林木基因型-环境互作算法研究进展与思考

doi:10.11707/j.1001-7488.LYKX20240800

Abstract

Abstract:

With global climate change, traditional forest tree breeding means are facing challenges, and unable to meet the urgent demands for rapid climate adaptation and optimized resource allocation. The complex interaction between tree genotype (G) and environment (E) is central to tree growth and development research. It has become a crucial research question to elucidate the G×E interaction mechanisms to enhance breeding efficiency and precision. This review focuses on study progress of the genotype-environment interaction (G×E) algorithms in tree breeding. It analyzes the mechanisms by which genotype and environment interact to shape phenotypic traits, including the association between genomic and phenotypic characteristics, and the impact of environmental factors on phenotypic expression. Additionally, it explores the role of multi-source heterogeneous data integration in deciphering interaction mechanisms and breeding applications, covering data mining techniques, integration strategies, and real-time data processing. Furthermore, this paper elaborates on the evolution and application of G×E interaction algorithms in tree breeding, including the historical development and application in trait prediction and analysis. The review also introduces the framework for developing G×E interaction algorithms for tree breeding, encompassing data acquisition, integration, algorithm design, and model optimization. Finally, future research directions are proposed, emphasizing explainable artificial intelligence, data fusion, breeding validation, and climate adaptability prediction. These advancements aim to provide more precise predictive tools and decision support for tree breeding, ultimately enhancing the ecological adaptability and productivity of trees in the face of climate change.

Key words: interaction between genotypes and environment, forest tree breeding, data fusion, machine learning, deep learning

CLC Number:

S771.8

Xiaoning Ge,Xinqiao Xu,Huaiqing Zhang,Jing Zhang,Jie Yang,Zeyu Cui,Rurao Fu,Jinjie Liang,Tianhua Zou,Linlong Wang,Yang Liu. Progress and Reflection on Genotype-Environment Interaction Algorithms in Forest Tree Breeding[J]. Scientia Silvae Sinicae, 2025, 61(3): 1-15.

Figures/Tables 4

Fig.1

Table 1

Algorithm and characteristics of genotype-environment interaction"

方法 Method	算法设计 Algorithm design	应用 Application	优点 Advantages	缺点 Disadvantages
统计学习 Statistical learning	线性混合模型、广义加性模型、贝叶斯网络 Linear Mixed Model (LMM)， Generalized Additive Model (GAM)， Bayesian Network	基因型与环境互作的固定效应和随机效应分析、非线性关系拟合、因果推断 Analysis of fixed and random effects of genotype - environment interaction， fitting of non - linear relationships, causal inference	模型解释性强，适用于不确定性较高的数据分析，能够量化环境因子的贡献度 The model has strong interpretability, is suitable for data analysis with high uncertainty, and can quantify the contribution of environmental factors	计算复杂度较高，处理高维数据时可能面临维度灾难，难以捕捉复杂的非线性关系 High computational complexity. When dealing with high - dimensional data, it may face the curse of dimensionality and has difficulty capturing complex non - linear relationships
机器学习 Machine learning	随机森林、支持向量机、 LASSO回归、 XGBoost Random Forest (RF)， Support Vector Machine (SVM)， LASSO Regression， XGBoost	林木性状预测、抗性评价、产量估计、基因组选择、环境适应性预测 Plant trait prediction, resistance evaluation, yield estimation, genomic selection, environmental adaptability prediction	处理高维数据能力强，特征选择效果好，模型解释性较好，适用于小样本数据 Strong ability to handle high - dimensional data, good feature selection effect, relatively good model interpretability, and suitable for small - sample data	对非线性关系的处理能力有限，计算复杂度较高，可能忽略复杂的互作关系 Limited ability to handle non - linear relationships, high computational complexity, and may ignore complex interaction relationships
深度学习 Deep learning	卷积神经网络、循环神经网络、长短时记忆网络、 Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short - Term Memory Network (LSTM)	林木表型分析、图像识别、时序数据分析、复杂表型预测 Plant phenotyping analysis, image recognition, time - series data analysis, complex phenotype prediction	自动提取复杂特征，处理非线性关系能力强，适用于大规模数据 Automatically extracts complex features, has a strong ability to handle non - linear relationships, and is suitable for large - scale data	需要大量训练数据，计算资源消耗大，模型解释性较差（“黑箱”问题） Requires a large amount of training data, consumes a lot of computing resources, and has poor model interpretability (the "black - box" problem)
集成学习 Ensemble learning	Stacking， Blending， XGBoost	多模态数据整合、复杂特征关系捕捉、林木表型分析、产量预测 Multi - modal data integration, capturing complex feature, relationships, plant phenotyping analysis, yield prediction	结合多种模型的优势，提升预测的稳健性和准确性，适用于多模态数据融合 Combines the advantages of multiple models, improves the robustness and accuracy of prediction, and is suitable for multi - modal data fusion	模型复杂度高，计算资源消耗大，模型解释性较差 High model complexity, large consumption of computing resources, and poor model interpretability
迁移学习 Transfer learning	预训练模型、轻量化模型（如MobileNet、EfficientNet） Pre - trained models， light weight models (such as MobileNet, EfficientNet)	小样本数据场景、林木表型分析、跨环境适应性评价 Small - sample data scenarion, plant phenotyping analysis, cross - environmental adaptability evaluation	减少训练数据需求，提升模型在新环境中的预测准确度，适用于小样本问题 Reduces the demand for training data, improves the prediction accuracy of the model in new environments, and is suitable for small - sample problems	预训练模型的适用性有限，可能无法完全适应新环境的数据分布 The applicability of pre - trained models is limited, and they may not fully adapt to the data distribution of new environments
知识图谱 Knowledge graph	作物表型知识图谱 Crop phenotype knowledge graph	表型性状的智能解析、基因调控网络与表型数据整合、复杂互作关系模拟 Intelligent analysis of phenotypic traits, integration of gene regulatory networks and phenotypic data, simulation of complex interaction relationships	促进表型性状的智能解析，整合多源数据，揭示基因与环境因子的耦合效应 Promotes the intelligent analysis of phenotypic traits, integrates multi - source data, and reveals the coupling effect of genes and environmental factors	构建和维护知识图谱的成本高，模型复杂度高，计算资源消耗大 High cost of constructing and maintaining the knowledge graph, high model complexity, and large consumption of computing resources

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分类 Classification	模型/算法 Model/Algorithm	应用 Application	文献 References
高通量表型学 High-throughput phenotyping	ResNet-50 CNN Transformer	减少人工标注工作量 Reduce manual labeling workload 提高测量准确性与精度 Improve measurement accuracy and precision	Ghosal et al.，2019； Liu et al.，2025； He et al.，2016
基因功能与基因组预测 Gene function and genome prediction	GS DeepGOplus CNN DNNGP	识别候选基因 Identify candidate genes 结合多组学数据实现更多的性能 Integrating multi-omics data to achieve enhanced performance	Yang et al.，2023a； Wang et al.，2023
多组学数据整合 Multi-omic data integration	GWAS CNN GCNN	结合多组学数据与深度学习制定路线图 Develop a roadmap by integrating multi-omics data with deep learning	Kang et al.，2022
G×E互作 G×E Interaction	GS RRM BLUP GBLUP-AD	对环境梯度的函数建模以预测性能 Modeling functions of environmental gradients to predict trait performance 得到更准确的遗传方差估计 Obtain more accurate estimates of genetic variance	Chen et al.，2019； Papin et al.，2024； El-Dien et al.，2018

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Progress and Reflection on Genotype-Environment Interaction Algorithms in Forest Tree Breeding

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