A method for constructing a model for evaluating the ability of corn to resist southern corn blight, and a method for evaluating the ability to resist southern corn blight

Abstract

The application discloses a kind of construction methods of evaluating corn southern corn rust resistance ability model, method for evaluating the ability of resistance to southern corn rust, it is related to plant molecular breeding technical field.The construction method of the model is universal for the construction of a variety of corn southern corn rust resistance ability model, the model construction method is provided, it is favorable for people to screen excellent line of extremely resistant or broad-spectrum resistance.The application also creates the method for evaluating the ability of resistance to southern corn rust of plant, the value of dependent variable is predicted by multiple linear regression equation, and the influence degree of each independent variable on dependent variable (resistance to southern corn rust) is evaluated.In addition, the application mines a rust-resistant candidate gene based on the above-mentioned method for evaluating the ability of corn southern corn rust resistance and the size of site effect, and has good application prospect for corn rust resistance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plant molecular breeding, in particular to 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. BACKGROUND

[0002] Corn rust is an air-borne disease caused by fungi, and there are mainly three types, namely common corn rust, southern corn rust and tropical corn rust. The pathogenic bacteria of the three types of corn rust are different, and the southern corn rust in China is caused by Puccinia sorghi and Puccinia polysora, and mainly occurs in the northern summer corn planting area and the Huang-Huai-Hai region. The outbreak of southern corn rust will seriously threaten corn production and food security. The most economical and effective method to control the disease is to find corn lines carrying resistance genes, and evaluating the resistance of plants is a key step to achieve this goal. Evaluating the resistance of plants by whole genome rust resistance site-associated markers is of great significance for cloning new sites resistant to southern corn rust and screening excellent lines with extremely resistant or broad-spectrum resistance.

[0003] So far, although 11 corn dominant resistance genes (Rpp1 to Rpp11) and 8 corn major 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 resistance to plants, and the method for identifying RppK has been developed, RppK is relatively rare, and most excellent lines do not contain it at present, so it is impossible to judge the resistance of plants by identifying whether they contain RppK.

[0004] In view of this, the present application is proposed. SUMMARY

[0005] The present application aims 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 application is implemented as follows:

[0007] In a first aspect, the present application provides a method for constructing a model for evaluating the ability of plants to resist southern corn rust, comprising the following steps:

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

[0009] S2: exclude significant SNPs with minor allele frequency less than the minimum allele frequency from the SNP set;

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

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

[0012] S5: construct a multiple regression linear equation with the genotype of the subset of SNP sites significantly associated with resistance to southern corn rust as the independent variable and the resistance phenotype as the dependent variable; randomly divide the plant samples into a training set and a test set, and solve the coefficients in the multiple regression linear equation using the training set.

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

[0014] Obtaining the genotype of the SNP site significantly associated with resistance to southern corn rust in the sample to be tested, inputting the genotype into the model constructed by the above method, and evaluating the ability of the plant to resist southern corn rust according to the phenotype evaluation result of the model.

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

[0016] In a fourth aspect, the present application also provides a computer readable storage medium having a program stored thereon, and the program is executed by a processor to implement the steps in the method for constructing the model for evaluating the ability of a plant to resist southern corn rust or the method for evaluating the ability of a plant to resist southern corn rust.

[0017] The present application has the following advantages:

[0018] The present application 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 the ability of various corn varieties to resist southern corn rust. The construction of the model for evaluating the ability of corn to resist southern corn rust is beneficial to screening excellent lines with high resistance or broad-spectrum resistance.

[0019] The present application 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 multiple linear regression equation, and evaluates the degree of influence of each independent variable on the dependent variable (resistance to southern corn rust). BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0021] Figure 1 GWAS results; wherein A is a QQ plot, B is a Manhattan plot;

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

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

[0024] The embodiments of the present application will now be described in detail with reference to the accompanying drawings. One or more examples of an embodiment of the present application are described below. Each example is provided as an explanation and not a limitation on the present application. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope or spirit of the present application. For example, features described or illustrated as part of one embodiment can be used in another embodiment to produce a further embodiment.

[0025] Name annotation:

[0026] SNP refers to a single nucleotide variation on a genome, including transition, transversion, deletion, and insertion, forming a genetic marker.

[0027] Minimum allele frequency (MAF): the frequency of occurrence of an uncommon allele in a given population.

[0028] In a first aspect, the present application provides a method for constructing a model for evaluating the ability of corn to resist southern corn rust, comprising the following steps:

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

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

[0031] S3: obtaining covariate data for whole genome association analysis;

[0032] S4: using linear model to perform correlation analysis on the covariate data, and obtaining a subset of SNP sites significantly associated with resistance to southern corn rust;

[0033] S5: taking the genotypes of the subset of SNP sites significantly associated with resistance to southern corn rust as independent variables, and taking the resistance phenotype as dependent variable, to construct a multiple regression linear equation therebetween; and dividing the plant samples into a training set and a test set at random, and solving the coefficients in the multiple regression linear equation by using the training set.

[0034] The SNP chip technology mainly forms a dense oligonucleotide probe array by fixing SNP markers on a carrier, and then performs allele-specific reaction with the target DNA, and determines the polymorphism of the SNP site according to the presence or absence and strength of the signal after the reaction. This technology can realize rapid and high-density scanning of the whole genome of crops, especially when performing throughput genotyping on population samples in breeding work, the cost of a single detection site is very low, and it is a high-integration, high-throughput, miniaturization and automated means for detecting SNPs.

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

[0036] The above correlation analysis or whole genome association analysis refers to a method of analyzing the relationship between a large number of single nucleotide polymorphism sites and phenotypes, scanning the entire genome, and determining the polymorphic nucleotide sites and candidate genes related to the phenotype. With the iteration and improvement of sequencing technology and statistical analysis methods, the accuracy of whole genome association analysis gradually improves, and the markers linked to the target trait regulation site increase and are closer. These markers can represent the site to some extent for analysis and prediction.

[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 degree of each independent variable on the dependent variable can be evaluated.

[0038] In order to evaluate the resistance of corn to rust, the present application uses whole genome association analysis to obtain a plurality of SNP sites significantly associated with resistance to southern corn rust, and then constructs a multiple regression linear equation to obtain the coefficient (value) of each SNP site, i.e. the effect value. Thus, the resistance of the plant to rust can be predicted by the equation, and the functional genes of the large-effect sites can be mined to accelerate the breeding process of the rust-resistant varieties.

[0039] In the preferred embodiment of the application, step S5 comprises: firstly, using the processing software to numerize the genotypes of the subset of SNPs significantly associated with resistance to southern corn blight and supplement the missing genotype values; and then constructing a multiple regression linear equation with the genotypes of the subset of SNPs significantly associated with resistance to southern corn blight as the independent variables and the resistance phenotype as the dependent variable.

[0040] For example, if the genotypes of 52 significant loci of the inbred lines are taken as independent variables x1, x2, x3,..., x52, and the resistance phenotype is taken as dependent variable y, a multiple regression linear equation similar to y = a1x1 + a2x2 + a3x3 +... + a52x52 + b is attempted to be constructed, where a represents the additive effect value of the 52 significant loci. The specific operation is as follows: the genotypes of 52 loci of 386 inbred lines are numerized by using the TASSEL 5.0 software, and the missing genotype values are supplemented, then 70% of the 386 inbred lines, i.e. 271 inbred lines, are randomly selected as the training set, and a1, a2, a3,..., a52 and b are solved, and finally the remaining 30% of the inbred lines, i.e. 115 inbred lines, are used as the test set to detect the model. 52 52 52 52

[0041] In the preferred embodiment of the application, after step S5, the model is further modified, and the modification comprises: removing at least one of the following types of samples in the training set and / or the test set:

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

[0043] Outlier samples refer to those points that are inconsistent with the general behavior or characteristics of other sample points in the sample population. These points are usually referred to as outliers or outliers, which are different from the rest of the data set, possibly because they are affected by 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 small number of points in a data set, but they have a disproportionate impact on the coefficients and properties of the model, which is an unfavorable situation for the model. Therefore, these points are found and the impact of these strong influence points on the model is evaluated. If the values of these strong influence points are wrong or problematic, they should be removed from the sample, but if they do not exist, they still need to be understood and considered.

[0045] ​​​​High-leverage sample: where the leverage value represents the distance between the ith observation of the independent variable x and the mean value of the independent variable; high-leverage value represents the outlier in the independent variable X space, which is called high-leverage point, that is, its X value is abnormal, and X value is far away from the mean value of X. High-leverage points may affect the nature of the model, but not all high-leverage points will affect the regression coefficient.

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

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

[0048] In the preferred embodiment of the application, after the model is modified, the model evaluation is further included, and the model evaluation includes: testing the accuracy of the model in evaluating the ability of plants to resist southern corn rust using the test set.

[0049] In the preferred embodiment of the application, step S3 includes: obtaining the covariate data for the whole genome association analysis by at least one of the following analysis methods:

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

[0051] In the preferred embodiment of the application, the significant SNP data obtained in step S2 is subjected to principal component analysis and kinship analysis to obtain a PCA matrix and a K matrix; the PCA matrix and the K matrix are used as covariate data for establishing the association analysis of the mixed linear model of the southern corn rust resistance phenotype and the significant SNPs;

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

[0053] y = Xβ + Zμ + ε where y is the phenotype 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 unknown design matrices of fixed effects and random effects, respectively, ε is the residual effect, μ and ε are subject to and normal distribution, K is the genomic kinship matrix, is the additive genetic variance, and is the residual variance; a 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 phenotype variation, and the smaller the P value, the higher the degree of association between the marker and the phenotype variation; different significance thresholds are set to screen out significant point sites for association analysis.

[0054] In a preferred embodiment of the application, the minimum allele frequency (MAF) is 0.05. Data less than the minimum allele frequency is deleted to reduce the possibility of data errors due to sequencing itself as a SNP site.

[0055] In a preferred embodiment of the application, 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 application also provides a method for evaluating the ability of a plant to resist southern corn blight, comprising the following steps:

[0057] The genotype of the SNP site significantly associated with southern corn blight resistance in the sample to be tested is obtained, which is input into the model constructed by the above method, and the ability of the plant to resist southern corn blight is evaluated according to the phenotype evaluation result (phenotype score) of the model. In an alternative embodiment, a threshold value of the disease-resistant phenotype is set, and if the score output by the model is higher than or equal to the threshold value, it is judged to be resistant, and if it is lower than the threshold value, it is judged to be a plant that does not resist southern corn blight. The phenotype can be further divided into: extremely resistant, high resistant, medium resistant, resistant and not resistant.

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

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

[0060] The control module comprises: an SNP screening module, a model construction module, a model evaluation module and a southern corn blight resistance ability evaluation module;

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

[0062] The model construction module is configured to construct a multiple regression linear equation of the genotype of the subset of SNP sites significantly associated with southern corn blight resistance as the independent variable and the rust-resistant phenotype as the dependent variable;

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

[0064] The southern corn rust resistance ability evaluation module is configured to input the genotype of the SNP site significantly associated with the southern corn rust resistance in the sample to be tested into the constructed model.

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

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

[0067] In a fifth aspect, the present application further provides an electronic device, comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for constructing a model for evaluating the southern corn rust resistance ability of corn or the above-mentioned method for evaluating the southern corn rust resistance ability of corn when executing the program.

[0068] Specifically, the electronic device can comprise a memory, a processor, a bus and a communication interface, which are electrically connected to each other directly or indirectly to realize the transmission or interaction of data. For example, these elements can be electrically connected to each other through one or more buses or signal lines. The processor can process information and / or data related to target identification to execute one or more functions described in the present application.

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

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

[0071] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below. If specific conditions are not indicated in the embodiments, the conventional conditions or the conditions recommended by the manufacturers are adopted. If the manufacturers of the reagents or instruments are not indicated, the conventional products that can be purchased in the market are adopted.

[0072] The features and performances of the present application will be described in further detail below in combination with the embodiments.

[0073] The present application obtains 37823 SNP markers by using a corn 50K high-density SNP chip, and obtains 52 sites significantly related to southern corn rust resistance by using whole genome association analysis. Then, the coefficient value of each site, i.e., the effect value, is obtained by constructing a multiple regression linear equation. Thus, the plant rust resistance can be predicted by the equation, and the functional genes of large-effect sites can be mined to accelerate the breeding process of rust-resistant varieties.

[0074] Embodiment 1

[0075] The present embodiment provides a method for constructing a model for evaluating the ability of plants to resist southern corn rust.

[0076] (1) In summer 2023, 386 core inbred line parents from corn production areas across the country were planted in Xinxiang, Henan in the regular season in the Huanghuaihai corn production area, with 1 row of each inbred line, 5 meters long, and a total of 21 plants. In October 2023, before the corn was harvested, a statistical investigation was conducted on the resistance of southern corn rust. The 386 corn inbred lines were fully diseased, and the overall disease was severe, with 10% of the resistance grades being 1 and 3, and 67% of the resistance grades being 7 and 9 (the numerical value is 1-9, and the numerical value represents the severity of southern corn rust, i.e., the smaller the numerical value, the stronger the resistance). The control variety planted in the same plot, the improved line of the rust-resistant inbred line K22 had a resistance grade of 3, and the susceptible inbred line ZHENG58 had a resistance grade of 9.

[0077] (2) Using high-density gene chip Maize 50K (provided by Hainan Chip Corn Technology Co., Ltd.), the genotypes of the above-mentioned 386 inbred lines were identified according to the standard operation procedure of Illumina Infinium gene chip detection, and 37283 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 significant SNP data obtained were subjected to principal component analysis and kinship analysis to obtain PCA matrix and K matrix; the PCA matrix and K matrix were used as covariate data for association analysis of the mixed linear model of resistance to southern corn rust.

[0078] Subsequently, mixed linear model analysis was performed, and a total of 52 sites significantly associated with resistance to southern corn rust were obtained (for GWAS results, refer to Figure 1 ).

[0079] The method for mixed linear model analysis is: y = Xβ + Zμ + ε, wherein y is the phenotype 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 unknown design matrices of fixed effects and random effects, respectively, ε is the residual effect, μ and ε obey and normal distribution, K is the genomic kinship matrix, σa2 is the additive genetic variance, σe2 is the residual variance; the Bonferroni correction factor multiple test is used to determine the significant level P of the point, which reflects the degree of association between the marker and the variation of the phenotype, and the smaller the P value, the higher the degree of association between the marker and the variation of the trait; different significance thresholds are set to screen out significant point sites for association analysis.

[0080] (3) The genotypes of the 52 significant sites of the inbred lines were used as independent variables x1, x2, x3,..., x 52 , and the resistance phenotype was used as the dependent variable y, and a multiple regression linear equation similar to y = a1x1 + a2x2 + a3x3 +... + a 52 x 52 +b was attempted to be constructed, wherein a represents the additive effect value of the 52 significantly associated sites. Specifically, the genotype values of the 52 sites of the 386 inbred lines were numerized using TASSEL5.0 software, and the missing genotype values were supplemented, then 70% of the 386 inbred lines, i.e. 271 inbred lines, were randomly selected as the training set, and a1, a2, a3,..., a 52 and b were solved, finally the remaining 30% of the inbred lines, i.e. 115 inbred lines, were used as the test set, and the detection results were obtained.

[0081] (4) The linear regression function lm() in R language was used for construction, and the function summary() was used for interpretation, and the results showed that the model effect was significant, but R2 Only 0.66, that is, only 66% of the phenotypic value can be explained, so the sample is subjected to outlier detection, through the function influencePlot(), it is found that there are indeed outliers, strong influence points, high leverage values in the sample, and these samples are removed, and the final result R 2 reaches 0.76 Figure 2 (as shown in the A and B graphs in FIG. 1).

[0082] The obtained equation is used to predict the remaining 115 inbred lines, and the obtained predicted value is rounded and subtracted from the actual phenotypic value to detect the accuracy of the equation. The results show that the average value of the absolute value of the difference between the two is 1.32, and the standard deviation is 0.97, which is basically close to 0, indicating that the result is good Figure 2 (as shown in the C graph in FIG. 1, the horizontal coordinate is the number of samples).

[0083] Example 2

[0084] In the article "Cloning southern corn rust resistant gene RppK and its cognate gene AvrRppK from Puccinia polysora" published by the Lai Zhigang team and the Yan Jianbing team in Nature Commerunication, about 345 natural population phenotypes resistant to southern corn rust are used, combined with about 0.56M SNP genotype data published on "MAIZE GO", and the same method as described above is used to perform mixed linear model association analysis of resistance to southern corn rust.

[0085] A total of 69 sites significantly associated with resistance to southern corn rust are obtained by using mixed linear model analysis. The genotypes of the 69 significant sites of the inbred lines are used as independent variables x1, x2, x3,..., x 69 , and the rust resistance phenotype is used as the dependent variable y, to construct a multiple regression linear equation similar to y=a1x1+a2x2+a3x3+...+a 69 x 69 +b, and similarly, 70% of about 345 inbred lines are randomly selected as a training set to solve a1, a2, a3,..., a 69 and b, and finally the remaining 30% of the inbred lines are used as a test set to detect the results.

[0086] The linear regression function lm() in R language is used to construct, and the function summary() is used to interpret. After correction, the results show that the model effect is significant and can explain about 85% of the phenotypic value.

[0087] The obtained equation is used to predict the rest of the inbred lines, and the difference between the obtained predicted value and the actual phenotype value is taken to detect the accuracy of the equation. The results show that the average of the absolute value of the difference is 0.81, and the standard deviation is 0.54, which is basically close to 0, indicating that the prediction result is good (for reference Figure 3

[0088] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.​

Claims

1. A method for constructing a model for evaluating the ability of corn to resist southern corn blight, characterized by, It comprises the following steps: S1: detecting the whole genome data of corn by using corn whole genome SNP chip and / or SNP detection kit, and obtaining a SNP set; S2: screening out significant SNPs less than the minimum allele frequency from the SNP set; S3: obtaining covariate data for whole genome association analysis; S4: performing association analysis on the covariate data by using a linear model, and obtaining a SNP site subset significantly associated with southern corn rust resistance; S5: constructing a multiple regression linear equation of the genotype of the SNP site subset significantly associated with southern corn rust resistance as the independent variable and the southern corn rust resistance phenotype as the dependent variable; and dividing the plant samples into a training set and a test set randomly, and solving the coefficients in the multiple regression linear equation by using the training set.

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

3. The method of claim 1, wherein the model is constructed by using a dataset of corn plants with known resistance to southern corn leaf blight. The step S5 further comprises modifying the model, and the modification comprises: removing at least one type of sample in the training set and / or the test set, such as an outlier sample, a strong influence point sample and a high leverage value sample. Preferably, the function influencePlot is used to remove at least one type of sample in the training set and / or the test set, such as an outlier sample, a strong influence point sample and a high leverage value sample. After modifying the model, the model evaluation is further included, and the model evaluation comprises: testing the accuracy of the evaluation of the southern corn rust resistance of corn by using the test set. The step S3 comprises: obtaining the covariate data for whole genome association analysis by using at least one of the following analysis methods:

4. The method for constructing a model to evaluate maize resistance to southern maize rust according to claim 1, characterized in that, principal component analysis, environmental analysis and construction of a kinship matrix; 5. The method of claim 1, wherein the model is constructed by using a training dataset comprising a plurality of corn plants having a known resistance to southern corn leaf blight. Preferably, the principal component analysis and the kinship analysis are performed on the significant SNP data obtained in the step S2, and a PCA matrix and a K matrix are obtained; and the PCA matrix and the K matrix are used as the covariate data for the association analysis of the mixed linear model of the southern corn rust resistance phenotype and the significant SNPs. Preferably, the step S4 uses the mixed linear model for analysis, and a SNP site subset significantly associated with southern corn rust resistance is obtained, wherein the formula of the association analysis is as follows: y=Xβ+Zμ+ε wherein, y is a phenotype observation value, β is a fixed effect vector, including genetic markers and population structure, μ is a random additive genetic effect vector of individuals / lines, X and Z are unknown design matrices of fixed effects and random effects, respectively, ε is a residual effect, μ and ε obey and normal distribution, K is a genomic kinship matrix, is an additive genetic variance, and is a residual variance; a correction factor multiple test is used to determine the significant level P of the point position, which reflects the degree of association of the marker with the variation of the phenotype, and the smaller the P value, the higher the degree of association of the marker with the variation of the trait; different significance thresholds are set to screen out the significant point positions of the association analysis. ​ ​ 6. The method of claim 1, wherein the model is constructed by using a training dataset comprising a plurality of corn plants having a known resistance to southern corn leaf blight. The minimum allele frequency is 0.

05.

7. The method for constructing a model to evaluate maize resistance to southern maize 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 of assessing the ability of a maize plant to resist southern corn blight, characterized in that, It comprises the following steps: Obtaining the genotype of the SNP site significantly associated with resistance to southern corn rust in the sample to be tested, inputting it into the model constructed by the method of any one of claims 1-7, and evaluating the corn resistance to southern corn rust according to the phenotype evaluation result of the model.

9. A device for evaluating the ability of corn to resist southern corn blight, constructed based on the method according to 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 the whole genome data of the plant; The control module comprises an SNP screening module, a model construction module, a model evaluation module and a southern corn rust resistance evaluation module; The SNP screening module is configured to detect the whole genome data of the plant of the same type as the plant detected by the chip using the plant whole genome SNP chip and / or SNP detection kit, obtain a SNP set, screen out significant SNPs less than the minimum allele frequency from the SNP set, obtain covariate data for whole genome association analysis, and perform association analysis on the covariate data using a linear model to obtain a subset of SNP sites significantly associated with resistance to southern corn rust; The model construction module is configured to construct a multiple regression linear equation of the genotype of the subset of SNP sites significantly associated with resistance to southern corn rust as the independent variable and the resistance phenotype as the dependent variable; The model evaluation module is configured to test the accuracy of the evaluation of the plant's resistance to southern corn rust using the test set to test the model; The southern corn rust resistance evaluation module is configured to input the genotype of the SNP site significantly associated with resistance to southern corn rust 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 stored thereon a program, characterized in that, The program is executed by the processor to implement the steps in the method of constructing the model for evaluating the corn resistance to southern corn rust according to any one of claims 1-7 or the method of evaluating the corn resistance to southern corn rust according to claim 8.

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