Article

Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization.

Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
Cancer epidemiology 09/2009; 33(2):147-54. DOI: 10.1016/j.canep.2009.07.001
Source: PubMed

ABSTRACT High-throughput single nucleotide polymorphism (SNP) genotyping generates a huge amount of SNP data in genome-wide association studies. Simultaneous analyses for multiple SNP interactions associated with many diseases and cancers are essential; however, these analyses are still computationally challenging.
In this study, we propose an odds ratio-based binary particle swarm optimization (OR-BPSO) method to evaluate the risk of breast cancer.
BPSO provides the combinational SNPs with their corresponding genotype, called SNP barcodes, with the maximal difference of occurrence between the control and breast cancer groups. A specific SNP barcode with an optimized fitness value was identified among seven SNP combinations within the space of one minute. The identified SNP barcodes with the best performance between control and breast cancer groups were found to be control-dominant, suggesting that these SNP barcodes may prove protective against breast cancer. After statistical analysis, these control-dominant SNP barcodes were processed for odds ratio analysis for quantitative measurement with regard to the risk of breast cancer.
This study proposes an effective high-speed method to analyze the SNP-SNP interactions for breast cancer association study.

0 Bookmarks
 · 
92 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: ORAI1 channels play an important role for breast cancer progression and metastasis. Previous studies indicated the strong correlation between breast cancer and individual single nucleotide polymorphisms (SNPs) of ORAI1 gene. However, the possible SNP-SNP interaction of ORAI1 gene was not investigated. To develop the complex analyses of SNP-SNP interaction, we propose a genetic algorithm (GA) to detect the model of breast cancer association between five SNPs (rs12320939, rs12313273, rs7135617, rs6486795 and rs712853) of ORAI1 gene. For individual SNPs, the differences between case and control groups in five SNPs of ORAI1 gene were not significant. In contrast, GA-generated SNP models show that 2-SNP (rs12320939-GT/rs6486795-CT), 3-SNP (rs12320939-GT/rs12313273-TT/rs6486795-TC), 5-SNP (rs12320939-GG/rs12313273-TC/rs7135617-TT/rs6486795-TT/rs712853-TT) have higher risks for breast cancer in terms of odds ratio analysis (1.357, 1.689, and 13.148, respectively). Taken together, the cumulative effects of SNPs of ORAI1 gene in breast cancer association study were well demonstrated in terms of GA-generated SNP models.
    Cancer Cell International 03/2014; 14(1):29. · 2.09 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.
    Annals of General Psychiatry 05/2014; 13:15. · 1.57 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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 interaction models. Index Terms—Single nucleotide polymorphism (SNP), particle swarm optimization (PSO), breast cancer.
    International Journal of Machine Learning and Computing. 10/2014; 4(5):468-473.

Full-text

View
39 Downloads
Available from
May 23, 2014