Abstract
With the explosion in genomic and functional genomics information, methods for disease gene identification are rapidly evolving. Databases are now essential to the process of selecting candidate disease genes. Combining positional information with disease characteristics and functional information is the usual strategy by which candidate disease genes are selected. Enrichment for candidate disease genes, however, depends on the skills of the operating researcher. Over the past few years, a number of bioinformatics methods that enrich for the most likely candidate disease genes have been developed. Such in silico prioritisation methods may further improve by completion of datasets, by development of standardised ontologies across databases and species and, ultimately, by the integration of different strategies.
Highlights
With the increase in accessible data and the development of novel molecular biology techniques, new methods for the identification of disease genes are evolving
Linkage studies and mutation screening are becoming easier and the number of identified genes is increasing rapidly. 2003 saw the completion of the human genome sequence and the number of genes is set to 20,000 – 25,000.1,2 With all the genetics technology in place, identification of disease-related mutations in Mendelian single-gene disorders mainly depends on having the right patients and families
Genetic mapping by linkage is a mainstay of human genetics research
Summary
With the increase in accessible data and the development of novel molecular biology techniques, new methods for the identification of disease genes are evolving. While positional information reduces the number of genes that are candidates for causing the disease, this reduction is often not sufficient for rapid disease gene identification. The aim of candidate gene prioritisation methods is to choose those genes for detailed mutation analysis that are most likely to be the cause of the disease. This is especially relevant since positional methods may leave up to 100 different genes as candidates. Integration of data based on genomic context, such as in the University of California, Santa Cruz genome browser and Ensembl,[6,7] resulted in step by step interfaces (eg EnsMart8) which extract data based on chromosomal position, gene expression[9] and gene ontology (GO).[10] Enrichment for disease candidate genes using these database interfaces, depends heavily on the operation skills of the researcher. This paper presents an overview of such methods and their accessibility
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