Because of the stochastic way in which lineages sort during speciation, gene trees may differ in topology from each other and from species trees. Surprisingly, assuming that genetic lineages follow a coalescent model of within-species evolution, we find that for any species tree topology with five or more species, there exist branch lengths for which gene tree discordance is so common that the most likely gene tree topology to evolve along the branches of a species tree differs from the species phylogeny. This counterintuitive result implies that in combining data on multiple loci, the straightforward procedure of using the most frequently observed gene tree topology as an estimate of the species tree topology can be asymptotically guaranteed to produce an incorrect estimate. We conclude with suggestions that can aid in overcoming this new obstacle to accurate genomic inference of species phylogenies.
Different genomic regions evolving along the branches of a tree of species relationships can have different evolutionary histories. Consequently, estimates of species trees from genetic data may be influenced by the particular choice of genomic regions used in an analysis. Recent work has focused on circumventing this problem by combining information from multiple regions to attempt to produce accurate species tree estimates.
The authors show that the use of multiple genomic regions for species tree inference is subject to a surprising new difficulty, the problem of “anomalous gene trees.” Not only can individual genes or genomic regions have genealogical histories that differ in shape, or topology, from a species tree, the gene tree topology most likely to evolve can differ from the species tree topology. As a result, the “democratic vote” procedure of using the most frequently observed gene tree topology as an estimate of the species tree topology can converge on the wrong species tree as more genes are added. As it becomes more feasible to simultaneously investigate many regions of a genome, species tree inference algorithms will need to begin taking the problem of anomalous gene trees into consideration.