Application of a Zm00001d030087 gene in regulating protein content in corn kernels

Abstract

The present invention provides an application of a Zm00001d030087 gene in regulating the protein content of corn kernels, and relates to the field of agricultural biotechnology. The Zm00001d030087 gene sequence is shown in SEQ ID NO: 1, and the gene is determined to be a functional gene for regulating the protein content of kernels. The gene can be used to regulate the protein content of corn kernels, and can provide a basis for breeding corn varieties with high protein content.

Description

Technical Field

[0001] The invention relates to the field of agricultural biotechnology, and in particular to an application of a Zm00001d030087 gene in regulating the protein content of corn grains. Background Art

[0002] Protein is one of the main nutrients stored in corn kernels. In addition to maintaining normal physiological functions of the human body, corn protein is also widely used in food processing and animal husbandry. Proteins in corn kernels are divided into prolamines / zeins, albumins, globulins, and glutelins according to their solubility (Huang et al., 2022). Most corns have poor quality because prolamines account for a large proportion of endosperm and glutelins account for a small proportion. Faced with the challenges of the growing global population and drastic environmental changes, breeders continue to transform corn to improve its nutritional value and its utility as food and feed. In addition, with the continuous progress of society and the improvement of human living standards, people pay more and more attention to the quality of corn, and protein content as an important indicator of corn quality has also attracted widespread attention. At present, corn is mainly used for feed production in China, but the protein content of corn kernels is low and cannot meet the daily nutrition and growth needs of animals. It is necessary to rely on the addition of high-protein products such as soybeans to make up for this defect. Since China is heavily dependent on soybean imports, discovering the relevant genes that increase the protein content of corn kernels and developing corn varieties with high protein content have become important directions of corn genetic research.

[0003] Tropical and subtropical maize germplasm contains a lot of rich genetic variation that is lacking in temperate maize, and is an important germplasm resource for maize breeding. In this study, a multi-parent population with rich variation in grain protein content was constructed using five maize inbred lines, mainly from tropical and subtropical regions, with obvious differences in grain protein content. All of these five parental materials are inbred lines with important breeding value (Yin et al., 2022; Jiang et al., 2023). Functional genes closely related to maize grain protein content were mined from the multi-parent population of these five parent combinations, providing a theoretical basis for molecular marker-assisted selection of high-protein maize. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an application of the Zm00001d030087 gene in regulating the protein content of corn kernels, explores the functional gene Zm00001d030087 closely related to the protein content of corn kernels, and provides a theoretical basis for molecular marker-assisted selection of high-protein corn.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented through the following technical solutions:

[0006] An application of the Zm00001d030087 gene in regulating the protein content of corn grains. The sequence of the Zm00001d030087 gene is shown in SEQ ID NO: 1.

[0007] A use of a Zm00001d030087 protein in regulating the protein content of corn grains, wherein the Zm00001d030087 protein is obtained by using the gene encoding as shown in SEQ ID NO:1.

[0008] A product comprising a substance of the Zm00001d030087 gene whose regulatory sequence is shown in SEQ ID NO:1.

[0009] Preferably, the product is a kit.

[0010] The above products can be applied to molecular marker-assisted breeding of gramineous crops or improvement of agronomic traits of gramineous crops.

[0011] Preferably, the agronomic trait is: changing the protein content of gramineous crops, changing the protein content of gramineous plant seeds, or improving the quality of gramineous crops.

[0012] Preferably, the grass plant is corn.

[0013] In addition, the present invention also provides a corn breeding method for increasing the protein content of grains, which comprises: manipulating the expression level of the Zm00001d030087 gene or protein in the overexpressed corn, wherein the Zm00001d030087 gene sequence is shown in SEQ ID NO:1.

[0014] A corn breeding method for reducing the protein content of corn kernels comprises: silencing, knocking out or knocking down the Zm00001d030087 gene or protein in corn, wherein the Zm00001d030087 gene sequence is shown in SEQ ID NO: 1. In practical applications, the requirements for corn quality traits are not uniform, so reducing the protein content of corn kernels to obtain corn with high starch content, high oil content, etc. is also one of the requirements.

[0015] The present invention provides an application of a Zm00001d030087 gene in regulating the protein content of corn kernels, which has the following advantages over the prior art:

[0016] In this study, a multi-parent maize population with significant differences in grain protein content was constructed by using the temperate maize inbred line Ye107 with low grain protein content as a common parent and hybridizing it with four subtropical maize inbred lines with high grain protein content. GWAS analysis and genetic linkage analysis co-localized SNP_104665306 on chromosome 1, which was significantly associated with grain protein content, and then excavated the functional gene Zm00001d030087 that regulates grain protein content. Gene Zm00001d030087 can explain 10.06% of the phenotypic variation in grain protein content. Haplotype analysis showed that among 601 RILs, Zm00001d030087 had two haplotypes (i.e., Hap1 and Hap2), and the grain protein content of Hap2 was significantly higher than that of Hap1. Therefore, Hap2 of the Zm00001d030087 gene is a haplotype type that significantly improves the grain protein content. The results of this study will help further study the regulatory mechanism of corn grain protein content, and also provide a theoretical basis for the development of high-protein corn varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The group structure of 601 RILs in the embodiment of the present invention; wherein: (a) principal component analysis; (b) unrooted tree; (c) phylogenetic tree; (d) Bayesian clustering diagram of 601 RILs when K=4;

[0018] Figure 2 LD attenuation diagram of 601 RILs in an embodiment of the present invention;

[0019] Figure 3 is the genetic linkage map of POP2 in the embodiment of the present invention;

[0020] Figure 4 is the protein significant QTL identified by pop2 in Yanshan in the embodiment of the present invention;

[0021] Figure 5 is the protein significant QTL identified by pop2 in Jinghong in the embodiment of the present invention;

[0022] Figure 6 The GWAS results of protein content in the embodiments of the present invention; wherein: (a) the GWAS results based on the mean of protein phenotype in Yanshan; (b) the GWAS results based on the mean of protein phenotype in Jinghong; (c) the GWAS results based on the protein BLUP value;

[0023] Manhattan plot (left), QQ plot (right). The dotted line in the Manhattan plot indicates the significance threshold, the points of different colors indicate the physical position of the SNP on the corresponding chromosome, and the y-axis indicates the negative significance logarithm with a base of 10. The red line in the QQ plot indicates the expected significance value, and the blue point indicates the actual significance value.

[0024] Figure 7 This is a haplotype analysis of the protein candidate gene in the embodiment of the present invention; the distribution of the haplotype of Zm00001d030087 in four subgroups. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention is clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment 1:

[0027] 1. Experiment

[0028] 1.1 Plant materials and field trials

[0029] In order to ensure that the experimental materials have rich diversity, this study selected temperate elite maize inbred line Ye107 and tropical and subtropical backbone maize inbred lines CML384, CML395, YML46 and YML32 as parents. These five parents are derived from the Reid, non-Reid and Suwan1 heterosis groups.

[0030] The test sites were Yanshan (23°19′-23°59′N, 103°35′-104°45′E) and Jinghong (21°27′-22°36′N, 100°25′-101°31′E), Yunnan Province, China.

[0031] Ye107 was used as the common male parent and was crossed with CML384, CML395, YML46 and YML32 to breed four hybrids (F1). After nine generations of single cross selfing, an MPP consisting of four subpopulations was produced (pop1: CML384×Ye107; pop2: CML395×Ye107; pop3: YML46×Ye107; pop4: YML32×Ye107). The MPP includes 601 RIL families, of which pop1, pop2, pop3 and pop4 have 161, 123, 145 and 172 families, respectively. Table 1 below lists the pedigrees, ecotypes and protein contents of the five parental lines.

[0032] Table 1 Parent information

[0033]

[0034]

[0035] The experiment adopted a completely randomized block design with three replicates at each location. The field trial plot was 3 meters long, with a row spacing of 0.70 meters, 14 plants per row, and two rows per plot. The experiment was conducted in YS and JH in 2022 and 2023, respectively.

[0036] 1.2 Data Collection and Analysis

[0037] The protein content of grains of 601 RILs was determined by NIRS (No. S-14105 Kungens Kurva, Sweden), with three replicates for each line, and the mean was used as the final value. The phenotypic data were statistically analyzed using IBM SPSS26.0 software, including mean, standard deviation, skewness, kurtosis, range of variation, and coefficient of variation. In order to eliminate the influence of environmental factors, a linear mixed model was used to calculate the best linear unbiased prediction (BLUP) value for each line. In addition, the 'cor.test' function in RStudio was used to calculate the correlation coefficient and P value of protein under different environments. The broad-sense heritability was calculated as follows (Knapp et al., 1983; Moran and Smith, 1918):

[0038]

[0039] where σg 2 Refers to genetic variance, σge 2 Refers to the variance caused by the environment x genotype interaction, σε 2 refers to the residual, e refers to the position, and r refers to the year.

[0040] 1.3 Sequencing genotyping, SNP extraction and filtering

[0041] Leaf tissues were collected at early maize growth stages and freeze-dried at -80°C, and genomic DNA was isolated and extracted using the CTAB protocol (Poland et al., 2012). DNA from 601 RILs and their parents was deeply sequenced using the genotyping by sequencing (GBS) method (Zhou et al., 2016), and genomic DNA was digested using PstI and MspI. PCR products were purified using the QIAquick PCR purification kit (QIAGEN, Valencia, CA, United States). Fragments of 200 to 300 bp (including adapters and tags) were isolated and purified using the Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA) and sequenced. The raw data were then filtered to remove adapters and low-quality sequences. The measured data were used to identify SNPs using the Genome Analysis Toolkit software (McKenna et al., 2010) and the maize B73 reference genome (Jiao et al., 2017). To ensure the quality of the map, Plink v 1.9 (Purcell et al., 2007) was used to filter out sites with a missing rate higher than 10% and SNPs with a minimum allele frequency of less than 5% at the site, with the parameters set to --geno 0.2 and --maf0.05 (SNP missing rate <20 and minor allele frequency <0.05).

[0042] 1.4 Principal component analysis and population structure analysis

[0043] Principal component analysis (PCA) was performed using GCTA (Yang et al., 2011), and the results were visualized using the scatterplot3d software package.

[0044] Admixture v1.3.0 was used to analyze the population structure. First, the K value was set for cross-validation. We believed that the K value with the smallest cross-validation error rate corresponded to the optimal number of clusters. Finally, the ggplot2 software package was used to visualize the population structure.

[0045] 1.5 Linkage disequilibrium analysis

[0046] The non-random association between two or more gene loci may be caused by historical recombination, selection pressure or population structure. The R2 value is used to measure the degree of linkage disequilibrium, ranging from 0 to 1. The closer the value is to 1, the higher the degree of linkage disequilibrium between the two loci.

[0047] PopLDdecay (Zhang et al., 2019) software was used to calculate the linkage disequilibrium between markers (r2 ) and used the Plot_OnePop.pl software package to draw linkage disequilibrium decay maps.

[0048] 1.6 Map construction and QTL positioning

[0049] The genetic linkage map of pop2 was constructed using JoinMap4.0 software, and the linkage group was determined by setting the LOD threshold ≧ 5. The maximum likelihood method was used to rank the markers within the linkage group, and the genetic distance between markers was calculated using the Kosambi function. The composite interval mapping (CIM) method was used to determine the QTL position of protein content in the two populations in the two environments. The LOD threshold was determined to be 2.5 using 1000 random permutation tests (P<0.05). When the genetic distance between intervals exceeding the threshold line was less than 10 cM, it was determined to be one interval. The square of the partial correlation coefficient (R2) was used to measure the degree to which a single QTL explained the phenotypic variation. The QTL naming rule is as follows: q+P+chromosome sequence number+detected QTL sequence number, where q represents QTL and P represents protein.

[0050] 1.7 Genome-wide association analysis of grain protein content

[0051] The MLM in GEMMA software was used to perform genome-wide association analysis on the phenotypic mean and BLUP values ​​of proteins, with the parameter set to -lmm 1 (Zhou et al. 2013), and population structure and kinship were introduced as covariates to reduce errors (Yu et al., 2006). Bedtools v1.7 was used to extract SNP sites that met or exceeded the threshold (Strable et al., 2017), and ANNOVAR software was used to annotate significant SNPs. The final results were visualized using CMplot v3.6.2 (Yin et al., 2021). Manhattan plots showed the distribution of markers, and QQ plots evaluated the accuracy of association analysis results.

[0052] 1.8 Candidate gene prediction and functional annotation

[0053] Candidate genes were predicted within the 20 kb range of the significant SNP and its upstream and downstream. The prediction of candidate genes was based on the maize B73 v4 reference genome sequence in the MaizeGDB (https: / / www.maize gdb.org / ) genome browser. Functional annotations of candidate genes were obtained using the MaizeGDB and NCBI (https: / / www.ncbi.nlm.nih.gov / ) databases.

[0054] 1.9 Haplotype analysis

[0055] Haploview v4.2 software was used to analyze candidate genes related to seed protein content detected in the two environments. First, a haplotype map was constructed using high-density whole-genome SNPs. Then, based on the position information of significant sites associated with the target trait and the results of LD analysis, the haplotypes where the significantly associated SNPs were located were found. Finally, the genes in the haplotypes were annotated to find functionally related gene loci.

[0056] 2. Results

[0057] 2.1 Analysis of grain protein content

[0058] The protein phenotype data of the four subgroups were statistically analyzed, as shown in Table 2 below:

[0059] Table 2 Statistical analysis of protein content

[0060]

[0061]

[0062] 2.2 Structural analysis

[0063] The PCA results showed that the 601 RILs were divided into four subgroups, which was consistent with the experimental design of this study ( Figure 1 a). The phylogenetic tree showed that the 601 RILs were mainly divided into four groups, which was consistent with the PCA results ( Figure 1 b and 1c). Population structure analysis showed that when K = 4, 601 RILs were divided into four subgroups ( Figure 1 d) Admixture between populations may result from genetic drift or natural hybridization.

[0064] 2.3 Linkage disequilibrium decay analysis

[0065] LD decay diagram ( Figure 2 ) showed that LD decayed rapidly as the physical distance between loci increased, indicating that LD decayed rapidly. 2 When the critical value was 0.3, the estimated physical distance of LD decay was about 20 kb. Since LD decayed at 20 kb, this study screened the upstream and downstream 20 kb regions of the significantly associated SNPs to identify candidate genes.

[0066] 2.4 Linkage map and QTL location

[0067] In this study, a high-density linkage map of pop2 was constructed for further QTL localization of grain protein content. The linkage map of Pop2 was constructed from 1503 SNPs, with a total genetic distance of 4593.45 cM and an average genetic distance of 2.27 cM between markers ( Figure 3 ).

[0068] The significant QTLs for grain protein content were screened based on linkage maps, with a LOD threshold of 2.5. A total of 8 significant QTLs related to protein content were screened in pop2 ( Figure 4 and Figure 5 ). There were 4 significant QTLs in Yanshan located on chromosomes 1, 6, 7 and 10, among which qP6-1 located on chromosome 6 had the highest phenotypic explanation rate of 26.78%. There were 4 significant QTLs in Jinghong located on chromosomes 1, 3, 6 and 7 (Table 3).

[0069] Table 3 Significant QTL for proteins

[0070]

[0071]

[0072] 2.5 QTL mapping combined with GWAS to reveal candidate genes

[0073] Using a single research method to reveal candidate genes may result in large errors, so this study integrated and compared the QTL positioning results with the GWAS results, and finally located 7 candidate genes (Table 4). Zm00001d030087 screened in Jinghong by GWAS based on the mean of phenotypic data co-localized with qP1-1 located by pop2 in Yanshan.

[0074] Table 4 Protein candidate genes co-localized between QTL and GWAS

[0075]

[0076] 2.6 Haplotype analysis

[0077] Analysis of the phenotypic variation explanation rate showed that the candidate gene Zm00001d030087 could explain 10.06% of the phenotypic variation in grain protein content. Haplotype analysis showed that among the 601 RILs, Zm00001d030087 had two haplotypes, namely Hap1 and Hap2, of which the distribution frequency of Hap1 was 151 and the distribution frequency of Hap2 was 93. There was a significant difference between Hap1 and Hap2, and the grain protein content of Hap2 was significantly higher than that of Hap1, so Hap2 was the dominant haplotype of Zm00001d030087 ( Figure 7 Left), and the gene sequence of Zm00001d030087 is shown in SEQ ID NO: 1:

[0078] ACAAACGGGATGGGTAACACTGCTGTAGGGTGATTCTCGAATCTCGATCTCCCTGCTCTACCCGCATCAC

[0079] CCCGCCGTTGCCGGCCCCTTCCTCCCGCTTCCCACCTCGCCGTCGCCACCTCTTCCTCTTCCTCATCGCC

[0080] GGCGCTTGCGCGCCCCTTCTCCACCCCGTTGACCGCCGCACCCACGGCCTCGCCGTTGCAGCACTCGTGC

[0081] GATCCGGTCACGAGTGGAGCGCACGAGCTCATTCGCTGCTTCCGCCTCCCCCTCTTCCTTGCCGGAGCCC

[0082] GCGCCTCCTCTCCTCGCCGGCGCCTTCGCCTGCTCCTGCGCCCCCTTCTCCACGTCGCACCCACGGCCTC

[0083] GTGTTGCTGCCCTCGTGAGATCCGGTCGCGAGCGCCCGACCTAGGCCGCTGCCTCAGTCTCTCCTCGCTC

[0084] GGTCGCATCACCACCGCCGGAGACGGACCGAGGTGCTCTGCGAGACTCCGCGCCATAGAGCTCCTCCTAC

[0085] GGCGGGTTCTTTTCCGTCCGGTAGCTGGGTTCCCTGAAGCCAGTCATGGCGATGACCACCGCCGTGACGG

[0086] GCCTCGCCGTCGCAGCAACCGCTCCCATTCGCTCCTTCCTCTCGTCCGCCGCCGCCTCTGTCAGCCTGCC

[0087] CGCCGATCTTCGTGACCTCGCCTCCGACCTAGCCTCGGACCCTGCCGTCTCCTACCGCTCTCTGCGTGCC

[0088] ATCTGGTGCGCCACCTCGCCGGACACCCGCCCGCCGCTCCGCGACCTCCTCCAGGGCGCCGACTTCGTGC

[0089] TACCCAGCCCGAAACCTCGTGAGAAGGTTCTTTCTCTCAGTCTTTGGTGGCACTGTCTGTCCCGGCCTGT

[0090] GCGTACCGGCAAATAGGTGGGTTTTGATTCTGATTCGGTTTAGTCTGTCATGTGGTTGGTTTGCAGAGCG

[0091] ATGAGCTGAAGGCGAGGCTGGAGAAGCTCCGGGAGACACAGGAGAGGAAGGAGTATGCTGAGCTCGTCAG

[0092] GGACGTCGCTCCGCCCAGCAAGGATGACGCTCCTGAGCCCTTTTCATCCTACAAGGATCAGATAGGATTT

[0093] GGTACTACCCAACCGCCCTATTTTCAAATTGCTCTATGCAATCTTCCTACTGCTTGTTTCATGGATTACA

[0094] CTTGTTCCATTGAAGTAATAGGAATATAGGATGACACCGTGTGTGTATTTCAACATAATTGGAGCAATAT

[0095] AGTTCAAAAGCTATAACATACGAGTTGTGGAGCAATATCTTCTCTCTTCATCAATTGTTCCAAATGCAGA

[0096] GGTAGGATCTACTATTTTTTAAAACACACACACATAAAGACAAGAATAAACAAATAAGATAATGTAAAAT

[0097] AAATTTGAGAATAATATTTTTGTAAATACCACTCGAACAAAGATCAATCTTCCGTGTGCTCGTATCTAAA

[0098] GCAATGATGCCATTAGAATTTAGACCTGAGGTGCAGCACATGAAAATTGTTCTACCTCCACCACTACATT

[0099] AGTGTATACGTCCAAGAGGCATTTATCTAGATTTAAAGTGAACAACGAACTACTAAAAATCTAGTTTCAG

[0100] GATTTGTTTTTTTGAACTATCAACTAAATCAGGTTATAATAACCTGAGTTTAATCTTTAATCAAGATTAC

[0101] ATTAATAACTTGGTGGTGTGAAAATCTATTTCTCGCTCAAAGGTCTGAAGTTCAGAGGCCATGTATTGTG

[0102] TTGTTTTTCTTCTTTATTTTTAAAGTTATTATTACTTTACCGGTTTCTATTTTTATATTTATATTGAACG

[0103] TACGTGACAAAAAACAAAATTTAATCTAACCGGAAATATAAAAAAATGAAATCGTGAAAAGTCATAGAAA

[0104] AAGAGAAGGAACACGATACATCTACCTGACCGGCCAGCAGCCATCGCTGCGCAGTGTGCACTTGTGTCCG

[0105] GCAGCACAAGCCCGCATCTGGCTGCCTCCGCGTGCACCCACGCCTGGACGCCCGTCGCCCGCTGCACCAG

[0106] GCGGCCCGGGGCCTGGATCTACCCGAGCTTTCCTGGGCTCAGTCTTTGGATTTGAAAAACTATCTTGAAG

[0107] ACTCATGTATAAGCATGAATGATTGCCAATAGAAGAATTCTTCTTTTCAGAATTAGAAACTTTAAATTTT

[0108] GATGCGGTCTGTGACTCTGTGCTACCCTACTTGGTGCAAAAGCATTTTGGTTATGTACTACTCCCTCCGT

[0109] TCTTTTTTATTTGTCGCGATCTAGTTCAAAAATGAATTAGCGGGCGACAAATATTCGAGAACGGAGGTAG

[0110] TATATAATAGCTAGAGGCCAAAGTTTGATGGATCTATTTAATTTGTGCACCATGTTTACTTTTTGTTTGT

[0111] TGTTGGCTTCAGCGTGTACTGTAATTCCTCTATCTTGTTTGGGAATCTGGGGATTTTAAGGGAAAAACAGA

[0112] CTCCTTAAGTTTAAATTAGTGTTGGTGAAACTTGTAATCAAGTTTTCAGGATGGAGTAACCATGCATGCC

[0113] AATAGTAGTGAAATGATACAAATGTTTCTTCACTTAAATGCACTACTACACAAACCTGCAATGCGCAATT

[0114] AAGAATGATTGCTATGATGCGAGAATTGTTCTTTTACGAATTCAGAACTTTTTAACTCCAAGAAGTTTAG

[0115] CCAAGTACTGGTCTTGTATGGTAATGTCAGTAAGAACGAGTGGGATCTAACATTTCTGTGCTTATTTGCA

[0116] ATGGGAGTAGAGTTATACAATAAATCCCTCGTTCATTTTTAACTATAGCTAGCAAAAATGGTTTATGTT

[0117] TTTTTTGTTAACGTGTAAAAATTGGTGTTTATGAGTCTTCATAAATTCAAAAAAATAAGTTGAAACATGG

[0118] ATGCTAGATTCAGGTTTGAACTTGTTTTTGTTGCAACTAATCGAGCATTTTGGTTATCTACAATGTAATT

[0119] GACTGAAGCTGAACCGCTAAAGCTTGTTGGATCTACTTTAATTTCTGCATTTCCTTTATTTTCTTTCAA

[0120] TTGTGGTTATTACTGGGAACTATAATTCCTTTACCTTTGCTAATGGGACCCTAGGGTCTTGTGCAAAAAG

[0121] GATTTCTCAATTAAACAGGTGCATGTGAAACTTGCAATCAAGTATCCAGGAAGTGTTAATAGACTAGTGG

[0122] TAAATGAAAGCCTACAAAATTTGTACAGTTATGCATGCCATCGGTAATGACATCATACAAATACGTTTTT

[0123] GACTGAAAATTTACTGCTAGACAAACCCACGGTGCACAATCAGGAATAGTTTGTCACTTCAAGTTAATG

[0124] GTGGCTTTTTCATCTGAATGCTTTGGCCTCTAGAATCACTCTTCGCGTATCCATAATCACCAGTCACTTT

[0125] CATTAAAAGGGGGGATTTTCTATCTAATTCTGCTAGCACCTTTTTAAGTAAAATGATATTAGGTGATCT

[0126] TTTGTTTTGGTTATATCGATCCAATTTATCATCAATATTAAATCCACATGCCTCTAAAATAGAGGCAAAC

[0127] TTTTCTATGTACAAAGTGATCGATAGTGTTCTTTTCCTATAAGTATATTTATCCTCTTAAGATGACATAC

[0128] AGTCTATATCTGAGCTGAGGCTGCTAGGATTTTCTTGTATGTTTTGTATGGTTTTCATTGTTCAATTGAC

[0129] TTTTCTTCTTTTTCAGGTCTGCATGTCGTGGTTATTATGTTCACAGGTTACTTGGTAGGATTTGCTATGT

[0130] TCAAAGCTCTGTTCAACAATAATGCTGTACTGGTAAGTTCATTAGGCCACTTCTTGTAGGGATGTAGTAT

[0131] CTTGTACATTGAATGGGAAAGCCAAATAATTTTGCCTTCCTCTGTAGCTGCTAATTTGTTTGCCATTCCT

[0132] GATAAGATTAAACCTCACAGAGTTAACCTTGTCATGGCTTTTTAGAACGCTGCTGGAGGTATCTTAGGAT

[0133] TAGTTGGTGGCATGCTGGTTGAGACCATTCTCTTCATCATCAGATCATCGAGTAAAGAGTTAGCCTCTGT

[0134] TCCAAGATCAAAGAAAGCTCAGTAGACTCTTGAAATGCTATCATCAGTTAGTGGTGAGAGCTATAAGTGG

[0135] CCTTAGCTTGTGGAAGTCTATGGAGCATTGGTATGGGTTTGAAAATGCAATTCATCGAATATATGCAATT

[0136] GACATCCGATTTGAATTTGCATCATGAAGGAATAGGGCAGAGGGATTTGATTTCAATATTATTAGCTACC

[0137] CAGAATTTTGTATCTCGCCCAATGATTGGCTCGATGATAATTGAAGAACATGTACCTACGGATATGTAAA

[0138] AAGTACATACAAGAAGATGGTATGCTTAACAGCTTAAGTACCATTGGAGAGTATATTGGCTTGAAATTTT

[0139] GCAGTTAAAACTTAAAAGTAAGTTGTTATGTTTGCACGTGAGGTTTTGTTTGTGAATAAGCATATTTTAT

[0140] CCTAAGATTTTTCTCAGATTTCTTTTTTATCATAGCATTTTGAGTAAATCGAAATGAATTTTTTGTTGGT

[0141] ATATGCACGATTGTTTAGAGCAACTCCAATAGTTACGTAAATTTTAGCTCTCTAAATAATAGATTTAAGA

[0142] AGTTGCTAAATGGCTTTTGGAGTAAAAAATGCGAGTTATCCAATAGTTTTCTAAATATAGGTTGTAATTT

[0143] TGTTTTGTATCTATCCATATAAAAAAATAAGTCAAAAAAACTACATAATGCAGTCAACATTTTTGTTTAG

[0144] AGAGTTGTTAAATGGTTGCCAAATGTAGAGAGAAAATGAGGTTAGATGACAACTTGTTAAATTTAGAAAG

[0145] TTCATTTAAAGAACTGTTGGAGAATAATTTTTATATTAACTACCTACATTATTGATTTAGTAAGTCTTTT

[0146] AGAGAACTACTGGAGTTGCTCTTATTTAGCTAGTTAAAAGGATTAAGACACTTTAGAGCATCTTCAATAG

[0147] TTATGTAAATTTAGCACCCTAAGAGCAACTCCAGTAGTTCTCTAAAAGACTTCCTAAATCAATAATTTAG

[0148] GTAGTTAACATGAAAACTATTCTCCAACAGTTCCCTAAATTAACTTTCTAAATTTAACAACTTGTCATCT

[0149] AACCTCATTTTCTCTCTCACATTTGGCAACCATTTAACAACTCCCTAAACAAATTGTTGACTGCATTGTA

[0150] TAGTTTTTGTGACTTATTTTTTATGTGGATAAATACAAAACAAAATTACAACCTATATTTAGAGAACTAT

[0151] TGGAGAACTCACATTTATTTACTCCAAAAGCCATTTAGCAACTTCTTAAATATGTGATTTAGAGAGCTAA

[0152] AATTTACATAACTATTGGAGTTGCTCTAAATTATAGATTTAAGAATTTGCTAAATCATTTTAAGGAGTAG

[0153] AAAAATGTGTGAGCTCCAACAGTTCTCTATTTTATTAGTTACTAATTTTTAGAATTATCTACATCAACAA

[0154] ATAAAGTGAGCATGTTTTCTATTTTTAATTGAGTTAGCACATCAATAACCTTTGAAAATGTCTTGAAAGG

[0155] TCGAAACAAAATATCTGGAAAGGTCGAAGTTATCGATGGAAGATCAGAATGTCGCCGCGGCGACGACATG

[0156] AAGACAACACATGGCACTGTAGATAGATTTGTGGGATGACGCAAAACAGTGGCCGAAATATGGTTTTTCGG

[0157] GATGGATTTAGAACCAACATTGATTTAGGGAGTCCCAACGAGGTGGTAAATATAGGGATGAAATTGGATT

[0158] TTGTATGGAGTTTGTAAATTTAAGGATCTGCTTTATATAACTGTTGGAAAAGAGTTTTTCCCAAAAAA.

[0159] 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. An application of Zm00001d030087 gene in molecular marker-assisted breeding of corn kernel protein content, characterized in that: The sequence of the Zm00001d030087 gene is shown in SEQ ID NO: 1.

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