Molecular marker related to ear thickness of corn and application of molecular marker

Abstract

The invention relates to the technical field of agricultural biology, and particularly discloses a molecular marker related to the ear thickness of corn and application of the molecular marker. The invention provides application of a corn ear thickness molecular marker in ear thickness corn molecular marker assisted breeding, the molecular marker is a Zm00001d020000 gene, the nucleotide sequence of the Zm00001d020000 gene is as shown in SEQ ID NO: 1, the expression quantity of the gene is positively correlated with the corn ear thickness character, a plurality of SNP sites related to the ear thickness are analyzed and screened out by utilizing GWAS, and the SNP sites related to the ear thickness are used for identifying the corn ear thickness. Meanwhile, QTL (quantitative trait loci) point analysis is utilized for co-localization to SNP84054216 located on a No.7 chromosome, the locus is excavated to a marker gene Zm00001d020000 for regulating and controlling the ear thickness, and the result of the invention provides technical support for molecular marker breeding of a thick-ear corn variety.

Description

Technical Field

[0001] The present invention relates to the field of agricultural biotechnology, and specifically provides a molecular marker related to corn ear diameter and an application thereof. Background Art

[0002] Ear diameter is a key agronomic trait in maize and a crucial determinant of its yield and quality. Improving maize ear diameter can help optimize yield by increasing ear and kernel number, thereby meeting growing food demands. Therefore, research on this trait is of great significance. Maize is not only an important food crop worldwide but also a vital source of feed and industrial raw materials. Its high yield and wide adaptability make it a key crop in global agricultural production. However, compared to other food crops, maize ear diameter exhibits considerable genetic variability. Therefore, breeders strive to improve ear diameter by analyzing the relationship between ear diameter and different maize genotypes. Therefore, increasing ear diameter is a key goal in maize breeding and biotechnology improvement. Identifying marker genes associated with ear diameter in maize can provide technical support for molecular marker-assisted selection of high-yield maize. Summary of the Invention

[0003] To solve the above problems, the present invention provides molecular markers for regulating corn ear diameter and their applications. In addition, the gene Zm00001d020000 discovered by the present invention is a marker gene for regulating ear diameter, and its associated SNP has a high positive additive effect and dominant effect.

[0004] In order to achieve the above object, the present invention provides the following technical solutions: The present invention provides an application of a molecular marker for corn ear diameter in molecular marker-assisted breeding of corn ear diameter, wherein the molecular marker is Zm00001d020000 gene, the Zm00001d020000 The nucleotide sequence of the gene is shown in SEQ ID NO: 1, and the expression level of the gene is positively correlated with the corn ear thickness trait.

[0005] Furthermore, the present invention provides the application of corn ear thickness molecular markers in molecular marker-assisted breeding of ear thickness corn, wherein the molecular markers are a combination of SNP sites, including the 38900012 allele on chromosome 5 of corn being T and the 158922707 allele being A; the 84054216 allele on chromosome 7 being T; the 84060078 allele being C or the 84060181 allele being T. When one or more of the SNP sites present corresponding bases, the corn variety is judged to have the advantageous trait of ear thickness, and the corresponding genome version of the sites is Zm-B73-REFERENCE-NAM-4.0.

[0006] Furthermore, the present invention provides the application of corn ear thickness molecular markers in the whole genome selection breeding of ear thickness corn, wherein the molecular markers are a combination of SNP sites, including the 158942707th position from the 5' end on corn chromosome 5; and the 84054216th position from the 5' end on corn chromosome 7, and the corresponding genome version of the sites is Zm-B73-REFERENCE-NAM-4.0.

[0007] Furthermore, the present invention provides the use of a product for detecting the molecular marker in the application in molecular marker-assisted breeding of coarse corn, wherein the product detects the expression level of the molecular marker.

[0008] Furthermore, the present invention provides the use of a product for detecting the molecular markers in the application in molecular marker-assisted breeding of coarse corn, wherein the product detects the genotype of the molecular markers.

[0009] Furthermore, the application is to screen or assist in screening corn varieties with advantageous ear thickness traits.

[0010] Furthermore, the product includes a reagent or a kit.

[0011] Furthermore, 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.

[0012] Furthermore, the present invention provides a method for screening corn with an advantageous trait of ear thickness, characterized in that a corn sample to be tested is taken, and the molecular markers used in the application of claim 1 or 2 are detected. If the markers are consistent, a corn variety with an advantageous trait of ear thickness is obtained.

[0013] 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.

[0014] The term "genomic selection (GS)" refers to a modern breeding technology that uses whole-genome marker information for genetic evaluation and selection. It aims to predict individual breeding values ​​or phenotypic performances through high-density molecular markers, thereby accelerating the breeding process and improving selection efficiency.

[0015] The technical effects achieved by the present invention are: This study used the temperate maize inbred line Ye107, known for its smaller ear diameter, as a common parent and hybridized it with four tropical maize inbred lines to construct a maize multi-parent population with significant differences in ear diameter. GWAS and QTL analysis co-localized SNPs 7_84054216 on chromosome 7 and 5_158942707 on chromosome 5, both of which are significantly associated with ear diameter. Furthermore, the functional genes Zm00001d020000 and Zm00001d016356 regulating ear diameter were identified. SNP 7_84054216 had an additive effect of 0.20 and a dominant effect of 0.25. Haplotype analysis of Zm00001d020000 revealed that HAP-1 (CT) and HAP-2 (CC) were significantly more frequent in the NK40-1 population across all studied environments. Specifically, the HAP-1 haplotype was more frequently distributed in the population and positively correlated with increased ear diameter, while HAP-2 was relatively less frequent and exhibited a smaller effect. Furthermore, statistical analysis revealed that individuals carrying HAP-1 (CT) had significantly greater average ear diameter than those carrying HAP-2 (CC), suggesting that HAP-1 may have a significant genetic contribution to improving ear diameter. This result supports the role of HAP-1 as a favorable haplotype, potentially influencing ear diameter development by regulating genes related to cell growth and development. This study, through the analysis and screening of tagging SNPs, facilitates further research into the regulatory mechanisms of maize ear diameter. This reduces redundant testing costs during GS or MAS breeding, while preserving the integrity of genetic information. This approach aims to provide innovative genetic resources and precise improvement strategies for significantly increasing maize yield per plant, thereby promoting the optimization and efficiency of maize production. Specifically, this study also conducted genome-wide selection on the study population, further demonstrating the reliability of the identified loci and enabling the selection of potentially superior breeding materials with coarse ear diameters through genetic potential prediction (GEBV). BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below.

[0017] Figure 1. A pedigree of a NAM population with significant differences in ear diameter was constructed by hybridizing four thick-eared parents with parent Ye107. Figure 2. Phenotypic variation and correlation analysis of ear diameter of four populations in three environments. (a) Distribution of ear diameter phenotypes of four populations (population 1, population 2, population 3, and population 4) in three environments (Dehong, Baoshan, and Yanshan). Each box plot represents the ear diameter data of a specific population in a given environment, where the center line in the box represents the median, and the upper and lower edges of the box correspond to the third and first quartiles, respectively. Outliers outside the normal distribution range are displayed as separate points; (b) Pearson correlation coefficient of ear diameter phenotype of four populations (population 1, population 2, population 3, and population 4) in three environments (Dehong, Baoshan, and Yanshan). Each sub-graph corresponds to a population, and the matrix within the sub-graph shows the correlation between different environments; Figure 3. Marker density heat map of 10 maize chromosomes; Figure 4 Evolutionary tree of the four groups; Fig. 5 Principal component analysis of four groups; Fig. 6 LD decay of four populations; Figure 7 Manhattan plot (left) and QQ plot (right) of significant SNPs in maize ear diameter analyzed using GWAS in different environments, (a) BLUP, (b) Baoshan environment, (c) Dehong environment, and (d) Yanshan environment, showing SNPs associated with ear diameter; Figure 8 Significant QTLs associated with ear diameter (four QTLs co-localize with SNP7_84054216 identified by GWAS); Figure 9 Significant QTL associated with ear diameter (one QTL co-localized with SNP5_158942707 identified by GWAS) Figure 10 The relative position of the SNP and gene Zm00001d020000; Figure 11 haplotype of gene Zm00001d020000; Figure 12 (a) The location of gene Zm00001d020000 in four environments; (b) The distribution of the two haplotypes in NK40-1; (c) The differences in the performance of the two haplotypes in different environments; Figure 13 amino acid sequence changes; Figure 14 The expression of gene Zm00001d020000 in different tissues of different groups; Figure 15Comprehensive GEBV assessment results for (a) the Dehong environment, (b) the Baoshan environment, and (c) the Yanshan environment. A Venn diagram was drawn using the results of the common breeding samples from the 13 models. The number of intersections in the middle represents the number of samples screened by the 13 models. (d) Materials selected across all three environments. Figure 16 (a) Genes associated with two SNPs in the same interval on chromosome 7, (b) Genes associated with two SNPs in the same interval on chromosome 5. DETAILED DESCRIPTION

[0018] In order to further illustrate the present invention, the present invention is described in detail below with reference to the accompanying drawings and embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0019] The present invention provides a method for regulating corn ear diameter Zm00001d020000 Gene, Zm00001d020000 The nucleotide sequence of the gene is shown in SEQ ID NO. 1, which corresponds to bases 84056033 to 84060419 on chromosome 7 of maize in genome version Zm-B73-REFERENCE-NAM-4.0.

[0020] In the present invention, Ear Diameter (ED) is a commonly used abbreviation for corn ear diameter, which represents the cross-sectional diameter (ear diameter) of a corn ear.

[0021] The positions and bases of the molecular marker sites provided by the present invention are shown in Table 1 below. The corresponding genome version of the positions is Zm-B73-REFERENCE-NAM-4.0, calculated from the 5' end of chromosomes 5 and 7, respectively.

[0022] Table 1 Molecular marker site location and base information Example 1

[0023] 1.1 Plant materials Four tropical maize inbred lines, CML444, YML46, YML32, and NK40-1, were selected as female parents. These four female parents were hybridized with the excellent temperate maize inbred line Ye107 (male parent) to obtain the first generation (F1). The first generation (FI) was self-pollinated continuously until the F7 generation, and finally four multi-parent populations ( Figure 1), namely Population 1 (CML444 × Ye107), Population 2 (YML46 × Ye107), Population 3 (YML32 × Ye107), and Population 4 (NK40 - 1 × Ye107). During the selfing process, some inbred lines failed to survive due to inbreeding depression and other environmental stresses. Ultimately, this NAM (Nested Association Mapping) population, comprising 858 F7 RILs, was used to map genes affecting ED (ear diameter). Parental information is provided in Table 2.

[0024] Table 2 Parent information

[0025] 1.2 Experimental Design Four recombinant inbred lines (RILs) (Population 1 (CML444 × Ye107), Population 2 (YML46 × Ye107), Population 3 (YML32 × Ye107), and Population 4 (NK40-1 × Ye107) were used for the study. These lines were planted in Baoshan City, Yanshan County, and Dehong, Yunnan Province. A Latin square design was used, with 4-m-long rows, 14 plants per row, and 25-cm plant spacing, with three replicates under standard field management. Sampling was performed near maturity (generally at the milky or waxy stage) during the corn growing season, when ear diameter had stabilized. Approximately 10 ears per row were measured to ensure representativeness and to avoid kernel damage. Ear diameter was measured using a vernier caliper, measured from base to tip, with the accuracy recorded to the nearest millimeter.

[0026] 1.3 Heritability analysis After preliminary processing of the phenotypic data collected at three time locations, Excel-2021 and IBMSPSS Statistics 20 were used to perform statistical analysis on the phenotypic data, and the mean, minimum, maximum, standard deviation (SD), coefficient of variation (CV), skewness and kurtosis were calculated. The kurtosis and skewness were used to evaluate the normality of the frequency distribution and calculate the broad-sense heritability of ED.

[0027] DNA extraction and genome sequencing During the reproductive stage, genomic DNA was extracted from leaves of RILs of the NAM population using the cetyltrimethylammonium bromide (CTAB) method. The genomic DNA was then digested with PstI and MspI restriction endonucleases. Next, barcoded adapters were ligated to the digested DNA fragments using T4 DNA ligase. All samples were purified using the QIAquick PCR Purification Kit (QIAGEN, Valencia, CA, USA). Primers complementary to the adapters were then amplified using polymerase chain reaction (PCR), and PCR products were purified and quantified using the Qubit dsDNA HS Assay Kit (Life Technologies, Grand Island, NY, USA). PCR products of 200–300 bp were selected for library construction, and fragment selection was performed using the Egel system (Life Technologies). The concentration of each library was then determined using the Qubit 2.0 Fluorometer and the Qubit dsDNA HS Assay Kit (Life Technologies). Template preparation and library sequencing were performed using the Ion PI HiQ Chef Kit (Thermo Fisher, USA). Sequencing was performed on an Ion Proton sequencer (Life Technologies, software version 5.10.1) using a P1v3 chip. The Ion Proton system generates variable-length sequencing reads. After sequencing, the raw data were quality-controlled to remove adapter sequences and low-quality reads (sequences with a base quality score (Q) ≤ 5 exceeding 50%). Sequencing data from the four subpopulations of RILs were cleaned, and the resulting high-quality sequences were analyzed using TASSEL v5.0 software. The maize B73_V4 (full name: Zm-B73-REFERENCE-NAM-4.0) genome was used as the reference genome for alignment, and sequence alignment was performed using BWA software with the following parameters: mem -t 4 -k 32 -M -R. Alignment results were converted to SAM / BAM files using SAMtools, and SNP detection was performed using the Genome Analysis Toolkit (GATK) software. SNP filtering was performed using PLINK v1.9 software with the -geno parameter set to 0.2 and -maf parameter set to 0.05. SNPs with a missingness rate exceeding 10% and a minimum allele frequency (MAF) below 5% were removed. SNP annotation was performed using ANNOVAR software.

[0028] 1.5 Phylogenetic tree, population structure, and linkage disequilibrium analysis Phylogenetic trees were constructed to analyze genetic differentiation among populations. Genetic distance matrices between 858 RILs in the NAM population were calculated using TreeBeST software (version 1.9.2) based on the filtered SNP dataset. Subsequently, a phylogenetic tree was constructed using the neighbor-joining method, with support values ​​calculated using 1000 bootstrap replicates. Population structure was inferred using Admixture software, using default parameters. Admixture used maximum likelihood estimation to infer ancestral composition based on the SNP genotyping dataset. The optimal number of clusters was determined using the cross-validation error rate (CV error), with the K value corresponding to the minimum CV error being the optimal number of clusters. Linkage disequilibrium (LD) between pairwise markers (r²) was calculated using PopLDdecay software (v3.40). LD decay curves were plotted using the built-in script Plot_OnePop.pl. LD decay describes the process by which linkage disequilibrium decreases over time or generations. The r² value ranges from 0 to 1, with values ​​closer to 1 indicating greater linkage disequilibrium between two loci. LD decay analysis helps determine the minimum number of markers required for GWAS and assess the detection efficiency and accuracy of GWAS.

[0029] GWAS and QTL A genome-wide association study (GWAS) of ED was conducted using a mixed linear model (MLM) implemented in EMMAX. Individual kinship and population stratification are major factors contributing to false positives during GWAS analysis. MLM models account for population structure and individual kinship, reducing false positives during GWAS. This study employed an MLM model for marker-trait associations, using population structure as a fixed effect and individual kinship as a random effect to mitigate the influence of these factors. The following formula was used for the mixed linear model analysis:

[0030] y is the phenotypic trait, X is the indicator matrix of fixed effects, α is the estimated parameter of fixed effects; Z is the indicator matrix of SNPs, β is the effect of SNPs; W is the indicator matrix of random effects, μ is the predicted random individual, and e is the random residual, which obeys e~(0, δe 2).

[0031] QQ plots and Manhattan plots were plotted by R software (v4.3.3). Independent markers were calculated using PLINK (parameters: -indep-pairwise 50 5 0.2). The adjusted significance threshold -log10(P) > 4.5 was calculated using the formula -log10(l / total number of SNPs) and Bonferroni correction for identifying significant SNPs.

[0032] Linkage analysis was performed to determine whether the important SNPs identified by GWAS overlap and lie within the QTL intervals identified by QTL mapping. Linkage analysis was performed on four populations, totaling 858 RILs. Genetic linkage maps of the four populations were constructed using JoinMap v4. QTL associated with ED were identified using Windows QTL Cartographer v2.0 by composite interval mapping (CIM). The LOD threshold was set according to 1000 permutation tests, with a significance level P < 0.05. QTL with a LOD threshold > 2.5 were considered significant. The percentage of phenotypic variance explained (PVE) for individual QTL was calculated by squaring the partial correlation coefficient (r 2 ).

[0033] 1.7 Identification and functional annotation of candidate genes B73_V4 reference genome was used to identify and annotate candidate genes associated with ear diameter (ED). QTL and GWAS identified candidate genes were compared with previous studies in public databases, such as NCBI, Maize GDB, Inter Pro, and UniProt.

[0034] 1.8 Haplotype analysis The 20 Kb region upstream and downstream of important loci were screened to identify candidate genes. Haploview v4.2 software was used to analyze important SNPs / candidate genes to identify dominant haplotypes. Subsequently, haplotypes were classified according to their corresponding phenotypes and boxplots were generated.

[0035] 1.9 Genome-wide selection Implementation of genomic selection requires first constructing a reference population for establishing genomic prediction equations. Individuals in this reference population have both phenotypes and marker genotypes, and mathematical models are used to estimate the genetic effects of markers on traits. Second, a candidate population is required, where individuals provide only marker genotypes. The genomic estimated breeding value (GEBV) for each individual in the candidate population is calculated by summing marker effects. High-performing individuals are then selected for breeding based on the GEBV. The models used in this study for estimating breeding values ​​include gBLUP, rrBLUP, Bayes A (BA), Bayes B (BB), Bayes C (BC), Bayesian Lasso (BL), Bayes Ridge Regression (BRR), RKHS (Reproducing Kernel Hilbert Space), Random Forest, lasso Regression, Ridge Regression, SVR, and LightGBM.

[0036] 2. Results 2.1 Analysis of ear diameter phenotype The data of ear diameter ED phenotype of four multi-parent populations under three environments were collected and statistically analyzed. The results are shown as follows (Table 3, Figure 2 ): In the three environments, the absolute values ​​of the skewness and kurtosis of ED in different populations were all less than 1, showing a normal distribution, which is consistent with the genetic characteristics of quantitative traits; we have four populations (population 1, population 2, population 3, population 4). In order to compare whether there are significant differences in ear diameter among different populations in different locations, we used ANOVA (analysis of variance). The results showed that in the three environments of Dehong, Baoshan, and Yanshan, there were significant differences in ear diameter among different populations (p < 0.00001); we used correlation analysis to explore the relationship between the ear diameter phenotypes of the same population in different environments, and calculated the Pearson correlation coefficient between different environments (Dehong, Baoshan, Yanshan) to evaluate the linear relationship between them. The results showed that r was greater than 0.7 in the four populations in different environments, which means that the ear diameter phenotypes measured in different environments are consistent and show a positive correlation.

[0037] Table 3 Statistical analysis of ear diameter phenotype

[0038] Note: Dehong, Baoshan and Yanshan represent the tests conducted in Dehong in 2018, Baoshan in 2019 and Yanshan in 2021, respectively.

[0039] 2.2 Population structure, principal component analysis (PCA), and linkage disequilibrium analysis Phylogenetic tree and PCA principal component analysis showed that 858 RILs were divided into four groups, which was consistent with the experimental selection design. The admixture observed in these four groups may be due to gene introgression or hybridization during breeding ( Figure 4 , Figure 5 ); Figure 3 A heat map of marker density for the 10 maize chromosomes is shown. The number of SNPs on chromosomes 1 to 10 is 81,417, 66,909, 66,084, 75,031, 57,271, 47,553, 52,316, 49,743, 44,543, and 43,284, respectively; the original SNP dataset for each polygenetic population was used for linkage disequilibrium (LD) decay analysis ( Figure 6 We calculated the LD decay of the four populations and found that 2 The threshold value of 20 kb of physical distance tended to be stable at 0.2, so we subsequently screened genes 20 kb upstream and downstream of the significantly associated SNP.

[0040] 2.3 Genome-wide association analysis of ear diameter In this study, the ED phenotype data from the multi-parent population in three environments all followed a normal distribution and were suitable for GWAS analysis. We used a mixed linear model (MLM) to conduct a GWAS of ED in different environments, with a threshold set at −log10p >4.5. Figure 7 Fifteen SNPs with high concordance across multiple environments were identified. In particular, SNP 5-38900012 (Ref / Alt = C / T) on chromosome 5 was found to have a PVE (Phenotypic Variation Explained) greater than 10%.

[0041] 2.4 QTL mapping in panicle populations To identify QTLs associated with ED, QTL mapping was performed in four polyphyletic populations (populations 1, 2, 3, and 4) under three environments. Best linear unbiased predictions (BLUPs) for ED were performed for all environments. SNP markers with a missingness rate greater than 10% and sites with a minimum allele frequency less than 5% were filtered out, and the LOD threshold was set at ≥2.5. The results are shown below: A total of 14 significant QTLs were detected in populations 1, 3, and 4 in the three environments and BLUP (Table 4). These QTLs with LOD ≥ 2.5 were considered to be significantly associated with ED. These 14 QTLs were distributed on chromosomes 1, 2, 3, 4, 5, and 7. Among these QTLs, qED7-3 had the largest LOD of 4.3, which came from the Dehong environment of population 4. At the same time, this QTL explained the highest phenotypic effect of 10.8%, with an additive effect of 18.6, which was positive. In addition, the phenotypic explanation rate of qED7-2 reached 10.1%, with an additive effect of 17.2. Given the high phenotypic explanation rates and additive effects of these two QTLs, we believe that these two QTLs may affect ear diameter.

[0042] Table 4 Significant QTLs associated with ear diameter

[0043] 2.5 Joint analysis of GWAS and QTL The QTL and GWAS results were combined to obtain more accurate genetic loci and candidate genes. The results showed that SNP7-84054216 on chromosome 7 identified by GWAS was located in the interval of qED7-1, qED7-2, qED7-3, and qED7-4 in population 4 (NK40-1 population) (Table 5, Figure 8 ), there is co-localization between them; in addition, SNP5-158942707 on chromosome 5 identified by GWAS is located in the interval of qED5 - 1 of population 1 (CML444 population) (Table 5, Figure 9 ) also co-localizes. Notably, SNP 7-84054216 has a high positive additive effect and a dominant effect, meaning that individuals carrying this SNP or QTL perform better on the trait. Linkage disequilibrium (LD) decay analysis screened candidate genes within 20 kb upstream and downstream of the tSNPs. Comparing the genes identified by GWAS with those obtained from QTL mapping, we found that gene Zm00001d020000 was consistently identified, suggesting that this gene is associated with ear diameter.

[0044] Table 5 Candidate genes identified by joint GWAS and QTL mapping analysis

[0045] 2.6 Genes Zm00001d020000 We first determined the location of gene Zm00001d020000 for haplotype analysis ( Figure 10), two haplotypes, HAP-1 (CT) and HAP-2 (CC), were identified to be significantly associated with ear diameter in three environments: BLUP and two environments. The haplotypes were located on chromosome 7 of maize from 84060078 to 84060181 (5' end) Figure 11 ). For example, in NK40-1 population, the analysis showed that the frequency of HAP-1 was significantly higher than that of HAP-2 in all the studied environments. Specifically, the haplotype HAP-1 had a higher distribution frequency in the population, which increased the ear diameter of the population, i.e., the distribution frequency of the haplotype HAP-1 in the population was positively correlated with the ear diameter. In addition, statistical analysis further revealed that the average ear diameter of individuals carrying HAP-1 (CT) was significantly greater than that of individuals carrying HAP-2 (CC), which indicated that HAP-1 might have an important genetic contribution to the improvement of ear diameter, which supported the role of HAP-1 as a favorable haplotype, which might affect the development process of ear diameter by regulating genes related to cell growth and development Figure 12 ).

[0046] Through statistics, in the four populations, the average ear diameter of individuals was significantly higher than that of individuals with other base types when the SNP sites SNP5_38900012, SNP5_158942707, and SNP7_84054216, SNP7_84060078, and SNP7_84060181 in Table 1 were T, A, T, C, and T, respectively.

[0047] From the results of the phenotypic data, we found that the ear diameter of different families in population 1 (NK40-1) was significantly different. In order to explore whether the gene Zm00001d020000 in NK40-1 was mutated, further research found that there was a non-synonymous variation in the nucleotide sequence of the conserved domain of Zm00001d020000 (the 64th base of the Zm00001d020000 gene was mutated from T to A). This variation changed the 19th amino acid sequence (from valine to aspartic acid), resulting in a change in the motif Figure 13 ). Therefore, it is speculated that the function of the gene Zm00001d020000 in NK40-1 is changed by SNP mutation, which subsequently affects the ear diameter. In order to further verify whether the gene Zm00001d020000 exists in the ear development, we analyzed the expression profile of Zm00001d020000 in multiple tissues in the four populations Figure 14). The results showed that the expression level of this gene reached a higher level after normalization in population 4 (the population co-localized to this gene), especially the average ear diameter of individuals with higher expression level of this gene was significantly higher than that of individuals with lower expression level. In addition, we found that the expression of Zm00001d020000 in the ear tissue of other populations was also relatively high, which further supported the finding that this gene affected the size of ear diameter.

[0048] 2.7 Whole genome selection To further explore the genetic mechanism behind ear diameter and improve the efficiency of genetic improvement, we integrated the whole genome data through the GS method to explore the genotype-phenotype relationship related to ear diameter in multiple parent populations, and screened potential excellent breeding materials through genetic potential prediction (GEBV). While estimating GEBV, the effect value of SNP site was also estimated. According to the ranking of GEBV, the top 20% of GEBV was selected as the breeding sample, and 13 individuals were further selected from the 20% model to obtain the comprehensive evaluation results of GEBV (Figure 15). In the environment of Dehong, the number of individuals selected was the largest, reaching 163, indicating that the stability of GEBV prediction in this environment was higher. In the environment of Yanshan, the prediction results of GEBV were relatively scattered, and only 71 common individuals were selected, showing that the environment had a greater impact on the prediction of GEBV. In addition, we found that 45 individuals were selected in Baoshan, Dehong, and Yanshan environments, representing the most robust candidate breeding materials. These individuals had consistent GEBV rankings in different environments, indicating that they had strong genetic potential and wide adaptability, and they had larger ear diameter and SNP effect value, making them suitable as the core breeding population. While estimating the effect value of SNP site, we ranked all SNP effect values and found that SNP7-84054329 on chromosome 7 ranked in the front (top 5%) among all SNPs. This finding is related to SNP7-84054216 previously co-localized, which belongs to the same segment (chromosome 7, 84034216-84074221) as the candidate gene Zm00001d020000 that we believe affects the size of ear diameter Figure 16 (a). In addition, SNP5_158942627 located on chromosome 5 was also detected in GS, and SNP5_158942707, which is 80 bp away from it, was previously co-localized, and the candidate gene Zm00001d016356 associated with this segment was also considered by us to affect the size of ear diameter Figure 16 (b).

[0049] Although the above embodiments have been described in detail, it should be understood that these are only some embodiments of the present application, but not all embodiments. Other embodiments can be obtained on the basis of the above embodiments without creativity, and these embodiments all belong to the protection scope of the present application.

Claims

1. Application of a molecular marker for corn ear diameter in molecular marker-assisted breeding of corn ear diameter, characterized in that: The molecular marker is Zm00001d020000 gene, the Zm00001d020000 The nucleotide sequence of the gene is shown in SEQ ID NO: 1, and the expression level of the gene is positively correlated with the corn ear thickness trait.

2. Application of a molecular marker for corn ear diameter in molecular marker-assisted breeding of corn ear diameter, characterized in that: The molecular marker is a combination of SNP sites, including the 38900012 allele on chromosome 5 of corn, which is T, and the 158922707 allele is A; the 84054216 allele on chromosome 7 is T; the 84060078 allele is C or the 84060181 allele is T. When one or more of the SNP sites present corresponding bases, it is judged to be a corn variety with an ear thickness advantage trait, and the corresponding genome version of the site is Zm-B73-REFERENCE-NAM-4.

0.

3. Application of corn ear diameter molecular markers in whole genome selection breeding of corn with ear diameter, characterized in that: The molecular marker is a combination of SNP sites, including position 158942707 from the 5' end on chromosome 5 of maize; It is located at position 84054216 from the 5' end on chromosome 7 of maize, and the corresponding genome version of the site is Zm-B73-REFERENCE-NAM-4.

0.

4. Detection of the use of the product of molecular markers in the application of claim 1 in molecular marker-assisted breeding of coarse corn, characterized in that: The product detects the expression level of the molecular marker.

5. Detection of the use of the product of molecular markers in the application of claim 2 in molecular marker-assisted breeding of coarse corn, characterized in that: The product detects the genotype of the molecular marker.

6. The use according to claim 4 or 5, characterized in that The application is to screen or assist in screening corn varieties with advantageous ear thickness traits.

7. The use according to claim 4 or 5, characterized in that The product includes a reagent or a kit.

8. The use according to claim 4 or 5, characterized in that The products include those prepared using PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization, TaqMan probe method, ARMS-PCR method or KASP method.

9. A method for screening corn for the dominant trait of ear thickness, characterized in that: A corn sample to be tested is taken and the molecular marker used in the application of claim 1 or 2 is tested. If the marker is consistent, a corn variety with an ear thickness advantage trait is obtained.

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