After decades of debate, a mostly satisfactory resolution of relationships among the 11 recognized holometabolan orders of insects has been reached based on nuclear genes, resolving one of the most substantial branche sof the tree-of-life,but the relationships are still not well established with mitochondrial genome data. The main reasons have been the absence of sufficient data in several orders and lack of appropriate phylogenetic methods that avoid the systematic errors from compositional and mutational biases in insect mitochondrial genomes. In this study, we assembled the richest taxon sampling of Holometabola to date (199 species in 11 orders), and analyzed both nucleotide and amino acid data sets using several methods. We find the standard Bayesian inference and maximum-likelihood analyses were strongly affected by systematic biases, but the site-heterogeneous mixture model implemented in PhyloBayes avoided the false grouping of unrelated taxa exhibiting similar base composition and accelerated evolutionary rate. The inclusion of rRNA genes and removal of fast-evolving sites with the observed variability sorting method for identifying sites deviating from the mean rates improved the phylogenetic inferences under a site-heterogeneous model, correctly recovering most deep branches of the Holometabola phylogeny. We suggest that the use of mitochondrial genome data for resolving deep phylogenetic relationships requires an assessment of the potential impact of substitutional saturation and compositional biases through data deletion strategies and by using site-heterogeneous mixture models. Our study suggests a practical approach for how to use densely sample dmitochondrial genome data in phylogenetic analyses.
|Number of pages||16|
|Journal||Genome Biology and Evolution|
|State||Published - May 2016|
Bibliographical noteFunding Information:
The authors thank Dr Yang Liu (University of Connecticut, USA) for assistance with phylogenetic analysis and Dr Peter Foster (Natural History Museum, London, UK) for discussing likelihood models. This work was supported by grants from the National Basic Research Program of China (grant no. 2013CB127600), the National Natural Science Foundation of China (grant nos. 31420103902, 31372229, and 31401991), the Beijing Natural Science Foundation (grant nos. 6144027 and 6152016), and the Chinese Universities Scientific Fund (grant nos. 2015NX001, 2016QC025, and 2016QC072).
© The Author 2016.
- Compositional bias
- Holometabola phylogeny
- Mitochondrial phylogenomics
- Rate variation
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics