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林业科学 ›› 2019, Vol. 55 ›› Issue (8): 73-83.doi: 10.11707/j.1001-7488.20190809

• 论文与研究报告 • 上一篇    下一篇

基于BLUP和GGE双标图的华北落叶松家系区域试验分析

郑聪慧, 张鸿景, 王玉忠, 代剑锋, 党磊, 杜子春, 刘建婷, 高运茹   

  1. 河北省林业科学研究院 河北省林木良种工程技术研究中心 石家庄 050061
  • 收稿日期:2019-02-14 修回日期:2019-05-27 发布日期:2019-09-05
  • 基金资助:
    国家重点研发计划项目子课题“华北落叶松优良品系选配及适应性评价”(2017YFD060040304);河北省科技计划项目子课题“河北省华北落叶松遗传资源收集整理及新品种选育”(16226309D-1);中央财政林草业科技推广示范项目“华北落叶松‘龙林系列’良种示范与推广”(冀TG[2019]001号)。

An Analysis of a Regional Trial of Larix principis-rupprechtii Families Based on BLUP and GGE Biplot

Zheng Conghui, Zhang Hongjing, Wang Yuzhong, Dai Jianfeng, Dang Lei, Du Zichun, Liu Jianting, Gao Yunru   

  1. Hebei Engineering and Technology Center of Forest Improved Variety Hebei Academy of Forestry Shijiazhuang 050061
  • Received:2019-02-14 Revised:2019-05-27 Published:2019-09-05

摘要: [目的]为综合评价华北落叶松参试家系的速生丰产性、稳定性及各试验地点的区分力和代表性,利用基因型主效加基因型-环境互作效应(GGE)双标图对2017年度华北落叶松区域试验中参试家系的生长数据进行分析。[方法]基于冀北地区4个试验地点26个华北落叶松家系的胸径数据,首先拟合3个线性混合效应模型,各个模型均具有相同的固定效应("地点"和"地点中的区组")和残差方差结构(行、列自回归AR1×AR1,以进行空间分析),其中,模型1为随机效应中包含2个公因子的因子分析模型(FA模型),模型2、3分别为随机效应里不含测量误差以及包含测量误差的非结构化矩阵模型(US模型);基于AIC信息准则选出一个最优模型,之后利用最佳线性无偏预测(BLUP)法得到各家系在各地点的胸径BLUP数据;基于胸径BLUP数据做GGE双标图分析,对华北落叶松家系和试验地点进行评价。[结果]基于AIC信息准则,模型3(空间变异结合包含测量误差的非结构化矩阵模型(US模型))被选为最优模型;基于胸径BLUP数据的GGE双标图的前2个主成分的方差解释百分比之和为92.4%,表明结果可靠; 4个试验地点被分成2组,地点L1(张家口赤城马营沟)、地点L3(张家口沽源柳条沟)和地点L4(承德围场查字)为一组(以111号家系的胸径最大),地点L2(承德围场御道口)为一组(以78号家系的胸径最大),相对而言,地点L3(张家口沽源柳条沟)能更有效地选择速生丰产且稳定的家系;各家系在不同试验地点上的表现有所不同,总体而言,26个华北落叶松家系中,111号的胸径(产量)最大,接着是78、72、82、76、59、100、77、56、86、96等系号,胸径(产量)最小的是1号,97、116、53、35、46、66和49号等家系的胸径(产量)也较低,68和42号家系的胸径(产量)接近总体均值; 96、86、100和76号是速生丰产且稳定的家系,速生丰产家系111、72、56号的稳定性居中,速生丰产家系78、82和77号的稳定性中等偏下,而速生丰产家系59号则不稳定。[结论]模型3(空间变异结合包含测量误差的非结构化矩阵模型(US模型))较另外2个模型而言,结果更为可靠。地点L3(张家口沽源柳条沟)既具高区分力,又具高代表性,能更有效地评价家系。家系96、86、100和76号兼具速生性、丰产性和稳定性,可被广泛推广。基于BLUP的GGE双标图能有效应用于华北落叶松家系及试验地点的评价,本研究可为冀北地区华北落叶松的家系选择和应用提供决策支持。

关键词: 线性混合效应模型, 最佳线性无偏预测(BLUP), 基因型主效加基因型-环境互作效应(GGE)双标图, 华北落叶松, 区域试验

Abstract: [Objective] In order to comprehensively evaluate fast-growing, high-yielding and stability of Larix principis-rupprechtii families and discrimitiveness and representiveness of test sites, genotype main effect plus genotype-environmental interaction effect (GGE) biplot was applied to the analysis of 2017 annual growth data of the regional family trial.[Method] Based on the data of diameter at breast height (DBH) for 26 families of L. principis-rupprechtii at four experimental sites in north Hebei, three linear mixed effect models with the same fixed effects ("site" and "block at site") and the same residuals variance matrix (autoregression for row and column AR1×AR1,to be used for spatial effects analysis) were firstly fitted. A factor analysis model with two factors was used in random effects for Model 1 (FA model), while an unstructured matrix model (US model) was used in random effects for Model 2 (without measurement errors) and Model 3 (with measurement errors simultaneously). The optimal model was selected based on Akaike information criterion. Through the best linear unbiased prediction (BLUP), the BLUP data of DBH for each family at each site was obtained. Genotype main effect plus genotype-environmental interaction effect (GGE) biplot based on BLUP data of DBH was analyzed to evaluate families and sites.[Result] Model 3 (fitted by spatial effects with the unstructured matrix model including measurement errors (US model)) was selected as the optimal model based on Akaike information criterion. The sum of variance interpretation percentage for the first two principal components of GGE biplot based on BLUP data of DBH was 92.4%, which suggests that results were reliable. The four sites were divided into two groups. Group 1 included site L1 (Mayinggou of Chicheng), site L3 (Liutiaogou of Guyuan) and site L4 (Zhazi of Weichang), where family 111 had the largest DBH; while group 2 included site L2 (Yudaokou of Weichang), in which family 78 had the largest DBH. Site L3 (Liutiaogou of Guyuan) was relatively more effective in selecting families with characteristics of fast-growing, high-yielding and stability. The performance of each family varied at different sites. Overall, among the 26 families, family 111 had the largest DBH, followed by families 78, 72, 82, 76, 59, 100, 77, 56, 86 and 96. Family 1 had the smallest DBH, and the DBH of families 97, 116, 53, 35, 46, 66 and 49 were small too. The DBH of families 68 and 42 were close to the overall mean. Families 96, 86, 100 and 76 were fast-growing, high-yielding and stable. The stability of fast-growing and high-yielding families 111, 72 and 56 was moderate. The stability of families 78, 82 and 77 was below the average level. The fast-growing and high-yielding family 59 was unstable.[Conclusion] In this study, Model 3 (fitted by spatial effects with the unstructured matrix model including measurement errors (US model)) was more reliable than the other two models. Site L3 (Liutiaogou of Guyuan) with high discrimination and high representation can be used to effectively evaluate families. Families 96, 86, 100 and 76 with characteristics of fast-growing, high-yielding and stability can be widely promoted. GGE biplot based on BLUP data can be effectively used for evaluation of L. principis-rupprechtii families and test sites. This study can provide decision support for the selection and application of L. principis-rupprechtii families in north Hebei.

Key words: linear mixed-effects model, best linear unbiased prediction(BLUP), genotype main effect plus genotype-environmental interaction effect(GGE) biplot, Larix principis-rupprechtii, regional trial

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