Maximum likelihood inference of reticulate evolutionary histories.

Yun Yu, Jianrong Dong, Kevin J Liu, Luay Nakhleh
Author Information
  1. Yun Yu: Departments of Computer Science and nakhleh@cs.rice.edu yy9@cs.rice.edu.
  2. Jianrong Dong: Departments of Computer Science and.
  3. Kevin J Liu: Departments of Computer Science and Ecology and Evolutionary Biology, Rice University, Houston, TX 77005.
  4. Luay Nakhleh: Departments of Computer Science and Ecology and Evolutionary Biology, Rice University, Houston, TX 77005 nakhleh@cs.rice.edu yy9@cs.rice.edu.

Abstract

Hybridization plays an important role in the evolution of certain groups of organisms, adaptation to their environments, and diversification of their genomes. The evolutionary histories of such groups are reticulate, and methods for reconstructing them are still in their infancy and have limited applicability. We present a maximum likelihood method for inferring reticulate evolutionary histories while accounting simultaneously for incomplete lineage sorting. Additionally, we propose methods for assessing confidence in the amount of reticulation and the topology of the inferred evolutionary history. Our method obtains accurate estimates of reticulate evolutionary histories on simulated datasets. Furthermore, our method provides support for a hypothesis of a reticulate evolutionary history inferred from a set of house mouse (Mus musculus) genomes. As evidence of hybridization in eukaryotic groups accumulates, it is essential to have methods that infer reticulate evolutionary histories. The work we present here allows for such inference and provides a significant step toward putting phylogenetic networks on par with phylogenetic trees as a model of capturing evolutionary relationships.

Keywords

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Grants

  1. R01 LM009494/NLM NIH HHS
  2. T15 LM007093/NLM NIH HHS
  3. R01LM009494/NLM NIH HHS
  4. T15LM007093/NLM NIH HHS

MeSH Term

Algorithms
Animals
China
Computer Simulation
Eukaryota
Europe
Evolution, Molecular
Genetic Speciation
Haplotypes
Kazakhstan
Likelihood Functions
Mice
Models, Genetic
Mutation Rate
Phylogeny
Sequence Alignment
Sequence Homology, Nucleic Acid

Word Cloud

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