SNPs (Single Nucleotide Polymorphisms) refer to genetic variations at the single nucleotide level. There are more than one million SNPs in the human genome. From a large set of SNP measurements, SNP-SNP interactions have been recognized to be basically important for understanding genetic causes of complex disease traits or individuals' responses to a certain medicine. Nowadays, identifying SNP-SNP interactions are computationally challenging and may take hours, weeks or months to complete. In addition, Logic regression is a powerful regression methodology used to discover predictors that are Boolean or logical combinations of binary covariates. Logic regression can be used in many situations, especially identification of SNP-SNP interactions for finding mixtures of SNPs that are associated with risk of a certain disease and evaluating the strength of the association. However, analyses of SNP associations in genome-wide association (GWA) studies using logic regression typically involve thousands of individuals, a half million of SNPs, and a great number of permutation rounds, requiring vast computing time, approximately hours to months. While parallel computing is a strong method to speed up computing time, it is arduous to apply this method to logic regression analyses of SNP-SNP interactions because it require advanced programming skills to accurately partition and distribute data, control and monitor tasks across the several computers, and merge output files.

     In this research, we present novel software to automatically speed up analyses of SNP-SNP interaction applying parallel computing. Therefore, user can use the software to parallelize logic regression analyses of SNP-SNP interactions without the advanced programming skills. The Rheumatoid Arthritis data set from the North American Rheumatoid Arthritis Consortium (NARAC) and the Crohn’s disease GWA studies dataset from the Wellcome Trust Case Control Consortium (WTCCC) will be analyzed using logic regression on a computer cluster to evaluate the performance of software. This software can be executed not only the Rheumatoid Arthritis data set and the Crohn’s disease data set but also other diseases data sets. The benefit of this research is that researchers can use the information from the software to develop better strategies to detect, treat and prevent the diseases more quickly than before. Consequently, patients and our country will save a lot of medical expenses.