Deep learning empowers genomic selection of pest-resistant grapevine

作者：Yu Gan, Zhenya Liu, Zhenya Liu, Fan Zhang, Xu Wang, Xu Wang, Hui Xue, Xiangnian Su, Wenqi Ma, Qiming Long, Anqi Ma, Guizhou Huang, Wenwen Liu, Xiaodong Xu, Lei Sun, Yingchun Zhang, Yuting Liu, Xinyue Fang, Chaochao Li, Xuanwen Yang, Pengcheng Wei, Xiucai Fan, Pengpai Zhang, Pengpai Zhang, Chonghuai Liu, Lianzhu Zhou, Zhiwu Zhang, Yiwen Wang, Zhongjie Liu, Zhongjie Liu, Yongfeng Zhou · 发表于：Horticulture Research · 年份：2025 · DOI：10.1093/hr/uhaf128 · 被引用次数：11 · 研究领域：Horticultural and Viticultural Research、Plant and Fungal Interactions Research、Insect-Plant Interactions and Control

Abstract Crop pests significantly reduce crop yield and threaten global food security. Conventional pest control relies heavily on insecticides, leading to pesticide resistance and ecological concerns. However, crops and their wild relatives exhibit varied levels of pest resistance, suggesting the potential for breeding pest-resistant varieties. This study integrates deep learning (DL)/machine learning (ML) algorithms, plant phenomics, quantitative genetics, and transcriptomics to conduct genomic selection (GS) of pest resistance in grapevine. Building deep convolutional neural networks (DCNNs), we accurately assess pest damage on grape leaves, achieving 95.3% classification accuracy (VGG16) and a 0.94 correlation in regression analysis (DCNN-PDS). The pest damage was phenotyped as binary and continuous traits, and genome resequencing data from 231 grapevine accessions were combined in a Genome-Wide Association Studies, which maps 69 quantitative trait locus (QTLs) and 139 candidate genes involved in pest resistance pathways, including jasmonic acid, salicylic acid, and ethylene. Combining this with transcriptome data, we pinpoint specific pest-resistant genes such as ACA12 and CRK3, which are crucial in herbivore responses. ML-based GS demonstrates a high accuracy (95.7%) and a strong correlation (0.90) in predicting pest resistance as binary and continuous traits in grapevine, respectively. In general, our study highlights the power of DL/ML in plant phenomics and GS, facil...