Comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties

Abstract

The invention discloses a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, and relates to the technical field of rice variety screening, and the method comprises the following steps: determining ecological points, collecting environmental data, selecting different variety groups according to different ecological points, and carrying out genotype analysis; the method comprises the following steps: collecting phenotype data of rice varieties, establishing a correlation model between genotype data and phenotype data, and screening high-quality genes; and selecting a target variety according to a screening result, directionally editing key genes of the rice variety, evaluating a gene editing effect, carrying out precise cultivation after gene editing, further verifying the performance of the rice variety, and carrying out variety screening. According to the method, ecological data, gene data and phenotype data can be effectively integrated for comprehensive evaluation, the rice variety problem is optimized from the global perspective, the actual performance of the rice variety is improved through precise cultivation management, and a variety layout foundation is laid for large-area rice yield, quality and efficiency collaborative improvement.

Description

Technical Field

[0001] The invention relates to the technical field of rice variety screening, in particular to a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties. Background Art

[0002] As one of the most important food crops in the world, rice is responsible for providing staple food. However, with the challenges of climate change, land resource limitations and agricultural production efficiency, traditional rice breeding methods have been unable to meet the demand for high-yield, high-quality and high-efficiency rice varieties. Therefore, how to screen rice varieties scientifically, accurately and efficiently has become an important research direction in the current agricultural field. In recent years, the rapid development of genomics, phenomics and precision cultivation technology has promoted the innovation of rice variety screening methods. The screening of rice varieties has gradually transformed from the traditional breeding model to the modern breeding model based on genes. The application of genomics technology enables researchers to screen out gene markers related to key traits such as yield, disease resistance and rice quality through genotyping analysis; at the same time, the precise collection and analysis of phenotypic data, through non-destructive technologies such as near-infrared diffuse reflectance spectroscopy (NIRDRS), realizes the refined phenotypic evaluation of rice grains. Combined with big data technology, the combination of genomics and phenomics provides a new direction for the screening of rice varieties. At the same time, the application of precision cultivation management technologies, such as the Internet of Things (IoT) and smart irrigation technology, has enabled more detailed regulation of the rice cultivation environment, further improving rice production efficiency and quality.

[0003] Although these emerging technologies have brought great development potential to rice breeding, existing technologies still have several shortcomings. First, although the association analysis between genotype and phenotype has been widely used, most of the existing analysis methods focus on the analysis of a single feature and lack in-depth discussion of the nonlinear relationship between multi-dimensional features. The traditional means of phenotypic data collection are relatively simple and lack long-term real-time monitoring of the rice growth environment and its changes. Secondly, although the application of gene editing technology can change the key genes of rice varieties in a targeted manner, the variety verification process after gene editing still lacks sufficient refined cultivation management and long-term field trials, resulting in unstable evaluation results and unable to fully verify the actual impact of gene editing on rice varieties. Finally, the existing rice variety screening methods are still insufficient in the design of adaptability assessment and comprehensive evaluation indicators in different ecological environments. They fail to effectively integrate ecological data, genetic data and phenotypic data for comprehensive evaluation, making it difficult to optimize rice varieties from a global perspective. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, which solves the problem that the existing rice variety screening methods are still insufficient in the design of adaptability assessment and comprehensive evaluation indicators in different ecological environments, fail to effectively integrate ecological data, genetic data and phenotypic data for comprehensive evaluation, and are difficult to optimize rice varieties from a global perspective.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties, comprising:

[0008] Identify ecological sites and collect environmental data, select different breed groups according to different ecological sites and conduct genotyping analysis;

[0009] Collect phenotypic data of rice varieties, establish a correlation model between genotypic data and phenotypic data, and screen high-quality genes;

[0010] Based on the screening results, the target varieties are selected and the key genes of the rice varieties are edited in a targeted manner. The gene editing effects are evaluated and precise cultivation is carried out after gene editing to further verify the performance of the rice varieties and conduct variety screening.

[0011] As a preferred solution of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties of the present invention, the step of determining ecological points and collecting environmental data comprises:

[0012] The said eco-site refers to an eco-site containing various climatic conditions;

[0013] The environmental data includes climate data, soil type, nutrient content, and water conditions.

[0014] As a preferred solution of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties of the present invention, the method of selecting different variety groups according to different ecological points and performing genotype analysis comprises:

[0015] The selecting of different variety groups according to different ecological points refers to selecting corresponding rice varieties from a rice variety library by analyzing environmental data of the ecological points, classifying the selected varieties according to cultivation types, and constructing rice variety groups according to the selected rice varieties;

[0016] The genotype analysis refers to using gene chip technology to perform genotype analysis on a rice variety group, and screening out genotype data related to key traits based on the gene chip data;

[0017] The key traits are yield, disease resistance, rice quality and drought resistance.

[0018] As a preferred embodiment of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties of the present invention, the phenotypic data refers to growth traits, yield traits, quality traits, resistance traits and physiological traits;

[0019] The collected phenotypic and genotypic data were standardized, and high yield and high quality were defined as target phenotypes;

[0020] The rice phenotypic data and genotypic data were merged according to the sample number to form a unified data matrix, and the LMC Copula model was used to calculate the dependency measure C between each pair of features u and v after standardization. LMC (u, v):

[0021] C LMC (u,v)=[u -a +v -a +b·log(u)·log(v)-1] -1 / a

[0022] Where a and b are adjustment parameters;

[0023] After calculating the dependency measure, the covariance of each pair of features is calculated, and combined with the standard deviation, the correlation coefficient between each pair of features is obtained and the correlation matrix is ​​constructed;

[0024] Set the correlation threshold A, filter out redundant features with correlation greater than the threshold A, use Kendall's Tau to calculate the ranking consistency between each pair of features, further confirm redundant features through Tau, and remove confirmed redundant features;

[0025] The data set after removing redundant features is input into the PCA algorithm, the covariance between each pair of features is calculated and the covariance matrix is ​​constructed, the covariance matrix is ​​decomposed by eigenvalues, and the eigenvalues ​​and eigenvectors are extracted;

[0026] Sort the eigenvectors by eigenvalue and select the principal component with the largest explained variance;

[0027] Project the data into a new low-dimensional space through the selected principal components to obtain a reduced-dimensional data set;

[0028] Extract the feature load matrix of each principal component from the PCA results, calculate the absolute value of the feature weight in each principal component, and calculate the absolute weight sum of each feature in all principal components to obtain the total contribution of the feature, set the threshold S, and select the features whose total contribution exceeds S as the feature set through the load matrix;

[0029] Randomly select several feature combinations from the feature set to generate initial tree species. Each tree species represents a feature subset. Create an array to store all tree species. Calculate the fitness of each tree based on the information gain and redundancy of the feature subset.

[0030] Sort by fitness from high to low, select the tree species with the highest fitness as the parent tree species, select the characteristics of the parent tree species for mating, generate new tree species, and after mating, perform small mutations on the newly generated tree species to generate a new set of tree species and perform cross-validation evaluation to obtain an optimized dimensionality reduction data set;

[0031] The phenotypic data after dimension reduction were input into the PLS-DA model as independent variables, and the genotypic data after dimension reduction were input into the PLS-DA model as dependent variables. The weight matrix W was extracted from the PLS-DA model to determine the influence of each principal component on the phenotypic data and genotypic data. According to the PLS-DA analysis results, the high-quality genes in the weight matrix that had the greatest influence on the target phenotype were selected.

[0032] As a preferred scheme of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties described in the present invention, wherein: the selecting of target varieties according to the screening results and directed editing of key genes of rice varieties refers to selecting rice varieties whose target phenotypes meet the screening requirements, analyzing the historical cultivation phenotypes of the selected rice varieties, selecting varieties whose target phenotype values ​​are higher than the average under different environmental conditions, using bioinformatics tools to design gRNA, targeting specific regions of the target gene, combining gRNA and Cas9 protein, and introducing the CRISPR-Cas9 system into the cells of the target rice variety through transformation technology.

[0033] As a preferred scheme of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties described in the present invention, the evaluation of gene editing effect refers to designing specific primers, performing PCR amplification on the target gene, detecting the success rate of the target gene, sequencing the PCR product using high-throughput sequencing technology, further confirming the marking effect of the target gene and analyzing the accuracy of insertion through gene comparison, cultivating the gene-edited rice varieties in the experimental field, and comparing various characteristics of the rice varieties before and after editing.

[0034] As a preferred solution of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties of the present invention, wherein: the precision cultivation after gene editing further verifies the performance of rice varieties, and the variety screening refers to designing corresponding cultivation management plans according to the characteristics of the target varieties, determining standardized cultivation management methods, implementing the cultivation plans in experimental fields, and recording data of each link, monitoring the cultivation environment in real time through sensors and automatic irrigation systems, and using Internet of Things devices to monitor field environmental data, transmitting the data to the data platform in real time, and adjusting the cultivation management measures according to the environmental data;

[0035] After determining the key core indicators for rice variety screening through the Delphi method, Delphi expert consultation was used again to clarify the key indicators and critical values ​​for evaluating rice yield, quality and efficiency at each test point, establish comprehensive evaluation indicators and grading standards for high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region, and screen out high-yield, high-quality and high-efficiency rice varieties.

[0036] As a preferred embodiment of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties described in the present invention, the storing and backing up of the screened rice variety data in a database refers to integrating the genotype and phenotypic data of the screened rice varieties into a variety table according to the sample numbers, generating an information table based on the basic information of the rice, and storing the data table in a central database.

[0037] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties as described in the first aspect of the present invention is implemented.

[0038] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program, when executed by a processor, implements any step of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties as described in the first aspect of the present invention.

[0039] The beneficial effects of the present invention are as follows: the present invention systematically collects diversified ecological data, phenotypic data and genetic data, uses big data and machine learning algorithms to accurately establish a correlation model between genotype and phenotype, and screens out rice varieties with high-quality characteristics. At the same time, the target gene is modified in a targeted manner in combination with gene editing technology, and the gene-edited varieties are verified and evaluated for a long time under precise cultivation management. This method can not only effectively integrate ecological data, genetic data and phenotypic data for comprehensive evaluation, optimize rice variety issues from a global perspective, but also improve the actual performance of rice varieties through precise cultivation management, laying a foundation for variety layout for the coordinated improvement of rice yield, quality and efficiency over a large area. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 This is a flow chart of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties in Example 1. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0045] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides a method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties, comprising the following steps:

[0046] S1. Identify ecological points and collect environmental data, select different variety groups according to different ecological points and conduct genotyping analysis;

[0047] Specifically, the steps of determining ecological points and collecting environmental data include:

[0048] The said eco-site refers to an eco-site containing various climatic conditions;

[0049] The environmental data includes climate data, soil type, nutrient content, and water conditions.

[0050] The present invention combines climate, soil and water conditions, precise sensor technology and real-time data collection to ensure the comprehensiveness and accuracy of rice variety screening. This method can not only accurately monitor environmental changes, but also screen out the most suitable rice varieties through the combination of genotype analysis, phenotypic data and environmental data, thereby improving the yield, quality and efficiency of rice.

[0051] Furthermore, different varieties were selected according to different ecological points and genotype analysis was performed including:

[0052] The selecting of different variety groups according to different ecological points refers to selecting corresponding rice varieties from the rice variety library by analyzing the environmental data of the ecological points, classifying the selected varieties according to the cultivation type (such as medium-ripening, medium-glutinous rice, soft-glutinous rice, etc.), and constructing rice variety groups according to the selected rice varieties;

[0053] The genotype analysis refers to using gene chip technology to perform genotype analysis on a rice variety group, and screening out genotype data related to key traits based on the gene chip data;

[0054] The key traits are yield, disease resistance, rice quality and drought resistance;

[0055] The genotype data include single nucleotide polymorphisms, simple sequence repeats, structural variations, genome-wide association data and genotype assembly data.

[0056] Through the precise selection of different ecological points, it is possible to ensure that the selected rice varieties are adapted to specific environmental conditions. For example, in the humid climate of the Yangtze River Delta, varieties suitable for high humidity environments are selected, while in the cold climate of the Northeast Plain, varieties adapted to low temperatures are selected. In this way, the adaptability of rice varieties can be improved and yield fluctuations caused by climate change can be reduced. In addition, classifying rice varieties according to cultivation types and constructing variety groups can help select the most suitable variety group for a specific environment from a wide range of variety libraries, thus laying the foundation for variety screening. Genotyping analysis uses gene chip technology to comprehensively scan and decode rice genes, and can accurately identify genes related to rice yield, disease resistance, rice quality and other characteristics. This not only helps to deeply understand the genetic background of rice, but also provides accurate markers for molecular breeding of rice and guides variety improvement. Classifying the selected rice varieties according to cultivation types, constructing rice variety groups, and combining gene chip technology for genotyping analysis can more accurately screen out those varieties with strong adaptability and excellent traits. Gene chip technology can quickly identify genes related to key traits such as yield, disease resistance, and drought resistance. These genes usually show high stability in different ecological environments. Through the above steps, accurate variety screening and genotype analysis can quickly identify rice varieties with high yield, high quality and disease resistance. This refined screening method can improve the breeding efficiency of rice varieties, reduce the breeding cycle, and increase the success rate of variety improvement.

[0057] S2. Collect phenotypic data of rice varieties, establish a correlation model between genotypic data and phenotypic data, and screen high-quality genes;

[0058] Specifically, we collected phenotypic data of rice varieties, established a correlation model between genotypic data and phenotypic data, and screened high-quality genes, including:

[0059] The phenotypic data refers to growth traits, yield traits, quality traits, resistance traits and physiological traits;

[0060] The collected phenotypic and genotypic data were standardized, and high yield and high quality were defined as target phenotypes;

[0061] The standardization process includes removing outliers and converting data into standard scores;

[0062] The rice phenotypic data and genotypic data were merged according to the sample number to form a unified data matrix, where each row represented a rice sample and each column represented a feature;

[0063] The genotype data are standardized and the LMC Copula model is used to calculate the dependency measure C between each pair of features u and v after standardization. LMC (u, v):

[0064] C LMC (u,v)=[u -a +v -a +b·log(u)·log(v)-1] -1 / a

[0065] In the formula, a and b are adjustment parameters, which control the strength of the dependency relationship between data in the model and the influence of the logarithmic term, respectively, and are estimated by the maximum likelihood estimation method;

[0066] The covariance of each pair of features is calculated, and combined with the standard deviation, the correlation coefficient between each pair of features is obtained and the correlation matrix is ​​constructed. The generated matrix not only contains the linear correlation, but also reflects the nonlinear correlation calculated by LMC Copula.

[0067] Set the correlation threshold A according to business needs, filter out redundant features with correlation greater than threshold A, use Kendall's Tau to calculate the sorting consistency between each pair of features, and further confirm redundant features through Tau, and remove the confirmed redundant features. If the Tau value is close to 1, it means that the redundancy of this pair of features is high, and one of them should be deleted;

[0068] The final filtered correlation matrix and the data set after removing redundant features are stored in the database;

[0069] The data set after removing redundant features is input into the PCA algorithm, the covariance between each pair of features is calculated and the covariance matrix is ​​constructed, the covariance matrix is ​​decomposed by eigenvalues, and the eigenvalues ​​and eigenvectors are extracted;

[0070] The eigenvalue represents the contribution of each principal component to the data variance, and the eigenvector represents the principal component direction of the data;

[0071] Sort the eigenvectors by eigenvalue and select the principal component with the largest explained variance;

[0072] Project the data into a new low-dimensional space through the selected principal components to obtain a reduced-dimensional data set;

[0073] Extract the feature load matrix of each principal component from the PCA results. The matrix dimension is n*w, where n is the number of original features and m is the number of principal components. Calculate the absolute value of the feature weight in each principal component and calculate the absolute weight sum of each feature in all principal components to obtain the total contribution of the feature. Set the threshold S through statistical analysis and select the features whose total contribution exceeds S as the feature set through the load matrix.

[0074] Randomly select several feature combinations from the feature set to generate initial tree species. Each tree species represents a feature subset, and each feature subset contains multiple features randomly selected from the dimensionality reduction data.

[0075] Create an array to store all tree species. Each tree species is represented as a subset of several features. The fitness of each tree is calculated based on the information gain and redundancy of the feature subset:

[0076] Q(T)=E(T)-τ×L(T)

[0077] Where Q(T) is the fitness of tree species T, E(T) is the information gain brought by the features selected in the tree species, which is calculated by the information entropy formula, L(T) is the redundancy of the feature set, which is measured by calculating the correlation matrix between the features, and τ is the penalty factor for redundancy, which is set by experience;

[0078] Sort by fitness from high to low, select the tree species with the highest fitness as the parent tree species, select the characteristics of the parent tree species for mating, generate new tree species, and after mating, perform small mutations on the newly generated tree species to generate a new set of tree species and perform cross-validation evaluation to obtain an optimized dimensionality reduction data set;

[0079] The phenotypic data after dimension reduction were input into the PLS-DA model as independent variables, and the genotypic data after dimension reduction were input into the PLS-DA model as dependent variables. The phenotypic data and genotypic data were input simultaneously using the PLS algorithm to establish a regression model. The weight matrix W was extracted from the PLS-DA model to determine the degree of influence of each principal component on the phenotypic data and genotypic data. According to the PLS-DA analysis results, the high-quality genes with the greatest impact on the target phenotype were selected in the weight matrix. The screened high-quality genes were compared with literature and queried in the database to confirm the known biological functions and verify the association with the target traits.

[0080] Copula function is a statistical tool used to describe the correlation between multidimensional random variables, and is particularly good at capturing nonlinear relationships between variables. In rice variety screening, the relationship between genotype data and phenotypic data is often not only linear, but may also involve complex nonlinear interactions. At this time, a single linear model cannot fully capture the potential connection. By introducing the Copula function, we can model the association between phenotypic and genotypic data more accurately, and both linear and nonlinear correlations can be effectively described. By calculating the correlation between phenotypic and genotypic features, the Copula function helps identify and remove redundant features, which lays a solid foundation for PCA dimensionality reduction and PLS-DA analysis. Therefore, the role of the Copula function is to ensure the accuracy of subsequent analysis by accurately describing the relationship between data, and to provide a clear direction for dimensionality reduction and high-quality gene screening. The role of PCA dimensionality reduction technology is to compress high-dimensional genotype and phenotypic data into low-dimensional space through linear transformation, highlighting the most informative features. PCA can not only reduce computational complexity and avoid interference from redundant features, but also retain the maximum variance of the data, ensuring that the most valuable information in the data is retained. Through PCA dimensionality reduction, we can simplify the phenotypic and genotypic data containing a lot of redundancy and correlation into fewer principal components. This step is particularly important in the subsequent PLS-DA analysis, because the reduced-dimensional data set is more representative and avoids multicollinearity problems, reducing noise interference. PLS-DA analysis is to identify which genotypic characteristics are significantly associated with specific phenotypic characteristics (such as yield, rice quality, etc.) by establishing a regression model between phenotypic and genotypic data. In rice variety screening, PLS-DA can help researchers screen out key genes related to target traits. Through the PLS-DA model, we can extract the weight matrix from the reduced-dimensional data set and analyze the contribution of each gene to the target phenotype.

[0081] Through non-destructive testing of rice samples using near-infrared diffuse reflectance spectroscopy (NIRDRS), the chemical composition of rice grains (such as starch content, protein content, etc.) can be accurately measured, and the efficiency and accuracy of data collection can be greatly improved. Compared with traditional manual evaluation methods, NIRDRS technology not only reduces human errors, but also improves the screening speed and convenience of data processing. The beneficial effect of this process is that the selection probability of high-quality gene combinations can be increased through the continuous optimization of intelligent algorithms, thereby accelerating the screening of excellent rice varieties. Through cross-validation evaluation, the most promising subset of variety characteristics can be screened out, which can effectively improve the production performance and quality of rice varieties.

[0082] Furthermore, selecting target varieties based on the screening results and directed editing of key genes of rice varieties refers to selecting rice varieties whose target phenotypes meet the screening requirements, analyzing the historical cultivation phenotypes of the selected rice varieties, selecting varieties whose target phenotype values ​​are higher than the average under different environmental conditions, using bioinformatics tools (such as CRISPR-Design, Benchling) to design gRNA, targeting specific regions of the target gene, combining gRNA and Cas9 protein, and introducing the CRISPR-Cas9 system into the cells of the target rice variety through transformation technology (such as Agrobacterium-mediated method or electroporation method), and the Cas9 protein performs gene cutting under the guidance of gRNA, cutting the DNA of the target gene, and inducing it to mutate or insert.

[0083] In the process of rice variety selection, by screening varieties whose phenotypes meet specific requirements, it is ensured that the selected varieties have good growth performance and target traits. In particular, varieties with phenotypic values ​​higher than the average under different environmental conditions can ensure the stability and adaptability of these varieties in various environments. This process effectively avoids the impact of environmental interference on rice performance and ensures the efficient output and high-quality performance of rice varieties under a wide range of climatic and soil conditions. Using bioinformatics tools such as CRISPR-Design and Benchling to design precise gRNA can ensure that the binding of gRNA to the target gene is very precise. This efficient gRNA design can effectively reduce off-target effects and accurately locate and cut target genes even in complex plant genomes. In addition, these tools can also be used to design multiple gRNAs to edit multiple genes at the same time, further improving the efficiency of gene editing. By combining the designed gRNA with the Cas9 protein and introducing the CRISPR-Cas9 system into the cells of the target rice variety using Agrobacterium-mediated or electroporation, the target gene can be accurately cut, DNA repair can be induced, and gene mutation or insertion can be ultimately achieved. This process enables rice varieties to have new and excellent characteristics, such as increased yield, enhanced disease resistance or improved rice quality. After gene editing, PCR amplification and gene sequencing are used to verify whether the target gene has been successfully edited, and field trials are used to evaluate the performance of the edited varieties. This process not only verifies the accuracy of gene editing, but also confirms the stability and excellent performance of the edited rice varieties under different environmental conditions.

[0084] S3. Select target varieties based on the screening results and edit key genes of rice varieties in a targeted manner, evaluate the gene editing effect, and perform precision cultivation after gene editing to further verify the performance of rice varieties and conduct variety screening;

[0085] Specifically, evaluating the gene editing effect means designing specific primers, performing PCR amplification on the target gene, detecting the success rate of the target gene, sequencing the PCR products using high-throughput sequencing technology, further confirming the marking effect of the target gene and analyzing the accuracy of the insertion through gene comparison, cultivating gene-edited rice varieties in experimental fields, comparing the yield of varieties before and after editing, detecting the hardness, protein content and starch content of rice grains, and evaluating changes in disease resistance of the edited varieties under high disease pressure.

[0086] In the process of gene editing, designing specific primers and amplifying the target gene by PCR can ensure that only the target gene region is amplified, avoiding the interference of non-specific amplification. This process can efficiently and accurately detect the effect of gene editing, ensuring the accuracy and efficiency of editing. High-throughput sequencing technology provides a more accurate means of verification for gene editing. By deep sequencing of PCR products, the mutation type, location and insertion accuracy of the gene can be fully identified. Compared with traditional Sanger sequencing, NGS not only improves the detection accuracy, but also can detect changes in multiple sites in the genome at the same time. Gene comparison analysis can accurately identify whether the gene editing is in line with expectations by comparing the differences in gene sequences before and after editing. This process can not only verify whether the gene is successfully inserted, deleted or mutated, but also further confirm whether the type of editing has achieved the optimization goal. Experimental field cultivation can expose gene-edited rice varieties to actual planting environments, simulate real growth conditions, and verify the actual impact of gene editing on rice performance. It is a very important step to evaluate the disease resistance of gene-edited varieties under high disease pressure. By increasing disease pressure, the resistance of rice varieties to pests and diseases under real farmland conditions can be tested. If the gene-edited varieties still show good resistance under high disease pressure, it means that the edited genes can effectively enhance the disease resistance of rice and provide more competitive varieties for subsequent variety promotion.

[0087] Furthermore, precision cultivation is carried out after gene editing to further verify the performance of rice varieties. Variety screening refers to designing corresponding cultivation management plans according to the characteristics of the target varieties, including sowing density, fertilization ratio and irrigation method, determining standardized cultivation management methods, implementing cultivation plans in experimental fields, and recording data of each link (such as soil moisture, air temperature and light intensity, etc.), monitoring the cultivation environment in real time through sensors and automatic irrigation systems, and using Internet of Things devices to monitor field environmental data, transmitting data to the data platform in real time, and adjusting cultivation management measures according to environmental data to ensure the best growth of edited rice varieties;

[0088] The Delphi method was used to determine the actual yield as the key core indicator for evaluating the yield, the polished rice rate and chalkiness as the key core indicators for evaluating the rice processing and appearance quality, the amylose content, protein content and taste value as the key core indicators for evaluating the rice taste quality, and the nitrogen fertilizer partial productivity (the rice yield that can be produced by unit fertilizer nitrogen input) and the effective accumulated temperature yield (yield / effective accumulated temperature during the entire growth period) as the key core indicators for evaluating the nutrient utilization efficiency and temperature and light utilization efficiency of rice.

[0089] Through Delphi expert consultation again, the key indicators for evaluating rice yield, quality and efficiency and their critical values ​​for grading were clarified at each test site, and comprehensive evaluation indicators and grading standards for high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region were established to screen out high-yield, high-quality and high-efficiency rice varieties.

[0090] The present invention effectively solves many challenges in rice breeding by comprehensively using gene editing technology, precision cultivation management and Internet of Things technology. Through accurate screening, genetic improvement and cultivation verification of rice varieties, not only can the yield and quality of rice be improved, but also its adaptability and disease resistance to environmental changes can be improved. Through the introduction of the Delphi method, a more scientific and authoritative basis is provided for the evaluation criteria of rice, ensuring the objectivity and accuracy of the evaluation during the screening process.

[0091] Furthermore, storing and backing up the screened rice variety data in a database means integrating the genotype and phenotypic data of the screened rice varieties into a variety table according to the sample number, generating an information table (yield distribution map, rice quality score map) based on the basic information of the rice, storing the data table in a central database, sorting the central database in chronological order, and marking the corresponding labels, using data visualization tools (such as Tableau, Power BI or Matplotlib) to display the data in the database, generating visualization charts of yield distribution maps, rice quality score maps, and genotype association maps of rice varieties, helping analysts and decision makers to understand the data more intuitively, setting up real-time dashboards, and dynamically displaying the latest rice variety data and analysis results.

[0092] Existing rice variety screening methods have many challenges in data processing and analysis, such as data dispersion, low analysis efficiency, insufficient real-time monitoring, etc. The present invention solves these problems by innovatively integrating and storing the genotype and phenotype data of rice varieties, using data visualization technology to display the analysis results, and setting up a real-time dashboard for dynamic monitoring.

[0093] Example 2: Select key core indicators and establish a comprehensive evaluation indicator system;

[0094] The Delphi method is a collective decision-making process based on expert opinions, which is often used to collect and integrate experts' views and opinions on a certain topic. It is essentially a feedback anonymous letter inquiry method. The general process is to obtain the opinions of experts on the issues to be predicted, sort them out, summarize them, and then anonymously feedback them to the experts, and then ask for their opinions again, collect them again, and then feedback them again until a consensus is reached. We use this method to determine the key core indicators related to rice yield, quality and efficiency.

[0095] We selected a group of experts with in-depth knowledge and practical experience in the field of rice research to conduct two rounds of questionnaire surveys. These experts came from the rice industry system, agricultural research institutions, rice production enterprises and related academic circles in Jiangsu Province. The questionnaire recovery rates of the two rounds of expert consultation were 94.6% (35 / 37) and 97.3% (36 / 37), respectively, both greater than 70%, indicating that the experts participated in the consultation actively. According to the experts' familiarity with the consultation content and the judgment basis, the authority coefficient (Cr) of the experts was 0.956 and 0.960, both greater than 70%, indicating that the authority of the consulting experts was high and the research results were more credible. Using the Kendall test in SPSS22.0, the Kendall coordination coefficient W of the first round of expert consultation was 0.434, the chi-square was 151.842, P < 0.01, indicating that the experts' evaluation of the importance of all items was consistent.

[0096] During the first round of expert consultation, many experts filled in the index modification suggestions. The research team collected and sorted out the opinions and made the following adjustments: the indicators of brown rice rate, polished rice rate and chalky grain rate were deleted, and a new expert consultation form was formulated based on the above revisions to carry out the second round of expert consultation. In this round of consultation, the Kendall coordination coefficient W was 0.442, which was more consistent than the previous round of results. The coefficient of variation of all indicators varied from 0.10 to 0.24, which was lower than the coefficient of variation of the previous round. The experts did not propose new index revision opinions, indicating that a consistent expert opinion had been formed and the consultation could be ended. The degree of coordination of expert opinions is shown in Table 1, and the degree of concentration of expert opinions is shown in Table 2.

[0097] Table 1. Degree of coordination of expert opinions

[0098]

[0099] Table 2 Concentration of expert opinions

[0100]

[0101] Finally, the actual yield was determined as the key core indicator for evaluating the yield, the whole polished rice rate and chalkiness were determined as the key core indicators for evaluating the rice processing and appearance quality, the amylose content, protein content, and taste value were determined as the key core indicators for evaluating the taste quality of rice, and the nitrogen fertilizer partial productivity (the rice yield that can be produced by unit fertilizer nitrogen input) and the effective accumulated temperature yield (yield / effective accumulated temperature during the entire growth period) were determined as the key core indicators for evaluating the nutrient utilization efficiency and temperature and light utilization efficiency of rice.

[0102] In order to determine the critical values ​​for grading key core indicators and establish a comprehensive evaluation index system for screening high-yield, high-quality and high-efficiency japonica rice varieties, the research team used the improved Delphi expert consultation method to launch a new round of expert questionnaires.

[0103] First, regarding the determination of the critical value of actual yield classification, based on the current situation of rice yield in Jiangsu Province in recent years, which has gradually exceeded 600kg / mu, we preliminarily proposed that the critical value of medium-yield rice with blanket seedling machine transplanting is 600kg / mu (9t / hm2), and the critical value of high-yield rice is increased by 50kg / mu on this basis, which is 650kg / mu (9.75t / hm2). The critical value of yield index under mechanical direct seeding conditions is 50kg / mu lower than the critical value under blanket seedling machine transplanting conditions, with high-yield rice ≥600kg / mu (9t / hm2) and medium-yield rice ≥550kg / mu (8.25t / hm2). The critical value of nitrogen fertilizer partial productivity is linked with yield, and it is preliminarily proposed that the nitrogen fertilizer partial productivity of medium-yield rice is ≥33.5kg / kg, and that of high-yield rice is ≥36.5kg / kg. Regarding the effective accumulated temperature yield, since the variation range of the effective accumulated temperature index of rice in the whole growth period of each test point due to the difference in latitude is much larger than the variation range of yield, for example, the average unit effective accumulated temperature yield of late-maturing medium-japonic rice and medium-maturing medium-japonic rice at each test point will tend to decrease with the southward shift of latitude, this study no longer divides the critical value of the unit effective accumulated temperature yield index of rice into two types: efficient utilization of temperature and light resources and moderate utilization. Instead, based on the range value of the unit effective accumulated temperature yield of each test point, the minimum limit of the unit effective accumulated temperature yield is given as 4.0kg / (℃·hm2) when rice reaches an efficient level of utilization of local temperature and light resources.

[0104] For the determination of the critical values ​​of the whole polished rice rate, chalkiness, taste value and amylose content, the initial reference is made to the national standard of the People's Republic of China "High-quality Rice GB / T17891-2017" for the whole polished rice rate (X) of japonica rice, first-grade high-quality rice: X ≥ 67.0%; second-grade high-quality rice: 67.0%>X ≥ 61.0%; third-grade high-quality rice: 61.0%>X ≥ 55.0%. Chalkiness (X), first-grade high-quality rice: X ≤ 2.0%; second-grade high-quality rice: 4.0% ≥ X > 2.0%; third-grade high-quality rice: 6.0% ≥ X > 4.0%. Taste value (X), first-grade high-quality rice: X ≥ 90; second-grade high-quality rice: 90>X ≥ 80; third-grade high-quality rice: 80>X ≥ 70. Amylose content 14% to 20%. Since soft rice is a type of japonica rice containing the Wx allele with low amylose content (such as Wxmp, Wxmq, Wxmw, etc.), the amylose content of soft rice generally does not exceed 14%. Based on the references at home and abroad, the amylose content of conventional japonica rice is preliminarily estimated to be 14% to 20%, and the amylose content of soft rice is preliminarily estimated to be 6% to 14%. Based on the references at home and abroad, the protein content of rice is preliminarily estimated to be 6.5% to 9% under the condition of 18kg pure nitrogen per mu.

[0105] The first round of expert consultation was conducted on the preliminary classification of the above-mentioned yield, quality and efficiency indicators. Many experts put forward modification opinions, which can be summarized as follows:

[0106] (1) The yield potential of rice of different growth types varies significantly. Generally, the yield of medium-maturing medium-japonica rice is lower than that of late-maturing medium-japonica rice and lower than that of late-maturing rice. Therefore, the critical values ​​of yield classification of rice of different growth types should be further divided. It is recommended to use 600 kg / mu (9 t / hm2) as the critical value of medium-yield type of medium-maturing medium-japonica rice planted by blanket seedling machine. The critical value of medium-yield type of late-maturing medium-japonica rice is 617 kg / mu, which is 0.25 t / hm2 higher than that of medium-maturing medium-japonica rice. The critical value of medium-yield type of late-maturing medium-japonica rice is 633 kg / mu, which is 0.5 t / hm2 higher than that of medium-maturing medium-japonica rice. The critical value of high-yield type of rice of the same growth type is 50 kg / mu higher than that of medium-yield type. The critical values ​​of the classification of the yield (X) of medium-maturing medium-glutinous rice are: high-efficiency type planted by blanket seedling machine: X≥650kg / mu, medium-efficiency type: 650kg / mu>X≥600kg / mu; high-efficiency type under direct seeding: X≥600kg / mu, medium-efficiency type: 600kg / mu>X≥550kg / mu. The critical values ​​of the classification of the yield (X) of late-maturing medium-glutinous rice are: high-efficiency type planted by blanket seedling machine: X≥667kg / mu, medium-efficiency type: 667kg / mu>X≥617kg / mu; high-efficiency type under direct seeding: X≥617kg / mu, medium-efficiency type: 617kg / mu>X≥567kg / mu. High-efficiency type of late-maturing medium-glutinous rice: X≥683kg / mu, medium-efficiency type: 683kg / mu>X≥633kg / mu.

[0107] (2) Due to the unique "cloudy" phenotype of soft rice, its chalkiness is generally lower than that of non-soft rice. Therefore, it is recommended to reduce the chalkiness (X) of high-quality soft rice: first-grade high-quality rice: X ≤ 6.0%; second-grade high-quality rice: 8.0% ≥ X>6.0%; third-grade high-quality rice: 10.0% ≥ X>8.0%.

[0108] (3) Soft japonica rice has a lower amylose content, which makes its rice taste better than non-soft rice. Therefore, the taste value classification standards of soft japonica rice and non-soft japonica rice need to be differentiated. It is recommended that the taste value (X) of soft japonica rice is: first-grade high-quality rice: X≥90; second-grade high-quality rice: 90>X≥85; third-grade high-quality rice: 85>X≥80. The taste value (X) of non-soft japonica rice is: first-grade high-quality rice: X≥75; second-grade high-quality rice: 75>X≥70; third-grade high-quality rice: 70>X≥65.

[0109] (4) The selection of the critical value of nitrogen fertilizer partial productivity should be linked to the critical value of yield classification. The recommended classification critical value of nitrogen fertilizer partial productivity (X) for medium-maturing medium-glutinous rice is: high-efficiency type transplanted by blanket seedling machine: X ≥ 36.5 kg / kg, medium-efficiency type: 36.5 kg / kg> X ≥ 33.5 kg / kg; high-efficiency type under direct seeding: X ≥ 33.5 kg / kg, medium-efficiency type: 33.5 kg / kg> X ≥ 30.5 kg / kg. The classification critical value of nitrogen fertilizer partial productivity (X) for late-maturing medium-glutinous rice is: high-efficiency type transplanted by blanket seedling machine: X ≥ 37.0 kg / kg, medium-efficiency type: 37.0 kg / kg> X ≥ 34.0 kg / kg; high-efficiency type under direct seeding: X ≥ 34.0 kg / kg, medium-efficiency type: 34.0 kg / kg> X ≥ 31.0 kg / kg. High-efficiency type of late japonica rice: X≥37.5kg / kg, medium-efficiency type: 37.5kg / kg>X≥34.5kg / kg.

[0110] Based on the collected expert opinions, we revised the relevant indicators and noted the reasons, then conducted a second round of expert consultation. In this round of consultation, no experts proposed new revision opinions, indicating that a consistent expert opinion had been formed and the consultation could be ended.

[0111] Through two Delphi expert consultations, the key indicators for evaluating rice yield, quality and efficiency and their critical values ​​for grading were clarified at each test site. Then, comprehensive evaluation indicators and grading standards for high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region were established for screening high-yield, high-quality and high-efficiency rice varieties.

[0112] Table 3 Comprehensive evaluation indexes and classification standards for high-yield and high-efficiency rice varieties

[0113]

[0114] Table 4 Comprehensive evaluation indexes and grading standards for high-quality rice varieties

[0115]

[0116] This embodiment also provides a computer device, which is suitable for the case of a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties proposed in the above embodiment.

[0117] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0118] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0119] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, characterized by: include, Identify ecological sites and collect environmental data, select different breed groups according to different ecological sites and conduct genotyping analysis; Collect phenotypic data of rice varieties, establish a correlation model between genotypic data and phenotypic data, and screen high-quality genes; Based on the screening results, the target varieties are selected and the key genes of the rice varieties are edited in a targeted manner. The gene editing effects are evaluated and precise cultivation is carried out after gene editing to further verify the performance of the rice varieties and conduct variety screening.

2. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties as claimed in claim 1, characterized in that: Determining ecological points and collecting environmental data include: The said eco-site refers to an eco-site containing various climatic conditions; The environmental data includes climate data, soil type, nutrient content, and water conditions.

3. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties as claimed in claim 2, characterized in that: The selecting of different variety groups according to different ecological points and conducting genotype analysis comprises: The selecting of different variety groups according to different ecological points refers to selecting corresponding rice varieties from a rice variety library by analyzing environmental data of the ecological points, classifying the selected varieties according to cultivation types, and constructing rice variety groups according to the selected rice varieties; The genotype analysis refers to using gene chip technology to perform genotype analysis on a rice variety group, and screening out genotype data related to key traits based on the gene chip data; The key traits are yield, disease resistance, rice quality and drought resistance.

4. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties as claimed in claim 3, characterized in that: The collecting of phenotypic data of rice varieties, establishing a correlation model between genotypic data and phenotypic data, and screening of high-quality genes include: The phenotypic data refers to growth traits, yield traits, quality traits, resistance traits and physiological traits; The collected phenotypic and genotypic data were standardized, and high yield and high quality were defined as target phenotypes; The rice phenotypic data and genotypic data were merged according to the sample number to form a unified data matrix, and the LMC Copula model was used to calculate the dependency measure between each pair of features u and v after standardization. : Where a and b are adjustment parameters; After calculating the dependency measure, the covariance of each pair of features is calculated, and combined with the standard deviation, the correlation coefficient between each pair of features is obtained and the correlation matrix is ​​constructed; Set the correlation threshold A, filter out redundant features with correlation greater than the threshold A, use Kendall's Tau to calculate the ranking consistency between each pair of features, further confirm redundant features through Tau, and remove confirmed redundant features; The data set after removing redundant features is input into the PCA algorithm, the covariance between each pair of features is calculated and the covariance matrix is ​​constructed, the covariance matrix is ​​decomposed by eigenvalues, and the eigenvalues ​​and eigenvectors are extracted; Sort the eigenvectors by eigenvalue and select the principal component with the largest explained variance; Project the data into a new low-dimensional space through the selected principal components to obtain a reduced-dimensional data set; Extract the feature load matrix of each principal component from the PCA results, calculate the absolute value of the feature weight in each principal component, and calculate the absolute weight sum of each feature in all principal components to obtain the total contribution of the feature, set the threshold S, and select the features whose total contribution exceeds S as the feature set through the load matrix; Randomly select several feature combinations from the feature set to generate initial tree species. Each tree species represents a feature subset. Create an array to store all tree species. Calculate the fitness of each tree based on the information gain and redundancy of the feature subset. Sort by fitness from high to low, select the tree species with the highest fitness as the parent tree species, select the characteristics of the parent tree species for mating, generate new tree species, and after mating, perform small mutations on the newly generated tree species to generate a new set of tree species and perform cross-validation evaluation to obtain an optimized dimensionality reduction data set; The phenotypic data after dimension reduction were input into the PLS-DA model as independent variables, and the genotypic data after dimension reduction were input into the PLS-DA model as dependent variables. The weight matrix W was extracted from the PLS-DA model to determine the influence of each principal component on the phenotypic data and genotypic data. According to the PLS-DA analysis results, the high-quality genes in the weight matrix that had the greatest influence on the target phenotype were selected.

5. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties as claimed in claim 4, characterized in that: The method of selecting a target variety based on the screening results and editing the key genes of the rice variety refers to selecting a rice variety whose target phenotype meets the screening requirements, analyzing the historical cultivation phenotypes of the selected rice varieties, selecting varieties whose target phenotype values ​​are higher than the average under different environmental conditions, using bioinformatics tools to design gRNA, targeting specific regions of the target gene, combining the gRNA and Cas9 protein, and introducing the CRISPR-Cas9 system into the cells of the target rice variety through transformation technology.

6. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties as claimed in claim 5, characterized in that: The evaluation of gene editing effect refers to designing specific primers, performing PCR amplification on the target gene, detecting the success rate of the target gene, sequencing the PCR product using high-throughput sequencing technology, further confirming the labeling effect of the target gene and analyzing the accuracy of insertion through gene comparison, cultivating the gene-edited rice varieties in experimental fields, and comparing various characteristics of the rice varieties before and after editing.

7. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties according to claim 6, characterized in that: The precision cultivation after gene editing is further used to verify the performance of rice varieties. Variety screening refers to designing corresponding cultivation management plans according to the characteristics of target varieties, determining standardized cultivation management methods, implementing cultivation plans in experimental fields, and recording data at each stage. The cultivation environment is monitored in real time through sensors and automatic irrigation systems, and field environmental data is monitored using IoT devices, and the data is transmitted to the data platform in real time, and cultivation management measures are adjusted according to environmental data. After determining the key core indicators for rice variety screening through the Delphi method, Delphi expert consultation was used again to clarify the key indicators and critical values ​​for evaluating rice yield, quality and efficiency at each test point, establish comprehensive evaluation indicators and grading standards for high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region, and screen out high-yield, high-quality and high-efficiency rice varieties.

8. The method for comprehensive screening of high-yield, high-quality and high-efficiency rice varieties according to claim 6, characterized in that: The storing and backing up of the screened rice variety data in a database refers to integrating the genotype and phenotype data of the screened rice varieties into a variety table according to the sample numbers, generating an information table based on the basic information of the rice, and storing the data table in a central database.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties described in any one of claims 1 to 7 are implemented.

Images