Effectiveness of in silico tagSNP selection methods: virtual analysis of the genotypes of pharmacogenetic genes

Abstract

INTRODUCTION: SNP tagging has been recently introduced, and the use of this strategy reduces the dimension of disease association studies and eventually saves on genotyping costs. There is no single set of tagging SNPs (tagSNPs) that will satisfy every association study design; thus, many different methods have been introduced. We evaluated various tagSNP selection methods using known haplotype data of pharmacogenetic genes. We also compared the selected tagSNPs among different ethnic groups. METHODS: We collected genotype data for the NAT2 and CYP2D6 genes from the previously published literature where the linkage phase was resolved directly through molecular haplotyping. Three computational tagSNP selection methods (ldSelect, Tagger and TagIT software) were evaluated with these data sets. RESULTS: Tagging effectiveness and efficiency were variable in all three tagSNP selection methods. No tagSNP sets were identical among the different ethnic groups. The haplotype r(2)-based method was more effective in determining genotype-phenotype correlation than the other methods employed. CONCLUSION: All of the three computational tagSNP selection methods showed acceptable efficiency and effectiveness. The selected tagSNPs were different from each other among the different ethnic groups.