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Supporting information for: The frequency and topology of pseudoorthologs

Cite this dataset

Smith, Megan; Hahn, Matthew (2021). Supporting information for: The frequency and topology of pseudoorthologs [Dataset]. Dryad.


Phylogenetics has long relied on the use of orthologs, or genes related through speciation events, to infer species relationships. However, identifying orthologs is difficult because gene duplication can obscure relationships among genes. Researchers have been particularly concerned with the insidious effects of pseudoorthologs—duplicated genes that are mistaken for orthologs because they are present in a single copy in each sampled species. Because gene tree topologies of pseudoorthologs may differ from the species tree topology, they have often been invoked as the cause of counterintuitive results in phylogenetics. Despite these perceived problems, no previous work has calculated the probabilities of pseudoortholog topologies, or has been able to circumscribe the regions of parameter space in which pseudoorthologs are most likely to occur. Here, we introduce a model for calculating the probabilities and branch lengths of orthologs and pseudoorthologs, including concordant and discordant pseudoortholog topologies, on a rooted three-taxon species tree. We show that the probability of orthologs is high relative to the probability of pseudoorthologs across reasonable regions of parameter space. Furthermore, the probabilities of the two discordant topologies are equal and never exceed that of the concordant topology, generally being much lower. We describe the species tree topologies most prone to generating pseudoorthologs, finding that they are likely to present problems to phylogenetic inference irrespective of the presence of pseudoorthologs. Overall, our results suggest that pseudoorthologs are less of a problem for phylogenetics than currently believed, which should allow researchers to greatly increase the number of genes used in phylogenetic inference.


National Science Foundation, Award: DBI-2009989

National Science Foundation, Award: DEB-1936187