林木基因型与环境互作的研究方法及其应用
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摘要
我国是全球第一大木材进口国和第二大木材消费国,木材对外依存度已连续多年超过50%。然而,我国每公顷森林年均生长量约为林业发达国家水平的一半,这说明我国林木育种水平与林业发达国家相比仍有较大差距。因此,加强林木的规模化试验与精准遗传评估,通过林木良种的精确选育与推广对提高我国人工林的生产力水平具有重要意义。基因型与环境互作是林木规模化试验与精准遗传评估的重要环节之一。基因型与环境互作(G×E)是指基因型的相对表现在不同环境下缺乏稳定性,表现为不同环境下基因型排序变化或基因型间差别不恒定。现有研究证实,林木G×E很普遍且通常很大,要找到具有广泛适应性的优良基因型往往较困难。由于G×E会减小遗传力和遗传增益,因此了解G×E效应及其驱动环境因子,对育种设计、良种选育和种苗配置至关重要。本文归纳了目前研究G×E的主流分析方法(包括因子分析法、BLUP-GGE联合分析法)和遗传力的估算方法,也比较了这些G×E分析方法(包括稳定性分析、B型遗传相关、AMMI分析、GGE双标法、因子分析法和BLUP-GGE联合分析法)的优缺点,其次综述了全球重要经济树种(湿地松、火炬松、欧洲云杉、巨桉、辐射松、花旗松,等)近年来在生长性状(胸径、树高、材积,等)、形质性状(通直度、分枝角度、分枝大小,等)和材性性状(木材密度、弹性模量,等)的G×E研究进展,进而讨论了林木G×E的环境驱动因子及其应对策略,最后针对林木G×E研究新方法开发、加强多性状的G×E分析以及将基因组选择融入G×E分析方面对未来研究方向提出建议:1)新的林木遗传分析模型与G×E分析的联合应用; 2)林木多环境、多性状的G×E的模式和幅度; 3)特定环境的林木基因组育种值的精准估计。
China is the largest wood importer and the second largest wood consumer in the world, and its dependence on external supply has exceeded 50% for several years. However, the average annual growth of forest per hectare in China is about half of that in the developed countries in forestry, which indicates that there is still larger gap in tree breeding in China, compared with the developed countries in forestry. Therefore, strengthening the large-scale experiments and accurate genetic evaluation of forest trees has great significance in improving the productivity of China's plantation forests through the precise selection and breeding of tree varieties. Genotype by environment interaction is one of the important contents of large-scale experiments and accurate genetic evaluation of forest trees. Genotype by environment interaction(G×E) refers to a lack of consistency in the relative performance of genotypes among different environments, and represents differences in genotype rankings or differences in performance inconstant among environments. Existing studies have confirmed that G×E is very common and often large in forest trees, and it is usually difficult to find consistently superior genotypes with broad adaptation. Since G×E can reduce heritability and genetic gain, understanding the G×E effects and their environmental drivers is vital to mating design, species/variety selection and genotype deployment. The paper reviews the current main analytical method for identifying G×E(including factor analytic method and BLUP-GGE joint analysis) and estimating heritability, and compares the strength and weakness of these analytical method(including stability analysis, type-B genetic correlation, AMMI, GGE biplot, factor analytic method and BLUP-GGE joint analysis), and also reviews the progress of G×E studies on growth traits(such as diameter at breast height, height and volume), form traits(such as stem straightness, branch angle and branch size) and wood properties(such as wood density and modulus of elasticity) in forest species(such as Pinus elliottii, Pinus taeda, Picea abies, Eucalyptus grandis, Pinus radiata and Pseudotsuga menziesii) of global economic importance. Moreover, the paper discusses the environmental drivers that cause G×E and strategies for dealing with G×E in tree breeding. Finally, the future research of G×E is proposed, alongside development of new analytical method, focusing on multi-variate model of G×E and integration of genomic selection with G×E. New genetic analysis model for forest trees should be adopted into G×E studies. The patterns and magnitude of G×E should be focused on multi-variate model for multi-environment trials. Accurate estimation of environment-specific genomic breeding values of forest trees should be performed.
China is the largest wood importer and the second largest wood consumer in the world, and its dependence on external supply has exceeded 50% for several years. However, the average annual growth of forest per hectare in China is about half of that in the developed countries in forestry, which indicates that there is still larger gap in tree breeding in China, compared with the developed countries in forestry. Therefore, strengthening the large-scale experiments and accurate genetic evaluation of forest trees has great significance in improving the productivity of China's plantation forests through the precise selection and breeding of tree varieties. Genotype by environment interaction is one of the important contents of large-scale experiments and accurate genetic evaluation of forest trees. Genotype by environment interaction(G×E) refers to a lack of consistency in the relative performance of genotypes among different environments, and represents differences in genotype rankings or differences in performance inconstant among environments. Existing studies have confirmed that G×E is very common and often large in forest trees, and it is usually difficult to find consistently superior genotypes with broad adaptation. Since G×E can reduce heritability and genetic gain, understanding the G×E effects and their environmental drivers is vital to mating design, species/variety selection and genotype deployment. The paper reviews the current main analytical method for identifying G×E(including factor analytic method and BLUP-GGE joint analysis) and estimating heritability, and compares the strength and weakness of these analytical method(including stability analysis, type-B genetic correlation, AMMI, GGE biplot, factor analytic method and BLUP-GGE joint analysis), and also reviews the progress of G×E studies on growth traits(such as diameter at breast height, height and volume), form traits(such as stem straightness, branch angle and branch size) and wood properties(such as wood density and modulus of elasticity) in forest species(such as Pinus elliottii, Pinus taeda, Picea abies, Eucalyptus grandis, Pinus radiata and Pseudotsuga menziesii) of global economic importance. Moreover, the paper discusses the environmental drivers that cause G×E and strategies for dealing with G×E in tree breeding. Finally, the future research of G×E is proposed, alongside development of new analytical method, focusing on multi-variate model of G×E and integration of genomic selection with G×E. New genetic analysis model for forest trees should be adopted into G×E studies. The patterns and magnitude of G×E should be focused on multi-variate model for multi-environment trials. Accurate estimation of environment-specific genomic breeding values of forest trees should be performed.
引文
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林元震,张卫华,程玲,等.2017.基于空间效应与竞争效应的林木遗传分析模型.华南农业大学学报,38(5):74-80.(Lin Y Z,Zhang W H,Cheng L,et al.2017.Genetic analysis model of forest based on space and competition effects.Journal of South China Agricultural University,38(5):74-80.[in Chinese])
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Baltunis B S,Huber D A,White T L,et al.2007.Genetic analysis of early field growth of loblolly pine clones and seedlings from the same full-sib families.Canadian Journal of Forest Research,37(1):195-205.
Bian L M,Shi J S,Zheng R H,et al.2014.Genetic parameters and genotype-environment interactions of Chinese fir.Canadian Journal of Forest Research,44(6):582-592.
Burdon R D.1977.Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding.Silvae Genetica,26(5/6):168-175.
Cappa E P,Muňoz F,Sanchez L,et al.2015.A novel individual-tree mixed model to account for competition and environmental heterogeneity:a Bayesian approach.Tree Genetics & Genomes,11(6):120-134.
Carson S D.1991.Genotype × environment interaction and optimal number of progeny test sites for improving Pinus radiata in New Zealand.New Zealand Journal of Forest Science,21(1):32-49.
Chen Z Q,Karlsson B,Wu H X.2017.Patterns of additive genotype-by-environment interaction in tree height of Norway spruce in southern and central Sweden.Tree Genetics & Genomes,13(1):25-38.
Costa S J,Borralho N M,Wellendorf H.2000.Genetic parameter estimates for diameter growth,pilodyn penetration and spiral grain in Picea abies (L.) Karst.Silvae Genetica,49(1):29-36.
Costa S J,Potts B,Dutkowski G.2006.Genotype by environment interaction for growth of Eucalyptus globulus in Australia.Tree Genetics & Genomes,2(2):61-75.
Cullis B R,Jefferson P,Thompson R,et al.2014.Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme.Theoretical and Applied Genetics,217(10):2193-2210.
Cullis B R,Smith A B,Coombes N E.2006.On the design of early generation variety trials with correlated data.Journal of Agricultural,Biological,and Environmental Statistics,11(4):381-393.
Diao S,Hou Y M,Xie Y H,et al.2016.Age trends of genetic parameters,early selection and family by site interactions for growth traits in Larix kaempferi open-pollinated families.BMC Genetics,17(1):104-115.
Dieters M J,White T L,Hodge G R.1995.Genetic parameter estimates for volume from full-sib tests of slash pine (Pinus elliottii).Canadian Journal of Forest Research,25(8):1397-1408.
Ding M,Tier B,Yan W,et al.2008.Application of GGE biplot analysis to evaluate genotype (G),environment (E) and G×E interaction on Pinus radiata:a case study.New Zealand Journal of Forest Science,38(1):132-142.
Dungey H S,Low C B,Lee J,et al.2012.Developing breeding and deployment options for Douglas-fir in New Zealand:breeding for future forest conditions.Silvae Genetica,61(3):104-115.
Dutkowski G.2005.Improved models for the prediction of breeding values in trees.Tasmania:PhD thesis of University of Tasmania.
El-Dien O G,Ratcliffe B,Kláp?tě J,et al.2015.Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing.BMC Genomics,16(1):1-16.
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