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Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions
Cheng-Hong Yang1, Yu-Da Lin1, Li-Yeh Chuang2
1Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80778, Taiwan.
Biomed Research International
|June 5, 2014
Summary
This study introduces an improved particle swarm optimization algorithm (DBM-PSO) to identify complex gene-gene interactions linked to breast cancer susceptibility. The DBM-PSO effectively discovered high-order interaction models, outperforming conventional methods.
Area of Science:
- Genetics
- Computational Biology
- Bioinformatics
Background:
- Gene-gene interactions, specifically single nucleotide polymorphisms (SNPs), are crucial for understanding disease susceptibility.
- Existing statistical methods struggle with analyzing high-order SNP interactions due to the vast number of potential genotype combinations.
Purpose of the Study:
- To develop and evaluate an improved particle swarm optimization algorithm (DBM-PSO) for identifying gene-gene interaction models.
- To assist statistical methods in analyzing SNP associations with disease susceptibility, particularly for high-order interactions.
Main Methods:
- An improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was employed.
- A simulated big data set based on published genotype frequencies of 26 SNPs across eight genes for breast cancer was utilized.
Main Results:
- The DBM-PSO successfully identified gene-gene interaction models of two- to six-order associated with breast cancer risk.
- The algorithm provided higher chi-square values compared to conventional particle swarm optimization (PSO).
- Identified models showed statistical significance (odds ratio > 1.0; P value <0.05).
Conclusions:
- The DBM-PSO is a robust and precise algorithm for determining gene-gene interaction models.
- This approach enhances the analysis of complex genetic variations contributing to breast cancer susceptibility.
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