Maize line grain number molecular marker gene Zm00001eb149810 and application thereof

Abstract

The invention belongs to the technical field of molecular breeding of crops, and particularly discloses a molecular marker gene Zm00001eb149810 for the row number of corn and application of the molecular marker gene Zm00001eb149810, the molecular marker is a Zm00001eb149810 gene or a protein coded by the gene, the nucleotide sequence of the gene is shown as SEQ ID NO: 1, and the molecular marker gene Zm00001eb149810 is a Zm00001eb149810 gene or a protein coded by the Zm00001eb149810 gene. According to the invention, research proves that the SNP 3-191281741 and the candidate gene Zm00001eb149810 located on the corn chromosome 3 have a remarkable effect in the aspect of increasing the corn row grain number, and the candidate gene and the gene locus found in the invention can deepen the understanding of people on the complex genetic pathway of the corn row grain number; a new marker site is provided for molecular marker-assisted breeding of the corn row grain number, and precious genetic resources and innovative strategies are provided for breeding of corn high-yield varieties.

Description

Technical Field

[0001] The present invention belongs to the technical field of crop molecular breeding, and particularly relates to the maize kernel number per row molecular marker gene Zm00001eb149810 and its application. Background Art

[0002] Maize ( Zea mays L.) is one of the most important food crops in the world, and its yield is directly related to global food security and economic stability. Maize yield is determined by multiple factors, among which the kernel number per row (KNR) is one of the important parameters of maize ear characteristics. The increase in kernel number per row can significantly increase the number of kernels per ear and the yield per ear of maize without significantly changing the biomass per plant. Therefore, exploring quantitative trait loci (QTLs) and functional genes related to maize kernel number per row is of great significance for maize genetic improvement and yield increase.

[0003] The genetic regulation of maize kernel number per row is a complex quantitative trait, which is jointly affected by multiple genes. Research shows that the genetic variation of kernel number per row is mainly jointly regulated by multiple minor-effect QTLs and a few major-effect QTLs. In recent years, some QTLs related to kernel number per row have been mapped through methods such as linkage analysis and association analysis. However, there are still some important research gaps. Most of the current QTL studies focus on certain specific germplasm resources of maize. The kernel number per row is regulated by multiple minor-effect QTLs and is significantly affected by environmental factors. The currently identified QTLs and functional genes show differences in different genetic backgrounds and environments, which limits their wide application in maize breeding. Therefore, a systematic study of the genetic variation of kernel number per row in different ecological regions and variety populations to identify QTLs that stably express under different environmental conditions is still an urgent problem to be solved.

[0004] In addition, as a complex quantitative trait, the kernel number per row is usually jointly affected by multiple genes and environmental factors. Although many QTLs related to kernel number per row have been mapped, the association between these QTLs and specific functional genes is still unclear. During the process of QTL mapping, although possible chromosomal intervals can be identified, the specific functional genes have not been accurately determined. Therefore, it is necessary to combine multi-dimensional omics data such as high-throughput sequencing technology, transcriptomics, and proteomics, and through more accurate gene mapping and function verification, to explore potential functional genes. Exploring more QTLs and functional genes related to maize kernel number per row not only helps to deeply understand the genetic regulation mechanism of maize kernel number per row, but also provides important gene resources for the genetic improvement of maize yield. Through molecular marker-assisted selection and gene editing technology, excellent QTLs and functional genes can be introduced into maize breeding, thereby achieving a significant increase in maize yield. Summary of the Invention

[0005] Based on genome-wide association analysis, the present invention uses the temperate germplasm Ye107 as a common parent and hybridizes it with 4 temperate germplasms (Q11, R-2-1-1, YML1218, Chang7-2). Multi-generation continuous self-crossing is carried out in tropical and temperate regions respectively to establish 4 recombinant inbred line populations. These recombinant inbred line populations are planted in Yanshan County, Wenshan Prefecture, Yunnan Province and Jinghong City, Xishuangbanna Prefecture, Yunnan Province respectively, and phenotypic identification is carried out. Through biparental QTL mapping and genome-wide association analysis (GWAS), QTL intervals and loci related to the number of kernels per row in maize are identified. Consensus sites SNP: 3-191281741 and candidate gene Zm00001eb149810 that are significantly related to the number of kernels per row are jointly identified in genome-wide association analysis and QTL. Subsequently, molecular breeding can be used to apply this candidate gene to improve maize yield.

[0006] Specifically, the present invention provides the following technical solutions: On the one hand, the present invention provides a molecular marker for the number of kernels per row in maize. The molecular marker is the Zm00001eb149810 gene or the protein encoded by this gene, and the nucleotide sequence of the gene is shown in SEQ ID NO: 1.

[0007] Furthermore, the expression level of the Zm00001eb149810 gene or the protein encoded by this gene is positively correlated with the number of kernels per row in maize ears.

[0008] On the other hand, the present invention provides a molecular marker for the number of kernels per row in maize. The molecular marker includes single nucleotide polymorphism (SNP) sites 191286413, 191286428, 191286494, 191286617, 191286625, 191286660, 191286667, 191286795, 191286797, 191286800, 191286803, 191286822, 191286836, 191286844, 191286845, 191286859, 191286862, 191286915, 191286920, 191286987, 191286995, 191287018, 19128702 on chromosome 3 and SNP 3-191281741 tightly linked to them. The reference genome version of these sites is B73_v5. The present invention screens out representative molecular marker sites within the Zm00001eb149810 gene and the SNP 3-191281741 marker linked to the Zm00001eb149810 gene, and this site presents C / G polymorphism. It can reduce the redundant detection cost in molecular breeding while retaining the integrity of genetic information.

[0009] On the other hand, the present invention provides an application of a product for detecting any one of the said molecular markers in identifying or assisting in the identification of the number of kernels per row trait in maize, wherein the product detects the genotype of the said molecular marker, or detects the expression level of the Zm00001eb149810 gene or the protein encoded by this gene.

[0010] Furthermore, the said product includes reagents, reagent kits or gene chips.

[0011] Furthermore, the said product includes products prepared by using methods such as PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization method, TaqMan probe method, ARMS-PCR method or KASP method.

[0012] On the other hand, the present invention provides any one of the following applications of any one of the said molecular markers: a) Genetic diversity analysis of the number of kernels per row in maize; b) Construction of a molecular genetic map of the number of kernels per row in maize; c) Genome-wide association analysis of the number of kernels per row in maize; d) Identification of maize varieties with multiple rows of kernels; e) Molecular marker-assisted selection breeding for the number of kernels per row trait in maize; f) Genome-wide selection breeding for the number of kernels per row trait in maize; g) Gene editing breeding for the number of kernels per row trait in maize.

[0013] On the other hand, the present invention provides a method for screening maize with a large number of kernels per row. Take a maize sample to be detected, detect the said molecular marker, and if it conforms to the molecular marker type, a maize variety with a large number of kernels per row can be obtained.

[0014] The term "Molecular Marker-assisted Selection (MAS)" is a breeding technique that selects offspring lines with the help of molecular markers of target traits, and then obtains excellent individual plants containing the target gene.

[0015] The term "Genomic Selection (GS)" is a modern breeding technique based on genomic marker information for genetic evaluation and selection, aiming to predict the breeding value or phenotypic performance of individuals through high-density molecular markers, so as to accelerate the breeding process and improve the selection efficiency; The term "GS-MAS" is a breeding strategy that combines Genomic Selection (GS) and Marker-assisted Selection (MAS). GS-MAS uses high-density marker information covering the whole genome and phenotypes to estimate the breeding value of individuals, and at the same time associates major and minor genes, and predicts and selects complex traits (low heritability, difficult to measure, etc.) at an early stage through breeding values, so as to shorten the generation interval, accelerate the breeding process, improve the selection accuracy and save costs.

[0016] The present invention is based on genome-wide association study ( Figure 9 ), using the temperate germplasm Ye107 as the common parent, hybridizing with 4 temperate germplasms (Q11, R-2-1-1, YML1218, Chang7-2), and conducting multi-generation continuous self-crossing in tropical and temperate regions to establish 4 recombinant inbred line populations. A consensus site SNP 3-191281741 significantly associated with kernel number per row was co-identified in genome-wide association study and QTL. This SNP can be located in multiple different environments, and the QTL interval where it is located explains 19.37% of the phenotypic variation. The associated candidate functional gene Zm00001eb149810 mainly encodes NAC domain-containing protein 68, which regulates cytokinin signaling during cell division and has a significant effect on regulating the kernel number per row in maize. The expression level of the Zm00001eb149810 gene in the maize ear primordium is significantly higher than that in other tissues. By using the marker SNP 3-191281741 and the functional gene Zm00001eb149810, the generation interval can be shortened, the breeding process can be accelerated, the selection accuracy can be improved, and the cost can be saved during the breeding process of high-yield maize with more kernels per row. At the same time, the results of the present invention contribute to further studying the regulation mechanism of the kernel number per row in maize, and provide more stable and accurate markers for genetic resource identification, MAS, genetic map construction, linkage mapping, and GS-MAS in high-yield maize breeding applications. Brief Description of the Drawings

[0017] Figure 1 Phenotype diagrams of parents and recombinant inbred line populations: Among them, the parents P1 (Q11), P2 (R-2-1-1), P3 (YML1218), P4 (Chang7-2) are hybridized with the common parent P5 (Ye107) to obtain the F1 generation, and then the recombinant inbred line populations of populations 1 - 4 are obtained through continuous self-crossing for multiple generations.

[0018] Figure 2 Distribution diagrams of single nucleotide polymorphisms (SNPs) on 10 chromosomes of 4 recombinant inbred line populations.

[0019] Figure 3 Genetic diversity analysis diagrams: a Principal component analysis; b Phylogenetic tree; c Kinship matrix.

[0020] Figure 4 Genome-wide association analysis results diagrams of kernel number per row resistance: From top to bottom are the results of Jinghong in 2021 (21JH), Yanshan in 2022 (22YS), Yanshan in 2023 (23YS), and best linear unbiased prediction. The Manhattan plot (left) and Q-Q plot (right) show the single nucleotide polymorphisms (SNPs) associated with GLS resistance.

[0021] Figure 5 Consensus locus map co-identified in genome-wide association studies and QTLs

[0022] Figure 6 Gene information analysis map of Zm00001eb149810: a represents the linkage disequilibrium block of the Zm00001eb149810 gene, b represents the position of the gene and single nucleotide polymorphisms (SNPs), c represents the distribution of each haplotype in the parents, and d represents the haplotype of the gene.

[0023] Figure 7 Haplotype map of single nucleotide polymorphisms (SNPs) corresponding to the Zm00001eb149810 gene

[0024] Figure 8 Expression map of the Zm00001eb149810 gene in various tissues of maize

[0025] Figure 9 Technical roadmap for the research of the present invention Detailed implementation manners

[0026] 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.

[0027] The gene sequence of Zm00001eb149810 marked by the present invention is shown in SEQ ID NO: 1, corresponding to positions 191284856 - 191287422 from the 5' end of chromosome 3 of the maize genome version B73_v5 (full name: Zm-B73-REFERENCE-NAM-5.0).

[0028] 1. Experimental materials

[0029] In the present invention, Ye107 was used as the common parent and was respectively crossed with 4 temperate maize varieties Q11, R-2-1-1, YML1218, and Chang7-2 from different heterotic groups to obtain F 1 generations. Four recombinant inbred line populations were obtained by continuous self-crossing for multiple generations using the single-seed descent method, namely population 1 (pop1: Q11×Ye107), population 2 (pop2: R-2-1-1×Ye107), population 3 (pop3: YML1218×Ye107), and population 4 (pop4: Chang7-2×Ye107). Approximately 200 lines were selected from each recombinant inbred line population to construct a nested association population. Information such as the pedigree and population size of the parents is listed in Table 1.

[0030] Table 1. Pedigree and ecotype of parental materials used in the research

[0031] During the self - crossing process, some inbred lines were lost due to inbreeding depression and other stresses. Eventually, a sub - population consisting of 540 maize recombinant inbred lines composed of 4 recombinant inbred lines was used in the present invention. The number of families in each recombinant inbred line sub - population varied, ranging from 111 in the R - 2 - 1 - 1×Ye107 recombinant inbred line population to 151 in the Chang7 - 2×Ye107 recombinant inbred line population.

[0032] 2. Field experiments and phenotypic data analysis

[0033] The field experiments were conducted in 3 different environments. The above - mentioned materials were planted in Jinghong City, Xishuangbanna Prefecture, Yunnan Province (24◦43′N, 98◦58′E, 920 m) in 2021, and in Yanshan County, Wenshan Prefecture, Yunnan Province (23◦59′N, 104◦33′E, 1830 m) in 2022 and 2023. A randomized complete block design (RCBD) was adopted with three replicates. Each experimental plot was 3.5 meters long, with 14 plants per row, a row spacing of 0.7 meters, and a plant spacing of 0.25 meters. After the ears matured, about 5 uniform ears were selected from each inbred line for the phenotypic identification of the number of kernels per row. Excel 2021 and SPSS (Statistics 20) were used to organize and calculate the phenotypic data in two environments, and then descriptive statistical analysis, normal distribution test, and correlation analysis were carried out.

[0034] To eliminate the influence of environmental factors on phenotypes, the mixed - linear model of the lme4 package in R (v3.3.3) was used to perform the best linear unbiased prediction (BLUP) on the original phenotypic data of the number of kernels per row of each recombinant inbred line in 3 environments. After normalizing the phenotypic data, the final best linear unbiased prediction phenotypic data values of each recombinant inbred line were obtained for subsequent genome - wide association analysis and QTL mapping.

[0035] 3. DNA extraction and WGS sequencing

[0036] The cetyltrimethylammonium bromide (CTAB) method was used to extract the genomic DNA of maize leaves during the reproductive period. The genomic DNA was extracted from maize seedling leaves by the CTAB method. The DNA concentration was quantified using Qubit and then diluted to 20 ng / μl according to the protocol of Poland et al. (2011) for library construction. Five parental lines and the progeny population were sequenced by whole - genome re - sequencing (WGS). Clean reads were obtained after quality control and aligned with the maize reference genome B73_v5 using the BWA software. The Genome Analysis Toolkit was used to identify single - nucleotide polymorphisms (SNPs).

[0037] 4. Population phylogenetic tree, principal component analysis, population genetic structure, and linkage disequilibrium analysis

[0038] We used TreeBeST (version: Treebest-1.9.2) software to calculate the distance matrix and construct the phylogenetic tree. We used GCTA software (version 1.94.1) to obtain the genetic relationship matrix (GRM) through the -make-grm parameter and used the -pca3 option to extract the first three principal components for principal component analysis (PCA). After obtaining the PCA results, the first two principal component values were used to perform three-dimensional visualization of the samples involved in the analysis.

[0039] We used Admixture software to infer population structure and determined the optimal number of clusters based on the cross-validation error rate (CV error). We used PopLDdecay software (version 3.04) to calculate the pairwise linkage disequilibrium degree (r2) between markers and used the built-in script Plot_OnePop.pl in the software to plot the linkage disequilibrium decay to determine the number of markers required for genome-wide association studies and to determine the detection efficiency and accuracy of genome-wide association studies.

[0040] 5. Genome-wide association study

[0041] Considering population structure and kinship among individuals, we used the mixed linear model association analysis method of EMMAX to perform genome-wide association study on the kernel number per row. We used R software (version 4.3.3) to create Q-Q scatter plots and Manhattan plots. We used plink to calculate independent markers with the parameter (--indep-pair-wise 50 5 0.2). The significance threshold -log10(P) > 5 calculated using the formula -log10(1 / number of SNPs) was used to identify significant single nucleotide polymorphisms (SNPs) related to the kernel number per row in maize (Bonferroni correction was used during calculation). We identified and annotated candidate genes related to the kernel number per row using the B73_v5 reference genome. During the genome-wide association study, individual kinship and population stratification are the main factors causing false positive associations. Therefore, a mixed linear model was used for trait association analysis, with population genetic structure as a fixed effect and individual kinship as a random effect to correct for the effects of population structure and individual kinship.

[0042] 6. Linkage map and QTL analysis

[0043] Using the single nucleotide polymorphisms (SNPs) that differ between the parents of 4 subgroups as molecular markers, a genetic linkage map was constructed using JoinMap v4 software. The QTLs for kernel number per row were determined using the composite interval mapping (CIM) method by Windows QTL Cartographer v2.0. The LOD threshold was set based on 1000 permutation tests, and the significance level was P ≤ 0.05. QTLs with an LOD threshold ≥ 2.5 were considered significant. The percentage of phenotypic variation explained (PVE) by a single QTL was calculated from the square of the partial correlation coefficient (r 2 ).

[0044] 7. Candidate gene prediction and functional annotation

[0045] The B73_v5 reference genome was used to identify and annotate candidate genes related to kernel number per row. The candidate genes identified by QTL and genome-wide association analysis were compared with the existing research results in public databases such as NCBI, Maize GDB, InterPro, and UniProt.

[0046] 8. Haplotype analysis

[0047] Regions 1 MB upstream and downstream of the significant loci or gene positions were selected, and the block values were calculated using haploview software and displayed as a heatmap. For the significant single nucleotide polymorphism (SNP) loci or gene positions, the haplotypes of the significant loci or gene regions were analyzed, and then the dominant haplotypes needed to be evaluated. Box plots were drawn based on the combined haplotypes and phenotypes, and asterisks were marked if the significant level was reached.

[0048] (2) Results 1. Phenotypic evaluation

[0049] In this invention, Ye107 was used as the common parent and crossed with 4 temperate maize inbred lines from different heterotic groups, and then self-crossed continuously for 7 generations to obtain 540 offspring ( Figure 1 ). The results of variance analysis showed that there were significant differences in this trait among the recombinant inbred lines and different environments (Table 2). The Pearson correlation coefficients between subgroups in three environments were calculated. As shown in Table 2, a highly positive correlation was observed among the subpopulations in the three environments. This result can be used for subsequent QTL and genome-wide association analysis.

[0050] Table 2 Phenotypic analysis of the recombinant inbred line subgroups resistant to kernel number per row in three environments

[0051] 21JH represents the experiment conducted in Jinghong in 2021; 22YS represents the experiment conducted in Yanshan in 2022; 23YS represents the experiment conducted in Yanshan in 2023.

[0052] 2. Single Nucleotide Polymorphism (SNP) Characterization, Linkage Disequilibrium Decay Distance, and Population Structure

[0053] After filtering SNPs with missing value < 0.2 and minor allele frequency (MAF) > 0.05, 6,348,639 valid SNPs from WGS were obtained and used for genome-wide association analysis. The average SNP density of the whole genome was 2,968 SNPs / Mb, which were evenly distributed along the chromosomes. The number of markers on chromosome 1 was the largest, and the number of markers on chromosome 10 was the smallest ( Figure 2 ). We used the above SNPs to evaluate the degree of linkage disequilibrium (LD) decay in the association population. When the linkage disequilibrium decay tends to be flat, it corresponds to 20 kb. At this time, the linkage disequilibrium decays slowly, indicating that the higher the degree of domestication, the greater the selection intensity, resulting in reduced genetic diversity.

[0054] The results of population structure analysis are as follows Figure 3 As shown in Figure 2, principal component analysis showed that despite the use of a common parent, Ye107, the four recombinant inbred line subpopulations remained independent of each other ( Figure 3 The phylogenetic tree shows that all recombinant inbred lines are divided into four groups ( Figure 3 b), there are some individuals mixed in the middle of these groups, which may indicate that gene introgression occurred during the breeding process; principal component analysis and kinship analysis ( Figure 3 The results of c) are basically consistent. In general, the results of principal component analysis, phylogenetic tree and phylogenetic relationship are consistent.

[0055] 3. Genome-wide association analysis and QTL mapping

[0056] The mixed linear model in EMMAX software was used to analyze the trait of row kernel number. Principal component analysis and kinship were used as covariates to control false positives in the association analysis. A threshold of −log10(P)>5 was set. The results showed that SNPs significantly associated with row kernel number could be identified on all 10 maize chromosomes in different environments, among which 21 Jinghong, 22 Yanshan, 23 Yanshan and best linear unbiased prediction (BLUP) identified multiple significant SNPs ( Figure 4 ).

[0057] Table 3 Location and effect of QTLs related to seed number per row detected in population 4

[0058] In order to identify high-effect consistent functional loci, QTL mapping was performed on four recombinant inbred line subpopulations. It was found that population 4 could locate a consistent QTL interval on chromosome 3 under different environments.qKNR3-2, qKNR3-3 and qKNR3-4, Among them, the highest single explanatory phenotypic effect was 19.37% (Table 3). The QTL positioning results were compared with the whole genome association analysis. The candidate SNP 3-191281741 obtained by the whole genome association analysis was located on chromosome 3 (Table 4), included in the three QTL intervals of qKNR3-2, qKNR3-3 and qKNR3-4, and was a consistent site significantly associated with the number of rows and grains ( Figure 5 , Table 5). According to the functional annotation of candidate genes and previous studies, a candidate gene significantly associated with row-grain number was mined in this consistent interval. Zm00001eb149810 , which encodes NAC domain-containing protein 68, a transcriptional activator activated by proteolytic cleavage through regulated intramembrane proteolysis (RIP) mediated by calpain or its functional homologs. It regulates cytokinin signaling during cell division and plays a significant role in regulating the number of kernels per row in maize.

[0059] Table 4 Distribution of major co-localized SNPs identified in different environments and candidate genes identified by genome-wide association analysis

[0060] Table 5 Information on consistent loci identified in GWAS and QTL

[0061] 4. Functional gene haplotype and expression analysis

[0062] The present invention locates the significant SNP site SNP 3-191281741 through genome-wide association analysis, which presents C / G polymorphism. Candidate genes are screened in the 20Kb region upstream and downstream of the significant SNP, and the candidate genes are annotated and functionally predicted using databases such as MaizeGDB, InterPro, UniProt and NCBI, and the candidate genes are identified. Zm00001eb149810 (chr3: 191284856-191287422) gene is located 3.115kb downstream of SNP 3-191281741 ( Figure 6 This SNP and gene were located in the 21-year Jinghong environment in the whole genome association analysis results, and were co-located in the three different environments of 21-year Jinghong, 22-year Yanshan, and 23-year Yanshan in the QTL analysis. The QTL interval in which it is located explains 19.37% of the phenotypic variation ( Figure 5 ).

[0063] Based on the above co-localization results, Zm00001eb149810Haplotype detection and analysis were performed in the four populations and it was found that the 191286413, 191286428, 191286494, 191286617, 191286625, 191286660, 191286667, 191286795, 191286797, 191286800, 191286803, 191286822, 191286836, 191286844, 191286845, 191286859, and 1912868677 of the gene Mutations occurred at bases 1286862, 191286915, 191286920, 191286987, 191286995, 191287018, and 19128702, and two haplotypes, Hap1: TGATGTGCAATGATACCGACGTT and Hap2: CAGGTCATGGCAGCGTTCGTATA, were finally detected. Haplotype analysis showed that both haplotypes had a significant effect on the increase in the number of corn kernels per row, and were distributed in the four populations. Among them, Hap 1: TGATGTGCAATGATACCGACGTT had a higher effect on increasing the number of corn kernels per row, and had a significant effect on increasing the number of corn kernels per row ( Figure 6 Middle c, d, Figure 7 ).

[0064] The public database MaizeGDB qTeller was used to identify candidate genes Zm00001eb149810 The expression of α-glucose in various maize tissues was analyzed ( Figure 8 ). The results showed that the expression level of this gene in maize cob primordium was significantly higher than that in other tissues, which further suggested that it was related to the development of kernel number per row in maize.

[0065] The gene encodes NAC domain-containing protein 68, which is a NAC domain protein and a transcription activator activated by proteolytic cleavage through regulated intramembrane proteolysis (RIP) mediated by calpain or its functional homologs. It regulates cytokinin signaling during cell division. NAC transcription factors can regulate plant cell division and growth and development by participating in a variety of hormone metabolic pathways, and play a significant role in regulating the number of kernels per row in maize.

[0066] In summary, the present invention has confirmed through research that SNP 3-191281741 and candidate gene Zm00001eb149810 located on chromosome 3 of maize have a significant effect on increasing the number of kernels per row in maize. The candidate genes and loci discovered in the present invention will deepen our understanding of the complex genetic pathways of the number of kernels per row in maize, not only laying the foundation for marker-assisted breeding of the number of kernels per row in the future, but also providing valuable genetic resources and innovative strategies for the breeding of high-yield maize varieties.

[0067] 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. 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 embodiments of the present invention.

Claims

1. A molecular marker for the number of kernels per row in corn, characterized in that: The molecular marker is the Zm00001eb149810 gene or the protein encoded by the gene, and the nucleotide sequence of the gene is shown in SEQ ID NO:

1.

2. The molecular marker according to claim 1, characterized in that The expression level of Zm00001eb149810 gene or the protein encoding this gene was positively correlated with the number of kernels per row in corn ears.

3. Molecular marker for kernel number per row in corn, characterized in that: The molecular markers include single nucleotide polymorphism sites 191286413, 191286428, 191286494, 191286617, 191286625, 191286660, 191286667, 191286795, 191286797, 191286800, 191286 803, 191286822, 191286836, 191286844, 191286845, 191286859, 191286862, 191286915, 191286920, 191286987, 191286995, 191287018, 19128702 and SNP 3-191281741 closely linked thereto, and the reference genome version of the site is B73_v5.

4. Use of a product for detecting the molecular markers of any one of claims 1 to 3 in identifying or assisting in identifying the trait of kernel number per row in corn, characterized in that: The product detects the genotype of the molecular marker, or detects the expression level of the Zm00001eb149810 gene or the protein encoding the gene.

5. The use according to claim 4, characterized in that: The products include reagents, kits or gene chips.

6. The use according to claim 4, 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.

7. Any of the following uses of the molecular marker according to any one of claims 1 to 3: a) Analysis of genetic diversity of corn kernel number per row; b) Construction of molecular genetic map of corn kernel number per row; c) Genome-wide association analysis of corn kernel number per row; d) Identification of corn varieties with high kernel number per row; e) Molecular marker-assisted selection breeding for corn kernel number per row; f) Genome-wide selection breeding for corn kernel number per row; g) Gene editing breeding for corn kernel number per row.

8. A method for screening corn with a large number of kernels per row, characterized in that: Take a corn sample to be tested, and detect the molecular markers described in claim 2 or 3. If it meets the type of molecular markers, obtain a corn variety with a large number of kernels in a fruit row.

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