4.8 Article

Improving Orthologous Signal and Model Fit in Datasets Addressing the Root of the Animal Phylogeny

Journal

MOLECULAR BIOLOGY AND EVOLUTION
Volume 40, Issue 1, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/molbev/msac276

Keywords

phylogenomic reconstruction; animal phylogeny; orthology; model fitness; systematic biases

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There is conflicting evidence regarding whether sponges or comb jellies are at the root of the animal phylogeny, and dataset size, construction, and model fit all influence animal root inference.
There is conflicting evidence as to whether Porifera (sponges) or Ctenophora (comb jellies) comprise the root of the animal phylogeny. Support for either a Porifera-sister or Ctenophore-sister tree has been extensively examined in the context of model selection, taxon sampling, and outgroup selection. The influence of dataset construction is comparatively understudied. We re-examine five animal phylogeny datasets that have supported either root hypothesis using an approach designed to enrich orthologous signal in phylogenomic datasets. We find that many component orthogroups in animal datasets fail to recover major lineages as monophyletic with the exception of Ctenophora, regardless of the supported root. Enriching these datasets to retain orthogroups recovering >= 3 major lineages reduces dataset size by up to 50% while retaining underlying phylogenetic information and taxon sampling. Site-heterogeneous phylogenomic analysis of these enriched datasets recovers both Porifera-sister and Ctenophora-sister positions, even with additional constraints on outgroup sampling. Two datasets which previously supported Ctenophora-sister support Porifera-sister upon enrichment. All enriched datasets display improved model fitness under posterior predictive analysis. While not conclusively rooting animals at either Porifera or Ctenophora, we do see an increase in signal for Porifera-sister and a decrease in signal for Ctenophore-sister when data are filtered for orthologous signal. Our results indicate that dataset size and construction as well as model fit influence animal root inference.

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