Considerable research has been devoted to investigating variations in disease susceptibility using SNPs associated with the individual cooccurrence of single nucleotide polymorphisms (SNPs) in genetic and phenotypic variability. Without the raw genotype data, these association studies are difficult to conduct and often omit SNP interactions, thus limiting their reliability and potential applicability. In this study, we apply a particle swarm optimization (PSO) algorithm to detect and identify the best protective SNP barcodes (i.e., SNP combinations and genotypes with a maximum difference between cases and controls) associated with chronic dialysis patients. SNP barcodes containing different numbers of SNPs were computed. We evaluated the combined effects of 27 SNPs related to nine published epigenetic modifier-related genes on breast cancer. Eleven different SNP combinations were found to be protective associated with the risk of breast cancer (odds ratio, OR p-value in silico analysis.