Application of Zm00001eb378820 gene in molecular marker-assisted breeding for regulating and controlling hundred-grain weight of corn grains

Abstract

The invention provides application of a Zm00001eb378820 gene in molecular marker-assisted breeding for regulating and controlling the hundred-grain weight of corn grains, and relates to the technical field of agricultural biology. A backbone maize inbred line Ye107 is used as a male parent and hybridized with three excellent inbred lines (D39, R-2-1-1 and YML1218), a multi-parent group with remarkable hundred-grain weight difference is constructed, and SNP27712538 positioned on a chromosome 9 is a remarkable associated site by measuring hundred-grain weight phenotype data in multiple environments and combining high-density SNP (Single Nucleotide Polymorphism) markers, GWAS analysis and QTL (Quantitative Trait Loci) positioning. And the functional gene Zm00001eb378820 which is closely related to the regulation and control of the corn hundred-grain weight is excavated. A new insight is provided for a genetic regulation mechanism of the corn hundred-grain weight, and a key target spot is provided for breeding of high-yield corn varieties, so that the breeding efficiency is remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of agricultural biotechnology, and in particular to an application of a Zm00001eb378820 gene in molecular marker-assisted breeding for regulating 100-grain weight of corn grains. Background Art

[0002] With the continuous growth of the global population, the shortage of feed grain has become the main challenge facing future food security. As the "king of feed", corn plays an important role in making up for the shortage of feed grain supply. Corn grain-related traits directly affect the yield level, are one of the core target traits of corn variety improvement, and show high heritability. In particular, 100-grain weight, as a key trait with strong heritability, is an important component in determining the yield level of corn and is often used as an indicator to measure grain quality. Although there is usually a positive correlation between 100-grain weight and grain weight, the two are not strictly linearly correlated. 100-grain weight is more representative and stable in explaining the variation of corn yield. Therefore, in-depth analysis of the genetic basis and regulatory mechanism of 100-grain weight has important theoretical value for revealing the genetic laws of corn yield formation. QTL positioning and genome-wide association analysis (GWAS) are the two main research methods for analyzing genetic loci related to corn kernel size and weight. These two methods provide important means for accurately locating the key genes or genomic regions that regulate corn 100-grain weight. This study combined QTL and GWAS methods to investigate the genetic basis and regulatory mechanism of 100-grain weight in maize, and analyzed its potential application value in breeding high-yield and high-quality maize, aiming to provide a theoretical basis and technical support for breeding new high-yield and high-quality maize varieties. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides an application of the Zm00001eb378820 gene in regulating the 100-grain weight of corn molecular marker-assisted breeding, which not only reveals the functional mechanism of the Zm00001eb378820 gene in regulating the 100-grain weight of corn, but also provides potential genetic resources for the breeding of high-yield corn varieties.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0005] A use of the Zm00001eb378820 gene in molecular marker-assisted breeding for regulating 100-grain weight of corn kernels, wherein the nucleotide sequence of the Zm00001eb378820 gene is shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the Zm00001eb378820 gene is shown in SEQ ID NO.2.

[0006] Preferably, regulating the 100-grain weight of corn kernels is to increase the 100-grain weight of corn kernels by adjusting the overexpression of the Zm00001eb378820 gene in corn.

[0007] Preferably, the breeding includes cultivating corn with a 100-grain weight, increasing corn yield, and breeding new corn varieties.

[0008] The present invention provides an application of the Zm00001eb378820 gene in regulating the 100-grain weight of corn kernels in molecular marker-assisted breeding, which has the following advantages over the prior art:

[0009] The present invention provides a gene Zm00001eb378820 for regulating the size of corn 100-grain weight and an application thereof. The present invention uses the temperate corn inbred line Ye107 with a larger 100-grain weight as a common parent, and crosses it with three tropical and temperate corn inbred lines with smaller 100-grain weights, thereby constructing a corn multi-parent population with significant differences in 100-grain weight. By measuring the 100-grain weight phenotypic data under multiple environments, and combining high-density SNP markers, GWAS analysis and QTL positioning, it is located that SNP_27712538 located on chromosome 9 is a significant association site. The site shows a large additive and dominant effect, and through further analysis, a functional gene Zm00001eb378820 closely related to the regulation of corn 100-grain weight is excavated. Haplotype analysis showed that among 485 RILs, gene Zm00001eb378820 had 4 haplotypes (Hap1, Hap2, Hap3 and Hap4), and the 100-grain weight phenotypes corresponding to Hap1 and Hap4 were significantly different (p<0.05), and the 100-grain weight corresponding to Hap4 was significantly lower than that of Hap1. Therefore, Hap4 of Zm00001eb378820 gene is a haplotype type that significantly improves the 100-grain weight of corn. The results of the present invention help to further provide new insights into the genetic regulation mechanism of 100-grain weight of corn, and provide key targets for the breeding of high 100-grain weight corn varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 The phenotypic distribution results of 100-grain weight of pop1, pop2 and pop3 in three environments are shown;

[0011] Figure 2 The results of correlation analysis of 100-grain weight phenotype data of pop1, pop2 and pop in three environments;

[0012] Figure 3 The distribution of SNPs detected by whole genome resequencing on 10 chromosomes of maize and their related characteristics;

[0013] Figure 4 To analyze the genetic structure of multi-parent populations;

[0014] Figure 5 The LD attenuation results of three recombinant inbred line populations;

[0015] Figure 6 Manhattan plots and QQ plots of BLUP, 21YS, 22YS and 23YS; the left plots in (a) to (d) are Manhattan plots, and the right plots are QQ plots. Each dot in the left plot represents a SNP, the black line represents the significance threshold, and different colors represent different chromosomes. The red line in the right plot is the trend line corresponding to the ideal QQ plot in each case; (a) is the GWAS result of BLUP, (b) is the GWAS result of YS21, (c) is the GWAS result of YS22, and (d) is the GWAS result of YS23;

[0016] Figure 7 The results of significant QTL detection that co-localizes with significant SNPs in GWAS;

[0017] Figure 8 The haplotype analysis results of Zm00001eb378820 gene, * indicates p≤0.05; (a) is the distribution frequency of the four haplotypes in the three populations, (b) is the box plot of the 100-grain weight changes corresponding to the four haplotypes;

[0018] Fig. 9 The genomic breeding values ​​(GEBV) and the additive and dominant effects of SNPs significantly associated with 100-grain weight. DETAILED DESCRIPTION

[0019] 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.

[0020] The present invention provides a Zm00001eb378820 gene for regulating the 100-grain weight of corn, and the amino acid sequence of the protein encoded by the Zm00001eb378820 gene is shown in SEQ ID NO. 2. In the present invention, the nucleotide sequence of the Zm00001eb378820 gene is preferably shown in SEQ ID NO. 1, and the specific sequence is as follows:

[0021] SEQ ID NO.1

[0022]

[0023]

[0024]

[0025] SEQ ID NO.2

[0026]

[0027]

[0028] Tropical and temperate maize germplasm is an important germplasm resource for maize breeding. The present invention constructs a multi-parent population with rich variation in 100-grain weight using 4 tropical and temperate maize inbred lines with obvious differences in 100-grain weight, and excavates the functional gene Zm00001eb378820 closely related to the 100-grain weight of maize from the multi-parent population of these 4 parent combinations. Haplotype analysis shows that among 485 RILs, gene Zm00001eb378820 has 4 haplotypes (Hap1, Hap2, Hap3 and Hap4), and there are significant differences in the 100-grain weight phenotypes corresponding to Hap1 and Hap4 (p<0.05), and the 100-grain weight corresponding to Hap4 is significantly lower than that of Hap1. Therefore, Hap4 of the Zm00001eb378820 gene is a haplotype type that significantly improves the 100-grain weight of corn. The results of the present invention help to further provide new insights into the genetic regulatory mechanism of 100-grain weight of corn and provide a key target for the breeding of high 100-grain weight corn varieties.

[0029] Embodiment 1:

[0030] 1.1 Plant materials

[0031] The experiment was carried out in three different ecological environments in Yanshan County, Yunnan Province (denoted as YS, 1540 m above sea level, longitude 104.5°E, latitude 23.6°N) in 2021, 2022 and 2023. Three multi-parent populations were obtained by single-seed transmission: Pop1 (D39×Ye107), Pop2 (R-2-1-1×Ye107) and Pop3 (YML1218×Ye107). The four parents (Ye107, D39, R-2-1-1 and YML1218) are disclosed in the literature [Jiang F, Liu L, Li Z, et al. Identification of Candidate QTLs and Genes for EarDiameter by Multi-Parent Population in Maize. Genes (Basel). 2023; 14 (6): 1305. Published 2023Jun 20. doi: 10.3390 / genes14061305] and [Bi, Y., Jiang, F., Yin, X. et al. Identification of candidate genes associated with maize northern leaf blight resistance in a multi-parent population. Plant Cell Rep 43,189(2024).https: / / doi.org / 10.1007 / s00299-024-03269-w】, among which, the parent Ye107 has been used to select and breed a number of excellent varieties with a planting area of ​​more than one million mu in southwest China, such as Ludan 8, Yunrui 8, Yunrui 88, Yunrui 888 and Yunrui 519. The information of the four parents is shown in Table 1.

[0032] Table 1

[0033]

[0034] 1.2 Field trials and phenotypic data collection

[0035] This study adopted a completely randomized block design (RCBD), and the research subjects included 485 F7 RILs and their four parents, which together formed a multi-parent population. Planting was carried out in Yanshan County (longitude 104.27°E, latitude 23.61°N), Yunnan Province, China in 2021, 2022, and 2023, and the experiment was set up with three replications to ensure the reliability and statistical significance of the results. In this experiment, each F7 RIL was planted in two rows of holes, with 14 plants per row, 4m long, 0.70m row spacing, and 25cm spacing between plants. Standard agronomic practices were followed during the experiment.

[0036] 1.3 Phenotypic data analysis

[0037] After preliminary processing of the phenotypic data collected three times in three years, the Anderson-Darling test in Minitab software was used to test the normality of the data, and the mean, standard deviation, variance, skewness, kurtosis, coefficient of variation and amplitude of variation of the data were analyzed. The Pearson test in R software (V4.1.3) was used to perform correlation analysis on the 100-grain weight data of different groups and years. Subsequently, the "aov" function was used to perform one-way analysis of variance, and the "lme4" package was used to calculate the heritability. The calculation of heritability was based on the method of Knapp et al. [Knapp SJ. Confidence intervals for heritability for two-factor mating design single environment linear models. Theor Appl Genet. 1986; 72(5): 587-591.]. In order to eliminate the influence of the environment on the variation of phenotypic values, this study used the R package lme4 to construct a mixed linear model and estimate the best linear unbiased prediction value (BLUE) of 100-grain weight. The formula for calculating heritability is shown in formula I:

[0038]

[0039] Among them, h 2 represents the broad heritability σg 2 is the genotype variance, σge 2 is the variance of the interaction between environment and genotype, σε 2 is the error variance, e is the environment, and r is the repetition.

[0040] 1.4 DNA extraction and whole genome sequencing (WGS)

[0041] The genomic DNA of the leaves of the seedlings of the maize multi-parent population was extracted using the cetyltrimethylammonium bromide (CTAB) method (Maroof, 1994), and the quality and purity of the extracted DNA were tested. After ensuring that the data met the experimental requirements, the IlluminaTruSeqDNA sample preparation kit was used to construct a genomic DNA library. After the genomic DNA was fragmented to about 350 bp, 150 bp paired-end reads were sequenced using the Illumina HiSeq X Ten platform. The raw reads generated by sequencing were processed by Trimmomatic software to remove adapter sequences and low-quality reads to obtain high-quality clean reads. Subsequently, the clean reads were aligned to the maize reference genome B73 (RefGen_v5) using the BWA software to generate the alignment file BAM. The GATK software was used to detect single nucleotide polymorphisms (SNPs) and insertion and deletion variations (InDels) and generate the variation file VCF. To ensure the reliability of the data, the PLINK tool was used to filter the variants in the VCF file, and the filtering criteria were set as missing rate>0.2 and minor allele frequency (MAF)<0.05 to retain high-quality variant data. ANNOVAR was used to annotate high-quality SNPs and determine the distribution of variant sites on the genome and their mutation types [Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38, e164.]. The annotated variant data were associated with phenotypic data, and subsequent analyses such as population structure analysis, GWAS and QTL positioning were carried out.

[0042] 1.5 Population structure analysis and LD decay

[0043] To understand the genetic structure of F7 RILs, this study used STRUCTURE software for population structure analysis. We set different population numbers (K values) and determined the optimal K value through cross-validation errors. PCA (principal component analysis) was performed on the genotype data using R4.1.3 software to further verify the stratification results of the population, and the PCA results were visualized using the scatterplot3d package. The linkage disequilibrium (LD) decay was analyzed across the genome, and the LD values ​​between SNPs were calculated using PopLDdecay software under the Linux system, and the degree of linkage disequilibrium (r 2), thereby generating a set of corresponding data points of LD value and physical distance. The physical distance and r of SNP were analyzed by fitting curve. 2 The relationship between the two groups was used to evaluate the decay trend of LD with increasing distance. The LD decay curve was then plotted using the Plot-OnePop.pl script provided by the software, and the “LD decay” package in the R language was used for visualization. 2 The minimum threshold was set to 0.2, which indicates the maximum physical distance at which LD is significantly decayed [Zhang C, Dong S, Xu J, et al. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files[J]. Bioinformatics, 2019, 35(10): 1786-1788.].

[0044] 1.6. Genome-wide association study (GWAS)

[0045] GWAS was performed using genotype and phenotype data from three different environments. First, we performed quality control on the genotype data obtained initially, filtered out low-quality SNP sites, and retained high-quality SNPs. The FarmCPU model in GEMMA (http: / / www.xzlab.org / software.html) was used to perform GWAS analysis on these high-quality SNPs, and PVE (phenotypic variation explanation rate) was calculated using the calculation formula proposed by Shim et al. [Shim H, Chasman DI, Smith JD, et al. A multivariate ge-nome-wide association analysis of 10LDL subfractions, and their response to statin treatment, in 1868 Caucasians [J]. PLoS One, 2015, 10 (4): e0120758.] In the model, we included the first few principal components of the population structure (such as PC1, PC2, and PC3) and other genetic markers (SNPs) with known significant associations as covariates in the model to better control possible spurious associations. The significance threshold was determined based on the total number of SNPs, and the formula -log10 (1 / total number of SNPs) was used to calculate at the level of p < 0.05. The candidate genes that were closely related to the variation of 100-grain weight and had specific functions were further identified in the significant SNPs with reference to the maize genome B73 RefGen_v5 (Zhou, 2012), and the Manhattan plot and QQ plot were generated using the "ggplot2" package in R software (V4.1.3). According to the LD decay results and the relative position of the SNPs, candidate genes were screened within 20 kb upstream and downstream of the significant SNPs [Zhang, X.; Ren, ZY; Luo, BW; Zhong, HX; Ma, P.; Zhang, HK; Hu, HM; Wang, YK; Zhang, HY; Liu, D.; et al. Genetic architecture of maize yield traits dissected by QTL mapping and GWAS in maize. Crop J. 2022, 10, 436-446.].

[0046] 1.7 Construction of genetic linkage map and QTL location

[0047] Based on high-density typing technology, high-quality SNP markers were screened, and markers with high missing rates and significant skewed segregation were excluded. JoinMap 4.0 software was used to perform linkage analysis on the screened high-quality SNP markers. The markers were assigned to different linkage groups according to the recombination frequency between SNP markers, and the maximum likelihood method was used to sort the markers in each linkage group. The Kosambi mapping function was used to calculate the genetic distance (cM) between markers, and finally the genetic linkage map of the three recombinant inbred line populations was constructed. Subsequently, the appropriate LOD threshold was determined by 1000 random permutation tests, and the significance level was set at p≤0.05 [Delannoy, E.; Stanley, WA; Bond, CS; Small, ID Pentatricopeptide repeat (PPR) proteins as sequence-specificity factors in post-transcriptional processes in organelles. Biochem. Soc. Trans. 2007, 35, 1643-1647.]. When the LOD value reaches or exceeds this threshold, the QTL is considered significant. Based on the phenotypic data of 100-grain weight and the constructed genetic linkage map, MapQTL 6.0 software was used to locate QTLs. The composite interval mapping (CIM) model was used to scan QTLs across the whole genome. The phenotypic data were combined with the linkage map to identify QTLs that were significantly associated with the target trait. The phenotypic variation explanation rate (R) of each QTL was calculated. 2 ) and its additive effects were calculated to screen out stable QTLs with significant effects.

[0048] 1.8. Combined GWAS and QTL analysis

[0049] Based on B73 RefGen_v5, the physical positions of the significant SNPs identified in the GWAS analysis were compared with the significant intervals obtained from the QTL positioning analysis to observe whether there was co-localization. Through GWAS and linkage analysis, candidate genes related to the target traits were obtained, and then the candidate genes were annotated and functionally predicted through databases such as Maize GDB, UniProt, NCBI, and InterPro to find candidate genes closely related to 100-grain weight.

[0050] 1.9. Gene haplotype analysis

[0051] Genes associated with the significantly associated SNP sites that were consistently detected in multiple environments and BLUP analyses and co-located with the QTL were selected for haplotype analysis. Haplotype blocks of the target region were constructed based on genotype data, the linkage disequilibrium (LD) structure of these regions was analyzed, and their effects on the 100-grain weight trait were evaluated. The calculation of LD values ​​was completed using Haploview software. By comparing the associations of different haplotypes with the 100-grain weight phenotype, the dominant haplotypes that may significantly affect the trait were screened out, further revealing potential functional variations and their genetic effects.

[0052] 1.10. SNP effect analysis and genomic estimated breeding value (GEBV) calculation

[0053] GCTA software was used to calculate the additive and dominant effects of SNPs and the epistatic effects between significant SNPs, and the GEBV of the three subpopulations was calculated at the same time. First, the genomic relationship matrix (GRM) was constructed using the genotype data in PLINK format, and the additive effect was estimated by the generalized linear mixed model (GREML) of GCTA. The dominant effect of SNPs was estimated by constructing a dominant GRM, and the epistatic effect of the significant SNP pairs was further analyzed. GEBV was then calculated using GRM and phenotypic data to obtain the GEBV of each population.

[0054] 2. Results

[0055] 2.1 Phenotypic analysis of 100-grain weight in multi-parent populations

[0056] The 100-grain weight data in this study were obtained by planting multiple parent populations in Yanshan, Yunnan for three years. The statistical analysis results of the 100-grain weight data of the three subpopulations are shown in Table 2. The average 100-grain weight of pop2 was the lowest, while that of pop3 was the highest. The coefficient of variation (CV) of the three subpopulations under the three environments ranged from 0.19 to 0.24, indicating the variability of 100-grain weight between samples. Further analysis showed that the frequency distribution histograms of the 100-grain weight of the three populations under different environments (e.g. Figure 1 ) is consistent with the normal distribution, and the absolute values ​​of skewness and kurtosis are less than 1, indicating that the data is less biased and consistent with the characteristics of normal distribution. The broad-sense heritability of 100-grain weight in the three populations was 85.96%, 84.87% and 91.59%, respectively. This high heritability indicates that the variation of this trait is mainly driven by genotype variation and is suitable for GWAS and QTL positioning studies.

[0057] Table 2 Descriptive statistical analysis of 100-grain weight in multi-parent populations

[0058]

[0059]

[0060] Correlation analysis was performed on the 100-grain weight data of the three groups under different environments. Figure 2 As shown in the figure, in the YS21, YS22 and YS23 environments, the correlation coefficients of 100-grain weight for pop1 were 0.68, 0.63 and 0.73, for pop2 were 0.65, 0.61 and 0.72, and for pop3 were 0.79, 0.75 and 0.81, respectively. The correlation coefficients were all significant (p<0.05). The consistent high correlations among the three environments indicated that the responses of pop1, pop2 and pop3 to 100-grain weight in different environments were stable, which once again showed that 100-grain weight was mainly affected by genetic factors, ensuring the phenotypic reliability of subsequent GWAS analysis and QTL positioning. The 100-grain weight data of the F7RIL population in different environments were subjected to variance analysis (ANOVA) (Table 3), and it was found that the 100-grain weight of different populations in different environments was extremely significantly different (p<0.001). This shows that the effects of different environments on 100-grain weight are extremely significant, which means that environmental factors such as climate, soil type, water supply, etc. have an impact on 100-grain weight that cannot be ignored.

[0061] Table 3 Analysis of variance for multi-parent populations, *** represents p < 0.001

[0062] group Degrees of Freedom sum of squares Mean Square F-number P-value F7RIL 2 4282 2141.2 67.3 <2e-16*** Pop1 2 2579 1289.7 43.27 <2e-16*** Pop2 2 883 441.7 12.83 3.82e-06*** Pop3 2 1151 575.7 19.63 6.87e-09***

[0063] 2.2 Population structure analysis and LD decay

[0064] A total of 194,963,454 variant sites were identified through whole genome resequencing (WGS). To ensure data quality, the raw data were strictly filtered, including removing sites with quality score (QUAL) ≤ 20, sequencing depth (DP) ≤ 3, missing rate > 20%, and minor allele frequency (MAF) < 0.05. Finally, 6,395,521 high-quality SNP sites were obtained, which were evenly distributed on the 10 chromosomes of maize ( Figure 3 ). Among them, chromosome 1 has the largest number of SNPs, reaching 2,467,471, while chromosome 10 has the least number of SNPs, only 1,200,136. The average marker density of the entire genome is 2975.46 SNPs / Mb, among which chromosome 4 has the highest marker density (3453.33 SNPs / Mb) and chromosome 6 has the lowest marker density (2776.07 SNPs / Mb). In the filtered SNP dataset, the average MAF is about 0.125, and the average missing rate is about 0.175, indicating that the data meets the high quality standards for subsequent GWAS.

[0065] Further principal component analysis divided F7RILs into three main subgroups: Figure 4a), this result is highly consistent with the subgroup structure revealed by kinship analysis. Phylogenetic tree analysis showed three genetic clusters, with the vast majority of families from the same subgroup clustered together, while a few families formed clusters with individuals from other subgroups. ( Figure 4 b. Figure 4 c), which is consistent with the distribution of population structure based on kinship, and the intermixing among subpopulations may be caused by gene introgression or hybridization of the common paternal parent Ye107 during the breeding process.

[0066] To assess LD decay in the association population, we analyzed the distribution of 6,395,521 high-quality SNPs on chromosomes using the r 2 The LD value (LD strength between two SNPs) is used to estimate LD. Figure 5 Shows r 2 The value tends to decrease gradually with the increase of physical distance. 2 The value decreases rapidly, indicating a strong LD effect. When the physical distance increases to about 20 Kb, r 2 The value decreased to 0.1 and tended to be stable at a larger distance. This shows that within the range of 20Kb, LD decayed significantly, and the rate of LD decay slowed down and gradually stabilized after exceeding this distance. This result provides an important reference for determining the physical distance of candidate genes upstream and downstream of significant SNPs in the whole genome.

[0067] 2.3 GWAS analysis and candidate gene mining in different environments

[0068] In the whole genome, the formula -log10(1 / SNP number) was used to determine 5.5 as the significance threshold of SNP, and 42 significantly associated SNP sites were identified at the p<0.05 level, with a PVE range of 0.37% to 8.91% and an average PVE of 5.54%, distributed on chromosomes 1, 2, 3, 4, 5, 6, 7, 9 and 10, respectively. The concentrated distribution of SNPs on chromosomes 5 and 9 was the most significant ( Figure 6 Most of these SNPs were detected in a single environment, but 4 SNPs showed consistent associations in multiple environments, among which 5-51394339 and 9-27712538 were consistently detected in the YS22, YS23 environments and BLUP, and 9-27712538 had the largest effect value (Table 4). These findings laid an important foundation for the screening of candidate genes.

[0069] Combined with the LD analysis results of the association population, by comparing the B73 RefGen_v5 on the maize GDB website, we mined 50 candidate genes in the 20kb region upstream and downstream of the 42 SNPs that were significantly associated with 100-grain weight. Based on the PVE value of the SNP, the functional annotation of the candidate gene and its consistency in different environments, a total of 5 candidate genes that may affect the 100-grain weight of maize were screened, namely Zm00001eb225640, Zm00001eb225650, Zm00001eb228310, Zm00001eb228320 and Zm00001eb378820, which are distributed on chromosomes 5 and 9 (Table 5).

[0070] Table 4 SNPs consistently identified as significantly associated with 100-grain weight in multiple environments

[0071]

[0072] Table 5 Candidate genes revealed by GWAS

[0073]

[0074]

[0075] 2.4. Construction of linkage map and QTL location

[0076] In this study, genetic linkage maps were constructed for three subpopulations (RIL-D39, RIL-R-2-1-1 and RIL-YML1218). The genetic maps of pop1, pop2 and pop3 contained 910, 2837 and 2400 SNP markers, respectively, covering all chromosomes, with total genetic distances of 588.07cM, 2487.83cM and 1501.77cM, respectively, and the average genetic distances between markers were 0.65cM, 0.88cM and 0.63cM, respectively. QTL positioning analysis was performed on the 100-grain weight and BLUP values ​​of the three populations under three different environments, and the LOD threshold was determined to be 2.5 at a significance level of p≤0.05 through 1000 permutation tests. The results showed that 17 significant QTLs were detected in the three populations, distributed on chromosomes 1, 2, 4, 6, 7, 8 and 9, with LOD ranging from 2.52 to 6.34, phenotypic variation explanation rate (R2) ranging from 5.73% to 13.93%, and an average R2 of 8.66% (Table 6). It is worth noting that in Pop2, the three significant QTLs (qHGW1-1, qHGW1-2 and qHGW1-3) located on chromosome 1 have overlapping intervals of 119,325,456-129,429,998. In Pop3, the two significant QTLs qHGW9-3 and qHGW9-4 located on chromosome 9 have overlapping intervals of 23,960,895-47,646,228 (Table 6).

[0077] Table 6 Distribution of significant QTLs in different populations

[0078]

[0079]

[0080] 2.5. Combined GWAS and QTL analysis

[0081] We compared the QTL mapping results of the three subpopulations with the GWAS results of MPP. In the GWAS, SNP 9-27712538 was identified as a significant association locus in YS22, YS23, and BLUP, and this SNP overlapped with two co-localized QTLs (qHGW9-3 and qHGW9-4) in pop3, respectively. The LOD values ​​of the two QTLs were 4.96 and 4.24, and the R2 were 13.09% and 12.32%, respectively. In addition, SNP 9-108046020 co-localized with qHGW9-1 in pop1, and the LOD value of qHGW9-1 was 3.46, and the R 2 7.89% (Table 6, Figure 7). Therefore, we speculated that the gene Zm00001eb378820 mined within 20kb upstream and downstream of site 9-27712538, and the genes Zm00001eb388530 and Zm00001eb388540 mined within 20kb upstream and downstream of site 9-108046020 may play a regulatory role in 100-grain weight. We queried Zm00001eb388530 and Zm00001eb388540 and found that they encode a pentapeptide repeat protein (PPR) and a transcription factor protein (GLK73), respectively, both of which are closely related to the growth and development of maize plants. Therefore, it was preliminarily speculated that the combined analysis of GWAS and QTL screened out a total of 7 candidate genes, namely Zm00001eb225640, Zm00001eb225650, Zm00001eb228310, Zm00001eb228320, Zm00001eb378820, Zm00001eb388530 and Zm00001eb388540.

[0082] 2.7. Haplotype analysis of genes

[0083] Gene Zm00001eb378820 was consistently identified in GWAS and QTL analysis and appeared in multiple environments. Its expression level in grain-related tissues was high, so it was selected as the key candidate gene in this study. Haplotype analysis was performed and a total of 4 haplotypes were identified (Table 6). There was a significant difference in the 100-grain weight phenotypes corresponding to Hap1 and Hap4 (p<0.05), and the 100-grain weight corresponding to Hap4 was significantly lower than that of Hap1 ( Figure 8 b) In the haplotype frequency distribution, the Pop2 population contains two haplotypes, Hap1 and Hap4, and the frequency of Hap4 exceeds 60%. In contrast, the Pop3 population only contains the Hap1 haplotype ( Figure 8 a). This haplotype distribution pattern is consistent with the 100-grain weight among populations. The mean 100-grain weight of the Pop2 population is the smallest, while the mean 100-grain weight of the Pop3 population is the largest. This indicates that Hap4 is closely associated with lower 100-grain weight, while Hap1 is closely associated with higher 100-grain weight.

[0084] Table 7 Summary of haplotypes corresponding to Zm00001eb378820

[0085]

[0086] 2.8. SNP effect analysis and genomic estimated breeding value (GEBV)

[0087] In this study, the additive, dominant and epistatic effects of four SNPs (5-51,394,339, 5-64,069,455, 6-73,588,308 and 9-27,712,538) significantly associated with 100-grain weight were evaluated. The results showed that 9-27712538 had the largest additive and dominant effects, and both were positive (Table 4, Fig. 9 ), indicating that the SNP has a positive effect on 100-grain weight and can increase the 100-grain weight of corn, and it not only works through the additive effect of individual alleles, but is also significantly affected by the dominant effect of different allele combinations. The epistatic effect between the four significant SNPs screened was analyzed, and the results showed that 5-51394339 had significant interactions with 6-73588308 and 5-64069445 (p<0.05), among which the epistatic effect between 5-51394339 and 6-73588308 was 2.24, and between 5-51394339 and 5-64069445 was -2.05. This indicates that there is an important gene-gene interaction between these three SNPs, which may lead to non-additive genetic effects, thereby affecting the size of 100-grain weight. In addition, by calculating the GEBV of the three subpopulations, it was found that the GEBV of the three populations had significant differences, among which pop3 showed the highest GEBV mean among the three subpopulations, and the GEBV difference between individuals was significant, indicating that this population has a large potential for genetic improvement. In contrast, pop2 had the lowest GEBV mean. These results provide a reference for further population selection and genotype optimization.

[0088] 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 Zm00001eb378820 gene in regulating 100-grain weight of maize by molecular marker-assisted breeding, characterized in that: The nucleotide sequence of the Zm00001eb378820 gene is shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the Zm00001eb378820 gene is shown in SEQ ID NO.

2.

2. The use according to claim 1, characterized in that: Regulating the 100-grain weight of corn kernels is to increase the 100-grain weight of corn kernels by adjusting the overexpression of the Zm00001eb378820 gene in corn.

3. The use according to claim 1, characterized in that: The breeding includes cultivating corn with 100-grain weight, increasing corn yield, and selecting new corn varieties.

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