4.5 Article

Genetic polymorphisms of angiotensinogen and essential hypertension in a Tibetan population

Journal

HYPERTENSION RESEARCH
Volume 30, Issue 11, Pages 1129-1137

Publisher

JAPANESE SOC HYPERTENSION CENT ACADEMIC SOC, PUBL OFFICE
DOI: 10.1291/hypres.30.1129

Keywords

essential hypertension; angiotensinogen; single-locus; haplotype; epistasis

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The human angiotensinogen gene (AGT) is a promising candidate for an essential hypertension-susceptibility gene. We aimed to explore the single-locus, haplotype and epistasis patterns of three polymorphisms of AGT(A-20C, A-6G and M235T) and their relation to the risk of essential hypertension in a Tibetan population. The three polymorphisms were genotyped in 333 essential hypertension patients and 235 healthy controls on the basis of a door-to-door cross-sectional study. Genotyping was performed using polymerase chain reaction (PCR)-restriction fragment length polymerase (RFLP) and direct sequencing techniques. The data were analyzed using the EH/EH+ program and the multifactor dimensionality reduction (MDR) method. Our single-locus analysis revealed that except for a marginal, significant association of A-20C allele distribution, no significant association between genotype and allele distributions of the A-20C, A-6G, or M235T polymorphism of AGT and essential hypertension was found. In haplotype analysis, we found that the H, haplotype may be the risk-conferring factor for hypertension, even after the Bonferroni correction. In epistasis analysis, we selected the final best model, which included the A-20C and A-6G polymorphisms with a strong synergistic effect. This model had a maximum testing accuracy of 0.564 and a maximum cross validation consistency of 10 out of 10 (p=0.001). The present study thus provides evidence of a strong synergistic effect of the A-20C and A-6G polymorphisms of AGT, which were not found to be associated with essential hypertension in the single-locus analysis. Moreover, we have proposed a promising data-mining analytical method using the open-source MDR software package for detecting and characterizing gene-gene interactions.

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