Sun Yingxia, Wang Xuan, Shang Junliang, Liu Jin-Xing, Zheng Chun-Hou, Lei Xiujuan
IEEE/ACM Trans Comput Biol Bioinform. 2020 Jul-Aug;17(4):1253-1261. doi: 10.1109/TCBB.2018.2879673. Epub 2018 Nov 5.
Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on the ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy. The heuristic information multi-SURF* is introduced into EACO for identifying epistasis, which is incorporated into ant-decision rules to guide the search with linear time. Two functionally complementary fitness functions, mutual information and the Gini index, are combined to effectively evaluate the associations between SNP combinations and the phenotype. Furthermore, a strategy for candidate solutions filtration is provided to adaptively retain all optimal solutions which yields a more accurate way for epistasis searching. Experiments of EACO, as well as three ACO based methods (AntEpiSeeker, MACOED, and epiACO) and four commonly used methods (BOOST, SNPRuler, TEAM, and epiMODE) are performed on both simulation data sets and a real data set of age-related macular degeneration. Results indicate that EACO is promising in identifying epistasis.
上位性学习旨在检测多个单核苷酸多态性（SNP）与复杂疾病之间的关联，在全基因组关联研究中受到越来越多的关注。尽管在绘制复杂疾病潜在的SNP方面已经做了很多工作，但由于缺乏启发式信息来加快搜索过程，检测上位性相互作用仍然存在困难。在本研究中，提出了一种基于蚁群优化（ACO）算法的EACO方法来检测上位性相互作用，其亮点在于引入的启发式信息、适应度函数和候选解过滤策略。将启发式信息multi-SURF*引入EACO以识别上位性，它被纳入蚂蚁决策规则以在线性时间内指导搜索。结合了两个功能互补的适应度函数，互信息和基尼指数，以有效评估SNP组合与表型之间的关联。此外，还提供了一种候选解过滤策略，以自适应地保留所有最优解，从而为上位性搜索提供一种更准确的方法。在模拟数据集和年龄相关性黄斑变性的真实数据集上进行了EACO以及三种基于ACO的方法（AntEpiSeeker、MACOED和epiACO）和四种常用方法（BOOST、SNPRuler、TEAM和epiMODE）的实验。结果表明，EACO在识别上位性方面很有前景。
