Stepped breeding method of multi-resistance eurytopic sweet corn inbred line

Abstract

The invention discloses a stepped breeding method of a multi-resistance eurytopic sweet corn inbred line, and belongs to the technical field of agricultural breeding. The method solves the problems that an existing method is long in period, linkage between characters is cumbersome and difficult to break, and multi-environment adaptability screening efficiency is low, molecular marker data, disease inoculation reaction phenotype data and environment factor data are integrated, characteristic variables related to disease resistance are screened by means of a correlation analysis method, and the disease resistance of the disease is screened out. It is ensured that the model can accurately evaluate the disease resistance of each single plant; by setting a scientific and reasonable disease resistance index threshold, optimization is performed in combination with historical data and actual breeding requirements, and the accuracy and stability of a screening result are ensured; therefore, not only is the screening efficiency of the disease-resistant single plants improved, but also the priority of the high-resistance single plants in subsequent breeding is determined by comprehensively evaluating other agronomic characters, and an innovative technical means is provided for efficient breeding of the sweet corn multi-resistance eurytopic selfing line.

Description

Technical Field

[0001] This invention relates to the field of agricultural breeding technology, specifically to a step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines. Background Technology

[0002] As an important crop for both fresh consumption and processing, the key challenge in breeding sweet corn is to synergistically improve its disease resistance, stress resistance, adaptability, and quality traits.

[0003] Traditional breeding methods suffer from problems such as long cycles, difficulty in breaking down linkages between traits, and low efficiency in screening for adaptability to multiple environments.

[0004] Therefore, not content with existing needs, a tiered breeding method for multi-resistant and widely adaptable sweet corn inbred lines is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines. By integrating molecular marker data, disease inoculation response phenotypic data, and environmental factor data, correlation analysis is used to screen characteristic variables related to disease resistance, ensuring that the model can accurately assess the disease resistance of each individual plant. By setting a scientifically reasonable disease resistance index threshold and optimizing it in combination with historical data and actual breeding needs, the accuracy and stability of the screening results are ensured. This not only improves the efficiency of screening disease-resistant individual plants, but also determines the priority of highly resistant individual plants in subsequent breeding by comprehensively evaluating other agronomic traits. This provides an innovative technical means for the efficient breeding of multi-resistant and widely adaptable sweet corn inbred lines and solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines includes the following steps:

[0008] Preparatory stage: By aggregating tropical and temperate germplasm resources and conducting multi-parent hybridization, a basic population is constructed to provide a material basis for subsequent tiered selection;

[0009] Step 1, Initial Screening for Disease Resistance: Early assisted selection was conducted using molecular markers linked to resistance to major diseases such as rust, leaf spot, and stem rot; an early disease resistance prediction model was constructed to output a disease resistance index to assist in the screening of highly resistant individual plants; and highly resistant individual plants were screened by combining artificial inoculation identification.

[0010] Step II, stress resistance and adaptability screening: Superior plants from Step I were evaluated at multiple locations under different abiotic stresses such as drought, low temperature and barrenness, and strains that showed stability and wide adaptability under various abiotic stresses were screened.

[0011] Step III: Synergistic Improvement of Yield and Quality: Based on Step II, the general combining ability, grain appearance and key sugar metabolism genes of the inbred lines are screened in the high-yield test area to ensure that the inbred lines have the characteristics of multiple resistance, wide adaptability and high yield and quality.

[0012] Furthermore, in Step I, an early disease resistance prediction model is constructed to output a disease resistance index to assist in the screening of highly resistant individual plants, including:

[0013] We acquire molecular marker data, disease inoculation response phenotypic data, and environmental factor data from the basic population, and integrate them to form a complete dataset.

[0014] Based on correlation analysis, characteristic variables related to disease resistance were selected, including genotypes of molecular marker sites, phenotypic indicators of disease response, and environmental factors.

[0015] The random forest algorithm is used to divide the integrated dataset into a training set and a test set; the random forest model is trained using the training set to optimize the model parameters; and the model is validated using the test set to evaluate its predictive performance.

[0016] The trained random forest model is applied to all individual plants in the base population to predict the disease resistance index of each individual plant.

[0017] Based on the disease resistance index, highly resistant single plants were screened out as candidate materials for subsequent step selection;

[0018] The disease resistance of the selected highly resistant individual plants was evaluated to verify their stability under different environmental conditions;

[0019] By combining other agronomic traits, highly resistant individual plants are comprehensively evaluated to determine their priority in subsequent tiered selection.

[0020] The selected highly resistant individual plants were then introduced into step II of the ladder process.

[0021] Furthermore, based on the disease resistance index, highly resistant individual plants were screened, including:

[0022] Obtain the mean and standard deviation of the disease resistance index from historical data samples, and set an initial threshold range;

[0023] Obtain the disease resistance index from historical data samples, and select an appropriate percentile as an auxiliary threshold based on the distribution of the disease resistance index;

[0024] Adjust the initial threshold range according to actual breeding goals and needs;

[0025] Cross-validation was used to divide the basic population into multiple subsets, and the screening results under different thresholds were calculated. The accuracy and stability of the highly resistant single plants screened under different thresholds were evaluated.

[0026] Based on known breeding data, the actual disease resistance performance of highly resistant single plants screened under different thresholds was evaluated, and the threshold with high consistency with the actual breeding results was selected.

[0027] Furthermore, in the preparatory stage, multi-parent hybridization is carried out by aggregating tropical and temperate germplasm resources, including:

[0028] Collect sweet corn germplasm resources with excellent traits from tropical regions, and select at least 5 representative tropical germplasm resources;

[0029] Collect sweet corn germplasm resources with excellent traits from temperate regions, and select at least 3 representative temperate germplasm resources;

[0030] A preliminary phenotypic assessment was conducted on the collected germplasm resources, including agronomic traits, disease resistance, and adaptability.

[0031] A multi-parental hybridization method was used to combine tropical germplasm with temperate germplasm for hybridization;

[0032] Under suitable environmental conditions, artificial pollination is performed on the selected parents to ensure successful hybridization, and the parent information and hybridization time of each hybrid combination are recorded;

[0033] Seeds of hybrid offspring were collected to construct a basic population with rich genetic background and complementary traits. Preliminary genetic diversity and trait complementarity analysis of the offspring population was conducted to ensure the genetic diversity of the basic population.

[0034] Furthermore, the preparatory stage, through multi-parent hybridization by aggregating tropical and temperate germplasm resources, also includes:

[0035] A certain number of individual plants were randomly selected from the basic population, and leaf samples were collected.

[0036] We employ DNA extraction methods suitable for plant tissues to extract high-quality DNA samples, and then test the concentration and purity of the extracted DNA to ensure that the DNA quality meets the requirements for subsequent analysis.

[0037] Select molecular markers linked to resistance to major diseases such as rust, leaf spot, and stem rot;

[0038] PCR amplification technology was used to detect molecular markers in individual plants of the basic population. PCR reactions were performed using specific primers for each marker to amplify the target fragments.

[0039] Genotype data for each individual plant at various molecular marker loci were recorded to construct a molecular marker database.

[0040] Furthermore, the preparatory stage, through multi-parent hybridization by aggregating tropical and temperate germplasm resources, also includes:

[0041] Rust, leaf spot and stem rot were selected as the main diseases, and artificial inoculation experiments were carried out. The individual plants of the basic population were divided into multiple treatment groups, each group was inoculated with one disease, and a control group was set up.

[0042] After a certain period of time following inoculation, observe and record the disease response phenotype of each plant, including but not limited to the number of lesions, the size of lesions, and the disease index;

[0043] Detailed records of disease response phenotypic data for each individual plant were kept, and a phenotypic database was constructed.

[0044] During the disease inoculation period, environmental meteorological data, including temperature, humidity, light intensity, and precipitation, were monitored and recorded.

[0045] Collect soil samples and determine the physicochemical properties of the soil;

[0046] The collected environmental factor data were organized into a database, corresponding to molecular marker data and phenotypic data.

[0047] Furthermore, in Step II, the superior plants from Step I were evaluated at multiple locations under different abiotic stresses, including drought, low temperature, and poor soil conditions, including:

[0048] Set up soil moisture content, stress duration, and soil fertility under different adverse conditions;

[0049] Multiple identification sites were set up in different geographical areas, and repeated experiments were set up at each identification site. A randomized block design was adopted, and 10-20 plants were planted in each plot.

[0050] The multidimensional fitness index of superior plants in each identification point was evaluated, including growth indicators, physiological indicators and yield-related indicators.

[0051] A comprehensive adaptability index model was constructed based on the multidimensional adaptability index. The growth index, physiological index and yield-related index were weighted and summed to calculate the adaptability index of each line.

[0052] Based on historical data samples, an adaptability index threshold was set to screen out strains that showed stability and broad adaptability under various adverse conditions.

[0053] The selected strains were further tested for abiotic stress, and then the selected strains were put into step III.

[0054] Furthermore, the multidimensional fitness index of superior strains at each identification site was evaluated, including:

[0055] The evaluation indicators for growth include: measuring the change in plant height under adverse stress to assess its growth stability;

[0056] Root vigor was determined using the TTC method to assess the root growth capacity of plants under adverse conditions.

[0057] The photosynthetic capacity of plants under stress was assessed by measuring chlorophyll content using SPAD values.

[0058] The assessment of physiological indicators includes: measuring the proline content in leaves as a physiological indicator of plant resistance to abiotic stress;

[0059] Measuring MDA content assesses the degree of cell membrane damage and reflects the plant's stress resistance;

[0060] The activities of antioxidant enzymes such as superoxide dismutase and catalase were measured to assess the antioxidant capacity of plants.

[0061] The yield-related indicators include: counting the number of grains per ear and assessing the plant's ability to set fruit under adverse conditions;

[0062] The weight of every 100 seeds was measured to assess seed plumpness and quality; the coefficient of variation of yield under different adverse conditions was calculated to assess yield stability.

[0063] Furthermore, in Step III, final screening of the general combining ability, grain appearance, and key sugar metabolism genes of the lines was conducted in the high-yield experimental area, including:

[0064] Assessing general combining ability includes: evaluating the general combining ability of lines through hybridization experiments, and selecting lines with high general combining ability for subsequent breeding;

[0065] Evaluate the specific combining ability of strains when crossed with different parents, and select combinations with high specific combining ability for hybrid development;

[0066] The evaluation of grain appearance includes: measuring the weight of 100 seeds to assess seed plumpness and quality; counting the number of grains per ear to assess the plant's fruit-setting capacity; calculating the yield per hectare to assess the high-yield potential of the line; and evaluating the color, shape, and size of the grains to ensure they meet market demands.

[0067] The assessment of key sugar metabolism genes includes: using high performance liquid chromatography to determine the sucrose, fructose and glucose content in the kernels to assess the sweetness of sweet corn; determining the starch content in the kernels to assess its processing adaptability; and determining the content of vitamin C and vitamin E to assess its nutritional value.

[0068] Furthermore, in Step III, the final screening of lines for general combining ability, grain appearance, and key sugar metabolism genes in high-yield experimental areas also includes:

[0069] A comprehensive quality index model was constructed based on general combining ability, grain appearance, and key sugar metabolism genes.

[0070] The yield, quality, and combining ability indices are weighted and summed to calculate the comprehensive quality index for each line.

[0071] Based on the preset comprehensive quality index threshold, strains with high yield, high quality and high combining ability were selected.

[0072] Compared with the prior art, the beneficial effects of the present invention are:

[0073] In this invention, by integrating molecular marker data, disease inoculation response phenotypic data, and environmental factor data, correlation analysis is used to screen characteristic variables related to disease resistance, ensuring that the model can accurately assess the disease resistance of each individual plant. By setting a scientifically reasonable disease resistance index threshold and optimizing it in combination with historical data and actual breeding needs, the accuracy and stability of the screening results are ensured. This not only improves the efficiency of screening disease-resistant individual plants, but also determines the priority of highly resistant individual plants in subsequent breeding by comprehensively evaluating other agronomic traits, providing strong support for multi-objective breeding. The highly resistant individual plants screened finally show good stability under different environmental conditions, significantly improving breeding efficiency and success rate, providing an innovative technical means for the efficient breeding of multi-resistant and widely adaptable inbred lines of sweet corn, and has important practical application value. Attached Figure Description

[0074] Figure 1 This is an overall flowchart of the step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines of the present invention;

[0075] Figure 2 This is a detailed flowchart of the step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines of the present invention. Detailed Implementation

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] To address the technical problems of existing methods, such as long time cycles, difficulty in breaking linkage burdens between traits, and low efficiency in screening for multi-environment adaptability, please refer to [link to relevant documentation]. Figure 1-2 This embodiment provides the following technical solution:

[0078] A step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines includes the following steps:

[0079] Preparatory stage: By aggregating tropical and temperate germplasm resources through multi-parental hybridization, a basic population with rich genetic background and complementary traits is constructed to provide a material basis for subsequent tiered selection; specifically including:

[0080] Collect sweet corn germplasm resources with excellent traits such as high sugar content, high temperature tolerance, and moisture tolerance from tropical regions, selecting at least 5 representative tropical germplasm resources; collect sweet corn germplasm resources with excellent traits such as cold resistance, drought resistance, and high yield from temperate regions, selecting at least 3 representative temperate germplasm resources; conduct preliminary phenotypic evaluations on the collected germplasm resources, including agronomic traits (e.g., plant height, ear length, and ear diameter), disease resistance (e.g., resistance to rust, leaf spot, and stalk rot), and adaptability (e.g., tolerance to drought, low temperature, and poor soil); and use multi-parental... This hybridization method combines tropical and temperate germplasm for hybridization. For example, a 4 tropical × 4 temperate parent hybridization design is used to ensure sufficient exchange of genetic material between the tropical and temperate germplasm. Under suitable environmental conditions, such as temperature, humidity, and light, the selected parents are artificially pollinated to ensure successful hybridization. The parent information and hybridization time of each hybridization combination are recorded. Seeds of the hybrid offspring are collected to construct a basic population with rich genetic background and complementary traits. Preliminary genetic diversity and trait complementarity analysis are performed on the offspring population to ensure the genetic diversity of the basic population.

[0081] A certain number of individual plants, such as 100, are randomly selected from the basic population, and leaf samples are collected. High-quality DNA samples are extracted using a suitable DNA extraction method for plant tissues, such as the CTAB method. The concentration and purity of the extracted DNA are tested to ensure that the DNA quality meets the requirements for subsequent analysis. Molecular markers linked to resistance to major diseases such as rust, leaf spot, and stem rot are selected; for example, markers homologous to genes such as Rp1 and Rhg1 are used. PCR amplification technology is used to detect molecular markers in individual plants of the basic population. PCR reactions are performed using specific primers for each marker to amplify the target fragments. Genotype data of each individual plant at each molecular marker locus are recorded, such as allele type, to construct a molecular marker database. Rust and leaf spot diseases are selected... In addition to stem rot, a major disease, artificial inoculation experiments were conducted. Individual plants in the baseline population were divided into multiple treatment groups, each inoculated with one disease, and a control group was set up. At certain time points after inoculation, such as 7, 14, and 21 days, the disease response phenotype of each plant was observed and recorded, including but not limited to the number, size, and disease index of lesions. Detailed records of the disease response phenotype data for each individual plant were compiled to construct a phenotypic database. During the disease inoculation period, environmental meteorological data, including temperature, humidity, light intensity, and precipitation, were monitored and recorded. Soil samples were collected, and the physicochemical properties of the soil, such as soil pH and soil nutrient content (nitrogen, phosphorus, potassium, etc.), were measured. The collected environmental factor data were organized into a database, corresponding to the molecular marker data and phenotypic data.

[0082] Step 1, Initial Screening for Disease Resistance: Early-stage assisted selection is performed using molecular markers linked to resistance to major diseases such as rust, leaf spot, and stem rot, such as markers homologous to Rp1 and Rhg1; an early disease resistance prediction model is constructed to output a resistance index to assist in the screening of highly resistant individual plants; combined with artificial inoculation identification, highly resistant individual plants are screened; specifically including:

[0083] Molecular marker data, disease inoculation response phenotypic data, and environmental factor data from the basic population were acquired and integrated to form a complete dataset. Based on correlation analysis, characteristic variables related to disease resistance were selected, including genotypes of molecular marker loci, disease response phenotypic indicators, and environmental factors. A random forest algorithm was used to divide the integrated dataset into training and testing sets. The random forest model was trained using the training set to optimize model parameters. The model was validated using the testing set to evaluate its predictive performance. The trained random forest model was applied to all individual plants in the basic population to predict the disease resistance index for each plant. Based on the disease resistance index, highly resistant plants (e.g., plants with a disease resistance index above a certain threshold) were selected as candidate materials for subsequent tiered selection. The disease resistance of the selected highly resistant plants was assessed to verify their stability under different environmental conditions. Combined with other agronomic traits, such as yield and quality, a comprehensive evaluation of the highly resistant plants was conducted to determine their priority in subsequent tiered selection. The selected highly resistant plants were then incorporated into the second tier of the tiered selection process.

[0084] Among them, highly resistant single plants were selected based on the disease resistance index, including:

[0085] Obtain the mean and standard deviation of the disease resistance index from historical data samples to set an initial threshold range; for example, choose the sum of the mean and standard deviation as the initial threshold. Obtain the disease resistance index from historical data samples and, based on the distribution of the disease resistance index, select an appropriate percentile as an auxiliary threshold; for example, select the 80th percentile of the disease resistance index, i.e., 80% of individual plants have a disease resistance index below this value, as an auxiliary threshold. Adjust the initial threshold range according to the actual breeding goals and needs; for example, if the breeding goal is to select individual plants with extremely high disease resistance, the threshold can be set higher; if the goal is to retain more germplasm resources... If necessary, the threshold can be appropriately lowered; cross-validation is used to divide the basic population into multiple subsets, calculate the screening results under different thresholds, and evaluate the accuracy and stability of highly resistant individual plants screened under different thresholds; combined with known breeding data, the actual disease resistance performance of highly resistant individual plants screened under different thresholds is evaluated, and a threshold with high consistency with the actual breeding results is selected; for example, if internal validation finds that highly resistant individual plants screened with a threshold of 0.8 have high accuracy and stability, and also show good disease resistance in external validation, then the disease resistance index threshold can be finally determined to be 0.8.

[0086] In one embodiment, assuming the disease resistance index distribution of individual plants in the base population is predicted by the model as follows: mean 0.65, standard deviation 0.15, 80th percentile 0.78, the initial threshold is based on the mean plus standard deviation: 0.65 + 0.15 = 0.80, and the auxiliary threshold is based on the 80th percentile: 0.78. Cross-validation shows that when the threshold is 0.80, the selected highly resistant plants exhibit the expected disease resistance performance. After comprehensive evaluation, the disease resistance index threshold is finally determined to be 0.80. Through the above steps, the disease resistance index threshold can be set scientifically and reasonably, ensuring that the selected highly resistant plants have high accuracy and stability, providing reliable candidate materials for subsequent tiered selection.

[0087] The beneficial effects achieved by the above are as follows: By scientifically setting the disease resistance index threshold and combining historical data with actual breeding needs, the screening process for highly resistant single plants has been optimized, enhancing the stability and reliability of breeding results. It has also improved the breeding efficiency of sweet corn inbred lines, providing strong technical support for high-yield, high-quality, and multi-resistant sweet corn cultivation, and has significant economic and social benefits.

[0088] Step II, Stress Resistance and Adaptability Screening: Superior strains from Step I were subjected to multi-site evaluation under different abiotic stresses, including drought, low temperature, and poor soil conditions, to screen for strains that exhibited stability and broad adaptability under various abiotic stresses; specifically including:

[0089] Set up soil moisture content, stress duration, and soil fertility under different adverse conditions; for example: under drought conditions, set the soil moisture content to 30%-40% of field capacity, and continuously stress for 15-20 days, observing the plant growth status, leaf wilting degree, root activity, and other indicators; under low temperature conditions, set the temperature to 5℃-10℃, and continuously stress for 10-14 days, observing the plant growth rate, leaf discoloration, frost damage, and other indicators; under infertile soil conditions, select low-fertility soils such as those with nitrogen, phosphorus, and potassium content lower than 50% of normal soils, and observe the plant growth and development, number of grains per ear, and 100-grain weight, and other indicators.

[0090] Multiple identification sites were set up in different geographical regions, such as northern, southern, and plateau areas, to ensure the diversity and representativeness of stress conditions. Repeated trials were conducted at each identification site using a randomized block design, with 10-20 plants planted per plot to ensure data reliability and reproducibility. The multidimensional adaptability index of superior plants at each identification site was evaluated, including growth indicators, physiological indicators, and yield-related indicators. A comprehensive adaptability index model was constructed based on the multidimensional adaptability index, weighted and summed to calculate the adaptability index of each line. Adaptability index thresholds were set based on historical data samples, such as ≥0.7, to screen lines that showed stability and broad adaptability under various stress conditions. The screened lines underwent further stress verification to ensure the stability of their adaptability under different environments, and were then incorporated into step III of the ladder experiment.

[0091] The evaluation of the multidimensional fitness index of superior plants at each identification point includes:

[0092] The growth indicators included: measuring plant height changes under abiotic stress to assess growth stability; measuring root activity using the TTC method to assess root growth capacity under stress; and measuring chlorophyll content using SPAD values ​​to assess photosynthetic capacity under stress.

[0093] The physiological indicators for assessment include: measuring the proline content in leaves as a physiological indicator of plant resistance to abiotic stress; measuring the MDA content to assess the degree of cell membrane damage and reflect the plant's resistance; and measuring the activities of antioxidant enzymes such as superoxide dismutase and catalase to assess the plant's antioxidant capacity.

[0094] The yield-related indicators include: counting the number of grains per ear to assess the plant's ability to set fruit under adverse conditions; measuring the weight of every 100 seeds to assess seed plumpness and quality; and calculating the yield variation coefficient under different adverse conditions to assess yield stability.

[0095] Step III: Synergistic Improvement of Yield and Quality: Building upon Step II, final screening of the general combining ability, grain appearance, and key sugar metabolism genes of the inbred lines is conducted in high-yield experimental areas to ensure that the developed inbred lines possess multiple resistances, wide adaptability, and high yield and quality characteristics; specifically including:

[0096] Assessing general combining ability (GFA) includes: evaluating the GFA of a line through hybridization experiments and selecting lines with high GFA for subsequent breeding; evaluating the specific combining ability (STA) of a line when hybridized with different parents and selecting combinations with high STA for hybrid development. Assessing kernel appearance includes: measuring the weight of 100 seeds to assess seed plumpness and quality; counting the number of kernels per ear to assess the plant's seed setting capacity; calculating yield per hectare to assess the high-yield potential of the line; and assessing the color, shape, and size of the kernels to ensure they meet market demands. Assessing key sugar metabolism genes includes: determining the sucrose, fructose, and glucose content in the kernels using high-performance liquid chromatography (HPLC) to assess the sweetness of sweet corn; determining the starch content in the kernels to assess its processing adaptability; and determining the vitamin C and vitamin E content to assess its nutritional value.

[0097] A comprehensive quality index model is constructed based on general combining ability, grain appearance, and key sugar metabolism genes. The yield, quality, and combining ability indices are weighted and summed to calculate the comprehensive quality index of each line. Based on a preset comprehensive quality index threshold, such as ≥0.8, lines with high yield, high quality, and high combining ability are selected. The selected lines are further validated for yield and quality to ensure their stability and consistency under different environments.

[0098] The beneficial effects achieved by the above are as follows: By integrating molecular marker data, disease inoculation response phenotypic data, and environmental factor data, correlation analysis was used to screen characteristic variables related to disease resistance, ensuring that the model can accurately assess the disease resistance of each individual plant; furthermore, by comprehensively evaluating other agronomic traits, the priority of highly resistant individual plants in subsequent breeding was determined, providing strong support for multi-objective breeding; finally, the highly resistant individual plants screened showed good stability under different environmental conditions, significantly improving breeding efficiency and success rate, providing innovative technical means for the efficient breeding of multi-resistant and widely adaptable inbred lines of sweet corn, and having important practical application value.

[0099] Working principle: Step I involves multi-parental hybridization of tropical and temperate germplasm resources to construct a basic population with rich genetic background and complementary traits, providing a diverse material basis for subsequent step selection. Step II utilizes molecular marker-assisted selection and early disease resistance prediction models, combined with artificial inoculation identification, to efficiently screen highly resistant individual plants, improving the accuracy and efficiency of disease resistance screening; it also screens lines that are stable and widely adaptable under various abiotic stresses through multi-site stress identification, enhancing the environmental adaptability of the selected inbred lines. Step III comprehensively evaluates the general combining ability, grain appearance, and key sugar metabolism genes of the lines in high-yield experimental areas to ensure that the bred inbred lines possess multiple resistances, wide adaptability, and high yield and quality characteristics.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or high-voltage switchgear that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or high-voltage switchgear.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines, characterized in that, Includes the following steps: Preparatory stage: By aggregating tropical and temperate germplasm resources and conducting multi-parent hybridization, a basic population is constructed to provide a material basis for subsequent tiered selection; Step 1, Initial Screening for Disease Resistance: Early assisted selection was conducted using molecular markers linked to resistance to major diseases such as rust, leaf spot, and stem rot; an early disease resistance prediction model was constructed to output a disease resistance index to assist in the screening of highly resistant individual plants; and highly resistant individual plants were screened by combining artificial inoculation identification. Step II, stress resistance and adaptability screening: Superior plants from Step I were evaluated at multiple locations under different abiotic stresses such as drought, low temperature and barrenness, and strains that showed stability and wide adaptability under various abiotic stresses were screened. Step III: Synergistic Improvement of Yield and Quality: Based on Step II, the general combining ability, grain appearance and key sugar metabolism genes of the inbred lines are screened in the high-yield test area to ensure that the inbred lines have the characteristics of multiple resistance, wide adaptability and high yield and quality.

2. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 1, characterized in that, In Step I, an early disease resistance prediction model is constructed to output a disease resistance index, which assists in the screening of highly resistant individual plants, including: We acquire molecular marker data, disease inoculation response phenotypic data, and environmental factor data from the basic population, and integrate them to form a complete dataset. Based on correlation analysis, characteristic variables related to disease resistance were selected, including genotypes of molecular marker sites, phenotypic indicators of disease response, and environmental factors. The random forest algorithm is used to divide the integrated dataset into a training set and a test set; the random forest model is trained using the training set to optimize the model parameters; and the model is validated using the test set to evaluate its predictive performance. The trained random forest model is applied to all individual plants in the base population to predict the disease resistance index of each individual plant. Based on the disease resistance index, highly resistant single plants were screened out as candidate materials for subsequent step selection; The disease resistance of the selected highly resistant individual plants was evaluated to verify their stability under different environmental conditions; Combined with other agronomic traits, highly resistant individual plants are comprehensively evaluated to determine their priority in subsequent tiered selection, and the selected highly resistant individual plants are put into tier II.

3. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 2, characterized in that, Based on the disease resistance index, highly resistant individual plants were selected, including: Obtain the mean and standard deviation of the disease resistance index from historical data samples, and set an initial threshold range; Obtain the disease resistance index from historical data samples, and select an appropriate percentile as an auxiliary threshold based on the distribution of the disease resistance index; Adjust the initial threshold range according to actual breeding goals and needs; Cross-validation was used to divide the basic population into multiple subsets, and the screening results under different thresholds were calculated. The accuracy and stability of the highly resistant single plants screened under different thresholds were evaluated. Based on known breeding data, the actual disease resistance performance of highly resistant single plants screened under different thresholds was evaluated, and the threshold with high consistency with the actual breeding results was selected.

4. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 1, characterized in that, In the preparatory stage, multi-parent hybridization is carried out by aggregating tropical and temperate germplasm resources, including: Collect sweet corn germplasm resources with excellent traits from tropical regions, and select at least 5 representative tropical germplasm resources; Collect sweet corn germplasm resources with excellent traits from temperate regions, and select at least 3 representative temperate germplasm resources; A preliminary phenotypic assessment was conducted on the collected germplasm resources, including agronomic traits, disease resistance, and adaptability. A multi-parental hybridization method was used to combine tropical germplasm with temperate germplasm for hybridization; Under suitable environmental conditions, artificial pollination is performed on the selected parents to ensure successful hybridization, and the parent information and hybridization time of each hybrid combination are recorded; Seeds of hybrid offspring were collected to construct a basic population with rich genetic background and complementary traits. Preliminary genetic diversity and trait complementarity analysis of the offspring population was conducted to ensure the genetic diversity of the basic population.

5. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 4, characterized in that, The preparatory stage, which involves multi-parent hybridization by combining tropical and temperate germplasm resources, also includes: A certain number of individual plants were randomly selected from the basic population, and leaf samples were collected. High-quality DNA samples were extracted using a DNA extraction method suitable for plant tissues, and the concentration and purity of the extracted DNA were tested. Select molecular markers linked to resistance to major diseases such as rust, leaf spot, and stem rot; PCR amplification technology was used to detect molecular markers in individual plants of the basic population. PCR reactions were performed using specific primers for each marker to amplify the target fragments. Genotype data for each individual plant at various molecular marker loci were recorded to construct a molecular marker database.

6. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 5, characterized in that, The preparatory stage, which involves multi-parent hybridization by combining tropical and temperate germplasm resources, also includes: Rust, leaf spot and stem rot were selected as the main diseases, and artificial inoculation experiments were carried out. The individual plants of the basic population were divided into multiple treatment groups, each group was inoculated with one disease, and a control group was set up. After a certain period of time following inoculation, the disease response phenotype of each plant was observed and recorded. Detailed records of disease response phenotypic data for each individual plant were kept, and a phenotypic database was constructed. During disease inoculation, environmental meteorological data should be monitored and recorded; Collect soil samples and determine the physicochemical properties of the soil; The collected environmental factor data were organized into a database, corresponding to molecular marker data and phenotypic data.

7. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 1, characterized in that, In Step II, the superior plants from Step I were evaluated at multiple locations under different abiotic stresses, including drought, low temperature, and poor soil conditions, including: Set up soil moisture content, stress duration, and soil fertility under different adverse conditions; Multiple identification points were set up in different geographical areas, and repeated tests were conducted at each identification point; The multidimensional fitness index of superior plants in each identification point was evaluated, including growth indicators, physiological indicators and yield-related indicators. A comprehensive adaptability index model was constructed based on the multidimensional adaptability index. The growth index, physiological index and yield-related index were weighted and summed to calculate the adaptability index of each line. Based on historical data samples, an adaptability index threshold was set to screen out strains that showed stability and broad adaptability under various adverse conditions. The selected strains were further tested for abiotic stress, and then the selected strains were put into step III.

8. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 7, characterized in that, The multidimensional fitness index of superior strains at each identification point was evaluated, including: The evaluation indicators for growth include: measuring the change in plant height under adverse stress to assess its growth stability; Root vigor was determined using the TTC method to assess the root growth capacity of plants under adverse conditions. The photosynthetic capacity of plants under stress was assessed by measuring chlorophyll content using SPAD values. The assessment of physiological indicators includes: measuring the proline content in leaves as a physiological indicator of plant resistance to abiotic stress; Measuring MDA content assesses the degree of cell membrane damage and reflects the plant's stress resistance; The activities of antioxidant enzymes such as superoxide dismutase and catalase were measured to assess the antioxidant capacity of plants. The yield-related indicators include: counting the number of grains per ear and assessing the plant's ability to set fruit under adverse conditions; The weight of every 100 seeds was measured to assess seed plumpness and quality; the coefficient of variation of yield under different adverse conditions was calculated to assess yield stability.

9. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 1, characterized in that, In Step III, final screening of the general combining ability, grain appearance, and key sugar metabolism genes of the lines was conducted in the high-yield experimental area, including: Assessing general combining ability includes: evaluating the general combining ability of a line through hybridization experiments and selecting lines with high general combining ability for subsequent breeding; and evaluating the specific combining ability of a line when hybridized with different parents and selecting combinations with high specific combining ability for hybrid development. The evaluation of grain appearance includes: measuring the weight of 100 seeds to assess seed plumpness and quality; counting the number of grains per ear to assess the plant's fruit-setting capacity; calculating the yield per hectare to assess the high-yield potential of the line; and evaluating the color, shape, and size of the grains to ensure they meet market demands. The assessment of key sugar metabolism genes includes: using high performance liquid chromatography to determine the sucrose, fructose and glucose content in the kernels to assess the sweetness of sweet corn; determining the starch content in the kernels to assess its processing adaptability; and determining the content of vitamin C and vitamin E to assess its nutritional value.

10. The step-by-step breeding method for multi-resistant and widely adaptable sweet corn inbred lines according to claim 9, characterized in that, In Step III, the final screening of lines for general combining ability, grain appearance, and key sugar metabolism genes was conducted in high-yield experimental areas, and also included: A comprehensive quality index model was constructed based on general combining ability, grain appearance, and key sugar metabolism genes. The yield, quality, and combining ability indices are weighted and summed to calculate the comprehensive quality index for each line. Based on the preset comprehensive quality index threshold, strains with high yield, high quality and high combining ability were selected.

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