|Title:||Particle swarm optimization algorithm for analyzing SNP-SNP interaction of renin-angiotensin system genes against hypertension||Authors:||Wu S.-J.
|Keywords:||Hypertension; Particle swarm optimization; Renin-angiotensin system; Single nucleotide polymorphism; SNP interactions||Issue Date:||2013||Journal Volume:||40||Journal Issue:||7||Start page/Pages:||4227-4233||Source:||Molecular Biology Reports||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. ? 2013 Springer Science+Business Media Dordrecht.
|URI:||https://scholars.lib.ntu.edu.tw/handle/123456789/551675||ISSN:||0301-4851||DOI:||10.1007/s11033-013-2504-8||SDG/Keyword:||article; controlled study; DNA barcoding; gene; gene frequency; gene interaction; genetic algorithm; genetic association; genetic database; genetic screening; genotype; human; hypertension; major clinical study; particle swarm optimization algorithm; prediction; renin angiotensin aldosterone system; renin angiotensin system gene; single nucleotide polymorphism; algorithm; biological model; biology; epistasis; genetic predisposition; genetics; hypertension; procedures; reproducibility; risk; Algorithms; Computational Biology; Epistasis, Genetic; Genetic Predisposition to Disease; Genotype; Humans; Hypertension; Models, Biological; Odds Ratio; Polymorphism, Single Nucleotide; Renin-Angiotensin System; Reproducibility of Results
|Appears in Collections:||醫學院附設醫院 (臺大醫院)|
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