Full dataset for high-throughput phenotyping-enabled genetic dissection of crop lodging in wheat (Triticum aestivum)

The primary objective of this research was to evaluate the potential of unmanned aerial systems-based imaging for quantification of crop lodging in wheat. This dataset contains images and associated files collected from large wheat breeding trials over the course of two seasons in 2016 and 2017. Using the validated digital lodging scores, we performed Genome-Wide Assocation Analysis on 1035 wheat genotypes and found a consistent peak at chromosome 2A. To follow up, we tested the hypothesis of complex genetic architecture of lodging in wheat by applying genomic prediction cross-validations within and across environments. Our findings provide a strong proof-of-concept in support of the application of unmanned aerial systems in breeding and genetic studies. This dataset provides a comprehensive collection of files to replicate the proposed methods and analysis. <br>

本研究的核心目标是评估基于无人机系统（unmanned aerial systems, UAS）成像技术量化小麦倒伏的应用潜力。本数据集收录了2016年与2017年两个生长季内，于大型小麦育种试验中采集的图像及相关配套文件。依托经验证的数字化倒伏评分体系，我们对1035份小麦基因型开展全基因组关联分析（Genome-Wide Association Analysis, GWAS），并在2A染色体上检测到稳定的关联信号峰。为进一步验证相关假说，我们通过在单一环境内及跨环境的基因组预测交叉验证试验，检验了小麦倒伏具有复杂遗传结构的假设。本研究结果为无人机系统在育种及遗传研究中的应用提供了强有力的概念验证依据。本数据集收录了完整的配套文件集，可用于复现本文所提出的研究方法与分析流程。