Genetic dissection of maize grain moisture content and dehydration rate using high-density bin mapping in a recombinant inbred line population

被引:0
|
作者
Zhang, Jun [1 ]
Zhang, Yingying [2 ]
Zhang, Fengqi [1 ]
Tian, Lei [3 ]
Ma, Zhiyan [1 ]
Wu, Xiaopan [4 ]
Zhou, Qingwei [5 ]
Zhang, Qianjin [1 ]
Mu, Xinyuan [1 ]
Fan, Yanping [1 ]
Xia, Laikun [1 ]
Ding, Yong [1 ]
机构
[1] Henan Acad Agr Sci, Cereal Crops Res Inst, Henan Prov Key Lab Maize Biol, Zhengzhou 450002, Peoples R China
[2] Anyang Acad Agr Sci, Anyang 455000, Peoples R China
[3] Henan Agr Univ, Coll Agron, Ctr Crop Genome Engn, State Key Lab Wheat & Maize Crop Sci, Zhengzhou 450046, Peoples R China
[4] Zhengzhou Beiqing Seed Ind Co Ltd, Zhengzhou 450002, Peoples R China
[5] Henan Sutai Agr Technol Co Ltd, Zhengzhou 450002, Peoples R China
来源
BMC PLANT BIOLOGY | 2025年 / 25卷 / 01期
关键词
Maize; Grain moisture content; Grain dehydration rate; Quantitative trait loci mapping; High-density bin mapping; Recombinant inbred line population; Marker-assisted selection; Transcriptomic analysis; QUANTITATIVE TRAIT LOCI; EAR MOISTURE; TESTCROSS PERFORMANCE; AGRONOMIC TRAITS; ZEA-MAYS; SELECTION; QTL; HARVEST; LINKAGE; CORN;
D O I
10.1186/s12870-025-06404-1
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Maize (Zea mays L.) grain moisture content (GMC) at harvest is a key determinant of seed preservation, grain quality, and drying costs, with the grain dehydration rate (GDR) playing a critical role in determining GMC. This study focused on understanding the genetic basis of GDR by utilizing a recombinant inbred line population of 310 lines derived from PB80 and PHJ65, assessed across three environments with high-density SNP markers. A genetic linkage map spanning 1237.36 cM with 5235 bin markers was constructed, leading to the identification of 23 quantitative trait loci (QTLs) associated with GMC and Area Under the Dry Down Curve (AUDDC) across multiple chromosomes, with several QTLs explaining over 10% of the phenotypic variance. Significant QTLs, including qGMC1.1, qGMC2.2, and qAUDDC2.2, were consistently detected across various environments and developmental stages. Transcriptomic analysis identified 21 candidate genes within these QTL regions, including key transcription factors and metabolism-related genes. These findings contribute to a better understanding of the genetic control of GMC and GDR, may serve as a foundation for future breeding efforts in maize breeding to enhance mechanized production efficiency and reduce post-harvest drying costs.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7
    Xufeng Bai
    Lijun Luo
    Wenhao Yan
    Mallikarjuna Rao Kovi
    Wei Zhan
    Yongzhong Xing
    BMC Genetics, 11
  • [42] Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7
    Bai, Xufeng
    Luo, Lijun
    Yan, Wenhao
    Kovi, Mallikarjuna Rao
    Zhan, Wei
    Xing, Yongzhong
    BMC GENETICS, 2010, 11
  • [43] Multi-Environment Quantitative Trait Loci Mapping for Grain Iron and Zinc Content Using Bi-parental Recombinant Inbred Line Mapping Population in Pearl Millet
    Singhal, Tripti
    Satyavathi, C. Tara
    Singh, S. P.
    Kumar, Aruna
    Sankar, S. Mukesh
    Bhardwaj, C.
    Mallik, M.
    Bhat, Jayant
    Anuradha, N.
    Singh, Nirupma
    FRONTIERS IN PLANT SCIENCE, 2021, 12
  • [44] Genetic mapping of quantitative trait locus for the leaf morphological traits in a recombinant inbred line population by ultra-high-density maps across multi-environments of maize (Zea mays)
    Zhang, Kuangye
    Lv, Xiangling
    Li, Fenghai
    Wang, Jia
    Yu, Hongwei
    Li, Jianbo
    Du, Wanli
    Diao, Yulin
    Wang, Jiaxu
    Weng, Jianfeng
    PLANT BREEDING, 2020, 139 (01) : 107 - 118
  • [45] Quantitative trait locus mapping for plant height and branch number in an upland cotton recombinant inbred line with an SNP-based high-density genetic map
    Zhen Zhang
    Aiying Liu
    Zhen Huang
    Senmiao Fan
    Xianyan Zou
    Xiaoying Deng
    Qun Ge
    Juwu Gong
    Junwen Li
    Wankui Gong
    Yuzhen Shi
    Liqiang Fan
    Zhibin Zhang
    Xiao Jiang
    Kang Lei
    Youlu Yuan
    Aixia Xu
    Haihong Shang
    Euphytica, 2019, 215
  • [46] Quantitative trait locus mapping for plant height and branch number in an upland cotton recombinant inbred line with an SNP-based high-density genetic map
    Zhang, Zhen
    Liu, Aiying
    Huang, Zhen
    Fan, Senmiao
    Zou, Xianyan
    Deng, Xiaoying
    Ge, Qun
    Gong, Juwu
    Li, Junwen
    Gong, Wankui
    Shi, Yuzhen
    Fan, Liqiang
    Zhang, Zhibin
    Jiang, Xiao
    Lei, Kang
    Yuan, Youlu
    Xu, Aixia
    Shang, Haihong
    EUPHYTICA, 2019, 215 (06)
  • [47] QTL mapping for grain number per spikelet in wheat using a high-density genetic map
    Yu Lin
    Xiaojun Jiang
    Haiyan Hu
    Kunyu Zhou
    Qing Wang
    Shifan Yu
    Xilan Yang
    Zhiqiang Wang
    Fangkun Wu
    Shihang Liu
    Caixia Li
    Mei Deng
    Jian Ma
    Guangdeng Chen
    Yuming Wei
    Youliang Zheng
    Yaxi Liu
    TheCropJournal, 2021, 9 (05) : 1108 - 1114
  • [48] QTL mapping for grain number per spikelet in wheat using a high-density genetic map
    Lin, Yu
    Jiang, Xiaojun
    Hu, Haiyan
    Zhou, Kunyu
    Wang, Qing
    Yu, Shifan
    Yang, Xilan
    Wang, Zhiqiang
    Wu, Fangkun
    Liu, Shihang
    Li, Caixia
    Deng, Mei
    Ma, Jian
    Chen, Guangdeng
    Wei, Yuming
    Zheng, Youliang
    Liu, Yaxi
    CROP JOURNAL, 2021, 9 (05): : 1108 - 1114
  • [49] QTL mapping of general combining abilities of four traits in maize using a high-density genetic map
    WANG Hai
    HE Yan
    WANG Shou-cai
    JournalofIntegrativeAgriculture, 2017, 16 (08) : 1700 - 1707
  • [50] Quantitative trait loci mapping of yield and related traits using a high-density genetic map of maize
    Chen, Lin
    Li, Chunhui
    Li, Yongxiang
    Song, Yanchun
    Zhang, Dengfeng
    Wang, Tianyu
    Li, Yu
    Shi, Yunsu
    MOLECULAR BREEDING, 2016, 36 (09)