Application of Zm00001d028188 gene in corn breeding

Abstract

The invention provides application of a Zm00001d028188 gene in corn breeding, and relates to the technical field of agricultural biology. The Zm00001d028188 gene can be applied to regulation and control of the hundred-grain weight of corn grains and can be applied to molecular marker-assisted breeding, the nucleotide sequence of the Zm00001d028188 gene is shown as SEQ ID NO.1, and the amino acid sequence of protein coded by the gene is shown as SEQ ID NO.2. The invention further discloses a preparation method of the Zm00001d028188 gene. The defects in the prior art are overcome, the functional gene Zm00001d028188 associated with the hundred-grain weight of the corn grains is excavated, and a theoretical basis is provided for molecular marker-assisted selection and regulation of the hundred-grain weight of the corn grains.

Description

Technical Field

[0001] The invention relates to the field of agricultural biotechnology, and in particular to an application of a Zm00001d028188 gene in corn breeding. Background Art

[0002] Maize (Zeamays L.) is one of the most important crops in the world and plays an important role in the source of food, feed and industrial products. Maize 100-kernel weight (HKW) is an important component affecting yield, and improving yield is an important goal of maize breeding (Z. Zhang, Z. Liu, Y. Hu, W. Li, Z. Fu, D. Ding, H. Li, M. Qiao, J. Tang, QTL analysis of Kernel-related traits in maize using an immortalized F2 population, PLoS One, 9 (2014) e89645).

[0003] As a typical quantitative trait, HKW is regulated by multiple genes and environmental factors (Y. Xiao, H. Liu, L. Wu, M. Warburton, J. Yan, Genome-wide Association Studies in Maize: Praise and Stargaze, Mol Plant, 10 (2017) 359-374; Z. Zhang, Z. Liu, Y. Hu, W. Li, Z. Fu, D. Ding, H. Li, M. Qiao, J. Tang, QTL analysis of Kernel-related traits in maize using animmortalized F2 population, PLoS One, 9 (2014) e89645). A deeper understanding of the natural genetic variation and its molecular mechanism regulating HKW is of great significance for improving maize yield. In summary, improving 100-grain weight is a key goal of plant breeding and biotechnology-assisted improvement. Therefore, exploring the functional genes closely related to 100-grain weight of maize can provide technical support for molecular marker-assisted selection of high-quality maize. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an application of the Zm00001d028188 gene in corn breeding, explores the functional gene Zm00001d028188 associated with the 100-grain weight of corn kernels, and provides a theoretical basis for molecular marker-assisted selection to regulate the 100-grain weight of corn kernels.

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

[0006] A use of the Zm00001d028188 gene in molecular marker-assisted breeding for regulating 100-grain weight of maize grains, wherein the nucleotide sequence of the Zm00001d028188 gene is shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the gene is shown in SEQ ID NO.2.

[0007] 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 Zm00001d028188 gene in corn.

[0008] Preferably, regulating the 100-grain weight of corn kernels is to reduce the 100-grain weight of corn kernels by reducing the expression level of the Zm00001d028188 gene in corn.

[0009] Preferably, the method of reducing the expression level of the Zm00001d028188 gene in corn is any one or more of silencing, knocking out or knocking down.

[0010] The present invention provides an application of Zm00001d028188 gene in corn breeding, which has the following advantages compared with the prior art:

[0011] The present invention constructs a multi-parent population (MPP) of 813 F2:7 RILs including 5 recombinant inbred line (RIL) subpopulations by hybridizing the common parent Ye107 (male parent) with 5 donor parents (female parents) with significant differences in grain size and weight. GWAS analysis and genetic linkage analysis were used to co-locate SNP_25750352 located on chromosome 1, which is significantly associated with the 100-grain weight of corn, and then the functional gene Zm00001d028188 regulating the 100-grain weight was excavated, which can explain 6.3% of the phenotypic variation of the 100-grain weight. Haplotype analysis showed that among the 813 RILs, the gene Zm00001d028188 had 2 haplotypes (CC and GG), and the number of haplotype B (allele GG) of pop3 was significantly higher than that of haplotype A (allele CC) ( Figure 7 dg). Haplotype analysis showed that the GG gene played an important role in maize HKW. Therefore, the GG of the Zm00001d028188 gene is a haplotype type that significantly increases 100-grain weight. The results of the present invention are helpful for further studying the regulatory mechanism of maize 100-grain weight, and also provide technical support for breeding high-quality maize varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1is the comparison of population development and 100-grain weight, where a is the number of each RIL in the multi-parent population, b is the grain phenotype of each parent, c is the 100-grain weight of the parent, d is the 100-grain weight of the RIL in the DH, and e is the 100-grain weight of the RIL in the BS;

[0013] Figure 2 The correlation results of pop1, pop2, pop3, pop4 and pop5 with HKW in two environments;

[0014] Figure 3 a is the phylogenetic tree of pop1, pop2, pop3, pop4 and pop5; b is the relationship analysis of 813 maize RILs; c is the Bayesian clustering diagram of 813 maize RILs when k=5;

[0015] Figure 4 Figure 2 is a graph of genotype diversity and LD decay, where a is the chromosome-specific SNP density in 1Mb intervals, and the range of SNP numbers is represented by green to red scales; b is the frequency distribution of SNP missing data points; c is the allele frequency (MAF) distribution; d is the change in genome-wide LD decay (r2) of 813 maize RILs with physical distance (Kb);

[0016] Figure 5 Phenotypic distribution, GWAS Manhattan plot and qq plot of 100-grain weight (HKW) trait in a Dehong, b Baoshan and c BLUP (from left to right);

[0017] Figure 6 QTL positioning of 5 subpopulations, where a is the QTL positioning of HKW in Pop1; b is the QTL positioning of HKW in Pop2; c is the QTL positioning of HKW in Pop3; d is the QTL positioning of HKW in Pop4; e is the QTL positioning of HKW in Pop5; and blue represents bin markers, orange represents DH, and purple represents BS environments;

[0018] Figure 7 Identification of candidate genes related to HKW in maize, where a is the QTL identification of pop3 on different chromosomes of maize under DH environment; b is the QTL identification of pop3 on different chromosomes of maize under BS environment; c is the haplotype analysis of Zm00001d028188; d is the comparison of 100-grain weight of two haplotypes (CC and GG) in DH and e BS; f is the comparison of HKW of two haplotypes in each RIL in DH and g BS; h is the ratio of two haplotypes in RIL; i is the location of Zm00001d028188 and its related SNPs. 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 Zm00001d028188 gene for regulating 100-grain weight of corn. The nucleotide sequence of the Zm00001d028188 gene is preferably as shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the Zm00001d028188 gene is as shown in SEQ ID NO.2:

[0021] SEQ ID NO.1:

[0022] 5'

[0023]

[0024] AGACTACTGTTAAATGTAGTAGCA

[0025]

[0026] SEQ ID NO.2:

[0027] MLIRQRVICSPTSFRFKLSDCLGRRIGPSFLGRQGGDSTRLVQDLYRTFDQVNNEEESPSDEKLPESFRDFLLEMKDNHYDARTFAVRLKATMENMDKEVKRSRLAEQLYKHYAATAIPKGIH CLSLRLTDEYSSNAHARKQLPPPELLPLLSDNSFQHYILASDNILAASVVVSSTVRSSSVPEKVVFHVITDKKTYPGMHSWFALNSVSPAIVEVKGVHQFDWLTRENVPVLEAIESHRGVRN HYHGDHGTVSSASDNPRMLASKLQARSPKYISLLNHLRIYLPELFPNLNKVVFLDDDIVVQRDLSPLWAINLEGKVNGAVETCRGEDSWVMSKRFRTYFNFSHPVIARSLDPDECAWAYGMN IFDLAVWRKTNIRDTYHFWLKENLKSGLTLWKFGTLPPALIAFRGHVHGIDPSWHLLGLGYQDKTDIESVRRAAVIHYNGQCKPWLDIAFKNLQPFWTNHVNYSNDFVRNCHILEPERVKE.

[0028] Tropical and subtropical corn germplasm contains rich genetic variation that is lacking in temperate corn, and is an important germplasm resource for corn breeding. The present invention constructed a multi-parent population with rich variation in grain oil content using 6 corn inbred lines, mainly tropical and subtropical, with obvious differences in grain size and weight. The functional gene Zm00001d028188, which is closely related to corn 100-grain weight, was mined from the multi-parent population composed of these 6 parents. This gene can explain 6.3% of the phenotypic variation in 100-grain weight. Haplotype analysis showed that among 813 RILs, gene Zm00001d028188 had 2 haplotypes (CC and GG), and the number of haplotype B (allele GG) of pop3 was significantly higher than that of haplotype A (allele CC) ( Figure 7 dg). Haplotype analysis showed that the GG gene played an important role in maize HKW. The results of the present invention are helpful for further studying the regulatory mechanism of maize 100-grain weight, and also provide an important reference for molecular assisted selection of maize high-yield breeding.

[0029] Embodiment 1:

[0030] 1.1 Plant materials

[0031] The experiment was conducted in 2019 in two different ecological environments: DH (longitude: 98.6°E, longitude: 24.4°N) and BS (longitude: 99.2°E, longitude: 25.1°N). Five multi-parent populations were obtained by single-grain transmission: pop1 (Ye107×CML312), pop2 (Ye107×YML32), pop3 (Ye107×CML373), pop4 (Ye107×CML395) and pop5 (Ye107×Q11); the six parents (Ye107, CML312, YML32, CML373, CML395 and Q11) were 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 [Jiang F, Liu L, Li Z, et al. Identification of Candidate QTLs and Genes for Ear Diameter by Multi-Parent Population in Maize. Genes (Basel). 2023; 14 (6): 1305. Published 2023Jun20. doi: 10.3390 / genes14061305], among which the common parent Ye107 is a key superior inbred line bred from two lines from different heterosis groups in the Chinese breeding program and has been used as a parent in many commercial hybrids planted in a considerable production area in China [Zhen, S., Gao, G., Wang, X., Ning, H., and Duan, X. (2004). Appraisal of drought-enduring quality of several maize inbred lines. J Maize Sci. (in Chinese) 12, 18-19.].

[0032] The pedigree, ecotype and 100-grain weight of the six parental lines are shown in Table 1. Among them, pop1 has 125 RILs, pop2 has 156 RILs, pop3 has 156 RILs, pop4 has 196 RILs, and pop5 has 180 RILs. Finally, an MPP population of 813 maize RILs was constructed.

[0033] Table 1 Parent information

[0034]

[0035]

[0036] 1.2 Experimental Design

[0037] A randomized block design (RCBD) was used in Dehong (denoted as DH) in 2019 and Baoshan (denoted as BS) in 2019, with three replicates in each location. Each experimental field had a row length of 4.0m, a row spacing of 0.7m, a spacing of 0.25m between plants, 14 plants per row, and 10 plants were sampled from the middle of each row. The corn in the experimental field was planted according to local standard agronomic practices. The measurement method was to randomly select 100 kernels, and the average weight after 3 repeated measurements was weighed with an electronic balance.

[0038] 1.3 BLUP analysis

[0039] After preliminary processing of the phenotypic data collected at two locations, the Ime4 package in R software (V4.0.5) was used to perform correlation analysis on the 100-grain weight (HKW) of different populations and environments, and the mean, standard deviation, skewness, kurtosis, and coefficient of variation of oil content were calculated to perform a normal distribution test. The lme4 package in R (v3.2.2) was used to perform the best linear unbiased prediction (BLUP) analysis on the collected data. The formula is shown in Formula I:

[0040] Y=μ+Line+Loc+(Line×Loc)+Rep(Loc)+ε Formula I;

[0041] Among them, Y, μ, Line and Loc represent phenotype, intercept, variety effect and environment effect respectively. Rep represents different replications, and ε represents random effects. Line×Loc represents the interaction between variety and environment, and Rep(Loc) represents the association effect of replication effects within environment.

[0042] 1.4 DNA extraction and genome sequencing

[0043] First, genomic DNA from maize seedling leaves was extracted using the cetyltrimethylammonium bromide (CTAB) method. Subsequently, the genomic DNA isolated from each F7RIL was digested with restriction endonucleases PstI and MspI and then ligated with barcode adapters using T4 ligase (New England BioLabs). GBS DNA libraries were constructed and sequenced according to the GBS protocol.

[0044] All connected samples were combined and purified using the QIAquick PCR purification kit (QIAGEN, Valencia, California, USA). Polymerase chain reaction (PCR) amplification was performed using primers matching the adapter. Finally, the PCR products were purified and quantified using the Qubit dsDNA HS analysis kit (Life Technologies, Grand Island, New York, USA). After selecting 200-300bp PCR products using the Egel system (Life Technologies), the library concentration was estimated using the Qubit 2.0 fluorometer and the Qubit dsDNA HS analysis kit (Life Technologies). Subsequently, sequencing reads were generated using TASSELv5.0 (Li C, Guan H, Jing X, et al. Genomic insights into historical improvement of heterotic groups during modern hybrid maize breeding. Nat Plants. 2022; 8 (7): 750-763.). Before TASSEL analysis, 80 poly(A) bases were attached to the 3' end of all sequencing reads. For comparative analysis, the B73_V4 reference genome sequence was used and analyzed using Sentieon software (parameter "bwa mem-k 32-MR") (Pei S, Liu T, Ren X, Li W, Chen C, Xie Z. Benchmarking variant callers in next-generation and third-generation sequencing analysis. Brief Bioinform. 2021; 22(3): bbaa148). Samtools (using the parameter rmdup) was used to compare the results for sorting and deduplication. Finally, 591,483 high-quality SNPs were generated and annotated using the ANNOVAR (Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16): e164.) software tool.

[0045] 1.5. SNP characterization, phylogenetic tree, PCA and linkage disequilibrium analysis

[0046] Phylogenetic tree analysis was performed using Tassel v5.0 software, and 591,483 high-quality SNPs were used to evaluate the genetic relationships among 813 RILs. Principal component analysis (PCA) was performed using R package 4.3.2, and the results were visualized using the scatterplot3d package. The decay of LD was evaluated using Pop LD decay v3.42 (Zhang C, Dong SS, Xu JY, HeWM, Yang TL. PopLDdecay: a fast and effective tool for linkage disequilibriumdecay analysis based on variant call format files. Bioinformatics. 2019; 35(10): 1786-1788.) using the raw SNP data. The parameters for calculating r2 (correlation coefficient) values ​​were set to default values. LD decay plots were drawn using default parameters.

[0047] 1.6. Genome-wide association analysis

[0048] GWAS was performed using the efficient mixed model association (EMME) analysis method in the GEMMA (Genome-wide efficient mixed-model analysis for association studies) software package (Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012; 44(7): 821-824. Published 2012Jun 17.). The following mixed model methods were used for the analysis:

[0049] y=Xa+Sb+Km+e Formula II;

[0050] Wherein, y represents phenotype, a and b are fixed effects, representing marker and non-marker effects, respectively, and m represents unknown random effects. The occurrence matrices of a, b and m are represented by X, S and K, respectively, and e is the vector of random residual effects. In order to correct the population structure, the present invention uses the first three principal components (PCs) to construct the S matrix, while the kinship (K) matrix is ​​constructed using a simple matching coefficient matrix. The genetic relationship between individuals is modeled as a random effect using the K matrix. In the association analysis, the significant P value threshold is set to p<1×10-6 to control the I type error.

[0051] The present invention uses PLINK (Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81 (3): 559-575.) to calculate independent markers, with a parameter of -independent airwise 5050.2. The formula -log10 (1 / SNP number) is used to calculate the significance threshold -log10 (p)> 4.5 to identify significant SNPs associated with maize HKW. SNPs that meet or exceed the threshold are extracted using bedtools v1.7 (Strable J, Wallace JG, Unger-Wallace E, et al. Maize YABBY Genes drooping leaf1 and drooping leaf2 Regulate Plant Architecture. Plant Cell. 2017; 29 (7): 1622-1641.), and candidate genes associated with maize HKW are identified in the 50 kb region upstream and downstream of the significantly associated SNPs based on the B73 (RefGen_v4) reference genome and annotation information. Based on the observation that the r2 value in the LD decay map appears a platform at 50 kb, the present invention decides to screen candidate genes associated with maize HKW in the 50 kb region upstream and downstream of the significantly associated SNPs.

[0052] 1.7. Construction of genetic map and QTL location

[0053] The SNP data were filtered by comparing with the maize reference genome B73 (RefGen_v4) by setting the parameter MAF ≥ 0.05. Then, a genetic linkage map was constructed by allelic SNPs using JoinMap4.0 (Ooijen, JW, Ooijen, JW, Verlaat, JV, Ooijen, JW, Tol, J., Dalén, J., Buren, JB, Meer, JV, Krieken, JH, Ooijen, JW, Kessel, JS, Van, O., Voorrips, RE, & Heuvel, LP (2006). 4, Software for the calculation of genetic linkage maps in experimental populations.). Linkage groups were formed using a LOD threshold of ≥2.5. QTL markers for HKW were performed using the composite interval mapping (CIM) method using Windows QTL Cartographer v2.0 (Zeng ZB. Precision mapping of quantitative trait loci. Genetics. 1994; 136 (4): 1457-1468). The LOD threshold was set based on 1000 random permutation tests, with a significance level of p ≤ 0.05. QTLs with an LOD threshold of ≥2.5 were considered significant. The proportion of phenotypic variation (PVE) explained by each QTL was determined using the square of the correlation coefficient (R2). The QTL name is constructed by starting with the letter "q" to indicate QTL, followed by the abbreviation of the trait name, the corresponding chromosome number and the marker position (Ribaut JM, Hoisington DA, Deutsch JA, Jiang C, Gonzalez-de-Leon D. Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interval. Theor Appl Genet. 1996; 92(7): 905-914.).

[0054] 1.8 Identification and functional annotation of candidate genes

[0055] The significant SNPs identified in GWAS were compared with the QTL mapping results to identify consistent sites, and SNPs overlapping in the QTL interval were selected to screen candidate genes. Candidate genes were searched within 50 kb upstream and downstream of the significant SNPs. Candidate genes were predicted in MaizeGDB (https: / / www.maizegdb.org / ) using the maize B73 v4 reference genome. Functional annotations of candidate genes were obtained using the InterPro database.

[0056] 1.9 Haplotype analysis

[0057] Haplotype analysis of SNPs associated with HKW in the two environments was performed using Haploview v4.2 software. First, a haplotype map was constructed using high-density genome-wide SNPs, and the haplotypes of SNPs significantly associated with maize 100-grain weight were determined based on the location of these sites and the results of LD analysis. Finally, the genes within the haplotypes were annotated to identify functionally related gene loci.

[0058] 2. Results

[0059] 2.1. 100-grain weight phenotype analysis

[0060] Construct an MPP population consisting of 813 RILs from 5 parents ( Figure 1 a) The differences in grains between parents in the MPP population are as follows Figure 1 As shown in b. Figure 1 As shown in c, the phenotypic value of the male parent Ye107 in HKW traits was significantly lower than that of all female parents. Variance analysis showed that the HKW traits of the five groups were significantly different ( Figure 1 de). Table 2 gives the descriptive statistical analysis of HKW of the five RIL subgroups and calculates the coefficient of variation of each subgroup in the two environments. CV showed that the variation of HKW in the five subgroups ranged from 14.3% (YML32) to 26.8% (CML395) in DH and from 15.6% (YML32) to 28.9% (CML395) in BS. In order to further evaluate the reliability of phenotypic identification, we calculated the correlation coefficient between the two environments. The results showed that the HKW phenotype had a very significant positive correlation. The correlation coefficients of the five subgroups in the two environments were 0.767, 0.734, 0.987, 0.979, and 0.975, respectively (p<0.001). The absolute values ​​of the skewness of the five subgroups were all less than 1, indicating a small degree of bias. Overall, the HKW of the MPP population showed a wide range of differences; however, the variation of HKW was consistent between the two environments, indicating that the phenotypic data had a high reliability for further analysis.

[0061] Table 2 Statistical analysis results of 100-grain weight phenotype

[0062]

[0063]

[0064] Note: DH represents the test conducted in Dehong in 2019, and BS represents the test conducted in Baoshan in 2019.

[0065] Pearson correlation analysis (significance level p < 0.001) showed that there was a significant and strong correlation between the overall performance of RILs in the same group under different environmental conditions, with correlation coefficients ranging from 0.73 to 0.99 ( Figure 2 ). This strong correlation indicates that the response pattern of RILs to 100-grain weight is very consistent under multiple environments, and their performance remains highly consistent even under different environmental changes. This phenomenon not only proves the stability and reliability of the experimental data, but also further indicates that the genetic control of the trait of 100-grain weight in different environments may be relatively stable. In addition, the strong correlation results also indicate that environmental factors may not produce significant variations in the performance of 100-grain weight, or even if they do, the genetic background between RILs makes their response to environmental changes have strong internal consistency.

[0066] 2.2. Group structure of RIL groups

[0067] In this study, Admixture software was used to analyze the population structure of 813 materials. The distribution of pop1, pop2, and pop5 populations was relatively concentrated, indicating that the genetic variation within these populations was small, while the significant separation between populations reflected the large genetic differences. Due to the influence of the common parent Ye107, there was some overlap between pop3 and pop4, but the other three RIL populations were relatively independent ( Figure 3 a, 3c). The significant overlap between CML373 (pop3) and CML395 (pop4) indicates that they have similar genetic backgrounds and belong to the non-Reid population, showing high genetic diversity and frequent gene exchange. CML312 (pop1), YML32 (pop2) and Q11 (pop5) formed their own independent branches, showing different evolutionary trajectories and relatively single genetic backgrounds. Figure 3 As shown, population structure, principal component analysis (PCA) and genetic distance or relatedness ( Figure 3 ac) are consistent. In the PCA graph, scattered points may come from heterogeneity or outliers within the population. Based on pedigree or genetic background, the MPP population can be divided into five major clusters. When K = 5 ( Figure 3 c), the population structure of the MPP population becomes clear. Phylogenetic tree analysis also showed five genetic clusters, which is consistent with the population structure based on kinship.

[0068] 2.3 SNP characterization and LD decay

[0069] A total of 591,483 high-quality genome-wide SNPs were identified through genotyping-by-sequencing (GBS), distributed across all 10 chromosomes of maize. Figure 4a Heat map showing the marker density of SNPs on maize chromosomes. The number of SNPs found on chromosomes 1-10 is as follows: 82,889, 67,249, 67,934, 75,637, 57,899, 47,683, 53,494, 50,217, 44,840, and 43,631. The highest number of SNPs is found on chromosome 1, while the lowest is found on chromosome 10. The SNP densities per million bases for chromosomes 1-10 are 269.99, 265.05, 275.11, 288.26, 306.30, 258.60, 273.99, 293.31, 277.25, and 280.65, respectively. SNPs are evenly distributed on chromosomes. In the filtered SNP dataset, the average missingness rate was 0.19 and the average minor allele frequency (MAF) was 0.20, indicating that the dataset is suitable for subsequent GWAS ( Figure 4 b, c). 591,483 SNPs were used to assess linkage disequilibrium (LD) decay in the association mapping population. LD in the association mapping panel was estimated using the r2 of pairwise combinations of SNPs across chromosomes. The minimum threshold was set to 0.38. The physical distance range for which LD decay was estimated was approximately 50 kb ( Figure 4 d) The rapid decay of LD indicates that the higher the degree of domestication, the greater the selection intensity, and the more favorable genetic relationship between populations.

[0070] 2.4. Genome-wide association analysis of HKW

[0071] In this study, there were significant differences in HKW traits between the MPP populations under the DH and BS environments ( Figure 5 ). The GWAS analysis of HKW traits in two environments was performed using the MLM model, which takes into account population structure and kinship. In association studies, population structure and kinship matrix are used as covariates to reduce false positives. The qq plot shows that the false positives of HKW traits have been effectively controlled ( Figure 5 ac). A threshold of -log10(P)>4.5 was set, and 21 SNPs significantly associated with HKW were identified, of which 5 were identified in DH, 6 were identified in BS, and 10 were identified in BLUP ( Figure 5, Table 3). These SNPs were distributed on 7 chromosomes (c1, c2, c4, c5, c6, c8, and c9). SNP-25750352 on chromosome 1 was detected in BS and BLUP; SNP-73091888 on chromosome 2 was detected in DH, BS, and BLUP, and SNP-176207154 was detected in DH and BLUP; SNP-233438083 on chromosome 4 was detected in DH, BS, and BLUP, and SNP-182679880 was detected in BS and BLUP; SNP-51610984 on chromosome 5 was detected in DH and BLUP; SNP-36178213 on chromosome 9 was detected in DH and BLUP. Using MaizeGDB, InterPro, UniProt and NCBI, combined with relevant published studies, candidate genes were comprehensively screened within the 50 Kb flanking region of the significant SNPs. Four potential candidate genes (Table 4) (Zm00001d028185, Zm00001d028186, Zm00001d028187, Zm00001d028188) were identified within the 50 Kb region on both sides of these loci.

[0072] Table 3 Details of SNPs significantly associated with HKW traits

[0073]

[0074]

[0075] Table 4 Potential candidate genes and their functions identified within the 50 Kb region on both sides of the important SNP sites

[0076]

[0077] 2.5. QTL Mapping in MPP Population

[0078] QTL positioning and effect analysis were performed on HKW in five subpopulations under two different environments, and SNP markers with a missing rate of more than 10% and loci with a minimum allele frequency of less than 5% were screened, with the LOD threshold set at ≥2.5. pop1( Figure 6 ) A total of three HKW QTLs were detected, namely qHKW4-1, qHKW1-1 and qHKW6-1, which explained 7.3%, 7.8% and 7.4% of the phenotypic variation in the two different environments, respectively (Table 5). Among them, qHKW4-1 identified on chromosome 4 had the highest LOD of 3.26 and the largest additive effect of 0.97 in the two different environments. The remaining QTLs were only detected in the BS environment, and the additive effect values ​​were all negative. Figure 6) were detected in both populations, qHKW3-1 and qHKW4-2 (Table 5), explaining 6.9% and 6.5% of the phenotypic variation, respectively. Among them, qHKW4-2 had the largest LOD value of 4.3. Figure 6 ) A total of four HKW QTLs were detected, namely qHKW1-2, qHKW3-2, qHKW5-1 and qHKW1-3, which explained 6.3%, 5.6%, 5.9% and 6.2% of the phenotypic variation in the two different environments, respectively (Table 5). qHKW1-3 identified on chromosome 1 had the highest LOD of 4.34 in both environments. The additive effect values ​​of qHKW1-2 and qHKW1-3 were both positive, indicating that these two QTLs had a positive effect on HKW. pop4( Figure 6 ) A total of 11 HKW QTLs were detected, with phenotypic variation ranging from 4.7% to 8.0%. Among them, qHKW2-1 had the highest LOD of 4.82 in two different environments. The additive effect values ​​of qHKW8-1, qHKW10-1, qHKW5-2, qHKW7-2, qHKW10-3 and qHKW10-4 were all positive, indicating that these 6 QTLs had a positive effect on HKW. Pop5( Figure 6 ) A total of 10 HKW QTLs were detected, with phenotypic variation ranging from 4.6% to 15.2%. qHKW5-8 had the highest LOD of 7.95 in two different environments. The additive effect values ​​of qHKW5-4 and qHKW5-7 were both positive, indicating that these two QTLs had a positive effect on HKW.

[0079] Table 5 Location and effect of HKW QTL detected in the MPP population

[0080]

[0081]

[0082]

[0083] 2.6 Identification of HKW-related genes

[0084] This study used QTL mapping and GWAS analysis to identify genes associated with HKW. Comparison of the results of the two analyses showed that a SNP (SNP-25750352) on chromosome 1 was located in qHKW1-2 ( Figure 7 a) and qHKW1-3( Figure 7 b). Therefore, we believe that these QTL intervals are reliable and consistent (Table 6). Based on functional annotation, one candidate gene Zm00001d028188 ( Figure 7c). SNP-25750352 is located 9,968 bp downstream of candidate gene Zm0000d028188 ( Figure 7 i). Further analysis based on 813 maize RILs showed that in 19DH and 19BS, the number of haplotype B (allele GG) of pop3 was significantly higher than that of haplotype A (allele CC) ( Figure 7 dg). Haplotype analysis showed that the GG gene played an important role in maize HKW. Figure 7 As shown in h, CC haplotype exists in pop1, pop2, pop3, pop4 and pop5, while GG haplotype exists in pop1, pop2, pop3 and pop4.

[0085] Table 6 GWAS and QTL co-localized genes

[0086]

[0087] 2.7. SNP Additive and Dominant Analysis

[0088] For the candidate genes related to HKW, a significant SNP site was deeply analyzed: SNP-26750352. By evaluating the additive and dominant effects of this site, we found that SNP-26750352 showed strong positive additive effects and dominant effects under different environmental conditions. This result indicates that this SNP site has a major positive effect in regulating maize HKW and shows stable synergistic characteristics in all environments, suggesting its value as a potential important regulatory gene. In addition, the above results further indicate that additive effects and dominant effects play an important control role in the development of maize HKW. It is particularly noteworthy that the parent Ye107 showed a significant increase effect in HKW, which indicates that Ye107 has good donor potential in improving maize grain-related traits.

[0089] Table 7 Additive and dominant effect values ​​of SNPs

[0090]

[0091] 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 Zm00001d028188 gene in corn breeding, characterized in that: The gene is used for regulating the 100-grain weight of corn kernels in molecular marker-assisted breeding. The nucleotide sequence of the Zm00001d028188 gene is shown in SEQ ID NO.1, and the amino acid sequence of the protein encoded by the 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 Zm00001d028188 gene in corn.

3. The use according to claim 1, characterized in that: Regulating the 100-grain weight of corn kernels is to reduce the 100-grain weight of corn kernels by reducing the expression level of the Zm00001d028188 gene in corn.

4. The use according to claim 3, characterized in that: The method of reducing the expression level of the Zm00001d028188 gene in corn is any one or more of silencing, knocking out or knocking down.

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