Date of this Version
Advances in Neural Information Processing Systems
Statistical evolutionary models provide an important mechanism for describing and understanding the escape response of a viral population under a particular therapy. We present a new hierarchical model that incorporates spatially varying mutation and recombination rates at the nucleotide level. It also maintains sep- arate parameters for treatment and control groups, which allows us to estimate treatment effects explicitly. We use the model to investigate the sequence evolu- tion of HIV populations exposed to a recently developed antisense gene therapy, as well as a more conventional drug therapy. The detection of biologically rele- vant and plausible signals in both therapy studies demonstrates the effectiveness of the method.
Braunstein, A., Wei, Z., Jensen, S. T., & McAuliffe, J. D. (2008). A Spatially Varying Two-Sample Recombinant Coalescent, With Applications to HIV Escape Response. Advances in Neural Information Processing Systems, 21 Retrieved from https://repository.upenn.edu/statistics_papers/464
Date Posted: 27 November 2017