Sinusoidal Map Based Particle Swarm Optimization Detect the SNP Barcode in Breast Cancer to Disease Susceptibility


  • Li-Yeh Chuang
  • Cheng-Han Wu
  • Yu-Da Lin
  • Cheng-Hong Yang


Sinusoidal map, Particle Swarm Optimization, SNP barcode


Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the human genome and are widely used to investigate the association analysis of diseases. SNP barcode is a combination of SNPs with genotypes (AA, Aa, and aa for an SNP) to find the difference between case data set and control data set for analyzing the disease association amongst SNPs. Currently, the computational time of statistical method becomes the weak to analyze the big data to find the significant SNP barcode. Here, we applied a sinusoidal particle swarm optimization (SPSO) algorithm facilitate the statistical methods to analyze the associated SNPs. We systematically evaluated the synergistic effect of 26 SNPs from eight epigenetic modifier-related genes in breast cancer. The 2- to 5-order SNP barcodes were found to determine the risk effects in breast cancer. We found that five of eight genes (BAT8, DNMT3A, EHMT1, DNMT3A, and BAT8) were statistically significant to breast cancer and play the important role in the SNP barcode. In addition, we compared the search ability between PSO and SPSO in the 2- to 5-order SNP barcodes. The results indicated that SPSO can find the better SNP barcode than PSO. In conclusion, SPSO is a precise algorithm for finding a significant model of SNP barcode.



L. E. Mechanic, B. T. Luke, J. E. Goodman, S. J. Chanock, and C. C. Harris, "Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions," BMC bioinformatics, 2008, pp. 146-146.

P. Kraft and C. A. Haiman, "GWAS identifies a common breast cancer risk allele among BRCA1 carriers," Nature genetics, vol. 42, 2010.

J.-C. Yu, C.-N. Hsiung, H.-M. Hsu, B.-Y. Bao, S.-T. Chen, G.-C. Hsu, W.-C. Chou, L.-Y. Hu, S.-L. Ding, and C.-W. Cheng, "Genetic variation in the genome-wide predicted estrogen response element-related sequences is associated with breast cancer development," Breast Cancer Res, 2011, pp. R13-R13.

X. Li, H. Chen, J. Li, and Z. Zhang, "Gene function prediction with gene interaction networks: a context graph kernel approach," Information Technology in Biomedicine, IEEE Transactions on, 2010, pp. 119-128.

J. H. Moore, F. W. Asselbergs, and S. M. Williams, "Bioinformatics challenges for genome-wide association studies," Bioinformatics, 2010, pp. 445-455.

C.-H. Yang, L.-Y. Chuang, Y.-J. Chen, H.-F. Tseng, and H.-W. Chang, "Computational analysis of simulated SNP interactions between 26 growth factor-related genes in a breast cancer association study," Omics: a journal of integrative biology, 2011, pp. 399-407.

P. D. Pharoah, J. Tyrer, A. M. Dunning, D. F. Easton, B. A. Ponder, and S. Investigators, "Association between common variation in 120 candidate genes and breast cancer risk," PLoS genetics, 2007, pp. e42-e42.

J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of IEEE international conference on neural networks, 1995, pp. 1942-1948.

Y. Shi and R. C. Eberhart, "Empirical study of particle swarm optimization," in Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 1999.

A. Ratnaweera, S. Halgamuge, and H. C. Watson, "Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients," Evolutionary Computation, IEEE Transactions on, 2004, pp. 240-255.




How to Cite

Chuang, L.-Y., Wu, C.-H., Lin, Y.-D., & Yang, C.-H. (2014). Sinusoidal Map Based Particle Swarm Optimization Detect the SNP Barcode in Breast Cancer to Disease Susceptibility. Asian Journal of Engineering and Technology, 2(5). Retrieved from

Most read articles by the same author(s)