Corn chromosome 4 white spot disease resistance-related molecular marker and application

Abstract

The application belongs to the technical field of molecular marker assisted breeding, and particularly relates to a corn chromosome 4 white spot disease resistance related molecular marker and application, specifically, the application provides a corn white spot disease resistance related molecular marker, the molecular marker gene is a Zm00001eb168510 gene and / or a sequence as shown in SEQ ID NO:1, a CDs sequence of the Zm00001eb168510 gene is as shown in SEQ ID NO:2, through years of experiments in multiple points, the application is co-located to a white spot disease resistance QTL qMWS4-4 interval and a resistance SNP:4-14146725 and an associated gene Zm00001eb168510 on a corn chromosome 4, the SNP site can be located in different environments through QTL linkage analysis and GWAS correlation analysis, and can explain 15.14% of the phenotype variation; the associated candidate functional gene mainly encodes a cysteine protease superfamily protein, the cysteine protease superfamily contains a deubiquitinating enzyme, and plays an important role in plant development and adversity response.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of molecular marker assisted breeding, and particularly relates to a molecular marker site SNP:4-14146725 on the 4th chromosome of corn and application of a candidate functional gene Zm00001eb168510 associated with the molecular marker site in improving white spot disease resistance of corn. BACKGROUND

[0002] Corn (Zea mays L.) is one of the important food crops, which directly affects the development of world food security and animal husbandry. In recent years, corn white spot disease caused by Pantoea ananatis is a leaf disease that seriously affects corn production. The disease was first reported in India in 1965, and it was prevalent in Brazil and the United States in the 1980s and 1990s. It is reported that severe white spot disease can cause more than 60% reduction in corn yield in Brazil. Since mid-July 2020, white spot disease has broken out in the southwest corn ecological zone of China, especially in Yunnan, causing large-scale yield reduction of corn in some areas, with a loss of 10% to 50%, seriously endangering corn production and leading to a significant decline in farmers' income. Since 2023, the disease has spread to the main corn producing areas in the north, and now it has become a nationwide corn disease. At the early stage of corn white spot disease, lesions appear on the basal leaves, and then spread rapidly to the upper part of the plant, appearing a faded green appearance at the later stage of the disease, thereby affecting plant carbon cycle and photosynthesis. If part of the leaves are infected before the corn filling stage, it can cause a significant decrease of 40% in net photosynthetic rate, thereby leading to the early end of the reproductive maturation stage, and finally causing corn ear malformation, low seed yield, and serious reduction in corn yield and quality.

[0003] At present, the prevention and control of white spot disease mainly focuses on optimizing field tillage system and using agricultural chemical agents. Recent studies have shown that white spot disease resistance is largely dependent on genetic factors. Therefore, breeding white spot disease resistant varieties from a genetic perspective is the most economical and effective way to control the disease. However, since white spot disease is a newly broken out corn disease in China, the functional genes related to white spot disease resistance have not been cloned. Based on genome-wide association analysis and QTL mapping, the present application uses tropical corn inbred lines (YML32, TRL418, CML171, TML139, YML226, NK40-1) as resistant parents, and crosses them with temperate susceptible excellent inbred line Ye107, and then selects 6 F9 recombinant inbred lines (RIL) through 9 generations of single seed descent selfing. These RILs are phenotyped by planting in Yanshan County, Yunnan Province for three consecutive years, and the functional genes significantly related to corn white spot disease resistance are identified through GWAS analysis and linkage analysis combined with high-quality SNPs of RIL populations. SUMMARY

[0004] The main purpose of the present application is to provide a corn chromosome 4 white spot disease resistance related molecular marker site SNP: 4-14146725 and the application of the candidate functional gene Zm00001eb168510 associated therewith in improving the corn white spot disease resistance, so as to assist the improvement of the corn white spot disease resistance, avoid the complicated and time-consuming resistance screening process, shorten the breeding period, and improve the efficiency of the corn white spot disease resistance molecular breeding.

[0005] Specifically, the present application provides the following technical solutions:

[0006] In one aspect, the present application provides a molecular marker related to corn white spot disease resistance, wherein the molecular marker gene is Zm00001eb168510 gene and / or a sequence as shown in SEQ ID NO: 1, and the CDs sequence of the Zm00001eb168510 gene is as shown in SEQ ID NO: 2.

[0007] Further, the molecular marker of the present application, i.e., the sequence as shown in SEQ ID NO: 1, has a T / C polymorphism at the 201st site from the 5' end; the Zm00001eb168510 gene sequence corresponds to the sequence of corn Chr4: 14186915-14205314, and has a T / C polymorphism at the 14187198th site from the 5' end; and / or the expression amount of the Zm00001eb168510 gene or its translated protein is positively correlated with the corn white spot disease resistance.

[0008] In still another aspect, the present application provides a molecular marker associated with resistance to Goss' Wilt in maize, said molecular marker is a SNP locus, including at least one of 14187198, 14187379, 14189243, 14189271, 14189273, 14189298, 14190223, 14190242, 14190268, 14190492, 14190497, 14190498, 14193823, 14193854, 14193910, 14193911, 14193925, 14194315, 14194384, 14194394, 14194412, 14194456, 14194500, 14194562, 14194574, 14194666, 14194669, 14194670, 14194759, 14194837, 14194851, 14199719, 14199734, 14199742, 14199751, 14201833, 14201844, 14201888, 14201909 or 14202725 on chromosome 4 of the reference genome version Zm-B73-REFERENCE-NAM-5.0.

[0009] Further, the present application provides any of the following applications of any of the described molecular markers,

[0010] a) analysis of genetic diversity of maize for resistance to Goss' Wilt; b) construction of a molecular genetic map of maize for resistance to Goss' Wilt; c) genome-wide association analysis of maize for resistance to Goss' Wilt; d) identification of maize varieties for resistance to Goss' Wilt; e) marker-assisted selection breeding of maize for resistance to Goss' Wilt; f) genome-wide selection breeding of maize for resistance to Goss' Wilt.

[0011] Further, the present application provides a product for detecting any of the described molecular markers, said product includes a reagent, a kit or a gene chip, said product detects the genotype of the molecular marker, or detects the expression amount of the sequence as shown in SEQ ID NO: 2 or its translated protein.

[0012] Further, the present application provides a product for detecting any of the described molecular markers, said product includes a product prepared by PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization method, TaqMan probe method, ARMS-PCR method or KASP method, said product detects the genotype of the molecular marker, or detects the expression amount of the sequence as shown in SEQ ID NO: 2 or its translated protein.

[0013] Further, the present application provides any of the following applications of the described product,

[0014] a) white spot disease resistance corn genetic diversity analysis; b) white spot disease resistance corn molecular genetic map construction; c) white spot disease resistance corn genome-wide association analysis; d) white spot disease resistance corn variety identification or auxiliary identification; e) white spot disease resistance corn molecular marker assisted selection breeding; f) white spot disease resistance corn whole genome selection breeding.

[0015] In still another aspect, the present application provides a method for screening white spot disease resistant corn, taking corn samples to be detected, detecting the sequence of the gene as shown in SEQ ID NO: 1, and when the base at position 201 from the 5' end of the sequence shown in SEQ ID NO: 1 is C, obtaining a corn variety with white spot disease resistance.

[0016] In still another aspect, the present application provides a method for screening white spot disease resistant corn, taking corn samples to be detected, detecting the sequence of the gene as shown in SEQ ID NO: 1, and when the base at position 201 from the 5' end of the sequence shown in SEQ ID NO: 1 is C, obtaining a corn variety with white spot disease resistance.

[0017] In still another aspect, the present application provides a method for screening white spot disease resistant corn, taking corn samples to be detected, detecting the sequence of the gene as shown in SEQ ID NO: 1, and when the base at position 201 from the 5' end of the sequence shown in SEQ ID NO: 1 is C, obtaining a corn variety with white spot disease resistance.

[0018] In still another aspect, the present application provides a method for screening white spot disease resistant corn, taking corn samples to be detected, detecting the sequence of the gene as shown in SEQ ID NO: 1, and when the base at position 201 from the 5' end of the sequence shown in SEQ ID NO: 1 is C, obtaining a corn variety with white spot disease resistance.

[0019] Technical effects achieved by the present application:

[0020] The present application proves that the white spot disease resistance QTL qMWS4-4 interval of corn chromosome 4 and the resistance SNP: 4-14146725 (i.e. the base at position 201 from the 5' end of the sequence shown in SEQ ID NO: 1) exist in the present application, and have a strong phenotype explanation rate for corn white spot disease resistance. The associated gene Zm00001eb168510 (the CD sequence is shown in SEQ ID NO: 2) is highly expressed in corn leaves, which proves that it is related to corn leaf spot disease. The resistance of corn white spot disease is a quantitative trait controlled by multiple genes, and the technical scheme provided by the present application provides a new method for cultivating new varieties of corn resistant to white spot disease by using modern molecular breeding technology, which can assist in improving the resistance of corn white spot disease, avoid the complicated and time-consuming resistance screening process, shorten the breeding period, and improve the efficiency of corn white spot disease resistance molecular breeding. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1Leaf images of maize with different grades of white spot disease; HR: highly resistant to white spot disease, R: resistant to white spot disease, M: moderately resistant to white spot disease, S: susceptible to white spot disease, HS: highly susceptible to white spot disease.

[0022] Figure 2 A heatmap showing the correlation of vitiligo resistance in six groups; it describes the overall correlation of RILs in the six groups under different environments. The narrower the circle and the redder the color, the stronger the correlation.

[0023] Figure 3 SNP density map and LD decay map; (a) Chromosome-specific SNP density within a 1Mb interval, with the vertical axis representing chromosomes and the horizontal axis representing the position on each chromosome. The redder the color of the corresponding position, the more variant sites there are. (b) Genome-wide LD decay (r2) of all chromosomes in 941 maize RILs as a function of physical distance (Kb).

[0024] Figure 4 Colocation analysis of vitiligo resistance QTL qMWS4-4 and resistance site SNP:4-14146725;

[0025] Figure 5. A diagram showing the three haplotypes of the vitiligo resistance candidate gene Zm00001eb168510;

[0026] Figure 6 Analysis of candidate gene Zm00001eb168510 for resistance to white spot disease; (a) Overall differences in resistance levels of the three haplotypes to white spot disease in maize, * indicates p<0.05, **** indicates p<0.0001; (b) Relative positions of Zm00001eb168510 and SNP:4-14146725, and base and amino acid variations in the resistant parents TRL418 and TML139; (c) Expression levels of the Zm00001eb168510 gene in different maize tissues, with the red box indicating the expression of the Zm00001eb168510 gene in leaves. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0028] Based on genome-wide association analysis and QTL mapping, this invention uses tropical white spot disease resistant maize inbred lines (YML32, TRL418, CML171, TML139, YML226, NK40-1) as resistant parents, crosses them with temperate susceptible superior inbred line Ye107, and selects 6 F9 recombinant inbred lines (RILs) after 9 consecutive generations of single-seed self-pollination. These RILs were planted in Yanshan County, Yunnan Province for three consecutive years for phenotypic identification. Based on the high-quality SNPs of the RIL population, GWAS and linkage analysis were used to identify the SNP locus SNP:4-14146725 (i.e., the 201 bp site from the 5′ end of the sequence shown in SEQ ID NO:1) and its associated functional gene Zm00001eb168510 (Chr4:14186915-14205314) that is significantly associated with maize white spot disease resistance. The CDs sequence is shown in SEQ ID NO:2. Molecular breeding can be used to apply this resistance gene to maize white spot disease resistance breeding.

[0029] Example 1

[0030] 1. Experimental Materials and Design

[0031] This experiment selected six tropical maize inbred lines (YML32, TRL418, CML171, TML139, YML226, and NK40-1) resistant to white spot disease and exhibiting wide genetic variation as female parents. The superior temperate maize inbred line Ye107, susceptible to white spot disease, was used as the common parent for hybridization (the pedigree, heterosis groups, ecological groups, and related resistance levels of the seven parents are detailed in Table 1). Through single-seed propagation and nine consecutive generations of self-pollination, six recombinant inbred line (RIL) populations were generated: pop1 (YML32 × Ye107), pop2 (TRL418 × Ye107), pop3 (TRL418 × Ye107), pop4 ... The RIL lines were initially 200 lines each, including Ye107, pop3 (CML171×Ye107), pop4 (TML139×Ye107), pop5 (YML226×Ye107), and pop6 (NK40-1×Ye107). However, due to environmental selection, inbreeding depression, and other factors, 904 RIL lines were ultimately available for this study (pop1: 145; pop2: 141; pop3: 147; pop4: 160; pop5: 152; pop6: 159). These RIL lines were planted in Yanshan County, Yunnan Province (23°19'-23°59'N, 103°35'-104°45'E) in 2021, 2022, and 2023. The experimental design was a completely randomized block design, with 14 plants per row, a row length of 4m, and a plant spacing of 25cm, and the experiment was conducted according to standard farmland management.

[0032] Table 1. Pedigrees, ecotypes, and vitiligo resistance of the seven parental lines used in the experiment.

[0033]

[0034] 2. Disease severity score

[0035] Phenotypic identification of white spot disease was conducted on multi-parental RIL populations in Yanshan County, Yunnan Province, China in 2021, 2022, and 2023 (different environmental years are abbreviated as 21YS, 22YS, and 23YS). White spot disease outbreaks in maize occur annually from early August to late September, when maize is in the grain-filling to maturity stage, which is suitable for resistance assessment. The severity grading of white spot disease follows the symptom identification criteria outlined in the "Handbook of Maize Diseases and Pests," and the average severity of disease in a single plot is evaluated on a scale of 1 to 9. Figure 1 As shown in Table 2.

[0036] Table 2. Criteria for classifying the severity of maize white spot disease

[0037]

[0038] 3. Phenotypic identification and statistical analysis

[0039] Descriptive analysis of phenotypic data was performed using SPSS (SPSS Statistics) and ORIGIN (Origin 2022) software. Measures such as mean, minimum, maximum, standard deviation (SD), coefficient of variation (CV), skewness, and kurtosis were calculated. The frequency distribution of the phenotypic data was determined using SPSS software. Kurtosis and skewness were used to assess the normality of the frequency distribution, and broad-sense heritability was calculated according to the method outlined by Knapp et al.

[0040] 4. Whole genome resequencing

[0041] DNA was extracted from maize seedling leaves using a modified CTAB method, and whole-genome resequencing was performed. DNA libraries were prepared according to standard procedures and sequenced using Illumina Hi Seq. TM The platform performed sequencing. Subsequently, the filtered readings were aligned to the maize reference genome B73_RefGen_v5 (full name: Zm-B73-REFERENCE-NAM-5.0) to identify SNP markers, and they were annotated using SNPeff software.

[0042] 5. Population structure analysis and LD decline

[0043] The distance matrix was calculated using TreeBeST (version: Treebest-1.9.2), and a phylogenetic tree was constructed based on this matrix using the neighbor-joint (NJ) method. To ensure accuracy, the bootstrap values ​​were obtained through up to 1000 calculations. PCA analysis was performed using GCTA, and the first two PC values ​​were used to create a two-dimensional representation of the analyzed samples. The computational linkage disequilibrium (r) between pairwise labels was calculated using PopLDdecay software. 2 The software's built-in script, Plot_OnePop.pl, was used to plot the LD decay.

[0044] 6. Genome-wide association analysis

[0045] Genome-wide association analysis (GWAS) was performed using GEMMA software (http: / / www.xzlab.org / software.html) based on an analysis of 6,390,967 high-quality SNPs. This analysis employed a mixed linear model (MLM) for GWAS, considering phenotypes across all environments and the best unbiased linear uptake (BLUP). A significance threshold of log10(p) > 5 was used to identify significant SNPs associated with maize white spot disease resistance. SNPs meeting or exceeding the threshold were extracted using bedtools v1.7. Based on the Zm-B73-REFERENCE-NAM-5.0 reference genome and annotation information, candidate genes associated with maize white spot disease resistance were screened within a 50 kb range upstream and downstream of significant SNPs.

[0046] 7. Construction of genetic linkage maps

[0047] Linkage maps were constructed using JoinMap 4.0 software, and the LOD threshold was determined using a 1000-permutation test. A QTL was considered significant if the LOD threshold was ≥2.5. Linkage groups were defined based on an LOD threshold ≥2.5, and genetic linkage maps were constructed using eligible SNP markers. Genotyping filtering of progeny markers was performed based on a 0.8 integrity score and a 0.001 partial segregation score, resulting in six population markers. Binary markers were then plotted every 15 non-linked markers within each population to obtain the final population markers. JoinMap 4.0 was used to sort the binary markers for each population, and the Kosambi function was used to calculate the genetic distance (cM) between markers.

[0048] 8QTL Positioning Analysis

[0049] QTL mapping was performed using the Composite Interval Mapping (CIM) method in Windows QTL Cartographer 2.0. Phenotypic data of vitiligo were integrated into a high-density genetic linkage map, excluding SNP markers with a deletion rate ≥0.2 and loci with minor allele frequencies below 0.05. A 0.21 cM threshold was used to identify parental polymorphic QTLs. Thresholds were determined using 1000 random permutations with 95% confidence intervals. The LOD threshold associated with flanking markers was set to 2.5 to identify QTLs controlling MWS.

[0050] 9 candidate gene analysis

[0051] Haploview v4.2 software was used to perform candidate gene haplotype analysis. Based on the candidate gene ID, the corresponding gene sequence was extracted from the reference genome Zm-B73-REFERENCE-NAM-5.0 and compared with the parental TRL418 and TML139. The corresponding coding regions and amino acid variation information were extracted, and the functional gene motif was predicted using the MEME online software (https: / / meme-suite.org / meme / tools / meme).

[0052] Results Analysis

[0053] 1. Phenotypic Data Analysis

[0054] Data on vitiligo resistance phenotypes of six RIL populations were collected over three years in Yanshan and descriptive statistical analysis was performed. The results showed that the coefficients of variation for the six RIL populations ranged from 0.25 to 0.79 over the three years, indicating differences among the samples. Simultaneously, the average vitiligo severity of the plants in the six RIL populations ranged from 2.9 to 5.81. The absolute values ​​of the skewness and kurtosis coefficients of the six populations over the three years were close to 1, indicating that the vitiligo resistance phenotypes of the tested populations conformed to a normal distribution and were consistent with quantitative trait characteristics during the three-year resistance identification. Further analysis showed that the heritability of vitiligo severity in each RIL population ranged from 76.47% to 98.79%, and there was a strong correlation (0.46-0.98) between the resistance phenotypes of the same population under different environments. Figure 2 The results indicate that the RIL lines showed strong consistency in resistance to white spot disease under different environments. Phenotypic identification results show that maize white spot disease resistance has high heritability, and maize white spot disease resistance is mainly determined by genes.

[0055] 2SNP density and LD decay

[0056] Whole-genome resequencing identified 6,390,967 high-quality SNPs across the entire genome. These SNPs were relatively evenly distributed across the 10 chromosomes of maize. Figure 3a) The number of SNPs identified on chromosomes 1 through 10 is as follows: 1354402, 699661, 716354, 880880, 668865, 485243, 563392, 545220, 472482, and 462679. Chromosome 1 has the highest number of SNPs, while chromosome 10 has the lowest. The 6,390,967 identified SNPs were used to assess linkage disequilibrium (LD) decay in the association-mapped population. Figure 3 As shown in b, when r 2 When the rate of decline tends to level off, the physical distance is approximately 50 kb. Therefore, we selected a 50 kb range upstream and downstream of significant SNP sites as the criterion for screening candidate genes.

[0057] 3. Group Structure Analysis

[0058] Phylogenetic analysis of the population was performed using TreeBeST, dividing the 941 RIL lines into 6 subpopulations. Principal component analysis (PCA) using GCTA subsequently divided the 941 RIL lines into 6 subpopulations, consistent with our 6-RIL population analysis. Some mixed or overlapping families were observed, possibly due to the shared parent Ye107 among the 6 subpopulations, and gene introgression during breeding. Therefore, the possibility of false positives in GWAS due to population structure needs to be considered in subsequent analyses.

[0059] 4. Genome-wide association analysis

[0060] GWAS analysis was performed using GEMMA software (http: / / www.xzlab.org / software.html) on vitiligo resistance phenotype data of 941 RIL lines in a multi-parent population over three years in Yanshan (YS) and 6,390,967 high-quality SNPs obtained from whole-genome resequencing. A mixed linear model (MLM) was used to simultaneously correct for population structure and individual kinship, identifying multiple SNPs. Among the significant SNPs located, 4-14146725 SNPs showed significant co-location in three or more environments (21YS, 22YS, 23YS, BLUP). Because artificial populations may cause false positives in GWAS analysis, QTL analysis will be used to further identify candidate loci to improve accuracy.

[0061] 5. QTL positioning

[0062] Using the white spot disease resistance phenotypes of different populations under different environments, combined with a constructed high-density genetic map, QTL mapping for maize white spot disease resistance was performed. The mapping results were compared with the GWAS analysis results. Finally, it was identified that the maize white spot disease resistance QTL qMWS4-4 interval (Chr4:11404224-48099500) from the NK40-1×Ye107 population overlapped with the resistance locus SNP:4-14146725 mapped by GWAS. Figure 4 This QTL explains 6.55% of the resistance phenotype variation. 6. Candidate Gene Discovery

[0063] This invention uses the Zm-B73-REFERENCE-NAM-5.0 reference genome to screen the 50Kb region upstream and downstream of the significant SNP:4-14146725 within the qMWS4-4 interval. Candidate genes were annotated and functionally predicted using databases such as MaizeGDB, InterPro, UniProt, and NCBI, identifying the candidate functional gene Zm00001eb168510 (Chr4:14186915-14205314). SNP:4-14146725 was co-located in 21YS, 22YS, 23YS, and BLUP, explaining 15.14% of the phenotypic variation, and this site is contained within the QTL qMWS4-4 interval. Figure 4 The results indicate that the SNP within the qMWS4-4 region is a highly reliable molecular marker. The associated gene, Zm00001eb168510, encodes a cysteine ​​protease superfamily protein, which contains deubiquitinase (DUB), an enzyme that plays a crucial role in plant development and stress responses.

[0064] 7. Candidate gene analysis

[0065] To clarify the correlation between the selected candidate functional gene Zm00001eb168510 and resistance to maize white spot disease, analysis was performed on Zm00001eb168510. The analysis revealed that the gene is associated with the following gene positions: 14187198, 14187379, 14189243, 14189271, 14189273, 14189298, 14190223, 14190242, 14190268, 14190492, 14190497, 14190498, 14193823, 14193854, 14193910, 14193911, and 14193... Mutations at base positions 925, 14194315, 14194384, 14194394, 14194412, 14194456, 14194500, 14194562, 14194574, 14194666, 14194669, 14194670, 14194759, 14194837, 14194851, 14199719, 14199734, 14199742, 14199751, 14201833, 14201844, 14201888, 14201909, and 14202725 resulted in the study population exhibiting three main haplotypes. Figure 5)

[0066] Hap1:TCACTAGTAGGGATTGCCCCGGCGCTAGTAGAGATAAGAC,

[0067] Hap2: CCACTAGTAGGGGCTGTTCGGGCGCGAGTAAAGATAGGGC,

[0068] Hap3: CCGCTGACGAAAGCTGTCTCGTTGCGAGCGGAAAATAAGGA),

[0069] Among them, plants with haplotype Hap2 (CCACTAGTAGGGGCTGTTCGGGCGCGAGTAAAGATAGGGC) showed better resistance to maize white spot disease. Figure 6 a). By comparing the gene sequences in the resistance materials TRL418 and TML139, a specific T / C mutation was found at base position 14187198 in both TRL418 and TML139, resulting in a mutation from cysteine ​​to arginine in the corresponding amino acid sequences of the resistance materials TRL418 and TML139, respectively. Figure 6(b) These amino acid mutations alter the protein motif. Studies of gene expression at different stages and in different tissues of maize revealed that the expression level of the Zm00001eb168510 gene was higher in leaves than in other tissues. Figure 6 c) This further demonstrates that the gene is closely related to maize leaf disease resistance and can be used as a molecular marker for breeding maize resistant to white spot disease.

[0070] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. Use of a molecular marker associated with resistance to corn lesion nematode in a lesion nematode resistant corn molecular marker assisted selection breeding program, characterized in that, The molecular marker is a SNP site, including 14187198, 14187379, 14189243, 14189271, 14189273, 14189298, 14190223, 14190242, 14190268, 14190492, 14190497, 14190498, 14193823, 14193854, 14193910, 14193911, 14193925, 14194315, 14194384, 14194394, 14194412, 14194456, 14194500, 14194562, 14194574, 14194666, 14194669, 14194670, 14194759, 14194837, 14194851, 14199719, 14199734, 14199742, 14199751, 14201833, 14201844, 14201888, 14201909 and 14202725 sites on the corn chromosome 4, wherein the reference genome version is Zm-B73-REFERENCE-NAM-5.0; when the SNP sites of the molecular marker are CCACTAGTAGGGGCTGTTCGGGCGCGAGTAAAGATAGGGC in turn, it is determined that the corn variety has resistance to white spot disease.

2. Use of the product of detecting the molecular marker as claimed in claim 1 in the marker-assisted selection breeding of white spot disease resistant corn, characterized in that, The product includes reagents, kits or gene chips, which detect the genotype of the molecular marker.

3. A method of screening white spot disease resistant corn, characterized by, A corn sample to be detected is taken, and the molecular marker in claim 1 is detected; when the SNP sites of the molecular marker are CCACTAGTAGGGGCTGTTCGGGCGCGAGTAAAGATAGGGC in turn, it is determined that the corn variety has resistance to white spot disease.

Images