An Ant Colony Optimization and Tabu List Approach to the Detection of Gene-Gene Interactions in Genome-Wide Association Studies [Research Frontier]
An Ant Colony Optimization and Tabu List Approach to the Detection of Gene-Gene Interactions in Genome-Wide Association Studies [Research Frontier]
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DOI：
10.1109/mci.2015.2471236
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发表时间：
2015-10
影响因子：
9
通讯作者：
Emmanuel Sapin;E. Keedwell;T. Frayling
中科院分区：
文献类型：
--
作者：
Emmanuel Sapin;E. Keedwell;T. Frayling
在本文中，提出了一种新的蚁群优化和禁忌列表方法，用于发现全基因组关联研究数据中的基因间相互作用。该方法在多种疾病上进行了测试，这些疾病取自 Wellcome Trust 病例控制联盟大型数据库，该数据库包含数十万个称为单核苷酸多态性的小 DNA 变化。为了分析全面的全基因组关联研究数据，我们对标准蚁群优化算法进行了调整，算法中包含了锦标赛路径选择、基于子集的方法和禁忌列表。除了使用单核苷酸多态性相互作用的显着性统计测试作为适应度函数之外，这些修改大大提高了执行速度并允许发现可以区分病例和对照的单核苷酸多态性的组合。该方法适用于几种大规模全基因组关联研究疾病数据集，即炎症性肠病、类风湿性关节炎、I 型糖尿病和 II 型糖尿病患者，以在适度的硬件上在合理的时间内发现假定的基因-基因相互作用。
In this paper, a novel ant colony optimization and tabu list approach for the discovery of gene-gene interactions in genome-wide association study data is proposed. The method is tested on a number of diseases drawn from the large established database, the Wellcome Trust Case Control Consortium which contains hundreds of thousands of small DNA changes known as single nucleotide polymorphisms. To analyze full scale genome-wide association study data, the standard ant colony optimization algorithm has been adapted, with tournament path selection, a subset based approach, and tabu list included in the algorithm. These modifications, in addition to the use of a statistical test of significance of single nucleotide polymorphism interactions as a fitness function, greatly increase execution speeds and permit the discovery of combinations of single nucleotide polymorphisms that can discriminate cases and controls. The methodology is applied to several large-scale genome-wide association study disease datasets namely, inflammatory bowel disease, rheumatoid arthritis, type I diabetes and type II diabetes patients to discover putative gene-gene interactions in reasonable time on modest hardware.