Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Gossypium hirsutum)
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Le résumé fourni par la source
Plant height (PH) is a key agronomic trait influencing plant architecture. Suitable PH values for cotton are important for lodging resistance, high planting density, and mechanized harvesting, making it crucial to elucidate the mechanisms of the genetic regulation of PH. However, traditional field PH phenotyping largely relies on manual measurements, limiting its large-scale application. In this study, a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field. Different strategies were used to extract PH values from two sets of sensor data, and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions. These predictions were consistent with manual measurements of the PH for the LiDAR (R 2 = 0.934) and RGB (R 2 = 0.914) data. The predicted PH values were used for GWAS analysis, and 34 PH-related genes, two of which have been demonstrated to regulate PH in cotton, namely, GhPH1 and GhUBP15 , were identified. We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G. hirsutum cultivar ZM24 harvested on the 15th, 35th, and 70th days after sowing compared with those from a dwarf mutant ( pag1 ), which presented shortened stem and internode phenotypes. The overexpression of GhPH_UAV1 significantly promoted cotton stem development, whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth, suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH. This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations, which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Gossypium hirsutum)
- Date Crossref
- 01/03/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Xinjiang Agricultural University pays non établi dans la noticeUniversité ou école supérieure
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Cotton Research Institute pays non établi dans la noticeStructure de recherche
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Xinjiang Academy of Agricultural Sciences pays non établi dans la noticeInstitution
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Xinjiang Academy of Agricultural and Reclamation Science pays non établi dans la noticeStructure de recherche
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Zhengzhou University pays non établi dans la noticeUniversité ou école supérieure
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Xinjiang Institute of Engineering pays non établi dans la noticeUniversité ou école supérieure
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Xinjiang University pays non établi dans la noticeUniversité ou école supérieure
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Ministry of Education/College of Agriculture Engineering Research Centre of Cotton pays non établi dans la noticeUniversité ou école supérieure
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State Key Laboratory of Cotton Bio-breeding and Integrated Utilization pays non établi dans la noticeStructure de recherche
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Xinjiang Key Laboratory of Crop Gene Editing and Germplasm Innovation pays non établi dans la noticeStructure de recherche
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School of Agricultural Sciences State Key Laboratory of Cotton Bio-breeding and Integrated Utilization pays non établi dans la noticeUniversité ou école supérieure
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College of Smart Agriculture (Research Institute) pays non établi dans la noticeUniversité ou école supérieure
Xinjiang Agricultural University, Cotton Research Institute et Xinjiang Academy of Agricultural Sciences, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.