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Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights

Author

Listed:
  • Weiwei Wen

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Dong Li

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Xiang Li

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Yanqiang Gao

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Wenqiang Li

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Huihui Li

    (Institute of Crop Science, CIMMYT China Office, Chinese Academy of Agricultural Sciences)

  • Jie Liu

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Haijun Liu

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Wei Chen

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Jie Luo

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

  • Jianbing Yan

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University)

Abstract

Plants produce a variety of metabolites that have a critical role in growth and development. Here we present a comprehensive study of maize metabolism, combining genetic, metabolite and expression profiling methodologies to dissect the genetic basis of metabolic diversity in maize kernels. We quantify 983 metabolite features in 702 maize genotypes planted at multiple locations. We identify 1,459 significant locus–trait associations (P≤1.8 × 10−6) across three environments through metabolite-based genome-wide association mapping. Most (58.5%) of the identified loci are supported by expression QTLs, and some (14.7%) are validated through linkage mapping. Re-sequencing and candidate gene association analysis identifies potential causal variants for five candidate genes involved in metabolic traits. Two of these genes were further validated by mutant and transgenic analysis. Metabolite features associated with kernel weight could be used as biomarkers to facilitate genetic improvement of maize.

Suggested Citation

  • Weiwei Wen & Dong Li & Xiang Li & Yanqiang Gao & Wenqiang Li & Huihui Li & Jie Liu & Haijun Liu & Wei Chen & Jie Luo & Jianbing Yan, 2014. "Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights," Nature Communications, Nature, vol. 5(1), pages 1-10, May.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4438
    DOI: 10.1038/ncomms4438
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    Cited by:

    1. Wenjin Hu & Xinli Pan & Fengfeng Li & Wubei Dong, 2018. "UPLC-QTOF-MS metabolomics analysis revealed the contributions of metabolites to the pathogenesis of Rhizoctonia solani strain AG-1-IA," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-17, February.

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