Integrating UAV-based dynamic phenotyping and GWAS to decipher the genetic basis of cotton defoliation

Abstract

Upland cotton (Gossypium hirsutum L.) is a critical economic crop, yet the efficiency of mechanized harvesting is heavily contingent upon effective pre-harvest defoliation. Traditional manual assessment of defoliation is labor-intensive and subjective, posing a significant bottleneck for large-scale genetic dissection of this dynamic trait. In this study, we established an integrated “high-throughput phenotyping-to-gene discovery” framework by utilizing UAV-based multispectral imaging to monitor 306 cotton cultivars across 4 environments. A Partial Least Squares Regression (PLSR) model was optimized to accurately estimate Leaf Area Index (LAI), and Gaussian curve fitting was employed to standardize LAI time series (ΔLAI) into a comparable dynamic phenotypic dataset. Genome-wide association studies (GWAS) based on these dynamic phenotypes identified 472 significant SNPs and 39 candidate genes. By integrating GWAS signals with transcriptome profiling of the petiole abscission zone and haplotype analysis, we identified 3 core regulatory genes: Ghi_A01G08401 (GhPIN3a), Ghi_D08G10716, and Ghi_D11G03091. Functional validation via virus-induced gene silencing (VIGS) and qRT-PCR demonstrated that Ghi_D08G10716 (encoding oxalyl-CoA synthetase) and Ghi_D11G03091 (encoding a VQ motif-containing protein) act as negative regulators in the defoliation process. These results provide a scalable technical paradigm and critical genetic resources for the precision breeding of cotton cultivars optimized for mechanized harvesting.

Plant phenotyping relevance

UAVマルチスペクトル画像からLAIを推定し、時系列を動的表現型データへ変換する高スループット表現型解析手法が研究の中心であり、GWASへの実質的適用も行っている。

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