Molecular marker associated with ear diameter on chromosome 7 of maize and its application

Abstract

The present application relates to the field of corn molecular marker assisted breeding, and specifically discloses a molecular marker related to corn ear thickness on chromosome 7 of corn and application thereof, and specifically proposes a corn ear thickness molecular marker, the molecular marker is a Zm00001eb303690 gene with a sequence as shown in SEQ ID NO:1, which can be applied in corn molecular marker assisted breeding, the present application uses GWAS analysis to locate a SNP_21950015 on chromosome 7 which is significantly related to ear thickness in multiple environments, and further excavates a functional gene Zm00001eb303690 for regulating ear thickness, the ear thickness trait is regulated by multiple genes, by performing molecular marker on the functional gene, analyzing the genotype, the germplasm resources can be comprehensively and efficiently evaluated and utilized, and the material with the target gene or trait can be quickly screened, and the utilization rate of genetic resources is improved.

Description

Technical Field

[0001] This invention relates to the field of marker-assisted breeding of maize, specifically to a molecular marker on maize chromosome 7 that is associated with maize ear diameter and its application. Background Technology

[0002] As one of the world's most important food crops, maize plays a crucial role in global agricultural production. However, with the continued growth of the global population and the ever-increasing demand for food, maize production faces significant challenges.

[0003] The yield per maize plant is closely related to inflorescence development, especially the optimization of ear traits, which plays a decisive role in increasing yield. Studies have shown that ear diameter, as an important component of ear traits, has a significant impact on maize yield per plant. A thicker ear can accommodate more rows of kernels or larger kernel diameters, thus directly increasing yield. Therefore, optimizing ear diameter is considered a key breakthrough for improving maize yield and a key direction for future maize breeding.

[0004] The expression of crop traits depends not only on the function of individual genes but also on the influence of gene-gene interactions (epistesis). The contribution of genes to traits may vary under different environmental conditions. This invention aims to identify novel genes and SNPs related to ear diameter in a multi-parental population of tropical maize using genome-wide association analysis (GWAS) through whole-genome sequencing (WGS) technology. Tag SNPs will be screened through haplotype analysis. This reduces redundant testing costs during GS or MAS breeding processes while preserving the integrity of genetic information, with the goal of providing innovative genetic resources and precise improvement strategies for significantly increasing maize yield per plant, thereby promoting the optimization and efficiency of maize production. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a molecular marker on chromosome 7 of maize that is associated with ear thickness and its application. It identifies the functional gene Zm00001eb303690, which is closely related to ear thickness, and provides more marker genes for marker-assisted selection of maize with thicker ears, thereby enabling more precise screening of germplasm with the target trait.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The present invention provides a crude molecular marker for maize ears, wherein the molecular marker is the gene Zm00001eb303690 with the sequence shown in SEQ ID NO:1.

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

[0009] Furthermore, 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 of the gene, starting from the 5′ end, are sequentially TCGTTGTGGGCGCTCAGGCC, the maize exhibits the dominant trait of thick ears.

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

[0011] Furthermore, the present invention provides products for detecting the molecular marker genotype, the products including those prepared using PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization, TaqMan probe method, ARMS-PCR method or KASP method.

[0012] Furthermore, the present invention provides the application of the product in identifying or assisting in the identification of corn ear thickness.

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

[0014] Furthermore, the present invention provides a method for screening maize germplasm with thick ears, wherein a maize sample to be tested is taken, the molecular marker is detected, and germplasm that matches the molecular marker genotype is screened, which is maize germplasm with thick ears.

[0015] Furthermore, the present invention provides a method for screening high-yield maize germplasm, which involves taking a maize sample to be tested, detecting the molecular markers, and screening germplasm that matches the molecular marker genotypes, which are then identified as high-yield maize germplasm.

[0016] The term "Molecular Marker-Assisted Selection (MAS)" refers to a breeding technique that uses molecular markers of a target trait to select offspring lines, thereby obtaining superior individual plants containing the target gene.

[0017] The term "Genomic Selection (GS)" is a modern breeding technique that uses whole-genome marker information for genetic evaluation and selection. It aims to accelerate the breeding process and improve selection efficiency by predicting the breeding value or phenotypic performance of an individual through high-density molecular markers.

[0018] The term "GS-MAS" refers to a breeding strategy that combines Genomic Selection (GS) with Marker-Assisted Selection (MAS). GS-MAS uses high-density marker information and phenotypes covering the entire genome to estimate the breeding value of an individual, while also associating major and minor genes. By using breeding values, it can predict and select for complex traits (low heritability, difficult to determine, etc.) at an early stage, thereby shortening the generation interval, accelerating the breeding process, improving selection accuracy, and saving costs.

[0019] The technical effects achieved by this invention are as follows:

[0020] This invention constructs a multi-parental maize population with significant differences in ear diameter by crossing the temperate maize inbred line Ye107 (with a relatively small ear diameter) with six tropical and subtropical maize inbred lines (with a relatively large ear diameter). GWAS analysis located the SNP_21950015 on chromosome 7, which is significantly associated with ear diameter, and further identified the functional gene Zm00001eb303690 that regulates ear diameter. Haplotype analysis showed that among 1107 RILs (recombinant inbred lines), Zm00001eb303690 had 13 haplotypes (Hap1, Hap2, ..., Hap13). Among them, Hap10 had significantly higher ear diameter than the other 12 haplotypes. Therefore, Hap10 of the Zm00001eb303690 gene is a haplotype that significantly increases ear diameter. The results of this invention help to further study the regulatory mechanism of maize ear diameter and provide more marker genes for molecular marker-assisted selection of maize with thicker ears, thereby screening germplasm with the target trait more accurately. Attached Figure Description

[0021] Figure 1 The following is a population structure diagram of 1107 RILs in an 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 2The following are GWAS results for ear diameter in embodiments of the present invention: (a) GWAS results based on the mean ear diameter phenotype in Yanshan in 2022; (b) GWAS results based on the mean ear diameter phenotype in Yanshan in 2023; (c) GWAS results based on the mean ear diameter phenotype in Jinghong in 2024; and (d) GWAS results based on the BLUP value of ear diameter.

[0023] Figure 3 This is a schematic diagram illustrating the relationship between significant SNPs and candidate genes in an embodiment of the present invention;

[0024] Figure 4 Candidate gene LD block analysis diagram;

[0025] Figure 5 A schematic diagram of mutation sites that form different haplotypes in the Zm00001eb303690 gene;

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

[0027] Figure 7 This is a graph showing the expression levels of the Zm00001eb303690 gene in different tissues. Detailed Implementation

[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 the purchased goods in the test methods, if no specific conditions are specified, they shall be carried out according to conventional conditions or conditions recommended by the manufacturer. If the manufacturers of the reagents or instruments used are not specified, they can be conventional products that can be purchased from the market.

[0029] The gene sequence of Zm00001eb303690 marked in this invention is shown in SEQ ID NO:1, corresponding to positions 21959514-21961325 on chromosome 7 from the 5′ end 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] This invention uses the excellent temperate backbone maize inbred line Ye107 as the common male parent, and crosses 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 are then continuously self-pollinated to F9 using the single-seed propagation method, constructing a multi-parent population containing 1107 recombinant inbred lines. The number of recombinant inbred lines in each subpopulation (pop1-pop6) is 142, 148, 303, 178, 200, and 136, respectively (Table 1).

[0034] This experiment was conducted 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 each location replicated three times. The planting pattern for the experimental materials was set as follows: row length 3m, row spacing 0.70m, 14 plants per row, and 2 rows per plot. The planting process was managed according to local standard agronomic practices. After the ears matured, nine ears were randomly selected from each row to record the ear diameter, and the average diameter was taken to determine the average ear diameter.

[0035] Table 1. Detailed information about the parents

[0036]

[0037] 1.2 Phenotypic Data Analysis

[0038] First, the phenotypic data were preprocessed using Excel 2019. SPSS 26.0 was used to assess whether the phenotypic data conformed to a normal distribution and to calculate basic statistics to help visually understand the central tendency and dispersion of the data. R4.3.2 was used to create heatmaps to assess the correlation between phenotypic data under different subgroups and environmental conditions. A two-way ANOVA was performed using the aov() function to assess whether the influence of subgroups and environment on the phenotypic data was statistically significant. The generalized heritability was calculated using the lme4 software package, with the formula H = Vg / (Vg + (Ve / L)), where Vg represents the genetic variance, Ve is the residual variance, and L is the environmental number.

[0039] 1.3 Whole genome sequencing

[0040] 1.3.1 Sample Collection and DNA Extraction

[0041] Healthy leaves were sampled during the seedling to jointing stage of maize. The collected leaf samples were 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 using agarose gel electrophoresis to confirm the concentration (above 100 ng / μl) and purity (A260 / A280 ratio should generally be between 1.8 and 2.0) to ensure the extracted DNA was suitable for subsequent whole-genome sequencing.

[0042] 1.3.2 Library Construction and Sequencing

[0043] DNA fragmentation was performed using a Vibra-Cell series ultrasonic fragmentation system (Sonics & Materials, Inc., Connecticut, America), and the fragmented DNA samples were subjected to gel electrophoresis to assess quality (ideally, the DNA fragment size should be between 200-500 bp). End repair was performed using T4 DNA polymerase (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America), ligating short DNA adapters to both ends of the DNA fragments. To ensure library quality, exonuclease I enzyme (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America) was used to remove DNA fragments without adapter ligations. DNA fragments of 200-500 bp in length (including portions of the adapter sequence) were selected using the AgencourtAMPureXP magnetic bead method. PCR amplification was performed on the library to increase the number of target DNA fragments. Finally, the concentration of the library was quantified using Qubit, typically targeting a concentration between 10-20 nM. Bioanalyzer (Agilent Technologies, Santa Clara, California, America) was used to detect the size distribution of the library, with fragment sizes typically ranging from 200 to 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 in FASTQ format. The quality of the sequencing data was evaluated using FastQC (Babraham Institute, Bioinformatics Group, Cambridge, United Kingdom). The evaluation criteria were: (1) Q-score ≥ 20, (2) GC content of the genome was 40% to 60%, (3) repetitive sequence ratio was less than 30%, (4) N content was less than 1-2%, and (5) read length was consistent with the expected length.

[0045] 1.4 Group Stratification Analysis

[0046] 1.4.1 Principal Component Analysis

[0047] To assess population genetic structure and control for the impact of population stratification on GWAS results, we performed principal component analysis (PCA) using GCTA software. First, genotypic data were standardized, and a genetic similarity matrix between samples was calculated. Then, the first few principal components were extracted based on this matrix; these principal components characterize the population structure.

[0048] 1.4.2 Construction of Unrooted Trees

[0049] To further analyze the genetic relationships among subpopulations, we constructed an unrooted tree using MEGA (Molecular Evolutionary Genetics Analysis) software to assess the genetic distance and phylogenetic relationships between subpopulations. During the construction process, we used the Neighbor-Joining (NJ) method, which efficiently and accurately reflects the genetic differentiation of a population by minimizing the sum of genetic distances between samples.

[0050] 1.4.3 Population genetic structure analysis

[0051] We used ADMIXTURE software for population stratification analysis. This method infers the contribution proportion of different subpopulations to each individual's genome by assuming that each individual is a mixture of components from multiple subpopulations. We first selected and set the expected subpopulation size (K value) based on our research objectives. By trying different K values ​​and selecting the optimal K value through cross-validation, we avoided 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.

[0052] 1.4.4 Chaining Imbalance Analysis

[0053] We used PopLDdecay software to assess the genetic linkage disequilibrium (LD) status of the population. The purpose of LD analysis is to measure the degree of linkage disequilibrium between SNPs, i.e., whether the genotypes of a pair of markers are independently inherited. In particular, the r² value is often used to represent the degree of LD between SNPs. 2 A higher value indicates a stronger linkage and a closer genetic association between the two SNPs. The r-values ​​between different SNP markers were calculated using PopLDdecay software. 2 The values ​​were further analyzed to determine the LD decay at different genetic distances. The Plot_OnePop.pl script included with PopLDdecay was used to generate visualizations of LD decay. These graphs clearly show the LD structure at different locations 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] Genetic sweep spectroscopy (GWS) of maize ear thickness was performed using a mixed linear model (MLM) in GEMMA software. In the model, population genetic structure was treated as a fixed effect, while individual kinship was treated as a random effect to correct for the influence of population structure and kinship on trait performance. Bonferroni correction was used for multiple comparisons to reduce false positives and false negatives. The p-value of each individual SNP was compared to the significance level after Bonferroni correction. If the p-value was less than p, the SNP was considered statistically significant. Furthermore, to ensure the reliability and accuracy of the results, FDR correction was used to reduce errors from multiple comparisons. Generally, a p-value less than 5 × 10⁻⁸ was considered significant. For identified significant SNPs, ANNOVAR was used for functional annotation to determine the location, region, and mutation type of the variant sites in the genome. Additionally, Manhattan plots and QQ plots were generated using PLINK software to visualize the association analysis results. The Manhattan plot assessed the strength of the association between SNPs and the trait, while the QQ plot assessed the deviation of the p-value distribution 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, phenotypic differences, and statistical significance of different haplotypes. Based on frequency distribution considerations, box plots only show haplotypes with high frequency in the sample. Low-frequency haplotypes (≤1%) were not shown separately; these low-frequency haplotypes had little statistical impact on the final results and did not affect the interpretation of the main conclusions.

[0058] 2. Results

[0059] 2.1 Analysis of Maize Ear Coarseness Phenotype Data

[0060] The results showed that pop4 consistently exhibited high and stable panicle diameter in all environments, while pop1 showed the lowest average panicle diameter (Table 2). Statistical analysis of the panicle diameter phenotypic data of the six subgroups is shown in Table 2 below. The coefficient of variation (CV%) of panicle diameter varied significantly among the six subgroups in each environment, indicating significant differences in panicle diameter among the subgroups. The absolute values ​​of skewness and kurtosis of panicle diameter in different environments were generally less than 1. The broad-sense heritability of panicle row number in the six subgroups ranged from 70.15% to 97.28%, indicating a strong genetic influence on this trait. The consistency of phenotypic variation across different environments highlights the reliability of the phenotypic data used for subsequent analysis. Significant differences in panicle diameter were observed among the subgroups due to the influence of genetic and environmental factors. This variability provides a solid foundation for further GWAS analysis and identification of important loci associated with panicle diameter.

[0061] Table 2. Analysis of spikelet diameter phenotypic data

[0062]

[0063] Note: 22YS refers to Yanshan in 2022, 23YS refers to Yanshan in 2023, and 24JH refers to Jinghong in 2024. 2.2 Group Stratification Analysis

[0064] By observing the 3D PCA plot, the clustering distribution of the samples is relatively clear, and the data can be roughly divided into 6 clusters, each representing a potential subgroup. Although most subgroups form a clear clustered structure in 3D space, there are overlapping regions between some clusters. For example, there is significant overlap between pop3 and pop6, which may indicate that these two subgroups have high similarity in the main feature dimensions. Figure 1 a).

[0065] In rootless tree analysis, although there is some degree of confounding 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 relatively close in the feature space, reflecting their similarity on some key features.

[0066] In the population structure analysis, when K=6, the data were clearly divided into 6 subgroups. Although some overlap still exists between certain samples, these 6 subgroups are generally well-defined. Figure 1 c). This overlap indicates that there may be overlapping areas between different subgroups, with some samples sharing similar characteristics, making their affiliation within certain subgroups unclear. Overall, despite the overlap, these subgroups still reflect the main structural characteristics of the population relatively well.

[0067] LD decay in MPP was evaluated using 16,223,980 valid SNPs. The LD decay plot shows that LD decreases rapidly with increasing physical distance between markers. 2 When the value drops to 0.24, the physical distance of LD attenuation is approximately 10kb. Figure 1 d).

[0068] 2.3 Genome-wide association analysis of spikelet thickness trait

[0069] This invention used 16,223,980 valid SNPs for GWAS analysis of maize ear diameter. The results showed that multiple significant SNPs associated with maize ear diameter were identified on all 10 chromosomes across all environments. Figure 2 Notably, we consistently identified one significant SNP associated with maize ear diameter across all environments (22YS, 23YS, 24JH, BLUP): 7_21950015. Gene screening within the relevant significant SNP and its upstream and downstream 10kb range revealed a consistent gene, Zm00001eb303690, across multiple environments. Figure 3 Based on the existing functional annotations and trait correlation assessment, this invention considers this to be a candidate gene worthy of further study. (Table 3)

[0070] Table 3. Candidate genes co-localized by GWAS

[0071]

[0072] 2.4 Candidate gene haplotype analysis

[0073] like Figure 4-6Haplotype analysis of Zm00001eb303690 (the gene sequence of Zm00001eb303690 is shown in SEQ ID NO:1, corresponding to positions 21959514-21961325 on chromosome 7 from the 5′ end of reference genome version Zm-B73-REFERENCE-NAM-5.0) revealed 13 distinct haplotypes (Hap1, Hap2, ..., Hap13) among 1107 RILs. Box plots only show the seven haplotypes with the highest frequencies in the sample. These haplotypes exhibited different frequency distributions in the different populations studied. Hap10 showed the highest phenotypic value and had a wide frequency distribution in pop4. Furthermore, Hap10 differed significantly from several other haplotypes; therefore, we conclude that Hap10 is the dominant haplotype regulating ear diameter in maize.

[0074] The specific haplotype loci, using Zm-B73-REFERENCE-NAM-5.0 as the reference genome, are as follows on chromosome 7 of the genome version, viewed from left to right starting at the 5′ end:

[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 different haplotype bases 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 candidate gene Zm00001eb303690 in various maize tissues was analyzed. Figure 7 The results showed that the gene was highly expressed in the maize ear primordia, suggesting that it is related to the development of maize ear diameter.

[0101] In summary, the Zm00001eb303690 gene, as a functional marker for maize ear coarseness, 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 of traditional selection and ensuring the accuracy of offspring selection. It can be applied in breeding for genetic resource identification, MAS (Magnetic Analytic Hierarchy Process), genetic map construction, linkage mapping, and GS-MAS (Graduate-Growth Syndrome-Mass Analysis). MAS can rapidly and accurately identify crop genotypes, avoiding the tediousness, time-consuming nature, and screening difficulties of traditional selection, greatly improving 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 it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The application of products containing SNP molecular markers for ear diameter in marker-assisted selection breeding of maize with ear diameter, characterized in that, The specific SNP molecular marker sites, using Zm-B73-REFERENCE-NAM-5.0 as the reference genome, are the SNP sites on chromosome 7 of the genome version, starting from the 5' end: 21959583, 21959626, 21959747, 21959798, 21959805, 21959813, 21959824, 21959842, 21959869, 21959881, 21959950, 21959968, 21960001, 21960757, 21960775, 21960783. When the bases at positions 21960822, 21961015, 21961082, 21961210, and 21961282 are sequentially TCGTTGTGGGCGCTCAGGCC, the maize exhibits the dominant trait of thick ears. The maize in question is a hybrid offspring of maize variety Ye107 and CML312, CML395, YML46, YML32, NK40-1, or YML1218, respectively.

2. The application according to claim 1, characterized in that, The products include reagent kits, reagents, or bases. Because of the chip.

3. The application according to claim 1, characterized in that, The products include those prepared using high-throughput sequencing, fluorescence in situ hybridization, TaqMan probe method, ARMS-PCR method, or KASP method.

4. A method for screening maize germplasm with thick ears, characterized in that, Take a corn sample to be tested, detect the SNP molecular markers as described in claim 1, and screen germplasm that meets the molecular marker genotypes of the ear thickness dominant trait in claim 1. This germplasm is ear thick maize germplasm. The maize is the offspring of a cross between maize variety Ye107 and CML312, CML395, YML46, YML32, NK40-1 or YML1218, respectively.

Images