A molecular marker related to resistance to corn gray leaf spot and its application

Abstract

The present invention relates to the field of genetic engineering technology, and in particular to a molecular marker related to corn gray spot resistance and its application. The present invention provides a molecular marker related to corn gray spot resistance, the molecular marker is located at bases 138, 153, and 206 of chromosome 4 of corn, and the mutation type is A or G. The molecular marker provided by the present invention can help us gain a deeper understanding of the complex genetic pathway of corn disease resistance. This research can provide a basis for future molecular marker-assisted selection of corn resistant to gray spot.

Description

Technical Field

[0001] The invention relates to the technical field of genetic engineering, and in particular to a molecular marker related to corn gray leaf spot resistance and an application thereof. Background Art

[0002] Gray leaf spot (GLS) of maize is a fungal foliar disease caused by two pathogenic fungi, Cercospora zeae-maydis and Cercospora zeina. It is one of the most serious foliar diseases of maize worldwide. The disease causes significant economic losses to maize in East and Southern Africa, with yield losses exceeding 70% under favorable environmental conditions. Cercospora zeae-maydis is more common in the north, while Cercospora zeina is more prevalent in the south. Conidia mainly remain in the diseased plant debris on the soil surface to overwinter, and stomatal penetration, vegetative growth and production of new conidia from the matrix within the leaf lesions are then spread to the maize plant by wind and rain splash, causing secondary infections throughout the growing season. Cercospora produces cercosporin, which is considered a virulence factor. Under light conditions, cercosporin produces reactive oxygen species that damage plant cell membranes. Therefore, in the early stage of the disease, long rectangular lesions appear on the leaves, and due to the accumulation of spores on the surface, a gray mold layer is produced on both sides of the leaves; mature gray spot lesions are parallel to the veins, with rectangular spots ranging from gray to brown. In severe cases, it will cause leaf wilt, loss of photosynthetic capacity, and stalk lodging, ultimately leading to a large reduction in yield. Therefore, a deep understanding of the genetic resistance mechanism of GLS and the identification of resistance genes are crucial to improving corn yield and disease control.

[0003] Tropical and subtropical corn germplasm contains rich genetic variation that is lacking in many temperate corns, and is an important germplasm resource for corn breeding. The present invention hybridizes 8 corn inbred lines mainly from temperate germplasm Ye 107 and tropical and subtropical lines with obvious differences in gray leaf spot resistance, and constructs a multi-parent population with rich gray leaf spot resistance variation. These 9 materials used as parents are all inbred lines with important breeding value. Molecular markers closely related to corn gray leaf spot resistance are mined from these parent-matched multi-parent populations, which can provide a theoretical basis for molecular marker-assisted selection of corn with gray leaf spot resistance. Summary of the invention

[0004] The main purpose of the present invention is to explore molecular markers closely related to corn gray leaf spot resistance, and to provide a theoretical basis for molecular marker-assisted selection of corn with high resistance to gray leaf spot.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] The invention provides a molecular marker related to corn gray spot resistance, wherein the molecular marker is located at bases 138, 153, and 206 of chromosome 4 of corn, and the mutation type is A or G.

[0007] Preferably, the molecular marker has two genotypes, AA and GG, and corn plants with the AA genotype have significantly higher resistance to gray leaf spot than corn plants with the GG haplotype.

[0008] Preferably, the molecular marker is located in a 0.318 kb long terminal inverted repeat element downstream of the candidate gene Zm00001d051039, and the repeat element is located 0.499 kb downstream of the candidate gene.

[0009] Preferably, the nucleotide sequence of the candidate gene Zm00001d051039 is shown in SEQ ID NO:1.

[0010]

[0011] The invention also provides the application of the molecular marker in screening or identifying corn gray leaf spot resistance germplasm resources.

[0012] The present invention also provides the application of the molecular marker in molecular marker-assisted breeding of corn gray leaf spot resistance.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] The present invention uses the temperate corn inbred line Ye107 susceptible to gray spot disease as a common parent and crosses it with 8 subtropical corn inbred lines with high gray spot disease resistance to construct a corn multi-parent population with significant differences in gray spot disease resistance. GWAS analysis was used to detect consistent SNP_138,153,206 located on chromosome 4 that was significantly associated with gray spot disease resistance under multiple environments. By analyzing the relationship between the site and gray spot disease resistance, it was found that the site had two haplotypes, AA and GG, and the gray spot disease resistance of plants with the AA haplotype was significantly higher than that of plants with the GG haplotype.

[0015] Furthermore, the present invention counted the 100 families with the strongest and weakest resistance to gray leaf spot disease, and found that among the 100 families with the strongest resistance, AA haplotype accounted for 71%, while among the 100 families with the weakest resistance, AA haplotype accounted for only 22%. By using linkage analysis, the candidate intervals related to corn gray leaf spot disease resistance were excavated, and the interval where the site was located was once again located in Baoshan and Dehong, that is, the effect of the site on improving corn gray leaf spot disease resistance was verified by different methods. At the same time, the study found that the site was located in the terminal inverted repeat element of gene Zm00001d051039, and gene Zm00001d051039 encodes Protein IN2-1 homolog B protein, which is regulated by glutathione in corn and is significantly related to corn resistance to multiple leaf spot diseases. The identification of molecular markers by the present invention can help us gain a deeper understanding of the complex genetic pathways of corn disease resistance. This study can provide a basis for future molecular marker-assisted selection of corn resistant to gray leaf spot disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the genetic linkage map of the Pop8 population at the BS, DH and YS points.

[0017] Figure 2 (a) Distribution of SNPs on 10 chromosomes in 8 RIL populations for phenotypic diversity analysis. (b) SNP deletion frequency distribution. (c) Minimum allele frequency (MAF) distribution. (d) LD decay of the whole genome as a function of physical distance.

[0018] Figure 3Genetic diversity analysis: (a) K value; (b) phylogenetic tree; (c) principal component analysis; (d) cluster diagram of 1653 maize inbred lines when K=8.

[0019] Figure 4 The Manhattan plot (left) and QQ plot (right) for Baoshan (a), Dehong (b), and Yanshan (c) show the SNPs associated with GLS resistance. Each dot in the left figure represents a SNP, the black dashed line represents the threshold, different colors represent different chromosomes, and the blue lines represent co-localized SNPs; the red line in the right figure is the trend line corresponding to the ideal QQ plot in each case.

[0020] Figure 5 The red line represents the co-localization of QTL and GWAS results. The purple line represents the co-localization of Baoshan environment and Yanshan environment in the QTL results. The green line represents the co-localization of QTL and GWAS results.

[0021] Figure 6 It is the linkage disequilibrium region corresponding to the SNP of Zm00001d051039 gene.

[0022] Figure 7 The AA haplotype of molecular marker SNP_138,153,206 can significantly improve the resistance to corn gray leaf spot.

[0023] Figure 8 The light blue represents the location of the gene and SNP.

[0024] Fig. 9 The proportion of AA haplotype of molecular marker SNP_138,153,206 in the 100 families with the strongest resistance was significantly higher than that in the 100 families with the weakest resistance.

[0025] Fig.10 The gene Zm00001d051039 associated with the molecular marker SNP_138,153,206 has resistance effects to multiple leaf spot diseases. DETAILED DESCRIPTION

[0026] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0027] Example 1

[0028] 1.1 Plant materials and field trials

[0029] In order to ensure that the test materials have rich diversity, the present invention selects temperate elite maize inbred line Ye107 as a common parent and hybridizes it with tropical and subtropical backbone maize inbred lines CML312, CML373, CML384, CML395, CML444, YML32, YML16, and TML139 (i.e., D39) (the varieties have been disclosed in the literature "Fine mapping of candidate quantitative trait loci for plant and ear height in a maize nested-association mapping population" and "Studies on Candidate Genes Related to Flowering Time in a Multiparent Population of Maize D erived from Tropical andTemperateGermplasm”) to obtain the F1 generation, and then 8 RILs (F2:8) subpopulations were obtained through continuous self-pollination of single seed propagation for multiple generations, namely pop1~pop8, and about 200 lines were selected from each RILs population to construct the NAM population. The pedigree information and disease resistance grades of the parents are listed in Table 1.

[0030] The experimental site of the present invention is Mangshi, Dehong Prefecture, Yunnan (DH) ( 920m), Baoshan City, Yunnan Province (BS) ( 1650m) and Yanshan County, Wenshan Prefecture, Yunnan Province (YS) ( 1830m) in three different environments.

[0031] A randomized complete block design (RCBD) was used, with each experimental field having a row length of 4 meters, 14 plants per row, a row spacing of 0.7 meters, and a spacing of 0.25 meters between plants. The disease situation was observed and recorded 15 days after the pollen was dispersed. The GLS resistance survey was based on the average disease severity of a single plot, and the severity was based on the percentage of lesion area on the leaves. The survey standard adopted a 1-9 grade grading method, where the susceptible leaf area of ​​grade 1 was 0-5%, grade 3 was 6-10%, grade 5 was 11-30%, grade 7 was 31-70%, and grade 9 was 71-100%, representing high resistance, medium resistance, resistance, susceptible, and high sensitivity, respectively.

[0032] Table 1 Test materials and sources

[0033]

[0034]

[0035] 1.2 Data Collection and Analysis

[0036] Excel 2021 and SPSS (Statistics 20) were used to organize and calculate the phenotypic data under the two environments, and 3 times the standard deviation was used to eliminate outliers, and descriptive statistical analysis, normal distribution test and correlation analysis were performed.

[0037] In order to eliminate the influence of environmental factors on the phenotype, the mixed linear model of the lme4 package in R (v3.3.3) was used to perform the best linear unbiased prediction (BLUP) of the GLS raw phenotypic data of each recombinant inbred line under the three environments, and the following formula was used for analysis:

[0038] Yijlk=μ+Linei+Locj+(Line×Loc)ij+Rep(Loc)jl+εijlk

[0039] where Yijllk, μ, Linei and Locj represent the phenotypic value, intercept, genetic effect of the i-th genotype, and effect of the j-th environment, respectively; Repj represents the j-th replication effect, εijlk represents the random effect, (Line×Loc)ij is used to show the interaction of the i-th row at the j-th position; Rep(Loc)jl is used to show the effect of the i-th replication in the j-th position.

[0040] The broad-sense heritability (H2) is estimated as the ratio of genotypic to phenotypic variance. The broad-sense heritability (H2) of each trait was calculated using the following formula:

[0041] in is the genotype variance, is the genotype-environment interaction variance, is the error variance, l is the number of environments, and r is the number of replicates. It is used to obtain the best linear unbiased prediction (BLUP) for each genotype across environments.

[0042] After homogenizing the phenotypic data, the final BLUP phenotypic data value of each RILs line was obtained for subsequent GWAS analysis and QTL positioning.

[0043] 1.3 Sequencing genotyping, SNP extraction and filtering

[0044] Genomic DNA from reproductive maize leaves was extracted using the cetyltrimethylammonium bromide (CTAB) method. Genomic DNA isolated from each RIL was then digested with restriction endonucleases PstI and MspI (New England BioLabs, Ipswich, MA, USA) and ligated with barcode adapters using T4 ligase (New En gland BioLabs). Construction and sequencing of the GBS DNA library followed the GBS protocol.

[0045] All samples were combined and purified using the QIAquick PCR purification kit (QIAGEN, Valencia, CA, USA). The genome was digested with restriction endonucleases, and after adding adapters with different barcodes, the samples containing different barcodes were mixed and PCR amplified with different primers.

[0046] The PCR products were then purified and quantified using the Qubit dsDNA HS Assay Kit (Life Technologies, Grand Island, NY, USA). After selecting PCR products of 200-300 bp in size in the Egel system (Life Technologies), the library concentration was estimated using the Qubit 2.0 fluorometer and the Qubit dsDNA HS Assay Kit (Life Technologies). Sequencing was performed using an ion proton sequencer with a P1v3 chip (Life Technologies software version 5.10.1). Final reads were generated using TASSEL v5.0 (https: / / github.com / E uphrasiologist / GBS_V2_Tassel5). Before TASSEL analysis, 80 poly(A) bases were attached to the 30 ends of all sequencing reads. SNPs were called using the GenomeAnalysis Toolkit software and the maize B73 reference genome (B73_V4, ftp: / / ftp.ensemblgenomes.org / pub / plants / release-37 / fasta / zea_mays / DNA). ANNOVAR software was used to further annotate S NPs.

[0047] 1.4 Principal component analysis and population structure analysis

[0048] The distance matrix was calculated using TreeBeST (version: Treebest-1.9.2) software to construct a phylogenetic tree. Bootstrap values ​​were obtained after 1000 calculations. PCA analysis was performed using GCTA. After obtaining the PCA results, the values ​​of the first two PCs were used to perform a two-dimensional display of the samples involved in the analysis, and the genetic classification of the samples could be seen.

[0049] Admixture software was used to infer population structure, and the optimal number of clusters was determined based on the cross-validation error (CV error). The K value with the smallest cross-validation error rate corresponded to the optimal number of clusters. PopLDdecay software (version 3.04) was used to calculate the degree of linkage disequilibrium (r) between pairwise markers. 2 ), and used the software's own script Plot_OnePop.pl to plot LD decay to determine the number of markers required for GWAS and to determine the detection efficiency and accuracy of GWAS.

[0050] 1.5 Map construction and QTL positioning

[0051] The present invention defines the best result based on whether the SNP identified by GWAS and the SNP identified by QTL linkage analysis exist in the same position. Ye107×TML139 is the only group with overlapping SNPs and QTLs. We used this subgroup and its 224 RIL families for genetic linkage analysis, and then used SNPs to construct a genetic linkage map using JoinMap 4 software. The QTL of GLS was determined using the composite interval mapping (CIM) method using Windows QTLCartographer v2.0. The LOD threshold was set based on 1000 random permutation tests, and the significance level was P≤0.05. QTLs with an LOD threshold of ≥2.5 were considered significant. The percentage of phenotypic variation (PVE) explained by a single QTL was calculated by the square of the partial correlation coefficient (r2).

[0052] 1.6 Genome-wide association analysis of gray leaf spot resistance

[0053] GWAS is the study of the correlation between genetic mutations and phenotypes in a specific population. Taking into account the population structure and the affinity between individuals, the GWAS analysis of GLS was performed using the general linear model (GLM) association analysis method of EMMAX. Quantile-Quantile scatter plots (QQ plots) and Mahatton association plots were made using R software (4.3.3). The present invention uses plink calculation parameters as independent markers (--indep-pair-wise 5050.2). The significance threshold -log10(P)>4.5 calculated using the formula -log10(1 / SNP numbers) was used to identify significant SNPs associated with corn GLS (Bonferroni correction was used during calculation). The B73 reference genome (B73_V4) was used to identify and annotate candidate genes associated with GLS. The general linear model was used for trait association analysis, with the population genetic structure as a fixed effect and the individual affinity as a random effect to correct the effects of population structure and individual affinity:

[0054] y=Xα+Zβ+Wμ+e

[0055] y is the phenotypic trait, X is the indicator matrix of fixed effects, α is the estimated parameter of fixed effects; Z is the indicator matrix of SNPs, β is the effect of SNPs; W is the indicator matrix of random effects, μ is the predicted random individual, e is the random residual, and obeys e~(0,δe2).

[0056] 1.7 Candidate gene prediction and functional annotation

[0057] 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.

[0058] 1.8 Haplotype analysis

[0059] Select the region 1MB upstream and downstream of the significant site or gene position, calculate the block value through haploview software and display it through heat map. Analyze the haplotype of the significant site or gene region by the significant SNP site or gene position, and then evaluate the dominant haplotype. Draw a box diagram based on the division of haplotype and phenotype, and mark it with an asterisk if it reaches a significant level.

[0060] 2. Results

[0061] 2.1 Analysis of resistance to gray leaf spot

[0062] The field planting conditions and GLS occurrence of eight RIL populations were investigated and data were collected at three different locations. The GLS phenotypes of all populations in the three environments of Dehong, Baoshan and Yanshan showed a wide range of phenotypic variation, with the coefficient of variation ranging from 18.6% to 48.5% (Table 2). The results of the normal distribution test showed that the absolute values ​​of the skewness of the GLS traits of the populations in each environment were all around 1, with a small degree of bias. The frequency distribution of the GLS phenotypes between different locations conformed to the normal distribution, which was consistent with the genetic characteristics of quantitative traits.

[0063] In order to further evaluate the reliability of phenotypic identification, the present invention calculated the correlation coefficients between the three environments of each population. It can be seen from Table 2 that the phenotypes of each population are extremely significantly positively correlated in different environments, which is helpful to obtain the location of related molecular markers. At the same time, the heritability of the eight populations is relatively high, ranging from 93.9% to 99.4%. This shows that GLS is a quantitative trait controlled by multiple genes. Therefore, the following population phenotypic data can be used for QTL positioning analysis and genome-wide association analysis of corn GLS.

[0064] Table 2 Phenotypic variation analysis of RIL populations under three environments

[0065]

[0066]

[0067] 2.2 GLS linkage analysis and QTL mapping in three populations

[0068] To identify QTLs for GLS, we screened the SNPs with different alleles in the parents in the RILs of Pop8 and constructed a linkage map. Markers with completeness (>0.3) and segregation distortion (p<0.001) were screened, and the LOD threshold was set to ≥2.5, and a high-density genetic map was constructed ( Figure 1 ). A total of 11 GLS QTLs were detected under the three environments, distributed on chromosomes 1, 2, 3, 4, and 5, and named qGLS1-1, qGLS4-1, qGLS1-2, qGLS1-3, qGLS2-1, qGLS3-1, qGLS3-2, qGLS4-2, qGLS1-4, qGLS3-3, and qGLS5-1.

[0069] 2.3 SNP characterization, LD decay distance, and population structure

[0070] Missing value < 0.2( Figure 2 ), minor allele frequency (MAF)>0.05( Figure 3c) were used for GWAS. The average SNP density of the whole genome was 281.65 SNPs / Mb, which was evenly distributed along the chromosomes. The SNP density ranged from 259.81 SNPs / Mb on chromosome 5 to 309.29 SNPs / Mb on chromosome 4 ( Figure 2 We used the above SNPs to assess the linkage disequilibrium (LD) decay of the association population. When the LD strength dropped to half of its maximum value, the LD delay was shown in r 2 = 0.25 corresponds to 20kb, at which point the LD decay tends to be gentle. The results of population structure analysis are as follows Figure 3 As shown in the figure, when K=8, an inflection point appears and the curve slows down; the phylogenetic tree shows that all RILs are divided into 8 groups, and some individuals are mixed in the middle of these groups, which may indicate that gene introgression occurs during the breeding process; the results of principal component analysis (PCA) are basically consistent with those of population structure analysis. In general, the results of population structure, principal component analysis and kinship are consistent.

[0071] 2.4GLS genome-wide association analysis

[0072] The general linear model (GLM) in EMMAX was used to analyze the GLS traits. Principal component analysis and kinship as covariates were used in the association analysis to control false positives. Manhattan plots and QQ plots showed that the false positives of each trait were well controlled ( Figure 4 ). A threshold of -log10(P)>4.5 was set, and 113 SNPs were identified to be significantly associated with GLS, of which 40 were in Baoshan, 42 in Dehong, and 31 in Yanshan.

[0073] In addition, SNP sites located on chromosomes 1, 4, and 9 were detected in the three environments of BS, DH, and YS ( Figure 4 We used data from MaizeGDB, InterPro, UniProt, and NCBI, combined with bioinformatics analysis and relevant published research results, to comprehensively screen candidate genes within the 20 kb region upstream and downstream of the significant SNPs, and detected four possible candidate genes within the 20 kb region upstream and downstream of the three sites.

[0074] 2.5 QTL mapping combined with GWAS to reveal candidate genes

[0075] The present invention adopts QTL positioning and GWAS analysis. The analysis results show that the candidate SNP-138, 153, 206 obtained by GWAS analysis is located on chromosome 4, and is co-located in the BS and YS environments, and is located in the two QTL intervals of qGLS4-1 and qGLS4-2. Therefore, we believe that these two intervals are consistent intervals ( Figure 5 Based on the functional annotation of candidate genes and previous studies, we identified 11 candidate genes Zm00001d051039 associated with GLS resistance in this set of consensus intervals.

[0076] 2.6 Haplotype analysis

[0077] According to the genotyping results, we further analyzed the significant SNP sites SNP-138,153,206 and the candidate gene Zm00001d051039 (Gene ID: 103655544) on chromosome 4. The SNP is located in a 0.318kb long terminal inverted repeat element downstream of the candidate gene, and the repeat element is located 0.499kb downstream of the candidate gene. Haplotype analysis showed that the site has two haplotypes, AA and GG. Plants with the AA haplotype performed significantly better in maize resistance to GLS ( Figure 6 , 7 , 8).

[0078] Furthermore, we counted the 100 families with the strongest and weakest resistance to gray leaf spot and found that among the 100 families with the strongest resistance, AA haplotype accounted for 71%, while among the 100 families with the weakest resistance, AA haplotype accounted for only 22%. By using linkage analysis, we mined candidate intervals related to resistance to corn gray leaf spot, and the interval where this site is located was once again located in Baoshan and Dehong, that is, the role of this site in improving resistance to corn gray leaf spot was verified by different methods. At the same time, the study found that the site is located in the terminal inverted repeat element of gene Zm00001d051039. Gene Zm00001d051039 encodes Protein IN2-1 homolog B protein, which is regulated by glutathione in corn and is significantly associated with resistance to multiple leaf spot diseases in corn. The identification of molecular markers in this study can help us gain a deeper understanding of the complex genetic pathways of corn disease resistance. This study can provide a basis for future molecular marker-assisted selection of corn resistant to gray leaf spot ( Fig. 9 and Fig.10 ).

[0079] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An application of a molecular marker in screening or identifying maize germplasm resources resistant to gray leaf spot disease, characterized in that: The molecular marker is located at bases 138, 153, and 206 of chromosome 4 of maize, and the mutation type is A or G; The molecular marker has two genotypes, AA and GG, and the corn plants with the AA genotype have significantly higher resistance to gray leaf spot than the corn plants with the GG haplotype; The molecular marker is located at the candidate gene Zm00001d051039 The downstream terminal inverted repeat element is 0.318 kb long, and the repeat element is located 0.499 kb downstream of the candidate gene; the candidate gene Zm00001d051039 The nucleotide sequence is shown in SEQ ID NO:

1.

2. An application of a molecular marker in molecular marker-assisted breeding for corn gray leaf spot resistance, characterized in that: The molecular marker is located at bases 138, 153, and 206 of chromosome 4 of maize, and the mutation type is A or G; The molecular marker has two genotypes, AA and GG, and the corn plants with the AA genotype have significantly higher resistance to gray leaf spot than the corn plants with the GG haplotype; The molecular marker is located at the candidate gene Zm00001d051039 The downstream terminal inverted repeat element is 0.318 kb long, and the repeat element is located 0.499 kb downstream of the candidate gene; the candidate gene Zm00001d051039 The nucleotide sequence is shown in SEQ ID NO:1.

Images