生物
表达数量性状基因座
基因调控网络
计算生物学
基因
数量性状位点
基因表达调控
转录组
基因表达
遗传学
调节顺序
转录因子
特质
基因型
单核苷酸多态性
程序设计语言
计算机科学
作者
Peng Zhou,Zhi Li,Erika Magnusson,Fabio Gomez-Cano,Peter A. Crisp,Jaclyn M. Noshay,Erich Grotewold,Candice N. Hirsch,Steven P. Briggs,Nathan M. Springer
出处
期刊:The Plant Cell
[Oxford University Press]
日期:2020-03-17
卷期号:32 (5): 1377-1396
被引量:47
摘要
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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