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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 ? 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. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. However, it would be extremely inefficient to test all the 10 million common SNPs for an association study. Here we review haplotype analysis and pattern-recognition techniques to systematically select candidate SNPs for candidate-gene association studies in pharmacogenomics. First, we survey linkage disequilibrium methods to identify tag SNPs and explore the use of haplotypes as genetic markers that are correlated and associated with drug efficacy. Secondly, we investigate pattern-recognition algorithms and statistical analyses to assess drug efficacy based on SNPs and other factors. Finally, we study pattern-recognition approaches to evaluate the epistasis among genes and SNPs. These techniques may provide tools for clinical association studies and help find genes/SNPs involved in responses to therapeutic drugs or adverse drug reactions. [ABSTRACT FROM AUTHOR]

Interferon taken alone or in combination with ribavirin can be used for the treatment of persons with chronic hepatitis C. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the treatments. In this work, our goal is to develop a prediction model resulting from the analysis of chronic hepatitis C patients' single nucleotide polymorphisms, viral genotype, viral load, age and gender, to predict the responsiveness of interferon combination treatment. Materials and methods: We collected blood samples from 523 chronic hepatitis C patients that had received interferon and ribavirin combination therapy. Based on the current treatment strategy for chronic hepatitis C patients, we focused our search for candidate genes involved in pathways related to interferon signaling and immunomodulation. With artificial neural network algorithms, we then developed pattern recognition methodologies to achieve predictions among the patients. The artificial neural network model was trained by an algorithm with an adaptive momentum and learning rate. Results: There were seven single nucleotide polymorphisms selected from six candidate genes including adenosine deaminase-RNA-specific, caspase 5, interferon consensus sequence binding protein 1, interferon-induced protein 44, phosphoinositide-3-kinase catalytic γ polypeptide and transporter 2 ATP-binding cassette subfamily B genes. We further applied the artificial neural network algorithms with these seven single nucleotide polymorphisms, viral genotype, viral load, age and gender information to build tools for predicting the responsiveness of interferon. Based on the fivefold cross-validation method to evaluate the performance, the model achieved a high success rate of prediction. Conclusion: We demonstrated that a trained artificial neural network model is a promising method for providing the inference from factors such as single nucleotide polymorphisms, viral genotype, viral load, age and gender to the responsiveness of interferon. [ABSTRACT FROM AUTHOR]