Selection method for excellent individuals of Chinese firs

A Chinese fir and individual technology, which is applied in the field of selection of excellent Chinese fir individuals, can solve problems such as the reduction of genetic diversity, and achieve the effects of high accuracy and good practicability.

Inactive Publication Date: 2013-08-07
NANJING FORESTRY UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The excellent individuals obtained according to the breeding value and selection index are often

Method used

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  • Selection method for excellent individuals of Chinese firs
  • Selection method for excellent individuals of Chinese firs
  • Selection method for excellent individuals of Chinese firs

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0031] Source of material: The 2nd generation Chinese fir seed garden of Guanzhuang State-owned Forest Farm, Shaxian County, Fujian Province is located in Shijingshan, Shijingshan, Chicun Work Area, 40 Forest Class, 54 Large Class and 2 Small Class, with an area of ​​195 mu. The anvil was fixed in February 1982, and grafted from March to April 1, 1984. The front stubble of the seed garden is a natural forest. After felling, mountain refining and clearing, the whole seed garden is divided into 17 groups and 34 plots, of which 7, 14, 26, and 27 sub-districts are divided into two groups. The system adopts the sequential dislocation design and configuration method. Among them, 256 families came from the provenance test forest, the excellent trees selected locally in Fujian, and the hybrid progenies of the excellent trees selected in Nanping area in the early stage.

[0032] The seedlings used in the present invention are all provided by the state-owned forest farm in Guanzhuang, ...

Embodiment 2

[0063] Taking the data measured in Example 1 as the original data, using the spatial analysis method to estimate the spatial residual variance and spatial autocorrelation, the restricted maximum likelihood method to estimate the genetic variance and the best linear unbiased prediction of the mixture of individual breeding values ​​of Chinese fir The linear model method analyzes the genetic variation, genetic control and genetic correlation between the economic traits of Chinese fir, and predicts the breeding value of each individual trait of Chinese fir; the specific process is as follows:

[0064] (1) Data collation and standardization, so that the data conform to the normal distribution.

[0065] (2) Spatial analysis. The spatial analysis model is as follows:

[0066] Y=Xb+Zf+ξ+η

[0067] Among them, Y is the data vector, the fixed effect vector is the mean vector, b and f are the random effect vectors, X and Z are the design matrices corresponding to the observations and ...

Embodiment 3

[0078] According to the multi-trait index selection formula of Chinese fir, the excellent individuals of Chinese fir with both excellent growth and wood properties were selected; the multi-trait index selection formula is as follows:

[0079] I i = 0.7 a ^ i 1 + 0.3 a ^ i 2

[0080] In the formula, I i is the selection index value of genotype i; 0.7 and 0.3 are the weights of growth traits and wood traits of Chinese fir; and is the best linear unbiased prediction breeding value of each character of genotype i (ie, the breeding value of diameter at breast height and the breeding value of wood density calculated in Example 2).

[0081] (1) Guanzhuang State-owned Forest Farm, Sha County, Fujian Province

[008...

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Abstract

The invention discloses a selection method for excellent individuals of Chinese firs. The selection method is a mixed linear model method, utilizes a spatial analysis method for estimating a spatial residual variance and spatial autocorrelation, utilizes a restrictive maximum likelihood method for estimating a genetic variance, and utilizes best linear unbiased prediction for individual breeding value of the Chinese firs. The method analyzes genetic variation, genetic control and genetic correlation among characters for main economic characters such as tree height, diameter at breast height, wood basic density, micro fiber angle and the like of the Chinese fir, predicts the breeding value of each character for each individual of the Chinese firs, and according to a multi-character index selection formula of the Chinese fir, picks out individuals which are genetically excellent both in growth and wood property. According to the method, when the environmental impact is eliminated in generic parameter estimation, the multi-character index selection based on the breeding value can select the individuals with generically excellent comprehensive characters effectively, the accuracy is high, and the selected excellent individuals can serve as both the material for asexual reproduction of the Chinese firs and the breeding material for next-generation breeding population of the Chinese firs.

Description

technical field [0001] The invention relates to the technical field of forest tree breeding, in particular to a method for selecting fine Chinese fir individuals. Background technique [0002] At present, the excellent individual plants of Chinese fir are often selected according to the phenotype, and the phenotype is the result of the interaction between the genotype and the environment. A good phenotype depends on the position effect caused by the environment and genetic factors, whether it can be passed on to offspring and How much strength is unknown, so it is very difficult to determine the quality of offspring and establish high-generation seed gardens based on the quality of expression alone. In the high-generation breeding of Chinese fir, phenotypic selection is mainly used at present, and genetically superior individuals cannot be truly selected. The most important advantage of the best linear unbiased prediction is that all data from multi-generational genetic test...

Claims

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Application Information

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IPC IPC(8): A01G23/00A01G7/00
Inventor 边黎明郑仁华施季森陈金慧杨立伟
Owner NANJING FORESTRY UNIV
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