Use of the gene zm00001eb023590 in ear thick corn breeding

Abstract

The application relates to the field of corn molecular marker assisted breeding, and particularly discloses application of a Zm00001eb023590 gene in breeding of corn with thick ears, and provides a corn ear thickness molecular marker, the molecular marker is shown as a sequence in SEQ ID NO:1, the application is positioned to a functional gene Zm00001eb023590 for regulating ear thickness by means of GWA analysis and genetic linkage analysis together and located on a chromosome 1, and the functional gene Zm00001eb023590 is mined, the corn ear thickness genetic gene is enriched, a representative molecular marker site in the gene is screened out through haplotype analysis of the gene, redundancy detection cost is reduced in molecular breeding, and meanwhile, the genetic information integrity is reserved.

Description

TECHNICAL FIELD

[0001] The application relates to the field of maize molecular marker assisted breeding, and particularly relates to application of a Zm00001eb023590 gene in ear thickness maize breeding. BACKGROUND

[0002] As one of the most important food crops in the world, maize plays a vital role in global agricultural production. With the continuous growth of global population, the demand for food is rising, and the production of maize is facing great challenges. According to the report of the Food and Agriculture Organization (FAO), although the yield per hectare in some major maize producing countries (such as the United States, China, Brazil, etc.) has increased, the overall growth rate has slowed down, and in some areas, it has even decreased. Therefore, improving the yield per hectare of maize has become an urgent task for global food security.

[0003] The yield per plant of maize is closely related to the development of inflorescences, and the optimization of ear traits plays a decisive role in improving the yield per plant. Studies have shown that ear thickness, as an important component of ear traits, has a significant impact on the yield per plant of maize. Thicker ears can accommodate more row numbers or larger grain sizes, thereby directly increasing the yield per plant. Therefore, the optimization of ear thickness is considered as a key breakthrough for improving the yield per plant of maize, and is also the focus of future maize breeding.

[0004] The performance of crop traits depends not only on the action of a single gene, but also on the interaction between genes (epistasis). Under different environmental conditions, the contribution of genes to traits may be different. By screening multiple genes, a stable gene combination under various environmental conditions can be screened, thereby improving the adaptability and stability of crops. The application aims to identify new genes related to ear thickness through whole genome sequencing (WGS) technology, whole genome association analysis (GWAS) and QTL positioning of a tropical maize multi-parent population, and to screen tag SNPs through haplotype analysis. In the GS or MAS breeding process, the redundancy detection cost is reduced while the genetic information integrity is preserved, so as to provide innovative genetic resources and precise improvement strategies for significantly improving the yield per plant of maize, and to promote the optimization and efficiency of maize production. SUMMARY

[0005] In view of the deficiencies in the prior art, the application aims to provide application of a Zm00001eb023590 gene in ear thickness maize breeding. The application locates a functional gene Zm00001eb023590 for regulating ear thickness to chromosome 1 by GWA analysis and genetic linkage analysis, enriches the genetic genes of ear thickness traits of maize, and screens representative molecular marker sites in the gene through haplotype analysis of the gene, so as to reduce the redundancy detection cost in molecular breeding while preserving the genetic information integrity.

[0006] To achieve the above object, the present application provides the following technical solutions.

[0007] The present application provides a corn ear thick molecular marker, which is a Zm00001eb023590 gene with a sequence as shown in SEQ ID NO:1.

[0008] Further, when the bases at positions 93789748, 93789902, 93789924, 93789976, 93789992, 93790070, 93790397, 93790404, 93790425, 93790456, 93790535, 93790606, 93790620, 93790651, 93790742, 93790745, 93790852, 93790861, 93790882, 93790884, 93790885, 93790894, 93790961, 93790974, 93790978, 93791080, 93791082, 93791139, 93791178, 93791224, 93791231, 93791277, 93791279, 93791422, 93791483, 93791486, 93791513, 93791574, 93791595, 93791684, 93791724, 93791745 and 93791817 from the 5' end of the gene are CGGAGAGAGATCGACCGTGACGCCTCAACTCACGGGCGGTCGC in turn, the corn presents the dominant trait of ear thick.

[0009] Further, the present application provides a product for detecting the genotype of the molecular marker, which includes a kit, a reagent or a gene chip.

[0010] Further, the present application provides a product for detecting the genotype of the molecular marker, which includes a product prepared by using PCR, qPCR, Sanger sequencing, high-throughput sequencing, fluorescence in situ hybridization method, TaqMan probe method, ARMS-PCR method or KASP method. The product is applied in identifying or assisting in identifying the corn ear thick trait.

[0011] Further, the present application provides an application of the molecular marker in any one of the following: a) genetic diversity analysis of corn ear thick; b) construction of a molecular genetic map of corn ear thick; c) whole genome association analysis of corn ear thick; d) identification or auxiliary identification of corn ear thick varieties; e) molecular marker assisted selection breeding of corn ear thick or high yield; f) whole genome selection breeding of corn ear thick.

[0012] Further, the present application provides a method for screening a maize inbred line with thick ear, taking a maize sample to be detected, detecting the molecular marker, and screening the inbred line with the genotype of the molecular marker, which is the maize inbred line with thick ear.

[0013] Further, the present application provides a method for screening a maize inbred line with high yield, taking a maize sample to be detected, detecting the molecular marker, and screening the inbred line with the genotype of the molecular marker, which is the maize inbred line with high yield.

[0014] The term "Molecular Marker-assisted selection (MAS)" is a breeding technique for selecting offspring lines by means of molecular markers of target traits, and then obtaining excellent single plants containing target genes.

[0015] The term "Genomic Selection (GS)" is a modern breeding technique for genetic evaluation and selection based on whole genome marker information, aiming to accelerate the breeding process and improve the selection efficiency by predicting the breeding value or phenotypic performance of individuals through high-density molecular markers.

[0016] The term "GS-MAS" is a breeding strategy combining Genomic Selection (GS) and Marker-assisted Selection (MAS). GS-MAS estimates the breeding value of individuals by using high-density marker information covering the whole genome and phenotypes, and associates major and minor genes, so as to predict and select complex traits (low heritability, difficult to determine, etc.) in early stage through breeding value, thereby shortening the generation interval, accelerating the breeding process, improving the selection accuracy, and saving the cost.

[0017] The technical effects achieved by the present application are as follows:

[0018] The application constructs a corn multi-parent population with significant difference in ear diameter by using the temperate corn inbred line Ye107 with thin ear diameter as a common parent and crossing with six tropical and subtropical corn inbred lines with thick ear diameter. GWAS analysis and genetic linkage analysis are used to locate the functional gene Zm00001eb023590 on chromosome 1 for regulating ear thickness. Haplotype analysis shows that in 1107 RILs (recombinant inbred lines), Zm00001eb023590 has 12 haplotypes (Hap1, Hap2, …, Hap12), and the ear thickness of Hap11 is significantly higher than that of the remaining 11 haplotypes, so Hap11 of Zm00001eb023590 gene is a haplotype type that significantly improves ear thickness. The results of the application help to further study the regulation mechanism of corn ear thickness, and provide more stable and accurate markers for genetic resource identification, MAS, genetic map construction, linkage mapping and GS-MAS in corn ear thickness breeding application. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a population structure diagram of 1107 RILs in the embodiments of the application, wherein: (a) principal component analysis; (b) unrooted tree; (C) Bayesian clustering diagram of 1107 RILs when K=6; (d) LD decay diagram;

[0020] Figure 2 It is the GWAS result of ear thickness in the embodiments of the application, wherein: (a) GWAS result based on the average of ear thickness phenotype in Yanshan in 2022; (b) GWAS result based on the average of ear thickness phenotype in Yanshan in 2023; (c) GWAS result based on the average of ear thickness phenotype in Jinghong in 2024; (d) GWAS result based on the BLUP value of ear thickness;

[0021] Figure 3 It is the significant QTLs related to corn ear thickness identified in different environments by six subpopulations in the embodiments of the application, wherein: (a) pop1; (b) pop2; (c) pop3; (d) pop4; (e) pop5; (f) pop6;

[0022] Figure 4 It is a schematic diagram of the relationship between significant SNPs and candidate genes in the embodiments of the application.

[0023] Figure 5 It is a haplotype distribution diagram of a candidate gene in the embodiments of the application.

[0024] Figure 6 It is a haplotype analysis diagram of the ear thickness candidate gene in the embodiments of the application, wherein A is the percentage in different subpopulations, and B is the difference statistics in different subpopulations. DETAILED DESCRIPTION

[0025] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the embodiments of the present application.

[0026] The gene sequence of the marker Zm00001eb023590 of the present application is shown as SEQ ID NO: 1, corresponding to the 1st chromosome from the 5' end of 93789616-93792425 of the genomic version Zm-B73-REFERENCE-NAM-5.0.

[0027] Embodiment 1

[0028] 1. Experiment

[0029] 1.1 Plant material and experimental design

[0030] In the present application, the temperate elite inbred line Ye107 is used as the common paternal parent, and is crossed with six tropical and subtropical corn inbred lines (CML312, CML395, YML46, YML32, NK40-1, YML1218) with thicker ears, respectively, to form six F1 hybrid combinations, and then the single kernel transmission method is used for continuous selfing to F9, thereby constructing a multi-parent population containing 1107 recombinant inbred lines. The number of recombinant inbred lines contained in each subpopulation of pop1-pop6 is 142, 148, 303, 178, 200 and 136, respectively (Table 1).

[0031] The experiment was carried out in Yanshan County, Yunnan Province, China in 2022 and 2023, and in Jinghong City, Yunnan Province, China in 2024. The experiment followed a completely randomized block design, and was repeated three times at each location. The planting pattern of the experimental material was set as row length 3 m, row spacing 0.70 m, 14 plants per row, and 2 rows per plot. During the planting process, the local standard agricultural practices were followed for management. After the fruiting ear matured, nine fruiting ears were randomly selected from each row to record the ear thickness, and the average ear thickness was determined by taking the average value.

[0032] Table 1 Specific information of parents

[0033]

[0034] 1.2 Phenotype data analysis

[0035] Firstly, the phenotypic data were pre-processed using Excel 2019. SPSS 26.0 was used to assess whether the phenotypic data were normally distributed and to calculate basic statistics to help intuitively understand the central tendency and dispersion of the data. R 4.3.2 was used to draw a heat map to assess the correlation between phenotypic data under different subgroups and environmental conditions. Two-way ANOVA was performed using the aov() function to assess whether the effects of subgroups and environment on phenotypic data were statistically significant. The lme4 package was used to calculate the general genetic force, with the formula H = Vg / (Vg + (Ve / L)), where Vg represents genetic variance, Ve is residual variance, and L is the number of environments.

[0036] 1.3 Whole genome sequencing

[0037] 1.3.1 Sample collection and DNA extraction

[0038] Healthy leaves were selected for sampling during the maize seedling to jointing stage. The collected leaf samples were immediately frozen in liquid nitrogen to prevent tissue degradation and DNA degradation. Tiangen plant genomic DNA extraction kit (Tiangen Biotech Co., Ltd., Beijing, China) was used to extract leaf DNA. The quality of the extracted DNA samples was controlled using agarose gel to confirm the concentration (100 ng / μl or more) and purity (A260 / A280 ratio should generally be between 1.8-2.0) to ensure that the extracted DNA was suitable for subsequent whole genome sequencing operations.

[0039] 1.3.2 Library construction and sequencing

[0040] DNA fragmentation was performed using Vibra-Cell series ultrasonic cell disruptor (Sonics & Materials, Inc., Connecticut, America), and the fragmented DNA samples were subjected to gel electrophoresis to evaluate the quality (the ideal DNA fragment size should be between 200-500 bp). End-repair was performed using T4 DNA polymerase (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America), and short DNA adapters were ligated to both ends of the DNA fragments. To ensure the quality of the library, Exonuclease I enzyme (Thermo Fisher Scientific Inc., Waltham, Massachusetts, America) was used to remove DNA fragments without adapter ligation. DNA fragments of 200-500 bp in length (including part of the adapter sequence) were selected by Agencourt AMPure XP magnetic bead method. The library was subjected to PCR amplification to increase the number of target DNA fragments. Finally, the concentration of the library was quantitatively detected using Qubit, and the target concentration was usually between 10-20 nM. The size distribution of the library was detected using Bioanalyzer (Agilent Technologies, Santa Clara, California, America), and the fragment size was usually 200-500 bp.

[0041] All samples were sequenced using the NovaSeq 6000 platform, with a double-end sequencing mode, a sequencing depth of 5x, and the sequencing results stored as FASTQ format files. FastQC (Babraham Institute, Bioinformatics Group, Cambridge, United Kingdom) was used to evaluate the quality of the sequencing data, with the following evaluation criteria: (1) Q-score ≥ 20, (2) GC content of the genome is 40% to 60%, (3) proportion of repetitive sequences is less than 30%, (4) proportion of N content is less than 1-2%, (5) read length is consistent with the expected length.

[0042] 1.4 Population stratification analysis

[0043] 1.4.1 Principal component analysis

[0044] To evaluate the population genetic structure and control the influence of population stratification on GWAS results, we performed principal component analysis (PCA) using the GCTA software. First, the genotype data was standardized, and the genetic similarity matrix between samples was calculated. Subsequently, the first few principal components were extracted based on this matrix, which were used to represent the population structure.

[0045] 1.4.2 Unrooted tree construction

[0046] To further analyze the genetic relationship among subpopulations, we constructed an unrooted tree using the MEGA (Molecular Evolutionary Genetics Analysis) software to assess the genetic distance and relationship between subpopulations. In the construction process, we used the Neighbor-Joining (NJ) method, which efficiently and accurately reflects the genetic differentiation of the population by minimizing the total genetic distance between samples.

[0047] 1.4.3 Population genetic structure analysis

[0048] We used the ADMIXTURE software for population stratification analysis. This method assumes that each individual is a mixture of components from multiple subpopulations, and estimates the contribution of different subpopulations to each individual's genome. We first selected and set the expected number of subpopulations (K value) according to the research purpose. By trying different K values and selecting the optimal K value through cross-validation, we can avoid overfitting or underfitting. We analyzed the population structure under different K values and determined the genetic background of the samples according to the population stratification pattern.

[0049] 1.4.4 Linkage disequilibrium analysis

[0050] We used the PopLDdecay software to evaluate the genetic linkage disequilibrium (LD) of the population. The purpose of LD analysis is to measure the degree of linkage disequilibrium between SNPs, i.e., whether a pair of markers is independently inherited. In particular, r 2 value is commonly used to represent the degree of LD between SNPs, and the higher the r 2 value, the stronger the linkage between the two SNPs and the closer the genetic association. We used the PopLDdecay software to calculate the r 2 value between different SNP markers and further analyzed the LD decay under different genetic distances. We used the Plot_OnePop.pl script provided by PopLDdecay to generate visualizations of LD decay. Through these graphs, we can clearly show the LD structure at different positions in the population and identify high-LD regions in the genome, further helping to determine marker selection and genetic region positioning for GWAS.

[0051] 1.5 Genome-wide association analysis

[0052] GEMMA software. In the model, population genetic structure was treated as a fixed effect and individual relatedness as a random effect to correct for the effects of population structure and relatedness on trait performance. To reduce false positives and false negatives, a Bonferroni correction was used for multiple comparison adjustment. The P-value of all individual SNPs was compared with the Bonferroni corrected significance level. If the P-value was less than p, the SNP was considered statistically significant. In addition, to ensure the reliability and accuracy of the results, FDR correction was further used to reduce the error caused by multiple comparisons. Generally, a p-value less than 5 x 10"8 was considered significant. For the identified significant SNPs, ANNOVAR tool was used for functional annotation to determine the location, region and mutation type of the variation site on the genome. In addition, Manhattan plot and QQ plot were generated using PLINK software to visualize the results of association analysis, where Manhattan plot evaluates the association strength of SNPs with traits, and QQ plot evaluates the deviation of p-value distribution from the expected value.

[0053] 1.6 Linkage mapping and QTL mapping

[0054] First, the offspring genotyping was filtered based on 0.8 completeness and 0.001 segregation distortion to obtain population markers, and then the final population markers were obtained by binning based on population markers (every 15 markers and no linkage). Joinmap 4.0 was used to sort the bin markers of each population and the Kosambi function was used to calculate the genetic distance between markers. The LOD threshold was determined to be 2.5 by 1000 random permutation tests (P < 0.05), and the QTL position of corn ear thickness was determined by composite interval mapping (CIM). If the genetic distance of the interval exceeding the threshold line is less than 10 cM, it is determined as an interval.

[0055] 1.7 Candidate gene haplotype analysis

[0056] Haploview v4.2 software was used for haplotype analysis of candidate genes. For each gene, we analyzed the frequency distribution of different haplotypes, phenotypic differences and their statistical significance. Based on the consideration of frequency distribution, box plots only showed haplotypes with high frequency in the sample. Low frequency haplotypes (≤1%) were not shown separately, as these low frequency haplotypes had little statistical impact on the final results and did not affect the interpretation of the main conclusions.

[0057] 2. Results

[0058] 2.1 Analysis of corn ear thickness phenotype data

[0059] The results showed that pop4 consistently exhibited high and stable ear thickness traits across all environments, while pop1 exhibited the lowest average ear thickness (Table 2). Statistical analysis of the ear thickness phenotypic data for the six subpopulations showed that the range of variation coefficients (CV%) of ear thickness between the six subpopulations in each environment was large, indicating significant differences in ear thickness between the subpopulations. The absolute values of skewness and kurtosis of ear thickness in different environments were generally less than 1. The broad-sense heritability of ear row number in the six subpopulations ranged from 70.15% to 97.28%, indicating a strong genetic influence on this trait. The consistency of phenotypic variation in different environments highlighted the reliability of the phenotypic data for subsequent analysis. Significant differences in ear thickness were observed among the subpopulations, influenced by genetic and environmental factors. This variability provided a solid foundation for further GWAS analysis and identification of important loci associated with ear thickness.

[0060] Table 2 Analysis of ear thickness phenotypic data

[0061]

[0062]

[0063] Note: 22YS refers to 2022 Yanshan, 23YS refers to 2023 Yanshan, 24JH refers to 2024 Jinghong

[0064] 2.2 Subpopulation analysis

[0065] By observing the three-dimensional PCA plot, the clustering distribution of samples was relatively clear, and the data could be roughly divided into six clusters, with each cluster representing a potential subpopulation. Although most subpopulations formed obvious cluster structures in the three-dimensional space, there were overlapping areas between some clusters. For example, there was obvious overlap between pop3 and pop6, which may indicate that these two subpopulations have high similarity in the main feature dimensions Figure 1 a).

[0066] In the unrooted tree analysis, although there was a certain degree of mixing between different clusters, the overall data could still be clearly divided into six main clusters Figure 1 b). This overlapping phenomenon may indicate that these clusters are relatively close in the feature space, reflecting their similarity in certain key features.

[0067] In the population structure analysis, when K = 6, the data was clearly divided into six subpopulations. Although there was still a certain degree of overlap between some samples, these six subpopulations were relatively clear overall Figure 1 c). This overlap indicates that there may be overlapping areas between different subpopulations, and some samples have similarities in features, leading to their unclear attribution in certain subpopulations. Overall, although there is overlap, these subpopulations still reflect the main structural features of the population.

[0068] The LD decay in MPP was assessed using 16223980 effective SNPs. The LD decay plot showed that the LD dropped rapidly with the increase of the physical distance between markers. When r 2 The physical distance of LD decay was about 10 kb when r Figure 1 d).

[0069] 2.3 Genome-wide association study of ear thickness traits

[0070] The GWAS of ear thickness in maize was performed using 16223980 effective SNPs. The results showed that multiple significant SNPs associated with ear thickness were identified on 10 chromosomes in all environments( Figure 2 ).

[0071] 2.4 QTL mapping of ear thickness traits

[0072] A total of 41 significant QTLs associated with ear thickness traits were identified in 6 subpopulations, which were distributed on chromosomes 1-10, with the most QTLs located on chromosome 1. A total of 5 significant QTLs were identified in Pop1, with qE6-1 identified in BLUP having the highest phenotypic variation explanation rate of 11.99%( Figure 3 a, Table 3). A total of 8 significant QTLs were identified in Pop2, with qE2-2 identified in 23YS having the highest phenotypic variation explanation rate of 10.99%( Figure 3 b, Table 3). A total of 8 significant QTLs were identified in Pop3, with qE9-1 identified in 24JH having the highest phenotypic variation explanation rate of 6.55%( Figure 3 c, Table 3). Only 1 significant QTL was identified in Pop4. A total of 9 significant QTLs were identified in Pop5, with qE1-9 identified in 23YS having the highest phenotypic variation explanation rate of 12.87%( Figure 3 d, Table 3). A total of 10 significant QTLs were identified in Pop6, with qE1-12 identified in 23YS having the highest phenotypic variation explanation rate of 12.87%( Figure 3 f, Table 3).

[0073] Table 3 Significant QTLs of ear thickness in maize

[0074]

[0075]

[0076] 2.5 Co-localization analysis of GWAS and QTL for candidate genes of ear thickness in maize

[0077] The present application integrates GWAS and QTL mapping results, aiming to identify candidate genes related to corn ear roughness traits. The significant SNP sites screened by GWAS are compared with the significant genomic regions determined by QTL mapping, and the overlapping regions are identified to provide clues for candidate genes of corn ear roughness traits.

[0078] Pop1 in 22YS identified qE1-1 and Pop3 in 24JH identified qE1-7 overlap with the significant SNP 1_93800687 identified by GWAS. Pop1 in 23YS identified qE1-2 overlaps with the significant SNP 1_114967032 identified by GWAS. Pop2 in 24JH identified qE1-5 overlaps with the significant SNP 1_194955056 identified by GWAS. Pop6 in 23YS, 24JH and BLUP identified qE1-12, qE1-13 and qE1-14 all overlap with the significant SNP 1_241482042, 1_241649260 identified by GWAS. Within the range of 20kb upstream and downstream of these overlapping sites, multiple co-localization candidate genes are found, and further located to the gene Zm00001eb023590 (Table 4). We consulted the functional annotation of these genes, and further analyzed through related literature. The results show that Zm00001eb023590 is closely related to the development of corn ear roughness. Therefore, we finally decided to conduct further functional analysis on this gene.

[0079] Table 4 Candidate genes co-localized by GWAS and QTL

[0080]

[0081] Note: 24JH refers to Jinghong in 2024

[0082] 2.6 Haplotype analysis of candidate genes

[0083] The haplotype analysis results of Zm00001eb023590 (the gene sequence of Zm00001eb023590 is shown in SEQ ID NO: 1, corresponding to chromosome 1 of genome version Zm-B73-REFERENCE-NAM-5.0 from 5' end 93789616-93792425) show that 12 different haplotypes (Hap1, Hap2, …, Hap12) are identified in 1107 RILs, and the box plot only shows 7 haplotypes with higher frequency in the sample (Hap1, Hap2, …, Hap7). Figure 5 These haplotypes show different frequency distributions in different populations studied. Hap11 shows the highest phenotype value ( Figure 6 ), and has a wide distribution frequency in pop4.Figure 6 ). Therefore, we consider Hap11 is the favorable haplotype to regulate the ear diameter of maize.

[0084] Haplotype specific loci, with Zm-B73-REFERENCE-NAM-5.0 as the reference genome, the sequence from left to right on chromosome 1 of the genome version is as follows:

[0085] 93789748 (REF: G / ALT: C) 93789902 (REF: A / ALT: G)

[0086] 93789924 (REF: G / ALT: T) 93789976 (REF: A / ALT: G)

[0087] 93789992 (REF: G / ALT: T) 93790070 (REF: A / ALT: T)

[0088] 93790397 (REF: C / ALT: G) 93790404 (REF: G / ALT: A)

[0089] 93790425 (REF: G / ALT: T) 93790456 (REF: G / ALT: A)

[0090] 93790535 (REF: C / ALT: T) 93790606 (REF: C / ALT: T)

[0091] 93790620 (REF: T / ALT: G) 93790651 (REF: A / ALT: G)

[0092] 93790742 (REF: A / ALT: C) 93790745 (REF: A / ALT: C)

[0093] 93790852 (REF: G / ALT: A) 93790861 (REF: C / ALT: T)

[0094] 93790882 (REF: G / ALT: T) 93790884 (REF: G / ALT: A)

[0095] 93790885 (REF: C / ALT: T) 93790894 (REF: A / ALT: G)

[0096] 93790961 (REF: C / ALT: G) 93790974 (REF: T / ALT: C)

[0097] 93790978 (REF: C / ALT: T) 93791080 (REF: G / ALT: C)

[0098] 93791082 (REF: A / ALT: G) 93791139 (REF: A / ALT: T)

[0099] 93791178 (REF: C / ALT: T) 93791224 (REF: T / ALT: C)

[0100] 93791231 (REF: C / ALT: T) 93791277 (REF: G / ALT: A)

[0101] 93791279 (REF: C / ALT: G) 93791422 (REF: G / ALT: C)

[0102] 93791483 (REF: G / ALT: C) 93791486 (REF: A / ALT: G)

[0103] 93791513 (REF: C / ALT: T) 93791574 (REF: G / ALT: C)

[0104] 93791595 (REF: G / ALT: A) 93791684 (REF: T / ALT: A)

[0105] 93791724 (REF: C / ALT: T) 93791745 (REF: G / ALT: A)

[0106] 93791817 (REF: T / ALT: C)

[0107] Hapl:

[0108] GAGAGAGGTACTGAAAGCGGCACCTCATTCCGGCCACGGACGT

[0109] Hap2:

[0110] GATAGTCGTGCCTGAAGCGACAGTCGGACTTGCGGACGGTCGT

[0111] Hap3:

[0112] GAGATAGGGACCGAAAGCGGCACCTGAACTCGGGGACGGTCGT

[0113] Hap4:

[0114] GAGGGAGGTACTGAAAGCGGCACCTCATTCCGGCGACGGACGT

[0115] Hap5:

[0116] GAGAGACGGGCCTGAAGCGACAGTCGGACTTGCGGACGGTCGT

[0117] Hap6:

[0118] GAGAGACGGGCCTAAAGCGGCACTCGAACTCGCGGACGGTCGT

[0119] Hap7:

[0120] GAGATAGGTACTGAAAGCGGCACCTCATTCCGGCGACGGACGT

[0121] Hap8:

[0122] GATAGTCGTGCCTGAAGCGACAGTCGGACTTGCGGACGAATAT

[0123] Hap9:

[0124] GATAGTGGTACTGAAAGCGGCACCTCATTCCGGCGACGGACAT

[0125] Hap10:

[0126] GAGGGAGGGACCGAAAGCGGCACCTGAACTCGCGGACGAATAT

[0127] Hap11:

[0128] CGGAGAGAGATCGACCGTGACGCCTCAACTCACGGGCGGTCGC

[0129] Hap12:

[0130] GAGAGAGGTACTGAAAGCGGCACCTCATTCCGGCGACGGACGT

[0131] In summary, the Zm00001eb023590 gene as a corn ear rough molecular marker functional gene identification can develop an allele-specific marker, the molecular marker can directly detect the crop genotype, accurately identify the presence or absence of the target gene or trait, avoid the blindness and uncertainty in traditional selection, ensure the accuracy of the offspring selection, and can be applied to genetic resource identification, MAS, genetic map construction, linkage mapping and GS-MAS in breeding, and through MAS, the crop genotype can be quickly and accurately identified, the tediousness, time consumption and screening difficulty of traditional selection are avoided, and the selection efficiency is greatly improved, and the breeding process is accelerated.

[0132] The above examples are only used to illustrate the technical solutions of the present application, but not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. Use of a product for detecting the genotype of a kernel row number molecular marker in the breeding of maize by assisted selection of the kernel row number, characterized in that, The molecular marker is a SNP site; with Zm-B73-REFERENCE-NAM-5.0 as the reference genome, the SNP site is located at position 93789748, 93789902, 93789924, 93789976, 93789992, 93790070, 93790397, 93790404, 93790425, 93790456, 93790535, 93790606, 93790620, 93790651, 93790742, 93790745, 93790852, 93790861, 93790882, 93790884, 93790885, 93790894, 93790961, 93790974, 93790978, 93791080, 93791082, 93791139, 93791178, 93791224, 93791231, 93791277, 93791279, 93791422, 93791483, 93791486, 93791513, 93791574, 93791595, 93791684, 93791724, 93791745 and 93791817 from the 5' end of the chromosome 1 of the genome, and when the SNP site presents CGGAGAGAGATCGACCGTGACGCCTCAACTCACGGGCGGTCGC in turn, the corn presents the advantageous ear thickness trait.

2. Use according to claim 1, characterized in that, The product includes a kit, a reagent or a gene chip.

3. A method of screening maize germplasm for ear thickness, comprising, Taking a corn sample to be detected, detecting the molecular marker in claim 1, screening the germplasm meeting the molecular marker genotype, that is, the corn germplasm with thick ear. The product includes a kit, a reagent or a gene chip.

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