Probe combination, gene chip for corn starch content prediction and application thereof

Abstract

The application belongs to the technical field of molecular marker assisted breeding, and particularly relates to a SNP combination for corn starch content prediction, a gene chip and application thereof. The application screens out SNPs positioned in the range of 2kb upstream and downstream of a starch function gene by collecting known corn starch synthesis regulation genes and mining potential related genes; further uses whole genome correlation analysis to identify SNP sites associated with corn starch content in the non-starch function gene region, and adds starch function SNP sites reported in research for supplementation; finally, high confidence SNP sites associated with corn starch content are obtained. The developed gene chip for detection has high accuracy, detection rate and consistency of repeated sample genotypes of SNP sites, and has the advantages of low cost, strong specificity, high stability of detection results, good accuracy, and provides a reliable molecular detection tool for corn whole genome selection breeding application.

Description

Technical Field

[0001] This invention relates to the field of molecular marker-assisted breeding technology, and in particular to a probe combination, gene chip, and their applications for predicting corn starch content. Background Technology

[0002] Maize is an important dual-purpose crop for both food and feed. Because its kernels are rich in starch (>60%), it is typically used as an energy source in animal feed. Therefore, the identification and breeding of high-starch maize varieties has significant social value. Maize is one of the most successful crops in applying heterosis, and the selection of its parents is the core of heterosis breeding. Previously, parent selection largely relied on the experience and phenotypic selection of breeders, consuming a large amount of manpower and suffering from drawbacks such as long breeding cycles and low efficiency. With the development of genomics, molecular biology, and high-throughput sequencing technologies, molecular marker-assisted selection breeding technology based on single nucleotide polymorphisms (SNPs) has emerged. By detecting SNP molecular markers, plants with genotypes or alleles associated with dominant traits are screened, thereby enabling the discovery and breeding of superior germplasm resources. This technology has advantages such as short breeding cycles, low costs, and high genetic stability, showing promising application prospects in the field of maize hybridization breeding.

[0003] SNPs are the most widely distributed genetic markers in the genome, characterized by high density and high genetic stability, and have become the most commonly used molecular markers for studying plant genetic variation. Gene chips designed and prepared based on SNPs involve immobilizing probes for detecting SNP molecular markers on a vector to form a dense array of oligonucleotide probes. These probes then undergo allele-specific hybridization reactions with the target genome, allowing the determination of SNP loci polymorphism and genotype based on the presence and strength of the resulting signal. SNP chips exhibit excellent detection rates and stability in sample locus detection. Developing efficient and low-cost maize SNP chips is an important tool in molecular breeding.

[0004] Currently, several SNP chips have been developed for maize. For example, the Institute of Crop Science of the Chinese Academy of Agricultural Sciences selected representative SNPs from the resequencing data of 1,604 maize inbred lines and integrated other specific markers to successfully develop the SNP liquid phase chip MaizeGerm50K (Guan et al., Development of a MaizeGerm50K array and application to maize genetic studies and breeding. The Crop Journal, 12(6):1686-1696); the Maize Research Institute of the Beijing Academy of Agricultural and Forestry Sciences developed the high-density chip Maize 6H-60K based on the whole genome resequencing data of 388 representative maize inbred lines from home and abroad (Tian et al., New resources for geneticstudies in maize (Zea mays L.): a genome-wide Maize 6H-60K single nucleotide polymorphism array and its application. Plant J, 105(4):1113-1122). Although gene chip technology has been applied in various crop breeding fields, currently developed gene chips are mainly high-density and medium-to-high-density. When used for genotyping breeding targeting specific or certain agronomic traits, the cost of genotyping is high, limiting its widespread application in maize breeding. Starch is the main storage substance in maize kernels, serving as an important energy source for humans and animals, as well as a crucial industrial raw material. However, chips specifically designed for genotyping maize starch trait traits are yet to be developed. Current gene chips used for starch phenotype prediction often suffer from marker redundancy and poor specificity. Therefore, developing low-density, specific, and / or low-cost maize starch trait genotyping chips has significant application value. Summary of the Invention

[0005] To address the problems existing in current technologies, this invention provides a SNP combination for predicting corn starch content, its detection probe, gene chip, and their applications. This invention screens SNPs located within a 2kb range upstream and downstream of starch-related functional genes by collecting known corn starch synthesis regulatory genes and mining potentially related genes. Furthermore, genome-wide association analysis (GWAS) is used to identify SNP loci associated with corn starch content in non-starch functional gene regions. In addition, starch-related SNP loci from previously reported studies are added. Ultimately, high-confidence SNP loci associated with corn starch content are obtained, providing new genetic resource information for breeding high-starch-content corn varieties. By detecting the genotypes of the aforementioned SNP loci, the starch content of harvested materials from corn inbred lines or hybrids can be efficiently predicted, with high consistency between predicted and actual values. This facilitates breeders in developing reasonable parental pairing strategies and accelerates breeding progress.

[0006] This invention is specifically achieved through the following technical solutions:

[0007] In view of this, the first aspect of the present invention provides a combination of SNP molecular markers for detecting corn starch content, comprising 850 SNP variant sites, named SNP1-SNP850 respectively. The physical locations of the 850 SNP variant sites are determined by sequence alignment based on a corn reference genome, the version number of which is Zm-B73-REFERENCE-GRAMENE-4.0. The chromosomal physical locations and base polymorphisms corresponding to SNP1-SNP850 are shown in Table 1 of the specification.

[0008] A second aspect of the present invention provides a probe array for recognizing SNP molecular marker arrays as described above.

[0009] Furthermore, each probe in the probe assembly has a CG content of 20%-80%, a theoretical melting temperature greater than or equal to 65°C, and the probe sequence contains no N bases, has a sequence complexity greater than or equal to 0.9, a maximum homopolymer length less than or equal to 6, and a maximum number of consecutive repetitions of multiple bases less than or equal to 3.

[0010] Furthermore, the probe has a sequence length of 110 bp.

[0011] Furthermore, the probe assembly comprises 853 probes, and the nucleotide sequences of the 853 probes correspond to the chromosome positions shown in Table 2 of the specification.

[0012] A third aspect of the present invention provides a gene chip comprising the probe combination described above.

[0013] Optionally, the gene chip is a liquid-phase chip.

[0014] The fourth aspect of the present invention provides the application of the probe combination or gene chip as described above, the application being selected from at least one of the following: (1) analysis of maize genetic diversity; (2) molecular marker-assisted selection breeding of maize starch content; (3) whole genome selection breeding; (4) identification of maize germplasm resources; (5) maize genotyping; (6) analysis of maize population structure.

[0015] A fourth aspect of this invention provides a method for predicting corn starch content, comprising the following steps:

[0016] S1. Extract genomic DNA from the maize to be tested;

[0017] S2. Construct a library of genomic DNA, hybridize it with probes, and sequence the captured library;

[0018] S3. Perform quality control and data analysis on sequencing data to obtain genotyping results;

[0019] S4. Perform superior allele analysis on the genotyping results to predict corn starch content.

[0020] Furthermore, in step S4, the criteria for determining the corn starch content by performing superior allele analysis on the genotyping results are as follows:

[0021] The genotyping results of each SNP in maize materials were compared with the superior alleles, and the number of superior alleles was counted. The fewer the number of superior alleles, the lower the starch content, and vice versa.

[0022] The advantages and positive effects of this invention are as follows:

[0023] The SNP molecular marker combinatorial developed in this invention for the starch trait in maize has advantages such as strong correlation, high specificity, low density, and low cost, providing high-quality molecular markers for efficient prediction or detection of starch content in maize germplasm resources and for selection breeding. The detection probe combinatorial and gene chip developed based on the aforementioned molecular markers achieves a SNP locus genotyping accuracy >99%, a detection rate >98%, and a genotypic consistency rate >99% for duplicate samples. It exhibits advantages such as high specificity, high stability of detection results, and good accuracy. Furthermore, the predicted starch content based on the SNP molecular marker combinatorial shows high consistency with the actual value, providing a reliable molecular detection tool for maize genome-wide selection breeding applications, which is beneficial for accelerating the breeding process of superior varieties such as high-starch-content maize. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments are briefly introduced below.

[0025] Figure 1 This is a distribution map of SNP variant sites across the entire genome in an embodiment of the present invention;

[0026] Figure 2 This is a correlation diagram between the predicted and actual starch content values ​​of maize materials BY815, DAN340, and K22 based on the number of dominant SNP alleles in an embodiment of the present invention.

[0027] Figure 3 This is a correlation diagram showing the relationship between predicted and actual starch content values ​​of maize associated populations based on the number of dominant SNP alleles in an embodiment of the present invention.

[0028] Figure 4 This is a correlation diagram between the predicted and actual starch content values ​​of the maize B73 and BY804 recombinant inbred line populations based on the number of dominant SNP alleles in an embodiment of the present invention.

[0029] Figure 5 This is a correlation diagram showing the correlation between the predicted and actual starch content values ​​of commercial maize hybrid materials based on the number of dominant SNP alleles in an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The embodiments described herein are for illustrative purposes only and are not intended to limit the invention. All figures and other numerical values ​​used in this invention to indicate amounts, percentages, etc., should be understood in all cases to be modified by the word "approximately". Therefore, unless specifically stated otherwise, the numerical parameters listed in the specification and appended claims are approximate values ​​and may vary depending on the desired properties being sought. Each numerical parameter should at least be considered as obtained based on reported significant figures and by conventional rounding methods. The meanings of words such as "comprising," "including," "containing," "having," etc., are non-limiting, allowing for the addition of other steps and other components that do not affect the result. "And / or" should be considered as a specific disclosure of each of the two specified features or components, with or without the other. For example, "A and / or B" would be considered to include (i) A, (ii) B, and (iii) A and B.

[0031] Experimental methods in the following examples that do not specify specific conditions are generally performed under conventional conditions, such as those described in Cold Spring Harbor Laboratory's "Molecular Cloning: A Laboratory Manual (4th Edition)" or as recommended by the manufacturer.

[0032] 1. Development of SNP molecular markers associated with starch content in maize parent kernels

[0033] 1.1. Collect genes related to corn starch synthesis metabolism and regulation.

[0034] Using the keywords "Maize starch; Maize endosperm; Maize seed development," a literature search was conducted in the NCBI PubMed biomedical literature retrieval system developed by the National Center for Biotechnology Information (NCBI) in the United States. Review articles from the past decade related to maize starch synthesis metabolism research were retrieved, and genes involved in the starch synthesis metabolism pathway and related regulatory genes were collected. These genes were verified using relevant research articles cited in the review articles. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the PATHWAY module was used with the Metabolism option to search for the starch synthesis metabolism pathway (map00500: Starch and sucrose metabolism), selecting maize (zma) as the species, and retrieving all genes involved in the starch synthesis metabolism pathway. The genes retrieved from KEGG were annotated and analyzed in the Maize Genome Database MazieGDB (https: / / www.maizegdb.org / ), removing genes without starch-related annotation information. A total of 50 maize starch synthesis metabolism genes and 23 related transcription factors were identified. The gene IDs are as follows: Zm00001d000002, Zm00001d000021, Zm00001d002256, Zm00001d003817, Zm00001d004438, Zm00001d005546, Zm00001d010821, Zm00001d011301, Zm00001d013428, Zm 00001d014150, Zm00001d014844, Zm00001d015200, Zm00001d015746, Zm00001d016684 , Zm00001d018033, Zm00001d019266, Zm00001d019479, Zm00001d020799, Zm00001d022 206. Zm00001d022510, Zm00001d026337, Zm00001d027242, Zm00001d029087, Zm00001d 029091, Zm00001d032385, Zm00001d033746, Zm00001d033910, Zm00001d033937, Zm000 01d034074、Zm00001d034256、Zm00001d035037、Zm00001d037234、Zm00001d038121、Zm 00001d039131, Zm00001d042536, Zm00001d042842, Zm00001d044129, Zm00001d044211,Zm00001d045042、Zm00001d045261、Zm00001d045462、Zm00001d047253、Zm00001d049753、Zm00001d050032、Zm00001d051976、Zm00001d052263、Zm00001d051837、Zm00001d035038、Zm00001d020771、Zm00001d005925、Zm00001d011712、Zm00001d018971、Zm00001d005100、Zm00001d029512、Zm00001d008403、Zm00001d005028、Zm00001d040189、Zm00001d021537、Zm00001d026447、Zm00001d003677、Zm00001d002654、Zm00001d026113、Zm00001d041781、Zm00001d038001、Zm00001d020043、Zm00001d009646、Zm00001d031620、Zm00001d000361、Zm00001d043131、Zm00001d010730、Zm00001d032095、Zm00001d036298、Zm00001d045026。、

[0035] Based on the 50 maize starch synthesis metabolism genes and 23 transcription factors collected above, potential candidate genes were identified using gene co-expression networks and protein interaction network analysis. The steps included: (1) The maize DNA demethylase mutant obtained in the previous study of this invention (Xu et al., DNA demethylation affects imprinted gene expression in maizeendosperm. Genome Biol. 23:77) was found to have reduced kernel starch content through multi-year and multi-location phenotypic detection. Whole-genome methylation sequencing and transcriptome sequencing showed that the extensive DNA methylation variation caused by the mutant perturbed the expression of genes in the starch synthesis pathway. Transcriptome sequencing was further performed on wild-type and DNA demethylase mutant maize kernels at different developmental stages of endosperm to identify the expression levels of genes at different developmental stages of endosperm. Differential gene expression analysis was performed using the DESeq2 R package. Simultaneously, differential expression analysis was conducted on the expression levels of the 50 corn starch biosynthesis genes and 23 transcription factors collected above. A total of 13 corn starch biosynthesis genes and 10 transcription factors were identified as differentially expressed in wild-type and DNA demethylase mutants. (2) A co-expression network of differentially expressed genes and annotated differentially expressed transcription factors in corn was constructed using GENIE3 software. The co-expression network constructed from the top 100,000 gene pairs was used for analysis. Based on the co-expression network, the transcription factors involved in gene pairs with a weight value ≥0.01 were selected as potential candidate genes, and the DNA methylation level of these potential candidate genes in wild-type and DNA demethylase mutants was analyzed. The maize genome was divided into discontinuous 100bp windows. The CG and CHG methylation levels of the 100bp windows of wild-type and DNA demethylase mutants at different developmental stages were statistically analyzed. The 100bp windows with differential DNA methylation levels of CG ≥ 50% or CHG ≥ 50% were identified as differentially methylated segments. Based on the above analysis results, genes with differentially methylated segments within a 2kb range upstream and downstream of the gene were selected as candidate genes related to maize starch synthesis and metabolism. A total of 99 candidate transcription factor genes were identified. (3) Using the MaizeNetome database (http: / / minteractome.ncpgr.cn / ) developed by Li Lin's research group at Huazhong Agricultural University, the Interactome Highconfidence module was selected to identify candidate genes. The IDs of 50 maize starch synthesis and metabolism genes and 23 transcription factor genes were entered for retrieval. A total of 99 candidate genes associated with these 50 maize starch synthesis and metabolism genes and 23 transcription factors were retrieved.By combining the aforementioned candidate genes, 196 genes related to corn starch synthesis and metabolism were screened out, including 102 transcription factors and 94 non-transcription factor genes.

[0036] 1.2 Analysis of SNP sites associated with corn starch synthesis

[0037] We obtained the location information of 50 maize starch synthesis metabolism genes, 23 transcription factors, and 196 potential maize starch synthesis metabolism genes in the maize reference genome (Zm-B73-REFERENCE-GRAMENE-4.0 (B73 v4), Assembly accession: GCF_000005005.2). Based on the SNP information identified by maize associated populations (Gui et al., ZEAMAP, a Comprehensive Database Adapted to the Maize Multi-OmicsEra. iScience. 2020 Jun 26;23(6):101241.), we screened for SNPs located within a 2kb range upstream and downstream of the genes, and retained only SNPs with only two alleles and a minimum allele frequency (MAF) greater than 0.1. The starch content of grains in maize-related populations was determined (Zhou and Bao, High throughput method for measuring totalfermentables in small amount of plant part. US Patent 8,329,426 P. 201212-11). Genotyping of retained SNPs was performed, and a T-test was conducted on the starch content of maize plants from different genotypes. SNPs with P < 0.05 were selected. Then, the Linkage Disequilibrium (LD) among SNPs was calculated using PLINK, and SNPs with LD ≥ 0.1 were merged, ultimately identifying 325 SNPs associated with starch content synthesis.

[0038] Based on 75,680,644 SNP molecular markers from published maize association population materials (Gui et al., ZEAMAP, a Comprehensive Database Adapted to the Maize Multi-Omics Era. iScience. 2020Jun 26;23(6):101241.) and measured grain starch content information, genome-wide association analysis (GWAS) was performed using a mixed linear model (MLM) in Tassel5 software. SNPs with a deletion greater than 10% were removed, and 574 SNPs with a threshold p < 0.0001 and LD ≥ 0.1 were retained. Further, based on the relevant research of Professor Yang Xiaohong's team at China Agricultural University (Hu et al., Genetic basis of kernel starch content decoded in a maize multi-parent population. Plant Biotechnol J. 2021, 19(11):2192-2205, this study used 6 recombinant inbred line (RIL) populations to analyze the genetic variation of maize kernel starch content traits, added the analyzed QTL effects to the conventional linear model to form a new modified model for GWAS, and used the residuals of the model to test all SNPs on the current chromosome), forward and backward regression methods were used to screen variables, and the P-value threshold was determined by 500 permutation tests. Finally, 137 SNPs that were significantly related to the variation of maize kernel starch content were collected.

[0039] Following the above analysis, a total of 1036 SNPs were collected. SNPs located within the same gene (2kb upstream and downstream) with an LD ≥ 0.1 were then merged, retaining 951 SNPs related to corn kernel starch content. These SNPs generally exhibited a uniform distribution across the genome (see [link to analysis]). Figure 1 ).

[0040] 2. Design of liquid-phase probes for SNP molecular marker detection

[0041] Based on the location information of 951 SNP sites on the maize B73 v4 genome, liquid-phase probes were designed centered on these SNPs. The probe sequences were 110 bp in length, with a CG content between 20% and 80%, a theoretical melting temperature ≥65℃, zero N bases, a sequence complexity ≥0.9, a maximum homopolymer length (continuous repetition of the same base) ≤6, and a maximum number of consecutive repetitions of multiple bases ≤3. The liquid-phase probes were prepared by Beijing Baiyu Biotechnology Co., Ltd., and the combination of these probes constituted the gene chip.

[0042] A total of 853 probes, each 110 bp in length, were designed, covering 850 SNPs (some SNPs contained two probes, such as SNP324, SNP498, and SNP542). Of these, 83 SNPs came from 50 functionally known maize starch biosynthesis genes and 23 transcription factors; 216 SNPs came from genes related to maize starch biosynthesis; 421 SNPs came from GWAS analysis of associated populations; and 130 SNPs came from previous literature reports. Table 1 shows the physical location information of the 850 SNP molecular markers (SNP1 to SNP850), as well as their base polymorphisms and dominant alleles. Table 2 shows the physical location information of the 853 probe sequences for detecting SNP molecular markers in the genome.

[0043] Table 1 Physical location and allele information of SNP1 to SNP850 molecular markers

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] In the table, chr represents chromosome, the data before .s_ indicates chromosome number, the number after .s_ indicates the physical location of the bases on that chromosome, and polymorphism indicates the base type of the SNP site; for example, Chr1.s_2154074 indicates that SNP1 is located at base 2154074 on chromosome 1, its base type is C or G, the dominant allele is C, that is, maize plants containing this allele (including homozygous genotype CC and heterozygous genotype CG) are associated with high starch content.

[0057] Table 2 Physical location information of SNP1 to SNP850 molecularly labeled probes

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] 3. Evaluation of the effectiveness of gene chip detection for corn starch content in breeding

[0066] The genome of maize inbred line B73 (named B73) was extracted from leaf tissue using the 2% CTAB method. Beijing Baiyu Biotechnology Co., Ltd. used gene chips to detect and analyze maize inbred line B73. The process included: constructing a library of genomic DNA and hybridizing it with probes; sequencing the captured library; quality control and data analysis of the sequencing data; and obtaining genotyping results.

[0067] The raw sequencing data was quality controlled and adapters removed using the SOAPnuke filter to obtain clean data. The parameters were set to -n 0.1 -q 0.5 -l 20 -Q 2 -G -f AAGTCGGAGGCCAAGCGGTCTTAGGAAGACAA -r AAGTCGGATCGTAGCCATGTCGTTCTGTGAGCCAAGGAGTTG. The clean data was then aligned to the maize reference genome B73 v4-like using BWA mem mode (951 SNPs in the B73 v4 genome are replaced with non-B73 bases; for example, SNP1 in the B73 v4 genome is C, but in the B73 v4-like genome, it is replaced with G), with the default parameters. The SortSam.jar and MarkDuplicates.jar files from the picard-tools-1.119 package were used to remove duplicate data caused by PCR amplification during library construction from the aligned files. Further annotation of AddOrReplaceReadGroups was performed using picard.jar from the picard-tools-1.124 package. Finally, GenomeAnalysisTK.jar from GenomeAnalysisTK-3.7 was used to identify SNP sites among all samples, with the parameters set to -T HaplotypeCaller -nct 6 -dontUseSoftClippedBases -stand_call_conf20.0. The acquired SNP files were quality controlled using vcftools, with the parameters set to --minDP 10 --minQ 30. Genotype extraction was performed using vcftools with the parameter set to --extract-FORMAT-info GT. The results are shown in Table 3. The results indicate that the probe combination achieved good genotyping accuracy, detection rate, and genotypic consistency among duplicate samples.

[0068] Table 3. Effects of probe combination detection on SNP molecular markers

[0069]

[0070] 4. Evaluation of the effectiveness of gene chip in predicting corn starch phenotype

[0071] The genotyping results were analyzed for superior alleles, and the number of superior alleles was counted. The number of superior alleles is usually positively correlated with the phenotypic value of the material; that is, the fewer the number of superior alleles, the lower the starch content, and vice versa. Based on the SNP molecular marker information of the published maize associated population materials, the number of superior alleles at 850 SNP loci in each material was counted. Among them, the recombinant self-pollinated population parent materials BY815, DAN340, and K22, which were reported for the genetic analysis of kernel starch content, had 403, 430, and 474 superior alleles, respectively (Hu et al., Genetic basis of kernel starch content decoded in a maize multi-parent population. Plant Biotechnol J. 2021 Nov;19(11):2192-2205). Based on the number of superior alleles, BY815, DAN340, and K22 were predicted to be low, medium, and high starch content materials, respectively. The predicted results are consistent with the starch content measurements of BY815, DAN340, and K22, which are 54.0%, 63.8%, and 67.9%, respectively (see [link to data]). Figure 2 The horizontal axis of the figure represents the number of superior alleles in the plant, and the vertical axis represents the starch content (%). Further evaluation of the correlation between the predicted results and the actual phenotypic values ​​of all materials in the population yielded the following results: Figure 3 The horizontal axis of the graph represents the number of superior alleles, and the vertical axis represents the actual starch content. Specifically, the number of superior alleles is <= 200, 201-250, 251-300, 301-350, 351-400, 401-450, 451-500, and 501-550, with average numbers of superior alleles of 192, 228, 279, 330, 377, 424, 470, and 518, respectively. The average actual phenotypic values ​​are 61.47%, 61.61%, 63.39%, 63.98%, 65.54%, 66.67%, 67.91%, and 69.38%, respectively. The correlation coefficient R0 is [missing value]. 2 =0.99, indicating that the prediction results have good accuracy.

[0072] Maize lines B73 and BY804 are two inbred lines with significantly different starch contents. Genomic DNA was extracted from the B73 × BY804 recombinant inbred line population (71 accessions) using the 2% CTAB method, and analyzed using gene chips by Beijing Baiyu Biotechnology Co., Ltd. The number of superior alleles (heterozygous sites were denoted as superior alleles) in each material was counted, and the materials were classified according to the number of superior alleles, such as... Figure 4The horizontal axis represents the starch content of the population material, determined using polarimetry (GB / T 20378-2006 "Determination of Protostarch Content by Polarimetry"). The consistency between the predicted results and the actual phenotypic values ​​was evaluated. The results are as follows: Figure 4 Correlation coefficient R 2 =0.89, indicating that the prediction results have good accuracy.

[0073] Commercial maize hybrids are the seed source for maize applications. Accurate prediction of starch content in harvested materials based on hybrid genotypes is crucial for breeding and application. Genomic DNA was extracted from 66 collected commercial hybrid populations using the 2% CTAB method, and analyzed using gene chips by Beijing Baiyu Biotechnology Co., Ltd. The number of superior alleles (heterozygous loci were denoted as superior alleles) in each material was counted, and the materials were classified according to the number of superior alleles, such as... Figure 5 The horizontal axis represents the correlation coefficient R between the predicted starch content of the population material determined by optical rotation and the actual phenotypic value. 2 =0.95 ( Figure 5 This indicates that the prediction results have good accuracy.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A probe assembly, characterized in that, The probe combination is used to identify SNP molecular marker combinations for corn starch content detection. The SNP molecular marker combination consists of 850 SNP variant sites, named SNP1-SNP850 respectively. The physical locations of the 850 SNP variant sites are determined by sequence alignment based on the corn reference genome, and the version number of the corn reference genome is Zm-B73-REFERENCE-GRAMENE-4.

0. The chromosomal physical locations and base polymorphisms corresponding to SNP1-SNP850 are shown in the table below: ； ； ； ； ； ； ； ； 。 2. The probe assembly according to claim 1, characterized in that, The probe combination has a CG content of 20%-80% for each probe, a theoretical melting temperature greater than or equal to 65℃, no N bases in the probe sequence, a sequence complexity greater than or equal to 0.9, a maximum homopolymer length less than or equal to 6, and a maximum number of consecutive repetitions of multiple bases less than or equal to 3.

3. The probe assembly according to claim 1, characterized in that, The probe has a sequence length of 110 bp.

4. The probe assembly according to claim 1, characterized in that, The probe array comprises 853 probes, and the chromosomal positions corresponding to the nucleotide sequences of the 853 probes are shown in the table below: ； ； ； ； ； 。 5. A gene chip, characterized in that, Includes the probe combination as described in any one of claims 1-4.

6. The gene chip according to claim 5, characterized in that, The gene chip is a liquid-phase chip.

7. The application of the probe combination as described in any one of claims 1-4 or the gene chip as described in any one of claims 5-6, characterized in that, The application is in marker-assisted selection breeding of corn starch content.

8. A method for predicting corn starch content, characterized in that, Includes the following steps: S1. Extract genomic DNA from the maize to be tested; S2. Construct a library from genomic DNA and hybridize it with probes in the probe combination as described in any one of claims 1-4, and sequence the captured library. S3. Perform quality control and data analysis on sequencing data to obtain genotyping results; S4. Perform dominant allele analysis on the genotyping results to predict corn starch content; In step S4, the criteria for determining the corn starch content by performing dominant allele analysis on the genotyping results are as follows: The genotyping results of each SNP in maize materials were compared with the dominant alleles, and the number of dominant alleles was counted. The fewer the number of dominant alleles, the lower the starch content, and vice versa.

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