A combination of interferon-α (IFN-α) 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. 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. 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, β 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%. 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.