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

Abstract

The application discloses a kind of high yield high-quality efficient rice variety comprehensive screening method, it is related to rice variety screening technical field, including determining ecological point and collecting environmental data, according to different ecological point selection different variety group and carry out genotypic analysis;Collect the phenotypic data of rice variety, establish the correlation model between genotypic data and phenotypic data, screen high-quality genes;According to the selection result of screening, the key genes of target variety and directional editing rice variety are selected, the gene editing effect is evaluated, and the performance of rice variety is further verified after gene editing, and the variety screening is carried out.The application can not only effectively integrate ecological data, gene data and phenotype data for comprehensive evaluation, optimize the problem of rice variety from the global perspective, but also improve the actual performance of rice variety through precision cultivation management, and lay the foundation for the variety layout of large-area rice yield, quality and efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rice variety screening, and particularly relates to a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties. BACKGROUND

[0002] As one of the most important food crops in the world, rice bears the heavy responsibility of providing staple food. However, with the challenges of climate change, land resource constraints and agricultural production efficiency, traditional rice breeding methods have been difficult to meet the demand for high-yield, high-quality and high-efficiency rice varieties. Therefore, how to scientifically, accurately and efficiently screen rice varieties 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 traditional breeding patterns to modern breeding patterns based on genes. The application of genomics technology enables researchers to screen gene markers related to yield, disease resistance, rice quality and other key traits through genotype analysis; at the same time, through non-destructive technologies such as near-infrared diffuse reflectance spectroscopy (NIRDRS), precise phenotype evaluation of rice grains is realized through accurate collection and analysis of phenotype data. 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 Internet of Things (IoT) and intelligent irrigation technology enables the rice cultivation environment to be more carefully regulated, further improving the production efficiency and quality of rice.

[0003] Although these emerging technologies have brought great development potential for rice breeding, there are still some deficiencies in the existing technology. First, although the correlation analysis of genotype and phenotype has been widely applied, most of the existing analysis methods focus on the analysis of single characteristics, and lack of in-depth discussion on the non-linear relationship between multi-dimensional characteristics. Traditional phenotype data collection methods are relatively single, and lack of long-term real-time monitoring of rice growth environment and its changes. Second, although the application of gene editing technology can change the key genes of rice varieties in a targeted manner, the verification process of the edited varieties still lacks sufficient fine cultivation management and long-term field trials, resulting in unstable evaluation results and inability to fully verify the actual impact of gene editing on rice varieties. Finally, the existing rice variety screening methods still have deficiencies in the adaptability evaluation of different ecological environments and the design of comprehensive evaluation indexes, and fail to effectively integrate ecological data, gene data and phenotype data for comprehensive evaluation, making it difficult to optimize rice varieties from a global perspective. SUMMARY

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

[0005] Therefore, the application provides a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, solves the problem that the existing rice variety screening method is still insufficient in adaptability evaluation and comprehensive evaluation index design under different ecological environments, cannot effectively integrate ecological data, gene data and phenotype data for comprehensive evaluation, and is difficult to optimize rice varieties from a global perspective.

[0006] To solve the above technical problems, the application provides the following technical solutions.

[0007] In a first aspect, the application provides a comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, which comprises,

[0008] determining ecological points and collecting environmental data, selecting different variety groups according to different ecological points and performing genotype analysis;

[0009] Collecting phenotype data of rice varieties, establishing a correlation model between genotype data and phenotype data, and screening high-quality genes;

[0010] According to the screening result, the target variety is selected and the key genes of the rice variety are edited, the gene editing effect is evaluated, and the rice variety performance is further verified after gene editing and precision cultivation, and the variety screening is performed.

[0011] As a preferred scheme of the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties, wherein: the determination of ecological points and the collection of environmental data comprises:

[0012] The ecological point refers to an ecological point containing various climate conditions;

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

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

[0015] According to the environmental data of the ecological point, the corresponding rice varieties are selected from the rice variety library, the selected varieties are classified according to the cultivation type, and the rice variety group is constructed according to the selected rice varieties;

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

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

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

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

[0020] The phenotypic data and genotypic data of rice are merged according to sample numbers to form a unified data matrix, and the LMC Copula model is used to calculate the dependence measure C between each pair of characteristics 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] In the formula, a and b are adjustment parameters.

[0023] After calculating the dependence measure, the covariance of each pair of characteristics is calculated, and the correlation coefficient between each pair of characteristics is obtained and a correlation matrix is constructed by combining the standard deviation.

[0024] A correlation threshold A is set, redundant features with a correlation greater than the threshold A are screened out, Kendall's Tau is used to calculate the ordering consistency between each pair of characteristics, and redundant features are further confirmed by Tau, and the confirmed redundant features are removed.

[0025] The data set after removing the redundant features is input into the PCA algorithm, the covariance between each pair of characteristics is calculated and a covariance matrix is constructed, the covariance matrix is subjected to eigenvalue decomposition, and the eigenvalues and eigenvectors are extracted.

[0026] The eigenvectors are sorted according to the size of the eigenvalues, and the principal components with the largest explained variance are selected.

[0027] The data is projected into a new low-dimensional space by the selected principal components, and a reduced data set is obtained.

[0028] The feature loading matrix of each principal component is extracted from the PCA result, the absolute value of the feature weight in each principal component is calculated, and the absolute weight sum of each feature in all principal components is calculated to obtain the total contribution degree of the feature. A threshold S is set, and the features with a total contribution degree exceeding S are selected as the feature set through the loading 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 according to the information gain and redundancy of the feature subset;

[0030] According to the fitness from high to low, select the tree species with the highest fitness as the parent tree species, select the features in the parent tree species for crossbreeding to generate new tree species, after crossbreeding, a small amount of mutation is carried out on the newly generated tree species, a new tree species set is generated and cross-validation evaluation is carried out to obtain an optimized dimensionality reduction data set;

[0031] The reduced phenotype data is input into the PLS-DA model as the independent variable, and the reduced genotype data is input into the PLS-DA model as the dependent variable, the weight matrix W is extracted from the PLS-DA model to determine the influence degree of each principal component on the phenotype data and the genotype data, and according to the PLS-DA analysis result, the best quality gene with the greatest influence on the target phenotype in the weight matrix is selected.

[0032] As a preferred scheme of the comprehensive screening method for high-yield, high-quality and efficient rice varieties, wherein: the key gene of the target variety is selected according to the screening result, and the historical cultivation phenotype of the selected rice variety is analyzed, the variety with the target phenotype value higher than the average value under different environmental conditions is selected, gRNA is designed using bioinformatics tools, the specific region of the target gene is targeted, the gRNA is combined with the Cas9 protein, and the CRISPR-Cas9 system is introduced 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 efficient rice varieties, wherein: the evaluation of the gene editing effect refers to designing specific primers, performing PCR amplification on the target gene, detecting the success rate of the target gene, using high-throughput sequencing technology to sequence the PCR product, further confirming the marking effect of the target gene and the accuracy of the insertion through gene comparison analysis, and cultivating the genetically edited rice variety in the experimental field, and comparing the characteristics of the rice variety before and after editing.

[0034] As a preferred scheme of the comprehensive screening method for high-yield, high-quality and efficient rice varieties, wherein: the precise cultivation after gene editing further verifies the performance of the rice variety, and the variety screening refers to designing the corresponding cultivation management scheme according to the characteristics of the target variety, determining the standardized cultivation management method, implementing the cultivation scheme in the experimental field, and recording the data of each link, monitoring the cultivation environment in real time through sensors and automatic irrigation systems, and monitoring the field environment data using Internet of Things equipment, 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 indexes of rice variety screening by the Delphi method, the key indexes and their critical values for evaluating the yield, quality and efficiency of rice in each test site are determined again through the Delphi expert consultation, the comprehensive evaluation indexes and grading standards of high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region are established, and high-yield, high-quality and high-efficiency rice varieties are screened out.

[0036] As a preferred scheme of the comprehensive screening method of high-yield, high-quality and high-efficiency rice varieties, the genotype and phenotype data of the screened rice varieties are integrated into a variety table according to the sample number, an information table is generated according to the basic information of rice, and the data table is stored in the central database.

[0037] In a second aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program, when executed by the processor, implements any step of the comprehensive screening method of high-yield, high-quality and high-efficiency rice varieties according to the first aspect of the present application.

[0038] In a third aspect, the present application 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 of high-yield, high-quality and high-efficiency rice varieties according to the first aspect of the present application.

[0039] The present application has the following advantages: the present application collects diversified ecological data, phenotype data and gene data systematically, uses big data and machine learning algorithms to accurately establish a genotype and phenotype correlation model, and screens rice varieties with high-quality characteristics. At the same time, the target gene is modified directionally by combining gene editing technology, and the variety after gene editing is verified and evaluated for a long time under precise cultivation management. This method not only effectively integrates ecological data, gene data and phenotype data for comprehensive evaluation, optimizes rice variety problems from a global perspective, but also improves the actual performance of rice varieties through precise cultivation management, and lays a foundation for large-area rice yield, quality and efficiency improvement. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0041] Figure 1 The flowchart of the comprehensive screening method of high-yield, high-quality and high-efficiency rice varieties in Example 1. DETAILED DESCRIPTION

[0042] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0043] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the concept of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0044] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or mutually exclusive with other embodiments.

[0045] Embodiment 1, reference Figure 1 For the first embodiment of the present application, the embodiment provides a comprehensive screening method for high-yield, high-quality and efficient rice varieties, comprising the following steps:

[0046] S1, determining ecological points and collecting environmental data, selecting different variety groups according to different ecological points and performing genotype analysis;

[0047] Specifically, the determination of ecological points and the collection of environmental data comprises:

[0048] The ecological point refers to an ecological point containing various climate conditions;

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

[0050] The present application combines climate, soil and water source conditions, combines precise sensor technology and real-time data acquisition, and ensures the comprehensiveness and accuracy of rice variety screening. This method not only accurately monitors environmental changes, but also selects the most suitable rice variety by combining genotype analysis, phenotype data and environmental data, thereby improving the yield, quality and efficiency of rice.

[0051] Further, selecting different variety groups according to different ecological points and performing genotype analysis comprises:

[0052] The selection of different variety groups according to different ecological points means that by analyzing the environmental data of the ecological points, the corresponding rice varieties are selected from the rice variety library, the selected varieties are classified according to the cultivation types (such as medium-mature, medium-joint, soft-milled japonica rice, etc.), and the rice variety group is constructed according to the selected rice varieties;

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

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

[0055] The genotype data includes single nucleotide polymorphism, simple sequence repeat, structural variation, genome-wide association data, and genotype assembly data.

[0056] Through precise selection of different ecological points, it can be ensured that the selected rice varieties are suitable for specific environmental conditions. For example, in the humid climate of the Yangtze River Delta region, varieties suitable for high-humidity environments are selected, while in the cold climate of the Northeast Plain, varieties suitable for 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 large variety library, thereby laying the foundation for variety screening. Genotype analysis can accurately identify genes related to rice yield, disease resistance, rice quality, and other characteristics by using gene chip technology to comprehensively scan and decode rice genes. This not only helps to better understand the genetic background of rice, but also provides precise markers for molecular breeding of rice, guiding variety improvement. By classifying selected rice varieties according to cultivation types, constructing rice variety groups, and combining genotype analysis with gene chip technology, it is possible to more accurately screen varieties with strong adaptability and excellent traits. Through gene chip technology, genes related to yield, disease resistance, drought resistance, and other key traits can be quickly identified, and these genes often exhibit high stability in different ecological environments. Through the above steps of precise variety screening and genotype analysis, rice varieties with high yield, high quality, and disease resistance can be quickly identified. This fine screening method can improve the breeding efficiency of rice varieties, reduce the breeding cycle, and increase the success rate of variety improvement.

[0057] S2, collecting phenotype data of rice varieties, establishing a correlation model between genotype data and phenotype data, and screening high-quality genes;

[0058] Specifically, collecting phenotype data of rice varieties, establishing a correlation model between genotype data and phenotype data, and screening high-quality genes includes:

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

[0060] The collected phenotype data and genotype data are standardized, and high yield and high quality are defined as target phenotypes;

[0061] The standardization processing step includes removing outliers, and converting data into standard scores;

[0062] The phenotype data and the genotype data of the rice are merged according to sample numbers to form a unified data matrix, each row representing a rice sample and each column representing a feature;

[0063] The genotype data is standardized, and the LMC Copula model is used to calculate the dependence measure C between each pair of standardized features u and v 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, respectively controlling the strength of the dependence relationship between the data in the model and the influence of the logarithmic term, which are estimated by the maximum likelihood estimation method;

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

[0067] According to the business requirements, set a correlation threshold A, filter out redundant features with a correlation greater than the threshold A, use Kendall's Tau to calculate the ordering consistency between each pair of features, and further confirm the redundant features by 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 correlation matrix after the final screening and the data set after removing the redundant features are stored in the database;

[0069] The data set after removing the redundant features is input into the PCA algorithm, the covariance between each pair of features is calculated and a covariance matrix is constructed, the covariance matrix is subjected to eigenvalue decomposition, and the eigenvalue and eigenvector are extracted;

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

[0071] The eigenvectors are sorted according to the eigenvalue size, and the principal component with the largest explained variance is selected;

[0072] The data is projected into a new low-dimensional space by the selected principal component to obtain a reduced data set;

[0073] Extract the feature loading matrix of each principal component from the PCA result, the matrix dimension is n*w, where n is the number of original features, m is the number of principal components, take 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 a threshold S through statistical analysis, select the features whose total contribution exceeds S as the feature set through the loading matrix;

[0074] Randomly select several feature combinations from the feature set to generate initial species, each species represents a feature subset, and each feature subset contains multiple features randomly selected from the reduced data;

[0075] Create an array to store all species, each species is represented as a subset containing several features, and the fitness of each tree is calculated according to the information gain and redundancy of the feature subset:

[0076] Q(T) = E(T) - τ × L(T)

[0077] In the formula, Q(T) is the fitness of the species T, E(T) is the information gain brought by the selected features in the 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 features, τ is the penalty factor of redundancy, which is set by experience;

[0078] Sort them from high to low according to the fitness, select the species with the highest fitness as the parent species, select the features in the parent species for mating to generate new species, and after mating, perform small-scale mutation on the newly generated species to generate a new species set and perform cross-validation evaluation to obtain the optimized reduced data set;

[0079] Input the reduced phenotype data as the independent variable into the PLS-DA model, and input the reduced genotype data as the dependent variable into the PLS-DA model, use the PLS algorithm to input the phenotype data and genotype data at the same time, establish a regression model, extract the weight matrix W from the PLS-DA model to determine the influence degree of each principal component on the phenotype data and genotype data, according to the PLS-DA analysis result, select the best quality gene with the greatest influence on the target phenotype in the weight matrix, and perform literature comparison and database query on the selected best quality gene to confirm the known biological function and verify the relevance to the target trait.

[0080] Copula functions are statistical tools used to describe the correlation between multiple random variables, particularly adept at capturing nonlinear relationships between variables. In rice variety selection, the relationship between genotype data and phenotype data is often not only linear but also involves complex nonlinear interactions. A single linear model cannot fully capture the potential connections in this case. By introducing Copula functions, we can model the relationship between phenotype and genotype data more accurately, effectively describing both linear and nonlinear correlations. By calculating the correlation between phenotype and genotype features, Copula functions help identify and remove redundant features, laying a solid foundation for PCA dimensionality reduction and PLS-DA analysis. Therefore, the role of Copula functions is to accurately describe the relationship between data to ensure the accuracy of subsequent analysis and provide a clear direction for dimensionality reduction and high-quality gene selection. The role of PCA dimensionality reduction is to compress high-dimensional genotype and phenotype data into low-dimensional space through linear transformation, highlighting the most informative features. PCA not only reduces computational complexity and avoids interference from redundant features, but also preserves 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 phenotype and genotype data containing a large amount of redundancy and correlation into fewer principal components. This step is particularly important in subsequent PLS-DA analysis, as the reduced dataset is more representative and avoids the problem of multicollinearity, reducing noise interference. PLS-DA analysis establishes a regression model between phenotype and genotype data to identify which genotype features are significantly associated with specific phenotype features (such as yield, rice quality, etc.). In rice variety selection, PLS-DA can help researchers screen key genes related to target traits. Through the PLS-DA model, we can extract the weight matrix from the reduced dataset and analyze the contribution of each gene to the target phenotype.

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

[0082] Further, selecting target varieties according to the screening results and targeted editing of key genes of rice varieties involves selecting rice varieties with target phenotypes meeting the screening requirements, analyzing the historical cultivation phenotypes of the selected rice varieties, selecting varieties with target phenotype values higher than the average under different environmental conditions, designing gRNA targeting specific regions of target genes using bioinformatics tools such as CRISPR-Design and Benchling, combining gRNA and Cas9 protein, and introducing the CRISPR-Cas9 system into the cells of the target rice varieties through transformation techniques such as Agrobacterium-mediated method or electroporation method. Cas9 protein performs gene cutting under the guidance of gRNA, cuts the DNA of target genes, and induces mutations or insertions.

[0083] During the selection of rice varieties, by screening varieties with phenotypes meeting specific requirements, it is ensured that the selected varieties have good growth performance and target traits. In particular, varieties with phenotype values higher than the average under different environmental conditions can ensure the stability and adaptability of these varieties under various environments. This process effectively avoids the influence of environmental interference on the performance of rice, ensuring high efficiency and excellent performance of rice varieties under a wide range of climate and soil conditions. The use of bioinformatics tools such as CRISPR-Design and Benchling for precise gRNA design ensures that gRNA binds to target genes very accurately. This efficient gRNA design can effectively reduce off-target effects, accurately locating and cutting target genes even in complex plant genomes. In addition, multiple gRNAs can be designed to edit multiple genes simultaneously, further improving the efficiency of gene editing. By combining the designed gRNA and Cas9 protein, and using Agrobacterium-mediated method or electroporation method to introduce the CRISPR-Cas9 system into the cells of the target rice varieties, the target genes can be precisely cut, inducing DNA repair, and ultimately achieving gene mutation or insertion. This process enables rice varieties to possess new excellent characteristics, such as increased yield, enhanced disease resistance, or improved rice quality, etc. After gene editing, techniques such as PCR amplification and gene sequencing are used to verify whether the target genes have been successfully edited, and field trials are conducted 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, according to the screening results, selecting target varieties and targeted editing of key genes of rice varieties, evaluating the effect of gene editing and further verifying the performance of rice varieties after gene editing, and performing variety screening;

[0085] Specifically, evaluating the effect of gene editing involves designing specific primers, performing PCR amplification on the target gene, detecting the success rate of the target gene, using high-throughput sequencing technology to sequence the PCR product, further confirming the labeling effect of the target gene, and analyzing the accuracy of insertion through gene comparison. In the experimental field, the edited rice varieties are cultivated, and the yield of the varieties before and after editing is compared. The hardness, protein content, and starch content of the rice grains are detected, and the resistance of the edited varieties to diseases under high disease pressure is evaluated.

[0086] During gene editing, specific primers are designed and the target gene is amplified by PCR to ensure that only the target gene region is amplified, avoiding non-specific amplification interference. 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 verification method for gene editing. By deeply sequencing the PCR product, the mutation type, location, and accuracy of insertion of the gene can be comprehensively identified. Compared with traditional Sanger sequencing, NGS not only improves detection accuracy but also simultaneously detects changes in multiple sites in the genome. Gene comparison analysis can accurately identify whether the gene editing meets the expectations by comparing the differences in gene sequences before and after editing. This process not only verifies whether the gene has been successfully inserted, deleted, or mutated, but also further confirms whether the type of editing has reached the optimization goal. Experimental field cultivation can expose the genetically edited rice varieties to actual planting environments, simulate real growth conditions, and verify the actual impact of gene editing on rice performance. Evaluating the disease resistance of edited varieties under high disease pressure is a crucial step. By increasing disease pressure, the resistance of rice varieties to pests and diseases in real field conditions can be tested. If the edited varieties still exhibit good resistance under high disease pressure, it indicates that the edited genes can effectively enhance the disease resistance of rice, providing more competitive varieties for subsequent variety promotion.

[0087] Furthermore, after gene editing, precise cultivation is further carried out to verify the performance of rice varieties and conduct variety selection. According to the characteristics of the target variety, corresponding cultivation management schemes are designed, including seeding density, fertilizer ratio, and irrigation method. Standardized cultivation management methods are determined, and the cultivation scheme is implemented in the experimental field. Data such as soil moisture, air temperature, and light intensity are recorded at each stage. Sensors and automatic irrigation systems are used to monitor the cultivation environment in real time, and Internet of Things devices are used to monitor field environment data. The data is transmitted to the data platform in real time, and the cultivation management measures are adjusted according to the environmental data to ensure the optimal growth of the edited rice varieties.

[0088] The actual yield is determined as the key core index for evaluating the yield by the Delphi method, the whole milled rice rate and chalkiness are determined as the key core indexes for evaluating the rice processing and appearance quality, the amylose content, protein content and taste value are determined as the key core indexes for evaluating the rice taste quality, and the nitrogen fertilizer partial productivity (the rice yield produced by unit input of fertilizer nitrogen) and effective accumulated temperature yield (yield / effective accumulated temperature during the whole growth period) are determined as the key core indexes for evaluating the nutrient utilization efficiency and light and temperature utilization efficiency of rice;

[0089] Again through the Delphi expert consultation, the key indexes and their critical values for evaluating the yield, quality and efficiency of rice are respectively determined at each test point, the comprehensive evaluation indexes and grading standards of high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region are established, and high-yield, high-quality and high-efficiency rice varieties are screened out.

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

[0091] Furthermore, the genotype and phenotype data of the screened rice varieties are integrated into a variety table according to the sample number, and an information table (yield distribution graph, rice quality score graph) is generated according to the basic information of rice. The data table is stored in the central database, the central database is sorted in chronological order, and the corresponding labels are marked. The data visualization tools (such as Tableau, Power BI or Matplotlib) are used to display the data in the database, and the visual charts of yield distribution graph, rice quality score graph and genotype correlation graph about rice varieties are generated, which helps analysts and decision makers to understand the data more intuitively. Set up real-time dashboard to dynamically display the latest rice variety data and analysis results.

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

[0093] Example 2, select key core indexes, establish comprehensive evaluation index system;

[0094] Delphi method is a kind of collective decision-making process based on expert opinions, commonly used to collect and integrate experts' views and opinions on a certain topic. It is essentially an anonymous feedback method. The general process is to collect experts' opinions on the issues to be predicted, then organize, summarize and count, and then anonymously feedback to experts again, and then collect again, and then feedback again, until a consensus is reached. We used 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 survey. These experts come from Jiangsu rice industry system, agricultural research institutions, rice production enterprises and related academic circles. The questionnaire return rates of the two rounds of expert consultation were 94.6% (35 / 37) and 97.3% (36 / 37), both more than 70%, indicating that the enthusiasm of the experts participating in the consultation was high. According to the familiarity of experts with the consultation content and the judgment basis, the authority coefficient (Cr) of the experts was 0.956 and 0.960, both more than 70%, indicating that the authority of the consulting experts was high, and the research results were reliable. Using Kendall test in SPSS22.0, the Kendall coordination coefficient W of the first round of expert consultation was 0.434, chi-square 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, and the research group collected and organized the opinions and made the following adjustments: delete the rough rice rate, milled rice rate and chalky grain rate indicators, and develop a new expert consultation table according to the above revision to carry out the second round of expert consultation. The Kendall coordination coefficient W of this round of consultation was 0.442, which was more consistent than the previous round of results. The coefficient of variation of all indicators ranged from 0.10 to 0.24, which was lower than the coefficient of variation of the previous round. And experts did not put forward new index revision opinions, indicating that a consensus among experts has been formed and the consultation can be ended. The coordination degree of expert opinions is shown in Table 1, and the concentration degree of expert opinions is shown in Table 2.

[0097] Table 1 Coordination degree of expert opinions

[0098]

[0099] Table 2 Concentration degree of expert opinions

[0100]

[0101] The actual yield was determined as the key core index for evaluating yield, the whole milled rice rate and chalkiness were determined as the key core index for evaluating rice processing and appearance quality, the amylose content, protein content and eating quality were determined as the key core index for evaluating rice eating quality, and the nitrogen partial productivity (the yield of rice per unit of fertilizer nitrogen) and effective accumulated temperature yield (yield / effective accumulated temperature during the whole growth period) were determined as the key core index for evaluating the nutrient use efficiency and light and temperature use efficiency of rice.

[0102] In order to determine the critical value of the key core index classification, a comprehensive evaluation index system was established for screening high-yield, high-quality and high-efficiency japonica rice varieties. The research group carried out a new round of expert questionnaire survey by using the improved Delphi expert consultation method.

[0103] Firstly, for the determination of the critical value of the actual yield classification, according to the production status that the rice yield in Jiangsu Province has gradually broken through 600 kg / mu in recent years, we preliminarily determined the critical value of the medium-yield rice under the conditions of mulch seedling and machine transplanting as 600 kg / mu (9 t / hm2), and the critical value of the high-yield rice was increased by 50 kg / mu, i.e. 650 kg / mu (9.75 t / hm2). The yield index critical value under the condition of mechanical direct seeding was 50 kg / mu lower than that under the condition of mulch seedling and machine transplanting, the high-yield rice was ≥ 600 kg / mu (9 t / hm2), and the medium-yield rice was ≥ 550 kg / mu (8.25 t / hm2). The critical value of nitrogen partial productivity was linked with yield, and we preliminarily determined that the nitrogen partial productivity of medium-yield rice was ≥ 33.5 kg / kg, and that of high-yield rice was ≥ 36.5 kg / kg. As for the effective accumulated temperature yield, the variation range of the effective accumulated temperature index of rice in each test point due to the difference in latitude was much larger than that of yield, for example, the average unit effective accumulated temperature yield of late-maturing medium japonica rice and medium-maturing medium japonica rice in each test point tended to decrease with the southward movement of latitude. In this study, we no longer divided the unit effective accumulated temperature yield index into two types of high efficient use of light and temperature resources and medium efficient use of light and temperature resources, but gave the minimum value of the unit effective accumulated temperature yield of rice for the efficient use of local light and temperature resources as 4.0 kg / (℃·hm2).

[0104] For the determination of the grading critical value of milled rice rate, chalkiness, eating quality and amylose content, the preliminary reference is the National Standard of the People's Republic of China "High-quality Rice GB / T 17891-2017" in glutinous rice milled rice rate (X), first-class high-quality rice: X ≥ 67.0%; second-class high-quality rice: 67.0% > X ≥ 61.0%; third-class high-quality rice: 61.0% > X ≥ 55.0%. Chalkiness (X), first-class high-quality rice: X ≤ 2.0%; second-class high-quality rice: 4.0% ≥ X > 2.0%; third-class high-quality rice: 6.0% ≥ X > 4.0%. Eating quality (X), first-class high-quality rice: X ≥ 90; second-class high-quality rice: 90 > X ≥ 80; third-class high-quality rice: 80 > X ≥ 70. Amylose content 14% ~ 20%. Since soft rice glutinous rice is a kind of glutinous rice containing low amylose content Wx alleles (such as Wxmp, Wxmq, Wxmw, etc.), therefore the amylose content of soft rice glutinous rice is generally not more than 14%, according to the domestic and foreign reference literature, the preliminary range of amylose content of conventional glutinous rice is 14% ~ 20%, and the range of amylose content of soft rice glutinous rice is 6% ~ 14%. According to the domestic and foreign reference literature, the range of rice protein content under the condition of 18 kg of pure nitrogen per mu is 6.5% ~ 9%.

[0105] According to the preliminary grading of yield, quality and efficiency indicators, the first round of expert consultation was carried out, and many experts put forward modification opinions, which were summarized as follows:

[0106] (1) The yield potential of different growth types of rice is significantly different, generally mid-mature medium japonica < late-mature medium japonica < late japonica, therefore, the yield grading critical value of different growth types of rice should be further divided, it is suggested that 600 kg / mu (9 t / hm2) be taken as the yield critical value of mid-mature medium japonica rice of medium yield type by mulching seedlings and machine transplanting, the yield critical value of late-mature medium japonica rice of medium yield type is increased by 0.25 t / hm2 based on that of mid-mature medium japonica rice, which is 617 kg / mu, and the yield critical value of late japonica rice of medium yield type is increased by 0.5 t / hm2 based on that of mid-mature medium japonica rice, which is 633 kg / mu; the yield critical value of high yield type rice of the same growth type is increased by 50 kg / mu based on the yield critical value of medium yield type. The yield (X) grading critical value of mid-mature medium japonica rice is: high efficiency type under mulching seedlings and machine transplanting: X ≥ 650 kg / mu, medium efficiency type: 650 kg / mu > X ≥ 600 kg / mu; high efficiency type under direct seeding conditions: X ≥ 600 kg / mu, medium efficiency type: 600 kg / mu > X ≥ 550 kg / mu. The yield (X) grading critical value of late-mature medium japonica rice is: high efficiency type under mulching seedlings and machine transplanting: X ≥ 667 kg / mu, medium efficiency type: 667 kg / mu > X ≥ 617 kg / mu; high efficiency type under direct seeding conditions: X ≥ 617 kg / mu, medium efficiency type: 617 kg / mu > X ≥ 567 kg / mu. Late japonica high efficiency type: X ≥ 683 kg / mu, medium efficiency type: 683 kg / mu > X ≥ 633 kg / mu.

[0107] (2) Soft japonica rice due to its unique "cloudy" phenotype, resulting in its chalkiness generally less than non-soft japonica rice, so it is recommended to reduce the chalkiness of high quality soft rice (X), the first grade of high quality rice: X≤6.0%; the second grade of high quality rice: 8.0%≥X>6.0%; the third grade of high quality rice: 10.0%≥X>8.0%.

[0108] (3) Soft japonica rice due to its lower amylose content, resulting in its rice taste better than non-soft rice, so the taste value grading standards of soft japonica rice and non-soft japonica rice need to be distinguished, it is recommended that the taste value (X) of soft japonica rice, the first grade of high quality rice: X≥90; the second grade of high quality rice: 90>X≥85; the third grade of high quality rice: 85>X≥80. The taste value (X) of non-soft japonica rice, the first grade of high quality rice: X≥75; the second grade of high quality rice: 75>X≥70; the third grade of high quality rice: 70>X≥65.

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

[0110] According to the collected expert opinions, we revised the related indicators and marked the reasons, and then carried out the second round of expert consultation. No expert put forward new modification opinions in this round of consultation, indicating that a consistent expert opinion has been formed and the consultation can be ended.

[0111] Through two rounds of Delphi expert consultation, the key indicators and their classification critical values for evaluating rice yield, quality and efficiency in each test site were determined, and the comprehensive evaluation indicators and classification standards of high-quality, high-yield and high-efficiency rice in the Yangtze River Delta region were established, which were used to screen high-yield, high-quality and high-efficiency rice varieties.

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

[0113]

[0114] Table 4 Comprehensive evaluation index and grading standard of high-quality rice varieties

[0115]

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

[0117] The computer device can be a terminal, which comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises 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 operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. 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 can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0118] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to realize the comprehensive screening method for high-yield, high-quality and high-efficiency rice varieties proposed in the above embodiment. The storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0119] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A comprehensive screening method for high-yield, high-quality, and high-efficiency rice varieties, characterized in that: Comprising, determining ecological points and collecting environmental data, selecting different variety groups according to different ecological points and performing genotype analysis; collecting phenotype data of rice varieties, establishing a correlation model between genotype data and phenotype data, and screening high-quality genes; selecting target varieties according to the screening results and editing key genes of rice varieties, evaluating the effect of gene editing and further verifying the performance of rice varieties after gene editing, and performing variety screening; the genotype analysis includes: the selection 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 types, and constructing a rice variety group according to the selected rice varieties; the genotype analysis refers to using gene chip technology to perform genotype analysis on the rice variety group, and screening out genotype data related to key traits according to the gene chip data; the key traits refer to yield, disease resistance, rice quality and drought resistance; the collection of phenotype data of rice varieties, the establishment of a correlation model between genotype data and phenotype data, and the screening of high-quality genes include: the phenotype data refer to growth traits, yield traits, quality traits, resistance traits and physiological traits; the collected phenotype data and genotype data are standardized, and high yield and high quality are defined as target phenotypes; The phenotype data and the genotype data of the rice are merged according to sample numbers to form a unified data matrix, and the dependence measure between each pair of characteristics u and v after standardization is calculated using an LMC Copula model : wherein a and b are adjustment parameters; after calculating the dependence measure, the covariance of each pair of feature pairs is calculated, and combined with the standard deviation, the correlation coefficient between each pair of features is obtained and a correlation matrix is constructed; set a correlation threshold A, select redundant features with a correlation greater than the threshold A, use Kendall's Tau to calculate the ordering consistency between each pair of features, further confirm the redundant features through Tau, and remove the confirmed redundant features; input the data set after removing the redundant features into the PCA algorithm, calculate the covariance between each pair of features and construct a covariance matrix, perform eigenvalue decomposition on the covariance matrix, and extract the eigenvalues and eigenvectors; sort the eigenvectors according to the eigenvalue size, and select the principal components with the largest explained variance; project the data onto a new low-dimensional space through the selected principal components to obtain a reduced data set; extract the feature loading matrix of each principal component from the PCA result, take 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 degree of the feature, set a threshold S, select the features with a total contribution degree greater than S as the feature set through the loading 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, and calculate the fitness of each tree according to the information gain and redundancy of the feature subset; sort the fitness from high to low, select the tree species with the highest fitness as the parent tree species, select the features in the parent tree species for mating to generate new tree species, and after mating, perform small-scale mutation on the newly generated tree species to generate a new tree species set and perform cross-validation evaluation to obtain an optimized reduced data set; The reduced phenotype data is input into the PLS-DA model as an independent variable, the reduced genotype data is input into the PLS-DA model as a dependent variable, a weight matrix W is extracted from the PLS-DA model to determine the influence degree of each principal component on the phenotype data and the genotype data, and the best quality gene with the greatest influence on the target phenotype in the weight matrix is selected according to the PLS-DA analysis result.

2. The method for comprehensive screening of high yield, high quality and high efficiency rice varieties according to claim 1, characterized in that: The determining of the ecological point and the collecting of the environmental data comprise: The ecological point refers to an ecological point containing various climate conditions; The environmental data comprise climate data, soil type, nutrient content, and water source condition.

3. The method for comprehensive screening of high yield, high quality and high efficiency rice varieties according to claim 1, characterized in that: The selecting of the target variety according to the screening result and the targeted editing of the key gene of the rice variety refer to selecting a rice variety with a target phenotype meeting the screening requirement, analyzing the historical cultivation phenotype of the selected rice variety, selecting a variety with a target phenotype value higher than an average value under different environmental conditions, designing a gRNA using a bioinformatics tool, targeting a specific region of a target gene, combining the gRNA and a Cas9 protein, and introducing the CRISPR-Cas9 system into cells of the target rice variety through a transformation technology.

4. The method for comprehensive screening of high yield, high quality and high efficiency rice varieties according to claim 3, characterized in that: The evaluating of the gene editing effect refers to designing a specific primer, performing PCR amplification on the target gene to detect the success rate of the target gene, using a high-throughput sequencing technology to sequence the PCR product, further confirming the marking effect of the target gene and the accuracy of the insertion through gene comparison analysis, and cultivating the gene-edited rice variety in an experimental field to compare the characteristics of the rice variety before and after the editing.

5. The method for comprehensive screening of high yield, high quality and high efficiency rice varieties according to claim 4, characterized in that: The precise cultivation after the gene editing further verifies the performance of the rice variety and the selection of the variety refers to designing a corresponding cultivation management scheme according to the characteristics of the target variety, determining a standardized cultivation management method, implementing the cultivation scheme in an experimental field, recording data at each link, monitoring the cultivation environment in real time through sensors and an automatic irrigation system, and monitoring field environmental data using an Internet of Things device, transmitting the data to a data platform in real time, and adjusting the cultivation management measures according to the environmental data. After the key core indicators for the selection of the rice variety are determined through the Delphi method, the Delphi expert consultation is performed again, the key indicators and the critical values for grading for the evaluation of the yield, quality, and efficiency of the rice in each test point are determined, the comprehensive evaluation indicators and grading standards for the high-quality, high-yield, and high-efficiency rice in the Yangtze River Delta region are established, and the high-yield, high-quality, and high-efficiency rice variety is screened out.

6. The method for comprehensive screening of high yield, high quality and high efficiency rice varieties according to claim 4, characterized in that: The genotype and phenotype data of the screened rice variety are integrated into a variety table according to the sample number, an information table is generated according to the basic information of the rice, and the data table is stored in a central database. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the comprehensive screening method for the high-yield, high-quality, and high-efficiency rice variety according to any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the comprehensive screening method for the high-yield, high-quality, and high-efficiency rice variety according to any one of claims 1-5.

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