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

• Published in: Molecular biology reports Vol. 40; no. 7; pp. 4227 - 4233
• Main Authors: Wu, Shyh-Jong, Chuang, Li-Yeh, Lin, Yu-Da, Ho, Wen-Hsien, Chiang, Fu-Tien, Yang, Cheng-Hong, Chang, Hsueh-Wei
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2013; Springer Nature B.V
• Subjects: Algorithms; Angiogenesis; Animal Anatomy; Animal Biochemistry; Atoms & subatomic particles; barcoding; Biomedical and Life Sciences; Computational Biology - methods; data collection; Epistasis, Genetic; genes; Genetic Predisposition to Disease; Genotype; heritability; Histology; Humans; Hypertension; Hypertension - genetics; Life Sciences; Models, Biological; Morphology; Odds Ratio; Optimization algorithms; Polymorphism, Single Nucleotide; renin-angiotensin system; Renin-Angiotensin System - genetics; Reproducibility of Results; single nucleotide polymorphism; Hypertension; SNP interactions; Single nucleotide polymorphism; Renin-angiotensin system; Particle swarm optimization
• ISSN: 0301-4851; 1573-4978; 1573-4978
• DOI: 10.1007/s11033-013-2504-8

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.