The imputation of untyped SNPs enables researchers to validate association findings across SNP arrays and also enables them to test a large number of SNPs to reveal the fine structure of the association peak, facilitating interpretation of the results and the location of causal polymorphisms. In this study, we applied the imputation method to a genomewide association study and recapitulated the previously associated gene loci of blood pressure traits in Korean cohorts. A total of 1,827,004 SNPs were imputed by the IMPUTE program, and we conducted a genomewide association study for systolic and diastolic blood pressure. While no SNPs passed the Bonferroni correction p-value (p=$2.74{ imes}10^{-8}$ for 1,827,004 SNPs), 12 novel loci for systolic blood pressure and 16 novel loci for diastolic blood pressure were detected by imputed SNPs, with $10^{-5}$ < p-value < $10^{-4}$. Moreover, 7 regions (ATP2B1, 10p15.1, ARHGEF12, ALX4, LIPC, 7q31.1, and TCF7L2) out of 14 genetic loci that were previously reported revealed that the imputed SNPs had lower p-values than those of genotyped SNPs. Moreover, a nonsynonymous SNP in the CSMD1 gene, one of the 14 genes, was found to be associated with systolic blood pressure (p<0.05). These results suggest that the imputation method can facilitate the discovery of novel SNPs as well as enhance the fine structure of the association peak in the loci.