Deep learning empowers genomic selection of pest-resistant grapevine
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 <i>ACA12</i> and <i>CRK3</i>, 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.
Authors
- Anqi Ma (ORCID: https://orcid.org/0009-0006-5009-2072)
- Zhongjie Liu (ORCID: https://orcid.org/0000-0001-8406-6709)
- Xiaodong Xu (ORCID: https://orcid.org/0000-0002-9580-9835)
- Qi Xu (ORCID: https://orcid.org/0000-0003-2791-0429)
- Chonghuai Liu (ORCID: https://orcid.org/0000-0001-6517-566X)
- Zhiwu Zhang (ORCID: https://orcid.org/0000-0002-5784-9684)
- Zhenya Liu (ORCID: https://orcid.org/0000-0002-3487-5070)
- Pengcheng Wei (ORCID: https://orcid.org/0000-0001-5403-9451)
- Pengpai Zhang
- Wenqi Ma (ORCID: https://orcid.org/0000-0003-1656-0953)
- Lianzhu Zhou
- Wenwen Liu (ORCID: https://orcid.org/0000-0002-9902-0743)
- Chuan Zhang
- Yu Gan
- Fan Zhang (ORCID: https://orcid.org/0000-0002-8576-0273)
- Yingchun Zhang (ORCID: https://orcid.org/0000-0003-3303-7611)
- Xinyue Fang
- Xuanwen Yang
- Chaochao Li (ORCID: https://orcid.org/0000-0001-7536-0527)
- Hui Xue
- Lei Sun
- Xiucai Fan
- Yuting Liu (ORCID: https://orcid.org/0009-0006-0502-5110)
- Yiwen Wang (ORCID: https://orcid.org/0000-0002-7067-9093)
- Qi-ming Long
- Xiangnian Su
- Yongfeng Zhou
- Guizhou Huang (ORCID: https://orcid.org/0009-0000-8381-6299)
- Xu Wang (ORCID: https://orcid.org/0000-0002-7871-5749)
Institutions
- Chinese Academy of Tropical Agricultural Sciences (CN)
- Applied Science and Technology Research Institute (CN)
- Henan University (CN)
- Agricultural Genomics Institute at Shenzhen (CN)
- Tropical Crops Genetic Resources Institute (CN)
- Xinjiang Academy of Agricultural Sciences (CN)
- Zhengzhou Fruit Research Institute (CN)
- Children's Hospital of Chongqing Medical University (CN)
- Washington State University (US)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Horticulture Research
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1093/hr/uhaf128
- Citations
- 3
- Primary Topic
- Horticultural and Viticultural Research
- Type
- article
- Field-Weighted Citation Impact
- 5.25
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China