Skip to main content
SpringerLink
Log in

An Improved Ant Colony Optimization Algorithm for the Detection of SNP-SNP Interactions

  • Conference paper
  • First Online:
Intelligent Computing Methodologies (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9773))

Included in the following conference series:

Abstract

An increasing number of studies have found that one of the most important factors for emergence and development of complex diseases is the interactions between SNPs, that is to say, epistasis or epistatic interactions. Though many efforts have been made for the detection of SNP-SNP interactions, the algorithm of such studies is still ongoing due to the computational and statistical complexities. In this work, we proposed an algorithm IACO based on ant colony optimization and a novel introduced fitness function Svalue, which combined both Bayesian networks and mutual information, for detecting SNP-SNP interactions. Furthermore, a memory based strategy is also employed to improve the performance of IACO, which effectively avoids ignoring the optimal solutions that have already been identified. Experiments of IACO are performed on both simulation data sets and a real data set of age-related macular degeneration (AMD). Results show that IACO is promising in detecting SNP-SNP interactions, and might be an alternative to existing methods for inferring epistatic interactions. The software package is available online at http://www.bdmb-web.cn/index.php?m=content&c=index&a=show&catid=37&id=98.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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)

    Article  Google Scholar 

  2. Maher, B.: The case of the missing heritability. Nature 456(7218), 18–21 (2008)

    Article  Google Scholar 

  3. Rekaya, R., Robbins, K.: Ant colony algorithm for analysis of gene interaction in high-dimensional association data. Revista Brasileira de Zootecnia 38(SPE), 93–97 (2009)

    Article  Google Scholar 

  4. Wang, Y., Liu, X., Robbins, K., Rekaya, R.: AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res. Notes 3(1), 117 (2010)

    Article  Google Scholar 

  5. Jing, P., Shen, H.: MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. Bioinformatics 31, 634–641 (2014). btu702

    Article  Google Scholar 

  6. Christmas, J., Keedwell, E., Frayling, T.M., Perry, J.R.: Ant colony optimisation to identify genetic variant association with type 2 diabetes. Inf. Sci. 181(9), 1609–1622 (2011)

    Article  Google Scholar 

  7. Greene, C.S., White, B.C., Moore, J.H.: Ant colony optimization for genome-wide genetic analysis. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 37–47. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  8. Greene, C.S., Gilmore, J.M., Kiralis, J., Andrews, P.C., Moore, J.H.: Optimal use of expert knowledge in ant colony optimization for the analysis of epistasis in human disease. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 92–103. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Li, P., Guo, M., Wang, C., Liu, X., Zou, Q.: An overview of SNP interactions in genome-wide association studies. Briefings Funct. Genomics 14, 143–145 (2014). elu036

    Article  Google Scholar 

  10. Shang, J., Zhang, J., Sun, Y., Zhang, Y.: EpiMiner: a three-stage co-information based method for detecting and visualizing epistatic interactions. Digit. Sig. Process. 24, 1–13 (2014)

    Article  Google Scholar 

  11. 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 Genom. 35, 1–12 (2013)

    Article  Google Scholar 

  12. 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), 2014, pp. 151–155. IEEE (2014)

    Google Scholar 

  13. 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)

    Chapter  Google Scholar 

  14. Zhang, Y., Liu, J.S.: Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39(9), 1167–1173 (2007)

    Article  Google Scholar 

  15. Han, B., Chen, X.-w., Talebizadeh, Z., Xu, H.: Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks. BMC Syst. Biol. 6(Suppl 3), S14 (2012)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Frankel, W.N., Schork, N.J.: Who’s afraid of epistasis? Nat. Genet. 14(4), 371–373 (1996)

    Article  Google Scholar 

  18. Li, W., Reich, J.: A complete enumeration and classification of two-locus disease models. Hum. Hered. 50(6), 334–349 (2000)

    Article  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. 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, 524821 (2015)

    Google Scholar 

  21. 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)

    Article  Google Scholar 

Download references

Acknowledgments

This work was in part 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).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China

    Yingxia Sun, Junliang Shang, JinXing Liu & Shengjun Li

  2. Institute of Network Computing, Qufu Normal University, Rizhao, 276826, China

    Junliang Shang

Authors
  1. Yingxia Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junliang Shang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. JinXing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shengjun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junliang Shang .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Inha University , Incheon, Korea (Republic of)

    Kyungsook Han

  3. Liverpool John Moores University , Liverpool, United Kingdom

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Sun, Y., Shang, J., Liu, J., Li, S. (2016). An Improved Ant Colony Optimization Algorithm for the Detection of SNP-SNP Interactions. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42297-8_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42296-1

  • Online ISBN: 978-3-319-42297-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation