Regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent a map of potential transcriptional regulation. A consistent analysis of a large number of public maize transcriptome datasets including >6000 RNA-Seq samples was used to generate 45 co-expression based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, tissue-and-genotype, etc). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our co-expression based GRNs to accurately predict co-varying TF-target relationships in natural variation datasets we found that presence/absence expression changes - rather than quantitative changes - of a TF, are more likely to associate with target gene changes. Integrating information from our TF-target predictions and previous eQTL mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning span a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species for detecting putative regulators of important plant pathways and providing potential targets for breeding or biotechnology applications.