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FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm

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  • Shouheng Tuo
  • Junying Zhang
  • Xiguo Yuan
  • Yuanyuan Zhang
  • Zhaowen Liu

Abstract

Motivation: Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Method: In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. Results: We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset.

Suggested Citation

  • Shouheng Tuo & Junying Zhang & Xiguo Yuan & Yuanyuan Zhang & Zhaowen Liu, 2016. "FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-27, March.
    • Handle: RePEc:plo:pone00:0150669
    DOI: 10.1371/journal.pone.0150669
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    References listed on IDEAS

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    1. Qingrun Zhang & Quan Long & Jurg Ott, 2014. "AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-14, June.
    2. Walton, S. & Hassan, O. & Morgan, K. & Brown, M.R., 2011. "Modified cuckoo search: A new gradient free optimisation algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 44(9), pages 710-718.
    3. Wanwan Tang & Xuebing Wu & Rui Jiang & Yanda Li, 2009. "Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy," PLOS Genetics, Public Library of Science, vol. 5(5), pages 1-18, May.
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    Cited by:

    1. Shouheng Tuo & Zong Woo Geem & Jin Hee Yoon, 2020. "A New Method for Analyzing the Performance of the Harmony Search Algorithm," Mathematics, MDPI, vol. 8(9), pages 1-17, August.

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