This Article 
 Bibliographic References 
 Add to: 
Evaluation of Breast Cancer Susceptibility Using Improved Genetic Algorithms to Generate Genotype SNP Barcodes
March-April 2013 (vol. 10 no. 2)
pp. 361-371
Cheng-Hong Yang, Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan
Yu-Da Lin, Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan
Li-Yeh Chuang, Dept. of Chem. Eng., I-Shou Univ., Kaohsiung, Taiwan
Hsueh-Wei Chang, Dept. of Biomed. Sci. & Environ. Biol., Kaohsiung Med. Univ. Cancer Center, Kaohsiung, Taiwan
Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast cancer risks were subsequently analyzed by odds-ratio (OR) and risk-ratio analysis. The estimated OR of the best SNP barcode is significantly higher than 1 (between 1.15 and 7.01) for specific combinations of two to 13 SNPs. Analysis results support that the IGA provides higher ratio difference values than the GA between breast cancer cases and noncancer cases over 3-SNP to 13-SNP interactions. A more specific SNP-SNP interaction profile for the risk of breast cancer is also provided.
Index Terms:
statistical analysis,bioinformatics,cancer,genetic algorithms,genetics,genomics,random processes,single nucleotide polymorphism,breast cancer susceptibility,genotype SNP barcode,genetic association,gene identification,gene characterization,common complex multifactorial diseases,loci interaction detection,genotype frequency,noncancer case,statistical analysis,improved genetic algorithm,IGA,GA method,high-dimensional SNP-SNP interaction,random population process,search course,maximum ratio difference,SNP combination,steroid hormone metabolism,signaling-related genes,breast carcinogenesis pathways,significant ratio differences,breast cancer risks,odds-ratio,risk-ratio analysis,Breast cancer,Genetic algorithms,Genetics,Cancer,Classification,genetic algorithm,breast cancer,Single nucleotide polymorphism,SNP-SNP interactions
Cheng-Hong Yang, Yu-Da Lin, Li-Yeh Chuang, Hsueh-Wei Chang, "Evaluation of Breast Cancer Susceptibility Using Improved Genetic Algorithms to Generate Genotype SNP Barcodes," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 2, pp. 361-371, March-April 2013, doi:10.1109/TCBB.2013.27
Usage of this product signifies your acceptance of the Terms of Use.