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Abstract The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). 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 coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 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.
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Effects of hybridization and gene flow on gene co-expression networks
Abstract Gene co-expression networks are a widely used tool for summarizing transcriptomic variation between individuals, and for inferring the transcriptional regulatory pathways that mediate genotype–phenotype relationships. However, these co-expression networks must be interpreted with caution, as they can arise from multiple processes. Here, we investigate one such process, using simulations to demonstrate that hybridization and gene flow between populations can greatly modify co-expression networks. Admixture between populations produces correlated expression between genes experiencing linkage disequilibrium. This correlated expression does not reflect functional relationships between genes but rather depends on migration rates and physical linkage on chromosomes. Given the prevalence of gene flow and hybridization between divergent populations in nature, these introgression effects likely represent a significant force in network evolution, even in populations where hybridization is historical rather than contemporary. These findings emphasize the critical importance of considering both evolutionary history and genomic architecture when analyzing gene co-expression networks in natural populations.
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- PAR ID:
- 10597607
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- GENETICS
- Volume:
- 230
- Issue:
- 2
- ISSN:
- 1943-2631
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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