Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
BMC Bioinformatics. 2019 Aug 28;20(1):444. doi: 10.1186/s12859-019-3022-z.
Mining 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.
We 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.
The 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.
挖掘影响特定表型性状的上位性基因座是生物学领域的一个重要研究问题。贝叶斯网络（BN）是一种可以表达基因座与表型之间关系的图形模型。到目前为止，它已经在许多研究工作中被广泛应用于上位性挖掘。然而，这种方法有两个缺点：学习效率低，容易陷入局部最优。遗传算法具有快速全局搜索和避免陷入局部最优的优点。它具有可扩展性，易于与其他算法集成。本工作提出了一种基于遗传禁忌算法和贝叶斯网络的上位性挖掘方法（Epi-GTBN）。它将遗传算法应用于贝叶斯网络的启发式搜索策略中。个体结构可以通过遗传算法的选择、交叉和变异等遗传操作进行进化。它有助于找到最优的网络结构，从而有效地挖掘上位性基因座。为了增强种群的多样性，获得更有效的全局最优解，我们将禁忌搜索策略应用于遗传算法的交叉和变异操作中。它有助于加速算法的收敛。
我们使用模拟数据集和真实数据集，将 Epi-GTBN 与其他最近的算法进行了比较。实验结果表明，在不影响不同数据集效率的情况下，我们的方法在上位性检测精度方面有很大的提高。
所提出的方法（Epi-GTBN）是一种有效的上位性检测方法，可以看作是复杂性状分析中使用的武器库的一个有趣补充。
