Efficient wheat breeding method based on modern biotechnology

Abstract

The invention discloses a high-efficiency wheat breeding method based on a modern biotechnology, and belongs to the technical field of high-efficiency wheat breeding, and the method comprises the following steps: S1, collecting and identifying wheat germplasm resources, and screening materials with target character expressions; s2, performing phenotype identification on the screened material under a multi-ecological environment condition to obtain integrity expression data; s3, carrying out genetic typing on the identified material by adopting an SNP chip or a sequencing technology, and constructing a wheat high-density genetic map; s4, based on the genetic map and phenotype data, carrying out QTL positioning analysis on the target character, and determining a key control site; s5, establishing a whole genome selection model based on the genetic map and phenotype data, and performing predictive selection and breeding value evaluation on the germplasm material offspring; by integrating a QTL positioning result and a high-throughput SNP typing technology, target character recognition on a gene level is realized in a germplasm resource preliminary screening stage, the precision and efficiency of early-stage material selection are remarkably improved, and the screening burden of breeding populations is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of efficient wheat breeding, and more specifically, it relates to an efficient wheat breeding method based on modern biotechnology. Background Art

[0002] Currently, there are problems in wheat breeding such as difficult prediction of complex traits, low selection accuracy, and single gene high-throughput genotyping technology. The efficient breeding technology system is not perfect, which restricts the development of the wheat bio-breeding industry;

[0003] For example, in the prior art, the process integration from phenotypic screening of germplasm resources to precise modification of functional genes has never been achieved, and there is a lack of a stable and closed-loop efficient breeding system;

[0004] At the same time, traditional GS models mostly focus on linear statistics, and it is difficult to fully model the complex genetic and environmental interaction relationships, resulting in large prediction deviations and poor cross-environment promotion capabilities;

[0005] And in the existing regional trials, the evaluation of the stability and wide adaptability of breeding materials often relies on empirical judgment, and there is insufficient quantitative model support;

[0006] Therefore, we have proposed an efficient wheat breeding method based on modern biotechnology. Summary of the Invention

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

[0008] An efficient wheat breeding method based on modern biotechnology, comprising:

[0009] S1: Collect and identify wheat germplasm resources, and screen materials with target trait performances;

[0010] S2: Conduct phenotypic identification on the screened materials under multi-ecological environment conditions to obtain complete trait performance data;

[0011] S3: Genotype the identified materials using SNP chips or sequencing technology to construct a high-density genetic map of wheat;

[0012] S4: Based on the genetic map and phenotypic data, conduct QTL mapping analysis of target traits to identify key control loci;

[0013] S5: Establish a genome-wide selection model based on the genetic map and phenotypic data, and conduct predictive selection and breeding value evaluation on the offspring of germplasm materials;

[0014] S6: Use gene editing technology to target and regulate the located target genes to construct editing materials with specific excellent traits;

[0015] S7: Combine with KASP molecular markers to introduce target alleles into the main cultivated varieties and create new germplasms with breeding value.

[0016] S8: Through multi-location regional trials, use the GGE biplot model to analyze and screen the stability and adaptability of the materials, and complete the efficient breeding evaluation process.

[0017] Furthermore, the S1: Collect and identify wheat germplasm resources, and screen materials with target trait performances includes:

[0018] Wheat landraces, introduced materials and released varieties from different ecological regions;

[0019] Germplasm materials with at least one or more of the following target traits: high protein content, high wet gluten content, short and lodging-resistant, leaf rust-resistant, scab-resistant, efficient nitrogen and phosphorus absorption, excellent grain uniformity, etc.;

[0020] The identification of germplasm resources adopts phenotypic screening combined with molecular marker-assisted detection to ensure that the selected materials have excellent traits with clear genetic bases.

[0021] The molecular marker detection preferably uses SNP loci or KASP markers in known QTL intervals for rapid primary screening.

[0022] Furthermore, the S3: Genotype the identified materials using SNP chips or sequencing technologies to construct a high-density genetic map of wheat, including:

[0023] Use recombinant inbred line populations such as Jimai 20, Jimai 9088 or Jimai 22 as construction materials, and perform genome-wide genotyping through SNP markers.

[0024] Among them, the full length of the map shall not be less than 5000 cM, and the average marker interval needs to be less than 5 cM.

[0025] The map is constructed based on genetic analysis software such as ICiMapping or JoinMap, and the marker types include SNP, SSR or InDel, and the linkage groups are integrated according to chromosome distribution.

[0026] The map is used as the basic support for subsequent QTL mapping and genome-wide selection modeling, and is used to improve the accuracy and prediction reliability of molecular-assisted breeding.

[0027] Furthermore, the S4: Based on the genetic map and phenotypic data, conduct QTL mapping analysis of target traits to clarify the key control loci, including:

[0028] Collect phenotypic data in multi-location and multi-year field trials, and use the BLUE method for phenotypic data integration and standardization processing.

[0029] The QTL mapping uses QTLIciMapping or GEMMA association analysis software, combines the high-density marker data constructed in the genetic map, and performs association analysis using a mixed linear model;

[0030] Improve the mapping accuracy of medium and small effect QTLs, control the environmental main effects by integrating the results of principal component analysis, and simultaneously eliminate the co-localization interference regions;

[0031] The key loci are used for subsequent candidate gene mining and molecular marker development, forming the preferred input features of the genome-wide selection model.

[0032] Furthermore, the S5: Establish a genome-wide selection model based on the genetic map and phenotypic data, and perform predictive selection and breeding value evaluation on the offspring of germplasm materials, including:

[0033] Using the key control loci obtained from the aforementioned QTL mapping as the main variables, combined with the SNP marker matrix across the genome;

[0034] Construct a genome-wide selection model based on BLUP:

[0035] The inputs of the genome-wide selection model include:

[0036] Marker matrix G, phenotypic BLUE estimation matrix Y, population structure principal component PC, environmental perturbation factor E;

[0037] The output is the predicted breeding value of the candidate materials in the next generation And perform optimal sorting based on the breeding value;

[0038] To improve the robustness of the model, preferably integrate multi-model comparison strategies, including GBLUP, BayesC and random forest methods, and use the average prediction accuracy (R 2 ) as the evaluation index.

[0039] Furthermore, the S6: Use gene editing technology to target and regulate the located target genes, and construct editing materials with specific excellent traits, including:

[0040] Based on the QTL mapping results and candidate gene annotation information, screen functional genes related to wheat plant height, disease resistance, protein quality or efficient nitrogen and phosphorus utilization;

[0041] Use the CRISPR system to construct an editing vector, and the editing sites preferably include regions related to the functions of Rht-B1b, Fhb1, and Glu-D1;

[0042] Introduce the editing vector into wheat embryogenic callus by Agrobacterium-mediated transformation or gene gun method;

[0043] Under tissue culture conditions, gene-edited positive plants were screened, and their DNA was extracted and subjected to Sanger sequencing to verify the target mutation types.

[0044] Phenotypic analysis was performed on the edited materials to evaluate whether they had target excellent traits, including reduced plant height, enhanced disease resistance, increased protein content, etc.

[0045] The obtained stable editing lines were used for subsequent variety breeding or hybrid combination construction.

[0046] Furthermore, in S7: Combining KASP molecular markers, introducing target alleles into the main cultivated varieties to create new germplasms with breeding value includes:

[0047] According to the QTL mapping results, allele loci closely related to the target traits were screened, preferably including Rht-B1b, Fhb1, Yr26, Glu-D1;

[0048] Based on the known sequence information, corresponding KASP primers were designed, and the genotypes of the breeding offspring were rapidly screened through the high-throughput KASP reaction system;

[0049] Individuals carrying multiple excellent alleles were screened from the hybrid offspring to construct a germplasm resource group with a clear genotype background;

[0050] Multi-location field trials and phenotypic identification were carried out on the above germplasm materials to verify their stable expression in agronomic traits, quality traits and environmental adaptability;

[0051] The obtained new germplasms were incorporated into the core germplasm bank for subsequent parental combination optimization, molecular design breeding and commercial variety creation.

[0052] Furthermore, in S8: Through multi-location regional trials, the stability and adaptability of the screening materials were analyzed using the GGE biplot model to complete the efficient breeding evaluation process, including:

[0053] Set up test areas covering different ecological regions for more than 3 years;

[0054] The target materials were repeatedly cultivated at multiple locations using a randomized block design, and their agronomic traits such as yield, plant height, ear length, disease incidence rate, etc. were recorded;

[0055] After collecting the test data, principal component analysis was performed on the interaction between varieties and environments through the GGE biplot model;

[0056] Construct a functional graph to evaluate the comprehensive performance of candidate materials in multiple environments;

[0057] Preferably, materials with high yield, stable yield and wide adaptability are selected as the final varieties to complete the whole process of biological breeding evaluation.

[0058] In summary, the present invention has the following beneficial effects:

[0059] By integrating the QTL mapping results and high-throughput SNP genotyping technology, the identification of target traits at the gene level is realized in the initial screening stage of germplasm resources, significantly improving the accuracy and efficiency of early material selection and reducing the screening burden of the breeding population;

[0060] By using the GGE biplot model to deconstruct the variety and environment interaction of regional trial data and establish an evaluation system, it is convenient to select wheat materials with high yield, stable yield and stress resistance under multiple environments;

[0061] By introducing the principal component structure matrix and environmental perturbation factors, the interactive response of complex phenotypes under multi-locus and multi-environment conditions can be dynamically regulated, thereby improving the stability and cross-regional applicability of its predicted breeding value. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0063] Figure 1 It is a structural schematic diagram of the present invention;

[0064] Figure 2 It is a distribution diagram of wheat genetic linkage map markers of the present invention;

[0065] Figure 3 It is a mapping table of the leaf rust resistance QTL identified on chromosome 2B of the present invention;

[0066] Figure 4 It is a mapping table of the leaf rust resistance QTL identified on chromosome 3D of the present invention;

[0067] Figure 5 It is a mapping table of the QTLs controlling plant height and spike length located on chromosome 2D of the present invention;

[0068] Figure 6 It is a mapping table of the QTLs controlling traits such as grain-leaf ratio located on chromosome 4A of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0070] Embodiment:

[0071] The following will further describe the present invention in detail in conjunction with the attached Figures 1-6 drawings.

[0072] Please refer to Figures 1-6 , the present invention provides a technical solution: a high-efficiency wheat breeding method based on modern biotechnology, as Figures 1-6 shown, including:

[0073] S1: Collect and identify wheat germplasm resources, and screen materials with target trait performances;

[0074] S2: Conduct phenotypic identification of the screened materials under multi-ecological environment conditions to obtain complete trait performance data;

[0075] S3: Genotype the identified materials using SNP chips or sequencing technologies to construct a high-density genetic map of wheat;

[0076] S4: Based on the genetic map and phenotypic data, conduct QTL mapping analysis of target traits to identify key control loci;

[0077] S5: Establish a genome-wide selection model based on the genetic map and phenotypic data, and conduct predictive selection and breeding value evaluation on the offspring of germplasm materials;

[0078] S6: Use gene editing technology to target and regulate the located target genes to construct editing materials with specific excellent traits;

[0079] S7: Combine KASP molecular markers to introduce target alleles into the main cultivated varieties to create new germplasms with breeding value;

[0080] S8: Through multi-location regional trials, use the GGE biplot model to analyze the stability and adaptability of the screened materials to complete the high-efficiency breeding evaluation process;

[0081] As Figures 1-6 shown, S1: Collect and identify wheat germplasm resources, and screen materials with target trait performances including:

[0082] Wheat landraces, introduced materials and improved varieties from different ecological regions;

[0083] Containing at least germplasm materials with one or more of the following target traits: high protein content, high wet gluten content, short plant height with lodging resistance, resistance to leaf rust, resistance to scab, efficient absorption of nitrogen and phosphorus, excellent grain uniformity, etc.;

[0084] The identification of germplasm resources is carried out by combining phenotypic screening with molecular marker-assisted detection to ensure that the selected materials have excellent traits with a clear genetic basis;

[0085] For molecular marker detection, SNP loci or KASP markers in known QTL intervals are preferably used for rapid preliminary screening.

[0086] In this embodiment:

[0087] First, several wheat germplasm resources such as landraces, bred varieties, and introduced materials are collected from different ecological regions in China;

[0088] When collecting, record the origin, growth period, and adaptation area of each material, and store it in a 4°C dry storage for refrigeration to ensure seed viability and subsequent availability;

[0089] Set the screening indexes for target traits, including but not limited to: grain protein content, wet gluten content, lodging resistance of the plant body, resistance to scab, nitrogen and phosphorus nutrient absorption efficiency, and grain uniformity, etc.;

[0090] The above germplasm materials are planted in the field conventionally, and samples are collected at the jointing stage, filling stage, and maturity stage of wheat, and the following detection methods are used in sequence:

[0091] The protein content is detected by the Kjeldahl method;

[0092] The wet gluten content is determined using a gluten index meter;

[0093] The lodging resistance is evaluated by measuring the plant height and calculating the internode strength to obtain the lodging index;

[0094] The disease resistance is evaluated by artificial inoculation and field resistance scoring, and the resistance grading refers to the national standard GB / T3543;

[0095] The nitrogen and phosphorus absorption efficiency is determined by measuring the nutrient element content using inductively coupled plasma mass spectrometry after digesting the above-ground tissues;

[0096] The grain uniformity is statistically analyzed for particle size and distribution through an image analyzer;

[0097] Based on the above phenotypic evaluation results, several wheat materials with advantages in one or more target traits are preliminarily screened and enter the molecular marker-assisted detection stage;

[0098] In molecular detection, the KASP genotyping technology is used to detect SNP loci in screening materials, and QTL regions closely related to target traits are preferably selected as detection target sites. The selected core markers include, but are not limited to:

[0099] AX series loci located on chromosome 3B and related to lodging resistance traits;

[0100] Candidate loci located on chromosome 6A and related to grain protein content;

[0101] QTL segments located on chromosome 2D and related to nitrogen uptake efficiency;

[0102] For each sample, 5–10 KASP primers are used for multi-locus detection. Combining with control varieties for cluster analysis and genotype comparison to determine whether the type of favorable alleles it carries is consistent with the phenotypic results;

[0103] Finally, several wheat germplasm materials with consistent phenotypes and genotypes, stable target traits, and clear genetic backgrounds are screened out. A resource numbering system is established, and their seeds and DNA samples are collected and stored in the library, laying a foundation for subsequent construction of genetic maps, QTL mapping analysis, and precision breeding.

[0104] As Figures 1-6 shown, S3: Genotype the identified materials using SNP chips or sequencing technologies to construct a high-density genetic map of wheat, including:

[0105] Using recombinant inbred line populations such as Jimai 20, Jimai 9088, or Jimai 22 as construction materials, and performing genome-wide genotyping through SNP markers;

[0106] Among them, the full length of the map shall not be less than 5000 cM, and the average marker interval needs to be less than 5 cM;

[0107] The map is constructed based on genetic analysis software such as ICiMapping or JoinMap. The marker types include SNP, SSR, or InDel, and the linkage groups are integrated according to chromosome distribution;

[0108] The map is used as the basic support for subsequent QTL mapping and genome-wide selection modeling, and is used to improve the accuracy and prediction reliability of molecular-assisted breeding;

[0109] In this example, first, representative parental materials Jimai 20 and Jimai 9088 are selected to carry out hybridization experiments to construct several F2:3 generation recombinant inbred line populations as the basic population materials for map construction;

[0110] Each population contains no less than X individual plants for subsequent genotype detection.

[0111] Then, the simplified genome sequencing technology was used to genotype the individuals of the above population and the parental materials;

[0112] The obtained raw data was subjected to quality control processing through a standard process, including removing marker loci with a missing rate exceeding 20%, removing marker data with a minor allele frequency less than 0.05, and uniformly formatting it into a standard input format for genetic analysis;

[0113] Linkage analysis was performed using genetic mapping analysis software to construct a high-density genetic map of the wheat whole genome;

[0114] During the construction process:

[0115] The obtained map was not less than 5000 cM in length;

[0116] The average marker interval was controlled within 3 cM;

[0117] The marker types used included SNP, SSR or InDel;

[0118] During the construction of the linkage group, the Kosambi function was used for map distance conversion, and the LOD threshold was set to 3.0 to achieve accurate positioning of the markers on each chromosome;

[0119] The linkage groups were integrated according to the wheat chromosome numbers to form a complete chromosome map structure;

[0120] The finally constructed map covered the entire genome range, with characteristics such as high marker density, stable linkage structure, and strong discrimination. This map could be used as a basic data platform for subsequent QTL locus mining and target gene mapping, and could also be used as an important tool for constructing a genomic selection model and precision breeding analysis to improve the accuracy and predictability of assisted breeding.

[0121] As Figures 1-6 shown, S4: Based on the genetic map and phenotypic data, QTL mapping analysis of target traits was carried out to clarify the key control loci including:

[0122] Phenotypic data was collected in multi-location and multi-year field trials, and the BLUE method was used for phenotypic data integration and standardization processing;

[0123] QTL mapping was performed using QTLIciMapping or GEMMA association analysis software. Combining with the high-density marker data constructed in the genetic map, a mixed linear model was used for association analysis;

[0124] The positioning accuracy of medium and small effect QTLs was improved, the main effect of the environment was controlled by integrating the results of principal component analysis, and the co-localization interference regions were removed at the same time;

[0125] The key loci are used for subsequent mining of candidate genes and development of molecular markers, forming the preferential input features for the genome-wide selection model;

[0126] In this example, a certain strain of wheat was used as the research object. The aim was to construct a mixed linear QTL mapping model by combining high-throughput phenotypic measurement under multiple environmental conditions with a high-density genetic map, and to perform high-resolution mapping analysis of target traits by combining principal component dimensionality reduction and gradient perturbation function, so as to mine stable main effect loci for subsequent molecular marker development;

[0127] The specific experimental design content is as follows:

[0128] The experimental materials have the following requirements, including but not limited to:

[0129] Population type: Recombinant inbred line population, a total of 120;

[0130] Parent combination: Jimai 20 / Jimai 9088;

[0131] Phenotypic traits: Leaf rust resistance score, using a rating system, with ratings from 0 to 9; Grain width in mm; Plant height in cm;

[0132] Select the experimental site, which can be any location;

[0133] Select the experimental years;

[0134] Randomized block experiment with 3 replicates, a total of 3×2×3 = 18 environmental combinations;

[0135] Collect and standardize the phenotypic data. For each trait, phenotypic scoring or measurement is carried out in each environment, and the BLUE method is used for integrated estimation. The calculation method is as follows:

[0136]

[0137] Among them, represents the BLUE estimated value of the i-th wheat material for the j-th trait under the t-th environmental condition, and is used to describe the comprehensive evaluation of the material's performance for this trait after multiple environmental repeated observations; represents the measured value of the i-th material for the j-th trait in the t-th environment; n t represents the number of materials participating in the experiment under the t-th environmental condition; t represents the measured value of the s-th material for the j-th trait in the t-th environment;

[0138] The BLUE method proposed above is specifically the Best Linear Unbiased Estimation method, which is an important statistical tool for integrating phenotypic data mainly in the context of multi-environment trials. It can perform stability analysis and unbiased estimation of the trait performance of each material on the basis of excluding the interference of environmental effects. Compared with traditional methods such as mean processing or least squares regression, the BLUE method has advantages such as smaller variance and more robust results, and is particularly suitable for the high-density multi-point wheat breeding evaluation system involved in the present invention. By using this method, the accuracy of subsequent QTL mapping and the reliability of genetic contribution value identification have been effectively improved, thus achieving the goal of efficient and high-quality molecular breeding;

[0139] Start genotyping the genes, perform a genome-wide scan using a 660K SNP chip, and the screening criteria are to delete markers with a minor allele frequency < 0.05 and a missing rate > 10%; obtain approximately 18,000 final markers;

[0140] Start constructing a genetic map, use the QTLIciMapping4.1 software to construct a linkage population, and the average map distance during the construction process should be controlled at about 2.82 cM. The final total map length is about 5853.25 cM, and the SNP loci are mapped to 21 wheat chromosomes;

[0141] Perform principal component analysis for dimensionality reduction to control the main environmental effect. Use the trait-environment joint matrix X to perform covariance decomposition and extract the first principal component Substitute it into the subsequent QTL model for environmental elimination;

[0142] Establish a QTL mapping analysis model as follows:

[0143]

[0144] Among them, represents the comprehensive function result for characterizing the QTL genetic contribution intensity of the jth trait under multi-environment and multi-genotype conditions, which is used to reflect the cumulative genetic influence of specific loci on trait variation; T represents the total number of experimental time stages participating in the modeling, that is, the length of field experiment time for multiple years; N represents the total number of materials / genotype samples in the breeding population; K represents the total number of effective SNP loci in each sample; represents the principal component score value obtained by the i-th material under the t-th environmental condition through principal component analysis, which is used to control the main environmental effect; the symbol represents the gradient perturbation degree value corresponding to the k-th locus of the i-th material in the t-th environment, which is used to measure the genetic sensitivity of this locus; represents the partial derivative of the phenotypic perturbation amplitude caused by the k-th locus of the i-th material with respect to its observed genetic effect value, which is used to describe the response intensity between locus effect change and phenotypic response; the symbol Denote the observed genetic effect value of the \(k\)-th SNP locus of the \(i\)-th sample, and its value is generally encoded as 0, 1, 2 to represent allele types; Denote the genetic variance of this locus in the overall population, which is used to reflect its stable expression ability; Denote the allele frequency correction factor of the \(k\)-th SNP locus of the \(i\)-th material under the \(t\)-th environment;

[0145] Among them, it should be emphasized that \(p\) represents the minor allele frequency in the \(k\)-th SNP locus, which is often used in population genetics to describe the distribution of this locus in the sample population; the genotypes of SNP loci are usually divided into AA, Aa, and aa; the formula for deriving \(p\) is: For example, if a locus A and a are detected, where a is the minor allele, and if the occurrence frequency of a in the population is 0.2, then \(p = 0.2\); this parameter is used to calculate the allele frequency correction factor of the locus to reflect the representativeness and genetic variation contribution of this locus in the breeding population, and to ensure the stability in the subsequent QTL mapping and model weighting processes;

[0146] Use QTLIciMapping for pre - and post - verification; set the judgment criteria, and the judgment rules are as follows:

[0147] First, if the LOD value of the QTL mapping result is greater than 3.0, then this locus is identified as a significant QTL; second, if the contribution rate is greater than or equal to 10%, then this locus is judged as a main effect locus; third, if this locus shows stable expression in three or more different environments, then it is judged as a stable expression locus; loci that meet one of the above conditions can enter the subsequent molecular marker development or model construction process, and if multiple conditions are met simultaneously, they are regarded as core candidate loci with high credibility;

[0148] The final experimental output result is:

[0149] A total of 14 QTLs related to grain width traits were detected, and 3 loci met the main effect judgment; the loci are located on chromosomes 2B, 5A, and 6D; the maximum contribution rate reached 18.6%; through integrating high - throughput phenotyping, mixed linear modeling, principal component dimensionality reduction control, and allele frequency correction, high - precision modeling and stability screening of multi - environment multi - trait QTL mapping were achieved, which can provide highly reliable candidate loci for subsequent molecular design breeding.

[0150] Such as Figures 1-6 As shown, S5: Establish a genome - wide selection model based on genetic maps and phenotypic data, and conduct predictive selection and breeding value evaluation on the offspring of germplasm materials, including:

[0151] By taking the key control loci obtained from the aforementioned QTL mapping as the main variables and combining with the SNP marker matrix across the whole genome;

[0152] Construct a genome-wide selection model based on BLUP:

[0153] The inputs of the genome-wide selection model include:

[0154] Marker matrix G, phenotypic BLUE estimation matrix Y, population structure principal components PC, and environmental perturbation factor E;

[0155] The output is the predicted breeding value of candidate materials in the next generation And perform optimal ranking based on the breeding value;

[0156] To improve the robustness of the model, preferably integrate multi-model comparison strategies, including GBLUP, BayesC, and random forest methods, and use the average prediction accuracy (R 2 ) as the evaluation index;

[0157] The genome-wide selection model is used to pre-identify materials with excellent potential in the phenotypes of offspring that have not been actually measured, reduce the cost of field seed selection, and serve as the decision-making support basis for subsequent gene editing and variety combination design;

[0158] In this embodiment, several key QTL control loci located on the basis of the aforementioned constructed high-density genetic map are selected, the SNP genotype data of each material in the population at these loci are extracted, and a standardized matrix G is constructed;

[0159] According to the previous multi-point phenotypic measurement results, including plant height, protein content, disease resistance, etc., use the mixed linear model to estimate the BLUE of each material to form the phenotypic matrix Y;

[0160] Transform the genetic background structure of the population into the first three principal component matrices PC through principal component analysis, and collect the growth location and cultivation environment factors to construct the perturbation factor matrix E;

[0161] Taking G, Y, PC, and E as input variables, construct a genome-wide selection model based on the BLUP (Best Linear Unbiased Prediction) algorithm, and output the predicted breeding value of each candidate material in the next generation

[0162] Rank all materials based on the predicted values, and select the top X% of the materials as the core germplasm for subsequent breeding

[0163] To enhance the model's expression ability and the modeling accuracy of the multi-factor coupling relationship, the following formula is proposed:

[0164]

[0165] Wherein: is the predicted breeding value of sample i; the numerator is the integral of chromosomal interval information, which integrates the SNP coding value z ij , based on environmental information entropy and phenotypic mutual information of the exponential modulation function, the gradient response of the phenotypic estimate with respect to this locus and the frequency variation adjustment function log(1 + τ j ), and the integral kernel function is the marker distribution density function of this sample in the section Ω i ; The denominator is the absolute value of the prediction error in the principal component norm and the genome-wide complexity after normalization, representing the error scaling modulation term under the dominance of structural complexity;

[0166] Through comparative analysis, it is found that the BayesC model can better identify the marker loci that have a large impact on the phenotype when dealing with sparse SNP data, and shows higher prediction accuracy in traits such as protein content and disease resistance;

[0167] Finally, BayesC was selected as the modeling method for the target trait, and the GBLUP or BLUP model was used for other traits.

[0168] As Figures 1-6 shown, S6: Using gene editing technology to target and regulate the located target genes, constructing editing materials with specific excellent traits includes:

[0169] Based on the QTL mapping results and candidate gene annotation information, screening for functional genes related to wheat plant height, disease resistance, protein quality, or efficient use of nitrogen and phosphorus;

[0170] Using the CRISPR system to construct an editing vector, and the editing sites preferably include regions related to the functions of Rht-B1b, Fhb1, and Glu-D1;

[0171] Introducing the editing vector into wheat embryogenic callus by Agrobacterium-mediated transformation or the gene gun method;

[0172] Under tissue culture conditions, screening for gene-edited positive plants, extracting their DNA and performing Sanger sequencing to verify the target mutation types;

[0173] Performing phenotypic analysis on the editing materials to evaluate whether they possess the target excellent traits, including reduced plant height, enhanced disease resistance, and increased protein content;

[0174] Using the obtained stable editing lines for subsequent variety breeding or hybrid combination construction;

[0175] In this example,

[0176] As Figures 1-6 shown, S7: Combining KASP molecular markers, introducing target alleles into the main cultivated varieties, and creating new germplasms with breeding value includes:

[0177] According to the QTL mapping results, screening allele loci closely related to the target traits, preferably including Rht-B1b, Fhb1, Yr26, Glu-D1;

[0178] Designing corresponding KASP primers based on the known sequence information, and rapidly screening the genotypes of the breeding offspring through the high-throughput KASP reaction system;

[0179] Screening individuals carrying multiple excellent alleles in the hybrid offspring, and constructing a germplasm resource group with a clear genotype background;

[0180] Conducting multi-point field trials and phenotypic identification on the above germplasm materials to verify their stable expression in agronomic traits, quality traits, and environmental adaptability;

[0181] Incorporating the obtained new germplasms into the core germplasm bank for subsequent optimization of parental combinations, molecular design breeding, and commercial variety creation;

[0182] In this example, for the above three target genes, 2 pairs of targeting sequence sgRNAs were designed each to guide Cas9 to generate double-strand breaks at key sites in the coding region / promoter, resulting in small fragment deletion mutations;

[0183] The editing vector uses pBUE411 as the backbone, inserts the sgRNA expression cassette and the Cas9 nuclease fragment, and the constructed editing vector is transferred to the next step after PCR verification;

[0184] Introducing the constructed editing vector into wheat embryogenic callus by Agrobacterium-mediated transformation method, and performing tissue culture and regeneration using a screening medium containing BA, KT, and an appropriate amount of DA-6 to obtain editing positive plants;

[0185] Extracting genomic DNA from the regenerated plants, performing PCR amplification using primers on both sides of the target site, and performing Sanger sequencing;

[0186] Analyzing the mutation types, including insertion, deletion, or substitution, and judging whether it is a "knockout type" or a "base editing type".

[0187] A total of 21 editing positive materials were obtained, among which the mutation rate in the Rht-B1b region was 76%, Fhb1 was 57%, and Glu-D1 was 81%;

[0188] Systematic trait detection was carried out on the obtained edited plants under field conditions, including:

[0189] Plant height measurement at the full ear stage: an average reduction of 12.8%;

[0190] Fusarium head blight resistance score: upgraded by one level according to the national disease resistance grading standard;

[0191] Dry basis protein content measurement: an average increase of 1.7%;

[0192] There was no significant change in the regeneration ability and growth period.

[0193] The above-mentioned edited plants with stable performance and the expected trait optimization direction were used as the preferred edited line population to enter the next generation of mating, hybridization construction, and new strain creation processes.

[0194] As Figures 1-6 shown, S8: Through multi-location regional trials, the stability and adaptability of the screening materials were analyzed using the GGE biplot model, and the efficient breeding evaluation process was completed, including:

[0195] Setting up experimental areas covering different ecological regions for more than 3 years;

[0196] Using a randomized block design to cultivate the target materials repeatedly at multiple locations, and recording agronomic traits such as yield, plant height, ear length, and disease incidence;

[0197] After collecting the experimental data, principal component analysis was performed on the interaction between varieties and environments using the GGE biplot model;

[0198] Constructing a functional graph to evaluate the comprehensive performance of candidate materials in multiple environments;

[0199] Selecting materials with high yield, stability, and wide adaptability as the final selected varieties to complete the whole process of biological breeding evaluation.

[0200] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0201] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. An efficient wheat breeding method based on modern biotechnology, characterized in that, Including: S1: Collect and identify wheat germplasm resources, and screen materials with target trait performances; S2: Conduct phenotypic identification on the screened materials under multi-ecological environment conditions to obtain complete trait performance data; S3: Genotype the identified materials using SNP chips or sequencing technologies to construct a high-density genetic map of wheat; S4: Based on the genetic map and phenotypic data, conduct QTL mapping analysis of target traits to identify key control loci; S5: Establish a genome-wide selection model based on the genetic map and phenotypic data, and conduct predictive selection and breeding value evaluation on the offspring of germplasm materials; S6: Use gene editing technology to target and regulate the identified target genes to construct editing materials with specific excellent traits; S7: Combine KASP molecular markers to introduce the target gene into the main cultivated varieties to create new germplasms with breeding value; S8: Through multi-location regional trials, use the GGE biplot model to analyze the stability and adaptability of the screened materials to complete the efficient breeding evaluation process.

2. The wheat high-efficiency breeding method based on modern biotechnology according to claim 1, wherein The S1: Collect and identify wheat germplasm resources, and screen materials with target trait performances includes: Wheat landraces, introduced materials and released varieties from different ecological regions; Germplasm materials with at least one or more of the following target traits: high protein content, high wet gluten content, short straw and lodging resistance, leaf rust resistance, scab resistance, efficient nitrogen and phosphorus absorption, excellent grain uniformity; The identification of germplasm resources adopts phenotypic screening combined with molecular marker-assisted detection to ensure that the selected materials have excellent traits with a clear genetic basis; The molecular marker detection preferably uses SNP loci or KASP markers in known QTL intervals for rapid primary screening.

3. The wheat high-efficiency breeding method based on modern biotechnology according to claim 2, characterized in that The S3: Genotype the identified materials using SNP chips or sequencing technologies to construct a high-density genetic map of wheat includes: Use the recombinant inbred line populations of Jimai 20, Jimai 9088 or Jimai 22 as construction materials, and conduct genome-wide genotyping through SNP markers; Among them, the full length of the map shall not be less than 5000 cM, and the average marker interval needs to be less than 5 cM; The map is constructed based on ICiMapping or JoinMap genetic analysis software, and the marker types include SNP, SSR or InDel, and the linkage groups are integrated according to chromosome distribution; The map is used as the basic support for subsequent QTL mapping and genome-wide selection modeling to improve the accuracy and prediction reliability of molecular-assisted breeding.

4. The wheat high-efficiency breeding method based on modern biotechnology according to claim 3, characterized in that, The S4: Based on the genetic map and phenotypic data, conduct QTL mapping analysis of target traits to identify key control loci includes: Collect phenotypic data in multi-location and multi-year field trials, and use the BLUE method to integrate and standardize the phenotypic data; The QTL mapping uses QTLIciMapping or GEMMA association analysis software, combines the high-density marker data constructed in the genetic map, and uses the mixed linear model for association analysis; Improve the mapping accuracy of small and medium-effect QTLs, control the environmental main effects by integrating the results of principal component analysis, and at the same time eliminate the co-localization interference regions; The key loci are used for subsequent mining of candidate genes and development of molecular markers, forming the preferred input features of the genome-wide selection model.

5. The wheat high-efficiency breeding method based on modern biotechnology according to claim 4, characterized in that S5: Establish a genome-wide selection model based on genetic maps and phenotypic data, and conduct predictive selection and breeding value evaluation on the offspring of germplasm materials, including: Using the key control loci obtained from the aforementioned QTL mapping as the main variables, combined with the SNP marker matrix across the genome; Construct a genome-wide selection model based on BLUP: The input of the genome-wide selection model includes: Marker matrix G, phenotypic BLUE valuation matrix Y, population structure principal component PC, environmental perturbation factor E; The output is the predicted breeding value of the candidate materials in the next generation And perform selection and ranking based on the breeding value; To improve the robustness of the model, it is preferable to integrate multiple model comparison strategies, including GBLUP, BayesC, and the random forest method, and use the average prediction accuracy (R 2 ) as the evaluation index.

6. The wheat high-efficiency breeding method based on modern biotechnology according to claim 5, characterized in that, S6: Use gene editing technology to target and regulate the located target genes, and construct editing materials with specific excellent traits, including: Based on the QTL mapping results and candidate gene annotation information, screen functional genes related to wheat plant height, disease resistance, protein quality, or efficient utilization of nitrogen and phosphorus; Construct an editing vector using the CRISPR system, and the preferred editing sites include regions related to the functions of Rht-B1b, Fhb1, and Glu-D1; Introduce the editing vector into wheat embryogenic callus by Agrobacterium-mediated transformation or gene gun method; Under tissue culture conditions, screen for gene-edited positive plants, extract their DNA, and perform Sanger sequencing to verify the target mutation types; Conduct phenotypic analysis on the editing materials to evaluate whether they possess the target excellent traits, including reduced plant height, enhanced disease resistance, and increased protein content; Use the obtained stable editing lines for subsequent variety breeding or hybrid combination construction.

7. A high-efficiency wheat breeding method based on modern biotechnology according to claim 5, characterized in that S7: Combine KASP molecular markers to introduce the target genes into the main cultivated varieties and create new germplasms with breeding value, including: According to the QTL mapping results, screen the gene loci closely related to the target traits, preferably including Rht-B1b, Fhb1, Yr26, and Glu-D1; Design corresponding KASP primers based on the known sequence information, and rapidly screen the genotypes of the selected offspring through the high-throughput KASP reaction system; Screen individuals carrying multiple excellent gene loci simultaneously in the hybrid offspring, and construct a germplasm resource group with a clear genotype background; Conduct multi-point field trials and phenotypic identification on the above germplasm materials to verify their stable expression in agronomic traits, quality traits, and environmental adaptability; Incorporate the obtained new germplasms into the core germplasm bank for subsequent optimization of parental combinations, molecular design breeding, and commercial variety creation.

8. An efficient wheat breeding method based on modern biotechnology according to claim 5, characterized in that, S8: Through multi-location regional trials, use the GGE biplot model to analyze the stability and adaptability of the selected materials, and complete the efficient breeding evaluation process, including: Set up test areas covering different ecological regions for more than 3 years; Use the randomized block design to cultivate the target materials with multiple replicates at multiple locations, and record their agronomic traits such as yield, plant height, spike length, and disease incidence rate; After collecting the test data, conduct principal component analysis on the interaction between varieties and environments through the GGE biplot model; Construct a functional diagram to evaluate the comprehensive performance of the candidate materials in multiple environments; Select materials with high yield, stability, and wide adaptability as the final selected varieties to complete the whole-process biological breeding evaluation.

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