Shi Rongli, López-Malvar Ana, Knoch Dominic, Tschiersch Henning, Heuermann Marc C, Shaaf Salar, Madur Delphine, Santiago Rogelio, Balconi Carlotta, Frascaroli Elisabetta, Erdal Sekip, Palaffre Carine, Charcosset Alain, Revilla Pedro, Altmann Thomas
Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, 06466 Seeland, Germany.
Universidad de Vigo, As Lagoas Marcosende, Agrobiología Ambiental, Calidad de Suelos Y Plantas (UVIGO), Unidad Asociada a La MBG (CSIC), 36310 Vigo, Spain.
Data Brief. 2025 Aug 5;62:111947. doi: 10.1016/j.dib.2025.111947. eCollection 2025 Oct.
This dataset was generated to characterize the physiological and morphological mechanisms underlying tolerance and resilience to combined drought and heat stress using a panel of 106 Mediterranean maize inbred lines. To achieve this, high-throughput non-invasive phenotyping combined with genome-wide association analysis was applied to accurately capture the dynamic responses of the maize lines to stress and to dissect the genetic basis of maize tolerance and resilience. Two experiments were conducted under control (25/20 °C, 70 % field capacity (FC)) and stress conditions (35/25 °C, 30 % FC). Stress was applied from 18 to 32 DAS (days after sowing), followed by a recovery period under control conditions. Plants were grown under controlled air temperature and soil water content, and were harvested at 45 DAS. Throughout the cultivation period, multiple camera sensors captured images daily, allowing agronomic traits to be extracted for analysis. The dataset includes raw and processed images, phenotypic data obtained from these images, results of two photosynthesis related parameters, Genome-Wide Association Study (GWAS) results from one parameter as an example, and scripts used for data analysis. Additionally, metadata and a detailed description of the experimental setup are provided. This resource is suitable for researchers interested in stress phenotyping and quantitative genetics. It allows further exploration of genotype-by-environment interactions and integration with other omics datasets. The dataset provides a valuable foundation for studies aiming to understand and improve crop resilience to climate-related abiotic stresses.
该数据集是通过使用106个地中海玉米自交系组成的群体生成的，用于表征玉米对干旱和热胁迫复合胁迫的耐受性和恢复力背后的生理和形态机制。为实现这一目标，采用了高通量非侵入性表型分析与全基因组关联分析相结合的方法，以准确捕捉玉米品系对胁迫的动态响应，并剖析玉米耐受性和恢复力的遗传基础。在对照条件（25/20°C，70% 田间持水量 (FC)）和胁迫条件（35/25°C，30% FC）下进行了两项实验。在播种后18至32天（DAS）施加胁迫，随后在对照条件下进行恢复期。植株在可控的气温和土壤含水量条件下生长，并在播种后45天收获。在整个栽培期间，多个相机传感器每天拍摄图像，以便提取农艺性状进行分析。该数据集包括原始图像和处理后的图像、从这些图像中获得的表型数据、两个光合作用相关参数的结果、以一个参数为例的全基因组关联研究（GWAS）结果以及用于数据分析的脚本。此外，还提供了元数据和实验设置的详细描述。该资源适用于对胁迫表型分析和数量遗传学感兴趣的研究人员。它允许进一步探索基因型与环境的相互作用，并与其他组学数据集整合。该数据集为旨在理解和提高作物对气候相关非生物胁迫恢复力的研究提供了宝贵的基础。
