Abstract
Interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, SNP-SNP interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, the algorithmic development is still ongoing due to their computational complexities. In this study, we apply selectively informed particle swarm optimization (SIPSO) to determine SNP-SNP interactions with mutual information as its fitness function. The highlights of SIPSO are the introductions of scale-free networks as its population structure, and different learning strategies as its interaction modes, considering the heterogeneity of particles. Experiments are performed on both simulation and real data sets, which show that SIPSO is promising in inferring SNP-SNP interactions, and might be an alternative to existing methods. The software package is available online at http://www.bdmb-web.cn/index.php?m=content&c=index&a=show&catid=37&id=99.
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References
Maher, B.: The case of the missing heritability. Nature 456(7218), 18–21 (2008)
Yang, C.-H., Chang, H.-W., Cheng, Y.-H., Chuang, L.-Y.: Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization. Cancer Epidemiol. 33(2), 147–154 (2009)
Chang, H.-W., Yang, C.-H., Ho, C.-H., Wen, C.-H., Chuang, L.-Y.: Generating SNP barcode to evaluate SNP–SNP interaction of disease by particle swarm optimization. Comput. Biol. Chem. 33(1), 114–119 (2009)
Chuang, L.-Y., Lin, M.-C., Chang, H.-W., Yang, C.-H.: Analysis of SNP interaction combinations to determine breast cancer risk with PSO. In: 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 291–294. IEEE (2011)
Chuang, L.-Y., Chang, H.-W., Lin, M.-C., Yang, C.-H.: Chaotic particle swarm optimization for detecting SNP–SNP interactions for CXCL12-related genes in breast cancer prevention. Eur. J. Cancer Prev. 21(4), 336–342 (2012)
Chuang, L.-Y., Lin, Y.-D., Chang, H.-W., Yang, C.-H.: An improved PSO algorithm for generating protective SNP barcodes in breast cancer. PLOS One 7(5), e37018 (2012)
Chuang, L.-Y., Lin, Y.-D., Chang, H.-W., Yang, C.-H.: SNP-SNP interaction using gauss chaotic map particle swarm optimization to detect susceptibility to breast cancer. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 2548–2554. IEEE (2014)
Yang, C.-H., Tsai, S.-W., Chuang, L.-Y., Yang, C.-H.: An improved particle swarm optimization with double-bottom chaotic maps for numerical optimization. Appl. Math. Comput. 219(1), 260–279 (2012)
Yang, C.-H., Lin, Y.-D., Chuang, L.-Y., Chang, H.-W.: Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions. BioMed Res. Int. 2014, 10 (2014)
Hwang, M.-L., Lin, Y.-D., Chuang, L.-Y., Yang, C.-H.: Determination of the SNP-SNP interaction between breast cancer related genes to analyze the disease susceptibility. Int. J. Mach. Learn. Comput. 4(5), 468–473 (2014)
Wu, S.-J., Chuang, L.-Y., Lin, Y.-D., Ho, W.-H., Chiang, F.-T., Yang, C.-H., Chang, H.-W.: Particle swarm optimization algorithm for analyzing SNP–SNP interaction of renin-angiotensin system genes against hypertension. Mol. Biol. Rep. 40(7), 4227–4233 (2013)
Ma, C., Shang, J., Li, S., Sun, Y.: Detection of SNP-SNP interaction based on the generalized particle swarm optimization algorithm. In: 2014 8th International Conference on Systems Biology (ISB), pp. 151–155. IEEE (2014)
Shang, J., Sun, Y., Li, S., Liu, J.-X., Zheng, C.-H., Zhang, J.: An improved opposition-based learning particle swarm optimization for the detection of SNP-SNP interactions. BioMed Res. Int. 2015, 12 (2015)
Gao, Y., Du, W., Yan, G.: Selectively-informed particle swarm optimization. Scientific reports 5 (2015)
Liu, C., Du, W.B., Wang, W.X.: Particle swarm optimization with scale-free interactions. PLOS One 9(5), e97822 (2014)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Zhang, Y., Liu, J.S.: Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39(9), 1167–1173 (2007)
Tang, W., Wu, X., Jiang, R., Li, Y.: Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet. 5(5), e1000464 (2009)
Frankel, W.N., Schork, N.J.: Who’s afraid of epistasis? Nat. Genet. 14(4), 371–373 (1996)
Li, W., Reich, J.: A complete enumeration and classification of two-locus disease models. Hum. Hered. 50(6), 334–349 (2000)
Shang, J., Zhang, J., Sun, Y., Liu, D., Ye, D., Yin, Y.: Performance analysis of novel methods for detecting epistasis. BMC Bioinform. 12(1), 475 (2011)
Shang, J., Zhang, J., Lei, X., Zhao, W., Dong, Y.: EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis. Genes Genomics 35, 305–316 (2013)
Shang, J., Zhang, J., Lei, X., Zhang, Y., Chen, B.: Incorporating heuristic information into ant colony optimization for epistasis detection. Genes Genomics 34(3), 321–327 (2012)
Shang, J., Zhang, J., Sun, Y., Zhang, Y.: EpiMiner: a three-stage co-information based method for detecting and visualizing epistatic interactions. Digit. Signal Process. 24, 1–13 (2014)
Shang, J., Sun, Y., Fang, Y., Li, S., Liu, J.-X., Zhang, Y.: Hypergraph supervised search for inferring multiple epistatic interactions with different orders. In: Huang, D.-S., Jo, K.-H., Hussain, A. (eds.) ICIC 2015. LNCS, vol. 9226, pp. 623–633. Springer, Heidelberg (2015)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Adams, M.K., Simpson, J.A., Richardson, A.J., Guymer, R.H., Williamson, E., Cantsilieris, S., English, D.R., Aung, K.Z., Makeyeva, G.A., Giles, G.G.: Can genetic associations change with age? CFH and age-related macular degeneration. Hum. Mol. Genet. 21(23), 5229–5236 (2012)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (61502272, 61572284, 61572283), the Scientific Research Reward Foundation for Excellent Young and Middle-age Scientists of Shandong Province (BS2014DX004), the Science and Technology Planning Project of Qufu Normal University (xkj201410), the Opening Laboratory Fund of Qufu Normal University (sk201416), the Scientific Research Foundation of Qufu Normal University (BSQD20130119), The Innovation and Entrepreneurship Training Project for College Students of China (201510446044), The Innovation and Entrepreneurship Training Project for College Students of Qufu Normal University (2015A058, 2015A059).
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Zhang, W., Shang, J., Li, H., Sun, Y., Liu, JX. (2016). SIPSO: Selectively Informed Particle Swarm Optimization Based on Mutual Information to Determine SNP-SNP Interactions. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_11
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DOI: https://doi.org/10.1007/978-3-319-42291-6_11
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