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Issue No.02 - March-April (2013 vol.10)
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
ABSTRACT
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
Breast cancer, Genetic algorithms, Genetics, Cancer, Classification,genetic algorithm, breast cancer, Single nucleotide polymorphism, SNP-SNP interactions
CITATION
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
REFERENCES
[1] J. Li, K. Humphreys, H. Darabi, G. Rosin, U. Hannelius, T. Heikkinen, K. Aittomaki, C. Blomqvist, P.D. Pharoah, A.M. Dunning, S. Ahmed, M.J. Hooning, A. Hollestelle, R.A. Oldenburg, L. Alfredsson, A. Palotie, L. Peltonen-Palotie, A. Irwanto, H.Q. Low, G.H. Teoh, A. Thalamuthu, J. Kere, M. D'Amato, D.F. Easton, H. Nevanlinna, J. Liu, K. Czene, and P. Hall, "A Genome-Wide Association Scan on Estrogen Receptor-Negative Breast Cancer," Breast Cancer Research, vol. 12, no. 6, article 93, 2010.
[2] P. Kraft and C.A. Haiman, "GWAS Identifies a Common Breast Cancer Risk Allele among BRCA1 Carriers," Nature Genetics, vol. 42, no. 10, pp. 819-820, 2010.
[3] G. Thomas, K.B. Jacobs, P. Kraft, M. Yeager, S. Wacholder, D.G. Cox, S.E. Hankinson, A. Hutchinson, Z. Wang, K. Yu, N. Chatterjee, M. Garcia-Closas, J. Gonzalez-Bosquet, L. Prokunina-Olsson, N. Orr, W.C. Willett, G.A. Colditz, R.G. Ziegler, C.D. Berg, S.S. Buys, C.A. McCarty, H.S. Feigelson, E.E. Calle, M.J. Thun, R. Diver, R. Prentice, R. Jackson, C. Kooperberg, R. Chlebowski, J. Lissowska, B. Peplonska, L.A. Brinton, A. Sigurdson, M. Doody, P. Bhatti, B.H. Alexander, J. Buring, I.M. Lee, L.J. Vatten, K. Hveem, M. Kumle, R.B. Hayes, M. Tucker, D.S. Gerhard, J.F. FraumeniJr., R.N. Hoover, S.J. Chanock, and D.J. Hunter, "A Multistage Genome-Wide Association Study in Breast Cancer Identifies Two New Risk Alleles at 1p11.2 and 14q24.1 (RAD51L1)," Nature Genetics, vol. 41, no. 5, pp. 579-584, 2009.
[4] A. Meindl, "Identification of Novel Susceptibility Genes for Breast Cancer—Genome-Wide Association Studies or Evaluation of Candidate Genes?" Breast Care (Basel), vol. 4, no. 2, pp. 93-99, 2009.
[5] D. Fanale, V. Amodeo, L.R. Corsini, S. Rizzo, V. Bazan, and A. Russo, "Breast Cancer Genome-Wide Association Studies: There Is Strength in Numbers," Oncogene, vol. 31, pp. 2121-2128, 2011.
[6] 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, C.W. Cheng, P.E. Wu, and C.Y. Shen, "Genetic Variation in the Genome-Wide Predicted Estrogen Response Element-Related Sequences Is Associated with Breast Cancer Development," Breast Cancer Research, vol. 13, no. 1, article 13, 2011.
[7] A.M. Soto and C. Sonnenschein, "The Two Faces of Janus: Sex Steroids as Mediators of Both Cell Proliferation and Cell Death," J. Nat'l Cancer Inst., vol. 93, no. 22, pp. 1673-1675, 2001.
[8] F. Auricchio, A. Migliaccio, and G. Castoria, "Sex-Steroid Hormones and EGF Signalling in Breast and Prostate Cancer Cells: Targeting the Association of SRC with Steroid Receptors," Steroids, vol. 73, nos. 9/10, pp. 880-884, 2008.
[9] S. Ando, F. De Amicis, V. Rago, A. Carpino, M. Maggiolini, M.L. Panno, and M. Lanzino, "Breast Cancer: From Estrogen to Androgen Receptor," Molecular Cell Endocrinology, vol. 193, nos. 1/2, pp. 121-128, 2002.
[10] P. Giovannelli, M. Di Donato, T. Giraldi, A. Migliaccio, G. Castoria, and F. Auricchio, "Targeting Rapid Action of Sex Steroid Receptors in Breast and Prostate Cancers," Frontier Bioscience, vol. 17, pp. 2224-2232, 2011.
[11] E.W. LaPensee and N. Ben-Jonathan, "Novel Roles of Prolactin and Estrogens in Breast Cancer: Resistance to Chemotherapy," Endocrine-Related Cancer, vol. 17, no. 2, pp. 91-107, 2010.
[12] N. Fortunati, M.G. Catalano, G. Boccuzzi, and R. Frairia, "Sex Hormone-Binding Globulin (SHBG), Estradiol and Breast Cancer," Molecular Cell Endocrinology, vol. 316, no. 1, pp. 86-92, 2010.
[13] M.S. Udler, E.M. Azzato, C.S. Healey, S. Ahmed, K.A. Pooley, D. Greenberg, M. Shah, A.E. Teschendorff, C. Caldas, A.M. Dunning, E.A. Ostrander, N.E. Caporaso, D. Easton, and P.D. Pharoah, "Common Germline Polymorphisms in COMT, CYP19A1, ESR1, PGR, SULT1E1 and STS and Survival after a Diagnosis of Breast Cancer," Int'l J. Cancer, vol. 125, no. 11, pp. 2687-2696, 2009.
[14] P.D. Pharoah, J. Tyrer, A.M. Dunning, D.F. Easton, and B.A. Ponder, "Association between Common Variation in 120 Candidate Genes and Breast Cancer Risk," PLoS Genetics, vol. 3, no. 3, article e42, 2007.
[15] Y.L. Low, J.I. Taylor, P.B. Grace, A.A. Mulligan, A.A. Welch, S. Scollen, A.M. Dunning, R.N. Luben, K.T. Khaw, N.E. Day, N.J. Wareham, and S.A. Bingham, "Phytoestrogen Exposure, Polymorphisms in COMT, CYP19, ESR1, and SHBG Genes, and Their Associations with Prostate Cancer Risk," Nutrition and Cancer, vol. 56, no. 1, pp. 31-39, 2006.
[16] W. Han, K.Y. Kim, S.J. Yang, D.Y. Noh, D. Kang, and K. Kwack, "SNP-SNP Interactions between DNA Repair Genes Were Associated with Breast Cancer Risk in a Korean Population," Cancer, vol. 118, no. 3, pp. 594-602, 2011.
[17] J. Conde, S.N. Silva, A.P. Azevedo, V. Teixeira, J.E. Pina, J. Rueff, and J.F. Gaspar, "Association of Common Variants in Mismatch Repair Genes and Breast Cancer Susceptibility: A Multigene Study," BMC Cancer, vol. 9, article 344, 2009.
[18] G.T. Lin, H.F. Tseng, C.H. Yang, M.F. Hou, L.Y. Chuang, H.T. Tai, M.H. Tai, Y.H. Cheng, C.H. Wen, C.S. Liu, C.J. Huang, C.L. Wang, and H.W. Chang, "Combinational Polymorphisms of Seven CXCL12-Related Genes Are Protective against Breast Cancer in Taiwan," OMICS: A J. Integrative Biology, vol. 13, no. 2, pp. 165-172, 2009.
[19] M.D. Ritchie, L.W. Hahn, N. Roodi, L.R. Bailey, W.D. Dupont, F.F. Parl, and J.H. Moore, "Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer," Am. J. Human Genetics, vol. 69, no. 1, pp. 138-147, 2001.
[20] Y. Chung, S.Y. Lee, R.C. Elston, and T. Park, "Odds Ratio Based Multifactor-Dimensionality Reduction Method for Detecting Gene-Gene Interactions," Bioinformatics, vol. 23, no. 1, pp. 71-76, 2007.
[21] 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, vol. 9, article 146, 2008.
[22] S.H. Chen, J. Sun, L. Dimitrov, A.R. Turner, T.S. Adams, D.A. Meyers, B.L. Chang, S.L. Zheng, H. Gronberg, J. Xu, and F.C. Hsu, "A Support Vector Machine Approach for Detecting Gene-Gene Interaction," Genetic Epidemiology, vol. 32, no. 2, pp. 152-67, 2008.
[23] H.W. Chang, C.H. Yang, C.H. Ho, C.H. Wen, and L.Y. Chuang, "Generating SNP Barcode to Evaluate SNP-SNP Interaction of Disease by Particle Swarm Optimization," Computational Biology and Chemistry, vol. 33, no. 1, pp. 114-119, 2009.
[24] C.H. Yang, H.W. Chang, Y.H. Cheng, and L.Y. Chuang, "Novel Generating Protective Single Nucleotide Polymorphism Barcode for Breast Cancer Using Particle Swarm Optimization," Cancer Epidemiology, vol. 33, no. 2, pp. 147-154, 2009.
[25] L.Y. Chuang, H.W. Chang, M.C. Lin, and C.H. Yang, "Chaotic Particle Swarm Optimization for Detecting SNP-SNP Interactions for CXCL12-Related Genes in Breast Cancer Prevention," European J. Cancer Prevention, vol. 21, no. 4, pp. 336-342, 2012.
[26] H.W. Chang, L.Y. Chuang, C.H. Ho, P.L. Chang, and C.H. Yang, "Odds Ratio-Based Genetic Algorithms for Generating SNP Barcodes of Genotypes to Predict Disease Susceptibility," OMICS: A J. Integrative Biology, vol. 12, no. 1, pp. 71-81, 2008.
[27] 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 J. Integrative Biology, vol. 15, no. 6, pp. 399-407, 2011.
[28] S.K. Musani, D. Shriner, N.J. Liu, R. Feng, C.S. Coffey, N.J. Yi, H.K. Tiwari, and D.B. Allison, "Detection of Gene x Gene Interactions in Genome-Wide Association Studies of Human Population Data," Human Heredity, vol. 63, no. 2, pp. 67-84, 2007.
[29] J.H. Holland, Adaptation in Nature and Artificial Systems, MIT Press, 1992.
[30] J.J. Liu, G. Cutler, W.X. Li, Z. Pan, S.H. Peng, T. Hoey, L.B. Chen, and X.F.B. Ling, "Multiclass Cancer Classification and Biomarker Discovery Using GA-Based Algorithms," Bioinformatics, vol. 21, no. 11, pp. 2691-2697, 2005.
[31] C.H. Yang, Y.H. Cheng, L.Y. Chuang, and H.W. Chang, "Confronting Two-Pair Primer Design for Enzyme-Free SNP Genotyping Based on a Genetic Algorithm," BMC Bioinformatics, vol. 11, article 509, 2010.
[32] L.P. Li, C.R. Weinberg, T.A. Darden, and L.G. Pedersen, "Gene Selection for Sample Classification Based on Gene Expression Data: Study of Sensitivity to Choice of Parameters of the GA/KNN Method," Bioinformatics, vol. 17, no. 12, pp. 1131-1142, 2001.
[33] L.Y. Chuang, C.S. Yang, J.C. Li, and C.H. Yang, "Chaotic Genetic Algorithm for Gene Selection and Classification Problems," OMICS: A J. Integrative Biology, vol. 13, no. 5, pp. 407-420, 2009.
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