Generating SNP barcode to evaluate SNP–SNP interaction of disease by particle swarm optimization

Abstract

Genome-wide association analysis involved many single-nucleotide polymorphisms (SNPs) data is challenging mathematically and computationally. Hence, we propose the odds ratio-based discrete binary particle swarm optimization (OR-DBPSO) method that uses the OR as a new quantitative measure of disease risk among many SNP combinations with genotypes called “SNP barcode”. DBPSO are applied to generate SNP barcode, which computes the maximal difference of occurrence between the case and control groups, to predict disease susceptibility such as osteoporosis. Different SNP barcode patterns may occur several times in either low or high bone mineral density (BMD) groups. Our results showed that a DBPSO can effectively identify a specific SNP barcode with an optimized fitness value. SNP barcodes with a low fitness value will naturally be discarded from the population. A representative SNP barcode with a variable number of SNPs is processed to OR analysis to determine the maximum difference between the low and high BMD groups in statistics manner. Therefore, this paper introduces a powerful procedure to analyze disease-associated SNP–SNP interaction in genome-wide genes.