Application of the Zm00001eb155730 gene in maize breeding

Abstract

This invention provides an application of the Zm00001eb155730 gene in maize breeding, relating to the field of agricultural biotechnology. The Zm00001eb155730 gene is applied to marker-assisted breeding to regulate maize white spot disease resistance. This invention utilizes genome-wide association analysis (GWAS) and quantitative trait locus analysis of genetic linkage maps to locate S3-210892574 on chromosome 3, which is significantly associated with maize white spot disease resistance, thereby identifying the functional gene Zm00001eb155730 that regulates maize white spot disease resistance. The Zm00001eb155730 gene exhibits a dominant haplotype in GWAS, and this haplotype shows significantly higher resistance to white spot disease than other haplotypes, providing a theoretical basis for marker-assisted selection to regulate maize white spot disease resistance.

Description

Technical Field

[0001] This invention relates to the field of agricultural biotechnology, specifically to the application of the Zm00001eb155730 gene in maize breeding. Background Technology

[0002] Maize (Zea may L.) is a major food crop worldwide, and its production is of great significance to global food security and economic development. However, maize production faces threats from various diseases, among which 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] Maize white spot disease resistance is a complex quantitative trait. Loci for maize white spot disease resistance are distributed across almost every chromosome and may be related to resistance to other foliar diseases in maize. It is essential to discover maize white spot disease resistance genes and combine them with molecular marker-assisted selection to accelerate the development of disease-resistant germplasm. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an application of the Zm00001eb155730 gene in maize breeding, exploring the functional gene Zm00001eb155730 associated with maize white spot disease resistance, and providing a theoretical basis for molecular marker-assisted selection to regulate maize white spot disease resistance.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] An application of the Zm00001eb155730 gene in maize breeding, wherein the gene is used to regulate molecular marker-assisted breeding for resistance to maize white spot disease, 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.

[0007] Preferably, maize resistance to white spot disease is regulated by adjusting the mutations at positions 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 of the Zm00001eb155730 gene to A, C, C, G, A, G.

[0008] A product containing a substance of the gene with a regulatory sequence as shown in SEQ ID NO: 1, namely Zm00001eb155730.

[0009] Preferably, the product is a reagent kit.

[0010] The above products can be used to identify or assist in the identification of corn white spot disease resistance.

[0011] Preferably, the application method is to use product identification to detect mutations in bases at positions 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene range.

[0012] This invention provides an application of the Zm00001eb155730 gene in maize breeding, which has the following advantages compared with existing technologies:

[0013] This invention utilizes the temperate maize inbred line Ye107, which has low resistance to maize white spot disease, as a common parent, and crosses it with two tropical maize inbred lines YML32 and TML139, which have high resistance to maize white spot disease, to construct a multi-parental maize population with significantly different kernel starch content. GWAS and genetic linkage analyses jointly located a single nucleotide polymorphism (SNP) on chromosome 3 that is significantly associated with maize white spot disease resistance: SNP_210892574, with a p-value reaching a maximum of 6.5. Furthermore, the functional gene Zm00001eb155730, which regulates kernel starch content, was identified. Haplotype analysis revealed three haplotypes in Zm00001eb155730 among 171 recombinant inbred lines: haplotype 1 (ACCGAG), haplotype 2 (GCCCAT), and haplotype 3 (GCTGGG). Haplotype 1 exhibited a significantly lower disease severity for white spot disease than haplotypes 2 and 3. Therefore, SEQ ID NO: 1 of the Zm00001eb155730 gene is a haplotype that significantly enhances maize resistance to white spot disease. The results of this study contribute to further research on the regulatory mechanisms of maize resistance to white spot disease and also provide a theoretical basis for developing maize varieties with high starch content. Attached Figure Description

[0014] Figure 1 Population construction and frequency distribution of maize white spot disease scores; A represents the construction of a multi-parent population, with Ye107 as the common male parent of two recombinant inbred line populations, and 360 F7 generation recombinant inbred lines were bred; B represents the frequency of maize white spot disease scores of the 360 ​​recombinant inbred lines under three environments.

[0015] Figure 2 Diversity analysis of 360 recombinant inbred lines; A: phylogenetic tree; B: principal component analysis; C: population structure of multi-parent populations;

[0016] Figure 3: Linkage disequilibrium decay plots for population 1 and population 2;

[0017] Figure 4 Manhattan plot and QQ plot in GWAS analysis of maize white spot disease resistance; A represents environment 21 (Yanshan); B represents environment 22 (Yanshan); C represents environment 23 (Yanshan); D represents the best linear unbiased prediction.

[0018] Figure 5 QTL mapping for maize white spot disease resistance under multiple environments; A represents the white spot disease resistance QTLs identified in population 1 under the 21, 22, and 23 Yanshan environments and the best linear unbiased predictive value; B represents the white spot disease resistance QTLs identified in population 2 under the 21, 22, and 23 Yanshan environments and the best linear unbiased predictive value.

[0019] Figure 6 Location and haplotype analysis of the Zm00001eb155730 gene; A represents the relative positions of significant SNPs S3-210892574, Zm00001eb155730, and Zm00001eb155740.

[0020] B represents the haplotype types within the Zm00001eb155730 gene; C is a box plot showing the differences in maize white spot disease resistance among the three haplotypes of Zm00001eb155730 estimated based on the best linear unbiased predictor, where **** indicates extremely significant differences at the P<0.0001 level, and ns indicates no significant differences; D shows the distribution of the three haplotypes in the two populations. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example:

[0023] 1. Experiment

[0024] 1.1 Plant Materials and Experimental Design

[0025] Three superior strains, 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 literature "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 Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies. Plants, 13(10), p. 1410" (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 Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies. Plants, 13(10), p. 1410). Ye107 (1410.) was the common parent in this study. It was crossed with two other lines, and the single-seed propagation method was used to construct two recombinant inbred line populations up to F7 (Population 1: Ye107 × YML32, Population 2: Ye107 × TML139). Population 1 contained 175 recombinant inbred lines, and Population 2 contained 185 recombinant inbred lines.

[0026] Table 1 Parental lines used to construct the recombinant inbred line population

[0027]

[0028] 1.2 Field Trial Design and Phenotypic Evaluation

[0029] A total of 360 recombinant inbred lines from Group 1 and Group 2 were planted in Yanshan in 2021, 2022, and 2023, respectively designated as 21 Yanshan, 22 Yanshan, and 23 Yanshan. Each plot was planted in single rows with a row length of 4.0m, row spacing of 0.7m, and plant spacing of 0.25m, with 14 plants per row. Field management followed local standards. A 1-9 level scoring method was used to score maize white spot disease in the two subgroups of recombinant inbred lines across the three environments. For each maize plant, the top and bottom three leaves closest to the ear were selected for disease scoring. The score for the disease with the highest frequency of white spot disease occurrence in the middle 10 maize plants of each row was used as the score for that recombinant inbred line.

[0030] Table 2 Disease severity classification of white spot disease in maize

[0031]

[0032] 1.3 Statistical Analysis of Phenotypic Data

[0033] By analyzing the disease scoring phenotypes of maize white spot disease in three environments (21 Yanshan, 22 Yanshan, and 23 Yanshan), the mean, standard deviation, skewness, kurtosis, coefficient of variation, and heritability were calculated, and a normality test was performed. The pairwise correlations between the two subpopulations in the three environments were also calculated, and the best linear unbiased prediction (BLUP) values ​​for the phenotypic data in the three environments were calculated using a mixed linear model (MLM).

[0034] 1.4 Deoxyribonucleic acid (DNA) sequencing and SNP detection

[0035] Whole-genome resequencing (WGS) was performed on three parental lines and 360 recombinant inbred lines. Genomic DNA was extracted from young leaf tissues 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 (LifeTechnologies, Grand Island, NY, USA). Paired-end sequencing libraries were constructed using approximately 350 base pairs (bp) of DNA inserts according to standard protocols using the Illumina TruSeq DNA Sample Preparation Kit (Illumina Inc., San Diego, CA, USA). 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 fluorescence analyzer (Invitrogen, Carlsbad, CA, USA).

[0036] Sequencing was performed on an Illumina NovaSeq 6000 sequencing platform, producing 150 bp paired-end reads. Raw sequencing reads were filtered using FASTP with default parameters to remove spliced ​​sequences and low-quality reads.

[0037] The processed sequencing reads were aligned with 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.).

[0038] Use Samtools (parameter: rmdup) to sort aligned reads and remove duplicates.

[0039] SNPs were detected in recombinant inbred lines and parents using Sentieon software, and filtering criteria were set to exclude variants that supported SNP reads <4, RMSMappingQuality (MQ) <40, and Genotype Qualit (GQ) <5.

[0040] Use VCFtools v0.1.16 to filter SNPs in the population, setting the parameters: --geno 0.2 and --maf0.05, to remove sites with a deletion rate greater than 20% and a minimum allele frequency of less than 5%, filter out multi-allelic SNP sites, and retain only biallelic sites.

[0041] The obtained high-quality SNPs were annotated using ANNOVAR software (v2021-07-16).

[0042] 1.5. Population Structure Analysis and Chaining Disequilibrium Decay

[0043] Use vcf2philips.py v2.0 to convert VCF files to Philips format. Import the Philips file into MEGA v11.0. In MEGA v11.0, rebuild the tree using the Neighbor-Joining (NJ) method and set the bootstrap value to 1000.

[0044] 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-dimensional space using R program v4.3.2.

[0045] Population structure analysis was performed using Admixture v1.3.0 software, with K values ​​set between 1 and 30, and default parameters. The optimal K value was determined by selecting the model with the smallest cross-validation error. The population structure was visualized using the ggplot2 package in R software v4.3.2.

[0046] Linkage disequilibrium (LD) analysis was performed on genome-wide SNPs using PopLDdecay v3.42 software. The coefficient of determination (R²) of linkage disequilibrium between marker pairs as distance increased was calculated using default parameters. 2 Use Plot_OnePop.pl to plot the chain imbalance decay curve.

[0047] 1.6 GAWS Analysis

[0048] GWAS analysis was performed using a linear mixture model (LMM) in GEMMA (Genome-wide Efficient Mixed Model Association algorithm) software. Population structure and individual kinship were incorporated into the model to correct for their effects. The LMM formula is as follows:

[0049]

[0050] y represents the phenotypic trait, X represents the indicator matrix of the fixed effects, α represents the estimated parameters of the fixed effects, Z represents the indicator matrix of the SNP, β represents the effect of the SNP, W represents the indicator matrix of the random effects, μ represents the predicted random individuals, and e represents the random residuals, which follow the pattern e ~ (0, δe 2).

[0051] The significance threshold for p-values ​​was determined using the Bonferroni correction method. Specifically, the significance threshold was calculated by dividing 0.05 (significance level) by the number of valid SNPs in the genome.

[0052] 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 maize white spot disease. GWAS results were visualized using Manhattan and QQ plots generated using CMplot v3.6.2.

[0053] 1.7 Genetic linkage map construction and QTL mapping

[0054] Based on the genotyping results of maize parents, polymorphic markers were developed among parents, and polymorphic loci of the parents were screened. Genotyping of offspring was filtered based on 0.8 integrity and 0.001 partial segregation to obtain population markers. Then, bins were drawn based on the population markers to obtain the final population markers. Joinmap 4.0 was used to sort the bin markers of each population using the maximum likelihood method, and the genetic distance was calculated using the Kosambi function.

[0055] QTL localization was performed using composite interval mapping in Windows QTL Cartographer v2.5 software. The Logarithm of the Odds (LOD) threshold was determined through 1000 random permutation trials. The permutation trials simulated the distribution of LOD values ​​in the absence of associated signals by randomly shuffling the correspondence between phenotype and genotype. The distribution of LOD values ​​generated by the permutation trials was analyzed, and an LOD value > 2.5 (P < 0.05) was used as the criterion for determining significant QTLs (Broman KW, Wu H, Sen S & Churchill GA. 2003. R / qtl: QTL mapping in experimental crosses. Bioinformatics, 19(7), 889-890.).

[0056] 1.8 Candidate gene screening, functional annotation, and haplotype analysis

[0057] First, in multiple environments, the overlap between significant SNPs in GWAS and significant QTLs in genetic linkage maps was counted. SNPs and QTLs with significant overlap in multiple environments were selected, and SNPs located within QTLs were further selected as candidate SNPs. Using the physical region range determined 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.

[0058] Haplotype analysis was performed on candidate genes. Linkage disequilibrium block values ​​were calculated using Haploview v4.2. Dominant haplotypes that may significantly influence maize white spot disease were identified. Box plots were generated based on the phenotypes, and significant differences between haplotypes were analyzed using one-way ANOVA.

[0059] result

[0060] 2.1 Phenotypic Analysis of White Spot Disease in Maize

[0061] The incidence of maize white spot disease was investigated under three environmental conditions: Yanshan 21, Yanshan 22, and Yanshan 23. The maize white spot disease scores of recombinant inbred lines were also recorded. Figure 1 (A, 2B). Statistical results showed that the average phenotypic scores of the two subgroups 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 maize white spot disease, as a quantitative trait, showed a normal distribution in the response of recombinant inbred lines. The heritability was significantly high (89.7% to 94.1%), indicating that this trait is mainly influenced by genetic factors. The pairwise correlation coefficients between different environmental subgroups were highly significant (Table 3).

[0062] Table 3. Statistical analysis of maize white spot disease phenotypes in two recombinant inbred line subpopulations.

[0063]

[0064] In the table above: **** indicates p < 0.0001.

[0065] 2.2 Phylogenetic Tree, Principal Component Analysis (PCA), and Population Structure Analysis

[0066] Phylogenetic tree analysis showed that the 360 ​​maize recombinant inbred lines could be clearly clustered into two major groups, with most recombinant inbred lines in the same subgroup showing a concentrated distribution. 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 at K=2, the 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 paternal parent, Ye107, during the development of the recombinant inbred line subpopulations. The population structure analysis results are consistent with those of principal component analysis and phylogenetic analysis. Therefore, in the GWAS analysis, the first three principal components of the principal component analysis, along with the kinship matrix, were used as covariates.

[0067] 2.3 Chaining Imbalance Decay Analysis

[0068] The determination coefficients (R²) of linkage disequilibrium decay in populations 1 and 2 were estimated using genome-wide SNPs. 2 Chain imbalance decay diagram () Figure 3 The data shows that the decay rate of chain imbalance is rapid with increasing physical distance. When the chain imbalance R... 2 At a linkage disequilibrium value of approximately 0.1, the disequilibrium decays at a physical distance of about 20 kb, beyond which the decay gradually stabilizes. Based on the linkage disequilibrium decay distance, candidate genes were identified by screening 20 kb regions upstream and downstream of significant SNPs in populations 1 and 2.

[0069] 2.4 GWAS of resistance to maize white spot disease

[0070] GWAS results showed 68 significant SNPs in the 21 Yanshan samples, with the highest P-value being 6.65. The SNPs were distributed across chromosomes 1-10, with chromosome 1 having the most (12). Figure 4 A) (Table 4). A total of 84 significant SNPs were found in the 22 Yanshan samples, with the highest P-value being 6.50. These SNPs were distributed on chromosomes 1-10, with the 25 most significant SNPs located on chromosome 3. Figure 4 B). A total of 76 significant SNPs were found in Yanshan, with the highest P-value being 6.66. These SNPs were distributed on chromosomes 1 through 10. The 15 most significant SNPs were located on chromosome 1. Figure 4 C). A total of 81 SNPs were found in the best linear unbiased prediction, with the highest P-value being 6.69. These SNPs were distributed on chromosomes 1-10, with the 12 most significant SNPs located on chromosomes 1 and 3. Figure 4 D). Further screening identified 15 SNPs coexisting in more than three environments (including the best linear unbiased predictor) (Table 4). These SNPs were distributed across chromosomes 2, 3, 4, 6, 7, 8, and 9, with the most (5) on chromosome 3. In terms of p-value, S3-203999904 had the highest best linear unbiased predictor value (6.69); in terms of phenotypic variance explained, S6-164274636 had the highest (9.8%). These SNPs will be further used for QTL colocalization screening.

[0071] Table 4. SNPs shared by three or more environments

[0072]

[0073] 2.5 QTL mapping of maize white spot disease resistance

[0074] In QTL mapping, a total of 10 QTLs were detected on chromosomes 1, 3, 7, and 8 of population 1. Figure 5 In population A, the logarithmic odds ranged from 2.64 to 4.60, explaining 5.45% to 9.89% of the phenotypic variation. Additive effects ranged from -0.47 to 0.51, with 5 positive and 5 negative effects. Similarly, in population 2, 12 QTLs were identified on chromosomes 1, 2, 3, and 10. Figure 5 (B) The log odds values ​​of these QTLs ranged from 2.56 to 4.88, and the phenotypic variance explained ranged from 5.58% to 10.81%. The additive effects ranged from -0.65 to 0.58, including 3 positive effects and 9 negative effects (Table 5).

[0075] Further screening revealed overlap of multiple QTLs within two intervals. Interval 1, located on chromosome 3 (210173589-215907098 bp), showed overlap of qMWS3-2, qMWS3-3, qMWS3-4, and qMWS3-5. Interval 2, located on chromosome 3 (218750741-225913893 bp), also showed overlap of qMWS3-3, qMWS3-4, and qMWS3-6. Notably, all five QTLs (qMWS3-2, qMWS3-3, qMWS3-4, qMWS3-5, and qMWS3-6) exhibited a negative additive effect, indicating that the resistant parent (TML139 or YML32) played a role in reducing disease severity phenotypes and increasing population resistance. These two intervals will be further used for SNP co-location screening.

[0076] Table 5. Significant QTLs found in two subgroups in different environments

[0077]

[0078] 2.6 Colocalization and Candidate Gene Identification

[0079] By comparing the locations of overlapping QTLs and SNPs in multiple environments, it was found that qMWS3-2, qMWS3-3, qMWS3-4, and qMWS3-5 co-located with S3-210892574 and S3-211477681 (Table 6). Among them, S3-210892574 had a higher P-value than S3-211477681 in all three environmental species, so S3-210892574 was selected as a candidate SNP. Gene search was performed within a 20kb range upstream and downstream. Zm00001eb155730 and Zm00001eb155740 genes were identified at distances of 3164bp and 6807bp downstream of S3-210892574, respectively. Figure 6 A). Among them, 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, the Zm00001eb155730 gene was selected as a candidate gene for further analysis.

[0080] Table 6. Co-localization results of GWAS and QTL

[0081]

[0082] 2.7 Haplotype Analysis of Candidate Genes

[0083] Further haplotype analysis was performed on the candidate gene Zm00001eb155730 to investigate the impact of SNP changes on the phenotype of the recombinant inbred lines. Mutations at bases 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 within the Zm00001eb155730 gene range resulted in three major haplotypes in the study population (haplotype 1: ACCGAG, haplotype 2: GCCCAT, haplotype 3: GCTGGG). Figure 6 B), the sample sizes for haplotype 1, haplotype 2, and haplotype 3 in the recombinant inbred lines were 49, 80, and 42, respectively. Figure 6 C). In the best linear unbiased predictions, the phenotype of haplotype 1 was significantly lower than that of haplotypes 2 and 3. Figure 6 (D) indicates that haplotype 1 of Zm00001eb155730 has the effect of reducing maize white spot disease and improving resistance. Further investigation 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 the higher maize white spot disease resistance in population 2 is related to haplotype 1.

[0084] The gene sequence of Zm00001eb155730 is shown in SEQ ID NO.1 below:

[0085] SEQ ID NO.1:

[0086]

[0087] The amino acid sequence of Zm00001eb155730 is shown in SEQ ID NO.2 below:

[0088] SEQ ID NO.2:

[0089] MAASGRWRRLRTLGRGASGAVVSLASDAASGELFAVKSAGASGAATLRREHAVLRGLRSPHVVRCVGGGEGADGSYQVFLEYAPGGSVADAVARGGGALEERAIRALAADVL RGLAYLHGRSVVHGDVKARNVLLGADGRARLADFGCARTPGFSARRPLGTPAFMAPEVARGEAQGPAADVWALGCTVVEMATGRAPWGGADADVLAAVHRIGYTDAVPDAP SWMSAEARDFLARCFARDAAERWTAAQLLEHPFVAAPCHGHGDHEAPRVSPKSTLDAAFWEAEDDDDDADEAVSASASERIKSLACSACALPDWDGEDGWIEVLGDQQRVEV CGAVQVARSAPGKVSSVLAVPAGEMDVGGGGGGGGDELEAEDVSFGGEVPGSADASAERQKKRYLILRSHYCHVLSCQLVPCNLPLVVVNNAIKLWVPTNVLLCRSVRFPLS.

[0090] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The application of a Zm00001eb155730 gene in maize breeding, characterized in that, The gene is used in marker-assisted breeding to regulate resistance to maize white spot disease. 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 Zm00001eb155730 gene is shown in SEQ ID NO.

2. The nucleotide sequence of the Zm00001eb155730 gene corresponds to positions 210895739-210897400 on maize chromosome 3. Resistance to maize white spot disease is regulated by mutations in the bases at positions 210896020, 210896024, 210896089, 210896095, 210896135, and 210896137 on maize chromosome 3, which are A, C, C, G, A, and G, respectively.

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