A robust ANOVA approach to estimating a phylogeny from multiple genes

Ximing Xu, Katherine A. Dunn, Chris Field

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In this article, we address the issue of estimating the phylogenetic tree based on sequence data across a set of genes. Recognizing that the individual gene trees may not all share the same evolutionary history due to lateral gene transfer or differences in rates of evolution for instance, we develop a robust algorithm for tree estimation based on pairwise distances computed gene by gene. A robust analysis of variance (ANOVA) is used to combine the distances across all genes giving a summary distance for all genes. The tree can then be constructed using any distancemethod such as BIONJ. Using the weights from the robust ANOVA, we can then identify the outlying genes and taxa for further examination. As the method is based on distances, computation is much faster than maximum likelihood on the concatenated genes. It is also very straightforward to carry out a bootstrap analysis using standard methods for regression models. We test our methods in a comprehensive simulation study and apply them to three data sets recently analyzed in the literature.

Original languageEnglish
Pages (from-to)2186-2194
Number of pages9
JournalMolecular Biology and Evolution
Volume32
Issue number8
DOIs
Publication statusPublished - Aug 1 2015

Bibliographical note

Funding Information:
Acknowledgments The authors acknowledge the very helpful comments of the Associate Editor and the three referees. They thank Joseph Bielawski for valuable discussions. This research was funded in part by C.F.’s Natural Sciences and Engineering Research Council of Canada (RGPAS-429146) research grant. K.D. is funded by The Centre for Genomics and Evolutionary Bioinformatics (funded by the Tula Foundation), and The Canadian Institutes of Health Research (CMF-108026).

Publisher Copyright:
© The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.

ASJC Scopus Subject Areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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