SNP barcodes generated using particle swarm optimization to detect susceptibility to breast cancer

Cheng-Hong Yang¹, Yu-Da Lin¹, Li-Yeh Chuang², Hsueh-Wei Chang³

¹Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung.
²Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung.
³Department of Biomedical Science and Environmental Biology, Center of Excellence for Environmental Medicine, Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung.

DOI: 10.4236/ns.2013.53049                

Abstract

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 in silico analysis.

Keywords

Single Nucleotide Polymorphism; Particle Swarm Optimization; SNP-SNP Interaction; Breast Cancer

Conflicts of Interest

The authors declare no conflicts of interest.

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