An Improved Ant Colony Optimization Algorithm for the Detection of SNP-SNP Interactions
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 f
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School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China {sunyingxia026,shangjunliang110,qfnulsj}@163.com, [email protected] 2 Institute of Network Computing, Qufu Normal University, Rizhao 276826, China
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. Keywords: SNP-SNP interaction Ant colony Optimization
Bayesian network Mutual information
1 Introduction With the development of high-throughput sequencing technologies, it is universally acknowledged that SNP (single nucleotide polymorphism) is one of the most common forms of genetic variants in human genome, which usually affects complex diseases by their nonlinear interactions, namely, epistatic interactions or epistasis [1]. Currently, epistasis has caused the extensive concerns in exploring the pathogenic mechanism of non-Mendelian diseases, such as hypertension, diabetes, Alzheimer’s disease and many others [2]. Although many efforts have been made for the detection of SNP-SNP interactions, the algorithm of such studies is still ongoing due to their computational
© Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part III, LNAI 9773, pp. 21–32, 2016. DOI: 10.1007/978-3-319-42297-8_3
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and statistical complexities, including the complexity of pathogenesis, the complexity of genetic models and the influence of environment factors. Recently, a number of generic ant colony optimization (ACO) based methods have been proposed [1, 3–8] to detect the epistatic interactions. For instance, Christmas et al. [6] used generic ACO algorithm to identify the epistatic interactions in type 2 diabetes data. Results indicate that ACO algorithm is able to find statistically significant epistatic interactions. Wang et al. [4] proposed AntEpiSeeker based on ACO algorithm and designed two-stage optimization procedure for detection SNP-SNP in
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