Construction method of model for evaluating southern corn rust resistance of corn and method for evaluating southern corn rust resistance

Abstract

The invention discloses a construction method of a model for evaluating southern corn rust resistance of corn and a method for evaluating southern corn rust resistance, and relates to the technical field of plant molecular breeding. The construction method of the model has universality for construction of southern corn rust resistance capability models of various varieties of corns, and the provided model construction method is beneficial for people to screen excellent strains with extremely resistant or broad-spectrum resistance. The invention further provides a method for evaluating the southern corn rust resistance of plants, values of dependent variables are predicted through a multiple linear regression equation, and the influence degree of each variable on the dependent variables (resisting the southern corn rust) is evaluated. Besides, an anti-rust candidate gene is excavated on the basis of the method for evaluating the southern corn rust resistance of the corn and the site effect, and the method has a good application prospect in corn rust resistance.

Description

Technical Field

[0001] The present invention relates to the technical field of plant molecular breeding, and in particular to a method for constructing a model for evaluating corn resistance to southern corn rust and a method for evaluating corn resistance to southern corn rust. Background Art

[0002] Corn rust is an airborne disease caused by fungi. There are three main types: common corn rust, southern corn rust, and tropical corn rust. The three types of corn rust have different pathogens. Corn rust in southern my country is caused by Puccinia odorifera and Puccinia multisinicola, and mainly occurs in the northern summer corn planting area and the Huanghuaihai region. The outbreak of corn rust in the south will seriously threaten corn production and food security. The most economical and effective way to control the disease is to find corn lines carrying resistance genes, and evaluating the rust resistance of plants is a key step to achieve this goal. Assessing rust resistance through genome-wide rust resistance locus association markers is of great significance for cloning new loci for resistance to southern corn rust and screening excellent lines with extreme or broad-spectrum resistance.

[0003] So far, although 11 dominant maize resistance genes (Rpp1 to Rpp11) and 8 major maize resistance QTLs (RppC, RppCML470, RppD, RppM, RppP25, RppQ, RppS, and RppS313) have been reported, only RppC and RppK have been cloned (Ding et al., 2022; Chen et al., 2022). Although RppK confers strong rust resistance to plants and methods for identifying RppK have been developed, RppK is relatively rare and most elite lines do not contain it. It is impossible to determine the rust resistance of plants by identifying whether they contain RppK.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The object of the present invention is to provide a method for constructing a model for evaluating the ability of corn to resist southern corn rust and a method for evaluating the ability to resist southern corn rust to solve the above technical problems.

[0006] The present invention is achieved in that:

[0007] In a first aspect, the present invention provides a method for constructing a model for evaluating plant resistance to southern corn rust, comprising the following steps:

[0008] S1: Use a plant whole genome SNP chip and / or a SNP detection kit to detect the whole genome data of plants of the same type as the plants detected by the chip to obtain a SNP set;

[0009] S2: Screen out significant SNPs with a frequency less than the minimum allele from the SNP set;

[0010] S3: Obtaining covariate data for genome-wide association analysis;

[0011] S4: A linear model was used to perform association analysis on the covariate data to obtain a subset of SNP loci significantly associated with resistance to southern corn rust;

[0012] S5: The genotypes of the subset of SNP loci significantly associated with resistance to southern corn rust were used as independent variables, and the rust resistance phenotype was used as the dependent variable to construct a multiple regression linear equation for the two; the plant samples were randomly divided into a training set and a test set, and the training set was used to solve the coefficients in the multiple regression linear equation.

[0013] In a second aspect, the present invention also provides a method for evaluating the resistance of a plant to southern corn rust, comprising the following steps:

[0014] The genotypes of the SNP loci significantly associated with resistance to southern corn rust in the sample to be tested were obtained, and the genotypes were input into the model constructed by the above method. According to the phenotypic evaluation results of the model, the ability of the plant to resist southern corn rust was evaluated.

[0015] In a third aspect, the present invention also provides a device for evaluating the ability of plants to resist southern corn rust based on a model for evaluating the ability of plants to resist southern corn rust constructed based on the above method, the device comprising: an input module, a control module and an output module.

[0016] In a fourth aspect, the present invention also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a model for evaluating the ability of plants to resist southern corn rust or the steps in the above-mentioned method for evaluating the ability of plants to resist southern corn rust.

[0017] The present invention has the following beneficial effects:

[0018] The present invention provides a method for constructing a model for evaluating the ability of corn to resist southern corn rust, and the method for constructing the model is universal for constructing models for the ability of various corn varieties to resist southern corn rust. The construction of a model for evaluating the ability of corn to resist southern corn rust is conducive to screening excellent strains with extreme resistance or broad-spectrum resistance.

[0019] The present invention also creates a method for evaluating the ability of corn to resist southern corn rust, predicts the value of the dependent variable (resistance to southern corn rust) through a multivariate linear regression equation, and evaluates the degree of influence of each variable on the dependent variable (resistance to southern corn rust). BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is the GWAS result; A is the QQ graph and B is the Manhattan graph;

[0022] Figure 2 It is the construction of multiple regression linear equation; A is the model establishment result, B is the influence of outliers, strong influence points and high leverage values ​​in the sample, and C is the model detection result;

[0023] Figure 3 It is the construction of multiple regression linear equation; A is the model establishment result, B is the influence of outliers, strong influence points and high leverage values ​​in the sample, and C is the model detection result. DETAILED DESCRIPTION

[0024] References to embodiments of the present invention will now be provided in detail, one or more examples of which are described below. Each example is provided as an explanation rather than a limitation of the present invention. In fact, it will be apparent to those skilled in the art that various modifications and variations may be made to the present invention without departing from the scope or spirit of the present invention. For example, a feature illustrated or described as part of one embodiment may be used in another embodiment to produce a further embodiment.

[0025] Name Notes:

[0026] SNP refers to a genetic marker formed by the variation of a single nucleotide in the genome, including transition, transversion, deletion and insertion.

[0027] Minimum Allele Frequency (MAF): The frequency with which an uncommon allele occurs in a given population.

[0028] In a first aspect, the present invention provides a method for constructing a model for evaluating corn resistance to southern corn rust, which comprises the following steps:

[0029] S1: Use a corn whole genome SNP chip and / or a SNP detection kit to detect the whole genome data of corn to obtain a SNP set;

[0030] S2: Screen out significant SNPs with a frequency less than the minimum allele from the SNP set;

[0031] S3: Obtaining covariate data for genome-wide association analysis;

[0032] S4: A linear model was used to perform association analysis on the covariate data to obtain a subset of SNP loci significantly associated with resistance to southern corn rust;

[0033] S5: The genotypes of the subset of SNP loci significantly associated with resistance to southern corn rust were used as independent variables, and the rust resistance phenotype was used as the dependent variable to construct a multiple regression linear equation for the two; the plant samples were randomly divided into a training set and a test set, and the training set was used to solve the coefficients in the multiple regression linear equation.

[0034] SNP chip technology mainly fixes SNP markers on a carrier to form a dense oligonucleotide probe array, which then undergoes an allele-specific reaction with the target DNA, and determines the polymorphism of the SNP site based on the presence and strength of the signal after the reaction. This technology can achieve rapid and high-density scanning of the entire crop genome, especially for high-throughput genotyping of population samples in breeding work. The cost of a single detection site is very low, and it is a highly integrated, high-throughput, miniaturized and automated means of detecting SNPs.

[0035] When the plant to be tested is corn, the corn 50K high-density SNP chip can be selected.

[0036] The above-mentioned association analysis or whole-genome association analysis refers to: analyzing the relationship between a large number of single nucleotide polymorphism sites and phenotypes, scanning the entire genome, and thus determining the polymorphic nucleotide sites and candidate genes associated with the phenotype. With the iteration and improvement of sequencing technology and statistical analysis methods, the accuracy of whole-genome association analysis has gradually improved, and the number of markers linked to the target trait regulatory sites has increased and become more closely linked. These markers can represent the sites for analysis and prediction to a certain extent.

[0037] The above multiple linear regression equation is a statistical analysis tool used to explore the relationship between multiple independent variables and a dependent variable. By constructing a linear model containing multiple independent variables, the value of the dependent variable can be predicted and the influence of each variable on the dependent variable can be evaluated.

[0038] In order to evaluate the rust resistance of corn, the present invention uses whole genome association analysis to obtain multiple SNP sites that are significantly correlated with resistance to southern corn rust, and then constructs a multivariate regression linear equation to obtain the coefficient (value) of each SNP site, that is, the effect value. At this point, the equation can be used to predict the rust resistance of the plant and to explore the functional genes of large effect sites, thereby accelerating the breeding process of rust-resistant varieties.

[0039] In a preferred embodiment of the present invention, step S5 includes: first, using processing software to digitize the genotypes of the subset of SNP sites significantly associated with resistance to southern corn rust, and supplement the missing genotype values; then, using the genotypes of the subset of SNP sites significantly associated with resistance to southern corn rust as independent variables and the rust resistance phenotype as the dependent variable, a multivariate regression linear equation of the two is constructed.

[0040] For example, the genotypes of the 52 significant loci of the inbred line are used as independent variables x1, x2, x3, ..., x 52 , rust resistance phenotype as the dependent variable y, try to construct something like y = a1x1 + a2x2 + a3x3 + ... + a 52 x 52 +b, where a represents the additive effect value of 52 significant associated loci. The specific operation is: using TASSEL5.0 software to digitize the genotypes of 52 loci of 386 inbred lines, and supplement the missing genotype values. Then, 70% of the 386 inbred lines, that is, 271 inbred lines, were randomly selected as training sets, and a1, a2, a3, ..., a 52 and b, and finally the remaining 30% of inbred lines, that is, 115 inbred lines, are used as a test set to test the model.

[0041] In a preferred embodiment of the present invention, step S5 further includes modifying the model, and the modification includes: removing at least one of the following types of samples from the training set and / or the test set:

[0042] Outlier samples, strong influence point samples and high leverage value samples.

[0043] Outlier samples are those points that are inconsistent with the general behavior or characteristics of other sample points in the sample population. These points are usually called outliers or divergent points. They appear different in the data set, possibly because of external interference or abnormal factors. By removing outlier samples, it helps to reduce the linear impact of abnormal samples on the regression equation.

[0044] Strong influence points are usually a minority of points in a data set, but they exert a disproportionate influence on the coefficients and properties of the model, which is an unfavorable situation for the model. Therefore, it is necessary to find these points and assess the impact of these strong influence points on the model. If the values ​​of these strong influence points are wrong or problematic, they should be removed from the sample, but if they are not wrong, they have a greater impact on the final use results of the model, and it is still necessary to understand these points and consider them.

[0045] High leverage samples: The leverage value indicates the distance between the ith observation of the independent variable x and the mean value of the independent variable; high leverage values ​​indicate abnormal values ​​in the independent variable X space, which are called high leverage points, that is, they have abnormal X values, and the X values ​​are far from the mean value of X. High leverage points may affect the properties of the model, but not all high leverage points will affect the regression coefficient.

[0046] In a preferred embodiment of the present invention, the function influencePlot is used to remove at least one of the following types of samples from the training set and / or the test set:

[0047] Outlier samples, strong influence point samples and high leverage value samples.

[0048] In a preferred implementation mode of the present invention, after the model is modified, model evaluation is also included, and the model evaluation includes: using a test set to test the accuracy of the model in evaluating the ability of plants to resist southern corn rust.

[0049] In a preferred embodiment of the present invention, step S3 comprises: obtaining covariate data for genome-wide association analysis by at least one of the following analysis methods:

[0050] Principal component analysis, environmental analysis, and construction of kinship matrices.

[0051] In a preferred embodiment of the present invention, principal component analysis and kinship analysis are performed on the significant SNP data obtained in step S2 to obtain a PCA matrix and a K matrix; the PCA matrix and the K matrix are used as covariate data to establish a mixed linear model association analysis between the southern corn rust resistance phenotype and the significant SNP;

[0052] In a preferred embodiment of the present invention, step S4 uses a mixed linear model for analysis to obtain a subset of SNP sites significantly associated with resistance to southern corn rust, wherein the formula for association analysis is as follows:

[0053] y=Xβ+Zμ+ε, where y is the phenotypic observation, β is the fixed effect vector, including genetic markers and population structure, μ is the random additive genetic effect vector of individuals / lines, X and Z are the design matrices with unknown fixed and random effects, respectively, ε is the residual effect, μ and ε obey the normal distribution, K is the genomic kinship matrix, is the additive genetic variance, and is the residual variance; the correction factor multiple test is used to determine the significance level P of the point, which reflects the degree of association between the marker and the phenotypic variation. The smaller the P value, the higher the degree of association between the marker and the trait variation; different significance thresholds are set to screen out significant points in the association analysis.

[0054] In a preferred embodiment of the present invention, the minimum allele frequency (MAF) is 0.05. Data with a value less than the minimum allele frequency are deleted to reduce the possibility of using data errors caused by sequencing itself as SNP sites.

[0055] In a preferred embodiment of the present invention, the ratio of the training set to the test set is (1-9):(1-9); such as 7:3 or 7:2.

[0056] In a second aspect, the present invention also provides a method for evaluating the resistance of a plant to southern corn rust, comprising the following steps:

[0057] The genotype of the SNP loci significantly associated with resistance to southern corn rust in the sample to be tested is obtained, and the genotype is input into the model constructed by the above method. According to the phenotypic evaluation result (phenotypic score) of the model, the ability of the plant to resist southern corn rust is evaluated. In an optional embodiment, a threshold value of the disease resistance phenotype is set. If the score output by the model is higher than or equal to the threshold value, it is judged as resistant, and if it is lower than the threshold value, it is judged as a non-resistant southern corn rust plant. The phenotype can also be further divided into several levels: extremely resistant, highly resistant, moderately resistant, resistant and non-resistant.

[0058] In a third aspect, the present invention also provides a device for evaluating the ability of corn to resist southern corn rust based on the model for evaluating the ability of plants to resist southern corn rust constructed by the above method, the device comprising: an input module, a control module and an output module;

[0059] The input module is configured to: input plant whole genome data;

[0060] The control modules include: SNP screening module, model building module, model evaluation module and southern corn rust ability evaluation module;

[0061] The SNP screening module is configured as follows: using a plant whole genome SNP chip and / or a SNP detection kit to detect whole genome data of plants of the same type as the plants detected by the chip to obtain a SNP set; screening out significant SNPs with a frequency less than the minimum allele from the SNP set; obtaining covariate data for whole genome association analysis; using a linear model to perform association analysis on the covariate data to obtain a subset of SNP sites significantly associated with resistance to southern corn rust;

[0062] The model building module is configured as follows: taking the genotype of the subset of SNP loci significantly associated with resistance to southern corn rust as the independent variable and the rust resistance phenotype as the dependent variable, a multiple regression linear equation is constructed between the two;

[0063] The model evaluation module is configured to: test the accuracy of the model in evaluating the ability of corn to resist southern corn rust using the test set;

[0064] The southern corn rust resistance assessment module is configured as follows: the genotypes of the SNP loci significantly associated with southern corn rust resistance in the sample to be tested are input into the constructed model;

[0065] The output module is configured to: output the evaluation result of the plant's resistance to southern corn rust.

[0066] In a fourth aspect, the present invention also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a model for evaluating corn resistance to southern corn rust or the steps in the above-mentioned method for evaluating corn resistance to southern corn rust.

[0067] In a fifth aspect, the present invention also provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for constructing a model for evaluating the ability of corn to resist southern corn rust or the steps in the method for evaluating the ability of corn to resist southern corn rust are implemented.

[0068] Specifically, the electronic device may include a memory, a processor, a bus, and a communication interface, and the memory, processor, and communication interface are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these elements can be electrically connected to each other via one or more buses or signal lines. The processor can process information and / or data related to target identification to perform one or more functions described in this application.

[0069] The memory can be, but is not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc.

[0070] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0071] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be described clearly and completely below. If the specific conditions are not specified in the embodiments, they are carried out according to conventional conditions or conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments used is not specified, they are all conventional products that can be purchased commercially.

[0072] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0073] The present invention obtains 37823 SNP markers through the corn 50K high-density SNP chip, and uses whole genome association analysis to obtain 52 loci significantly associated with resistance to southern corn rust. Subsequently, by constructing a multiple regression linear equation, the coefficient value of each locus, that is, the effect value, is obtained. At this point, the equation can be used to predict the plant's rust resistance and explore the functional genes of large effect loci, thereby accelerating the breeding process of rust-resistant varieties.

[0074] Example 1

[0075] This example provides a method for constructing a model for evaluating plant resistance to southern corn rust.

[0076] (1) In the summer of 2023, 386 core inbred line parents from various corn producing areas across the country were planted in Xinxiang, Henan during the main season in the Huanghuaihai corn producing area. Each inbred line was planted in one row, with a row length of 5 meters and a total of 21 plants. In October 2023, before the corn was harvested, a statistical survey was conducted on its resistance to southern corn rust. The 386 corn inbred lines were fully diseased and the overall disease was serious. Among them, the resistance levels of 1 and 3 accounted for 10%, and the resistance levels of 7 and 9 accounted for 67% (the values ​​are 1-9, and the values ​​represent the severity of southern corn rust, that is, the smaller the value, the stronger the resistance). For the control varieties planted in the same plot, the resistance level of the improved line of the rust-resistant inbred line K22 was 3, and the resistance level of the susceptible inbred line ZHENG58 was 9.

[0077] (2) Using the high-density gene chip Maize 50K (provided by Hainan Xinyu Technology Co., Ltd.), the genotypes of the above 386 inbred lines were identified according to the standard process of Illumina Infinium gene chip detection, and 37,283 SNP markers covering the whole genome were obtained. Using TASSEL5.0 software, the minimum allele frequency was set to 0.05 for quality control, and the obtained significant SNP data were subjected to principal component analysis and kinship analysis to obtain the PCA matrix and K matrix; the PCA matrix and K matrix were used as covariate data for association analysis to establish a mixed linear model for resistance to southern corn rust.

[0078] A mixed linear model was then used to analyze the relationship between the 52 loci significantly associated with resistance to southern corn rust (GWAS results refer to Figure 1 shown).

[0079] The analysis method of mixed linear model is: y=Xβ+Zμ+ε, where y is the phenotypic observation value, β is the fixed effect vector, including genetic markers and population structure, μ is the random additive genetic effect vector of individuals / lines, X and Z are the design matrices with unknown fixed effects and random effects, respectively, ε is the residual effect, μ and ε obey the normal distribution, K is the genomic kinship matrix, is the additive genetic variance, and is the residual variance; the correction factor multiple test is used to determine the significance level P of the point, which reflects the degree of association between the marker and the phenotypic variation. The smaller the P value, the higher the degree of association between the marker and the trait variation; different significance thresholds are set to screen out significant points in the association analysis.

[0080] (3) The genotypes of the 52 significant loci of the inbred lines were used as independent variables x1, x2, x3, ..., x 52 , rust resistance phenotype as the dependent variable y, try to construct something like y = a1x1 + a2x2 + a3x3 + ... + a 52 x 52 +b, where a represents the additive effect value of the 52 significant associated loci. The specific operation is to use TASSEL5.0 software to digitize the genotypes of 52 loci of 386 inbred lines and supplement the missing genotype values. Then, 70% of the 386 inbred lines, that is, 271 inbred lines, were randomly selected as training sets, and a1, a2, a3, ..., a 52 And b are solved, and finally the remaining 30% inbred lines, that is, 115 inbred lines, are used as a test set to test the results.

[0081] (4) The model was constructed using the R language midline regression function lm() and interpreted using the summary() function. The results showed that the model was effective, but R2 Only 0.66, which means that it can only explain 66% of the phenotypic value. Therefore, the sample is tested for outliers. Through the function influencePlot(), it is found that there are indeed outliers, strong influence points, and high leverage values ​​in the sample. These samples are eliminated, and the final result is R 2 Reached 0.76 ( Figure 2 A and B in Figure 1).

[0082] The obtained equation was used to predict the remaining 115 inbred lines. The predicted values ​​were rounded and the difference between them and the actual phenotypic values ​​was taken to test the accuracy of the equation. The results showed that the average absolute value of the difference between the two was 1.32 and the standard deviation was 0.97, which was basically close to 0, indicating that the results were good ( Figure 2 As shown in Figure C, the horizontal axis is the number of samples).

[0083] Example 2

[0084] Using the southern corn rust resistance phenotypes of about 345 natural populations in the article "Cloningsouthern corn rust resistant gene RppK and its cognate gene AvrRppK from Puccinia polysora" published by Lai Zhibing's team and Yan Jianbing's team in Nature Communications, combined with about 0.56M SNP genotype data published on MAIZEGO, an association analysis of the mixed linear model for southern corn rust resistance was performed using the same method as mentioned above.

[0085] A mixed linear model was used to analyze the relationship between the 69 loci significantly associated with resistance to southern corn rust. The genotypes of the 69 significant loci in the inbred lines were used as independent variables x1, x2, x3, ..., x 69 , the rust resistance phenotype is taken as the dependent variable y, and a similar y=a1x1+a2x2+a3x3+...+a 69 x 69 + b, 70% of the approximately 345 inbred lines were randomly selected as training sets, and a1, a2, a3, ..., a 69 and b to solve, and finally use the remaining 30% inbred lines as a test set to test the results.

[0086] The model was constructed using the midline regression function lm() in R language and interpreted using the function summary(). After correction, the results showed that the model was significant and could explain about 85% of the phenotypic values.

[0087] The obtained equation was used to predict the remaining inbred lines, and the difference between the predicted value and the actual phenotypic value was taken to test the accuracy of the equation. The results showed that the average absolute value of the difference between the two was 0.81, and the standard deviation was 0.54, which was basically close to 0, indicating that the prediction results were good (refer to Figure 3 shown).

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a model for evaluating corn resistance to southern corn rust, characterized in that: It includes the following steps: S1: Use a corn whole genome SNP chip and / or a SNP detection kit to detect the whole genome data of corn to obtain a SNP set; S2: Screening out significant SNPs with a frequency less than the minimum allele from the SNP set; S3: Obtaining covariate data for genome-wide association analysis; S4: A linear model was used to perform association analysis on the covariate data to obtain a subset of SNP loci significantly associated with resistance to southern corn rust; S5: Using the genotype of the subset of SNP sites significantly associated with resistance to southern corn rust as the independent variable and the rust resistance phenotype as the dependent variable, construct a multiple regression linear equation for the two; randomly divide the plant samples into a training set and a test set, and use the training set to solve the coefficients in the multiple regression linear equation.

2. The method for constructing a model for evaluating corn resistance to southern corn rust according to claim 1, characterized in that: The step S5 comprises: firstly using processing software to digitize the genotypes of the subset of SNP sites significantly associated with resistance to southern corn rust, and supplementing the missing genotype values; then using the genotypes of the subset of SNP sites significantly associated with resistance to southern corn rust as independent variables and the rust resistance phenotype as dependent variable, constructing a multiple regression linear equation for the two.

3. The method for constructing a model for evaluating corn resistance to southern corn rust according to claim 1, characterized in that: The step S5 further includes modifying the model, wherein the modification includes: removing at least one of the following types of samples from the training set and / or the test set: Outlier samples, strong influence point samples and high leverage value samples; Preferably, the influencePlot function is used to exclude at least one of the following types of samples from the training set and / or the test set: Outlier samples, strong influence point samples and high leverage value samples.

4. The method for constructing a model for evaluating corn resistance to southern corn rust according to claim 1, characterized in that: After the model is modified, model evaluation is also included, and the model evaluation includes: using the test set to test the accuracy of the model in evaluating the ability of corn to resist southern corn rust.

5. The method for constructing a model for evaluating corn resistance to southern corn rust according to claim 1, characterized in that: The step S3 comprises: obtaining covariate data for genome-wide association analysis by at least one of the following analysis methods: principal component analysis, environmental analysis, and construction of kinship matrices; Preferably, principal component analysis and kinship analysis are performed on the significant SNP data obtained in step S2 to obtain a PCA matrix and a K matrix; the PCA matrix and the K matrix are used as covariate data to establish an association analysis of a mixed linear model between the southern corn rust resistance phenotype and the significant SNP; Preferably, the step S4 uses a mixed linear model for analysis to obtain a subset of SNP sites significantly associated with resistance to southern corn rust, wherein the formula for the association analysis is as follows: y=Xβ+Zμ+ε, where y is the phenotypic observation, β is the fixed effect vector, including genetic markers and population structure, μ is the random additive genetic effect vector of individuals / lines, X and Z are the design matrices with unknown fixed and random effects, respectively, ε is the residual effect, μ and ε obey the normal distribution, K is the genomic kinship matrix, is the additive genetic variance, and is the residual variance; the correction factor multiple test is used to determine the significance level P of the point, which reflects the degree of association between the marker and the phenotypic variation. The smaller the P value, the higher the degree of association between the marker and the trait variation; different significance thresholds are set to screen out significant points in the association analysis.

6. The method for constructing a model for evaluating corn resistance to southern corn rust according to claim 1, characterized in that: The minimum allele frequency is 0.

05.

7. The method for constructing a model for evaluating corn resistance to southern corn rust according to claim 6, characterized in that: The ratio of the training set to the test set is (1-9):(1-9).

8. A method for evaluating corn resistance to southern corn rust, characterized in that: It includes the following steps: The genotype of the SNP site significantly associated with resistance to southern corn rust in the sample to be tested is obtained, and the genotype is input into the model constructed by the method described in any one of claims 1 to 7, and the ability of corn to resist southern corn rust is evaluated according to the phenotypic evaluation results of the model.

9. A device for evaluating the ability of corn to resist southern corn rust based on the model for evaluating the ability of corn to resist southern corn rust constructed by the method described in any one of claims 1 to 7, characterized in that: The device comprises: an input module, a control module and an output module; The input module is configured to: input plant whole genome data; The control module includes: a SNP screening module, a model building module, a model evaluation module and a southern corn rust ability evaluation module; The SNP screening module is configured to: use a plant whole genome SNP chip and / or a SNP detection kit to detect the whole genome data of plants of the same type as the plant detected by the chip to obtain a SNP set; screen out significant SNPs with a frequency less than the minimum allele from the SNP set; obtain covariate data for whole genome association analysis; use a linear model to perform association analysis on the covariate data to obtain a subset of SNP sites significantly associated with resistance to southern corn rust; The model building module is configured to: take the genotype of the SNP locus subset significantly associated with southern corn rust resistance as an independent variable and the rust resistance phenotype as a dependent variable, and construct a multiple regression linear equation of the two; The model evaluation module is configured to: test the accuracy of the model in evaluating the ability of plants to resist southern corn rust using a test set; The southern corn rust resistance assessment module is configured to: input the genotype of the SNP site significantly associated with southern corn rust resistance in the sample to be tested into the constructed model; The output module is configured to output the evaluation result of the plant's resistance to southern corn rust.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the program implements the steps in the method for constructing a model for evaluating corn resistance to southern corn rust as described in any one of claims 1 to 7 or the method for evaluating corn resistance to southern corn rust as described in claim 8.

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