Research article

Meta-analysis of genome-wide expression patterns associated with behavioral maturation in honey bees

Heather A Adams^1,2 , Bruce R Southey^3,4 , Gene E Robinson^2,5,6 and Sandra L Rodriguez-Zas^1,2,5,7

¹Department of Animal Sciences, University of Illinois, Urbana, Illinois 61801, USA

²Institute for Genomic Biology, University of Illinois, Urbana, Illinois 61801, USA

³Department of Chemistry, University of Illinois, Urbana, Illinois 61801, USA

⁴Department of Computer Science, University of Illinois, Urbana, Illinois 61801, USA

⁵Neuroscience Program, University of Illinois, Urbana, Illinois 61801, USA

⁶Department of Entomology, University of Illinois, Urbana, Illinois 61801, USA

⁷Department of Statistics, University of Illinois, Urbana, Illinois 61801, USA

author email corresponding author email

BMC Genomics 2008, 9:503doi:10.1186/1471-2164-9-503

Published:	24 October 2008

Abstract

Background

The information from multiple microarray experiments can be integrated in an objective manner via meta-analysis. However, multiple meta-analysis approaches are available and their relative strengths have not been directly compared using experimental data in the context of different gene expression scenarios and studies with different degrees of relationship. This study investigates the complementary advantages of meta-analysis approaches to integrate information across studies, and further mine the transcriptome for genes that are associated with complex processes such as behavioral maturation in honey bees. Behavioral maturation and division of labor in honey bees are related to changes in the expression of hundreds of genes in the brain. The information from various microarray studies comparing the expression of genes at different maturation stages in honey bee brains was integrated using complementary meta-analysis approaches.

Results

Comparison of lists of genes with significant differential expression across studies failed to identify genes with consistent patterns of expression that were below the selected significance threshold, or identified genes with significant yet inconsistent patterns. The meta-analytical framework supported the identification of genes with consistent overall expression patterns and eliminated genes that exhibited contradictory expression patterns across studies. Sample-level meta-analysis of normalized gene-expression can detect more differentially expressed genes than the study-level meta-analysis of estimates for genes that were well described by similar model parameter estimates across studies and had small variation across studies. Furthermore, study-level meta-analysis was well suited for genes that exhibit consistent patterns across studies, genes that had substantial variation across studies, and genes that did not conform to the assumptions of the sample-level meta-analysis. Meta-analyses confirmed previously reported genes and helped identify genes (e.g. Tomosyn, Chitinase 5, Adar, Innexin 2, Transferrin 1, Sick, Oatp26F) and Gene Ontology categories (e.g. purine nucleotide binding) not previously associated with maturation in honey bees.

Conclusion

This study demonstrated that a combination of meta-analytical approaches best addresses the highly dimensional nature of genome-wide microarray studies. As expected, the integration of gene expression information from microarray studies using meta-analysis enhanced the characterization of the transcriptome of complex biological processes.