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 < 1.0; p-value < 0.05). The results suggest that SNPs 1 and 2 (gene BAT8), 9, 10, 11 and 13 (DNMT3A), 20 and 21 (EHMT1), 24 (HDAC2), 25 (MBD2), and 27 (SETDB1) are statistically very significant and that there may be interactive effects that play a role in the prevalence of breast cancer. A PSO-based on the Chi-Square test process allowed us to quickly identify the significant SNP combinations in a multi-locus association analysis, and then further detect interactive effects on complex genotypes amongst the SNPs. The PSO algorithm is robust and precisely identifies the best protective SNP barcodes. It can identify potential combined epigenetic modifier-related genes together with the SNP barcodes that were deemed protective against breast cancer by insilico analysis.
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