Genetic insights on host and hepatitis B virus in liver diseases.

Abstract

Hepatitis B virus (HBV) infection is a major global health problem and many studies have underlined the importance of inter individual variability and somatic mutations during the clinical course of HBV infection. In recent years, high-throughput technologies have provided new possibilities to study the genetic basis of many diseases. We reviewed all literature available on genome-wide association studies (GWASs), whole genome, exome and RNA sequencing studies as well as studies on HBV infection and the pathogenesis of related liver disease. Many GWASs conclude that the genetic variants in the HLA region (HLA-DP, HLA-DQ, HLA-DR and MICA), KIF1B, DEPDC5 and PNPLA3 influence HBV infection, its clinical course and the response to hepatitis B vaccination. The next generation sequencing approach provides important clues on the mutational landscape of genes involved in signaling pathways in particular JAK/STAT, Wnt/β-catenin, p53 pathways and multiple chromatin regulator genes that significantly promote hepatocarcinogenesis. In addition, the hotspots of recurrent integrations of HBV-DNA into host chromosomes such as hTERT, PDGF receptor, MLL are involved in pathogenesis of hepatocellular carcinoma (HCC). Additionally, the transitions T>C/A>G, C>T/G>A, C>A/G>T and T>A/A>T remain specific for HCC induced by viral infection and the DNA methylation in the CpG island is proposed as a biomarker for HCC. We have described common mutations in the HBV genome (G1896A, rtM204V, rtM204I) which modulate the pathogenesis and carcinogenesis of the liver. Further GWASs in different ethnic groups and additional functional studies are required to warrant the significance of such defined genetic factors. Such findings continue to shape our understanding of the genetic architecture of host-virus interactions and provide new clues and directions in determining genetic markers that modulate HBV infection and related liver diseases. The studies using high-throughput technologies help identifying potential genetic threats however the utility of mutational information can be complex in predicting prognostic significance and shall pose challenges to its clinical implementation.