火炬松基因资源的育种值预测与选择

doi:10.11707/j.1001-7488.20140809

Abstract

Abstract:

This study tested 258 families of Loblolly pine (Pinus taeda) with different genetically improved levels introduced from the USA. A mixed linear model in terms of restricted maximum likelihood estimate (REML) and best linear unbiased prediction (BLUP) was used to predict breeding values of height and diameter at breast height (DBH) for the tested families and single trees at the age of 14 years. The phylogenetic relationship matrix was used to promote the accurate of the prediction. The control-pollinated families had 1.40 cm of average gain for DBH, being the highest gain in the whole trail. Thirty nine excellent families, accounting for 15% of all the tested families, were selected based on the predicted breeding values. These families could be re-introduced from the USA and used for afforestation. By using combined selection 77 individuals, accounting for 1% of all the tested trees, were selected to enrich the second generation breeding population. Ten individuals among them were selected into the second generation nucleus breeding population, remaining 67 into the second generation main breeding population. The accurate prediction based on BLUP and the selection based on individual tree breeding values in the advanced-generation breeding program would improve the accuracy and the genetic gain of selection. The breeding program and the trail design should be improved to achieve the maximum effect when using the advanced statistics method such as ASReml. The balanced incomplete block design (BIB) could be used to conduct the field experiment design when the families involved in the trail were too many, which would help to evaluate the factors in the trail and award the maximum achievement.

Key words: Pinus taeda, gene resource, breeding value, selection

CLC Number:

S722.3

Liu Tianyi, Yang Huixiao, Liu Chunxin, Wang Jinbang, Huang Shaowei. Prediction of Breeding Values and Selection to the Gene Resources of Loblolly Pine[J]. Scientia Silvae Sinicae, 2014, 50(8): 60-67.

References

边黎明, 施季森, 童春发, 等.2012.ASReml及其在林木遗传分析中的应用前景.林业科技开发, 26(4): 9-12.
刘天颐, 刘纯鑫, 钟伟华, 等.2013.火炬松核心育种群体子代生长变异与选择.林业科学, 49(2): 27-32.
(Liu T Y, Liu C X, Zhong W H, et al. 2013. Growth variation and selection to the progeny of nucleus breeding population of Pinus taeda. Scientia Silvae Sinicae, 49(2): 27-32. [in Chinese] )
孙晓梅, 杨秀艳.2011.林木育种值预测方法的应用与分析.北京林业大学学报, 33(2): 65-71.
(Sun X M, Yang X Y. 2011. Applications and analysis of methods for breeding value prediction in forest trees. Journal of Beijing Forestry University, 33(2): 65-71. [in Chinese] )
朱军.2000.线性模型分析原理.北京: 科学出版社.
Gilmour A R, Thompson R, Cullis B R. 1995. Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics, 51(4): 1440-1450.
Gilmour A R. 1997. ASReml for testing fixed effects and estimating multiple trait variance components. Proceeding of the Association for the Advancement of Animal Breeding and Genetics, 12: 386-390.
Gilmour A R, Gogel B J, Cullis B R, et al. 2009. ASReml User Guide Release 3.0. Hemel Hempstead: VSN International Ltd.
Henderson C R. 1953. Estimation of variance and covariance components. Biometrics, 9 (2): 226-252.
Henderson C R. 1963. Selection index and expected genetic advance//Hanson W D, Robinsion H F. Statistical genetics and plant breeding. Washington DC: NAS-NRC Pub., 141-163.
Henderson C R. 1974. General flexibility of linear model techniques for sire evaluation. Journal of Dairy Science, 57 (8): 963-972.
Henderson C R. 1975. Use of all relatives in intraherd prediction of breeding values and producing abilities. Jounal of Dairy Science, 58(12):1910-1916.
Henderson C R. 1976a. Inverse of a matrix of relationships due to sires and maternal grandsires in an inbred population. Journal of Dairy Science, 59(9): 1585-1588.
Henderson C R. 1976b. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics, 32(1): 69-83.
Henderson C R. 1980. A simple method for unbiased estimation of variance components in the mixed model. Journal of Animal Science, 58(1): 119.
Kennedy B W, Sorenson D A. 1988. Properties of mixed-model methods for prediction of genetic merit//Proceedings of the Second International Conference on Quantitative Genetics. Sunderland, USA: Sinauer Associates, 91-103.
White T L, Hodge G R. 1989. Predicting breeding values with applications in forest tree improvement. Dordrecht: Kluwer Academic Publishers.
White T L, Thomas A W, Neale D B. 2007. Forest genetics. CAB International, Wallingford UK.

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Prediction of Breeding Values and Selection to the Gene Resources of Loblolly Pine

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