Abstract
BackgroundMining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.ResultsWe compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.ConclusionsThe presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.
Highlights
Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology
Later the improved logistic regression based on Group Lasso method is used into epistasis mining [2]
In order to improve the detection efficiency and get the global optimal solution, some researchers use the evolutionary algorithm into epistasis mining, such as genetic algorithm (GA) [25], particle swarm optimization [26], etc
Summary
With the rapid development of many high-throughput technologies, massive biological data has been produced in recent years, such as genome, transcription and phenotype data. On the basis of Boolean operation, BOOST detects epistasis using the stages of screening and testing [21] The efficiency of this method is relatively high, but it is limited to the interaction between two SNPs, which leads to limited utility. In order to improve the detection efficiency and get the global optimal solution, some researchers use the evolutionary algorithm into epistasis mining, such as genetic algorithm (GA) [25], particle swarm optimization [26], etc. Some research work use Bayesian network learning method to construct the network of gene loci and phenotype, and to detect the epistatic loci for specific phenotype [31, 32]. We use mutual information entropy calculation method to calculate the relationship between gene loci and phenotype, and to construct the initial network. Experiment results show Epi-GTBN has much better epistasis detection accuracy in the case of not affecting the efficiency
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