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.

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    • "However, SNP–SNP interaction analysis requires to simultaneously evaluate the complex interactions for all tested SNPs and thus computational help is needed (Chang et al., 2009; Chuang et al., 2012a, 2012b; Lane et al., 2012; Steen 2012; Yang et al., 2009, 2011, 2012). Genetic algorithm (GA) is an evolutionary algorithm loosely based on processes of biological evolution; its main components are comprised by encoding schemes, a fitness evaluation, population initialization, selection operation, and a crossover operation. "
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    ABSTRACT: Abstract Background and aims: Single nucleotide polymorphism (SNP) interaction analysis can simultaneously evaluate the complex SNP interactions present in complex diseases. However, it is less commonly applied to evaluate the predisposition of chronic dialysis and its computational analysis remains challenging. In this study, we aimed to improve the analysis of SNP-SNP interactions within the mitochondrial D-loop in chronic dialysis. Material & method: The SNP-SNP interactions between 77 reported SNPs within the mitochondrial D-loop in chronic dialysis study were evaluated in terms of SNP barcodes (different SNP combinations with their corresponding genotypes). We propose a genetic algorithm (GA) to generate SNP barcodes. The χ(2) values were then calculated by the occurrences of the specific SNP barcodes and their non-specific combinations between cases and controls. Results: Each SNP barcode (2- to 7-SNP) with the highest value in the χ(2) test was regarded as the best SNP barcode (11.304 to 23.310; p < 0.001). The best GA-generated SNP barcodes (2- to 7-SNP) were significantly associated with chronic dialysis (odds ratio [OR] = 1.998 to 3.139; p < 0.001). The order of influence for SNPs was the same as the order of their OR values for chronic dialysis in terms of 2- to 7-SNP barcodes. Conclusion: Taken together, we propose an effective algorithm to address the SNP-SNP interactions and demonstrated that many non-significant SNPs within the mitochondrial D-loop may play a role in jointed effects to chronic dialysis susceptibility.
    Mitochondrial DNA 06/2013; 25(3). DOI:10.3109/19401736.2013.796513 · 1.70 Impact Factor
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    • "Current studies focus on the combined effects of multiple SNPs on many cancer and disease risks, but association studies for multiple SNP candidates are hampered by complex computations. In recent years, many methods have been proposed to detect epistasis, such as MDR [5] [6], PIA [7], SVM [8] [9] [10], particle swarm optimization (PSO) [8] [9] [10], and genetic algorithms (GA) [11] [12]. The main difference amongst these methods is the computational time required. "
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    ABSTRACT: The analysis of disease-associated polymorphisms by genome-wide association (GWAS) is currently challenging for detection of influences upon their interaction with other genetic factors. Surmising that a gene can be examined in isolation without allowing for potential interactions with other unknown factors might miss the detection of these influences. However, SNP barcodes can be applied for disease prediction and allow the detection of these influences. A particle swarm optimization (PSO) algorithm is introduced and applied to the search of the available SNP barcodes. However, a simple PSO does not assure that every implemented result is indeed a good result. In this study, we use a Catfish particle swarm optimization (CatfishPSO) method for the SNP barcodes of a disease prediction study; breast cancer data was used to test and compare the two method abilities. The experiment demonstrates that the CatfishPSO method is robust and provides exact identification of the best protective SNP barcode for breast cancer.
    The International Association of Science and Technology for Development (IASTED); 07/2012
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    ABSTRACT: Genome-wide association studies have revealed that many single nucleotide polymorphisms (SNPs) are associated with breast cancer, and yet the potential SNP-SNP interactions have not been well addressed to date. This study aims to develop a methodology for the selection of SNP-genotype combinations with a maximum difference between case and control groups. We propose a new chaotic particle swarm optimization (CPSO) algorithm that identifies the best SNP combinations for breast cancer association studies containing seven SNPs. Five scoring functions, that is, the percentage correct, sensitivity/specificity, positive predictive value/negative predictive value, risk ratio, and odds ratio, are provided for evaluating SNP interactions in different SNP combinations. The CPSO algorithm identified the best SNP combinations associated with breast cancer protection. Some SNP interactions in specific SNPs and their corresponding genotypes were revealed. These SNP combinations showed a significant association with breast cancer protection (P<0.05). The sensitivity and specificity of the respective best SNP combinations were all higher than 90%. In contrast to the corresponding non-SNP-SNP interaction combinations, the estimated odds ratio and risk ratio of the SNP-SNP interaction in SNP combinations for breast cancer were less than 100%. This suggests that CPSO can successfully identify the best SNP combinations for breast cancer protection. In conclusion, we focus on developing a methodology for the selection of SNP-genotype combinations with a maximum difference between case and control groups. The CPSO method can effectively identify SNP-SNP interactions in complex biological relationships underlying the progression of breast cancer.
    European journal of cancer prevention: the official journal of the European Cancer Prevention Organisation (ECP) 11/2011; 21(4):336-42. DOI:10.1097/CEJ.0b013e32834e31f6 · 2.76 Impact Factor
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