Phylogenomic comparative methods: accurate evolutionary inferences in the presence of gene tree discordance

Abstract

Phylogenetic comparative methods have long been a mainstay of evolutionary biology, allowing for inferences of the tempo and mode of trait evolution across species while accounting for their common ancestry. These analyses typically assume a single, bifurcating phylogenetic tree that describes the shared history among species. However, modern phylogenomic analyses have shown that genomes are often composed of a mosaic of different histories that can disagree both with the species tree and with each other--so-called discordant gene trees. These gene trees describe shared histories that are not captured by the species tree, and therefore that are unaccounted for in classic comparative approaches. The application of standard phylogenetic comparative methods to species histories containing discordance leads to incorrect inferences about the timing, direction, and rate of evolution. Here, we develop two approaches for incorporating gene tree histories into comparative methods: one involves constructing a fuller phylogenetic variance-covariance matrix that includes relationships not found in the species tree, and another that applies Felsensteins pruning algorithm over a set of gene trees to calculate trait histories and likelihoods. Both approaches are agnostic to the biological causes of gene tree discordance, which may include incomplete lineage sorting and introgression. Using simulation, we demonstrate that our new approaches generate much more accurate estimates of tree-wide rates of trait evolution than standard methods. We apply our methods to two clades of the wild tomato genus Solanum with varying rates of discordance, demonstrating the contribution of gene tree discordance to variation in a set of floral traits and the ability of our approaches to provide more accurate inferences. Our new approaches have the potential to be applied to a broad range of classic inference problems in phylogenetics, including ancestral state reconstruction and the inference of lineage-specific rate shifts.