68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.

Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable response to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved focusing only on motifs within unmethylated regions near the transcription start site and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.

Changes in gene expression are important for response to abiotic stress. Transcriptome profiling performed on maize inbred and hybrid genotypes subjected to heat or cold stress identifies many transcript abundance changes in response to these environmental conditions. Motifs that are enriched near differentially expressed genes were used to develop machine learning models to predict gene expression responses to heat or cold. The best performing models utilize the sequences both upstream and downstream of the transcription start site. Prediction accuracies could be improved using models developed for specific co-expression clusters compared to using all up- or down-regulated genes or by only using motifs within unmethylated regions. Comparisons of expression responses in multiple genotypes were used to identify genes with variable response and to identify cis- or trans-regulatory variation. Models trained on B73 data have lower performance when applied to Mo17 or W22, this could be improved by using models trained on data from all genotypes. However, the models have low accuracy for correctly predicting genes with variable responses to abiotic stress. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and provides a framework for developing models to predict expression response to abiotic stress across multiple genotypes.One sentence summaryTranscriptome profiling of maize inbred and hybrid seedlings subjected to heat or cold stress was used to identify key cis-regulatory elements and develop models to predict gene expression responses.