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Journal of Integrative Agriculture
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QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data

Xin Zhang1, 2, 3, 4*, Jidong Zhang5*, Yunling Peng1, Xun Yu3, 4, Lirong Lu3, 4, Yadong Liu3, 4, Yang Song3, 4, Dameng Yin3, 4, Shaogeng Zhao2, Hongwu Wang5#, Xiuliang Jin3, 4#, Jun Zheng1, 2#

1 Agronomy College, Gansu Agricultural University, Lanzhou 730070, China

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

3 State Key Laboratory of Crop Physiology and Ecology, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

4 National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572000, China

5 National Engineering Research Center of Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

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摘要  

玉米(Zea mays L.)是全球重要的粮食作物,随着人口数目的增加,提高玉米产量至关重要株高是影响产量、抗倒伏性和生态适应性的关键性状之一,具有至关重要的意义。但传统的株高测量方法往往存在成本效益测量精度低等问题。本研究采用搭载激光雷达(LiDAR)传感器的无人机,收集了270个双单倍体(DH)系的点云数据,探索了无人机LiDAR技术在玉米育种中进行高通量表型分析的创新应用。评估了单株尺度和行尺度的预测精度,此外还构建了高密度遗传图谱,并对多个发育阶段的株高数据进行QTL定位。结果表明,对于多品种和小面积区域,单株尺度的预测精度优于行尺度,分别为0.670.56RMSE分别为0.12 m0.17 m。此外,在三亚和新乡两个环境中F1DHF2DH群体分别鉴定了12个和20个与株高相关的QTLs。本研究成功鉴定并验证了与植株高度相关的QTLs,揭示了控制株高的新遗传位点和候选基因。这项研究强调了基于无人机的遥感技术在推动精准农业方面的潜力,通过高效、大规模的表型分析和基因挖掘来促进玉米精确育种计划的发展。



Abstract  

Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population.  Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors.  Traditional methods for measuring plant height often lack cost-efficiency and accuracy.  In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines.  This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding.  We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds.  Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R² of 0.67 versus 0.56 and an RMSE of 0.12 m vs. 0.17 m, respectively.  Two high-density genetic maps were constructed based on SNP markers.  In Sanya and Xinxiang, the F1DH and F2DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively.  The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes.  This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

Keywords:  doubled haploid       light detection and ranging        quantitative trait loci        high-density genetic map        unmanned aerial vehicle  
Received: 12 April 2024   Online: 12 September 2024  
Fund: 

This research was supported by the National Key Research and Development Program of China (2023YFD1200500), the National Natural Science Foundation of China (32301395, 42071426, 51922072), the Nanfan Special Project of the Chinese Academy of Agricultural Sciences (YBXM2305), the Central Public-Interest Scientific Institution Basal Research Fund Program (Y2020YJ07, Y2022XK22), the Key Cultivation Program of the Xinjiang Academy of Agricultural Sciences of China (xjkcpy-2020003), and the Open Competition Project of Heilongjiang Province of China (2021ZXJ05A03).

About author:  #Correspondence Jun Zheng, E-mail: zheng.jun02@caas.cn; Xiuliang Jin, E-mail: jinxiuliang@caas.cn; Hongwu Wang, E-mail: wanghongwu@caas.cn *These authors contributed equally to this work

Cite this article: 

Xin Zhang, Jidong Zhang, Yunling Peng, Xun Yu, Lirong Lu, Yadong Liu, Yang Song, Dameng Yin, Shaogeng Zhao, Hongwu Wang, Xiuliang Jin, Jun Zheng. 2024. QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2024.09.004

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