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Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice

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  • Wanneng Yang

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology
    College of Engineering, Huazhong Agricultural University)

  • Zilong Guo

    (National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University)

  • Chenglong Huang

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology)

  • Lingfeng Duan

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology
    College of Engineering, Huazhong Agricultural University)

  • Guoxing Chen

    (MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, Huazhong Agricultural University)

  • Ni Jiang

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology)

  • Wei Fang

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology)

  • Hui Feng

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology)

  • Weibo Xie

    (National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University)

  • Xingming Lian

    (National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University)

  • Gongwei Wang

    (National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University)

  • Qingming Luo

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology)

  • Qifa Zhang

    (National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University)

  • Qian Liu

    (Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology
    MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology)

  • Lizhong Xiong

    (National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University)

Abstract

Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

Suggested Citation

  • Wanneng Yang & Zilong Guo & Chenglong Huang & Lingfeng Duan & Guoxing Chen & Ni Jiang & Wei Fang & Hui Feng & Weibo Xie & Xingming Lian & Gongwei Wang & Qingming Luo & Qifa Zhang & Qian Liu & Lizhong , 2014. "Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6087
    DOI: 10.1038/ncomms6087
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    Cited by:

    1. Alejandro del Pozo & Nidia Brunel-Saldias & Alejandra Engler & Samuel Ortega-Farias & Cesar Acevedo-Opazo & Gustavo A. Lobos & Roberto Jara-Rojas & Marco A. Molina-Montenegro, 2019. "Climate Change Impacts and Adaptation Strategies of Agriculture in Mediterranean-Climate Regions (MCRs)," Sustainability, MDPI, vol. 11(10), pages 1-16, May.
    2. Xiaohang Ma & Yongze Wu & Jingfang Shen & Lingfeng Duan & Ying Liu, 2021. "ML-LME: A Plant Growth Situation Analysis Model Using the Hierarchical Effect of Fractal Dimension," Mathematics, MDPI, vol. 9(12), pages 1-17, June.
    3. Tae-Ho Ham & Yebin Kwon & Yoonjung Lee & Jisu Choi & Joohyun Lee, 2021. "Genome-Wide Association Study Reveals the Genetic Basis of Cold Tolerance in Rice at the Seedling Stage," Agriculture, MDPI, vol. 11(4), pages 1-13, April.
    4. San Mar Lar & Jeonghwan Seo & Seong-Gyu Jang & Hongjia Zhang & Ah-Rim Lee & Fang-Yuan Cao & Ja-Hong Lee & Na-Eun Kim & Yoonjung Lee & Yong-Jin Park & Joohyun Lee & Soon-Wook Kwon, 2021. "Genome-Wide Association Study for Detecting Salt-Tolerance Loci and Candidate Genes in Rice," Agriculture, MDPI, vol. 11(11), pages 1-17, November.
    5. Wei Wang & Xue Gao & Yukun Cheng & Yi Ren & Zhihui Zhang & Rui Wang & Junmei Cao & Hongwei Geng, 2022. "QTL Mapping of Leaf Area Index and Chlorophyll Content Based on UAV Remote Sensing in Wheat," Agriculture, MDPI, vol. 12(5), pages 1-19, April.
    6. Yongqi He & Shan Sun & Jia Zhao & Zhibo Huang & Liling Peng & Chengwei Huang & Zhengbin Tang & Qianqian Huang & Zhoufei Wang, 2023. "UDP-glucosyltransferase OsUGT75A promotes submergence tolerance during rice seed germination," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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