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 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 1133 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.