Gene-gene and gene-environment interactions in interferon therapy for chronic hepatitis C.

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 gamma 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.

Pattern-recognition techniques with haplotype analysis in pharmacogenomics.

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.

An artificial neural network approach to the drug efficacy of interferon treatments.

INTRODUCTION: 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 gamma 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.

An algorithm for model construction and its applications to pharmacogenomic studies.

A model depicts the relationship between clinical phenotypes and genotypes on a set of genetic polymorphisms. After the model is constructed and validated, it may be used to predict clinical phenotypes such as traits of complex diseases. A pharmacogenomic model is used to predict the efficacies or adverse drug reactions of a medication. The construction of a model is a challenging task. This is because a single-locus polymorphism does not contain enough information to stratify patients in general, given the complex biological mechanisms involved. An exhaustive search for the correct combination of genotypes across multiple loci is, however, computationally infeasible. We are, thus, motivated to propose a novel algorithm for the construction of models using the multiple single-nucleotide polymorphism (SNP) information in diplotype forms. This algorithm utilizes the techniques of genetic algorithms and Boolean algebra (GABA). The proposed algorithm is tested on simulated data, as well as real genotype datasets of chronic hepatitis C patients treated with interferon-combined therapy. A model for predicting the treatment efficacy is constructed and validated. The results showed that the proposed algorithm is very effective in deriving models comprising multiple SNPs.

Genetic predisposition of responsiveness to therapy for chronic hepatitis C.

BACKGROUND: A combination of interferon-alpha (IFN-alpha) and ribavirin has been the choice for treating chronic hepatitis C (CHC) patients. It achieves an overall sustained response rate of approximately 50%; however, the treatment takes 6-12 months and often brings significant adverse reactions to some patients. It would therefore be beneficial to include a pretreatment evaluation in order to maximize the efficacy. In addition to viral genotypes, we hypothesize that patient genotypes might also be useful for the prediction of treatment response. METHODS: We retrospectively analyzed the genetic differences of CHC patients that are associated with IFN/ribavirin responses. The DNA polymorphisms among 195 sustained responders and 122 nonresponders of CHC patients of Taiwanese origin were compared. Statistical and algorithmic methods were used to select the genes associated with drug response and single nucleotide polymorphisms (SNPs) that permitted the construction of a predictive model. RESULTS: Association studies and haplotype reconstruction revealed selection of seven genes: adenosine deaminase, RNA-specific (ADAR), caspase 5, apoptosis-related cysteine peptidase (CASP5), fibroblast growth factor 1 (FGF1), interferon consensus sequence binding protein 1 (ICSBP1), interferon-induced protein 44 (IFI44), transporter 2, ATP-binding cassette, subfamily B (TAP2) and transforming growth factor, beta receptor associated protein 1 (TGFBRAP1) for the responsiveness trait. Based on confirmed linkage disequilibrium block in the population, a minimal set of 26 SNPs in the seven selected genes was inferred. To predict treatment outcome, a multiple logistic regression model was constructed using susceptible genotypes of SNPs. The performance of the resultant model had a sensitivity of 68.2% and specificity of 60.7% on 317 CHC patients treated with IFN-combined therapy. In addition, a prediction model with both the host genetic and viral genotype information was also constructed which enhanced the performance with a sensitivity of 80.7% and specificity of 67.2%. CONCLUSIONS: A genetic model was constructed to predict outcomes of the combination therapy in CHC patients with high sensitivity and specificity. Results also provide a possible process of selecting targets for predicting treatment outcomes and the basis for developing pharmacogenetic tests.

Analysis of epistasis for diabetic nephropathy among type 2 diabetic patients.

Diabetic nephropathy (DN) is one of the most serious complications of diabetes, accounting for the majority of patients with end-stage renal disease. The molecular pathogenesis of DN involves multiple pathways in a complex, partially resolved manner. The paper presents an exploratory epistatic study for DN. Association analysis were performed on 231 SNP loci in a cohort of 264 type 2 diabetes patients, followed by the epistasis analysis using the multifactor dimensionality reduction and the genetic algorithm with Boolean algebra. A two-locus epistatic effect of EGFR and RXRG was identified, with a cross-validation consistency of 91.7%.