Gene–gene and Gene–Environment Interactions in Interferon Therapy for Chronic Hepatitis C

Abstract

Introduction: In studies of pharmacogenomics, it is essential to address gene–gene and gene–environment interactions to describe complex traits involving pharmacokinetic and pharmacodynamic mechanisms. In this work, our goal is to detect gene–gene and gene–environment interactions resulting from an analysis of chronic hepatitis C patients‘ clinical factors including SNPs, viral genotype, viral load, age and gender. Materials & Methods: We collected blood samples from 523 chronic hepatitis C patients who had received interferon and ribavirin combination therapy. Based on the treatment strategy for chronic hepatitis C patients, we focused our search for candidate genes involved in pathways related to interferon signaling and immunomodulation. To investigate gene–gene and gene–environment interactions, we implemented an artificial neural network-based method for identifying significant interactions between clinical factors with the fivefold crossvalidation method and permutation tests. The artificial neural network model was trained by an algorithm with an adaptive momentum and learning rate. Results: A total of 20 SNPs were selected from six candidate genes including adenosine deaminase-RNA-specific (ADAR), caspase 5 (CASP5), interferon consensus sequence binding protein 1 (ICSBP1), interferon-induced protein 44 (IFI44), phosphoinositide-3-kinase catalytic γ polypeptide (PIK3CG), and transporter 2 ATP-binding cassette subfamily B (TAP2) genes. By applying our artificial neural network-based approach, IFI44 was found in the significant two-locus, three-locus and four-locus gene–gene effect models, as well as in the significant two-factor and three-factor gene–environment effect models. Furthermore, viral genotype remained in the best two-factor, three-factor and four-factor gene–environment models. These results support the hypothesis that IFI44 and viral genotype may play a role in the pharmacogenomics of interferon treatment. In addition, our approach identified a panel of ten clinical factors that may be more significant than the others for further study. Conclusion: We demonstrated that our artificial neural network-based approach is a promising method to assess the gene–gene and gene–environment interactions for interferon and ribavirin combination treatment in chronic hepatitis C patients by using clinical factors such as SNPs, viral genotype, viral load, age and gender.

Keywords: :

• chronic hepatitis C
• gene–environment interactions
• gene–gene interactions
• interferon
• pharmacogenomics
• ribavirin
• SNPs

Acknowledgement

The authors extend their sincere appreciation to Vita Genomics, Inc. for funding the research and to Pei-Jer Chen of the Hepatitis Research Center, National Taiwan University, You-Chen Chao of the Tri-Service General Hospital, Ming-Lung Yu of the Kaohsiung Medical University Hospital, and Chuan-Mo Lee of the Kaohsiung Chang-Gung Memorial Hospital for research collaboration. The authors also would like to thank Charles Wang for helpful suggestions.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The author states that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.