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

Shouheng Tuo1, Haiyan Liu1, Hao Chen1

  • 1School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.

Bioinformatics (Oxford, England)
|April 1, 2020
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Summary

This study introduces a multipopulation harmony search (MP-HS-DHSI) algorithm to detect high-order single nucleotide polymorphism (SNP) interactions. The method improves search speed and discrimination of disease models, outperforming existing swarm intelligence optimization algorithms.

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Area of Science:

  • Genetics and Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Swarm intelligence optimization (SIO) is used for disease model-free detection of high-order single nucleotide polymorphism (SNP) interactions.
  • Existing SIO methods face challenges including filtering relevant SNP combinations and lacking heuristic factors for interaction detection.

Purpose of the Study:

  • To propose a novel multipopulation harmony search (HS) algorithm, MP-HS-DHSI, for detecting high-order SNP interactions.
  • To enhance the ability to discriminate diverse disease models and address limitations of current SIO approaches.

Main Methods:

  • Developed a three-stage MP-HS-DHSI algorithm utilizing multipopulation harmony search.
  • Incorporated multiple criteria (K2-score, Jensen-Shannon divergence, likelihood ratio, ND-JE) and a novel ND-JE evaluation criterion.
  • Employed G-test and multifactor dimensionality reduction for verification of candidate SNP interactions.

Main Results:

  • MP-HS-DHSI demonstrated accelerated search speed and enhanced discrimination of epistasis models compared to state-of-the-art SIO algorithms.
  • The method was validated on 20 simulation disease models and a real-world age-related macular degeneration dataset.
  • Experimental results confirm the efficacy of MP-HS-DHSI in identifying high-order SNP interactions.

Conclusions:

  • MP-HS-DHSI offers an effective and efficient approach for detecting high-order SNP interactions.
  • The proposed algorithm overcomes limitations of existing methods, improving disease model discrimination.
  • This work contributes a valuable tool for genetic association studies and understanding complex diseases.