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J Exp Bot. 2014 Mar;65(4):1069-79. doi: 10.1093/jxb/ert464. Epub 2014 Jan 13.

An expression quantitative trait loci-guided co-expression analysis for constructing regulatory network using a rice recombinant inbred line population.

Author information

1
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, PR China.

Abstract

The ability to reveal the regulatory architecture of genes at the whole-genome level by constructing a regulatory network is critical for understanding the biological processes and developmental programmes of organisms. Here, we conducted an eQTL-guided function-related co-expression analysis to identify the putative regulators and construct gene regulatory network. We performed an eQTL analysis of 210 recombinant inbred lines (RILs) derived from a cross between two indica rice lines, Zhenshan 97 and Minghui 63, the parents of an elite hybrid, using data obtained by hybridizing RNA samples of flag leaves at the heading stage with Affymetrix whole-genome arrays. Making use of an ultrahigh-density single-nucleotide polymorphism bin map constructed by population sequencing, 13 647 eQTLs for 10 725 e-traits were detected, comprising 5079 cis-eQTLs (37.2%) and 8568 trans-eQTLs (62.8%). The analysis revealed 138 trans-eQTLs hotspots, each of which apparently regulates the expression variations of many genes. Co-expression analysis of functionally related genes within the framework of regulator-target relationships outlined by the eQTLs led to the identification of putative regulators in the system. The usefulness of the strategy was demonstrated with the genes known to be involved in flowering. We also applied this strategy to the analysis of QTLs for yield traits, which also suggested likely candidate genes. eQTL-guided co-expression analysis may provide a promising solution for outlining a framework for the complex regulatory network of an organism.

KEYWORDS:

Gene expression; network construction; phenotype; quantitative genomics; regulation.

PMID:
24420573
PMCID:
PMC3935569
DOI:
10.1093/jxb/ert464
[Indexed for MEDLINE]
Free PMC Article
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