Genome prediction method for combining ability of maize foreign germplasm resources

Abstract

The invention discloses a genome prediction method for combining ability of maize foreign germplasm resources. According to the method, 636 parts of foreign corn germplasm resources are collected and hybridized with two test species Jing 2416 and Zheng 58 to form two sets of test cross species, and two-point identification in two years is carried out. According to the method, an RR-BLUP program in R software is utilized, 4 / 5 germplasm is selected as a training set and 1 / 5 corn germplasm is selected as a verification set for all data of 636 foreign corn germplasm resource test cross species, a genome prediction (GP) model of the combining ability of the foreign corn germplasm is established by utilizing an RR-BLUP technology, the combining ability of the germplasm in the verification set is predicted, and a prediction result of the model is calculated. Test results show that the prediction effect of the GP model on all agronomic characters is 0.36-0.56. Wherein the best prediction effect of the ear thickness is 0.56, the prediction effect of the yield is 0.49, and the minimum number of grains per ear is 0.36.

Description

Technical Field

[0001] The invention relates to the field of crop genetic breeding, and in particular to a genome prediction method for the combining ability of corn exotic germplasm resources. Background Art

[0002] Germplasm resources are the basis of corn breeding. Before 2010, breeding work was mainly based on domestic corn germplasm, such as Tang Sipingtou group, Lvda Honggu group, PA and PB groups. As corn varieties from foreign seed companies entered the Chinese market, a large number of excellent corn germplasm resources were also used in domestic breeding. These germplasms have the advantages of lodging resistance, early maturity, fast dehydration, and mechanical harvesting. These resources have broadened the domestic corn germplasm base and formed a new type of hybrid advantage group, which has strong hybrid advantages with the domestic Tang Sipingtou and PA groups. How to use these foreign germplasms requires the establishment of rapid prediction technology for the combining ability of foreign germplasms.

[0003] Meuwissen et al. proposed a genomic prediction (GP) breeding strategy, which uses all molecular marker data or haplotype data throughout the genome and phenotypic data of each sample in the reference population to establish a prediction model, estimate the genetic effect of each marker, and use the genetic effect of each marker in other related breeding populations to predict the breeding value of individuals (Meuwissen et al. 2001). In hybrid breeding, the data of identified hybrids can be used to establish a GP model to predict the performance of unidentified hybrids. The main approach is to use the genotyping results of some parental inbred lines and the field performance of their hybrid combinations in the same heterosis group to make a genomic prediction (GP) model of combining ability, and then use the model to predict the combining ability of other inbred lines. This reduces the cost of testing and combination identification. Summary of the invention

[0004] To solve the above technical deficiencies, the present invention provides a genome prediction method for combining ability of foreign corn germplasm resources. The present invention provides a genome prediction model for combining ability of foreign corn inbred lines using whole genome prediction technology and verifies it.

[0005] The present invention is achieved through the following technical solutions:

[0006] The present invention provides a genome prediction method for combining ability of corn exotic germplasm resources, comprising the following steps:

[0007] ST1. Extract DNA from exotic maize germplasm for genotyping analysis using a 40K chip;

[0008] ST2. Hybridize the exotic corn germplasm with the test variety, and perform variance analysis on the data of the relevant traits of the hybrid;

[0009] ST3. Estimate the effect value of each marker based on the phenotypic values ​​of the test cross and the SNP genotype data of the inbred line using the linear unbiased estimation method. The model used is as follows:

[0010] Y=Xβ+Vb+Hg+Si+σ 2 ε

[0011] y is the phenotypic result of the hybrid; β is the fixed effect value of the experimental site; b is the number of blocks within the site; g is the genotype value of the hybrid; i is the interaction value of genotype and environment; ε is the random effect value of the experimental site; σ 2 ε The error estimate for each site; X, V, H, and S are the association matrices of β, b, g, and i, respectively.

[0012] The genome prediction method for combining ability of corn exotic germplasm resources provided by the present invention is suitable for predicting yield, plant height, ear length, ear diameter, number of grains per ear or 100-grain weight.

[0013] Preferably, the prediction method is suitable for predicting ear diameter or plant height.

[0014] More effectively, the prediction method is suitable for predicting ear diameter.

[0015] The genome prediction method for combining ability of corn exotic germplasm resources provided by the present invention can be used for testing combining ability of corn exotic germplasm resources.

[0016] The genome prediction method for combining ability of corn exotic germplasm resources provided by the present invention can be used for breeding of corn exotic germplasm resources.

[0017] The beneficial effects of the present invention are as follows: the present invention provides a genome prediction method for the combining ability of corn exotic germplasm resources, the method does not require field hybridization testing, and reduces the cost of matching and combination identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The genomic prediction model was established based on the average phenotypic values ​​of 1,272 hybrid combinations at two points in two years. The prediction results of various agronomic traits include yield (GY), plant height (PH), ear length (EL), ear diameter (EW), number of kernels per ear (EKN), and 100-kernel weight (HKW). DETAILED DESCRIPTION

[0019] Example Establishment and prediction of genomic prediction model for combining ability of exotic maize inbred lines 1. Materials and methods

[0020] 1.1 Experimental materials and genotyping methods

[0021] In 2016, based on the national key research and development plan, 636 foreign corn germplasm resources were collected to represent China's imported corn germplasm resources. DNA of 636 imported germplasms was extracted for genotyping analysis using the 40K chip of Boruidi.

[0022] 1.2 Identification methods of phenotypic data

[0023] In the winter of 2016, 636 parental inbred lines were hybridized with the same test varieties Zheng 58 and Jing 2416 at the same time, producing 1272 hybrid combinations. In 2017 and 2018, 1272 hybrid combinations were planted in two test sites in Jinan, Shandong and Xinxiang, Henan. Each combination was planted in 2 rows, 5 meters long, with a randomized block design and a density of 4500 plants / mu. A total of 9 traits were measured for the hybrid combinations, including plant height, grain yield, ear length, ear diameter, number of ear rows, number of grains per row, number of grains per ear, and 100-grain weight (as shown in Table 1). IBM SPASSstatistics23 software was used to perform variance analysis on the data of hybrid yield and its related traits.

[0024] Table 1 Investigation characteristics and measurement methods

[0025]

[0026] Heritability (h 2 ) is calculated according to the following formula:

[0027] H 2 =σ 2 g / (σ 2 g +σ 2 ge +σ 2 err )

[0028] H 2 is the heritability. 2 g is the genetic variance, σ 2 ge is the interaction effect of genotype and environment, σ 2 err is the residual variance.

[0029] 1.3 Principles of establishing gene prediction model

[0030] The genomic prediction model was established using R software version 3.4.2, based on the phenotypic values ​​of the test cross and the SNP genotype data of the inbred lines, using the linear unbiased estimation method (BLUP) to estimate the effect value of each marker. The applicable model is as follows:

[0031] Y=Xβ+Vb+Hg+Si+σ 2ε

[0032] Here y is the phenotypic result of the hybrid; β is the fixed effect value of the experimental site; b is the number of blocks within the site; g is the genotypic value of the hybrid; i is the interaction value of genotype and environment; ε is the random effect value of the experimental site. 2 ε Error estimate for each site. X, V, H, and S are the correlation matrices of β, b, g, and i, respectively.

[0033] 1.4 Validation of the gene prediction model

[0034] Using the RR-BLUP program in R software, all the data of 636 test crosses of exotic maize germplasm resources were selected, 4 / 5 of the germplasm was used as the training set, and 1 / 5 of the maize germplasm was used as the validation set. The calculation was repeated 100 times. The prediction model was established using the data of the training set to predict the theoretical phenotypic value of the validation set. The correlation coefficient between the predicted value of the validation set and the actual observed value was used as the predicted response (r MP ).

[0035] 2. Results and Analysis

[0036] 2.1 Results of variance analysis of test crosses and estimation of trait heritability

[0037] Table 2 Results of variance analysis of yield and its related traits, estimated heritability of traits

[0038]

[0039] The difference is less than 0.05

[0040] The difference is less than 0.001

[0041] The results in Table 2 show that the genotype variances of all traits reached extremely significant differences, indicating that there were significant differences between hybrid combinations. The genotype and environment interaction variances reached extremely significant differences, that is, the hybrid and environment interaction differences were significant. The variability (CV%) was between 7.42 and 36.91. The variability of ear diameter was the smallest (7.42), and the variability of yield was the largest (36.91). The heritability of the traits was between 0.39 and 0.75. The heritability of plant height was much higher than that of other traits.

[0042] 2.2 Genomic prediction results of yield and related traits

[0043] Figure 1Based on the performance of two test cross varieties formed by Zheng 58 and Jing 2416 in Jinan and Xinxiang, the RR-BLUP program in R software was used to select 4 / 5 germplasms as training sets and 1 / 5 corn germplasms as validation sets. The RR-BLUP technology was used to establish a genomic prediction (GP) model for the combining ability of foreign corn germplasm, and the combining ability of the validation set germplasm was predicted, and the prediction results of the model were calculated. The experimental results showed that the prediction effect of the GP model for all agronomic traits was 0.36-0.56. Among them, the prediction effect of ear diameter was the best at 0.56, followed by plant height at 0.53, and the third was yield at 0.49. The genetic prediction values ​​of ear length, 100-grain weight and number of grains per ear were all lower than 0.5, among which the number of grains per ear was the lowest at 0.36.

[0044] The above is only a preferred implementation of this patent. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of this patent. These improvements and substitutions should also be regarded as the scope of protection of this patent.

Claims

1. A method for genomic prediction of combining ability of corn exotic germplasm resources, comprising the following steps: ST1. Extract DNA from exotic maize germplasm for genotyping analysis using a 40K chip; ST2. Hybridize the exotic corn germplasm with the test variety, and perform variance analysis on the data of the relevant traits of the hybrid; ST3. Estimate the effect value of each marker based on the phenotypic values ​​of the test cross and the SNP genotype data of the inbred line using the linear unbiased estimation method. The model used is as follows: Y=Xβ+Vb+Hg+Si+σ 2 ε y is the phenotypic result of the hybrid; β is the fixed effect value of the experimental site; b is the number of blocks within the site; g is the genotype value of the hybrid; i is the interaction value of genotype and environment; ε is the random effect value of the experimental site; σ 2 ε The error estimate for each site; X, V, H, and S are the association matrices of β, b, g, and i, respectively.

2. The method for genomic prediction of combining ability of corn exotic germplasm resources according to claim 1, characterized in that: The prediction method is suitable for predicting yield, plant height, ear length, ear diameter, number of grains per ear or 100-grain weight.

3. The method for genomic prediction of combining ability of corn exotic germplasm resources according to claim 2, characterized in that: The prediction method is suitable for predicting ear diameter or plant height.

4. The method for genomic prediction of combining ability of corn exotic germplasm resources according to claim 3, characterized in that: The prediction method is suitable for predicting ear diameter.

5. A method for genomic prediction of combining ability of corn exotic germplasm resources according to claims 1-4, characterized in that: The prediction method is used for testing the combining ability of corn exotic germplasm resources.

6. A method for genomic prediction of combining ability of corn exotic germplasm resources according to claims 1-4, characterized in that: The prediction method is used for breeding of corn exotic germplasm resources.

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