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Fine-Tuning Quantitative Trait Loci Identified in Immortalized F 2 Population Are Essential for Genomic Prediction of Hybrid Performance in Maize

Author

Listed:
  • Pingxi Wang

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
    These authors contributed equally to this work.)

  • Xingye Ma

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
    These authors contributed equally to this work.)

  • Xining Jin

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Xiangyuan Wu

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Xiaoxiang Zhang

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Huaisheng Zhang

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Hui Wang

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Hongwei Zhang

    (State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Junjie Fu

    (State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Yuxin Xie

    (State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Shilin Chen

    (Henan Collaborative Innovation Center of Modern Biological Breeding, School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China)

Abstract

Maize breeding is greatly affected by hybrid vigor, a phenomenon that hybrids exhibit superior performance than parental lines. The immortalized F 2 population (IMF 2 ) is ideal for the genetic dissection and prediction of hybrid performance. Here, in this study, we conducted the QTL mapping and genomic prediction of six traits related to plant architecture using an IMF 2 population. Broad-sense heritability of these traits ranged from 0.85 to 0.94. Analysis of genetic effects showed that additive variance was the main contributor to phenotypic variations. The mapping of quantitative trait loci (QTLs) revealed 10 to 16 QTLs (including pleiotropic loci and epistatic QTLs) for the six traits. Additionally, we identified 15 fine-tuning QTLs for plant height (PH). For genomic prediction (GP), the model of additive and dominance (AD) exhibited higher prediction accuracy than those fitting general combining ability (GCA) and its combination with special combining ability (SCA) effects for all tested traits. And adding the epistasis (E) effect into the AD model did not significantly increase its prediction accuracy. Moreover, the identified 15 fine-tuning QTLs of PH, which exerted large genomic prediction effects, were verified by the marker effect of GP. Our results not only provide an approach for the fine-mapping of fine-tuning QTLs but also serve as references for GP breeding in crops.

Suggested Citation

  • Pingxi Wang & Xingye Ma & Xining Jin & Xiangyuan Wu & Xiaoxiang Zhang & Huaisheng Zhang & Hui Wang & Hongwei Zhang & Junjie Fu & Yuxin Xie & Shilin Chen, 2024. "Fine-Tuning Quantitative Trait Loci Identified in Immortalized F 2 Population Are Essential for Genomic Prediction of Hybrid Performance in Maize," Agriculture, MDPI, vol. 14(3), pages 1-14, February.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:3:p:340-:d:1343016
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