空间分析及其对杉木遗传试验效率的影响

doi:10.3969/j.issn.1000-2006.2015.05.007

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南京林业大学学报（自然科学版） ›› 2015, Vol. 39 ›› Issue (05): 39-44.doi: 10.3969/j.issn.1000-2006.2015.05.007

空间分析及其对杉木遗传试验效率的影响

边黎明¹,郑仁华²,肖晖²,甘振栋³,苏顺德²,施季森^1*

1.南京林业大学南方现代林业协同创新中心,江苏南京 210037;
2.福建省林业科学研究院, 福建福州 350012;
3.福建省邵武卫闽国有林场,福建邵武 354006

出版日期:2015-10-15 发布日期:2015-10-15
基金资助:
收稿日期:2014-10-20 修回日期:2015-01-20
基金项目:福建省林木种苗科技攻关三期、四期项目(闽林科[2009]4号和闽林科[2013]1号); 江苏高校优势学科建设工程资助项目(PAPD)
第一作者:边黎明,博士。*通信作者:施季森,教授。E-mail: jshi@njfu.edu.cn。
引文格式:边黎明,郑仁华,肖晖,等. 空间分析及其对杉木遗传试验效率的影响[J]. 南京林业大学学报:自然科学版,2015,39(5):39-44.

Spatial analysis and its effects on efficiency of genetic trial in Chinese fir

BIAN Liming¹, ZHENG Renhua², XIAO Hui², GAN Zhendong³, SU Shunde², SHI Jisen^1*

1.Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China;
2. Fujian Academy of Forestry, Fuzhou 350012, China;
3. Weimin Forest Farm, Fujian Province, Shaowu 354006, China

Online:2015-10-15 Published:2015-10-15

摘要/Abstract

摘要： 以位于福建卫闽国有林场10年生杉木80个半同胞家系的子代遗传测定林为材料,以ASReml程序进行林木遗传试验的空间分析,并分别利用试验设计模型和试验设计与空间分析整合模型对胸径生长进行遗传分析,检验空间分析对林木遗传试验效率的影响。模型选择结果表明,试验设计与空间分析整合模型中,空间效应完全取代了试验设计效应,模型可简化为空间分析模型。与试验设计模型相比,空间分析模型的AIC(赤池最小信息量准则)减少了129.2,模型的拟合程度更好; 同时,加性遗传方差增加了16.5%,残差方差降低了11.6%,单株狭义遗传力增加了31.3%; 行和列上的空间自相关参数分别为0.89和0.96,试验地点有明显的整体变异趋势。

Abstract: DBH at the age of 10 years in a progeny test of Chinese fir with 80 families from Fujian Weimin Forest Farm was examined by both experimental design model and combined design with spatial model, through which spatial analysis methods using ASReml was introduced, and efficiency of genetic trial was improved. All design effects was found replaced by spatial effects in combined model. AIC was reduced by 129.2 in spatial model, which was more effectively modelled the spatial variation than the design model, and greatly increased additive genetic variance by 16.5%, reduced residual variance by 11.6%, and improved individual tree narrow sense heritabilities by 31.3% correspondingly. Autocorrelations in row and column direction with 0.89 and 0.96 indicated that there are strong global trend in this site.

中图分类号:

S711
S722

边黎明,郑仁华,肖晖,等. 空间分析及其对杉木遗传试验效率的影响[J]. 南京林业大学学报（自然科学版）, 2015, 39(05): 39-44.

BIAN Liming, ZHENG Renhua, XIAO Hui, GAN Zhendong, SU Shunde, SHI Jisen. Spatial analysis and its effects on efficiency of genetic trial in Chinese fir[J].Journal of Nanjing Forestry University (Natural Science Edition), 2015, 39(05): 39-44.DOI: 10.3969/j.issn.1000-2006.2015.05.007.

参考文献

[1] Gilmour A R, Cullis B R, Verbyla A P. Accounting for natural and extraneous variation in the analysis of field experiments [J]. Journal of Agricultural, Biological, and Environmental Statistics, 1997, 2(3): 269-293.
[2] Ver Hoef J, Cressie N A. Spatial statistics: Analysis of field experiments[C]//Scheiner S, Gurevitch J. Design and analysis of ecological experiments. London: Chapman and Hall, 1993: 319-341.
[3] Costa e Silva J, Dutkowski G W, Gilmour A R. Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual [J]. Canadian Journal of Forest Research, 2001, 31(11): 1887-1893.
[4] 张建方. 关于试验设计的效率及有关问题 [J]. 数理统计与管理, 2007, 26(5): 792-801. Zhang J F.On the efficiency of experimental design and its some related problems [J]. Application of Statistics and Management, 2007, 26(5): 792-801.
[5] Costa e Silva J, Potts B M, Bijma P, et al. Genetic control of interactions among individuals: contrasting outcomes of indirect genetic effects arising from neighbour disease infection and competition in a forest tree [J]. New Phytol, 2013, 197(2): 631-641.
[6] Magnussen S. A method to adjust simultaneously for spatial microsite and competition effects [J]. Canadian Journal of Forest Research, 1994, 24(5): 985-995.
[7] Williams E R, Matheson A C, Harwood C E. Experimental design and analysis for tree improvement [M]. Collingwood, Australia: CSIRO Pulishing, 2002.
[8] White T L, Adams W T, Neale D B. Foerst genetics [M]. London:CABI Publishing, 2007.
[9] Fu Y, Yanchuk A D, Namkoong G. Spatial patterns of tree height variations in a series of Douglas-fir progeny trials: implications for genetic testing [J]. Canadian Journal of Forest Research, 1999, 29(6): 714-723.
[10] Ye T Z, Jayawickrama K J S. Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest [J]. Tree Genetics & Genomes, 2008, 4: 677-692.
[11] Dutkowski G W, Costa e Silva J, Gilmour A R, et al. Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials [J]. Canadian Journal of Forest Research, 2006, 36(7): 1851-1870.
[12] Dutkowski G W, Costa e Silva J, Gilmour A R, et al. Spatial analysis methods for forest genetic trials [J]. Canadian Journal of Forest Research, 2002, 32(12): 2201-2214.
[13] Gezan S A. Optimal design and analysis of clonal forestry trials using simulated data [D]. Gainesville: University of Florida, 2005.
[14] 郑仁华. 杉木遗传育种研究进展与对策 [J]. 世界林业研究, 2005, 18(3): 63-65. Zheng R H. Research advances in genetic breeding of Chinese fir[J]. World Forest Research, 2005, 18(3): 63-65.
[15] 国家林业局. 国家森林资源清查[R].
[2004-10-01]. http://211.167.243.162:8085/8/index.html.
[16] Walsh B, Lynch M. Genetics and analysis of quantitative traits [M]. Sunderland, Maryland: Sinauer, 1998.
[17] Stram D O, Lee J W. Variance components testing in the longitudinal mixed effects model [J]. Biometrics, 1994, 50(4): 1171-1177.
[18] Gilmour A R, Cullis B R, Thompson R. ASReml user guide release 3.0 [M]. Hemel Hempstead, UK: VSN International, 2009.
[19] Akaike H. A new look at the statistical model identification [J]. IEEE Transactions on Automatic Control, 1974, 6(19): 8.
[20] Falconer D S. Introduction to quantitative genetics [M]. 4th Edition. New York: Longman, 1996.
[21] Cotterill P P. On estimating heritability according to practical applications [J]. Silvae Genetica, 1987, 36: 46-48.
[22] Fu Y. On implementation of incomplete block designs in forest genetic field trials [J]. Forest Genetics, 2003, 10(1): 23-33.
[23] Fu Y, Clarke G P Y, Namkoong G, et al. Incomplete block designs for genetic testing: statistical efficiencies of estimating family means [J]. Canadian Journal of Forest Research, 1998, 28(7): 977-986.
[24] Gezan S A, White T L, Huber D A. Comparison of experimental designs for clonal forestry using simulated data [J]. Forest Science, 2006, 52(1): 108-116.
[25] Magnussen S. Application and comparison of spatial models in analyzing tree-genetics field trials [J]. Canadian Journal of Forest Research, 1990, 20(5): 536-546.
[26] Bian L, Shi J, Zheng R, et al. Genetic parameters and genotype-environment interactions of Chinese fir(Cunninghamia lanceolata)in Fujian Province [J]. Canadian Journal of Forest Research, 2014, 44: 582-592.

空间分析及其对杉木遗传试验效率的影响

Spatial analysis and its effects on efficiency of genetic trial in Chinese fir

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