ArticleData in brief2025
A high-throughput phenotyping dataset for GWAS analysis of maize under combined drought and heat stress.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Genomics, Multi-Omics, and Emerging Artificial Intelligence for Combined Drought-Heat Stress Resilience in Plants: A Structured Narrative Review.Plants (Basel, Switzerland) · 2026Review
- A multi-locus genome-wide association study uncovers candidate genes for alkali tolerance in maize (Zea Mays L.) seedlings.BMC genomics · 2025Article
- A high-throughput phenotyping dataset for GWAS analysis of maize under combined drought and heat stress.Data in brief · 2025Article
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Authors and funding
15 authors.
Funding
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Abstract
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.
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