In recent years, the detection of epistatic interactions of multiple genetic variants on the causes of complex diseases brings a significant challenge in genome-wide association studies (GWAS). However, most of the existing methods still suffer from algorithmic limitations such as single-objective optimization, intensive computational requirement, and premature convergence. In this paper, we propose and formulate an epistatic interaction multi-objective artificial bee colony algorithm based on decomposition (EIMOABC/D) to address those problems for genetic interaction detection in genome-wide association studies. First, to direct the genetic interaction detection, two objective functions are formulated to characterize various epistatic models; rank probability model is proposed to sort each population into different nondomination levels based on the fast nondominated sorting approach. After that, the mutual information based local search algorithm is proposed to guide the population search for disease model evaluations in an unbiased manner. To validate the effectiveness of EIMOABC/D, we compare EIMOABC/D against seven state-of-the-art methods on 77 epistatic models including eight small-scale epistatic models with marginal effects, eight large-scale epistatic models with marginal effects, 60 large-scale epistatic models without any marginal effect, and one case study. The experimental results indicate that our proposed algorithm EIMOABC/D outperforms seven state-of-the-art methods on those epistatic models. Furthermore, time complexity analysis and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.

中文翻译：

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全基因组关联研究对自然启发的多目标上位性阐明。

近年来，检测多种遗传变异在复杂疾病原因上的上位相互作用在全基因组关联研究（GWAS）中提出了重大挑战。但是，大多数现有方法仍然受到算法限制，例如单目标优化，密集的计算要求和过早收敛。在本文中，我们提出并制定了一种基于分解的上位相互作用多目标人工蜂群算法（EIMOABC / D），以解决全基因组关联研究中遗传相互作用检测的那些问题。首先，为了指导遗传相互作用的检测，制定了两个目标函数来表征各种上位模型。提出了一种基于快速非支配排序方法的等级概率模型，将每个人口分为不同的非支配级别。之后，提出了一种基于互信息的局部搜索算法，以无偏见的方式指导人群搜索疾病模型的评估。为了验证EIMOABC / D的有效性，我们将EIMOABC / D与77种上位模型的7种最新方法进行了比较，其中包括8种具有边际效应的小规模上位模型，8种具有边际效应的大型上位模型，60没有任何边际效应的大规模上位模型和一个案例研究。实验结果表明，在这些上位模型上，我们提出的算法EIMOABC / D优于七种最新方法。此外，