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

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 an...

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Veröffentlicht in:Molecular biology reports Jg. 40; H. 7; S. 4227 - 4233
Hauptverfasser: Wu, Shyh-Jong, Chuang, Li-Yeh, Lin, Yu-Da, Ho, Wen-Hsien, Chiang, Fu-Tien, Yang, Cheng-Hong, Chang, Hsueh-Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.07.2013
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:0301-4851
1573-4978
1573-4978
DOI:10.1007/s11033-013-2504-8