Particle swarm optimization algorithm for analyzing SNP–SNP interaction of renin-angiotensin system genes against hypertension

Abstract

Most non-significant individual single nucleotide polymorphisms (SNPs) were undiscovered in hypertension association studies. Their possible SNP-SNP interactions were usually ignored and leaded to missing heritability. In present study, we proposed a particle swarm optimization (PSO) algorithm to analyze the SNP-SNP interaction associated with hypertension. Genotype dataset of eight SNPs of renin-angiotensin system genes for 130 non-hypertension and 313 hypertension subjects were included. Without SNP-SNP interaction, most individual SNPs were non-significant difference between the hypertension and non-hypertension groups. For SNP-SNP interaction, PSO can select the SNP combinations involving different SNP numbers, namely the best SNP barcodes, to show the maximum frequency difference between non-hypertension and hypertension groups. After computation, the best PSO-generated SNP barcodes were dominant in non-hypertension in terms of the occurrences of frequency differences between non-hypertension and hypertension groups. The OR values of the best SNP barcodes involving 2-8 SNPs were 0.705-0.334, suggesting that these SNP barcodes were protective against hypertension. In conclusion, this study demonstrated that non-significant SNPs may generate the joint effect in association study. Our proposed PSO algorithm is effective to identify the best protective SNP barcodes against hypertension.

Metaheuristic Algorithms for Healthcare: Open Issues and Challenges

“…Similar to GA and ACO, particle swarm optimization (PSO) also has several successful results [35,77,80] in solving the classification problems or analyzing the data of a healthcare system. In [77] , PSO was used for analyzing the data of single nucleotide polymorphisms (SNPs) of renin-angiotensin system genes to understand the interaction of SNPs and hypertension, e.g., non-hypertension and hypertension.…”

“…In [77] , PSO was used for analyzing the data of single nucleotide polymorphisms (SNPs) of renin-angiotensin system genes to understand the interaction of SNPs and hypertension, e.g., non-hypertension and hypertension. Another promising research trend is to use PSO as a classification algorithm to recognize breast cancer [35,80] .…”

Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes

“…Thus, evolutionary computations have been applied to improve statistical methods for identifying significant genetic associations. Particle swarm optimization (PSO) has been applied to identify significant genetic associations for facial emotion perception [11] and hypertension [12]. Genetic algorithm (GA) has been successfully used to identify significant genetic associations for chronic dialysis [13] and breast cancer [14].…”

Detection of SNP-SNP interaction based on the generalized particle swarm optimization algorithm

“…Swarm intelligence algorithms, such as ant colony optimization algorithm, and particle swarm optimization (PSO) algorithm, might be good ways for solving these complex problems [5][6][7][8][9]. Among them, the PSO algorithm has several merits.…”

“…Third, the selection of parameters has a mature theoretical research. Wu et al [5] proposed a PSO based method to analyze the SNP-SNP interaction associated with hypertension. Chuang et al [6] used the Gauss PSO algorithm to detect and identify the best protective association with breast cancer.…”