New meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior

Seth A. Ament, Charles A. Blatti, Cedric Alaux, Marsha M. Wheeler, Amy L. Toth, Yves Le Conte, Greg J. Hunt, Ernesto Guzmán-Novoa, Gloria DeGrandi-Hoffman, Jose Luis Uribe-Rubio, Gro Amdam, Robert Page, Sandra L. Rodriguez-Zas, Gene E. Robinson, Saurabh Sinha

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


A fundamental problem in meta-analysis is how to systematically combine information from multiple statistical tests to rigorously evaluate a single overarching hypothesis. This problem occurs in systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other complex phenotypes are influenced by intrinsic and environmental determinants that act on the transcriptome, but little is known about how these determinants interact at the molecular level. We developed an informatic technique that identifies statistically significant meta-associations between gene expression patterns and transcription factor combinations. Deploying this technique for brain transcriptome profiles from ca. 400 individual bees, we show that diverse determinants of behavior rely on shared combinations of transcription factors. These relationships were revealed only when we considered complex and variable regulatory rules, suggesting that these shared transcription factors are used in distinct ways by different determinants. This regulatory code would have been missed by traditional gene coexpression or cis-regulatory analytic methods. We expect that our meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.

Original languageEnglish (US)
Pages (from-to)E1801-E1810
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number26
StatePublished - Jun 26 2012


  • Honey bee
  • Transcriptional regulation

ASJC Scopus subject areas

  • General


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