prioritized for detailed examination through other methods. The key here is that expression analysis and linkage analysis provide independent evidence of a given gene's involvement in a disease process. It is the synthesis of information from these two independent lines of evidence that makes this approach powerful.

Another very powerful application of microarray expression data is called classification analysis. This technique uses gene expression data to separate tissue samples into two or more groups. For example, one type of tumor may respond very well to an aggressive program of chemotherapy treatment, while another type may respond better to surgical removal followed by radiation therapy. Further, these two types of tumors may be difficult or impossible to tell apart under a microscope. Choosing the correct method of treatment and applying that treatment early in the course of disease could significantly improve a patient's chances of survival.

In such a case, expression analysis can be used to give a detailed picture of the genes that are expressed in the two types of tumor. A training set (a small set of samples in each category) can be used to find specific patterns of gene expression that are characteristic for each type of tumor. New tumors can then be analyzed, and their expression profiles can be used to predict the group to which they belong. These approaches are used with great success to refine the clinical management of cancer patients. A 2001 study by S. Dhanasekaran, "Delineation of Prognostic Biomarkers in Prostate Cancer," offers an example of this kind of work. Additional applications of microarrays are still being developed.

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