Genome selective breeding method based on wood character of poplar multi-generation hybrid population and application

Abstract

The invention discloses a genome selective breeding method based on poplar multi-generation hybrid population wood character and application, and belongs to the technical field of poplar genetic breeding. On the basis of re-sequencing data of multi-generation hybrid populations of 762 American black poplar plants, wood property character genome selection breeding research is systematically carried out, QTNs significantly related to the poplar wood property characters are identified through whole genome association analysis, the influence of 10 GS statistical models and different marker screening strategies on whole genome selection prediction precision is evaluated, and the prediction accuracy of the whole genome selection prediction is evaluated. The optimal genome selection prediction method for the populus americana wood property characters is established, and compared with random marking, the prediction precision is averagely improved by 157.55%. According to the method, early prediction of the poplar property characters can be realized, and offspring and core breeding parents with excellent wood quality are screened, so that the forest breeding period is shortened, the forest breeding precision is improved, the test scale is reduced, and the breeding process of excellent forest germplasm is accelerated.

Description

Technical Field

[0001] The invention relates to a method and application of selection breeding based on the genome selection of wood properties of a multi-generation hybrid population of poplars, belonging to the technical field of poplar biological breeding. Background Art

[0002] Poplars are important afforestation species in my country and are widely used in timber production, pulp and paper making, and ecological restoration. The basic density, microfibril angle, and fiber length and width of wood are directly related to wood quality and economic benefits. Existing new poplar varieties are difficult to meet breeding needs. In view of the different breeding needs of wood properties, it is urgent to accelerate the genetic improvement process of poplar wood properties and select new poplar varieties with corresponding improved wood properties.

[0003] Wood properties are characterized by multi-gene and multi-level regulation, and they change dynamically with tree age. Traditional genetic improvement relies on long-term phenotypic evaluation and intergenerational alternation, and the breeding cycle is long. In the process of genetic improvement, different wood traits often affect each other, and it is difficult to achieve the co-breeding of multiple traits by conventional breeding. Since the whole genome selection breeding (Genomic selection, GS) method was proposed in 2001, it has played an increasingly important role in shortening the breeding cycle and improving breeding efficiency, and to a certain extent solved the problem of the difficulty of co-breeding of multiple traits. At present, there are few reports on the research on genomic selection of wood traits in multi-generational hybrid populations of poplar.

[0004] The accuracy of genomic selection prediction is usually evaluated by prediction accuracy. Phylogenetic relationships, statistical models, marker density, etc. will affect the prediction accuracy (PA). Genomic prediction depends on the relationship between the reference population and the breeding population. For populations with close kinship, such as multi-generation hybrid populations, better prediction results can often be achieved. For whole genome selection models, different statistical models often have different prior assumptions and different assumptions about the distribution of marker effects. At present, no model can be well applied to most whole genome selection studies. In actual breeding, it is necessary to conduct a systematic evaluation in combination with breeding goals to select the most suitable model. How to optimize marker density and improve prediction efficiency has always been the key to GS breeding.

[0005] Therefore, it is a technical challenge that urgently needs to be solved in this technical field to systematically carry out genomic selection breeding of wood traits in multi-generation hybrid populations of American black poplar (Populus deltoides), establish a whole genome selection genetic improvement breeding system for wood traits, select asexual lines and core breeding parents with multiple excellent wood qualities, achieve genetic improvement of poplar wood, and increase genetic gain. Summary of the invention

[0006] The purpose of the present invention is to provide a genomic selection breeding method and application based on wood traits of multi-generation hybrid populations of poplars, which can achieve accurate screening of clones with excellent wood quality, shorten the breeding cycle, and accelerate the genetic improvement of poplar wood quality by identifying significant markers of genome-wide association study (GWAS), selecting the optimal GS model, and screening efficient prediction markers.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for selecting and breeding wood traits of a multi-generation hybrid population of poplars based on genomic selection, comprising the following steps:

[0009] Step 1: Resequencing of the genome selection population and determination of the phenotype of the reference population

[0010] Multi-generation hybridization was used to construct a genome selection reference population and breeding population. The Illumina HiSeq6000 high-throughput sequencing platform was used to perform double-end PE150 (PE refers to paired-end sequencing, and 150 refers to the number of bases measured at both ends) sequencing on the genome selection population. Populus deltoides'Danhong' (DHY) was used as the reference genome for genotyping and quality control to obtain high-quality single nucleotide polymorphism (SNP) markers. The pith of each tree in the reference population was drilled from south to north using a growth cone, and a complete and defect-free wood core sample was taken out. The outermost annual ring was removed, and the penultimate annual ring was taken to measure the basic density (BD), microfibril angle (MA), fiber length (MA) and fiber width (FW).

[0011] Step 2: Genome-wide association analysis of wood traits

[0012] Based on the genotype data of the reference population and the wood property phenotype data, the genome-wide association analysis (GWAS) model was selected for association analysis to identify the quantitative trait nucleotides (QTNs) that affect the phenotype;

[0013] Step 3: Evaluate wood traits using the best genomic selection model

[0014] A 5-fold cross validation was used to evaluate the performance of 10 GS models at different random marker densities and comprehensively screen the optimal statistical model for each trait;

[0015] Step 4: Screening strategy for optimal GS markers for wood properties

[0016] The screening strategy includes screening based on linkage disequilibrium (LD) levels among markers, correlation between markers and traits, and using equal amounts of random markers as controls.

[0017] Preferably, step 5 is also included: establishing the optimal prediction system for whole genome selection

[0018] The subset of trait-significantly associated markers, the optimal whole-genome selection statistical model and the optimal marker screening strategy constitute the optimal prediction system for whole-genome selection of poplar wood traits.

[0019] Preferably, in step 1, the whole genome resequencing data is aligned to the core parent Danhong Poplar genome using the BWA tool, and single nucleotide polymorphism (SNP) is detected using the Genome Analysis ToolKit (GATK); quality control is performed using the Variant Call Format tools (VCFtools), and missing markers are filled using the BEAGLE software. The basic density of wood is determined in accordance with the national standard of the People's Republic of China GB / T1993-2009, and the microfibril angle is determined by X-ray diffraction (XRD); fiber length and fiber width are determined using an L&W fiber analyzer; wherein the basic density is measured 5 times, the microfibril angle is measured 30 times, and the fiber morphology is observed more than 50 times.

[0020] Preferably, in step 2, the genome-wide association analysis is performed using the IIIVmrMLM model.

[0021] Preferably, in step 3, the 10 GS models are GBLUP, rrBLUP, BayesA, BayesB, BayesC, BRR, RF, GBDT, KRR and DNNGP; the different random marker densities are allsnp, 0.5snp, 0.2snp, 0.1snp, 0.05snp and 0.01snp; the 5-fold cross-validation method uses 80% of the reference population as a training population and 20% as a test population, and repeats the iteration 20 times; and the mean Pearson correlation coefficient between the test population breeding value and the actual observed value is used as an indicator for evaluating the prediction accuracy.

[0022] Preferably, in step 4, the linkage disequilibrium (LD) level between the markers is LD≤1, LD≤0.8, LD≤0.5, LD≤0.3, LD≤0.2 and LD≤0.1; the correlation level between the marker and the trait is p≤1, p≤0.5, p≤0.1, p≤0.01, p≤0.001 and p≤0.0001.

[0023] Preferably, in step 5, the numbers of trait-significantly associated marker subsets (basic density, microfibril angle, fiber length and fiber width-significantly associated marker subsets) are 22, 16, 26 and 17 respectively; the optimal whole-genome selection statistical models are BayesB, GBDT, RF and GBDT respectively; and the optimal marker screening strategy is based on the GWAS association analysis at the p≤0.01 level.

[0024] The above-mentioned method of genomic selection breeding based on wood traits of multi-generation hybrid populations of poplars is applied in poplar breeding.

[0025] Preferably, the optimal GS prediction system for each trait is used to predict the GEBV value of the breeding population, and the prediction results of multiple traits are integrated to screen excellent parents and progeny.

[0026] The beneficial effects of the present invention are as follows:

[0027] Compared with the prior art, the present invention establishes a method and application for genomic selection breeding of wood traits of multi-generation hybrid populations of American poplar (Populus deltoides), including a subset of trait-significantly associated markers, an optimal whole-genome selection statistical model and an optimal marker screening strategy; compared with random markers, the optimal prediction system for whole-genome selection of poplar wood traits of the present invention improves the prediction accuracy of basic density, microfibril angle, fiber length and fiber width by 168.85%, 217.46%, 60.05% and 183.83%, respectively, with an average increase of 157.55%, and improves the genomic prediction accuracy and selection efficiency of poplar wood traits controlled by multiple genes.

[0028] The present invention is further described below through specific implementation modes and drawings, but it is not intended to limit the protection scope of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a normal distribution diagram of wood property phenotypes of a reference population of poplar trees in Example 1 of the present invention;

[0030] Figure 2 The Manhattan plot of the genome-wide association analysis of wood traits of the poplar reference population in Example 1 of the present invention;

[0031] Figure 3It is a box plot of the prediction accuracy of the whole genome selection of wood traits of 10 statistical models under 6 SNP marker densities in Example 2 of the present invention;

[0032] Figure 4 This is a graph of model prediction accuracy under different marker screening strategies in Example 2 of the present invention;

[0033] Figure 5 It is a scatter plot of prediction accuracy under the optimal prediction system for each trait in Example 2 of the present invention;

[0034] Figure 6 This is the phylogenetic network of excellent asexual lines under the optimal prediction system for each trait in Example 2 of the present invention. DETAILED DESCRIPTION

[0035] The present invention is further described below in conjunction with specific examples. Operations not described in detail in the following examples can be implemented by referring to molecular cloning and the instructions for use of related kits.

[0036] Unless otherwise specified, the reagents involved in the following examples are all conventional reagents available on the market, and the methods used are all commonly used methods in the art.

[0037] Example 1

[0038] A method for selecting and breeding wood traits of a multi-generation hybrid population of poplars based on genomic selection, comprising the following steps:

[0039] (1) Population resequencing and phenotyping

[0040] The Illumina HiSeq6000 high-throughput sequencing platform was used to perform paired-end PE150 sequencing on the genome selection population. GATK was used for variation detection, and the GATK parameters were set as follows: QD<2.0, FS>50, MQ<20, MQRankSum>12.5, and ReadPosRankSum>8.0. VCFtools software was used to filter the genotypes, and the filtering parameters were as follows: sequencing depth greater than 5×, minimum allele frequency not less than 0.05, deletion rate less than 5%, and serious deviation from Hardy-Weinberg equilibrium markers. Finally, about 114 high-quality SNP markers were obtained.

[0041] For the reference population of American black poplar (Populus deltoides), the pith was drilled from south to north using a growth cone at the DBH, and the complete and defect-free wood core sample was taken out. The outermost annual ring was removed, and the penultimate annual ring was taken to measure the basic density (BD), microfibril angle (MA), fiber length (FL) and fiber width (FW). The basic density of wood was measured in accordance with the national standard of the People's Republic of China GB / T1993-2009; the microfibril angle was measured by X-ray diffraction (XRD); the fiber length and fiber width were measured by L&W fiber analyzer; among them, the basic density was measured 5 times, the microfibril angle was measured 30 times, and the fiber morphology was observed more than 50 times; Figure 1 As shown, the wood properties all conform to the normal distribution, and the phenotypic statistical results are shown in Table 1 below:

[0042] Table 1 Statistical results of wood properties

[0043]

[0044] (2) Genome-wide association analysis of wood traits

[0045] Based on the high-quality SNP markers obtained above, principal component analysis was performed using Tassel software, and the kinship matrix was calculated. Combined with the reference population phenotype, the IIIVmrMLM model was used for analysis. Genome scanning was used to obtain the P-values ​​of all markers, and markers with P-values ​​< 0.01 were retained as potential quantitative trait nucleotides (QTNs) for the next step of analysis. The potential QTNs obtained by genome scanning were incorporated into the multi-locus model, and the marker effect was estimated by Bayesian, and the likelihood ratio was output to test markers with LOD scores greater than 3.0. For significant and suggested QTNs, the threshold P value and LOD score were set to 0.05 / m and 3.0, respectively, where m is the number of markers. The Manhattan plot of wood quality trait GWAS was drawn using IIIVmrMLM software ( Figure 2 ), the numbers of QTNs with basic density, microfibril angle, fiber length and fiber width exceeding the threshold were 22, 16, 26 and 17, respectively; significant QTNs will be added as covariates in the subsequent GS model.

[0046] Example 2: Application of the breeding method based on the genomic selection of wood traits of multi-generation hybrid populations of poplars in poplar breeding

[0047] (1) Evaluation of the optimal genomic selection model for wood traits

[0048] The 5-fold cross-validation method was used to evaluate the differences in the prediction ability of the 10 mainstream GS models for the wood properties of American black poplar. In the 5-fold cross-validation method, 80% of the reference population was used as the training population and 20% as the test population. The process was repeated 20 times. The 10 models included 2 BLUP models (RR-BLUP and GBLUP), 4 Bayes models (BayesA, BayesB, BayesC, Bayesian LASSO and BRR), 3 machine learning models (GBDT, FR, XGBoost, SVR) and a deep learning model DNNGP.

[0049] In order to improve the accuracy of model evaluation, vcflib software was used to randomly generate 6 groups of high-density marker subsets; the 6 random marker densities were allsnp, 0.5snp, 0.2snp, 0.1snp, 0.05snp and 0.01snp; the mean Pearson correlation coefficient between the test population breeding value and the actual observed value was used as an indicator to evaluate the prediction accuracy; under the 6 marker densities, the prediction ability of each model for wood properties was as follows Figure 3 As shown; Based on the prediction performance of the models under the six marker densities, the BB model for BD traits is the best (0.244), the GBDT model for MA traits is the best (0.126), the RF model for FL traits is the best (0.423), and the GBDT model for FW traits is the best (0.167);

[0050] (2) Optimal GS marker screening strategy for wood properties

[0051] Based on the most suitable model for each trait, three strategies were used to generate different marker subsets (Table 2). According to the prediction results, the most suitable model predictions for wood traits under different strategies were statistically analyzed, such as Figure 4 As shown in the figure, there is no significant difference in the prediction ability of traits between LD screening markers and random markers, and the LD level has almost no effect on the prediction ability of the model; there are significant differences in the prediction accuracy of each trait under different P values; the diameter at breast height, basic density, microfibril angle, and fiber length show a trend of first increasing and then decreasing with the increase of correlation, while the tree height and fiber width show a trend of first increasing and then stabilizing with the increase of correlation ( Figure 4 ), when the P value of the marker and trait reached the 0.01 level, the model had a good prediction result; after adding the significant marker as a covariate to the model, the prediction accuracy was finally evaluated, and a scatter density map was drawn based on the GEBV value and the actual observed value, and the linear regression equation was fitted. The final prediction accuracy of BD, MA, FL, and FW was 0.673, 0.378, 0.670, and 0.458, respectively ( Figure 5 ), compared with random sampling, the improvements are 168.85%, 217.46%, 60.05%, and 183.83% respectively;

[0052] Table 2 Genomic selection marker screening strategy

[0053]

[0054] (3) Prediction of breeding population genome and screening of superior clones

[0055] Based on the optimal GS prediction system for each trait, the GEBV values ​​of 502 clones in the breeding population were predicted. Subsequently, the breeding population was ranked according to the GEBV value. Based on the ranking results of multiple traits, clones with multiple excellent wood qualities were selected according to a 20% seed retention rate to achieve genetic improvement of multiple traits of poplar wood properties. Wood properties need to be quantitatively improved according to breeding needs. Table 3 shows 31 clones with higher basic density and longer fiber length. These materials have better mechanical properties and stability and can be used for furniture manufacturing, building materials and pulp.

[0056] Table 3 Results of 31 clone systems of Populus deltoides with excellent wood properties

[0057]

[0058]

[0059] The genetic relationship of the clones with good wood quality was further integrated, and the genetic relationship network was constructed based on the information of the parents. The network was visualized using the Cytoscape software, and the importance was indicated by the size of the points. The excellent female parents DHY and 27-4 with good comprehensive performance in genetic improvement of wood quality traits, as well as the excellent male parents 2025, 17-2 and 3-54 ( Figure 6 ).

[0060] The present invention systematically carried out genomic selection breeding research on wood traits based on a multi-generation hybrid population of 762 American black poplars, identified QTNs significantly correlated with wood traits of poplars through whole genome association analysis, evaluated the effects of 10 GS statistical models and different marker screening strategies on the prediction accuracy of whole genome selection, and established the most suitable genomic selection prediction method for wood traits of poplars. Compared with random markers, the prediction accuracy was improved by an average of 157.55%.

[0061] The present invention can achieve early prediction of poplar wood properties, screen out excellent parents and offspring with multiple wood qualities, thereby shortening the forest breeding cycle, improving forest breeding accuracy, reducing the scale of experiments and accelerating the breeding process of excellent forest germplasm.

[0062] Although the purpose, concept and embodiments of the present invention have been described in detail above, a person skilled in the art will recognize that various improvements and modifications may be made to the present invention without departing from the scope of the claims, and such improvements and modifications shall still fall within the scope of protection of the present invention.

Claims

1. A method for selecting and breeding poplar trees based on the genomic selection of wood traits of multi-generation hybrid populations, comprising the following steps: Step 1: Resequencing of the genome selection population and determination of the phenotype of the reference population Multi-generation hybridization was used to construct a genomic selection reference population and breeding population. The Illumina HiSeq6000 high-throughput sequencing platform was used to perform double-end PE150 sequencing on the genomic selection population. Danhong Populus was used as the reference genome for genotyping and quality control to obtain high-quality single nucleotide polymorphism markers. Using a growth cone, drill the pith of each tree in the reference population from south to north, take out a complete and flawless wood core sample, remove the outermost annual ring, take the second to last annual ring, and measure the basic density, microfibril angle, fiber length, and fiber width; Step 2: Genome-wide association analysis of wood traits Based on the genotype data of the reference population and the wood property phenotype data, the GWAS model was selected for association analysis to identify the quantitative trait nucleotides that affect the phenotype; Step 3: Evaluation of the optimal genomic selection model for wood traits A 4-fold cross validation was used to evaluate the performance of 10 GS models at different random marker densities and comprehensively screen the optimal statistical model for each trait; Step 4: Screening strategy for optimal GS markers for wood properties The screening strategy included screening based on the level of linkage disequilibrium between markers, the correlation between markers and traits, and using an equal amount of random markers as controls.

2. The method for selective breeding based on the genomic characteristics of a multi-generation hybrid population of poplars as claimed in claim 1, characterized in that: In step 1, the core parent Danhong Poplar was selected, and GATK was used for variation detection with the following parameters: QD<2.0, FS>50, MQ<20, MQRankSum>-12.5, and ReadPosRankSum>-8.

0. VCFtools software was used for quality control with the following filtering parameters: sequencing depth greater than 5×, minimum allele frequency not less than 0.05, deletion rate less than 5%, and severe deviation from Hardy-Weinberg equilibrium markers. Finally, 114 high-quality SNP markers were obtained; fiber length and fiber width were determined using an L&W fiber analyzer; among them, basic density was measured 5 times, microfibril angle was measured 30 times, and fiber morphology was observed more than 50 times.

3. The method for selective breeding based on the genomic characteristics of a multi-generation hybrid population of poplars as claimed in claim 1, characterized in that: In step 2, genome-wide association analysis was performed using the IIIVmrMLM model.

4. The method for selective breeding based on the genomic selection of wood properties of multi-generation hybrid populations of poplars as claimed in claim 1, characterized in that: In step 3, the 10 GS models are GBLUP, rrBLUP, BayesA, BayesB, BayesC, BRR, RF, GBDT, KRR and DNNGP.

5. The method for selective breeding based on the genomic characteristics of a multi-generation hybrid population of poplars as claimed in claim 1, characterized in that: In step 3, the different random marker densities are all SNP, 0.5 SNP, 0.2 SNP, 0.1 SNP, 0.05 SNP and 0.01 SNP respectively.

6. The method for selective breeding based on the genomic selection of wood properties of multi-generation hybrid populations of poplars as claimed in claim 1, characterized in that: In step 3, the 5-fold cross-validation method uses 80% of the reference population as the training population and 20% as the test population, and repeats the iteration 20 times; and uses the mean Pearson correlation coefficient between the test population breeding value and the actual observation value as an indicator for evaluating the prediction accuracy.

7. The method for selective breeding based on the genomic characteristics of a multi-generation hybrid population of poplars as claimed in claim 1, characterized in that: In step 4, the linkage disequilibrium level between the markers, i.e., the LD level, is LD≤1, LD≤0.8, LD≤0.5, LD≤0.3, LD≤0.2, and LD≤0.1; the correlation levels between the markers and the traits are p≤1, p≤0.5, p≤0.1, p≤0.01, p≤0.001, and p≤0.0001, respectively.

8. Application of the method for genomic selection breeding based on wood traits of multi-generation hybrid populations of poplars as claimed in any one of claims 1 to 7 in poplar breeding.

9. The application of the method for selecting and breeding based on the genomic characteristics of multi-generation hybrid populations of poplars in poplar breeding as claimed in claim 8, characterized in that: The GEBV value of the breeding population was predicted using the optimal GS prediction system for wood properties, and the prediction results of multiple traits were integrated to screen parents and progeny with excellent wood quality.

10. Application of the method for selecting and breeding based on the genomic selection of wood properties of multi-generation hybrid populations of poplars in poplar breeding as claimed in claim 9, characterized in that: The significantly associated marker subsets, the optimal whole-genome selection statistical model and the optimal marker screening strategy constitute the optimal prediction system for the whole-genome selection of poplar wood traits; the number of significantly associated marker subsets are 22, 16, 26 and 17 respectively; the optimal whole-genome selection statistical models are BayesB, GBDT, RF and GBDT respectively; the optimal marker screening strategy is based on the GWAS association analysis p≤0.01 level.

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