Abstract—Investigation of the single nucleotide
polymorphism (SNP)-SNP interaction model can facilitate the
analysis of the susceptibility to disease. The model explains the
risk of association between the genotypes and the disease in
case-control study. Thus, many mathematic methods are widely
applied to identify the statistically significant model such as odds
ratio (OR), chi-square test, and error rate. However, a huge
number of data sets have been found to limit the statistical
methods to identify the significant model. In this study, we
propose a novel statistical method, complementary-logic particle
swarm optimization (CLPSO), to increase the efficiency of
significant model identification in case-control study. The
complementary-logic is implemented to improve the PSO search
ability and identify a better SNP-SNP interaction model. Six
important breast cancer genes including 23 SNPs and simulated
huge number of data sets were selected as the test data sets. The
methods of PSO and CLPSO were applied on the identification
of SNP-SNP interactions in the two-way to five-way. In results,
the OR evaluates the breast cancer risk of the identified
SNP-SNP interaction model. Compared to the corresponding
non-interaction model, if the OR value is greater than 1 that
indicates the model is significant risk between cases and controls.
The results showed that CLPSO is able to identify the significant
models for specific SNP-SNP interaction of two-way to five-way
(OR value: 1.153-1.391; confidence interval (CI): 1.05-1.79;
p-value: 0.01-0.003). The model suggests that the genes ESR1,
PGR, and SHBG may be an important role in the interactive
effects to breast cancer. In addition, we compared the search
abilities of PSO and CLPSO for identification of the significant
model. Results revealed that CLPSO can identify better model
with difference values between cases and controls than PSO; it
suggests CLPSO can be used to identify a better SNP-SNP
Index Terms—Single nucleotide polymorphism (SNP), particle swarm optimization (PSO), breast cancer.
Mei-Lee Hwang and Li-Yeh Chuang are with the Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan (e-mail: firstname.lastname@example.org, email@example.com).
Yu-Da Lin and Cheng-Hong Yang are with the Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Mei-Lee Hwang, Yu-Da Lin, Li-Yeh Chuang, and Cheng-Hong Yang, "Determination of the SNP-SNP Interaction between Breast Cancer Related Genes to Analyze the Disease Susceptibility," International Journal of Machine Learning and Computing vol. 4, no. 5, pp. 468-473, 2014.