Genes & Genomics

, Volume 34, Issue 3, pp 321–327 | Cite as

Incorporating heuristic information into ant colony optimization for epistasis detection

  • Junliang Shang
  • Junying Zhang
  • Xiujuan Lei
  • Yuanyuan Zhang
  • Baodi Chen
Research Article


Epistasis has been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for epistasis detection, genome-wide association study remains a challenging task: it makes the search space excessively huge while solution quality is excessively demanded. In this study, we introduce an ant colony optimization based algorithm, AntMiner, by incorporating heuristic information into ant-decision rules. The heuristic information is used to direct ants in the search process for improving computational efficiency and solution accuracy. During iterations, chi-squared test is conducted to measure the association between an interaction and the phenotype. At the completion of the iteration process, statistically significant epistatic interactions are ordered and then screened by a post-procedure. Experiments of AntMiner and its comparison with existing algorithms epiMODE, TEAM and AntEpiSeeker are performed on both simulation data sets and real age-related macular degeneration data set, under the criteria of detection power and sensitivity. Results demonstrate that AntMiner is promising for epistasis detection. In terms of detection power, AntMiner performs best among all the other algorithms on all cases regardless of epistasis models and single nucleotide polymorphism size; compared with AntEpiSeeker, AntMiner can obtain better detection power but with less ants and iterations. In terms of sensitivity, AntMiner is better than AntEpiSeeker in detecting epistasis models displaying marginal effects but it has moderate sensitivity on epistasis models displaying no marginal effects. The study may provide clues on heuristics for further epistasis detection. The software package is available online at


Epistasis detection Heuristic information Ant colony optimization Single nucleotide polymorphisms Genome-wide association study 


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Supplementary material

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  1. Balding DJ (2006) A tutorial on statistical methods for population association studies. Nat. Rev. Genet. 7: 781–791.PubMedCrossRefGoogle Scholar
  2. Cardon LR and Bell JI (2001) Association study designs for complex diseases. Nat. Rev. Genet. 2: 91–99.PubMedCrossRefGoogle Scholar
  3. Christmas J, Keedwell E and Frayling TM (2010) Ant colony optimisation to identify genetic variant association with type 2 diabetes. Inform. Sciences 181: 1609–1622.CrossRefGoogle Scholar
  4. Culverhouse R, Klein T and Shannon W (2004) Detecting epistatic interactions contributing to quantitative traits. Genet. Epidemiol. 27: 141–152.PubMedCrossRefGoogle Scholar
  5. Das S, Abraham A and Konar A (2008) Swarm intelligence algorithms in bioinformatics. Stud. Comp. Intell. 94: 113–147.CrossRefGoogle Scholar
  6. Fontanarosa J and Dai Y (2010) A block-based evolutionary optimization strategy to investigate gene-gene interactions in genetic association studies. IEEE Internat. Confer. Bioinform. Biomed. Workshop: 330–335.Google Scholar
  7. Frankel WN and Schork NJ (1996) Who’s afraid of epistasis? Nat. Genet. 14: 371–373.PubMedCrossRefGoogle Scholar
  8. Gilmore J, Greene C, Andrews P, Kiralis J and Moore J (2011) An Analysis of New Expert Knowledge Scaling Methods for Biologically Inspired Computing. Lect. Notes Comput. Sci. 5778: 286–293.CrossRefGoogle Scholar
  9. Greene C, Gilmore J, Kiralis J, Andrews P and Moore J (2009a) Optimal use of expert knowledge in ant colony optimization for the analysis of epistasis in human disease. Lect. Notes Comput. Sci. 5483: 92–103.CrossRefGoogle Scholar
  10. Greene CS, Penrod NM, Kiralis J and Moore JH (2009b) Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions. BioData Min. 2: 5.PubMedCrossRefGoogle Scholar
  11. Greene CS, White BC and Moore JH (2008) Ant Colony Optimization for Genome-Wide Genetic Analysis. Lect Notes Comput. Sci. 5217: 37–47.CrossRefGoogle Scholar
  12. Jiang R, Tang W, Wu X and Fu W (2009) A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics 10Suppl 1: S65.PubMedCrossRefGoogle Scholar
  13. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST et al. (2005) Complement factor H polymorphism in age-related macular degeneration. Science 308: 385–389.PubMedCrossRefGoogle Scholar
  14. Li W and Reich J (2000) A complete enumeration and classification of two-locus disease models. Hum. Hered. 50: 334–349.PubMedCrossRefGoogle Scholar
  15. Maher B (2008) Personal genomes: The case of the missing heritability. Nature 456: 18–21.PubMedCrossRefGoogle Scholar
  16. Moore JH, Asselbergs FW and Williams SM (2010) Bioinformatics challenges for genome-wide association studies. Bioinformatics 26: 445–455.PubMedCrossRefGoogle Scholar
  17. Quevedo J, Bahamonde A, Prez-Enciso M and Luaces O (2011) Disease liability prediction from large scale genotyping data using classifiers with a reject option. IEEE/ACM T. Comput. Bi. 9: 88–97.CrossRefGoogle Scholar
  18. Rekaya R and Robbins K (2009) Ant colony algorithm for analysis of gene interaction in high-dimensional association data. Rev. Bras. Zootecn. 38: 93–97.CrossRefGoogle Scholar
  19. Risch N and Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273: 1516–1517.PubMedCrossRefGoogle Scholar
  20. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF and Moore JH (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69: 138–147.PubMedCrossRefGoogle Scholar
  21. Shang J, Zhang J, Sun Y, Liu D, Ye D and Yin Y (2011) Performance analysis of novel methods for detecting epistasis. BMC Bioinformatics 12: 475.PubMedCrossRefGoogle Scholar
  22. Shen Y, Liu Z and Ott J (2010) Systematic removal of outliers to reduce heterogeneity in case-control association studies. Hum. Hered. 70: 227–231.PubMedCrossRefGoogle Scholar
  23. Tang W, Wu X, Jiang R and Li Y (2009) Epistatic module detection for case-control studies: a Bayesian model with a Gibbs sampling strategy. PLoS Genet. 5: e1000464.PubMedCrossRefGoogle Scholar
  24. Wan X, Yang C, Yang Q, Xue H, Tang NL and Yu W (2010) Detecting two-locus associations allowing for interactions in genome-wide association studies. Bioinformatics 26: 2517–2525.PubMedCrossRefGoogle Scholar
  25. Wang Y (2010) Computation Bioinformatics and Bioimaging Laboratory.
  26. Wang Y, Liu X, Robbins K and Rekaya R (2010) AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res. Notes 3: 117.PubMedCrossRefGoogle Scholar
  27. Wei B, Peng Q, Zhang Q and Li C (2011) Identification of a combination of SNPs associated with Graves’ disease using swarm intelligence. Sci. China Life Sci. 54: 139–145.PubMedCrossRefGoogle Scholar
  28. WTCCC (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.CrossRefGoogle Scholar
  29. Yuan X, Zhang J and Wang Y (2011) Mutual information and linkage disequilibrium based SNP association study by grouping case-control. Genes Genom. 33: 65–73.CrossRefGoogle Scholar
  30. Zhang X, Huang S, Zou F and Wang W (2010) TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 26: 217–227.CrossRefGoogle Scholar
  31. Zhang Y and Liu JS (2007) Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39: 1167–1173.PubMedCrossRefGoogle Scholar

Copyright information

© The Genetics Society of Korea and Springer Netherlands 2012

Authors and Affiliations

  1. 1.School of Computer Science & TechnologyXidian UniversityXi’anP.R.China
  2. 2.College of computer scienceShaanxi Normal UniversityXi’anP.R.China

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