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Accumulating evidence has shown that individual commonly occurring single nucleotide polymorphisms (SNPs) are associated with the cancer risks. Hence, determining the disease-causing SNPs have become an important issue. In order to explore SNP-SNP interactions in breast cancer, we used a genetic algorithm (GA) to simultaneously analyze multiple independent SNPs, and to compute the difference between the case and control groups of different SNP combinations with their corresponding genotypes. The best combination of SNP-SNP interactions is the maximal difference of co-occurrences between the case and control groups. We also used the odds ratio (OR) to further evaluate the impact of each SNP combination. In this study, we explored the SNP-SNP interactions for the simulated breast cancers SNP dataset including 19 SNPs in 372 control group and 398 cases of breast cancer group. Compared to their corresponding non-SNP combinations, the estimated OR of the best predicted SNP combination with genotypes for breast cancer is about 1.771 and 5.904 (confidence interval (CI): 1.223-20.275; p <; 0.05) for specific SNP combinations of two to six SNPs. The SNP-SNP interactions with a high risk of breast cancer could be successfully predicted by the GA method. The proposed algorithm may potentially be applied to SNP-SNP interaction combinations in other diseases and cancers.