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.