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. 2017 Jul 6:10:23.
doi: 10.1186/s13040-017-0143-7. eCollection 2017.

epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

Yingxia Sun  1 , Junliang Shang  1   2 , Jin-Xing Liu  1 , Shengjun Li  1 , Chun-Hou Zheng  1   3
Affiliations

epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

Yingxia Sun et al. BioData Min. .

Abstract

Background: Identifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.

Results: In this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype. Two path selection strategies, i.e., probabilistic path selection strategy and stochastic path selection strategy, are provided to adaptively guide ant behaviors of exploration and exploitation. The memory based strategy is designed to retain candidate solutions found in the previous iterations, and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis.

Conclusions: Experiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods.

Keywords: Ant colony optimization; Bayesian network; Epistatic interactions; Mutual information.

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The authors declare that they have no competing interests.

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Figures

Fig. 1
Fig. 1
The pseudo code of epiACO
Fig. 2
Fig. 2
The convergence and contribution of epiACO with the parameter settings being the same as those of the first experiment
See this image and copyright information in PMC

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