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Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids

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  • Lanzhi Li

    (Hunan Agricultural University)

  • Xingfei Zheng

    (Food Crop Institute, Hubei Academy of Agricultural Sciences
    Wuhan University)

  • Jiabo Wang

    (Southwest Minzu University)

  • Xueli Zhang

    (Hunan Agricultural University)

  • Xiaogang He

    (Hunan Agricultural University)

  • Liwen Xiong

    (Hunan Agricultural University)

  • Shufeng Song

    (Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural Sciences)

  • Jing Su

    (Hunan Agricultural University)

  • Ying Diao

    (Wuhan Polytechnic University)

  • Zheming Yuan

    (Hunan Agricultural University)

  • Zhiwu Zhang

    (Washington State University)

  • Zhongli Hu

    (Wuhan University
    Wuhan Polytechnic University)

Abstract

Genetic improvement of grain quality is more challenging in hybrid rice than in inbred rice due to additional nonadditive effects such as dominance. Here, we describe a pipeline developed for joint analysis of phenotypes, effects, and generations (JPEG). As a demonstration, we analyze 12 grain quality traits of 113 inbred lines (male parents), five tester lines (female parents), and 565 (113×5) of their hybrids. We sequence the parents for single nucleotide polymorphisms calling and infer the genotypes of the hybrids. Genome-wide association studies with JPEG identify 128 loci associated with at least one of the 12 traits, including 44, 97, and 13 loci with additive effects, dominant effects, and both additive and dominant effects, respectively. These loci together explain more than 30% of the genetic variation in hybrid performance for each of the traits. The JEPG statistical pipeline can help to identify superior crosses for breeding rice hybrids with improved grain quality.

Suggested Citation

  • Lanzhi Li & Xingfei Zheng & Jiabo Wang & Xueli Zhang & Xiaogang He & Liwen Xiong & Shufeng Song & Jing Su & Ying Diao & Zheming Yuan & Zhiwu Zhang & Zhongli Hu, 2023. "Joint analysis of phenotype-effect-generation identifies loci associated with grain quality traits in rice hybrids," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39534-x
    DOI: 10.1038/s41467-023-39534-x
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    1. Qian Liu & Ruixi Han & Kun Wu & Jianqing Zhang & Yafeng Ye & Shuansuo Wang & Jianfeng Chen & Yajun Pan & Qi Li & Xiaopeng Xu & Jiawu Zhou & Dayun Tao & Yuejin Wu & Xiangdong Fu, 2018. "G-protein βγ subunits determine grain size through interaction with MADS-domain transcription factors in rice," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    2. Dong-Sheng Zhao & Qian-Feng Li & Chang-Quan Zhang & Chen Zhang & Qing-Qing Yang & Li-Xu Pan & Xin-Yu Ren & Jun Lu & Ming-Hong Gu & Qiao-Quan Liu, 2018. "GS9 acts as a transcriptional activator to regulate rice grain shape and appearance quality," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    3. Xuehui Huang & Shihua Yang & Junyi Gong & Yan Zhao & Qi Feng & Hao Gong & Wenjun Li & Qilin Zhan & Benyi Cheng & Junhui Xia & Neng Chen & Zhongna Hao & Kunyan Liu & Chuanrang Zhu & Tao Huang & Qiang Z, 2015. "Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis," Nature Communications, Nature, vol. 6(1), pages 1-9, May.
    4. Xiaolei Liu & Meng Huang & Bin Fan & Edward S Buckler & Zhiwu Zhang, 2016. "Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 12(2), pages 1-24, February.
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