Unverified paper record
UAV-LiDAR high-throughput time-series phenotyping and genome-wide association analysis reveal the genetic basis of plant height in peanut ( Arachis hypogaea L.).
Plant Phenomics · 6 Nov 2025 · 10.1016/j.plaphe.2025.100139
Abstract
Plant height (PH) is closely linked to yield potential, lodging resistance, and mechanized harvesting efficiency in peanut cultivation. However, breeding efforts for optimized PH are hindered by limited understanding of its genetic architecture. In this study, we utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials. Using UAV-LiDAR data, we precisely measured time-series PH and applied Gaussian fitting and principal component analysis (PCA) to extract five dynamic growth parameters: parameter a (maximum plant height), b (time to reach maximum height), c (variation extent of PH), (interpreted as average height), and (growth rate). Genome-wide association studies (GWAS) identified 1,133 candidate genes associated with parameters a , b , c , and , and differential expression of genes (DEGs) analysis combined with weighted correlation network analysis (WGCNA) further identified Arahy.1026BX as a candidate gene. This gene is involved in the shikimate pathway and is crucial for the synthesis of auxin and lignin. Reverse transcription quantitative real-time PCR (RT-qPCR) and virus-induced gene silencing (VIGS) experiments validated the significant effect of Arahy.1026BX on peanut PH. Overall, our study integrates advanced UAV-LiDAR time-series phenotyping with genome-wide association study to identify potential candidate genes associated with PH, which providing valuable breeding insights for developing peanut varieties with ideal PH and improving peanut yield.
Plant phenotyping relevance
UAV-LiDARによる時系列の草丈取得と動的成長パラメータ抽出が研究の中心的手法であり、植物表現型解析プラットフォームを実質的に適用している。
abstractwe utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials.
abstractUsing UAV-LiDAR data, we precisely measured time-series PH and applied Gaussian fitting and principal component analysis (PCA) to extract five dynamic growth parameters
abstractOverall, our study integrates advanced UAV-LiDAR time-series phenotyping with genome-wide association study
Code and data availability
The paper's UAV-LiDAR time-series plant height data, Gaussian fitting/PCA parameters, and GWAS results are only said to be in the paper and its supplementary materials, with no public repository deposit for the phenotyping data or authors' analysis code. The IIIVmrMLM R package is a generic third-party tool, the SRP287
No evidence-backed public reproduction asset is currently recorded.
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