1. Evolutionary Biology
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Genes associated with ant social behavior show distinct transcriptional and evolutionary patterns

  1. Alexander S Mikheyev
  2. Timothy Linksvayer  Is a corresponding author
  1. Okinawa Institute of Science and Technology, Japan
  2. University of Pennsylvania, United States
Research Article
  • Cited 51
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Cite this article as: eLife 2015;4:e04775 doi: 10.7554/eLife.04775

Abstract

Studies of the genetic basis and evolution of complex social behavior emphasize either conserved or novel genes. To begin to reconcile these perspectives, we studied how the evolutionary conservation of genes associated with social behavior depends on regulatory context, and whether genes associated with social behavior exist in distinct regulatory and evolutionary contexts. We identified modules of co-expressed genes associated with age-based division of labor between nurses and foragers in the ant Monomorium pharaonis, and we studied the relationship between molecular evolution, connectivity, and expression. Highly connected and expressed genes were more evolutionarily conserved, as expected. However, compared to the rest of the genome, forager-upregulated genes were much more highly connected and conserved, while nurse-upregulated genes were less connected and more evolutionarily labile. Our results indicate that the genetic architecture of social behavior includes both highly connected and conserved components as well as loosely connected and evolutionarily labile components.

Article and author information

Author details

  1. Alexander S Mikheyev

    Ecology and Evolution Unit, Okinawa Institute of Science and Technology, Okinawa, Japan
    Competing interests
    The authors declare that no competing interests exist.
  2. Timothy Linksvayer

    Department of Biology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    tlinks@sas.upenn.edu
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Philipp Khaitovich, Partner Institute for Computational Biology, China

Publication history

  1. Received: September 15, 2014
  2. Accepted: January 23, 2015
  3. Accepted Manuscript published: January 26, 2015 (version 1)
  4. Version of Record published: February 17, 2015 (version 2)

Copyright

© 2015, Mikheyev & Linksvayer

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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