Abstract
Nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, epistatic interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, most only focus on the detection of pairwise epistatic interactions. In this study, a Hypergraph Supervised Search (HgSS) is developed based on the co-information measure for inferring multiple epistatic interactions with different orders at a substantially reduced time cost. The co-information measure is employed to exhaustively quantify the interaction effects of low order SNP combinations, as well as the main effects of SNPs. Then, highly suspected SNP combinations and SNPs are used to construct a hypergraph. By deeply analyzing the hypergraph, some clues for better understanding the genetic architecture of complex diseases could be revealed. Experiments are performed on both simulation and real data sets. Results show that HgSS is promising in inferring multiple epistatic interactions with different orders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Maher, B.: Personal genomes: the case of the missing heritability. Nature 456(7218), 18–21 (2008)
Maher, B.: The case of the missing heritability. Nature 456(7218), 18–21 (2008)
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)
Chanda, P., Sucheston, L., Zhang, A., Ramanathan, M.: The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors. Eur. J. Hum. Genet. 17(10), 1274–1286 (2009)
Chanda, P., Sucheston, L., Zhang, A., Brazeau, D., Freudenheim, J.L., Ambrosone, C., Ramanathan, M.: AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 180(2), 1191–1210 (2008)
Chanda, P., Zhang, A., Brazeau, D., Sucheston, L., Freudenheim, J.L., Ambrosone, C., Ramanathan, M.: Information-theoretic metrics for visualizing gene-environment interactions. Am. J. Hum. Genet. 81(5), 939–963 (2007)
Chanda, P., Sucheston, L., Liu, S., Zhang, A., Ramanathan, M.: Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits. BMC Genom. 10(1), 509 (2009)
Sucheston, L., Chanda, P., Zhang, A., Tritchler, D., Ramanathan, M.: Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity. BMC Genom. 11(1), 487 (2010)
Shang, J., Zhang, J., Sun, Y., Liu, D., Ye, D., Yin, Y.: Performance analysis of novel methods for detecting epistasis. BMC Bioinformatics 12(1), 475 (2011)
Moore, J.H., Gilbert, J.C., Tsai, C.-T., Chiang, F.-T., Holden, T., Barney, N., White, B.C.: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J. Theor. Biol. 241(2), 252–261 (2006)
McKinney, B.A., Crowe, J.E., Guo, J., Tian, D.: Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genet. 5(3), e1000432 (2009)
Hu, T., Sinnott-Armstrong, N.A., Kiralis, J.W., Andrew, A.S., Karagas, M.R., Moore, J.H.: Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics 12, 364 (2011)
Hu, T., Andrew, A.S., Karagas, M.R., Moore, J.H.: Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models. In: Pacific Symposium on Biocomputing, pp. 397–408. World Scientific, Singapore (2013)
Bell, A.J.: The co-information lattice. In: The 4th International Symposium on Independent Component Analysis and Blind Signal Separation, pp. 921–926 (2003)
Sun Han, T.: Multiple mutual informations and multiple interactions in frequency data. Inf. Control 46(1), 26–45 (1980)
Miller, D.J., Zhang, Y., Yu, G., Liu, Y., Chen, L., Langefeld, C.D., Herrington, D., Wang, Y.: An algorithm for learning maximum entropy probability models of disease risk that efficiently searches and sparingly encodes multilocus genomic interactions. Bioinformatics 25(19), 2478–2485 (2009)
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)
Klein, R.J., Zeiss, C., Chew, E.Y., Tsai, J.Y., Sackler, R.S., Haynes, C., Henning, A.K., SanGiovanni, J.P., Mane, S.M., Mayne, S.T.: Complement factor H polymorphism in age-related macular degeneration. Science 308(5720), 385–389 (2005)
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)
Wan, X., Yang, C., Yang, Q., Xue, H., Tang, N.L., Yu, W.: Detecting two-locus associations allowing for interactions in genome-wide association studies. Bioinformatics 26(20), 2517–2525 (2010)
Acknowledgments
This work was supported by 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 Project of Shandong Province Higher Educational Science and Technology Program (J13LN31), the Award Foundation Project of Excellent Young Scientists in Shandong Province (BS2014DX005), the Shenzhen Municipal Science and Technology Innovation Council (JCYJ20140417172417174), the Shandong Provincial Natural Science Foundation (ZR2013FL016), the China Postdoctoral Science Foundation Funded Project (2014M560264).
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Shang, J., Sun, Y., Fang, Y., Li, S., Liu, JX., Zhang, Y. (2015). Hypergraph Supervised Search for Inferring Multiple Epistatic Interactions with Different Orders. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-22186-1_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22185-4
Online ISBN: 978-3-319-22186-1
eBook Packages: Computer ScienceComputer Science (R0)