AUTHOR=Cheng Yukun , He Wanlong , Zheng Fuxing , Zhang Feifei , Bai Bin , Sun Na , Wang Wei , Geng Hongwei 

TITLE=Genome-wide association study of wheat chlorophyll dynamics under drought and irrigation using multispectral UAV phenotyping

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1607862

DOI=10.3389/fpls.2025.1607862

ISSN=1664-462X

ABSTRACT=High-throughput phenotypic analysis using multispectral unmanned aerial vehicle (UAV) technology is a critical approach for enhancing the efficiency and accuracy of gene mining. This study aimed to evaluate the feasibility of UAV-based remote sensing techniques in predicting chlorophyll content and conducting genome-wide association studies (GWAS) for winter wheat under both normal and drought stress conditions. The study was conducted in the fall of 2019 at the Zepu and Manas experimental bases using winter wheat. Chlorophyll content was measured manually during the heading, flowering, and grain filling stages and compared with data obtained via UAV-mounted multispectral sensors. A predictive model for chlorophyll content was developed using UAV data and validated against manual measurements. The predicted and measured chlorophyll values were then integrated into a GWAS to identify loci associated with chlorophyll content.Chlorophyll content values differed across growth stages, with both measured and predicted values increasing from the heading to grain filling stages. Under normal conditions, manual measurements ranged from 43.96 to 65.85, while UAV-predicted values ranged from 47.59 to 62.29. Under drought conditions, manual measurements ranged from 45.00 to 66.33, and UAV-predicted values ranged from 47.83 to 65.89. Correlation coefficients between measured and predicted values were high under normal conditions (0.90–0.93 during heading, 0.91–0.92 during flowering, and 0.88–0.90 during filling) and moderate to high under drought stress (0.57–0.70, 0.89–0.91, and 0.94–0.96, respectively). A neural network model demonstrated high accuracy in predicting chlorophyll content. GWAS revealed 308 loci associated with chlorophyll content, with UAV-predicted data identifying 206 loci across 21 chromosomes, explaining 7.58%–19.58% of the phenotypic variation. Measured values identified 102 loci across 21 chromosomes, accounting for 9.31%–15.83% of the variation. Eighteen overlapping loci were detected on chromosomes 1A, 1B, 2B, 3B, 4B, 5A, 5B, 5D, 6B, 6D, 7A, and 7B. This study confirms the reliability of UAV-based multispectral data for chlorophyll content inversion and GWAS. Site-specific differences in prediction quality were observed, with site P showing stronger correlations and higher prediction accuracy. Analysis of loci identified 21 candidate genes potentially related to chlorophyll content, including those encoding chlorophyll a/b-binding proteins, aquaporins, and chlorophyll kinases. These findings demonstrate the potential of UAV technology for high-throughput, efficient, and accurate phenotyping, facilitating better understanding of the genetic mechanisms underlying chlorophyll content variation.