Abstract
BackgroundIdentifying 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.ResultsIn 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.ConclusionsExperiments 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.
Highlights
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
The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype
Experiments of epiACO and its comparison with other recent methods Epistatic module detection (epiMODE), Tree-based epistasis association mapping (TEAM), Boolean operation-based screening and testing (BOOST), SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and an age-related macular degeneration real data set
Summary
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. Zhang et al [6] proposed TEAM (Tree-based Epistasis Association Mapping) which updates contingency tables by utilizing the structure of a minimum spanning tree to search two-SNP epistatic interactions. As far as we know, MDR is one of the most popular methods in this field Though these methods show great performance on identifying epistatic interactions in small scale data sets, they have poor ability on large scale data sets due to their exponential time complexities [1, 3]. Though heuristic search methods sometimes miss global optimal solutions and only obtain several local optimal solutions, they are still promising methods so far
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