Application of Zm00001eb155730 gene in corn breeding

Abstract

The invention provides application of a Zm00001eb155730 gene in corn breeding, and relates to the technical field of agricultural biology. The Zm00001eb155730 gene is applied to regulation and control of corn leukoderma resistance molecular marker assisted breeding. According to the invention, GWAS (Genome-Wide Association Study) analysis and QTL (Quantitative Trait Loci) analysis of a genetic linkage map are jointly positioned to S3-210892574 which is positioned in a chromosome 3 and is remarkably related to the resistance of the corn white spot disease, so that the functional gene Zm00001eb155730 for regulating and controlling the resistance of the corn white spot disease is excavated. The gene Zm00001eb155730 has a predominant haplotype in GWAS, the leukoderma resistance of the haplotype is significantly higher than that of other haplotypes, and a theoretical basis is provided for molecular marker-assisted selection and regulation of the corn leukoderma resistance.

Description

Technical Field

[0001] The present invention relates to the field of agricultural biotechnology, and in particular to an application of a Zm00001eb155730 gene in corn breeding. Background Art

[0002] Maize (Zea may L.) is a major food crop worldwide, and its production is crucial for global food security and economic development. However, maize production faces numerous threats from diseases. Maize White Spot (MWS) is a serious foliar disease that occurs in maize-growing areas worldwide, but is particularly severe in tropical and subtropical regions.

[0003] Resistance to corn white spot is a complex quantitative trait. The loci for corn white spot resistance are distributed on almost every chromosome and may be related to resistance to other leaf diseases of corn. It is very necessary to explore corn white spot resistance genes and combine them with molecular marker-assisted selection to accelerate the development of disease-resistant germplasm. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides an application of the Zm00001eb155730 gene in corn breeding, explores the functional gene Zm00001eb155730 associated with corn white spot disease resistance, and provides a theoretical basis for molecular marker-assisted selection to regulate corn white spot disease resistance.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A Zm00001eb155730 gene is used in corn breeding. The gene is used for regulating corn white spot disease resistance molecular marker-assisted breeding. The nucleotide sequence of the Zm00001eb155730 gene is shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the gene is shown in SEQ ID NO.2.

[0006] Preferably, the regulation of corn white spot disease resistance is to improve corn white spot disease resistance by adjusting the bases 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene to mutate to A, C, C, G, A, and G.

[0007] A product containing a substance of the Zm00001eb155730 gene whose regulatory sequence is shown in SEQ ID NO: 1.

[0008] Preferably, the product is a kit.

[0009] The above products can identify or assist in identifying corn white spot disease resistance.

[0010] Preferably, the application method is to use the product to identify mutations at bases 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene.

[0011] The present invention provides an application of the Zm00001eb155730 gene in corn breeding, which has the following advantages over the prior art: This study constructed a multi-parent maize population with significant differences in kernel starch content by using Ye107, a temperate maize inbred line with low resistance to corn white spot disease, as a common parent and crossing it with two tropical maize inbred lines, YML32 and TML139, both of which exhibit higher resistance. GWAS and genetic linkage analysis co-localized a single nucleotide polymorphism (SNP) on chromosome 3, SNP_210892574, which is significantly associated with corn white spot disease resistance, with a p-value of up to 6.5. Furthermore, the functional gene Zm00001eb155730, which regulates kernel starch content, was identified. Haplotype analysis showed that among 171 recombinant inbred lines, Zm00001eb155730 had three haplotypes: haplotype 1 (ACCGAG), haplotype 2 (GCCCAT), and haplotype 3 (GCTGGG). Haplotype 1 had a significantly lower white spot disease grade than haplotypes 2 and 3. Therefore, SEQ ID NO: 1 of the Zm00001eb155730 gene is a haplotype that significantly improves white spot resistance in maize. The results of this study will help further investigate the regulatory mechanisms of white spot resistance in maize and provide a theoretical basis for the development of maize varieties with high starch content. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 : Population construction and frequency distribution of corn white spot disease scores; A shows the construction of a multi-parent population, using Ye107 as the common male parent of two recombinant inbred line populations to breed 360 F7 generation recombinant inbred lines; B shows the frequency distribution of corn white spot disease scores of 360 recombinant inbred lines under three environments; Figure 2 :Diversity analysis of 360 recombinant inbred lines; A is the phylogenetic tree; B is the principal component analysis; C is the population structure of the multi-parent population; Figure 3 : Linkage disequilibrium decay diagram of population 1 and population 2; Figure 4: Manhattan plot and QQ plot in the GWAS analysis of corn white spot resistance; A is the 21 Yanshan environment; B is the 22 Yanshan environment; C is the 23 Yanshan environment; D is the best linear unbiased prediction value; Figure 5 : QTL mapping of maize white spot disease resistance under multiple environments; A is the white spot disease resistance QTL identified in population 1 under the 21 Yanshan, 22 Yanshan, and 23 Yanshan environments and the best linear unbiased prediction value; B is the rice white spot disease resistance QTL identified in population 2 under the 21 Yanshan, 22 Yanshan, and 23 Yanshan environments and the best linear unbiased prediction value; Figure 6 : Location and haplotype analysis of the Zm00001eb155730 gene; A is the relative position of the significant SNPs S3-210892574, Zm00001eb155730, and Zm00001eb155740; B is the haplotype type within the Zm00001eb155730 gene; C is a box plot showing the differences in corn white spot resistance among the three haplotypes of Zm00001eb155730 estimated based on the best linear unbiased prediction value, where **** indicates extremely significant differences at the P<0.0001 level, and ns indicates not significant; D is the distribution of the three haplotypes in the two populations. DETAILED DESCRIPTION

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention. Example:

[0014] 1. Experiment 1.1 Plant materials and experimental design Three excellent lines, Ye107, YML32, and TML139, were used as parents (Table 1). Ye107 came from temperate regions, while YML32 and TML139 came from tropical regions. The three parents (Ye107, YML32, TML139) are disclosed in the document "Li L, Jiang F, Bi Y, Yin X, Zhang Y, Li S, Zhang X, Liu M, Li J, Shaw RK, Ijaz B&Fan X. 2024. Dissection of Common Rust Resistancein Tropical Maize Multiparent Population through GWAS and Linkage Studies. Plants, 13(10), p. 1410". 1410.), with Ye107 serving as the common parent in this study. It was then crossed with two other lines and propagated through single-seed descent to the F7 stage to construct two recombinant inbred line populations (Population 1: Ye107 × YML32, Population 2: Ye107 × TML139). Population 1 contained 175 recombinant inbred lines, while Population 2 contained 185. Table 1 Parents for constructing recombinant inbred line populations

[0015] 1.2 Field trial design and phenotypic evaluation A total of 360 recombinant inbred lines from populations 1 and 2 were planted in Yanshan in 2021, 2022, and 2023, and were recorded as 21 Yanshan, 22 Yanshan, and 23 Yanshan, respectively. Each plot was planted in a single row, with a row length of 4.0 m, a row spacing of 0.7 m, a spacing of 0.25 m between plants, and 14 plants per row. Local field management standards were implemented. A 1-9 rating method was used to score corn white spot disease in two recombinant inbred line subpopulations in three environments. For each corn plant, three leaves above and below the ear were selected for disease scoring. The disease score of the 10 plants with the highest frequency of white spot disease in each row of recombinant inbred lines was used as the score of the recombinant inbred line.

[0016] Table 2 Disease grade classification of corn white spot

[0017] 1.3 Statistical analysis of phenotypic data The maize white spot disease phenotypes were scored for the 21 Yanshan, 22 Yanshan, and 23 Yanshan environments. The mean, standard deviation, skewness, kurtosis, coefficient of variation, and heritability were calculated, and a normal distribution test was performed. Pairwise correlations between the two subpopulations in the three environments were calculated, and a mixed linear model (MLM) was used to calculate the best linear unbiased prediction (BLUP) values ​​for the phenotypic data across the three environments.

[0018] 1.4 Deoxyribonucleic Acid (DNA) Sequencing and SNP Detection Whole-genome resequencing (WGS) was performed on three parental lines and 360 recombinant inbred lines. Genomic DNA was extracted from young leaf tissue using the cetyltrimethylammonium bromide (CTAB) method and purified using the QIAquick PCR Purification Kit (Qiagen, Valencia, CA, USA). The extracted DNA was quantified using the Qubit dsDNA HS Assay Kit (Life Technologies, Grand Island, NY, USA). Paired-end sequencing libraries were constructed using an Illumina TruSeq DNA Sample Preparation Kit (Illumina Inc., San Diego, CA, USA) using a DNA insert of approximately 350 base pairs (bp) according to standard protocols. After library construction, fragment size was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), and fragment concentration was quantified using a Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA).

[0019] Sequencing was performed on an Illumina NovaSeq 6000 sequencing platform, generating paired-end sequencing reads of 150 bp. Raw sequencing reads were filtered using fastp using default parameters to remove spliced ​​sequences and low-quality sequencing reads.

[0020] The processed sequencing reads were aligned to the Zea mays B73 RefGen_v5 reference genome using Sentieon software v2021-12-01 with the parameter “bwa mem-k 32-MR” (Pei S, Liu T, Ren X, Li W, Chen C&Xie Z. 2021. Benchmarking variant callers in next-generation and third-generation sequencing analysis. Brief Bioinform, 22(3), bbaa148.).

[0021] Aligned reads were sorted and duplicates removed using Samtools (parameter: rmdup).

[0022] SNPs were detected in recombinant inbred lines and parents using Sentieon software, and filtering criteria were set to exclude variants with supporting SNP reads <4, RMSMappingQuality (MQ) <40, and Genotype Qualit (GQ) <5.

[0023] VCFtools v0.1.16 was used to filter the SNPs in the population with the following parameters: -geno 0.2 and -maf 0.05. Sites with a missing rate greater than 20% and a minimum allele frequency less than 5% were removed. Multi-allelic SNP sites were filtered out, and only biallelic sites were retained.

[0024] The obtained high-quality SNPs were annotated using ANNOVAR software (v2021-07-16).

[0025] 1.5 Population structure analysis and linkage disequilibrium decay The VCF file was converted to the Philips format using vcf2philips.py v2.0. The Philips file was imported into MEGA v11.0, and the tree was rebuilt in MEGA v11.0 using the neighbor-joining (NJ) method with a bootstrap value of 1000.

[0026] Principal component analysis (PCA) was used to evaluate genetic variation among recombinant inbred lines. The first three principal components were calculated using Plink v1.9, and the results were displayed in three dimensions using R program v4.3.2.

[0027] Population structure analysis was performed using Admixture v1.3.0 software, with K values ​​set to 1 to 30 and default parameters. The optimal K value was determined by selecting the model with the lowest cross-validation error. Population structure was visualized using the ggplot2 package in R software v4.3.2.

[0028] PopLDdecay v3.42 software was used to analyze linkage disequilibrium (LD) of SNPs in the whole genome, and the coefficient of determination (R) of linkage disequilibrium between marker pairs with increasing distance was calculated using default parameters. 2 ). Use Plot_OnePop.pl to draw the linkage disequilibrium decay curve.

[0029] 1.6 GAWS Analysis GWAS analysis was performed using a linear mixed model (LMM) in the GEMMA (Genome-wide Efficient Mixed Model Association algorithm) software. Population structure and individual relatedness were incorporated into the model to correct for their effects. The LMM formula is as follows:

[0030] y is the phenotypic trait, X is the indicator matrix of fixed effects, α is the estimated parameter of fixed effects; Z is the indicator matrix of SNPs, β is the effect of SNPs; W is the random effect matrix, μ is the predicted random individual, and e is the random residual, which obeys e~(0, δe 2).

[0031] The Bonferroni correction method was used to determine the significance threshold of the p-value. Specifically, the significance threshold was calculated by dividing 0.05 (the significance level) by the number of effective SNPs in the genome.

[0032] The number of valid SNPs was estimated using plink, and a threshold (-log10(1 / SNPs)) of 5 was determined to identify SNPs significantly associated with corn white spot. GWAS results were visualized using Manhattan and QQ plots generated using CMplot v3.6.2.

[0033] 1.7. Construction of genetic linkage map and QTL mapping Based on the genotype test results of maize parents, polymorphic markers between parents were developed, and the polymorphic sites of the parents were screened. The offspring genotyping was filtered based on 0.8 completeness and 0.001 partial separation to obtain population markers. Then, bins were drawn based on the population markers to obtain the final population markers. Joinmap4.0 was used to sort the bin markers of each population by the maximum likelihood method, and the Kosambi function was used to calculate the genetic distance.

[0034] QTL mapping was performed using composite interval mapping with Windows QTL Cartographer v2.5 software. The logarithm of the odds (LOD) threshold was determined by 1000 random permutation tests. The permutation test simulates the distribution of LODs in the absence of an association signal by randomly disrupting the correspondence between phenotypes and genotypes. The LOD distributions generated by the permutation test were analyzed, and a LOD > 2.5 (P < 0.05) was used as the criterion for significant QTLs (Broman KW, Wu H, Sen S & Churchill G A. 2003. R / qtl: QTL mapping in experimental crosses. Bioinformatics, 19(7), 889-890.).

[0035] 1.8 Candidate gene screening, functional annotation, and haplotype analysis First, the overlap between significant SNPs in GWAS and significant QTLs in genetic linkage maps was counted across multiple environments. SNPs and QTLs with significant overlap across multiple environments were selected, and SNPs located within QTLs were further selected as candidate SNPs. Using the physical region defined by linkage disequilibrium decay, upstream and downstream genes of the SNPs were identified as candidate genes. Functional annotation of the candidate genes was performed using the MaizeGDB (http: / / www.maizegdb.org) and NCBI (http: / / www.ncbi.nlm.nih.gov) databases.

[0036] Haplotype analysis of candidate genes was performed. Linkage disequilibrium block values ​​were calculated using Haploview v4.2. Dominant haplotypes that may significantly affect corn white spot disease were identified. Box plots were generated based on phenotypes, and significant differences between haplotypes were analyzed using one-way analysis of variance.

[0037] result 2.1 Phenotypic analysis of corn white spot disease The incidence of corn white spot disease was investigated in three environments: 21 Yanshan, 22 Yanshan, and 23 Yanshan, and the corn white spot disease scores of the recombinant inbred lines were recorded ( Figure 1A, 2B). Statistical results showed that the average phenotypic scores for the two subpopulations ranged from 3.91 to 5.93, with coefficients of variation (CV) ranging from 25.8% to 52.6%. Kurtosis and skewness were within the range of -1 to 1, indicating that the response of the recombinant inbred lines to corn white spot, as a quantitative trait, followed a normal distribution. Heritability was significantly high (89.7% to 94.1%), indicating that this trait is primarily influenced by genetic factors. Pairwise correlation coefficients between subpopulations from different environments were highly significant (Table 3).

[0038] Table 3 Statistical analysis of maize white spot disease phenotypes in two recombinant inbred line subpopulations

[0039] In the above table: **** indicates p<0.0001.

[0040] 2.2 Phylogenetic tree, principal component analysis (PCA), and population structure analysis Phylogenetic tree analysis showed that 360 maize recombinant inbred lines could be clearly clustered into two major groups, and most of the recombinant inbred lines in the same subgroup were concentrated ( Figure 2 A). Principal component analysis showed that the recombinant inbred lines were basically divided into two major groups, confirming the experimental design of this study ( Figure 2 B). Population structure analysis showed that when K=2, 360 recombinant inbred lines were divided into two subpopulations ( Figure 2 C). Hybridization between subpopulations may be due to genetic drift or the use of a common sire, Ye107, during the development of the recombinant inbred line subpopulations. The results of population structure analysis are consistent with those of principal component analysis and phylogenetic analysis. Therefore, the first three principal components of the principal component analysis and the kinship matrix were used as covariates in the GWAS analysis.

[0041] 2.3 Linkage disequilibrium decay analysis The determination coefficient (R) of linkage disequilibrium decay between populations 1 and 2 was estimated using genome-wide SNPs. 2 Linkage disequilibrium decay plot ( Figure 3 ) shows that linkage disequilibrium decays rapidly with increasing physical distance. 2 When the linkage disequilibrium ratio is approximately 0.1, it decays at a physical distance of approximately 20 kb. Beyond this distance, the linkage disequilibrium decay gradually stabilizes. Based on the linkage disequilibrium decay distance, we screened the 20 kb regions upstream and downstream of the significant SNPs in populations 1 and 2 to identify candidate genes.

[0042] 2.4 GWAS of maize white spot disease resistance GWAS results showed that there were 68 significant SNPs in 21 Yanshan, with the highest P value being 6.65. SNPs were distributed on chromosomes 1-10, with the largest number of SNPs on chromosome 1 being 12 ( Figure 4 A) (Table 4). There were 84 significant SNPs in 22 Yanshan, with the highest P value of 6.50. SNPs were distributed on chromosomes 1-10, and the most significant 25 SNPs were located on chromosome 3 ( Figure 4 B). There are 76 significant SNPs in 23 Yanshan, with the highest P value of 6.66. SNPs are distributed on chromosomes 1 to 10. Among them, 15 of the most significant SNPs are located on chromosome 1 ( Figure 4 C). There are 81 SNPs in the best linear unbiased prediction value, of which the highest P value is 6.69. SNPs are distributed on chromosomes 1-10, and the most significant 12 SNPs are distributed on chromosomes 1 and 3 ( Figure 4 D). We further screened for 15 SNPs that co-occurred in more than three environments (including the best linear unbiased predictor value) (Table 4). These SNPs were distributed on chromosomes 2, 3, 4, 6, 7, 8, and 9, with the highest number of five on chromosome 3. S3-203999904 had the highest best linear unbiased predictor value of 6.69, while S6-164274636 had the highest explained phenotypic variance of 9.8%. These SNPs will be further used for QTL co-localization screening.

[0043] Table 4 SNPs shared by more than three environments

[0044] 2.5 QTL Mapping for Maize White Spot Resistance In QTL mapping, a total of 10 QTLs were detected on chromosomes 1, 3, 7, and 8 in population 1 ( Figure 5 A), with logarithmic odds values ​​ranging from 2.64 to 4.60 and phenotypic variance explained by 5.45% to 9.89%. The additive effect ranged from -0.47 to 0.51, with 5 positive effects and 5 negative effects. Similarly, in population 2, 12 QTLs were identified on chromosomes 1, 2, 3, and 10 ( Figure 5 The log odds values ​​of these QTLs ranged from 2.56 to 4.88, explaining 5.58% to 10.81% of the phenotypic variation. The additive effects ranged from -0.65 to 0.58, with three positive effects and nine negative effects (Table 5).

[0045] Further screening revealed two intervals containing overlapping QTLs. Interval 1, located at 210,173,589-215,907,098 bp on chromosome 3, overlaps with qMWS3-2, qMWS3-3, qMWS3-4, and qMWS3-5. Interval 2, located at 218,750,741-225,913,893 bp on chromosome 3, overlaps with qMWS3-3, qMWS3-4, and qMWS3-6. Notably, all five QTLs, qMWS3-2, qMWS3-3, qMWS3-4, qMWS3-5, and qMWS3-6, exhibited negative additive effects, suggesting that the resistant parent (TML139 or YML32) plays a role in reducing disease severity and improving population resistance. These two intervals will be further used for SNP co-localization screening.

[0046] Table 5 Significant QTLs found in two subpopulations in different environments

[0047] 2.6 Colocalization and candidate gene identification Comparison of the locations of overlapping QTLs and SNPs across multiple environments revealed that qMWS3-2, qMWS3-3, qMWS3-4, and qMWS3-5 colocalized with S3-210892574 and S3-211477681 (Table 6). S3-210892574 had a higher P value than S3-211477681 in all three environments, so S3-210892574 was selected as a candidate SNP. Gene searches were conducted within 20 kb upstream and downstream. Zm00001eb155730 and Zm00001eb155740 genes were identified 3164 bp and 6807 bp downstream of S3-210892574, respectively. Figure 6 A). Zm00001eb155730 encodes mitogen-activated protein kinase kinase 18 (MAPKKK18), and Zm00001eb155740 is a non-coding gene model. Since Zm00001eb155740 is farther from the SNP than Zm00001eb155730 and has no functional annotation, Zm00001eb155730 was selected as a candidate gene for further analysis.

[0048] Table 6 GWAS and QTL co-localization results

[0049] 2.7 Haplotype analysis of candidate genes Further haplotype analysis was performed on the candidate gene Zm00001eb155730 to study the effect of SNP changes on the phenotype of the recombinant inbred lines. Mutations occurred at bases 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene, resulting in three major haplotypes in the study population (haplotype 1: ACCGAG, haplotype 2: GCCCAT, and haplotype 3: GCTGGG) ( Figure 6 B), the number of samples of haplotype 1, haplotype 2 and haplotype 3 in the recombinant inbred lines were 49, 80 and 42 respectively ( Figure 6 C). The phenotype of haplotype 1 in the best linear unbiased prediction value is significantly lower than that of haplotype 2 and haplotype 3 ( Figure 6 D), indicating that haplotype 1 of Zm00001eb155730 has the effect of reducing corn white spot disease and improving resistance. Further findings revealed that haplotypes 2 and 3 were present in both populations 1 and 2, while haplotype 1 was only present in population 2, suggesting that haplotype 1 is associated with population 2's higher corn white spot disease resistance.

[0050] The gene sequence of Zm00001eb155730 is shown in SEQ ID NO.1: SEQ ID NO.1: The amino acid sequence of Zm00001eb155730 is shown in SEQ ID NO. 2: SEQ ID NO.2: MAASGRWRRLRTLGRGASGAVVSLASDAASGELFAVKSAGASGAATLRREHAVLRGLRSPHVVRCVGGGEGADGSYQVFLEYAPGGSVADAVARGGGALEERAIRALAADVL RGLAYLHGRSVVHGDVKARNVLLGADGRARLADFGCARTPGFSARRPLGTPAFMAPEVARGEAQGPAADVWALGCTVVEMATGRAPWGGADADVLAAVHRIGYTDAVPDAP SWMSAEARDFLARCFARDAAERWTAAQLLEHPFVAAPCHGHGDHEAPRVSPKSTLDAAFWEAEDDDDDADEAVSASASERIKSLACSACALPDWDGEDGWIEVLGDQQRVEV CGAVQVARSAPGKVSSVLAVPAGEMDVGGGGGGGGDELEAEDVSFGGEVPGSADASAERQKKRYLILRSHYCHVLSCQLVPCNLPLVVVNNAIKLWVPTNVLLCRSVRFPLS.

[0051] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An application of Zm00001eb155730 gene in corn breeding, characterized in that: The gene is used for regulating molecular marker-assisted breeding for corn white spot disease resistance. The nucleotide sequence of the Zm00001eb155730 gene is shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the gene is shown in SEQ ID NO.

2.

2. The use according to claim 1, characterized in that: The resistance to corn white spot disease is regulated by adjusting the bases 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene to mutate to A, C, C, G, A, and G to improve the resistance to corn white spot disease.

3. A product characterized by: The product contains a substance of the Zm00001eb155730 gene whose regulatory sequence is shown in SEQ ID NO:

1.

4. The product according to claim 3, characterized in that: The product is a test kit.

5. Use of the product according to any one of claims 3 to 4 in identifying or assisting in identifying resistance to corn white spot disease.

6. The use according to claim 5, characterized in that: The application method is to use the product to identify mutations in bases 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene.

Images