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With the development of genome-wide association studies (GWAS), computationally identifying the epistatic interactions associated with common diseases presents a significant challenge to bioinformatics society. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity. To address these issues, we propose the use of a Markov Blanket-based method, DASSO-MB, for epistatic interaction detection. We apply our method to both simulated data sets and a real data set, and demonstrate that DASSO-MB significantly outperforms other existing methods and is capable of finding SNPs that have a strong association with common diseases.