School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Genes (Basel). 2022 May 12;13(5):871. doi: 10.3390/genes13050871.
In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection.
在全基因组关联研究中，检测上位性对于复杂人类疾病的发生和诊断具有重要意义，但它也面临着高维性和小样本量等挑战。近年来，为了应对这些挑战，已经引入了几种群体智能方法来识别上位性。然而，现有的方法仍然存在一些局限性，例如高消耗和过早收敛。在这项研究中，我们提出了一种基于无标度网络的多目标人工蜂群（ABC）算法（SFMOABC）。SFMOABC 将无标度网络纳入 ABC 算法中，以指导解决方案的更新和选择。此外，SFMOABC 使用互信息和贝叶斯网络的 K2 分数作为目标函数，并使用基于对比例的学习策略来提高搜索能力。我们在模拟数据集和与年龄相关的黄斑变性（AMD）的真实数据集上进行了实验。模拟实验的结果表明，SFMOABC 比其他七种上位性检测方法具有更好的检测能力和效率。在真实的 AMD 数据实验中，SFMOABC 检测到的大多数单核苷酸多态性组合都与 AMD 疾病有关。因此，SFMOABC 是一种很有前途的上位性检测方法。
