文献类型： 外文期刊

作者： Li, Jinlong ¹ ; Zhang, Dongfeng ¹ ; Yang, Feng ¹ ; Zhang, Qiusi ¹ ; Pan, Shouhui ¹ ; Zhao, Xiangyu ¹ ; Zhang, Qi ¹ ; Han, Yanyun ¹ ; Yang, Jinliang ³ ; Wang, Kaiyi ¹ ; Zhao, Chunjiang ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Univ Nebraska, Dept Agron & Hort, Lincoln, NE 68583 USA

4.Univ Nebraska, Ctr Plant Sci Innovat, Lincoln, NE 68583 USA

关键词： crop; genotype to phenotype; transfer learning; yield prediction; multi-trait

期刊名称：PLANT COMMUNICATIONS （ 影响因子：9.4； 五年影响因子：9.4 ）

ISSN： 2590-3462

年卷期： 2024 年 5 卷 7 期

页码：

收录情况： SCI

摘要： Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/ TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.