林业科学2025，Vol.61Issue(3)：1-15,15.DOI:10.11707/j.1001-7488.LYKX20240800
林木基因型-环境互作算法研究进展与思考
Progress and Reflection on Genotype-Environment Interaction Algorithms in Forest Tree Breeding
摘要
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.关键词
基因型-环境互作/林木育种/数据融合/机器学习/深度学习Key words
interaction between genotypes and environment/forest tree breeding/data fusion/machine learning/deep learning分类
林学引用本文复制引用
葛晓宁,王林龙,刘洋,许新桥,张怀清,张京,杨杰,崔泽宇,傅汝饶,梁金洁,邹添华..林木基因型-环境互作算法研究进展与思考[J].林业科学,2025,61(3):1-15,15.基金项目
中国林业科学研究院基本科研业务费专项(CAFYBB2023PA003) （CAFYBB2023PA003）
科技创新2030重大项目(2023ZD0406103-02). （2023ZD0406103-02）
