TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

作者：Jinlong Li, Dongfeng Zhang, Feng Yang, Qiusi Zhang, Shouhui Pan, Xiangyu Zhao, Qi Zhang, Yanyun Han, Jinliang Yang, Kaiyi Wang, Chunjiang Zhao · 发表于：Plant Communications · 年份：2024 · DOI：10.1016/j.xplc.2024.100975 · 被引用次数：50 · 研究领域：Smart Agriculture and AI

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...