In Silico Gene Discovery

Discovery of disease-related genes improves our knowledge of disease etiology and pathogenesis, and subsequently will lead to novel diagnostic and therapeutic methods in treating common diseases. In silico gene discovery is a complementary strategy and significantly enhances the likelihood of finding genes. It integrates biology, computer science, and mathematics to facilitate locus mapping, gene search, and DNA variant identification. It is essential to construct a well-organized locus database with all relevant information such as retrieved sequences, DNA markers, LD blocks, and all available transcripts. This locus database can provide a platform for fine mapping and the subsequent gene isolation as well as DNA variant identification. It is one of the key tasks for in silico gene discovery to identify and select a set of appropriate DNA markers. High-quality (informativeness) of selected DNA markers can provide the valuable clue in fine mapping in also given families and greatly facilitate the identification of the target gene(s). These tools can increase the efficiency of marker selection via simulation in order to capture more genetic variation in candidate genes and to avoid multiple comparisons. In silico gene discovery accelerates the process or bypass many steps in the traditional gene discovery process and provides some answers in a fraction of the time that it once took in the laboratory. The in silico approach would help to differentiate a functional variant from a polymorphic non-synonymous change. It also provides a unique platform for identifying the promoter DNA variants at the conserved sites and potential binding motifs for transcription factors. The integration of the in silico approach with the genome-wide association studies will provide enormous scope for testing genetic effects or interactions across genetic regions. Such integration will lead the genetics of complex diseases to a point of success comparable to where Mendelian genetics now firmly resides.

Keywords:

Gene discovery , DNA variant , data mining , prediction , in silico prioritization