Datasets of "Association mapping for image-based root traits in tropical maize under water stress in semi-arid regions"

Water stress is the factor that most negatively impacts agricultural production. In this context, root system traits, such as length, surface area, volume, and mass, are paramount in water deficit studies, as they play a central role in plant growth, allocation, and acquisition of soil resources. However, the plant evaluation for them and under water stress is very difficult. Therefore, an alternative has been to obtain surrogate variables from image processing. Moreover, identifying genomic regions or genes associated with the expression of the root system under water deficit may allow breeding programs to outline more effective strategies for obtaining efficient genotypes. Hence, a public diversity panel composed of 360 inbred maize lines was evaluated via image-based root traits at phenological stage V6 (six expanded leaves) under well-water (WW) and water-stress (WS) conditions. Then, genetic association analyses (GWAS) were conducted for each image-based trait in WW and WS using the Fixed and Random Model Circulating Probability Unification (FarmCPU) method. A total of 23 markers were identified in association with all the traits in the two water supply conditions, 12 only in WW, four associated with traits in WW and WS, and seven exclusives to WS. All those genomic regions are associated with physiological mechanisms and molecular responses related to water deficit tolerance that can be explored in subsequent studies and by breeding programs to obtain more resilient genotypes for this condition. Furthermore, image-based features are a valuable tool to dissect root traits in WS conditions.' Here you can find all the data and scripts used to perform this study.水分胁迫(water stress)是对农业生产负面影响最为显著的环境因素。在此研究背景下，根系性状(root system traits)（包括根长、根表面积、根体积与根干重）在水分亏缺(water deficit)相关研究中占据核心地位，因其直接参与植物生长、资源分配及土壤资源获取过程。然而，在水分胁迫条件下针对上述根系性状开展植物表型评价难度极高。为此，学界转而采用图像处理技术获取替代性状作为解决方案。此外，挖掘与水分亏缺条件下根系性状表达相关的基因组区域或功能基因，可为育种计划制定高效策略提供支撑，助力培育抗旱高效的作物基因型。 本研究针对由360份玉米自交系(inbred maize lines)组成的公共多样性种质群体，在V6生育期（即展开6片完全叶的时期）分别设置正常供水(well-water, WW)与水分胁迫(water-stress, WS)两种处理，基于图像分析法测定其根系性状。随后，采用固定随机模型循环概率统一(Fixed and Random Model Circulating Probability Unification, FarmCPU)方法，针对两种供水条件下的各图像衍生根系性状开展全基因组关联分析(Genome-Wide Association Study, GWAS)。 研究共鉴定出23个与两种供水条件下所有目标性状相关的分子标记，其中12个仅在正常供水处理中被检测到，4个同时与正常供水及水分胁迫处理下的性状显著相关，另有7个仅在水分胁迫处理中特异性关联。上述所有基因组区域均与水分亏缺耐受性相关的生理机制及分子响应通路密切相关，可在后续研究中进一步挖掘，亦可供育种项目用于培育适应水分胁迫环境的高抗性基因型。此外，基于图像的表型特征分析技术是解析水分胁迫条件下根系性状的有效工具。 本研究所用全部数据与分析脚本均可在此处获取。