Assessing the Validity of the Fixed Tree Topology Assumption in Phylodynamic Inference

Abstract

Abstract Fixed tree topologies are widely used in phylodynamic analyses to reduce computational burden, yet the consequences of this assumption remain insufficiently understood. Here, we systematically assess the impact of various fixed-topology strategies on phylogenetic and phylodynamic parameter estimates across a diverse set of viral datasets. We compare fully Bayesian joint inference with fixed-topology strategies, including conditioning on maximum likelihood trees subsequently dated with LSD or TreeTime. Our analyses show that global parameters of the substitution and site models are largely robust to the fixed-topology assumption, whereas parameters that depend on the temporal structure of the tree, such as molecular clock rates, node ages, and demographic histories, can exhibit substantial systematic differences in their estimates. We do treat unconstrained Bayesian analyses as the reference, although we recognize that these too are model-based approximations. Nevertheless, our results highlight serious discordance associated with fixing the topology and underscore the need for faster, time-aware methods that simultaneously integrate topology and parameter estimation. These findings raise important questions about the balance between computational efficiency and inferential accuracy in phylodynamic studies. phylodynamic, tree topology, Bayesian inference, BEAST