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In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.