Large-scale metabolite quantitative trait locus analysis provides new insights for high-quality maize improvement

Kun Li, Weiwei Wen, Saleh Alseekh, Xiaohong Yang, Huan Guo, Wenqiang Li, Luxi Wang, Qingchun Pan, Wei Zhan, Jie Liu, Yanhua Li, Xiao Wu, Yariv Brotman, Lothar Willmitzer, Jiansheng Li*, Alisdair R. Fernie, Jianbing Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC2F7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling-stage leaf, grouting-stage leaf, young kernel and mature kernel) with clear tissue-specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi-tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs.

Original languageEnglish
Pages (from-to)216-230
Number of pages15
JournalPlant Journal
Volume99
Issue number2
DOIs
StatePublished - Jul 2019
Externally publishedYes

Funding

FundersFunder number
German Federal Ministry of Research and Education
Huazhong Agricultural University Scientific and Technological Self-Innovation Foundation
Horizon 2020 Framework Programme739582
National Natural Science Foundation of China31525017
Bundesministerium für Bildung und Forschung837031B0205A
Horizon 2020836
Huazhong Agricultural University
National Key Research and Development Program of China2016YFD010100303

    Keywords

    • genetic basis
    • maize
    • primary metabolism
    • quantitative trait locus
    • teosinte

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