Multi-population harmony search algorithm for the detection of high-order SNP interactions.

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Multi-population harmony search algorithm for the detection of high-order SNP interactions.

Bioinformatics. 2020 Mar 30;:

Authors: Tuo S, Liu H, Chen H

MOTIVATION: Recently, multi-objective swarm intelligence optimization (SIO) algorithms have attracted considerable attention as disease model-free methods for detecting high-order single nucleotide polymorphism (SNP) interactions. However, a strict Pareto optimal set may filter out some of the SNP combinations associated with disease status. Furthermore, the lack of heuristic factors for finding SNP interactions and the preference for discrimination approaches to disease models are considerable challenges for SIO.
METHOD: In this study, we propose a multi-population harmony search (HS) algorithm dedicated to the detection of high-order SNP interactions (MP-HS-DHSI). This method consists of three stages. In the 1st stage, HS with multi-population (multi-harmony memories) is used to discover a set of candidate high-order SNP combinations having an association with disease status. In HS, multiple criteria (Bayesian network-based K2-score, Jensen-Shannon (JS) divergence, likelihood ratio (LR) and normalized distance with joint entropy (ND-JE)) are adopted by four harmony memories to improve the ability to discriminate diverse disease models. A novel evaluation criterion named ND-JE is proposed to guide HS to explore clues for high-order SNP interactions. In the 2nd and 3rd stages, the G-test statistical method and multifactor dimensionality reduction (MDR) are employed to verify the authenticity of the candidate solutions, respectively.
RESULTS: We compared MP-HS-DHSI with four state-of-the-art SIO algorithms for detecting high-order SNP interactions for 20 simulation disease models and a real dataset of age-related macular degeneration (AMD). The experimental results revealed that our proposed method can accelerate the search speed efficiently and enhance the discrimination ability of diverse epistasis models.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 32227192 [PubMed - as supplied by publisher]