基于无人机LiDAR高通量表型数据的双单倍体群体玉米株高QTL定位研究

doi:10.1016/j.jia.2024.09.004

Journal of Integrative Agriculture ›› 2026, Vol. 25 ›› Issue (5): 1822-1835.DOI: 10.1016/j.jia.2024.09.004

基于无人机LiDAR高通量表型数据的双单倍体群体玉米株高QTL定位研究

收稿日期:2024-04-12 修回日期:2024-09-11 接受日期:2024-08-04 出版日期:2026-05-20 发布日期:2026-04-08

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

Xin Zhang^{1, 2, 3, 4*}, Jidong Zhang^5*, Yunling Peng¹, Xun Yu^{3, 4}, Lirong Lu^{3, 4}, Yadong Liu^{3, 4}, Yang Song^{3, 4}, Dameng Yin^{3, 4}, Shaogeng Zhao², Hongwu Wang^5#, Xiuliang Jin^{3, 4#}, Jun Zheng^{1, 2#}

¹Agronomy College, Gansu Agricultural University, Lanzhou 730070, China
²State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
³State Key Laboratory of Crop Physiology and Ecology, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
⁴National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572000, China
⁵National Engineering Research Center of Crop Molecular Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Received:2024-04-12 Revised:2024-09-11 Accepted:2024-08-04 Online:2026-05-20 Published:2026-04-08
About author:#Correspondence Jun Zheng, E-mail: zhengjun02@caas.cn; Xiuliang Jin, E-mail: jinxiuliang@caas.cn; Hongwu Wang, E-mail: wanghongwu@caas.cn * These authors contributed equally to this study.
Supported by:
This research was supported by the National Key Research and Development Program of China (2023YFD1200500), the National Natural Science Foundation of China (32301395, 42071426, and 51922072), the Nanfan Special Project of the Chinese Academy of Agricultural Sciences (YBXM2305), the Central Public-Interest Scientific Institution Basal Research Fund Program, China (Y2020YJ07 and Y2022XK22), the Key Cultivation Program of the Xinjiang Academy of Agricultural Sciences, China (xjkcpy-2020003), and the Open Competition Project of Heilongjiang Province, China (2021ZXJ05A03).

摘要/Abstract

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高和测量精度低等问题。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，R²分别为0.67和0.56，RMSE分别为0.12 m和0.17 m。此外，在三亚和新乡两个环境中F₁DH和F₂DH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 world’s growing 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, a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) was employed 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 trials. High-density genetic maps were constructed, and plant height was assessed at both single-plant and row scales across multiple developmental stages and genetic backgrounds. The findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with R² values of 0.67 vs. 0.56 and RMSE values 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. This study successfully identified and validated QTLs associated with plant height, thereby 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.

Key words: doubled haploid , light detection and ranging , quantitative trait loci , high-density genetic map , unmanned aerial vehicle

Xin Zhang, Jidong Zhang, Yunling Peng, Xun Yu, Lirong Lu, Yadong Liu, Yang Song, Dameng Yin, Shaogeng Zhao, Hongwu Wang, Xiuliang Jin, Jun Zheng. 基于无人机LiDAR高通量表型数据的双单倍体群体玉米株高QTL定位研究[J]. Journal of Integrative Agriculture, 2026, 25(5): 1822-1835.

Xin Zhang, Jidong Zhang, Yunling Peng, Xun Yu, Lirong Lu, Yadong Liu, Yang Song, Dameng Yin, Shaogeng Zhao, Hongwu Wang, Xiuliang Jin, Jun Zheng. QTL mapping of maize plant height based on a population of doubled haploid lines using UAV LiDAR high-throughput phenotyping data[J]. Journal of Integrative Agriculture, 2026, 25(5): 1822-1835.

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基于无人机LiDAR高通量表型数据的双单倍体群体玉米株高QTL定位研究

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

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