Bioinformatics strategies for disease gene identification

Disease gene identification based on chromosomal localisation is sometimes difficult and often time-consuming. It requires collecting as much information on the disease as possible. Combining positional information with disease characteristics might give hints by which candidate disease genes can be selected. Our hypothesis was that internet databases, which contain gene specific data as well as databases that contain phenotype descriptions, can be utilized systematically for identification of disease genes. We present two bioinformatics strategies that are able to identify human candidate disease genes and classify human disease phenotypes.Phenotype analysis is equally relevant to disease gene identification as it is to the functional annotation of the human genome: Over the course of the coming years major improvements are to be expected with regard to gene identification methods for monogenic, and multifactorial diseases, mutation detection, and various methods for cross-species comparisons. Bioinformatics approaches such as those described here, should be a useful addition to sequence and gene-based analyses. While much remains to be discovered from systematic phenotype-genotype analyses, an essential prerequisite will be the development of a standardized and internationally agreed nomenclature for phenotype definition that is applicable to humans as well as to the major model organisms that are in use today.