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An optimization approach to epistasis detection

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  • Wang, Lizhi
  • Nikouei Mehr, Maryam

Abstract

Epistasis refers to the phenomenon where the interaction of multiple genes affects a certain phenotype in addition to their individual additive effects. Similar epistatic effects are also ubiquitous in other application areas, such as gene-environment interactions, where a certain effect is triggered only when a particular combination of genes and environmental components is present. Epistasis detection has been recognized as a major challenge in the field of genetics. Previously proposed methods either focused on finding two-gene interactions using brute force enumeration or resorted to heuristic algorithms to search only a subset of the solution space. Herein we present an optimization approach that can identify the number of explanatory variables responsible for the epistasis as well as the exact combination of these variables. Results from simulation experiments using a soybean data set suggested that the proposed approach had a 95.5% chance of correctly detecting second-order to fifth-order epistases, which was a significant improvement over two alternative approaches in the literature.

Suggested Citation

  • Wang, Lizhi & Nikouei Mehr, Maryam, 2019. "An optimization approach to epistasis detection," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1069-1076.
    • Handle: RePEc:eee:ejores:v:274:y:2019:i:3:p:1069-1076
    DOI: 10.1016/j.ejor.2018.10.032
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    1. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    2. E. L. Lawler & D. E. Wood, 1966. "Branch-and-Bound Methods: A Survey," Operations Research, INFORMS, vol. 14(4), pages 699-719, August.
    3. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
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