Gan Yu, Liu Zhenya, Zhang Fan, Xu Qi, Wang Xu, Xue Hui, Su Xiangnian, Ma Wenqi, Long Qiming, Ma Anqi, Huang Guizhou, Liu Wenwen, Xu Xiaodong, Sun Lei, Zhang Yingchun, Liu Yuting, Fang Xinyue, Li Chaochao, Yang Xuanwen, Wei Pengcheng, Fan Xiucai, Zhang Chuan, Zhang Pengpai, Liu Chonghuai, Zhou Lianzhu, Zhang Zhiwu, Wang Yiwen, Liu Zhongjie, Zhou Yongfeng
National Key Laboratory of Tropical Crop Breeding, Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Xueyuan Road, Longhua District, Haikou, 571101, China.
National Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Buxin Road, Dapeng New District, Shenzhen, 518000, China.
Hortic Res. 2025 May 7;12(8):uhaf128. doi: 10.1093/hr/uhaf128. eCollection 2025 Aug.
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 and , 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, facilitating genomic breeding of pest-resistant grapevine.
农作物害虫会显著降低作物产量，并威胁全球粮食安全。传统的害虫防治严重依赖杀虫剂，导致了抗药性和生态问题。然而，作物及其野生近缘种表现出不同程度的抗虫性，这表明培育抗虫品种具有潜力。本研究整合了深度学习（DL）/机器学习（ML）算法、植物表型组学、数量遗传学和转录组学，对葡萄的抗虫性进行基因组选择（GS）。通过构建深度卷积神经网络（DCNN），我们准确评估了葡萄叶片上的害虫损害，在分类分析中达到了95.3%的准确率（VGG16），在回归分析中相关性为0.94（DCNN-PDS）。将害虫损害表型化为二元和连续性状，并将来自231份葡萄种质的基因组重测序数据用于全基因组关联研究，该研究定位了69个数量性状位点（QTL）和139个参与抗虫途径的候选基因，包括茉莉酸、水杨酸和乙烯途径。将此与转录组数据相结合，我们确定了特定的抗虫基因，如 和 ，它们在食草动物反应中至关重要。基于ML的GS在预测葡萄二元和连续性状的抗虫性方面分别显示出高精度（95.7%）和强相关性（0.90）。总体而言，我们的研究突出了DL/ML在植物表型组学和GS中的作用，促进了抗虫葡萄的基因组育种。
