Association between a Single Nucleotide Polymorphism in the 3'-UTR of ARHGEF18 and the Risk of Nonidiopathic Pulmonary Arterial Hypertension in Chinese Population

Abstract

ARHGEF18 has been identified as upregulated in the lung tissues of rat models of pulmonary artery hypertension introduced by hypoxia or monocrotaline (MCT). We used online SNP function prediction tools to screen the candidate SNPs that might be associated with the regulation of the ARHGEF18 expression. The result suggested that rs3745357 located in the 3'-untranslated region of ARHGEF18 is probably a genetic modifier in the process. In the present study, we aimed to investigate the association between ARHGEF18 rs3745357 polymorphism and nonidiopathic pulmonary arterial hypertension susceptibility (niPAH). A total of 293 participants were included in the case-control study (117 patients and 176 healthy controls). The rs3745357 variant was discriminated by using cleaved amplification polymorphism (CAP) sequence-tagged site technology. Although the overall allele and genotype frequencies of rs3745357 in niPAH patients were close to those of the control group, significant differences have been identified when we further divided the niPAH patients into subgroups with or without coronary heart disease (CHD). Rs3745357 C allele frequency was significantly higher in niPAH patients without CHD history (p = 0.001), while the frequency was significantly lower in niPAH patients with CHD history (p = 0.017) when compared to control subjects. The distribution of genotype frequencies was also quite different. After adjustment by gender and age, significant differences were found between patients with CHD history and controls. The results suggest that the ARHGEF18 rs3745357 variant may be used as a marker for the genetic susceptibility to niPAH.

Figure 1

Lanes 1 and 5 are CC genotypes; lanes 2, 4, and 6 are CT genotypes; lane 3 is a TT genotype; and M is the DNA ladder from 100 to 600 bp.

Figure 2

Expression values of ARHGEF18 in rats under different processing methods. Data from GEO datasets for GSE72707 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72707).