Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

20 Scopus Citations
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Original languageEnglish
Pages (from-to)226-237
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number1
Online published22 Jun 2018
Publication statusPublished - Jan 2020


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. Firstly, 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 79 epistatic models including eight small-scale epistatic models with marginal effects, eight large-scale epistatic models with marginal effects, sixty 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.

Research Area(s)

  • bioinformatics, computational biology, Computational modeling, Diseases, epistatic interaction, genome-wide association studies, Genomics, Linear programming, Optimization