TagSNP Prediction Based On Linkage Disequilibrium

Single nucleotide polymorphism (SNP) is the most common type of genetic variants in human genome,taking up more than 90% of human genetic polymorphisms. SNPs and haplotypes in the human genome have been widely used in the identification of disease-associated genes and the study of pharmacogenomics. Recent studies have revealed that many SNPs are strongly correlated, which makes it feasible to choose a small amount of SNPs that contain almost the entire genetic model information. The use of tagSNPs can greatly reduce genotyping expense and is promising to improve the effect of association studies. Therefore, how to effectively predict the tagSNP has become one of the most important studies in the field of Bioinformatics, and a study of tagSNP prediction is presented.A computational program tagSNPPRE is developed to predict tagSNPs on the genotype dataset. First, it partitions the haplotype blocks based on linkage disequilibrium of pairwise SNPs and uses the greedy exhaustive hybrid approach to work out all the potential tagSNP sets. Then it predicts the best tagSNP set out of potential tagSNP sets using three statistical features(%genotype,MAF and HWPval). Testing results on the widely used genotype dataset demonstrate that tagSNPPRE has better prediction accuracy.In order to mine new features for the prediction of tagSNPs, a secondary local SNP database is preliminarily constructed. The SNP database is built based on the information and biological data provided by the famous database dbSNP. The downloaded primary data is analyzed and processed, and then is successively shifted into local database for further research. The preliminary construction of SNP database has finished, and then the search service has been provided.