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There are limited data regarding the real world effectiveness of direct acting antivirals (DAA) for the therapy of chronic genotype 3 hepatitis C virus (HCV) infection. All HCV genotype 3 infected patients from the German hepatitis C cohort (GECCO), which is a prospective database of nine German hepatitis C treatment centers, were included in the study. Three hundred forty-two chronically infected HCV genotype 3 patients were analyzed (253 males [74.0%], mean age 47.3 years, 127 cirrhotic patients [37.1%] mostly with Child A cirrhosis, 113 treatment experienced patients [37.1%], 38 HCV/HIV co-infected patients [11.1%]). SVR12 rates in the "intention-to-treat" analysis were as follows: sofosbuvir/ribavirin 69.4% (75/108), sofosbuvir/peginterferon/ribavirin 80.6% (58/72), sofosbuvir/daclatasvir ± ribavirin for 12 weeks 88.3% (53/63), sofosbuvir/daclatasvir ± ribavirin for 24 weeks 79.3% (23/29), sofosbuvir/ledipasvir ± ribavirin for 12 weeks 71.4% (10/14), and sofosbuvir/ledipasvir ± ribavirin for 24 weeks 86.7% (26/30). Forty patients were lost to follow-up, 23 patients had a relapse, 4 patients stopped treatment prematurely and 1 patient died. Female sex (P = 0.038) and treatment with two different DAAs (P = 0.05) were predictors for SVR12 in the multivariate analysis. In conclusion, sofosbuvir/daclatasvir ± ribavirin for 12 weeks and sofosbuvir/ledipasvir ± ribavirin for 24 weeks are effective for the treatment of HCV genotype 3 infected patients including cirrhotic, treatment-experienced or HIV/HCV co-infected patients., (© 2017 Wiley Periodicals, Inc.)

Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.