Wu Hao, Han Rui, Zhao Liang, Liu Mengyao, Chen Hong, Li Weifu, Li Lin
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
Plant Commun. 2025 Apr 14;6(4):101240. doi: 10.1016/j.xplc.2025.101240. Epub 2025 Jan 8.
In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding. Furthermore, recently developed genomic selection (GS) models, such as deep learning (DL), are complicated and inconvenient for breeders to navigate and optimize within their breeding programs. Here, we present the development of an intelligent breeding platform named AutoGP (http://autogp.hzau.edu.cn), which integrates genotype extraction, phenotypic extraction, and GS models of genotype-to-phenotype data within a user-friendly web interface. AutoGP has three main advantages over previously developed platforms: 1) an efficient sequencing chip to identify high-quality, high-confidence SNPs throughout gene-regulatory networks; 2) a complete workflow for extraction of plant phenotypes (such as plant height and leaf count) from smartphone-captured video; and 3) a broad model pool, enabling users to select from five ML models (support vector machine, extreme gradient boosting, gradient-boosted decision tree, multilayer perceptron, and random forest) and four commonly used DL models (deep learning genomic selection, deep learning genomic-wide association study, deep neural network for genomic prediction, and SoyDNGP). For the convenience of breeders, we use datasets from the maize (Zea mays) complete-diallel design plus unbalanced breeding-like inter-cross population as a case study to demonstrate the usefulness of AutoGP. We show that our genotype chips can effectively extract high-quality SNPs associated with days to tasseling and plant height. The models show reliable predictive accuracy on different populations and can provide effective guidance for breeders. Overall, AutoGP offers a practical solution to streamline the breeding process, enabling breeders to achieve more efficient and accurate genomic selection.
面对气候变化和全球人口的不断增长，迫切需要加速高产作物品种的开发。为此，已收集了大量的基因型到表型数据，并开发了许多机器学习（ML）模型来根据给定的基因型预测表型。然而，对单核苷酸多态性（SNP）高密度的要求以及表型数据收集的劳动密集型性质阻碍了这些模型在育种中的应用。此外，最近开发的基因组选择（GS）模型，如深度学习（DL），对于育种者在其育种计划中进行导航和优化来说既复杂又不方便。在这里，我们展示了一个名为AutoGP（http://autogp.hzau.edu.cn）的智能育种平台的开发，该平台在用户友好的网络界面中集成了基因型提取、表型提取以及基因型到表型数据的GS模型。与先前开发的平台相比，AutoGP具有三个主要优势：1）一种高效的测序芯片，可在整个基因调控网络中识别高质量、高可信度的SNP；2）一个完整的工作流程，用于从智能手机拍摄的视频中提取植物表型（如株高和叶片数量）；3）一个广泛的模型库，使用户能够从五个ML模型（支持向量机、极端梯度提升、梯度提升决策树、多层感知器和随机森林）和四个常用的DL模型（深度学习基因组选择、深度学习全基因组关联研究、用于基因组预测的深度神经网络和大豆DNGP）中进行选择。为了方便育种者，我们以玉米（Zea mays）完全双列设计加上不平衡育种样杂交群体的数据集为例，来证明AutoGP的实用性。我们表明，我们的基因型芯片可以有效地提取与抽雄天数和株高相关的高质量SNP。这些模型在不同群体上显示出可靠的预测准确性，并可为育种者提供有效的指导。总体而言，AutoGP为简化育种过程提供了一个切实可行的解决方案，使育种者能够实现更高效、准确的基因组选择。
