Method for selectively breeding excellent DH line of crop by using whole genome

Abstract

The invention discloses a method for selectively breeding an excellent DH line of a crop by using a whole genome, and belongs to the technical field of biological breeding. The whole genome selection method comprises the following steps: (1) establishing a statistical genetic model; (2) calculating a marker effect value # imgabs0 # and a population mean value # imgabs1 #; (3) calculating a breeding value based on the marker effect value # imgabs2 # and the population mean value # imgabs3 #; (4) calculating an average breeding value; and (5) selecting an excellent DH line based on the average breeding value. By adopting the whole genome selection method disclosed by the invention, not only can the excellent DH line be efficiently and accurately bred, but also the breeding cost can be greatly reduced and the breeding efficiency can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biological breeding, and in particular to a method for breeding excellent DH lines of crops by whole genome selection. Background Art

[0002] Traditional breeding usually requires 6-8 generations of continuous self-pollination to obtain stable pure lines, while DH (double haploid) technology uses parthenogenetic induction to produce haploids, which are then doubled into dihaploids through treatment with colchicine and other agents. A large number of pure lines can be produced in just two generations, which greatly shortens the breeding period and improves breeding efficiency. With the widespread use of DH technology in crop breeding, millions of DH lines are produced every year, but some problems also arise, such as how to select excellent pure lines from a large number of DH lines. There are two main traditional methods for selecting excellent DH lines: (1) DH lines are evaluated in multiple environments to select excellent DH lines. This method can only evaluate the quality of the DH line itself, but cannot evaluate the performance of the DH line after hybridization with other inbred lines, so it is impossible to truly obtain a DH line that performs well in the hybrid combination; (2) DH lines are hybridized with a few inbred lines and then evaluated in the field, but this method cannot evaluate the general breeding value of DH lines. In summary, the traditional DH breeding method is not only time-consuming and labor-intensive, but also difficult to accurately evaluate and breed DH lines. Therefore, it is urgent to develop a method that can quickly and accurately breed excellent DH lines. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a method for breeding excellent DH lines of crops using whole genome selection, so as to solve the problem of low efficiency of existing DH excellent pure line breeding.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: a method for breeding excellent DH lines of crops using whole genome selection is provided, comprising the following steps:

[0005] (1) Establishment of statistical genetic model:

[0006] A whole genome selection statistical genetic model for constructing hybrid combinations under DH lines was established for phenotypic prediction. Assuming there are n DH lines, m DH lines were randomly selected to construct The whole genome selection statistical genetic model for the hybrid combination constructed for the DH line is as follows:

[0007] y=Xb+Zu+e

[0008] Among them, y is trait phenotypic value vector; X is the fixed effect matrix; b is the fixed effect vector; Z is the marker matrix; u is the marker effect vector; e is the residual vector;

[0009] (2) After obtaining the y value of step (1), the marker effect value is calculated using the best linear unbiased prediction and the population mean The formula is as follows:

[0010]

[0011] Q=V -1 -V -1 X(X T V -1 X) + X T V -1

[0012] Among them, Z is the labeling matrix; X is the fixed effect matrix; T is the matrix transpose; is the estimated genetic variance; Q is the intermediate parameter; V represents the total variance matrix; y is trait phenotypic value vector of ;

[0013] (3) Using the marker effect value obtained above and fixed effect values Calculate the total number of DH systems produced by the remaining nm The genomic breeding value of a hybrid combination is as follows:

[0014]

[0015] Among them, GEBV jk is the genomic breeding value of the hybrid combination produced by the j-th DH line and the k-th DH line; Z jk is the genotype matrix generated for the j-th DH line and the k-th DH line; X is the fixed effect matrix;

[0016] (4) Using the genomic breeding value of the hybrid combination obtained in step (3), calculate the average breeding value of each DH line in all hybrid combinations containing it, the formula is as follows:

[0017]

[0018] in, is the average breeding value of the jth DH line;

[0019] (5) The average breeding value of each DH line obtained in step (4) Sort them from high to low, and then select the DH lines with the top 5% average breeding values ​​as excellent lines.

[0020] Furthermore, the labeling effect vector u in step (1) obeys The residual vector e follows the normal distribution of The normal distribution of is the genetic variance; I is the identity matrix; is the residual variance.

[0021] Furthermore, the genetic variance and the residual variance The minimum norm quadratic unbiased estimation method is used for calculation, and the formula is as follows:

[0022]

[0023] Q=V -1 -V -1 X(X T V -1 X) + X T V -1

[0024] V=G+D

[0025] Among them, tr is the trace of the matrix; U m To solve the variable matrix; U n is the background variable matrix; y is trait phenotypic value vector; T is the matrix transpose; G is the genetic variance matrix; D is the residual variance matrix; represents the variance of the solved variable; Q is the intermediate parameter; X is the fixed effect matrix; V represents the total variance matrix.

[0026] The present invention has the following beneficial effects:

[0027] The whole genome selection method of the present invention comprises the following steps: (1) establishing a statistical genetic model; (2) calculating the marker effect value; and the population mean (3) Based on the marker effect value and the population mean Calculate the breeding value; (4) calculate the average breeding value; (5) select the excellent DH line based on the average breeding value. The whole genome selection method of the present invention can not only efficiently and accurately select excellent DH lines, but also greatly reduce breeding costs and improve breeding efficiency. DETAILED DESCRIPTION

[0028] The principles and features of the present invention are described below in conjunction with the examples, which are only used to explain the present invention and are not intended to limit the scope of the present invention. If no specific conditions are specified in the examples, the conditions are carried out according to conventional conditions or the conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments is not specified, they are all conventional products that can be purchased commercially.

[0029] Example 1

[0030] A method for breeding an excellent DH line of crops using whole genome selection comprises the following steps:

[0031] (1) Establishment of statistical genetic model:

[0032] A double haploid hybrid genomic best linear unbiased prediction (DHHGBLUP) model for phenotypic prediction was established for constructing hybrid combinations under DH lines. Here, we only focus on additive effects. Assuming there are n DH lines, m DH lines are randomly selected to construct The whole genome selection statistical genetic model for the hybrid combination constructed for the DH line is as follows:

[0033] y=Xb+Zu+e

[0034] Among them, y is The phenotypic value vector of the trait; X is the fixed effect matrix; b is the fixed effect vector; Z is the marker matrix; u is the marker effect vector, obey Normal distribution; e is the residual vector, obey The normal distribution of is the genetic variance; I is the identity matrix; is the residual variance.

[0035] (2) Using the minimum norm quadratic unbiased estimator (MINQUE) method to calculate genetic variance and the residual variance The formula is as follows:

[0036]

[0037] Q=V -1 -V -1 X(X T V -1 X) + X T V -1

[0038] V=G+D

[0039] Among them, tr is the trace of the matrix; U m To solve the variable matrix; U n is the background variable matrix; y is trait phenotypic value vector; T is the matrix transpose; G is the genetic variance matrix; D is the residual variance matrix; represents the variance of the solved variable; Q is the intermediate parameter; X is the fixed effect matrix; V represents the total variance matrix;

[0040] (3) After obtaining the y value of step (1), the marker effect value is calculated using the best linear unbiased prediction and the population mean The formula is as follows:

[0041]

[0042] Q=V -1 -V -1 X(X T V -1 X) + X T V -1

[0043] Among them, Z is the labeling matrix; X is the fixed effect matrix; T is the matrix transpose; is the estimated genetic variance; Q is the intermediate parameter; V represents the total variance matrix; y is trait phenotypic value vector of ;

[0044] (4) Using the marker effect value obtained in step (3) and fixed effect values Calculate the total number of DH systems produced by the remaining nm The genomic breeding value of a hybrid combination is as follows:

[0045]

[0046] Among them, GEBV jk is the genomic breeding value of the hybrid combination produced by the j-th DH line and the k-th DH line; Z jk is the genotype matrix generated for the j-th DH line and the k-th DH line; X is the fixed effect matrix;

[0047] (5) Using the genomic breeding value of the hybrid combination obtained in step (4), calculate the average breeding value of each DH line in all hybrid combinations containing it, the formula is as follows:

[0048]

[0049] in, is the average breeding value of the jth DH line;

[0050] (6) The average breeding value of each DH line obtained in step (5) Sort them from high to low, and then select the DH lines with the top 5% average breeding values ​​as excellent lines.

[0051] Test Example 1

[0052] The above method was used to predict the breeding value of the simulated population. The parameters of the simulated population were as follows: the DH population size was 300, the number of molecular markers was 1000, the genetic distance between two adjacent markers was 1cm, the marker loci overlapped with the minor effect gene loci controlling the trait, the effect of each minor effect gene obeyed the same normal distribution N(0,1), and the true heritability of the trait was 0.83. Based on the above parameters, 300 DH line phenotypic values ​​were generated, 100 DH lines were randomly selected to construct 4950 hybrid combinations and generate phenotypic values, and the average breeding value of the remaining 200 DH lines was predicted. The number of simulations was 10.

[0053] The results are as follows: The prediction accuracy of DHHGBLUP is 0.75, while the traditional method requires that the 19,900 hybrid combinations generated by the remaining 200 DH lines be planted in the field, which is costly, time-consuming and labor-intensive.

[0054] Test Example 2

[0055] The above method was used to predict the breeding value of the simulated population. The parameters of the simulated population were as follows: the DH population size was 500, the number of molecular markers was 1000, the genetic distance between two adjacent markers was 1cm, the marker loci overlapped with the minor effect gene loci controlling the trait, the effect of each minor effect gene obeyed the same normal distribution N(0,1), and the true heritability of the trait was 0.83. Based on the above parameters, 500 DH line phenotypic values ​​were generated, 300 DH lines were randomly selected to construct 44,850 hybrid combinations and generate phenotypic values, and the average breeding value of the remaining 200 DH lines was predicted. The number of simulations was 10.

[0056] The results are as follows: The prediction accuracy of DHHGBLUP is 0.81, while the traditional method requires that the 19,900 hybrid combinations generated by the remaining 200 DH lines be planted in the field, which is costly, time-consuming and labor-intensive.

[0057] The above results show that the method of the present invention for breeding excellent DH lines of crops using whole genome selection has good prediction accuracy, and increasing the size of the training population can also improve the prediction accuracy.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for breeding high-quality DH lines of crops using whole genome selection, characterized in that: The following steps are involved: (1) Establishment of statistical genetic model for whole-genome selection: A whole genome selection statistical genetic model for constructing hybrid combinations under DH lines was established for phenotypic prediction. Assuming there are n DH lines, m DH lines were randomly selected to construct The whole genome selection statistical genetic model for the hybrid combination constructed for the DH line is as follows: y=Xb+Zu+e Among them, y is trait phenotypic value vector; X is the fixed effect matrix; b is the fixed effect vector; Z is the marker matrix; u is the marker effect vector; e is the residual vector; (2) After obtaining the y value of step (1), the marker effect value is calculated using the best linear unbiased prediction and the population mean The formula is as follows: Q=V -1 -V -1 X(X T V -1 X) + X T V -1 Among them, Z is the labeling matrix; X is the fixed effect matrix; T is the matrix transpose; is the estimated genetic variance; Q is the intermediate parameter; V represents the total variance matrix; y is trait phenotypic value vector of ; (3) Using the marker effect value obtained in step (2) and fixed effect values Calculate the total number of DH lines produced by the remaining nm The genomic breeding value of a hybrid combination is as follows: Among them, GEBV jk is the genomic breeding value of the hybrid combination produced by the j-th DH line and the k-th DH line; Z jk is the genotype matrix generated for the j-th DH line and the k-th DH line; X is the fixed effect matrix; (4) Using the genomic breeding value of the hybrid combination obtained in step (3), calculate the average breeding value of each DH line in all hybrid combinations containing it, the formula is as follows: in, is the average breeding value of the jth DH line; (5) The average breeding value of each DH line obtained in step (4) Sort them from high to low, and then select the DH lines with the top 5% average breeding values ​​as excellent lines.

2. The method for breeding excellent DH lines of crops using whole genome selection according to claim 1, characterized in that: The labeling effect vector u in step (1) is subject to Normal distribution; the residual vector e obeys The normal distribution of is the genetic variance; I is the identity matrix; is the residual variance.

3. The method for breeding excellent DH lines of crops using whole genome selection according to claim 2, characterized in that: The genetic variance and the residual variance The minimum norm quadratic unbiased estimation method is used for calculation, and the formula is as follows: Q=V -1 -V -1 X(X T V -1 X) + X T V -1 V=G+D Among them, tr is the trace of the matrix; U m To solve the variable matrix; U n is the background variable matrix; y is trait phenotypic value vector; T is the matrix transpose; G is the genetic variance matrix; D is the residual variance matrix; represents the variance of the solved variable; Q is the intermediate parameter; X is the fixed effect matrix; V represents the total variance matrix.

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