Zhang Zhen, Qiu Xiaolong, Guo Guanghui, Zhu Xiaojing, Shi Jiawei, Zhang Ning, Ding Shenglong, Tang Nazhu, Qu Yunfeng, Sun Zhe, Li Huilin, Ma Feifei, Xie Shangyuan, Lv Qian, Fu Liming, Hu Ge, Cao Ying, Ge Haowei, Li Hao, Huang Jinling, Xu Weigang, Yang Wanneng, Zhou Yun, Song Chun-Peng
State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Kaifeng 475004, China.
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China.
Plant Physiol. 2025 Apr 30;198(1). doi: 10.1093/plphys/kiaf154.
The root system architecture (RSA) determines plant growth and yield. The characterization of optimal RSA and discovery of genetic loci or candidate genes that control root traits are therefore important research goals. However, the hidden nature of the root system makes it difficult to perform nondestructive, rapid analyses of RSA. In this study, we developed an automated, nondestructive, high-throughput root phenotyping platform (Root-HTP) and a corresponding data processing pipeline for efficient, large-scale characterization of wheat (Triticum aestivum L.) RSA. This system is capable of tracking root growth dynamics and RSA variation across all wheat developmental stages. In situ phenotyping using Root-HTP extracted 47 RSA traits, including 33 novel traits in wheat and 23 novel traits in other crops. We used root trait data from the phenotyping system and yield trait data to conduct a genome-wide association study (GWAS) of 155 wheat accessions, which identified 2,650 SNPs and 233 quantitative trait loci (QTLs) associated with aspects of root architecture. The candidate gene TaMYB93 was detected in a QTL for root tortuosity, and EMS mutants confirmed its effect on RSA in wheat. We explored the relationship between root- and yield-related traits and identified 20 root-related QTLs that were also associated with yield traits. Furthermore, we have built a predictive model for wheat yield based on 18 RSA traits and propose a parsimonious RSA ideotype associated with high yields. The data generated from this study provide insight into the genetic architecture of wheat RSA and support for RSA ideotype-based wheat breeding and yield prediction.
根系结构（RSA）决定了植物的生长和产量。因此，确定最佳RSA并发现控制根系性状的基因座或候选基因是重要的研究目标。然而，根系的隐蔽特性使得对RSA进行无损、快速分析变得困难。在本研究中，我们开发了一个自动化、无损、高通量的根系表型分析平台（Root-HTP）以及相应的数据处理流程，用于对小麦（Triticum aestivum L.）RSA进行高效、大规模的表征。该系统能够追踪小麦各个发育阶段的根系生长动态和RSA变化。使用Root-HTP进行原位表型分析提取了47个RSA性状，包括小麦中的33个新性状和其他作物中的23个新性状。我们利用表型分析系统的根系性状数据和产量性状数据，对155份小麦种质进行了全基因组关联研究（GWAS），鉴定出2650个单核苷酸多态性（SNP）和233个与根系结构相关的数量性状基因座（QTL）。在一个控制根弯曲度的QTL中检测到候选基因TaMYB93，甲基磺酸乙酯（EMS）突变体证实了其对小麦RSA的影响。我们探索了根系相关性状与产量相关性状之间的关系，鉴定出20个与产量性状也相关的根系相关QTL。此外，我们基于18个RSA性状建立了小麦产量预测模型，并提出了一种与高产相关的简约RSA理想型。本研究产生的数据为小麦RSA的遗传结构提供了见解，并为基于RSA理想型的小麦育种和产量预测提供了支持。
