FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis. (English) Zbl 1373.93185

Yuan, Lin; Yuan, Chang-An; Huang, De-Shuang

Complexity 2017, Article ID 5024867, 10 p. (2017).

Summary: The epistasis is prevalent in the SNP interactions. Some of the existing methods are focused on constructing models for two SNPs. Other methods only find the SNPs in consideration of one-objective function. In this paper, we present a unified fast framework integrating adaptive ant colony optimization algorithm with multiobjective functions for detecting SNP epistasis in GWAS datasets. We compared our method with other existing methods using synthetic datasets and applied the proposed method to Late-Onset Alzheimer’s Disease dataset. Our experimental results show that the proposed method outperforms other methods in epistasis detection, and the result of real dataset contributes to the research of mechanism underlying the disease.

MSC:

93C40	Adaptive control/observation systems
90C59	Approximation methods and heuristics in mathematical programming
92C50	Medical applications (general)

Keywords:

fast adaptive ant colony optimization algorithm

Software:

SFAPS; IMPUTE; AntEpiSeeker; eQTL epistasis; FAACOSE; MACOED; chifish

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