Answer to biological questions through hierarchical models

Due to recent advances in statistical methodology and to the explosion of genomic data, hierarchical models of molecular evolution and population genetics are increasingly being utilized. We have applied hierarchical models to a variety of evolutionary topics and we discuss these applications here with an emphasis on the important role played by hyperparameters. One use of hierarchical models is to study the stochastic process of changing evolutionary rates over time. By doing so, we can explore the extent of change in evolutionary rate, associations between selection pressure and functional diversification, and correlated patterns of evolution among genes. By bridging techniques from phylogenetics and population genetics, hierarchical models can also be applied to the analysis of serially collected viral sequences. Although population parameters themselves are already hyperparameters, modeling the distribution of adaptation processes among viral hosts necessitates an additional hierarchical level. In addition, hierarchical models are valuable when using genomic data to address ecological topics. In contrast to paternity analysis in humans where the goal is to identify an individual, the pertinent issue in conservation biology is often to study population parameters such as the effective distance of pollen dispersal.