Molecular marker related to corn ear thickness on corn chromosome 7 and application of molecular marker

Abstract

The invention relates to the field of corn molecular marker assisted breeding, particularly discloses a molecular marker related to corn ear thickness on a corn chromosome 7 and application of the molecular marker, and particularly provides a corn ear thickness molecular marker, the molecular marker is a Zm00001eb303690 gene with the sequence shown as SEQ ID NO: 1, and the gene can be applied to corn molecular marker assisted breeding. According to the invention, the GWAS is utilized to analyze SNP21950015 which is co-localized to a No.7 chromosome in a plurality of environments and is significantly related to the ear thickness, then a functional gene Zm00001eb303690 for regulating and controlling the ear thickness is excavated, the ear thickness character is regulated and controlled by a plurality of genes, the functional gene is subjected to molecular marking, the genotype of the functional gene is analyzed, germplasm resources can be comprehensively and efficiently evaluated and utilized, and the application prospect is wide. Materials with target genes or characters are rapidly screened out, and the utilization rate of genetic resources is increased.

Description

Technical Field

[0001] The present invention relates to the field of maize molecular marker-assisted breeding, and specifically relates to a molecular marker related to maize ear diameter on chromosome 7 of maize and its application. Background Art

[0002] As one of the important food crops in the world, maize plays a crucial role in global agricultural production. With the continuous growth of the global population and the increasing demand for food, the production of maize faces huge challenges.

[0003] The yield per plant of maize is closely related to inflorescence development. In particular, the optimization of ear traits plays a decisive role in increasing the yield per unit. Research shows that ear diameter, as an important part of ear traits, has a significant impact on the yield per plant of maize. A thicker ear can accommodate more grains per row or larger grain size, thus directly increasing the yield per unit. Therefore, the optimization of ear diameter traits is considered the key breakthrough point for increasing the yield per unit of maize and is also the key direction for future maize breeding.

[0004] The performance of crop traits not only depends on the action of individual genes but also is affected by the interaction between genes (epistasis). Under different environmental conditions, the contribution of genes to traits may vary. The purpose of the present invention is to identify new genes and SNPs related to ear diameter through genome-wide association analysis (GWAS) of a tropical maize multi-parent population by whole genome sequencing (WGS) technology, and to screen out tag SNPs through haplotype analysis, reducing redundant detection costs while retaining the integrity of genetic information during the GS or MAS breeding process, in order to provide innovative genetic resources and precise improvement strategies for a significant increase in the yield per plant of maize, thereby promoting the optimization and efficiency increase of maize production. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a molecular marker related to maize ear diameter on chromosome 7 of maize and its application, excavating the functional gene Zm00001eb303690 closely associated with maize ear diameter, providing more marker genes for molecular marker-assisted selection of maize with thicker ears, and thus more precisely screening germplasms with target traits.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0007] The present invention provides a maize ear diameter molecular marker, and the molecular marker is the Zm00001eb303690 gene shown in SEQ ID NO:1.

[0008] The expression level of the Zm00001eb303690 gene is positively correlated with maize ear diameter.

[0009] Further, when the bases at positions 21959583, 21959626, 21959747, 21959798, 21959805, 21959813, 21959824, 21959842, 21959869, 21959881, 21959950, 21959968, 21960001, 21960757, 21960775, 21960783, 21960822, 21961015, 21961082, 21961210, and 21961282 from the 5'-end of the gene successively present as TCCGTTGTGGGCGCTCAGGCC, maize presents the advantageous trait of thick ears.

[0010] Further, the present invention provides a product for detecting the molecular marker, and the product includes a kit, a reagent, or a gene chip. The product detects the genotype or expression level of the molecular marker.

[0011] Further, the present invention provides a product for detecting the genotype of the molecular marker, and the 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.

[0012] Further, the present invention provides the application of the product in identifying or assisting in identifying the thick ear trait of maize.

[0013] Further, the present invention provides the application of the molecular marker in any of the following: a) genetic diversity analysis of thick ear maize; b) construction of a molecular genetic map of thick ear maize; c) genome-wide association analysis of thick ear maize; d) identification or assisted identification of thick ear maize varieties; e) molecular marker-assisted selection breeding for thick ear or high-yield maize; f) genome-wide selection breeding for thick ear maize.

[0014] Further, the present invention provides a method for screening thick ear maize germplasm. Take a maize sample to be detected, detect the molecular marker, and screen the germplasm that conforms to the molecular marker genotype, which is the thick ear maize germplasm.

[0015] Further, the present invention provides a method for screening high-yield maize germplasm. Take a maize sample to be detected, detect the molecular marker, and screen the germplasm that conforms to the molecular marker genotype, which is the high-yield maize germplasm.

[0016] The term "Molecular Marker-assisted selection (MAS)" is a breeding technique that selects offspring lines with the help of molecular markers of target traits, and then obtains excellent individual plants containing the target gene.

[0017] The term "Genomic Selection (GS)" is a modern breeding technique for genetic evaluation and selection based on whole-genome marker information, aiming to predict the breeding value or phenotypic performance of individuals through high-density molecular markers, thereby accelerating the breeding process and improving the selection efficiency;

[0018] The term "GS-MAS" is a breeding strategy that combines Genomic Selection (GS) with Marker-assisted Selection (MAS). GS-MAS uses high-density marker information covering the whole genome and phenotypes to estimate the breeding value of individuals, while associating major and minor genes, and predicts and selects complex traits (low heritability, difficult to measure, etc.) at an early stage through breeding values, thereby shortening the generation interval, accelerating the breeding process, improving the selection accuracy, and saving costs.

[0019] The technical effects achieved by the present invention:

[0020] By using the temperate maize inbred line Ye107 with a thinner ear diameter as a common parent and crossing it with 6 tropical and subtropical maize inbred lines with a thicker ear diameter, the present invention constructs a maize multi-parent population with a significant difference in ear diameter thickness. Using GWAS analysis, SNP_21950015 significantly associated with ear thickness located on chromosome 7 was mapped, and then the functional gene Zm00001eb303690 regulating ear thickness was mined. Haplotype analysis showed that among 1107 RILs (recombinant inbred lines), Zm00001eb303690 had a total of 13 haplotypes (Hap1, Hap2,..., Hap13), and the ear thickness of Hap10 was significantly higher than the other 12 haplotypes. Therefore, Hap10 of the Zm00001eb303690 gene is a haplotype type that significantly increases ear thickness. The results of the present invention contribute to further studying the regulatory mechanism of maize ear thickness and providing more marker genes for marker-assisted selection of maize with a thicker ear, so as to more accurately screen germplasm with target traits. Description of the Drawings

[0021] Figure 1 It is the population structure diagram of 1107 RILs in the embodiment of the present invention; wherein: (a) Principal component analysis; (b) Unrooted tree; (C) Bayesian clustering diagram of 1107 RILs when K = 6; (d) LD decay diagram;

[0022] Figure 2GWAS results of ear diameter in the embodiments of the present invention; wherein: (a) GWAS results of ear diameter based on the phenotypic mean in Yanshan in 2022; (b) GWAS results of ear diameter based on the phenotypic mean in Yanshan in 2023; (c) GWAS results of ear diameter based on the phenotypic mean in Jinghong in 2024; (d) GWAS results based on the BLUP value of ear diameter.

[0023] Figure 3 Schematic diagram showing the relationship between significant SNPs and candidate genes in the embodiments of the present invention.

[0024] Figure 4 Analysis diagram of LD block of candidate genes.

[0025] Figure 5 Schematic diagram of mutation sites forming different haplotypes in the Zm00001eb303690 gene.

[0026] Figure 6 Differences of different haplotypes in subgroups are shown, where A is the percentage of difference and B is the box plot of difference.

[0027] Figure 7 Expression level diagram of the Zm00001eb303690 gene in different tissues. Detailed implementation manners

[0028] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. For purchased commodities in the test methods, if the specific conditions are not indicated, they shall be carried out according to the conventional conditions or the conditions recommended by the manufacturer. For reagents or instruments whose manufacturers are not indicated, they can all be obtained as conventional products through commercial purchase.

[0029] The gene sequence of Zm00001eb303690 marked in the present invention is shown in SEQ ID NO:1, corresponding to positions 21959514 - 21961325 from the 5′ end of chromosome 7 of the reference genome version Zm - B73 - REFERENCE - NAM - 5.0.

[0030] Example 1

[0031] 1. Experiment

[0032] 1.1 Plant materials and experimental design

[0033] The present invention uses the temperate excellent backbone maize inbred line Ye107 as the common male parent, and crosses it with 6 tropical and subtropical maize inbred lines with relatively thick ears (CML312, CML395, YML46, YML32, NK40-1, YML1218) respectively to form 6 F1 hybrid combinations. Subsequently, continuous self-crossing is carried out to the F9 generation by the single-seed descent method to construct a multi-parent population containing 1,107 recombinant inbred lines. The number of recombinant inbred lines contained in each sub-population of pop1-pop6 is 142, 148, 303, 178, 200, and 136 respectively (Table 1).

[0034] This experiment was carried out in Yanshan (YS) County, Yunnan Province, China in 2022 and 2023, and in Jinghong (JH) City, Yunnan Province, China in 2024. The experiment followed a completely randomized block design with three replicates at each location. The planting pattern of the experimental materials was set as a row length of 3 m, a row spacing of 0.70 m, 14 plants per row, and 2 rows per plot. The planting process was managed according to local standard agronomic practices. After the ear was mature, nine ears were randomly selected from each row to record the ear diameter, and finally the mean value was taken to determine the average ear diameter.

[0035] Table 1 Specific information of parents

[0036]

[0037] 1.2 Phenotypic data analysis

[0038] First, use Excel 2019 to preprocess the phenotypic data. Use SPSS26.0 to evaluate whether the phenotypic data conforms to the normal distribution, and calculate basic statistics to help intuitively understand the central tendency and dispersion degree of the data. Use R4.3.2 to draw a heat map to evaluate the correlation between phenotypic data under different sub-populations and environmental conditions. Use the aov() function for two-way analysis of variance to evaluate whether the effects of sub-populations and environments on phenotypic data are statistically significant. Use the lme4 software package to calculate the broad-sense heritability, and the formula is H = Vg / (Vg+(Ve / L)), where Vg represents the genetic variance, Ve is the residual variance, and L is the number of environments.

[0039] 1.3 Whole-genome sequencing

[0040] 1.3.1 Sample collection and DNA extraction

[0041] Healthy leaves were sampled during the maize seedling stage to jointing stage, and the collected leaf samples were immediately frozen in liquid nitrogen to prevent tissue degradation and DNA degradation. Tiangen Plant Genomic DNA Extraction Kit (Tiangen Biotech Co., Ltd., Beijing, China) was used to extract leaf DNA. Agarose gel was used to perform quality control on the extracted DNA samples to confirm the concentration (above 100 ng / μl) and purity (the A260 / A280 ratio should generally be between 1.8 - 2.0), ensuring that the extracted DNA was suitable for subsequent whole-genome sequencing operations.

[0042] 1.3.2 Library Construction and Sequencing

[0043] A Vibra-Cell series ultrasonic disruptor (Sonics & Materials, Inc., Connecticut, America) was used for DNA fragmentation, and gel electrophoresis was performed on the fragmented DNA samples to evaluate the quality (the ideal DNA fragment size should be between 200 - 500 bp). T4 DNA polymerase (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America) was used for end repair, and short DNA adapters were ligated to both ends of the DNA fragments. To ensure the quality of the library, Exonuclease I enzyme (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America) was used to remove DNA fragments that did not ligate the adapters. DNA fragments with a length of 200 - 500 bp (including the part of the adapter sequence) were selected by the Agencourt AMPure XP magnetic bead method. The library was amplified by PCR to increase the number of target DNA fragments. Finally, Qubit was used to quantitatively detect the concentration of the library, and the target concentration was usually between 10 - 20 nM. Bioanalyzer (Agilent Technologies, Santa Clara, California, America) was used to detect the size distribution of the library, and the fragment size was usually 200 - 500 bp.

[0044] All samples were sequenced using the Illumina NovaSeq 6000 platform (Illumina, Inc., San Diego, California, America). The sequencing mode was paired-end sequencing, the sequencing depth was 5×, and the sequencing results were stored as FASTQ format files. FastQC (Babraham Institute, Bioinformatics Group, Cambridge, United Kingdom) was used to evaluate the quality of the sequencing data. The evaluation criteria were as follows: (1) Q-score ≥ 20, (2) the GC content of the genome was 40% to 60%, (3) the proportion of repetitive sequences was less than 30%, (4) the proportion of N-containing was less than 1-2%, and (5) the read length was consistent with the expected length.

[0045] 1.4 Population stratification analysis

[0046] 1.4.1 Principal component analysis

[0047] To evaluate the population genetic structure and control the influence of population stratification on GWAS results, we used the GCTA software to perform principal component analysis (PCA). First, the genotype data were standardized, and the genetic similarity matrix between samples was calculated. Subsequently, the first few principal components were extracted based on this matrix, and these principal components were used to characterize the population structure.

[0048] 1.4.2 Unrooted tree construction

[0049] To further analyze the genetic relationships between subgroups, we used MEGA (Molecular Evolutionary Genetics Analysis) software to construct an unrooted tree to evaluate the genetic distances and kinship between subgroups. During the construction process, we used the Neighbor-Joining (NJ) method, which can efficiently and accurately reflect the genetic differentiation of the population by minimizing the total genetic distance between samples.

[0050] 1.4.3 Population genetic structure analysis

[0051] We used the ADMIXTURE software for population stratification analysis. This method infers the contribution proportions of different subpopulations in each individual's genome by assuming that each individual is composed of a mixture of components from multiple subpopulations. We first selected and set the expected number of subpopulations (K value) according to the research purpose. By trying different K values and selecting the optimal K value through cross-validation, overfitting or underfitting can be avoided. We analyzed the population structure under different K values and determined the genetic background of the samples according to the population stratification pattern.

[0052] 1.4.4 Linkage disequilibrium analysis

[0053] We used PopLDdecay software to assess the genetic linkage disequilibrium (LD) of the population. The purpose of LD analysis is to measure the degree of linkage disequilibrium between SNPs, that is, whether the genotypes between a pair of markers are independently inherited. In particular, the r2 value is often used to indicate the degree of LD between SNPs. 2 The higher the value, the stronger the linkage between the two SNPs and the closer the genetic association. PopLDdecay software was used to calculate the r between different SNP markers. 2 We further analyzed the LD decay at different genetic distances using the Plot_OnePop.pl script provided by PopLDdecay to generate visualizations of LD decay. Through these graphs, we can clearly display the LD structure at different positions in the population, identify high LD regions in the genome, and further help determine marker selection for GWAS and the location of genetic regions.

[0054] 1.5 Genome-wide association analysis

[0055] The GWS of maize ear thickness trait was performed using a mixed linear model (MLM) in the GEMMA software. In the model, population genetic structure was taken as a fixed effect, and individual kinship was taken as a random effect to correct the effects of population structure and kinship on trait performance. To reduce false positives and false negatives, the Bonferroni correction was used for multiple comparison correction. The P values ​​of all individual SNPs were compared with the significance level after Bonferroni correction. If the P value was less than p, the SNP was considered statistically significant. In addition, to ensure the reliability and accuracy of the results, the FDR correction was further used to reduce the error caused by multiple comparisons. Generally, a p-value less than 5×10^-8 was considered significant. For the identified significant SNPs, the ANNOVAR tool was used to annotate them functionally to determine the location, region, and mutation type of the variant site on the genome. In addition, the PLINK software was used to generate Manhattan plots and QQ plots to visualize the results of the association analysis, where the Manhattan plot evaluates the strength of the association between SNPs and traits, and the QQ plot evaluates the degree of deviation of the distribution of p-values ​​from the expected value.

[0056] 1.6 Candidate gene haplotype analysis

[0057] Haploview v4.2 software was used to perform haplotype analysis on candidate genes. For each gene, we analyzed the frequency distribution of different haplotypes, phenotypic differences, and their statistical significance. Based on the frequency distribution, box plots only showed the haplotypes with higher frequencies in the samples. Haplotypes with lower frequencies (≤1%) were not shown separately as their statistical impact on the final results was small and did not affect the interpretation of the main conclusions.

[0058] 2. Results

[0059] 2.1 Data analysis of maize ear diameter phenotype

[0060] The results showed that pop4 consistently exhibited high and stable ear diameter traits in all environments, while pop1 showed the lowest average ear diameter (Table 2). Statistical analysis of the ear diameter phenotype data of the six subgroups is shown in Table 2 below. The coefficient of variation (CV%) range of ear diameter among the six subgroups in each environment varied greatly, indicating significant differences in ear diameter among subgroups. The absolute values of skewness and kurtosis of ear diameter in different environments were generally less than 1. The broad-sense heritability range of ear row number among the six subgroups was 70.15% to 97.28%, indicating a strong genetic influence on this trait. The consistency of phenotypic variation in different environments highlighted the reliability of the phenotypic data for subsequent analysis. Affected by genetic and environmental factors, significant differences in ear diameter were observed among subgroups. This variability provided a solid foundation for further GWAS analysis and identification of important loci related to ear diameter.

[0061] Table 2 Data analysis of maize ear diameter phenotype

[0062]

[0063] Note: 22YS refers to Yanshan in 2022, 23YS refers to Yanshan in 2023, and 24JH refers to Jinghong in 2024. 2.2 Population stratification analysis

[0064] By observing the three-dimensional PCA plot, the clustering distribution of the samples was relatively clear, and the data could be roughly divided into 6 clusters, with each cluster representing a potential subgroup. Although most subgroups formed distinct cluster structures in three-dimensional space, there were overlapping regions between some clusters. For example, there was an obvious overlap between pop3 and pop6, which might indicate a high degree of similarity between these two subgroups in the main characteristic dimensions ( Figure 1 a).

[0065] In the unrooted tree analysis, although there was a certain degree of admixture between different clusters, the overall data could still be clearly divided into 6 main clusters ( Figure 1 b). This overlapping phenomenon might indicate that these clusters were relatively close in the feature space, reflecting their similarity in some key features.

[0066] In population structure analysis, when K = 6, the data was clearly divided into 6 subpopulations. Although there was still a certain degree of overlap between some samples, overall these 6 subpopulations were relatively distinct ( Figure 1 c). This overlap indicates that there may be overlapping regions between different subpopulations, and some samples have similar characteristics, resulting in their unclear belonging to certain subpopulations. Generally speaking, despite the overlap, these subpopulations can still better reflect the main structural characteristics of the population.

[0067] The LD decay in MPP was evaluated using 16,223,980 effective SNPs. The LD decay plot showed that as the physical distance between markers increased, LD decreased rapidly. When the r 2 value dropped to 0.24, the physical distance of LD decay was approximately 10 kb ( Figure 1 d).

[0068] 2.3 Genome-wide association analysis of ear diameter trait

[0069] The present invention used 16,223,980 effective SNPs to conduct GWAS analysis of maize ear diameter. The results showed that multiple significant SNPs related to maize ear diameter were identified on 10 chromosomes in all environments ( Figure 2 ). It is worth mentioning that we consistently identified 1 significant SNP related to maize ear diameter in all environments (22YS, 23YS, 24JH, BLUP): 7_21950015. Gene screening was carried out within 10 kb upstream and downstream of the relevant significant SNP and its vicinity. We consistently identified 1 gene Zm00001eb303690 ( Figure 3 , Table 3). Referring to its existing functional annotations for trait correlation assessment, the present invention believes that this is a candidate gene worthy of in-depth study.

[0070] Table 3 GWAS co-localized candidate genes

[0071]

[0072] 2.4 Haplotype analysis of candidate genes

[0073] As Figures 4 - 6, the haplotype analysis results of Zm00001eb303690 (the gene sequence of Zm00001eb303690 is shown in SEQ ID NO: 1, corresponding to positions 21,959,514 - 21,961,325 from the 5′ end of chromosome 7 of the reference genome version Zm - B73 - REFERENCE - NAM - 5.0) showed that a total of 13 different haplotypes (Hap1, Hap2, …, Hap13) were identified among 1107 RILs. The box plot only shows 7 haplotypes with relatively high frequencies in the samples. These haplotypes showed different frequency distributions in different populations studied. Hap10 had the highest phenotypic value and a relatively wide distribution frequency in pop4. In addition, there were significant differences between Hap10 and multiple haplotypes. Therefore, we concluded that Hap10 is the dominant haplotype regulating maize ear diameter.

[0074] For the specific loci of the haplotypes, with Zm - B73 - REFERENCE - NAM - 5.0 as the reference genome, the viewing order from the 5′ end of chromosome 7 of the genome version is from left to right as follows:

[0075] 21959583 (REF: T / ALT: A) 21959626 (REF: C / ALT: A)

[0076] 21959747 (REF: C / ALT: T) 21959798 (REF: G / ALT: A)

[0077] 21959805 (REF: T / ALT: G) 21959813 (REF: T / ALT: A)

[0078] 21959824 (REF: G / ALT: A) 21959842 (REF: T / ALT: G)

[0079] 21959869 (REF: G / ALT: C) 21959881 (REF: G / ALT: A)

[0080] 21959950 (REF: G / ALT: C) 21959968 (REF: C / ALT: T)

[0081] 21960001 (REF: G / ALT: A) 21960757 (REF: C / ALT: T)

[0082] 21960775 (REF: T / ALT: C) 21960783 (REF: C / ALT: T)

[0083] 21960822(REF:G / ALT:A)21961015(REF:G / ALT:A)

[0084] 21961082(REF:G / ALT:T)21961210(REF:G / ALT:C)

[0085] 21961282(REF:C / ALT:A)

[0086] The bases of different haplotypes are as follows:

[0087] Hap1:TCCGTTGTGGCCGCTCGGGGC

[0088] Hap2:TACAGAAGGACCACCTGATGA

[0089] Hap3:TCTAGTGGGGCCACCTGATGA

[0090] Hap4:TCCGTTGTGGGCGCTCGGGGC

[0091] Hap5:TCTAGAAGCGCTGTCTGATGA

[0092] Hap6:TACAGAAGGACCACTCAGGCC

[0093] Hap7:TCCGTTGTGGCCGCTCGGTGA

[0094] Hap8:ACTAGTAGCGCCGCTCGGGGC

[0095] Hap9:TCCAGTGGGGCCACTCAGGCC

[0096] Hap10:TCCGTTGTGGGCGCTCAGGCC

[0097] Hap11:TCCGTTGTGGCCGCTCAGGGC

[0098] Hap12:TCCGTTGTGGCCGCCTGATGA

[0099] Hap13:TCCAGAAGGACCACTCAATGA

[0100] The expression of the candidate gene Zm00001eb303690 in various tissues of maize was analyzed ( Figure 7 ). The results showed that the expression level of this gene was relatively high in the maize ear primordium, and it was further speculated that it was related to the development of maize ear diameter.

[0101] In summary, the identification of the Zm00001eb303690 gene as a functional gene for maize ear diameter molecular markers can develop allele-specific markers. Molecular markers can directly detect the genotypes of crops, accurately identify the presence or absence of target genes or traits, avoid the blindness and uncertainty in traditional selection, ensure the accuracy of offspring selection, and can be applied to genetic resource identification, MAS, genetic map construction, linkage mapping, and GS-MAS in breeding. Through MAS, the genotypes of crops can be identified quickly and accurately, avoiding the tediousness, time-consuming nature, and screening difficulty of traditional selection, greatly improving the selection efficiency, and accelerating the breeding process.

[0102] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A molecular marker for corn ear diameter, characterized in that: The molecular marker is the Zm00001eb303690 gene with a sequence as shown in SEQ ID NO:

1.

2. The molecular marker according to claim 1, characterized in that The expression level of Zm00001eb303690 gene was positively correlated with corn ear diameter.

3. The molecular marker according to claim 1, characterized in that When the 21959583rd, 21959626th, 21959747, 21959798, 21959805, 21959813, 21959824, 21959842, 21959869, 21959881, 21959950, 21959968, 21960001, 21960757, 21960775, 21960783, 21960822, 21961015, 21961082, 21961210 and 21961282nd bases from the 5′ end sequentially show TCCGTTGTGGGCGCTCAGGCC, corn shows the dominant trait of thick ear.

4. A method for detecting a product of a molecular marker according to claim 1, characterized in that: The product includes a kit, a reagent or a gene chip.

5. A product for detecting the molecular marker genotype according to claim 3, characterized in that: The products include products prepared by PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization, TaqMan probe method, ARMS-PCR method or KASP method.

6. Use of the product according to claim 4 or 5 in identifying or assisting in identifying the coarseness trait of corn ears.

7. Use of the molecular marker according to any one of claims 1 to 3 in any of the following: a) Genetic diversity analysis of coarse-ear corn; b) Construction of molecular genetic map of coarse-ear corn; c) Whole-genome association analysis of coarse-ear corn; d) Identification or auxiliary identification of coarse-ear corn varieties; e) Molecular marker-assisted selection breeding of coarse-ear or high-yield corn; f) Whole-genome selection breeding of coarse-ear corn.

8. A method for screening coarse corn germplasm, characterized in that: Take a corn sample to be tested, detect the molecular marker as described in claim 2 or 3, and screen the germplasm that meets the genotype of the molecular marker, that is, the coarse corn germplasm.

9. A method for screening high-yield corn germplasm, characterized in that: Take a corn sample to be tested, detect the molecular marker as described in claim 2 or 3, and screen the germplasm that meets the genotype of the molecular marker, which is the high-yield corn germplasm.

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