Application Of Tag SNP-set Analytical Method In Genome Wide Association Study

Genome wide association study(GWAS)is a research on the association of gene and disease of multi center,large scale,repeated verification in the whole genome level.SNP set analysis method in Genome-wide association studies is more and more attention.SNP set analysis method in order to study the association between original SNP set and the disease.The existing methods for SNP-set analysis only look for good results,but ignore the cost of genotyping and calculating.The genotyping of closely spaced SNP markers frequently yields highly correlated data,owing to extensive LD between markers,that is,the SNPs which are LD will have overlapping genetic information.So when we test the association of markers with disease,typing all available SNP markers is not necessary.We only need to select representative SNPs,and test the association between these SNPs and disease,which reduces the cost of genotyping and computational time complexity greatly.We develop an efficient tag SNP-set analytical method: TagSNP-Set Selection Method with Maximum Information(TSMI)to deal with the problem caused by the quality of the SNP set.In this study,we generate amounts of replicated datasets based on gene HTR2 A and the CEU sample of HapMap project.The intensive simulation results show that the power of TSMI is much higher than that of original SNP-set in test and TSMI reduces the cost of genotyping.We also compare the powers of TSMI on four types of tag SNP-·sets for the reason that the selection of tag SNP-set influence the test power greatly.The simulation results indicate the method of selecting tag SNP-set impacts the power greatly and the power of our proposed method is the highest.Besides,we evaluate the time complexity of our method and the others,and the comparison results show that the complexity of our method is the lowest.