biostudies-literatureAment SANIDDK NIH HHSNIDA NIH HHSNIH HHSNIGMS NIH HHSE1801-10https://www.ebi.ac.uk/biostudies/studies/S-EPMC3387076biostudies-literatureUnknown109(26)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 behProceedings of the National Academy of Sciences of the United States of AmericaNew meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior.PMC3387076R01 GM085233R21 DA027548R01 DK0826051DP1OD0064161R01GM085233-01DP1 OD0064161R01DK082605-01A1Wheeler MMLe Conte YBlatti CARodriguez-Zas SLAmdam GVGuzman-Novoa EUribe-Rubio JLAlaux CSinha SPage RERobinson GEToth ALHunt GJAment SADegrandi-Hoffman GfalseNew meta-analysis tools reveal common transcriptional regulatory basis for multiple determinants of behavior.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 beh2012-01-01T00:00:00Z2012 Jun2026-05-01T23:10:39.902Z2026-04-07T17:25:29.845ZS-EPMC33870762269150110.1073/pnas.1205283109