文献类型：期刊文献

中文题名：林木基因型-环境互作算法研究进展与思考

英文题名：Progress and Reflection on Genotype-Environment Interaction Algorithms in Forest Tree Breeding

作者：葛晓宁[1,2] 许新桥[3] 张怀清[1,2] 张京[1,2] 杨杰[1,2,4] 崔泽宇[1,2] 傅汝饶[1,2,5] 梁金洁[3] 邹添华[3] 王林龙[1,2,6] 刘洋[1,2]

第一作者：葛晓宁

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局林业遥感与信息技术重点实验室,北京100091;[3]国家林业和草原局信息中心,北京100714;[4]北京林业大学,北京100091;[5]中南林业科技大学,长沙410004;[6]中国林业科学研究院林业科技信息研究所,北京100091

年份：2025

卷号：61

期号：3

起止页码：1-15

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：;EI(收录号：20251518198713);北大核心:【北大核心2023】；

基金：中国林业科学研究院基本科研业务费专项(CAFYBB2023PA003);科技创新2030重大项目(2023ZD0406103-02)。

语种：中文

中文关键词：基因型-环境互作;林木育种;数据融合;机器学习;深度学习

外文关键词：interaction between genotypes and environment;forest tree breeding;data fusion;machine learning;deep learning

分类号：S771.8

摘要：随着全球气候变化,传统的林木育种方式面临挑战,难以满足快速气候适应与资源优化配置的迫切需求。林木基因型(G)与环境(E)之间的复杂互作关系是林木生长发育研究的核心,阐明G×E互作机制以提高林木育种效率和精准度成为研究的重点。本文围绕林木基因型-环境互作算法的相关研究进展,解析基因型与环境互作对表型特性塑造的机制,包括基因组与表型特征形成的关联机制、环境因子对表型的影响等;探讨多源异构数据融合在解析互作机制和育种中的应用,涵盖数据挖掘技术、融合策略和实时数据处理;阐述基因型与环境互作算法在林木育种中的演变与应用,包括历史演变、在性状预测和分析中的应用等;介绍林木基因型与环境互作算法研发体系,涉及数据获取、融合、算法设计和模型优化。最后,提出林木基因型-环境互作未来研究的方向,结合可解释人工智能、数据融合、育种验证和气候适应性预测,为林木育种提供更精准的预测工具和决策支持,尤其在应对气候变化挑战时,推动林木的生态适应性与生产力提升。
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.