Candidate Gene Predictions: Bioinformatics Significance in Linkage Analysis

The rationale behind this review article is based on the identification of candidate genes in linkage analysis by using in-silico strategies. In recent development, it is noted that computational approaches are widely used to find out disease-causing genes. This is achieved by applying various greedy algorithms, which are constructed either on mathematical modeling, or computational search and alignment methods, or both. The advantage of computer-assisted techniques is that, it reduces the search domain of disease-causing genes which may comprise of 100 to 1000's candidate genes mapped on a single locus. In this context, the common criterion for in-silico candidate-gene identification relies on gene ontology, protein-protein interaction, sequence based features, functional annotation, data mining, and microarray-expression data. In addition to improve the disease-causing genes identification, development of disease-predicting software by incorporating existing data (wet-lab) will be a straightforward step.