Molecular marker related to corn ear thickness on corn chromosome 1 and application of molecular marker

Abstract

The invention relates to the field of corn molecular marker-assisted breeding, and particularly discloses a corn ear thickness related molecular marker on a corn chromosome 1 and application of the corn ear thickness related molecular marker, and the corn ear thickness molecular marker is a Zm00001eb032370 gene with the sequence shown as SEQ ID NO: 1, and the Zm00001eb032370 gene is a Zm00001eb032370 gene with the sequence shown as SEQ ID NO: 2. According to the invention, the GWAS is utilized to analyze SNP181125354 which is jointly positioned to the chromosome 1 and is remarkably related to the ear thickness in multiple environments, then the functional gene Zm00001eb032370 for regulating and controlling the ear thickness is excavated, the SNP181125354 can explain 6.15% of the phenotypic variation of the ear thickness, and the generation interval can be shortened, the breeding process can be greatly accelerated, the selection precision can be improved and the cost can be saved through molecular marker breeding.

Description

Technical Field

[0001] The present invention relates to the field of maize molecular marker-assisted breeding, and in particular to a molecular marker on maize chromosome 1 related to maize ear diameter and an application thereof. Background Art

[0003] Corn yield per plant is closely related to inflorescence development. Optimizing ear traits, in particular, plays a decisive role in increasing yield per plant. Research has shown that ear diameter, a key component of ear traits, significantly impacts corn yield per plant. Thicker ears can accommodate more kernels per row or larger kernel sizes, directly increasing yield per plant. Therefore, optimizing ear diameter is considered a key breakthrough in increasing corn yield per plant and a key focus of future corn breeding.

[0004] The expression of crop traits depends not only on the effects of individual genes but also on the interactions between genes. The contribution of genes to traits may vary under different environmental conditions. The present invention aims to identify new genes and SNPs associated with ear diameter through genome-wide association analysis (GWAS) of tropical maize multi-parent populations using whole genome sequencing (WGS) technology, screen out tag SNPs through haplotype analysis, and reduce redundant testing costs during GS or MAS breeding while preserving the integrity of genetic information, in order to provide innovative genetic resources and precise improvement strategies for significantly improving maize yield per plant, thereby promoting the optimization and efficiency of maize production. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a molecular marker on maize chromosome 1 that is related to maize ear thickness and its application. The present invention uses whole genome sequencing (WGS) technology to perform genome-wide association analysis (GWAS) on tropical maize multi-parent populations, and explores the functional gene Zm00001eb032370 that is closely related to maize ear thickness, providing a new technical solution for molecular marker-assisted selection of maize germplasm with thicker ears.

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

[0007] A maize ear diameter molecular marker, wherein the molecular marker is the Zm00001eb032370 gene with a sequence as shown in SEQ ID NO: 1.

[0008] The expression level of Zm00001eb032370 gene was positively correlated with corn ear diameter.

[0009] Furthermore, the gene is 181114303, 181114391, 181114395, 181114424, 181114426, 181114432, 181114591, 181114593, 181114632, 181114635, 181114652, 181114669, 181114672, 181115212、181115221、181115371、181115372、181115384、 181115433、181115439、181115450、181115458、181115476、 181115485、181115535、181115544、181115569、181115586、 181115587、181115855、181115857、181115875、181115879、 181115917、181115936、181115945、181115957、181115959、 181115961、181116033、181116044、181116070、181116128、 181116133、181116175、181116253、181116324、181116469、 181116473、181116526、181116549、181116569、181116659、 181116694、181116801、181116923、181117253、181117327、 181117354、181117448、181117485、181117689、181117692、 181117769、181118174、181118180、181118210、181118218、

[0021] Bases 181118287, 181118398, 181118403, and 181118418 appear in sequence

[0022] TATTCTGCGACCGCTTCGTCTACACGATCGCTGACTCGTCTTCAGGGGTAAGCCCGACAGGCAACACCCCGC, corn shows the dominant trait of thick ear.

[0023] Furthermore, the present invention provides a product for detecting the molecular marker, the product comprising a kit, a reagent or a gene chip, and the product detecting the genotype or expression level of the molecular marker.

[0024] Furthermore, the present invention provides products for detecting the genotype of the molecular marker, including products prepared using PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization, TaqMan probes, ARMS-PCR, or KASP. These products are used in identifying or assisting in identifying the coarseness trait of corn.

[0025] Furthermore, the present invention provides the application of the molecular markers in any of the following: a) genetic diversity analysis of coarse-ear corn; b) construction of a molecular genetic map of coarse-ear corn; c) genome-wide association analysis of coarse-ear corn; d) variety identification or assisted identification of coarse-ear corn; e) molecular marker-assisted selection breeding of coarse-ear or high-yield corn; f) genome-wide selection breeding of coarse-ear corn.

[0026] Furthermore, the present invention provides a method for screening coarse-ear corn germplasm, which comprises taking a corn sample to be tested, detecting the molecular markers, and screening germplasm that meets the genotype of the molecular markers, namely, coarse-ear corn germplasm.

[0027] Furthermore, the present invention provides a method for screening high-yield corn germplasm, taking a corn sample to be tested, detecting the molecular markers, and screening germplasm that meets the genotype of the molecular markers, namely, the high-yield corn germplasm.

[0028] The term "Molecular Marker-assisted selection (MAS)" refers to a breeding technique that uses molecular markers of target traits to select offspring lines and thereby obtain superior individual plants containing the target gene.

[0029] The term "Genomic Selection (GS)" refers to a modern breeding technology based on genetic evaluation and selection based on whole-genome marker information. It aims to predict individual breeding values ​​or phenotypic performance through high-density molecular markers, thereby accelerating the breeding process and improving selection efficiency.

[0030] The term "GS-MAS" refers to a breeding strategy that combines genome-wide selection (GS) with marker-assisted selection (MAS). GS-MAS uses high-density marker information covering the entire genome and phenotypes to estimate individual breeding values, while simultaneously linking major and minor genes. This allows for early prediction and selection of complex traits (such as those with low heritability and difficult to measure) based on breeding values, thereby shortening generation intervals, accelerating the breeding process, improving selection accuracy, and reducing costs.

[0031] The technical effects achieved by the present invention are:

[0032] This study constructed a multi-parent maize population with significant differences in ear diameter by using the temperate maize inbred line Ye107, which exhibits a relatively thin ear diameter, as a common parent and crossing it with six tropical and subtropical maize inbred lines with relatively thick ear diameters. GWAS analysis pinpointed SNP_181125354 on chromosome 1, which is significantly associated with ear diameter. This gene, Zm00001eb032370, was then identified as a regulatory gene for ear diameter. SNP_181125354 can explain 6.15% of the phenotypic variation in ear diameter. Haplotype analysis showed that among 1,107 RILs (recombinant inbred lines), Zm00001eb032370 had a total of 12 haplotypes (Hap1, Hap2, ..., Hap12), among which Hap12 had significantly higher ear diameter than the other 11 haplotypes. Therefore, Hap12 of the Zm00001eb032370 gene is a haplotype type that significantly increases ear diameter. The results of this invention will help further study the regulatory mechanism of corn ear diameter and also provide a theoretical basis for the development of corn varieties with larger ear diameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 : 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;

[0034] Figure 2 GWAS analysis diagram of ear thickness in the embodiment of the present invention; wherein: (a) GWAS result based on the mean value of ear thickness phenotype in Yanshan in 2022; (b) GWAS result based on the mean value of ear thickness phenotype in Yanshan in 2023; (c) GWAS result based on the mean value of ear thickness phenotype in Jinghong in 2024; (d) GWAS result based on the BLUP value of ear thickness;

[0035] Figure 3 Schematic diagram of the relationship between significant SNPs and candidate genes in the embodiment of the present invention;

[0036] Figure 4 Candidate gene LD block analysis diagram;

[0037] Figure 5 Schematic diagram of the mutation sites that form different haplotypes in the Zm00001eb032370 gene;

[0038] Figure 6 The differences between different haplotypes in subpopulations are shown, where A is the difference percentage and B is the difference box plot.

[0039] Figure 7 The expression level of Zm00001eb032370 gene in different tissues. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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. For purchased commodities in the test method, if no specific conditions are specified, they are carried out according to conventional conditions or the conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments used is not specified, they can all be conventional products purchased from the market.

[0041] The gene sequence of Zm00001eb032370 marked by the present invention is shown in SEQ ID NO: 1, corresponding to positions 181114266 to 181118468 from the 5′ end of chromosome 1 of the reference genome version Zm-B73-REFERENCE-NAM-5.0.

[0042] Example 1

[0043] 1. Methods

[0044] 1.1 Plant materials and experimental design

[0045] The present invention used the temperate, high-quality, backbone maize inbred line Ye107 as the common male parent and hybridized it with six tropical and subtropical maize inbred lines with relatively thick ears (CML312, CML395, YML46, YML32, NK40-1, and YML1218) to form six F1 hybrid combinations. These combinations were then selfed continuously to F9 lines using single-kernel descent, resulting in a multi-parent population containing 1,107 recombinant inbred lines. The number of recombinant inbred lines contained in each subpopulation (pop1-pop6) was 142, 148, 303, 178, 200, and 136, respectively (Table 1).

[0046] The experiments were conducted in Yanshan County, Yunnan Province, China, in 2022 and 2023, and in Jinghong City, Yunnan Province, China, in 2024. A completely randomized block design was used, with three replications per site. The experimental material was planted in rows with a length of 3 m, a spacing of 0.70 m, 14 plants per row, and two rows per plot. Planting was managed according to local standard agronomic practices. After ripening, nine ears per row were randomly selected and their diameters were recorded. The average diameter was then determined.

[0047] Table 1 Parental information

[0048]

[0049] 1.2 Phenotypic data analysis

[0050] First, the phenotypic data were preprocessed using Excel 2019. SPSS 26.0 was used to assess whether the phenotypic data conformed to the normal distribution, and basic statistics were calculated to help intuitively understand the central tendency and dispersion of the data. R4.3.2 was used to draw heat maps and evaluate the correlation between phenotypic data under different subpopulations and environmental conditions. A two-way analysis of variance was performed using the aov() function to assess whether the effects of subpopulations and environments on phenotypic data were statistically significant. The lme4 software package was used to calculate the broad heritability using the formula H = Vg / (Vg+(Ve / L)), where Vg represents the genetic variance, Ve is the residual variance, and L is the number of environments.

[0051] 1.3 Whole-genome sequencing

[0052] 1.3.1 Sample collection and DNA extraction

[0053] Healthy maize leaves were sampled between the seedling and jointing stages and immediately frozen in liquid nitrogen to prevent tissue and DNA degradation. Leaf DNA was extracted using the Tiangen Plant Genomic DNA Extraction Kit (Tiangen Biotech Co., Ltd., Beijing, China). The extracted DNA samples were quality-controlled on agarose gels to confirm concentration (above 100 ng / μl) and purity (A260 / A280 ratio, typically between 1.8 and 2.0), ensuring that the extracted DNA was suitable for subsequent whole-genome sequencing.

[0054] 1.3.2 Library construction and sequencing

[0055] DNA fragmentation was performed using a Vibra-Cell series ultrasonic breaker (Sonics & Materials, Inc., Connecticut, America), and the DNA samples after fragmentation were subjected to gel electrophoresis to assess quality (ideal DNA fragment size should be between 200-500 bp). End repair was performed using T4 DNA polymerase (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America), and short DNA adapters were connected to both ends of the DNA fragments. In order to ensure the quality of the library, DNA fragments without adapters were removed using Exonuclease I enzyme (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America). DNA fragments of 200-500 bp in length (including the portion of the adapter sequence) were selected by the Agencourt AMPure XP magnetic bead method. The library was PCR amplified to increase the number of target DNA fragments. Finally, the concentration of the library was quantitatively detected using Qubit, and the target concentration was usually between 10-20 nM. The size distribution of the library was detected using a Bioanalyzer (Agilent Technologies, Santa Clara, California, America), and the fragment size was generally 200-500 bp.

[0056] All samples were sequenced using the Illumina NovaSeq 6000 platform (Illumina, Inc., San Diego, California, America) in paired-end sequencing mode at a sequencing depth of 5×. Sequencing results were stored as FASTQ files. The quality of sequencing data was assessed using FastQC (Babraham Institute, Bioinformatics Group, Cambridge, United Kingdom) based on the following criteria: (1) Q-score ≥ 20, (2) genomic GC content of 40% to 60%, (3) repetitive sequence proportion less than 30%, (4) N-containing proportion less than 1–2%, and (5) read length consistent with expected length.

[0057] 1.4 Group stratification analysis

[0058] 1.4.1 Principal Component Analysis

[0059] To assess population genetic structure and control for the effects of population stratification on GWAS results, we performed principal component analysis (PCA) using GCTA software. First, the genotype data were standardized, and a genetic similarity matrix between samples was calculated. The first few principal components were then extracted from this matrix, which was used to characterize population structure.

[0060] 1.4.2 Unrooted Tree Construction

[0061] To further analyze the genetic relationships between subpopulations, we used MEGA (Molecular Evolutionary Genetics Analysis) software to construct unrooted trees to assess the genetic distances and relatedness between subpopulations. During the construction process, we used the neighbor-joining (NJ) method, which efficiently and accurately reflects the genetic differentiation of populations by minimizing the sum of genetic distances between samples.

[0062] 1.4.3 Analysis of population genetic structure

[0063] We used ADMIXTURE software for population stratification analysis. This method assumes that each individual is composed of a mixture of components from multiple subpopulations and infers the proportion of contributions from different subpopulations to each individual's genome. We first selected and set the expected number of subpopulations (K value) based on the study objectives. We then tried different K values ​​and selected the optimal K value through cross-validation to avoid overfitting or underfitting. We analyzed the population structure under different K values ​​and determined the genetic background of the samples based on the population stratification pattern.

[0064] 1.4.4 Linkage disequilibrium analysis

[0065] 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, r 2 The value is often used to indicate the degree of LD between SNPs, r 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 LD decay at different genetic distances using the Plot_OnePop.pl script included with PopLDdecay to generate visualizations of LD decay. These plots clearly demonstrate the LD structure at different positions within a population, identify regions of high LD within the genome, and further aid in marker selection and mapping of genetic regions for GWAS.

[0066] 1.5 Genome-wide association analysis

[0067] A mixed linear model (MLM) in GEMMA software was used to perform a GWAS for maize ear diameter. In the model, population genetic structure was treated as a fixed effect, and individual kinship was treated as a random effect to correct for the effects of population structure and kinship on trait expression. To reduce false positives and false negatives, a Bonferroni correction was used for multiple comparisons. M represents the number of SNPs used in the GWAS analysis. The P values ​​of all individual SNPs were compared to the Bonferroni-corrected significance level. If the P value was less than p, the SNP was considered statistically significant. Furthermore, to ensure the reliability and accuracy of the results, an FDR correction was used to reduce the error caused by multiple comparisons. Generally, a p-value less than 5×10^-8 was considered significant. The identified significant SNPs were functionally annotated using the ANNOVAR tool to determine the location, region, and mutation type of the variant loci on the genome. In addition, PLINK software was used to generate Manhattan plots and QQ plots to visualize the association analysis results. The Manhattan plot evaluated the strength of the association between SNPs and traits, and the QQ plot evaluated the degree of deviation of the distribution of p-values ​​from the expected value.

[0068] 1.6 Candidate gene haplotype analysis

[0069] Haplotype analysis of candidate genes was performed using Haploview v4.2 software. For each gene, we analyzed the frequency distribution, phenotypic differences, and statistical significance of different haplotypes. Based on frequency distribution considerations, boxplots only display haplotypes with high frequency in the sample. Haplotypes with low frequency (≤1%) are not individually displayed; these low-frequency haplotypes have a minimal statistical impact on the final results and do not affect the interpretation of the main conclusions.

[0070] 2. Results

[0071] 2.1 Analysis of corn ear thickness phenotype data

[0072] The results showed that pop4 consistently exhibited a high and stable ear thickness trait in all environments, while pop1 exhibited the lowest average ear thickness (Table 2). The ear thickness phenotypic data of the six subpopulations were statistically analyzed, as shown in Table 2 below. The coefficient of variation (CV%) of ear thickness among the six subpopulations in each environment had a large range of data, indicating significant differences in ear thickness among the subpopulations. The absolute values ​​of the skewness and kurtosis of ear thickness in different environments were generally less than 1. The broad-sense heritability of the number of ear rows in the six subpopulations ranged from 70.15% to 97.28%, indicating a strong genetic influence on this trait. The consistency of phenotypic variation in different environments highlights the reliability of the phenotypic data used for subsequent analysis. Significant differences in ear thickness were observed among subpopulations, influenced by genetic and environmental factors. This variability provides a solid foundation for further GWAS analysis and identification of important loci associated with ear thickness.

[0073] Table 2 Analysis of ear thickness phenotype data

[0074]

[0075]

[0076] Note: 22YS refers to Yanshan in 2022, 23YS refers to Yanshan in 2023, and 24JH refers to Jinghong in 2024

[0077] 2.2 Group stratification analysis

[0078] By observing the 3D PCA graph, the cluster distribution of the samples is relatively clear. The data can be roughly divided into 6 clusters, each representing a potential subgroup. Although most subgroups form a clear cluster structure in the 3D space, there are overlapping areas between some clusters. For example, there is a significant overlap between pop3 and pop6, which may indicate that the two subgroups have a high similarity in the main feature dimensions ( Figure 1 a).

[0079] In the unrooted tree analysis, although there is a certain degree of confusion between different clusters, the overall data can still be clearly divided into 6 main clusters ( Figure 1 b) This overlap may indicate that these clusters are close in the feature space, reflecting their similarity in some key features.

[0080] In the population structure analysis, when K = 6, the data is clearly divided into 6 subgroups. Although there is still a certain degree of overlap between some samples, the 6 subgroups are relatively clear overall ( Figure 1 c) This overlap indicates that there may be overlapping areas between different subgroups, and some samples may have similar characteristics, making their attribution to certain subgroups unclear. Overall, despite this overlap, these subgroups still reasonably reflect the main structural characteristics of the population.

[0081] LD decay in MPP was evaluated using 16,223,980 valid SNPs. The LD decay plot showed that LD decreased rapidly with increasing physical distance between markers. 2 When the value drops to 0.24, the physical distance of LD decay is about 10 kb ( Figure 1 d).

[0082] 2.3 Genome-wide association analysis of ear diameter traits

[0083] The present invention uses 16,223,980 effective SNPs to conduct GWAS analysis of corn ear diameter. The results show that multiple significant SNPs associated with corn ear diameter were identified on 10 chromosomes in all environments ( Figure 2 ). It is worth mentioning that we consistently identified a significant SNP associated with corn ear diameter in all environments (22YS, 23YS, 24JH, BLUP): 1_181125354, which can explain 6.15% of the variation in ear diameter phenotype. Gene screening was performed within the 10kb range upstream and downstream of the associated significant SNP. We consistently identified a gene Zm00001eb032370 ( Figure 3 , Table 3), and the trait correlation was evaluated with reference to its existing functional annotation. The present invention believes that this is a candidate gene worthy of further study.

[0084] Table 3 GWAS co-localized candidate genes

[0085]

[0086] 2.4 Haplotype analysis of candidate genes

[0087] like Figure 4 、 5 6. Haplotype analysis of Zm00001eb032370 (the gene sequence of Zm00001eb032370 is shown in SEQ ID NO:1, corresponding to positions 181114266-181118468 of the reference genome version Zm-B73-REFERENCE-NAM-5.0) revealed 12 distinct haplotypes (Hap1, Hap2, …, Hap12) among 1107 RILs. The boxplot only displays the eight haplotypes with the highest frequency in the samples. These haplotypes exhibited varying frequency distributions across the different populations studied. Hap12 exhibited the highest phenotypic values ​​and was widely distributed in pop4. Furthermore, Hap12 was significantly different from Hap1 and Hap3 (P < 0.05, P < 0.01). Therefore, we conclude that Hap12 is the dominant haplotype regulating ear diameter in maize.

[0088] The specific haplotype sites, using Zm-B73-REFERENCE-NAM-5.0 as the reference genome, are as follows from left to right on chromosome 1 starting from the 5′ end:

[0089] 181114303(REF:T / ALT:C)181114391(REF:A / ALT:C)

[0090] 181114395(REF:T / ALT:C)181114424(REF:T / ALT:G)

[0091] 181114426(REF:C / ALT:T)181114432(REF:T / ALT:G)

[0092] 181114591(REF:G / ALT:A)181114593(REF:C / ALT:A)

[0093] 181114632(REF:G / ALT:T)181114635(REF:A / ALT:C)

[0094] 181114652(REF:T / ALT:C)181114669(REF:C / ALT:T)

[0095] 181114672(REF:G / ALT:A)181115212(REF:C / ALT:T)

[0096] 181115221(REF:T / ALT:A)181115371(REF:T / ALT:A)

[0097] 181115372(REF:C / ALT:A)181115384(REF:G / ALT:A)

[0098] 181115433(REF:T / ALT:G)181115439(REF:C / ALT:T)

[0099] 181115450(REF:T / ALT:A)181115458(REF:A / ALT:G)

[0100] 181115476(REF:C / ALT:T)181115485(REF:A / ALT:G)

[0101] 181115535(REF:C / ALT:A)181115544(REF:G / ALT:A)

[0102] 181115569(REF:A / ALT:C)181115586(REF:T / ALT:C)

[0103] 181115587(REF:C / ALT:A)181115855(REF:G / ALT:A)

[0104] 181115857(REF:C / ALT:G)181115875(REF:T / ALT:C)

[0105] 181115879(REF:G / ALT:A)181115917(REF:A / ALT:T)

[0106] 181115936(REF:C / ALT:T)181115945(REF:T / ALT:A)

[0107] 181115957(REF:C / ALT:T)181115959(REF:G / ALT:A)

[0108] 181115961(REF:T / ALT:C)181116033(REF:C / ALT:T)

[0109] 181116044(REF:T / ALT:C)181116070(REF:T / ALT:G)

[0110] 181116128(REF:C / ALT:G)181116133(REF:A / ALT:C)

[0111] 181116175(REF:G / ALT:A)181116253(REF:G / ALT:A)

[0112] 181116324(REF:G / ALT:A)181116469(REF:G / ALT:A)

[0113] 181116473(REF:T / ALT:A)181116526(REF:A / ALT:G)

[0114] 181116549(REF:A / ALT:G)181116569(REF:G / ALT:T)

[0115] 181116659(REF:C / ALT:T)181116694(REF:T / ALT:C)

[0116] 181116801(REF:C / ALT:T)181116923(REF:T / ALT:G)

[0117] 181117253(REF:A / ALT:G)181117327(REF:C / ALT:G)

[0118] 181117354(REF:C / ALT:A)181117448(REF:G / ALT:A)

[0119] 181117485(REF:G / ALT:T)181117689(REF:C / ALT:G)

[0120] 181117692(REF:G / ALT:A)181117769(REF:A / ALT:C)

[0121] 181118174(REF:C / ALT:T)181118180(REF:G / ALT:A)

[0122] 181118210(REF:C / ALT:A)181118218(REF:T / ALT:C)

[0123] 181118287(REF:C / ALT:T)181118398(REF:T / ALT:C)

[0124] 181118403(REF:G / ALT:T)181118418(REF:T / ALT:C)

[0125] Hap1:TCCGTGGCGCTCGTAAAAGTAGTACACCCGGCATCTCGTTCTGCAGGGAGGTTTTGACCGTGGACGCTCTTT

[0126] Hap2:TATTCTGCGACCGCTTCGTCTACACGATCGCTGACTCGTCTTCAGGGGAGGTTTTGGCCAGGGACGCTCTTT

[0127] Hap3:TATTCTGCTCTTGTTTCAGCTATACGATAAGTGATATGCTCTGCAGGGAGGTTTTGAGCGTCGCTGATTCGT

[0128] Hap4:TATTCTGCGACCGCTTCGTCTACACGATGCTGACTCGTCTTCAGGGGTAAGCTCTACCGGCGACGCTCTGT

[0129] Hap5:TATTCTGCGACCGCTTCGTCTACACGATGCTGACTCGTCTTCAGGGGAGGTTTTGGCCGGGGACGCTCTTT

[0130] Hap6:CATTCTGAGCTCGTAAAAGTAGTGAACCCGGCATCTCATTTGCAGAAAAGAGCCCGACAGGCAATGATCCGC

[0131] Hap7:CATTCTGCGATCGCTTCGTCTACACGATCGCTGACTCGTCTTCAGGGGTAAGCTCTACCGGCGACGCTCTTT

[0132] Hap8:TATTCTACGCTCACTTCGTCTACACGATCGCTGACTCGTCTTCAGGGGTAAGCTTGGCCAGGGACGCTCTTT

[0133] Hap9:TATTCTGCGATCGCTTCGTCTACACGATCGCTGACTCGTCTTCAGGGGTAAGCTCTACCGGCGACGCTCTGT

[0134] Hap10:TATTCTGCGACCGCTTCGGTCTACACGATGCTGACTC GTCTTCAGGGGGTAAGCTCTACCGTGGACGCTCTGT

[0135] Hap11:TATTCTGCGACCGCTTCGGTCTACACGATCGCTGACTC GTCTTCAGGGGTGGTTTTGGCCAGGGACCGCTCTTT

[0136] Hap12:TATTCTGCGACCGCTTCGGTCTACACGATCGCTGACTC GTCTTCAGGGGGTAAGCCCGACAGGCAACACCCCGC

[0137] The expression of candidate gene Zm00001eb032370 in various tissues of maize was analyzed ( Figure 7The results showed that the gene was highly expressed in maize cob primordium, which further suggested that it was related to the development of maize cob diameter.

[0138] In summary, the identification of the Zm00001eb032370 gene as a functional molecular marker for maize ear diameter can be used to develop allele-specific markers. Molecular markers can directly detect crop genotypes and accurately identify the presence or absence of target genes or traits, avoiding the blindness and uncertainty inherent in traditional selection and ensuring accurate progeny selection. These markers can be applied in breeding applications such as genetic resource identification, MAS, genetic map construction, linkage mapping, and GS-MAS. MAS allows for rapid and accurate identification of crop genotypes, avoiding the tedious, time-consuming, and difficult screening process of traditional selection, significantly improving selection efficiency and accelerating the breeding process.

[0139] 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. A molecular marker for corn ear diameter, characterized in that: The molecular marker is the Zm00001eb032370 gene with a sequence as shown in SEQ ID NO:

1.

2. The corn ear diameter molecular marker according to claim 1, characterized in that The expression level of Zm00001eb032370 gene was positively correlated with corn ear diameter.

3. The molecular marker according to claim 1, characterized in that The genes are 181114303, 181114391, 181114395, 181114424, 181114426, 181114432, 181114591, 181114593, 181114632, 181114635, 181114652、181114669、181114672、181115212、181115221、 181115371、181115372、181115384、181115433、181115439、 181115450、181115458、181115476、181115485、181115535、 181115544、181115569、181115586、181115587、181115855、 181115857、181115875、181115879、181115917、181115936、 181115945、181115957、181115959、181115961、181116033、 181116044、181116070、181116128、181116133、181116175、181116253、181116324、181116469、181116473、181116526、181116549、181116569、181116659、181116694、181116801、 The bases 181116923, 181117253, 181117327, 181117354, 181117448, 181117485, 181117689, 181117692, 181117769, 181118174, 181118180, 181118210, 181118218, 181118287, 181118398, 181118403 and 181118418 are presented in this order. TATTCTGCGACCGCTTCGTCTACACGATCGCTGACTCGTCTTCA GGGGTAAGCCCGACAGGCAACACCCCGC, 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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