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    林元震, 张卫华, 程玲, 等. 基于空间效应与竞争效应的林木遗传分析模型[J]. 华南农业大学学报, 2017, 38(5): 74-80. DOI: 10.7671/j.issn.1001-411X.2017.05.013
    引用本文: 林元震, 张卫华, 程玲, 等. 基于空间效应与竞争效应的林木遗传分析模型[J]. 华南农业大学学报, 2017, 38(5): 74-80. DOI: 10.7671/j.issn.1001-411X.2017.05.013
    shu
    LIN Yuanzhen, ZHANG Weihua, CHENG Ling, ZHANG Xinfei, ZHANG Xinxin. Genetic analysis model of forest based on space and competition effects[J]. Journal of South China Agricultural University, 2017, 38(5): 74-80. DOI: 10.7671/j.issn.1001-411X.2017.05.013
    Citation: LIN Yuanzhen, ZHANG Weihua, CHENG Ling, ZHANG Xinfei, ZHANG Xinxin. Genetic analysis model of forest based on space and competition effects[J]. Journal of South China Agricultural University, 2017, 38(5): 74-80. DOI: 10.7671/j.issn.1001-411X.2017.05.013
    shu

    基于空间效应与竞争效应的林木遗传分析模型

    Genetic analysis model of forest based on space and competition effects

      摘要
    • 摘要:
      目的  建立空间效应与竞争效应的分析模型，以提高林木遗传评估的准确性.
      方法  利用R软件及其程序包breedR模拟数据，结合实测数据，采用XFA1结构拟合加性效应、近邻竞争效应和AR1结构拟合空间效应，建立随机区组设计模型(RCBM)、空间模型(SM)、空间与测量误差模型(SUM)和空间与竞争模型(SCM)，运行ASReml估算遗传参数，进行模型比较。
      结果  对于模拟数据，估计的参数结果均显示 SCM是最优模型，其大大降低了随机误差方差，随机误差方差分别由7.56(RCBM)、5.72(SUM)降低到3.13(SCM)，分别降低了58.6%、45.3%，并估算到四周近邻竞争方差；SCM模型估算的单株狭义遗传力在0.40左右，高于RCBM(0.24)和SUM模型(0.30)；设置参数的不同初始值，SCM估计的参数结果均较为稳定；对于实测数据，估算结果与模拟结果比较一致。
      结论  SCM模型是新的单株混合模型，可用于林木遗传分析。

       

      Abstract:
      Objective  To improve the accuracy in genetic analysis of forest establishing an analysis model based on space and competition effects.
      Method  The data was simulated by R software and its package breedR. The additive effect and neighbor competition effect were fitted using XFA1 structure and the spatial effect was fitted using AR1 structure for both simulated and measured data. Four models (randomized block design model, RCBM; spatial model, SM; spatial with measured error model, SUM; spatial and competition model, SCM) were established and analyzed using ASReml to estimate genetic parameters for comparison.
      Result  The estimated results showed that SCM was the best model for the simulation data. SCM greatly reduced the random error variance from 7.56 (RCBM) and 5.72 (SUM) to 3.13 (SCM), decreased by 58.6% and 45.3%, respectively. SCM could estimate the genetic variance of competition from surrounding neighbors. The individual heritability assessed by SCM was around 0.40, higher than those of RCBM (0.24) and SUM (0.30). SCM obtained stable estimated results under different settings of initial values for parameters. Furthermore, for the measured data, the estimated results were consistent with the simulation data.
      Conclusion  SCM is a new individual-tree mixed model and could be used for genetic analysis of forest.

       

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