Discovery and application method and system of corn growth regulation molecular marker

Abstract

The invention belongs to the field of corn growth regulation, and discloses a discovery and application method and system of a corn growth regulation molecular marker, and the method comprises the following steps: carrying out whole genome sequencing on a large number of corn varieties (strains), and carrying out large-scale genome association analysis in combination with multi-environment and multi-site phenotypic data; a whole genome selection model is utilized to predict the genetic effect of corn complex characters, and efficient germplasm improvement is carried out; and carrying out functional verification on candidate molecular markers screened by GWAS and GS to ensure the stability and applicability of the candidate molecular markers. The application effect of the corn growth regulation molecular marker can be remarkably improved, the problems that in the prior art, markers are unstable, the analysis capacity is limited, and the number is insufficient are solved, and a more effective technical means is provided for corn growth regulation and breeding.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of corn growth regulation, and in particular relates to a method and system for discovering and applying a molecular marker for corn growth regulation. Background Art

[0002] In the field of corn growth regulation, molecular marker-assisted selection (MAS) breeding technology has become an important means. However, most of the existing molecular markers are discovered based on traditional phenotypic selection and gene linkage analysis methods. These markers usually only work under specific genetic backgrounds and environmental conditions, and the polymorphism of the markers is low, making it difficult to accurately identify and select the genotype of the target trait during the breeding process.

[0003] In view of the bottlenecks in the above fields, the technical problems that need to be solved urgently in the prior art are:

[0004] 1. Insufficient marker stability: Due to environmental changes and different genetic backgrounds, existing molecular markers are unstable in a variety of environments and different corn varieties, resulting in low selection efficiency;

[0005] 2. Limited ability to analyze complex traits: Corn growth regulation involves multi-gene interactions, and most existing markers can only analyze the genetic background of a single trait or simple traits, and cannot fully reveal the genetic mechanism of complex traits (such as stress resistance, yield, etc.);

[0006] 3. Limited number of markers: The number of available efficient molecular markers is limited, and the genomic regions they cover are very small, which cannot meet the needs of diversity monitoring across the entire genome. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention provides a method and system for discovering and applying molecular markers for regulating corn growth.

[0008] The present invention is achieved by a method for discovering and applying a molecular marker for regulating corn growth, characterized in that the method for discovering and applying a molecular marker for regulating corn growth specifically comprises:

[0009] S1: Conduct large-scale genome-wide association analysis by whole-genome sequencing of a large number of maize varieties and combining phenotypic data from multiple environments and locations;

[0010] S2: Predict the genetic effects of complex traits in maize and conduct efficient germplasm improvement by using a genome-wide selection model;

[0011] S3: Functionally validate the candidate molecular markers screened by GWAS and GS to ensure their stability and applicability.

[0012] Further, the S1 includes:

[0013] (1) Sample selection and whole genome sequencing

[0014] (1.1) Sample selection: Select representative corn varieties (lines) to ensure coverage of a wide range of genetic backgrounds, geographical distributions, and diverse phenotypic characteristics. Sample selection should take into account differences in corn planting areas, climate types, and growth cycles;

[0015] (1.2) Whole genome sequencing: Use high-throughput sequencing technology (such as Illumina or PacBio) to perform whole genome sequencing on the selected samples. The sequencing depth should be high enough (usually above 30×);

[0016] (1.3) Data quality control: Perform quality control on the obtained genomic data to remove low-quality reads, repeated sequences, and existing sequencing errors;

[0017] (2) Multi-environment phenotypic data collection

[0018] (2.1) Comprehensive collection of phenotypic data: Systematically collect phenotypic data related to maize growth under a variety of different environmental conditions. The main phenotypic indicators include plant height, number of leaves, flowering period, stress resistance (such as drought resistance, salt resistance, disease resistance), yield, etc. The collection of these phenotypic data needs to be carried out in multiple growing seasons and different locations to cover a wide range of environmental variations;

[0019] (2.2) Data standardization: Standardization processing includes data homogenization, variance stabilization, and environmental effect correction;

[0020] (3) Genotype-phenotype association analysis

[0021] (3.1) Selection of statistical analysis tools: Statistical analysis tools such as GEMMA (Genome-wide Efficient Mixed Model Association) or TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) were used to perform association analysis on genotype and phenotype data;

[0022] (3.2) Application of mixed linear model: Mixed linear model (MLM) is used to comprehensively consider fixed effects and random effects. The model also needs to consider population structure and genetic background;

[0023] (3.3) Association signal screening: Through whole genome scanning, SNPs sites significantly associated with the target trait are identified, and multiple hypothesis testing corrections (such as Bonferroni correction or FDR correction) are performed on the association signals.

[0024] Further, the S2 comprises:

[0025] (1) Training set construction

[0026] (1.1) Data integration: Integrate the genotype and phenotype data obtained in GWAS to construct a training set for training the whole genome selection model. The training set should include a variety of corn varieties (lines);

[0027] (1.2) Introduction of multi-environment data: Introduction of phenotypic data from multiple environments and multiple locations;

[0028] (2) Model training and verification

[0029] (2.1) Application of mixed linear models: Use mixed linear models such as G-BLUP (Genomic Best Linear Unbiased Prediction) or BayesB to estimate the genetic contribution of each SNP to the target trait, and predict the performance of each variety (line) by calculating the genomic breeding value (GBV);

[0030] (2.2) Cross-validation: The training set is randomly divided into multiple subsets and alternately trained and tested;

[0031] (2.3) Model optimization: According to the results of cross-validation, adjust the model parameters. If necessary, use weighted SNP information or introduce environmental factors;

[0032] (3) Genetic value prediction and selection

[0033] (3.1) Extensive testing: Apply the trained model to candidate corn varieties (lines) to predict their genomic breeding values ​​(GBVs) for target traits, and select varieties (lines) with excellent performance based on the predicted genetic values;

[0034] (3.2) Selection of superior varieties: Based on the genetic value, varieties (lines) with higher genetic values ​​are selected as superior varieties (lines) for further selection and improvement.

[0035] Further, the S3 includes:

[0036] (1) Functional verification of candidate genes

[0037] (1.1) Gene editing verification: Use gene editing technologies such as CRISPR-Cas9 to verify the function of the selected candidate genes, knock out or overexpress the candidate genes in corn, observe their effects on corn growth and development, and verify the causal relationship between the candidate genes and the target traits;

[0038] (1.2) Transgenic experiment: construct transgenic vectors and systems, insert candidate genes into target maize varieties, observe their phenotypic changes, and further confirm the function of the gene and its regulatory effect on the target trait;

[0039] (2) Multi-environment and multi-location verification

[0040] (2.1) Field trial design: Conduct multi-site field trials in different environments and geographic regions to test the stability and applicability of candidate molecular markers under a variety of conditions. These trials should cover different climate zones, soil types, and cropping patterns to verify the broad applicability of the markers.

[0041] (2.2) Data collection and analysis: In the experiment, phenotypic data and environmental factor data are systematically collected and combined with genotypic data to evaluate the stability and environmental adaptability of molecular markers, analyze the performance differences of markers in different environments, and screen out markers with strong environmental adaptability;

[0042] (3) Optimization and combination of markers

[0043] (3.1) Marker optimization: Based on the results of multi-environment and multi-location verification, optimize the candidate marker combination and select molecular markers that perform consistently in multiple environments and are highly associated with the target trait;

[0044] (3.2) Application of combined markers: Apply the optimized marker combination in breeding practice, especially in the multi-gene interaction regulation of complex traits.

[0045] Another object of the present invention is to provide a discovery and application system for maize growth regulation molecular markers, the system specifically comprising:

[0046] Association analysis module, used to perform large-scale genome-wide association analysis;

[0047] A genome-wide selection module for predicting genetic effects of complex traits in maize;

[0048] Verification and optimization module, used for functional verification of candidate molecular markers.

[0049] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0050] Wide applicability and improved stability of markers: Molecular markers screened through multi-environment and multi-location verification can maintain stability and applicability under different genetic backgrounds and environmental conditions, significantly improving the efficiency and accuracy of corn breeding;

[0051] Comprehensive analysis of complex traits: Whole-genome association analysis and whole-genome selection breeding technology can effectively analyze the multi-gene interaction relationship in complex traits of corn, identify the key gene network that affects corn growth regulation, and thus provide a scientific basis for breeding;

[0052] Shortened breeding cycle and improved precision: By optimizing marker combinations and using whole genome selection models for breeding, the breeding cycle can be significantly shortened, the workload of blind screening can be reduced, and the precision and genetic gain of breeding can be improved;

[0053] Enhanced adaptability to diverse environments: The marker combination verified in multiple environments and locations enables the newly developed corn varieties to have stronger environmental adaptability, especially in terms of stress resistance and yield stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the method for discovering and applying a molecular marker for regulating corn growth provided by an embodiment of the present invention;

[0055] Figure 2 is a flow chart of large-scale genome association analysis provided by an embodiment of the present invention;

[0056] Figure 3 is a flow chart of whole genome selection breeding provided by an embodiment of the present invention;

[0057] Figure 4 is a flow chart of function verification and marking optimization provided by an embodiment of the present invention;

[0058] Figure 5 It is a module diagram of the discovery and application system of maize growth regulation molecular markers provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] like Figure 1 As shown, the embodiment of the present invention provides a method for discovering and applying a molecular marker for regulating corn growth, and the method specifically includes:

[0061] S1: Conduct large-scale genome-wide association analysis by whole-genome sequencing of a large number of maize varieties and combining phenotypic data from multiple environments and locations;

[0062] S2: Predict the genetic effects of complex traits in maize and conduct efficient germplasm improvement by using a genome-wide selection model;

[0063] S3: Functionally validate the candidate molecular markers screened by GWAS and GS to ensure their stability and applicability.

[0064] like Figure 2 As shown, the S1 includes:

[0065] (1) Sample selection and whole genome sequencing

[0066] (1.1) Sample selection: Select a representative population of corn varieties to ensure coverage of a wide range of genetic backgrounds, geographical distributions, and diverse phenotypic characteristics. Sample selection should take into account differences in corn planting areas, climate types, and growth cycles in order to obtain more representative genomic data.

[0067] (1.2) Whole genome sequencing: Use high-throughput sequencing technology (such as Illumina or PacBio) to perform whole genome sequencing on the selected samples. The sequencing depth should be high enough (usually above 30×) to ensure that sufficient variation information, such as single nucleotide polymorphisms (SNPs) and insertion and deletion variations (Indels), is detected.

[0068] (1.3) Data quality control: After sequencing is completed, the obtained genomic data is quality controlled to remove low-quality reads, duplicate sequences, and existing sequencing errors to ensure the accuracy and reliability of subsequent analysis.

[0069] (2) Multi-environment phenotypic data collection

[0070] (2.1) Comprehensive collection of phenotypic data: Systematically collect phenotypic data related to corn growth under a variety of different environmental conditions. The main phenotypic indicators include plant height, number of leaves, flowering period, stress resistance (such as drought resistance, salt resistance, disease resistance), yield, etc. The collection of these phenotypic data needs to be carried out in multiple growing seasons and different locations to cover a wide range of environmental variations.

[0071] (2.2) Data standardization: In order to eliminate the influence of environment and experimental conditions on phenotypic data, data standardization is required. Standardization includes homogenization, variance stabilization and environmental effect correction, thereby reducing the interference of abiotic factors on the data and enhancing the association signal between genotype and phenotype.

[0072] (3) Genotype-phenotype association analysis

[0073] (3.1) Selection of statistical analysis tools: Statistical analysis tools such as GEMMA (Genome-wide Efficient Mixed Model Association) or TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) are used to perform association analysis on genotype and phenotype data. These tools can efficiently process large-scale data and identify SNPs that are significantly associated with complex traits.

[0074] (3.2) Application of mixed linear model: Mixed linear model (MLM) is used to comprehensively consider fixed effects and random effects to reduce false positive associations caused by environmental and genetic structure. The model also needs to consider population structure and genetic background to improve the accuracy of association analysis.

[0075] (3.3) Association signal screening: Through whole genome scanning, SNPs sites significantly associated with the target trait are identified. Multiple hypothesis testing corrections (such as Bonferroni correction or FDR correction) are performed on the association signals to ensure that the candidate markers finally screened are statistically significant.

[0076] like Figure 3 As shown, the S2 comprises:

[0077] (1) Training set construction

[0078] (1.1) Data integration: Integrate the genotype and phenotype data obtained in GWAS to construct a training set for training the whole genome selection model. The training set should include a variety of corn varieties to improve the universality of the model.

[0079] (1.2) Introduction of multi-environment data: When constructing the training set, it is necessary to introduce phenotypic data from multiple environments and locations to improve the model’s predictive ability in different environments. The diversity of the training set can effectively improve the model’s prediction accuracy for complex traits.

[0080] (2) Model training and verification

[0081] (2.1) Application of mixed linear models: Use mixed linear models such as G-BLUP (Genomic Best Linear Unbiased Prediction) or BayesB to estimate the genetic contribution of each SNP to the target trait. Predict the performance of each variety by calculating the genomic breeding value (GBV).

[0082] (2.2) Cross-validation: Cross-validation is used to evaluate the predictive ability of the model. The training set is randomly divided into multiple subsets, and training and testing are performed alternately to ensure the stability and generalization ability of the model.

[0083] (2.3) Model optimization: Based on the results of cross-validation, adjust the model parameters to improve the accuracy and robustness of the prediction. If necessary, weighted SNP information or environmental factors can be used to enhance the predictive ability of the model.

[0084] (3) Genetic value prediction and selection

[0085] (3.1) Extensive testing: Apply the trained model to candidate corn varieties to predict their genomic breeding values ​​(GBVs) for target traits. Use the predicted genetic values ​​to select varieties with excellent performance.

[0086] (3.2) Selection of superior varieties: Based on the genetic value, varieties with higher genetic values ​​are selected as superior varieties for further breeding and improvement. This process can significantly shorten the breeding cycle and improve breeding efficiency.

[0087] like Figure 4 As shown, the S3 includes:

[0088] (1) Functional verification of candidate genes

[0089] (1.1) Gene editing verification: Gene editing technologies such as CRISPR-Cas9 are used to verify the function of the selected candidate genes. The causal relationship between the candidate gene and the target trait is verified by knocking out or overexpressing the candidate gene in corn and observing its effect on corn growth and development.

[0090] (1.2) Transgenic experiment: construct transgenic vectors and systems, insert candidate genes into target corn varieties, observe their phenotypic changes, and further confirm the function of the gene and its regulatory effect on the target traits.

[0091] (2) Multi-environment and multi-location verification

[0092] (2.1) Field trial design: Multi-site field trials should be conducted in different environments and geographic regions to test the stability and applicability of candidate molecular markers under a variety of conditions. These trials should cover different climate zones, soil types, and cropping patterns to verify the broad applicability of the markers.

[0093] (2.2) Data collection and analysis: Phenotypic data and environmental factor data are systematically collected during the experiment, and combined with genotypic data to evaluate the stability and environmental adaptability of molecular markers. The performance differences of markers in different environments are analyzed to screen out markers with strong environmental adaptability.

[0094] (3) Optimization and combination of markers

[0095] (3.1) Marker optimization: Based on the results of multi-environment and multi-location verification, optimize the candidate marker combination and select molecular markers that perform consistently in multiple environments and are highly associated with the target trait.

[0096] (3.2) Application of combined markers: Apply optimized marker combinations to breeding practice, especially in the regulation of multi-gene interactions of complex traits. Through the pleiotropic effects of marker combinations, comprehensive improvement of complex traits can be achieved.

[0097] like Figure 5 As shown, a system for discovering and applying a maize growth regulating molecular marker provided by an embodiment of the present invention specifically includes:

[0098] Association analysis module, used to perform large-scale genome association analysis;

[0099] A genome-wide selection module for predicting genetic effects of complex traits in maize;

[0100] Verification and optimization module, used for functional verification of candidate molecular markers.

[0101] Example 1: Screening of molecular markers for drought resistance in maize by GWAS and application in drought resistance breeding

[0102] Background: Drought resistance is a key trait in maize growth, especially in maize cultivation in arid areas. However, since drought resistance is a complex trait controlled by multiple genes, traditional breeding methods are difficult to effectively improve the drought resistance of maize. Therefore, screening molecular markers related to drought resistance through large-scale genome association analysis (GWAS) and applying them to drought-resistant breeding is an effective strategy to improve the drought resistance of maize.

[0103] Implementation steps:

[0104] 1. Sample selection and whole genome sequencing

[0105] 200 maize varieties from different geographical regions with significant differences in drought resistance were selected as research samples. These varieties were sequenced whole genome at a depth of 30× to obtain comprehensive SNPs information.

[0106] 2. Multi-environment phenotypic data collection

[0107] These maize varieties were planted under three different environmental conditions (drought, high humidity, and moderate humidity) to systematically collect drought resistance-related phenotypic data, including leaf curling, plant height, root length, and final yield. Three replicates were set up in each environment to ensure data reliability.

[0108] 3. Genotype-phenotype association analysis

[0109] The GEMMA tool was used to conduct an association analysis between the genotype data and the drought resistance phenotype data of multiple environments. The results showed that multiple SNPs sites significantly associated with drought resistance were found on chromosomes 1 and 5.

[0110] 4. Whole genome selection breeding

[0111] The selected drought resistance-related SNPs markers were applied to the whole genome selection model. The genomic breeding values ​​(GBV) of drought resistance of different maize varieties were predicted by G-BLUP model through genotyping. According to the prediction results, varieties with higher genetic values ​​were selected for further hybrid breeding.

[0112] 5. Functional verification and tag optimization

[0113] The candidate genes were knocked out using CRISPR-Cas9 technology to observe their drought resistance performance in corn growth and verify the causal relationship between these SNPs and drought resistance. The stability of these molecular markers was verified in multi-environment and multi-location experiments, and finally a marker combination with strong stability was selected for actual breeding.

[0114] Technological progress: Through this example, multiple molecular markers significantly associated with drought resistance were successfully screened, and their stability under different environments was verified. The application of these markers for whole genome selection breeding significantly improved the breeding efficiency of drought-resistant corn varieties and reduced the workload of blind screening.

[0115] Example 2: High-yield corn breeding based on whole genome selection

[0116] Background: Corn yield is the core goal of breeding. However, yield is a typical complex trait affected by multiple factors such as environment and genetic background. In order to improve corn yield, screening and applying yield-related molecular markers combined with whole genome selection technology is the key to achieving efficient breeding.

[0117] Implementation steps:

[0118] 1. Sample selection and whole genome sequencing

[0119] 300 maize varieties with different yield performance were selected, covering a wide range of genetic backgrounds and geographical distributions. These varieties were sequenced whole genome at a sequencing depth of 35× to ensure that comprehensive genetic variation information was captured.

[0120] 2. Multi-environment phenotypic data collection

[0121] Corn yield data was collected at four different planting locations (including high-yield and low-yield areas), covering key indicators such as yield per hectare, number of grains per ear, and weight of single ear. Data from all locations were standardized to eliminate interference from environmental and seasonal factors.

[0122] 3. Genotype-phenotype association analysis

[0123] TASSEL tool was used to analyze the association between genotype data and yield data of each location. Several SNPs sites significantly associated with yield were found on chromosome 3 and chromosome 7.

[0124] 4. Whole genome selection breeding

[0125] A whole genome selection model was constructed, and the SNPs selected in the association analysis were applied to the selection of yield traits. The Bayes B model was used to predict the genomic breeding value (GBV) of yield of different varieties, and excellent varieties were selected for breeding based on the predicted values.

[0126] 5. Functional verification and tag optimization

[0127] Functional verification of the selected yield-related candidate genes was performed, and key genes were overexpressed using transgenic technology to observe their effects on yield. The applicability of these markers was verified in multi-environment experiments, and the marker combination was further optimized and used in high-yield corn breeding.

[0128] Technological progress: Through this embodiment, multiple molecular markers significantly associated with corn yield were successfully screened, and these markers were applied in whole genome selection for breeding. Ultimately, the yield per unit area of ​​corn was significantly improved, the breeding cycle was shortened, and the accuracy and efficiency of breeding were improved. These high-yield corn varieties showed excellent adaptability and yield stability in different geographical regions and environmental conditions.

[0129] The discovery and application method of the maize growth regulation molecular marker provided by the present invention realizes the accurate identification and application of the molecular markers related to maize growth traits through genome association analysis (GWAS), genome-wide selection (GS) model and functional verification of candidate markers. The specific implementation scheme is as follows:

[0130] S1: Genome-wide association study (GWAS)

[0131] 1. Whole genome sequencing: Perform whole genome sequencing on a large number of corn varieties to generate high-quality single nucleotide polymorphism (SNP) data. The sample size should cover multiple representative corn varieties to ensure genetic diversity and wide applicability.

[0132] 2. Phenotypic data collection: Collect phenotypic data such as corn growth cycle, stress resistance (such as drought resistance, disease resistance, etc.), yield (such as number of ears, ear length, etc.), quality (such as grain protein content, starch content, etc.) in multiple locations and environmental conditions. Ensure the accuracy and stability of phenotypic data through repeated experiments.

[0133] 3. Genome association analysis: Use statistical analysis tools (such as TASSEL, PLINK, etc.) to combine SNP data with phenotypic data to conduct genome-wide association analysis (GWAS). By calculating the correlation between each SNP site and the target phenotype, significant sites related to corn growth traits are screened out.

[0134] S2: Genome-wide selection model (GS)

[0135] 1. Model construction: Use the whole genome selection model, combined with the genetic background and complex traits of corn, to construct a mixed linear model (MLM) or Bayesian model. The MLM model can balance the influence of environmental and genetic effects, while the Bayesian model is used for more flexible genetic parameter estimation.

[0136] 2. Predict genetic effects: Use whole genome SNP data to predict the genetic contribution of corn varieties in complex traits and determine the genetic effect value of each candidate molecular marker. This process can accurately identify gene loci that contribute more to complex traits, facilitating subsequent germplasm improvement.

[0137] 3. Germplasm improvement: By screening out high-quality candidate genes or marker loci, breeding strategies are designed to carry out efficient germplasm improvement in order to improve the performance of corn in terms of growth traits, stress resistance and yield.

[0138] S3: Functional validation of candidate molecular markers

[0139] 1. Functional verification: Gene editing technology (such as CRISPR-Cas9) is used to verify the function of candidate molecular markers screened by GWAS and GS. By knocking out or activating candidate genes in corn plants, their effects on growth, yield or stress resistance are verified.

[0140] 2. Application of gene editing: Select different corn varieties to ensure the stability and applicability of candidate molecular markers in a variety of genetic backgrounds. Gene editing experiments are conducted under greenhouse or field conditions to evaluate the actual effects of candidate markers on corn growth traits.

[0141] 3. Marker stability verification: By tracking the phenotype and genotype of multiple generations, the marker's transmissibility and stability are confirmed. The experimental results must show that the marker can continue to work under different varieties and environmental conditions, thereby proving its applicability in actual breeding.

[0142] S4: Application of candidate molecular markers

[0143] 1. Marker-assisted selection (MAS): Based on verified functional markers, develop marker-assisted selection (MAS) technology for efficient breeding. Through the detection of molecular markers, corn lines with excellent traits can be directly screened to shorten the breeding cycle.

[0144] 2. Wide application in breeding process: Markers can be applied to multiple breeding objectives such as corn growth cycle, stress resistance, yield improvement, etc., and can be further developed into molecular breeding toolkits for breeders to use directly in practice.

[0145] In summary, the present invention screens and verifies molecular markers related to corn growth through in-depth analysis of corn genome data and multi-environment phenotypic data, combined with a whole genome selection model, and provides an efficient and accurate breeding method. This method not only shortens the breeding cycle, but also significantly improves the yield and stress resistance of corn varieties, and has broad industrial application prospects.

[0146] Experimental process: Discovery and application of molecular markers for maize growth regulation

[0147] Through genome-wide association study (GWAS) and genome-wide selection (GS) technology, molecular markers related to maize growth regulation were screened and verified, and their application effects in the actual breeding process were evaluated.

[0148] Experimental steps:

[0149] Step 1: Maize genome data collection and sequencing

[0150] 1. Sample selection: About 300 corn varieties with different growth performances were selected, covering a wide range of genetic backgrounds and environmental adaptability.

[0151] 2. Whole genome sequencing: Perform whole genome sequencing on these corn varieties to obtain single nucleotide polymorphism (SNP) data and analyze the genetic diversity of corn.

[0152] 3. Phenotypic data collection: Under multiple locations and environmental conditions, phenotypic data of these corn varieties are collected, focusing on growth cycle, stress resistance (such as drought resistance, disease resistance), yield (such as number of grains per ear, ear length), etc. Repeat the test many times to ensure data reliability.

[0153] Step 2: Genome-wide association study (GWAS)

[0154] 1. Data preprocessing: Clean and preprocess the whole genome sequencing data and phenotypic data to remove poor quality SNP sites and noise data.

[0155] 2. Association analysis: Use TASSEL and other analysis tools to combine SNP data with phenotypic data to conduct genome-wide association analysis (GWAS) to screen out SNP sites that are significantly associated with corn growth traits.

[0156] 3. Candidate marker screening: Identify multiple potential molecular markers and key gene loci related to maize growth regulatory traits.

[0157] Step 3: Genome-wide selection model (GS) prediction

[0158] 1. Model construction: Based on the molecular markers screened above, a mixed linear model (MLM) or Bayesian model is constructed to calculate the genetic effect value of each marker on corn traits.

[0159] 2. Model validation: Use the cross-validation method to evaluate the accuracy of the model in predicting complex traits of corn (such as yield and stress resistance) to ensure that the model has good generalization ability.

[0160] 3. Germplasm improvement: Based on the model prediction results, varieties with high genetic effects are selected for germplasm improvement experiments to observe their performance under different environments.

[0161] Step 4: Functional validation of candidate molecular markers

[0162] 1. Gene editing: For candidate markers screened by GWAS and GS, CRISPR-Cas9 gene editing technology is used to knock out or activate these genes in corn plants to verify their effects on corn growth traits.

[0163] 2. Greenhouse experiment: The gene-edited corn plants are planted in a greenhouse, and phenotypic observations are used to evaluate the performance of the plants in terms of growth cycle, stress resistance, and yield.

[0164] 3. Field experiments: Under field conditions, further test the stability of these markers in different varieties and observe the actual impact of gene editing on corn growth and yield.

[0165] Step 5: Practical application of molecular markers

[0166] 1. Marker-assisted selection (MAS): Apply verified molecular markers to the breeding process and design a marker-assisted selection program. By detecting molecular markers in seeds or seedlings, corn varieties with excellent traits can be quickly screened out.

[0167] 2. Breeding verification: Rapidly improve the stress resistance and yield of corn through marker-assisted selection (MAS) in multiple generations, and record the extent to which the breeding cycle is shortened and the improvement in breeding efficiency.

[0168] Effect:

[0169] 1. Efficient screening and improvement: Through the combination of genome association analysis and whole genome selection model, the experiment successfully screened out key molecular markers related to corn growth regulation. These markers can quickly screen out corn varieties with excellent traits in the early stage of breeding, greatly shorten the breeding cycle, and improve the efficiency of germplasm improvement.

[0170] 2. Accurate prediction and stable verification: The application of gene editing technology verifies the genetic stability and application value of candidate markers in different corn varieties. The experimental results show that the selected molecular markers have significantly improved corn yield, stress resistance and growth cycle under field conditions, proving their actual effect in breeding.

[0171] 3. Successful application of marker-assisted selection (MAS): Through marker-assisted selection (MAS) technology, researchers can directly screen out high-quality germplasm through molecular detection during the breeding process, reducing reliance on traditional phenotypic observations. MAS technology not only improves breeding efficiency, but also improves the accuracy and reliability of selection, especially in the improvement of complex traits.

[0172] 4. Reduce breeding resource input: Through the practical application of experiments, experimenters can use molecular markers to screen out varieties with higher genetic effects, avoiding a large number of complicated field trials and waste of resources, thereby optimizing the breeding process and significantly reducing breeding costs.

[0173] Overall, this experiment provided a complete and effective experimental process for the discovery and application of molecular markers for maize growth regulation. It not only promoted the application of molecular breeding technology in maize, but also provided strong technical support for large-scale germplasm improvement.

[0174] Example 1: Molecular marker screening and application of drought-tolerant corn varieties

[0175] S1: Sample selection and whole genome sequencing

[0176] 1. Sample selection:

[0177] 200 maize varieties from different geographical regions were selected, which showed significant differences in drought resistance, covering different genetic backgrounds and climate types of maize. The samples were mainly selected from arid and semi-arid areas and planting areas with irrigation conditions to ensure the diversity of genotype and phenotypic data.

[0178] 2. Whole genome sequencing:

[0179] Using the Illumina high-throughput sequencing platform, 30× deep whole-genome sequencing was performed on the selected corn varieties to obtain high-quality genomic data.

[0180] 3. Data quality control:

[0181] FastQC and BWA were used to perform quality control on sequencing data to remove low-quality reads and repeated sequences, and GATK tools were used to detect and filter SNP sites in genomic data to ensure the accuracy of subsequent analysis.

[0182] S2: Multi-environment phenotyping data collection

[0183] 1. Phenotypic data collection:

[0184] Corn growth experiments were conducted under three different environmental conditions (such as dryland planting, light irrigation and full irrigation conditions) to measure phenotypic data such as plant height, leaf number, flowering period and yield of each variety, with particular attention paid to traits related to drought resistance.

[0185] The experiment in each environment was repeated three times, covering two growing seasons, to ensure the accuracy and stability of the data.

[0186] 2. Data Standardization:

[0187] The collected phenotypic data were homogenized, and the variance stabilization method was used to eliminate the influence of different experimental conditions on the data. The mixed linear model (MLM) was used to correct the environmental effect and eliminate the existing random errors.

[0188] S3: Genotype-phenotype association analysis

[0189] 1. Correlation analysis:

[0190] Genome-wide association analysis (GWAS) was performed using GEMMA software on drought resistance phenotypic and genomic data, and mixed linear models (MLM) were used to consider population structure and random effects.

[0191] The Bonferroni correction method was used to screen out significantly associated SNP sites.

[0192] 2. Related signal screening:

[0193] Five SNP sites significantly associated with drought resistance were identified, which are located on chromosomes 2 and 9 of the genome. Functional verification of these SNP sites was performed, and preliminary experiments showed that these SNPs are related to genes regulating water use and stress resistance.

[0194] S4: Validation of candidate molecular markers

[0195] 1. Gene function verification:

[0196] The target gene was knocked out using CRISPR / Cas9 gene editing technology to verify its effect on corn drought resistance. The results showed that the edited corn varieties exhibited significant growth inhibition under drought conditions, verifying the functions of these markers.

[0197] 2. System optimization:

[0198] Based on the validation results, the relevant molecular markers were integrated into the breeding decision system to guide corn drought resistance breeding. After multiple growing seasons of trials, corn varieties selected using these molecular markers showed stronger stress resistance and higher yields under drought conditions.

[0199] Example 2: Molecular marker screening and application of high-yield corn varieties

[0200] S1: Sample selection and whole genome sequencing

[0201] 1. Sample selection:

[0202] A total of 300 different varieties of corn were selected from multiple corn breeding bases, with an emphasis on those that showed high yields in different geographical regions. The samples covered different corn growing environments, including high altitudes, low altitudes, and different soil types.

[0203] 2. Whole genome sequencing:

[0204] The PacBio high-throughput sequencing platform was used to perform 35× whole-genome sequencing of 300 maize varieties to ensure that the data were detailed enough for efficient GWAS analysis.

[0205] 3. Data quality control:

[0206] FastQC and SAMtools were used to perform quality control analysis on the sequencing data, duplicate sequences were removed, and low-quality SNP sites were identified and filtered through the GATK tool to ensure high reliability of the data.

[0207] S2: Multi-environment phenotyping data collection

[0208] 1. Phenotypic data collection:

[0209] Yield data (such as ear length, grain weight, yield per plant, etc.) were recorded for each variety at test sites in four different climate zones. The test areas included warm, humid and arid regions, ensuring that yield data covered a wide range of climate and soil conditions.

[0210] The experimental data were collected over multiple growing seasons to ensure the stability and representativeness of the data.

[0211] 2. Data Standardization:

[0212] The yield data were standardized using homogenization and variance stabilization methods. The environmental effect correction model was used to remove the interference caused by different growing regions and seasons on the data to ensure the accuracy of the association analysis.

[0213] S3: Genotype-phenotype association analysis

[0214] 1. Correlation analysis:

[0215] TASSEL software was used to perform association analysis on yield phenotypic data and genotypic data. The mixed linear model (MLM) was combined to consider the complexity of the population structure and genetic background of corn varieties and accurately identify SNP loci associated with yield.

[0216] FDR (False Discovery Rate) correction was used to screen out significantly associated gene loci.

[0217] 2. Related signal screening:

[0218] Through association analysis, eight SNP loci significantly associated with high yield were identified, which were concentrated on chromosomes 1 and 5. Subsequent studies have shown that these SNPs are closely related to genes related to corn kernel development and nutrient transport.

[0219] S4: Validation of candidate molecular markers

[0220] 1. Gene function verification:

[0221] The function of the candidate genes was verified by RNA interference technology (RNAi). The results showed that the grain yield of corn decreased significantly after inhibiting the expression of these genes, further verifying the functionality of these molecular markers.

[0222] 2. System optimization:

[0223] These molecular markers were integrated into the breeding system to guide the rapid screening of high-yield corn varieties. The breeding strategy was further optimized by validating the functions of the candidate markers. After multiple growing seasons of testing, corn varieties bred using these molecular markers showed higher yields under different climatic conditions.

[0224] The above two examples show how to achieve efficient screening and breeding applications of maize traits (drought resistance and high yield) through large-scale genome sequencing, collection of multi-environment phenotypic data, association analysis and molecular marker verification. These methods combine genome-wide association analysis (GWAS) and genome-wide selection (GS), provide important tools for maize germplasm improvement, and show great potential in practical breeding applications.

[0225] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for discovering and applying a molecular marker for corn growth regulation, It is characterized in that The method specifically includes: S1: Conduct large-scale genome-wide association analysis by whole-genome sequencing of a large number of maize varieties and combining phenotypic data from multiple environments and locations; S2: Predict the genetic effects of complex traits in maize and conduct efficient germplasm improvement by using a genome-wide selection model; S3: Functionally validate the candidate molecular markers screened by GWAS and GS to ensure their stability and applicability.

2. The method for discovering and applying the molecular marker for corn growth regulation as claimed in claim 1, It is characterized in that Said S1 comprises: (1) Sample selection and whole genome sequencing (1.1) Sample selection: Select a representative group of corn varieties to ensure coverage of a wide range of genetic backgrounds, geographical distributions, and diverse phenotypic characteristics. Sample selection should take into account differences in corn planting areas, climate types, and growth cycles; (1.2) Whole genome sequencing: Use high-throughput sequencing technology (such as Illumina or PacBio) to perform whole genome sequencing on the selected samples. The sequencing depth should be high enough (usually above 30×); (1.3) Data quality control: Perform quality control on the obtained genomic data to remove low-quality reads, duplicate sequences, and existing sequencing errors; (2) Multi-environment phenotypic data collection (2.1) Comprehensive collection of phenotypic data: Systematically collect phenotypic data related to maize growth under a variety of different environmental conditions. The main phenotypic indicators include plant height, number of leaves, flowering period, stress resistance (such as drought resistance, salt resistance, disease resistance), yield, etc. The collection of these phenotypic data needs to be carried out in multiple growing seasons and different locations to cover a wide range of environmental variations; (2.2) Data standardization: Standardization processing includes data homogenization, variance stabilization, and environmental effect correction; (3) Genotype-phenotype association analysis (3.1) Selection of statistical analysis tools: Statistical analysis tools such as GEMMA (Genome-wide Efficient Mixed Model Association) or TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) were used to perform association analysis on genotype and phenotype data; (3.2) Application of mixed linear model: Mixed linear model (MLM) is used to comprehensively consider fixed effects and random effects. The model also needs to consider population structure and genetic background; (3.3) Association signal screening: Through whole genome scanning, SNPs sites significantly associated with the target trait are identified, and multiple hypothesis testing corrections (such as Bonferroni correction or FDR correction) are performed on the association signals.

3. The method for discovering and applying the molecular marker for regulating corn growth as claimed in claim 1, It is characterized in that The S2 comprises: (1) Training set construction (1.1) Data integration: Integrate the genotype and phenotype data obtained from GWAS to construct a training set for training the whole genome selection model. The training set should include a variety of corn varieties; (1.2) Introduction of multi-environment data: Introduction of phenotypic data from multiple environments and multiple locations; (2) Model training and verification (2.1) Application of mixed linear models: Use mixed linear models such as G-BLUP (Genomic Best Linear Unbiased Prediction) or BayesB to estimate the genetic contribution of each SNP to the target trait, and predict the performance of each variety by calculating the genomic breeding value (GBV); (2.2) Cross-validation: The training set is randomly divided into multiple subsets and alternately trained and tested; (2.3) Model optimization: According to the results of cross-validation, adjust the model parameters. If necessary, use weighted SNP information or introduce environmental factors; (3) Genetic value prediction and selection (3.1) Extensive testing: Apply the trained model to candidate corn varieties to predict their genomic breeding values ​​(GBVs) for target traits, and select varieties with excellent performance based on the predicted genetic values; (3.2) Selection of superior varieties: Based on the genetic value, varieties with higher genetic values ​​are selected as superior varieties for further breeding and improvement.

4. The method for discovering and applying the molecular marker for regulating corn growth as claimed in claim 1, It is characterized in that The S3 includes: (1) Functional verification of candidate genes (1.1) Gene editing verification: Use gene editing technologies such as CRISPR-Cas9 to verify the function of the selected candidate genes, knock out or overexpress the candidate genes in corn, observe their effects on corn growth and development, and verify the causal relationship between the candidate genes and the target traits; (1.2) Transgenic experiment: construct transgenic vectors and systems, insert candidate genes into target corn varieties, observe their phenotypic changes, and further confirm the function of the gene and its regulatory effect on the target trait; (2) Multi-environment and multi-location verification (2.1) Field trial design: Conduct multi-site field trials in different environments and geographic regions to test the stability and applicability of candidate molecular markers under a variety of conditions. These trials should cover different climate zones, soil types, and cropping patterns to verify the broad applicability of the markers. (2.2) Data collection and analysis: In the experiment, phenotypic data and environmental factor data are systematically collected and combined with genotypic data to evaluate the stability and environmental adaptability of molecular markers, analyze the performance differences of markers in different environments, and screen out markers with strong environmental adaptability; (3) Optimization and combination of markers (3.1) Marker optimization: Based on the results of multi-environment and multi-location verification, optimize the candidate marker combination and select molecular markers that perform consistently in multiple environments and are highly associated with the target trait; (3.2) Application of combined markers: Apply the optimized marker combination in breeding practice, especially in the multi-gene interaction regulation of complex traits.

5. The method for discovering and applying the molecular marker for corn growth regulation according to claim 1, It is characterized in that In the step S1, genome association analysis is performed by combining single nucleotide polymorphism (SNP) data with corn phenotype data to find key sites related to corn growth.

6. The method for discovering and applying the molecular marker for regulating corn growth as claimed in claim 1, It is characterized in that The phenotypic data collection in the S1 step covers multiple complex traits of corn, such as growth cycle, stress resistance, yield and quality, and is collected through repeated experiments in multiple environments and multiple locations to ensure the comprehensiveness and reliability of the data.

7. The method for discovering and applying the molecular marker for regulating corn growth as claimed in claim 1, It is characterized in that In the S2 step, the whole genome selection model is constructed using a mixed linear model (MLM) or a Bayesian model, and is used to improve the efficiency of germplasm improvement by predicting the genetic effect of each candidate gene on corn growth traits.

8. The method for discovering and applying the molecular marker for regulating corn growth as claimed in claim 1, It is characterized in that In the S3 step, the functional verification of the candidate molecular markers is performed on corn plants through gene editing technology to ensure their stability in different corn varieties and evaluate their applicability in practical applications.

9. A discovery and application system based on the maize growth regulation molecular markers as described in claims 1-8, It is characterized in that The system specifically includes: Association analysis module, used to perform large-scale genome association analysis; A genome-wide selection module for predicting genetic effects of complex traits in maize; Verification and optimization module, used for functional verification of candidate molecular markers.

Images