A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios

Abstract

AbstractThe reconstruction of ancestral scenarios is widely used to study the evolution of characters along a phylogenetic tree. In the likelihood framework one commonly uses the marginal posterior probabilities of the character states, and the joint reconstruction of the most likely scenario. Both approaches are somewhat unsatisfactory. Marginal reconstructions provide users with state probabilities, but these are difficult to interpret and visualize, while joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences.We propose a simple and fast approach, which is in between these two extremes. We use decision-theory concepts and the Brier criterion to associate each node in the tree to a set of likely states. A unique state is predicted in the tree regions with low uncertainty, while several states are predicted in the uncertain regions, typically around the tree root. To visualize the results, we cluster the neighboring nodes associated to the same states and use graph visualization tools. The method is implemented in the PastML program and web server.The results on simulated data consistently show the accuracy and robustness of the approach. The method is applied to large tree comprising 3,619 sequences from HIV-1M subtype C sampled worldwide, which is processed in a few minutes. Results are very convincing: we retrieve and visualize the main transmission routes of HIV-1C; we demonstrate that drug resistance mutations mostly emerge independently under treatment pressure, but some resistance clusters are found, corresponding to transmissions among untreated patients.