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Plant Physiol. 2017 Oct;175(2):746-757. doi: 10.1104/pp.17.00694. Epub 2017 Aug 14.

Transcriptome Association Identifies Regulators of Wheat Spike Architecture.

Author information

1
State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
2
Department of Crop Genomics and Bioinformatics, College of Agronomy and Biotechnology, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China.
3
University of Chinese Academy of Sciences, Beijing 100049, China.
4
State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
5
Department of Crop Genomics and Bioinformatics, College of Agronomy and Biotechnology, National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China yljiao@genetics.ac.cn sysbio@cau.edu.cn.
6
State Key Laboratory of Plant Genomics and National Center for Plant Gene Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China yljiao@genetics.ac.cn sysbio@cau.edu.cn.

Erratum in

Abstract

The architecture of wheat (Triticum aestivum) inflorescence and its complexity is among the most important agronomic traits that influence yield. For example, wheat spikes vary considerably in the number of spikelets, which are specialized reproductive branches, and the number of florets, which are spikelet branches that produce seeds. The large and repetitive nature of the three homologous and highly similar subgenomes of wheat has impeded attempts at using genetic approaches to uncover beneficial alleles that can be utilized for yield improvement. Using a population-associative transcriptomic approach, we analyzed the transcriptomes of developing spikes in 90 wheat lines comprising 74 landrace and 16 elite varieties and correlated expression with variations in spike complexity traits. In combination with coexpression network analysis, we inferred the identities of genes related to spike complexity. Importantly, further experimental studies identified regulatory genes whose expression is associated with and influences spike complexity. The associative transcriptomic approach utilized in this study allows rapid identification of the genetic basis of important agronomic traits in crops with complex genomes.

PMID:
28807930
PMCID:
PMC5619896
DOI:
10.1104/pp.17.00694
[Indexed for MEDLINE]
Free PMC Article

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