Diverse phylogenomic datasets uncover a concordant scenario of laurasiatherian interordinal relationships.

Resolving the interordinal relationships in the mammalian superorder Laurasiatheria has been among the most intractable problems in higher-level mammalian systematics, with many conflicting hypotheses having been proposed. The present study collected three different sources of genome-scale data with comprehensive taxon sampling of laurasiatherian species, including two protein-coding datasets (4,186 protein-coding genes for an amino acid dataset comprising 2,761,247 amino acid residues and a nucleotide dataset comprising 5,516,340 nucleotides from 1st and 2nd codon positions), an intronic dataset (1,210 introns comprising 1,162,723 nucleotides) and an ultraconserved elements (UCEs) dataset (1,246 UCEs comprising 1,946,472 nucleotides) from 40 species representing all six laurasiatherian orders and 7 non-laurasiatherian outgroups. Remarkably, phylogenetic trees reconstructed with the four datasets using different tree-building methods (RAxML, FastTree, ASTRAL and MP-EST) all supported the relationship (Eulipotyphla, (Chiroptera, ((Carnivora, Pholidota), (Cetartiodactyla, Perissodactyla)))). We find a resolution of interordinal relationships of Laurasiatheria among all types of markers used in the present study, and the likelihood ratio tests for tree comparisons confirmed that the present tree topology is the optimal hypothesis compared to other examined hypotheses. Jackknifing subsampling analyses demonstrate that the results of laurasiatherian tree reconstruction varied with the number of loci and ordinal representatives used, which are likely the two main contributors to phylogenetic disagreements of Laurasiatheria seen in previous studies. Our study provides significant insight into laurasiatherian evolution, and moreover, an important methodological strategy and reference for resolving phylogenies of adaptive radiation, which have been a long-standing challenge in the field of phylogenetics.

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.