Association between TGFB1 915G/C polymorphism and susceptibility to chronic hepatitis C virus infection: A meta-analysis

Abstract

The human transforming growth factor-β1 (TGF-β1) gene, namely TGFB1, contains several single-nucleotide polymorphisms (SNPs) and some of the polymorphic variants were shown to affect the TGF-β1 protein levels. A number of studies reported the association between 915G/C polymorphism and susceptibility to chronic hepatitis C virus (HCV) infection. However, the results were inconsistent. This meta-analysis was conducted to assess the association of TGFB1 915G/C polymorphism with susceptibility to chronic HCV infection. PubMed, ISI Web of Knowledge, ScienceDirect and Google Scholar databases were systematically searched up to August, 2013 to identify relevant studies. The pooled odds ratios (ORs) with their corresponding 95% confidence intervals (95% CIs) were calculated in 5 genetic comparison models (C vs. G, CC vs. GG, GC vs. GG, CC vs. GG+GC and CC+GC vs. GG). The Galbraith plot and subgroup analyses based on ethnicity, genotyping methods, sample size and fibrosis were performed to investigate possible sources of heterogeneity. A sensitivity analysis and assessment of publication bias were also conducted. Finally, 8 eligible case-control studies on TGFB1 915G/C polymorphism, including a total of 910 cases and 632 controls, were included in this meta-analysis. Overall, there was no evidence of any gene-disease association obtained from the subgroup analyses. Therefore, this meta-analysis demonstrated that there is no association between TGFB1 915G/C polymorphisms and susceptibility to chronic HCV infection.

Keywords: 915G/C; hepatitis C virus; meta-analysis; polymorphism; transforming growth factor β1.

Figure 1

Flowchart of the study selection process.

Figure 2

Identification of studies acting as sources of heterogeneity by the Galbraith plot under the CC+GC vs. GG genetic model. Each name represents a separate study for the indicated association. The random effects model was used.

Figure 3

Sensitivity analysis through exclusion of one study at a time to reflect the effect of individual datasets on the pooled ORs under the CC+GC vs. GG genetic model.