CN112143829A - Typing and application of universal SNP (single nucleotide polymorphism) markers - Google Patents

Abstract

The invention discloses the typing and application of a group of universal SNP markers. The typing of the present invention includes the following steps: extracting the genomic DNA of the tomato to be tested; based on the site set information composed of multiple SNP sites, the SNP site is detected by reducing the amplification temperature programmatically; based on the detection result, the tomato to be tested is detected. Perform genotyping and analysis of genotyping results. The SNP marker of the present invention has good polymorphism, and has good stability and repeatability. The SNP marker is detected by competitive allele-specific PCR, which can realize a higher throughput detection process. In addition, the present invention provides a set of core SNP markers. The use of the marker set can efficiently and accurately identify the authenticity of tomato varieties, ensure the reliability of tomato germplasm resources collection, and significantly reduce the detection cost.

Description

Typing and application of a set of universal SNP markers

technical field

The invention relates to the technical field of germplasm resources and molecular breeding, in particular to a universal SNP marker composition, typing method and application thereof.

Background technique

Tomato belongs to Solanineae (Lycopersicon), is rich in tomato and multivitamins, has a short growth cycle and high economic value. It is not only one of the important vegetable crops with the largest planting area in my country, but also a succulent fruit. Model plants for research. With the rapid development of breeding technology, the number of tomato varieties is increasing day by day, and the improvement of living standards has prompted the increasing demand for variety diversification. In production, counterfeit varieties, impure varieties, mixed varieties and other phenomena are common, which greatly damages the interests of breeders and seed companies, and also brings confusion to seed demanders when choosing seeds. Variety identification is an important link in the process of agricultural development. How to scientifically identify and analyze the huge tomato germplasm resources, avoid repeated collection of the same tomato germplasm, protect the intellectual property rights of new plant varieties, and promote the healthy development of the tomato variety market. One of the urgent problems in tomato production.

Traditional identification methods of tomato varieties mainly rely on morphological markers to visually identify relative differences in traits such as stems, leaves, flowers, plant height, and fruits, or rely on some easily testable traits, such as typical resistance to pests and diseases. , physiological characteristics, this method is time-consuming and labor-intensive, some characters are easily affected by the environment, and morphological characteristics often begin to appear in a specific growth stage of the plant or the distinction of some varieties is not reflected in the shape. With the development of molecular biology technology, the identification of varieties by molecular marker technology has become a trend, which excludes the influence of environmental factors and makes the results more reliable. The International Union for the Protection of New Varieties (UPOV) has adopted SSR (simple sequence repeat) markers and SNP (single-nucleotide polymer-phism) markers as the recommended molecular markers for plant variety identification in the BMT molecular testing guidelines (Button P, 2007 ). SNP markers have the advantages of abundant variation, high accuracy, and easy automation. They can overcome the limitations of limited SSR markers and time-consuming operations. They are recognized as molecular markers with great application prospects (Rasheed Awaiset al., 2017). At the same time, the completion of tomato genome sequencing provides a good foundation for the development of tomato SNP markers (Lin et al., 2014).

At present, SNP marker technology is widely used in the identification of varieties of rice, corn, soybean and other crops, and has produced positive effects in variety management and protection. The identification of tomato varieties mainly uses the InDel labeling method (Gao Jianchang et al., 2013), and polyacrylamide gel electrophoresis is used for genotyping and analysis. This method has low throughput, cumbersome steps and difficult data integration. In addition, the problems of low efficiency and vulnerability to environmental factors in the authenticity identification of traditional methods still need to be further improved.

SUMMARY OF THE INVENTION

In order to solve at least part of the technical problems in the prior art, the present invention provides a group of universal SNP marker typing methods and applications. The composition and genotyping method of the invention can identify commercial tomato varieties widely existing in the market, not only can realize high-throughput detection, but also efficiently and accurately identify the authenticity of tomato varieties, and ensure the reliability of tomato germplasm resources collection , while significantly reducing detection costs. Specifically, the present invention includes the following.

The first aspect of the present invention provides a kind of typing method based on SNP marker, it comprises the following steps:

(1) the step of extracting the genomic DNA of the tomato to be tested;

(2) Based on the site set information composed of multiple SNP sites, the detection step of the tomato genome SNP site to be tested is performed by reducing the amplification temperature programmatically, wherein the site set includes the following sites: SNP01-3, SNP02- 1. SNP02-3, SNP03-1, SNP03-3, SNP04-1, SNP04-2, SNP05-3, SNP06-3, SNP06-6, SNP07-4, SNP07-5, SNP07-8, SNP07-9, SNP08-1, SNP09-1, SNP10-1, SNP11-3, SNP12-4, SNP12-6 and SNP12-8;

(3) The step of genotyping the tomato to be tested and analyzing the genotyping results.

According to the typing method based on the combination of SNP markers of the present invention, preferably, the locus set further comprises:

SNP01-1, SNP01-2, SNP02-2, SNP02-4, SNP03-2, SNP03-4, SNP03-5, SNP03-6, SNP04-3, SNP04-4, SNP04-5, SNP05-1, SNP05- 2. SNP05-4, SNP05-5, SNP05-6, SNP05-7, SNP06-1, SNP06-2, SNP06-4, SNP06-5, SNP07-1, SNP07-2, SNP07-3, SNP07-6, SNP07-7, SNP07-10, SNP07-11, SNP07-12, SNP07-13, SNP11-1, SNP11-2, SNP12-1, SNP12-2, SNP12-3, SNP12-5, SNP12-7 and SNP12- 9.

According to the typing method based on the combination of SNP markers of the present invention, preferably, a primer set consisting of three primers, a first forward primer, a second forward primer and a reverse primer, is used to detect the information of each site in the genomic DNA site set .

According to the typing method based on the combination of SNP markers of the present invention, preferably, the primers shown in SEQ ID NOs: 1-63 are used to detect the locus set in the genomic DNA.

According to the typing method based on the combination of SNP markers of the present invention, preferably, the primers shown in SEQ ID NOs: 1-177 are used to detect the locus set in the genomic DNA.

According to the typing method based on the combination of SNP markers of the present invention, preferably, step (2) includes the step of constructing a reaction system containing a buffer and a plurality of primer sets dissolved in the buffer, wherein each primer in the reaction system The final concentration of the first forward primer, the second forward primer and the reverse primer in the group is generally 50-200 μmol/L, preferably 80-150 μmol/L, such as 100 μmol/L.

According to the typing method based on the combination of SNP markers of the present invention, preferably, step (2) programmatically reducing the amplification temperature to detect the SNP site includes:

Denaturation step, denaturation at 94°C for 15min; denaturation at 94°C for 20s;

The first cycle step, first 61°C, annealing for 60s, then lowering by 0.6°C for each cycle, annealing for 60s, a total of 5-20 cycles;

In the second cycle step, denaturation at 94°C for 20s, and annealing and extension at 55°C for 60s, a total of 26-32 cycles, preferably 2-5 cycles, preferably 3-5 cycles, on the basis of 26 cycles.

A second aspect of the present invention provides a composition for typing based on SNP markers, comprising primers for detecting information of a locus set consisting of a plurality of SNP loci, wherein the locus set includes SNP01- 3. SNP02-1, SNP02-3, SNP03-1, SNP03-3, SNP04-1, SNP04-2, SNP05-3, SNP06-3, SNP06-6, SNP07-4, SNP07-5, SNP07-8, SNP07-9, SNP08-1, SNP09-1, SNP10-1, SNP11-3, SNP12-4, SNP12-6 and SNP12-8.

According to the composition for typing based on SNP markers of the present invention, preferably, the site set further comprises SNP01-1, SNP01-2, SNP02-2, SNP02-4, SNP03-2, SNP03-4, SNP03-5, SNP03-6, SNP04-3, SNP04-4, SNP04-5, SNP05-1, SNP05-2, SNP05-4, SNP05-5, SNP05-6, SNP05-7, SNP06-1, SNP06- 2. SNP06-4, SNP06-5, SNP07-1, SNP07-2, SNP07-3, SNP07-6, SNP07-7, SNP07-10, SNP07-11, SNP07-12, SNP07-13, SNP11-1, SNP11-2, SNP12-1, SNP12-2, SNP12-3, SNP12-5, SNP12-7 and SNP12-9.

The third aspect of the present invention provides the use of the composition according to the second aspect in the identification of tomato varieties.

In a fourth aspect of the present invention, there is provided a kit for typing based on SNP markers, which includes a primer set including the sequences shown in SEQ ID NOs: 1-177.

The invention designs and selects a set of SNP markers related to the identification of tomato varieties based on the SNPs generated by the tomato retest data and the whole genome sequence alignment of tomato varieties, and the marker set includes 59 SNP markers. These SNP markers have good polymorphism and good stability and reproducibility. The detection of SNP markers by competitive allele-specific PCR enables a higher-throughput detection process. In addition, the present invention provides a core set of SNP markers, which includes 21 SNP markers. The use of the marker set can efficiently and accurately identify the authenticity of tomato varieties, ensure the reliability of tomato germplasm resources collection, and significantly reduce the detection cost. The SNP marker set of the present invention is particularly suitable for the identification of varieties whose morphological characteristics are indistinguishable or difficult to distinguish.

Description of drawings

Figure 1 is a boxplot of the comparison of the discrimination of random combinations of markers before and after MAF screening.

FIG. 2 is an example of a fluorescent reading result of the labeled SNP04-1 (scanning result of Matrix Scanner from HC Scientific).

Figure 3 shows the number of SNPs corresponding to the MAF value interval.

Figure 4 shows the number of SNPs corresponding to the missing rate interval.

Detailed ways

Various exemplary embodiments of the present invention will now be described in detail, which detailed description should not be construed as a limitation of the invention, but rather as a more detailed description of certain aspects, features, and embodiments of the invention.

It should be understood that the terms described in the present invention are only used to describe particular embodiments, and are not used to limit the present invention. Additionally, for numerical ranges in the present disclosure, it should be understood that the upper and lower limits of the range, and every intervening value therebetween, are specifically disclosed. Every smaller range between any stated value or intervening value in a stated range and any other stated value or intervening value in that stated range is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention relates. Although only the preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference for the purpose of disclosing and describing the methods and/or materials in connection with which the documents are referred. In the event of conflict with any incorporated document, the content of this specification controls. "%" is a percentage by weight unless otherwise stated.

Unless otherwise specified, gene positions or loci in the present invention refer to positions relative to tomato variety Heinz 1706 (version number SL3.0).

[Methods for Genotyping Using SNP Markers]

The genotyping method of the present invention comprises the following steps:

(1) the step of extracting the genomic DNA of the tomato to be tested;

(2) based on the site set information composed of multiple SNP sites, the detection step of the tomato genome SNP site to be tested is performed by reducing the amplification temperature programmatically;

(3) The step of genotyping the tomato to be tested and analyzing the genotyping results.

In step (1), the method for extracting the genomic DNA of the tomato to be tested is not particularly limited, and methods known in the art can be used, and the genomic DNA extraction method can refer to known textbooks, such as "Molecular Cloning Experiments in Cold Spring Harbor". Publications such as the Fourth Edition of the Guide. Tomato samples used for extraction can be tomato cotyledons or true leaves, roots and other parts of tomato plants.

In step (2), the specific manner of obtaining the tomato DNA sequence polymorphism site is not particularly limited. Preferably, the present invention adopts a platform based on the Illumina Genome Analyzer sequencing system to perform resequencing of the samples. It further includes the processing of sequencing data to obtain polymorphism data containing SNP loci. The data processing includes taking the tomato variety Heinz 1706 as the reference genome and using PopSeq2Geno to obtain SNP variant loci.

Preferably, in step (2), the following parameters are set to obtain reliable SNP sites: filter out the minimum allele frequency (MAF) less than 0.01, genotype deletion rate (Miss Rate) greater than 0.2, and no SNP within 50bp other sites of variation. Further preferably, the SNP locus set is obtained based on an average minimum allele frequency of 0.379 and an average deletion rate of 0.002. In certain specific embodiments, the set of SNP loci is shown in Table 1.

Preferably, the SNP loci are respectively corresponding to each chromosome of tomato, and then the formula R=(minimum allele frequency)*0.8-(genotype deletion rate)*0.2 is used to calculate the number of SNP loci present in each chromosome. R, size-sort the R value of each SNP site in each chromosome. The R value of the present invention only considers the physical location and genetic distance position of the molecular marker on the chromosome, and does not involve the function or phenotypic characteristics of the molecular marker. Functional traits or phenotypes referred to in the present invention include, but are not limited to, for example, plant height, leaf size, fruit color, seeds, or some biological characteristics associated with an ecosystem process and associated with a species according to physiology, distribution area, or other to divide the characteristics. The method of the present invention is particularly suitable for solving the problem that, for example, tomato seeds grow to seedlings, and cultivated or semi-cultivated tomato seedlings cannot be correctly typed, resulting in ineffective identification and analysis of germplasm resources.

Preferably, the core SNP site set of the present invention is obtained by size sorting by R value, and the site set is SNP01-3, SNP02-1, SNP02-3, SNP03-1, SNP03-3, SNP04-1, SNP04 -2, SNP05-3, SNP06-3, SNP06-6, SNP07-4, SNP07-5, SNP07-8, SNP07-9, SNP08-1, SNP09-1, SNP10-1, SNP11-3, SNP12-4 , SNP12-6 and SNP12-8.

Reports on the identification and analysis of tomato varieties based on SNP markers are rare. There are many detection methods for SNP markers. The present invention uses competitive allele-specific PCR to perform related detection, and many detection platforms with a high degree of automation have been launched at home and abroad. Therefore, the development of generally representative tomato SNP markers can provide technical support for the authenticity identification of tomato varieties, collection of germplasm and large-scale management, save time and cost, and improve work efficiency.

Preferably, the present invention utilizes a primer set consisting of three primers, a first forward primer, a second forward primer and a reverse primer, to detect the information of each site in the genomic DNA site set. Further preferably, the primers shown in SEQ ID NOs: 1-63 are used to detect the set of loci in the genomic DNA. Also preferably, the detection of the set of loci in the genomic DNA is performed using the primers shown in SEQ ID NOs: 64-177.

Preferably, step (2) includes the step of constructing a reaction system comprising a buffer and a plurality of primer sets dissolved in the buffer. The concentration of each primer in the reaction system is not particularly limited, and is generally between 50-200 μmol/L, preferably between 80-150 μmol/L, for example, 100 μmol/L. This concentration is the initial concentration. When the primer is a dry powder, the dry powder can be diluted to 100 μmol/L as a storage solution. Before use, the reaction system is obtained as a working solution by diluting the storage solution according to the specified ratio and mixing with other components. For example, the concentration of the first forward primer, the second forward primer and the reverse primer in each primer set can be prepared to be 100 μmol/L as a storage solution. The three primers can be prepared as three different stock solutions, or the three primers can be prepared as the same stock solution containing all three primers. When constructing the reaction system, the first forward primer solution, the second forward primer solution and the reverse primer solution can be in a specific volume ratio, for example (10-15):(10-15):(28-32) volume ratio mixing. Further preferably, the solution volume ratio of the first forward primer, the second forward primer and the reverse primer is (11-14):(11-14):(29-31). Also preferably, the solution volume ratio of the first forward primer, the second forward primer and the reverse primer is (11-13):(11-13):(29-31).

The total volume of the reaction system is not limited and may be any suitable volume. In an exemplary reaction system, the total system is about 5 μL, specifically, 2.5 μL of genomic DNA (60 ng·μL ⁻¹ ), 0.07 μL of primer mixture, 2.5 μL of KASP V4.0 2×Master Mix from LGC Company, suitable for 384 orifice plate. In another exemplary reaction, the total reaction volume is 10 μL, specifically, 5 μL of genomic DNA (60 ng·μL ⁻¹ ), 0.14 μL of primer mix, 5 μL of KASP V4.02×Master Mix from LGC, suitable for 96 wells plate. The primer mixture is obtained by mixing the first forward primer A1, the second forward primer A2, the reverse primer C, and ddH ₂ O according to the volume ratio of 12:12:30:46 (the initial concentration of each primer is 100 μmol/L) . A blank control of water can be set during the test.

In the present invention, the detection step of the tomato genome SNP site to be tested is performed by reducing the amplification temperature programmatically. Before starting, use high temperature amplification to ensure the rigor of amplification. After the abundance of the target gene increases, reduce the temperature of amplification, thereby improving the efficiency of amplification. In addition, the appearance of non-specific PCR products is avoided, especially when complex genomic DNA templates are used, where non-specific annealing is prone to occur. Preferably, it includes a denaturation step, denaturation at 94°C for 15min; denaturation at 94°C for 20s; a first cycle step, firstly at 61°C, annealing for 60s, then decreasing by 0.6°C and annealing for 60s in each cycle, a total of 10 cycles; the second cycle step , denaturation at 94°C for 20s, and annealing and extension at 55°C for 60s, for a total of 25-32 cycles, preferably 25-30 cycles, and also preferably 25-27, such as 26 cycles. According to the actual typing results, 3-5 cycles can be appropriately increased.

[Composition for Genotyping]

In a second aspect of the present invention, there is provided a composition for typing based on SNP markers, the composition comprising primers for detecting information of a locus set consisting of a plurality of SNP loci, wherein the locus set includes SNP01- 3. SNP02-1, SNP02-3, SNP03-1, SNP03-3, SNP04-1, SNP04-2, SNP05-3, SNP06-3, SNP06-6, SNP07-4, SNP07-5, SNP07-8, SNP07-9, SNP08-1, SNP09-1, SNP10-1, SNP11-3, SNP12-4, SNP12-6 and SNP12-8.

Preferably, the site set further comprises SNP01-1, SNP01-2, SNP02-2, SNP02-4, SNP03-2, SNP03-4, SNP03-5, SNP03-6, SNP04-3, SNP04-4, SNP04-5, SNP05-1, SNP05-2, SNP05-4, SNP05-5, SNP05-6, SNP05-7, SNP06-1, SNP06-2, SNP06-4, SNP06-5, SNP07-1, SNP07- 2. SNP07-3, SNP07-6, SNP07-7, SNP07-10, SNP07-11, SNP07-12, SNP07-13, SNP11-1, SNP11-2, SNP12-1, SNP12-2, SNP12-3, SNP12-5, SNP12-7 and SNP12-9.

[use]

The third aspect of the present invention provides the use of the composition described in the second aspect in the identification of tomato varieties.

In a fourth aspect of the present invention, there is provided a kit for typing based on SNP markers, which includes a primer set including the sequences shown in SEQ ID NOs: 1-177.

Example

1. Development of SNP markers related to identification of tomato varieties

The SNP marker in the present invention is obtained based on the alignment of 38 tomato resequencing data with the whole genome sequence of tomato variety Heinz 1706 (version number is SL3.0). Specifically, 38 tomato genomic DNAs were extracted respectively with a concentration range of 900-1200 ng/ul, and 10 ng of each sample was resequenced on the Illumina Genome Analyzer sequencing system platform of the sequencing company.

First, use fastp software to filter the sequencing data, and filter out the sequences whose quality value is less than 10 and more than 30%, contains adapters, and the proportion of undetected bases exceeds 10%. Taking the tomato variety Heinz 1706 (version number SL3.0) as the reference genome, PopSeq2Geno was used to obtain SNP variant sites, and the minimum allele frequency was less than 0.01, the genotype deletion rate was greater than 0.2, and there was no other within 50bp near the SNP. The mutated sites can obtain reliable tens of thousands of SNP sites.

These SNP loci are respectively corresponding to each chromosome of tomato, and then use the formula R=(minimum allele frequency)*0.8-(genotype deletion rate)*0.2 to calculate the R of each SNP locus present in each chromosome, The R value of each SNP locus in each chromosome was sorted by size. The loci with the largest R value distributed on each chromosome of tomato were selected, and a total of 120 loci were transformed into competitive allele-specific PCR markers.

Next, the comparison of the discrimination of random combinations of markers before and after MAF screening was analyzed. Specifically, the degree of discrimination of 100 random combinations of markers corresponding to the gradient of the number of markers before and after the MAF screening calculated by the Python script was drawn into a boxplot using R. 83 markers before MAF screening and 59 markers after MAF screening.

Before MAF filtering, when the number of markers is 50, the average discrimination of 100 possible samples is 99.17% of the 241 samples, when the number of markers is 40, the average degree of discrimination is 96.03%, and when the number of markers is 30, the average degree of discrimination is 93.9% , when the number of markers is 20, the average degree of discrimination is 90.3%, and when the number of markers is 10, the average degree of discrimination is 75.04%. After MAF filtering, when the number of markers is 80, the average discrimination of 100 possible samples is 98.96% of the 241 samples, when the number of markers is 70, the average degree of discrimination is 98.34%, and when the number of markers is 60, the average degree of discrimination is 97.51% , when the number of marks is 50, the average degree of discrimination is 96.39%, when the number of marks is 40, the average degree of discrimination is 94.68%, when the number of marks is 30, the average degree of discrimination is 92.15%, when the number of marks is 20, the average degree of discrimination is 88.81%, and when the number of markers is 10, the average discrimination is 69.97%.

The results are shown in Figure 1. The discrimination degree of the 59 markers after MAF screening is the same as that of the 83 markers before MAF screening, both being 99.59%. At the same time, the discrimination degree of the random combination of markers after MAF screening is higher than that of the random combination of markers before MAF screening. The 59 markers are shown in Table 1 below:

Table 1

The mean minimum allele frequency (MAF) was 0.379 and the mean missing rate (Miss Rate) was 0.002.

Based on the analysis of the competitive allele-specific PCR typing results of 241 tomato materials, the above 59 SNP markers were randomly combined by bioinformatics, and the number of markers was randomly output as 50, 40, 30 , 20, and 10 correspond to 100 possible combinations respectively. When the number of markers is 50, the average degree of discrimination of 100 possible samples is 99.17% of the 241 samples, when the number of markers is 40, the average degree of discrimination of 100 possible samples is 96.03% of the 241 samples, and when the number of markers is 30, the average degree of discrimination of 100 samples The possible average discrimination is 93.9% of the 241 samples, and when the number of markers is 20, the average discrimination of the 100 possible samples is 90.3% of the 241 samples, and when the number of markers is 10, the average discrimination of the 100 possible samples is 241 75.04% of the sample. In order to ensure that the degree of discrimination of varieties is above 90%, and considering the physical location and genetic distance of the markers on the chromosome, a core marker set consisting of 21 SNP markers with excellent effects was finally selected. The discrimination of this set of core markers was 92.53% of the 241 samples.

3. Genotyping method based on the SNP marker of the present invention:

3.1 Extraction of tomato genomic DNA.

3.2 Construction of PCR reaction system:

The total system is about 5 μL, specifically 5 ng of genomic DNA, 0.07 μL of primer mix, 2.5 μL of KASP V4.02×Master Mix from LGC Company, and ddH ₂ O to make up to 5 μL, suitable for 384-well plate.

The total reaction system is 10 μL, specifically 10 ng of genomic DNA, 0.14 μL of primer mixture, 5 μL of KASP V4.0 2×Master Mix from LGC Company, and ddH ₂ O to make up to 10 μL, suitable for 96-well plates. The primer mixture was prepared by mixing forward primer A1, forward primer A2, reverse primer C, and ddH ₂ O in a volume ratio of 12:12:30:46 (the initial concentration of each primer was 100 μmol/L). A blank control of water can be set during the test.

The present invention provides primer pairs for PCR amplification of tomato SNP markers, the primers include 59 primer sets, each primer set is used to amplify the corresponding SNP marker, and consists of two forward primers (A1/A2) and one reverse primer The primers consisted of a FAM fluorescent linker (GAAGGTGACCAAGTTCATGCT) added to the 5' end of the forward primer A1, and a VIC fluorescent linker (GAAGGTCGGAGTCAACGGATT) added to the 5' end of the forward primer A2. Specifically, the sequences are shown in SEQ ID NOs: 1-177.

4. Procedure for PCR Amplification

Denaturation at 94°C for 15min; denaturation at 94°C for 20s, annealing at 61°C (decreased by 0.6°C per cycle) for 60s, a total of 10 cycles;

Denaturation at 94°C for 20s, and annealing and extension at 55°C for 60s, for a total of 25-32 cycles, preferably 25-30 cycles, and also preferably 25-27, such as 26 cycles.

The reading and analysis of the fluorescence data can be carried out using the ScientificQuantStudio ^TM 12K Flex real-time PCR system of Applied Biosystems, the reading temperature of the fluorescence data is 30°C, the reading time is 60s, and the fluorescence is FAM and HEX fluorescence. Alternatively, use the HC Scientific GeneMatrix Genotyping System, or other PCR systems capable of performing competitive allele-specific PCR experiments. Among them, Fig. 2 shows the fluorescent reading result of labeled SNP04-1 (the scanning result of Matrix Scanner of HC Scientific Corporation). Figure 3 shows the number of SNPs corresponding to the MAF value interval. Figure 4 shows the number of SNPs corresponding to the missing rate interval.

The marker set of the present invention contains 59 SNP markers. The 59 SNP markers have good polymorphism, and have good stability and repeatability. The SNP markers are detected by competitive allele-specific PCR technology, and the detection process is more high-throughput. The set of markers can be used to efficiently and accurately identify the authenticity of tomato varieties and ensure the reliability of tomato germplasm resources collection.

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<211> 24

<212> DNA

<213> Artificial Sequence

<400> 24

tcatttttgg gcgggcttac tttg 24

<210> 25

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 25

gaaggtgacc aagttcatgc ttctcgggcg tgactcagtt ttag 44

<210> 26

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 26

gaaggtcgga gtcaacggat ttctcgggcg tgactcagtt ttac 44

<210> 27

<211> 27

<212> DNA

<213> Artificial Sequence

<400> 27

catggtttct ggcatggaaa agtggac 27

<210> 28

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 28

gaaggtgacc aagttcatgc tgtgcaaact ttggagacta gggt 44

<210> 29

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 29

gaaggtcgga gtcaacggat tgtgcaaact ttggagacta gggc 44

<210> 30

<211> 22

<212> DNA

<213> Artificial Sequence

<400> 30

ggtgcttgtt gcctcttact gg 22

<210> 31

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 31

gaaggtgacc aagttcatgc taaacaagtt gggatcgggt aagcg 45

<210> 32

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 32

gaaggtcgga gtcaacggat taaacaagtt gggatcgggt aagca 45

<210> 33

<211> 22

<212> DNA

<213> Artificial Sequence

<400> 33

tgtggactaa cgatttcacg gg 22

<210> 34

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 34

gaaggtgacc aagttcatgc tcattacagg agtggtacgt ctgac 45

<210> 35

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 35

gaaggtcgga gtcaacggat tcattacagg agtggtacgt ctgaa 45

<210> 36

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 36

aagaacaaca cgtcctttag tgcc 24

<210> 37

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 37

gaaggtgacc aagttcatgc tgctggttaa gtgccaagaa catggac 47

<210> 38

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 38

gaaggtcgga gtcaacggat tgctggttaa gtgccaagaa catggat 47

<210> 39

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 39

gtagccctct catcttccat ggac 24

<210> 40

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 40

gaaggtgacc aagttcatgc tgcgctcaac atgttctgtt cctcatc 47

<210> 41

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 41

gaaggtcgga gtcaacggat tgcgctcaac atgttctgtt cctcatt 47

<210> 42

<211> 28

<212> DNA

<213> Artificial Sequence

<400> 42

cgaaaaacca ctcttatcaa aggctcgc 28

<210> 43

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 43

gaaggtgacc aagttcatgc tcccattttc ttttagccga cgggtc 46

<210> 44

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 44

gaaggtcgga gtcaacggat tcccattttc ttttagccga cgggtt 46

<210> 45

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 45

taattccaca ggtgaggtct agg 24

<210> 46

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 46

gaaggtgacc aagttcatgc tgttgtgtcc gtgaccagag agagttt 47

<210> 47

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 47

gaaggtcgga gtcaacggat tgttgtgtcc gtgaccagag agagttc 47

<210> 48

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 48

tcgttacttg tccctgttca cacc 24

<210> 49

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 49

gaaggtgacc aagttcatgc tcctaagtgc ggggaagtta acaacc 46

<210> 50

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 50

gaaggtcgga gtcaacggat tcctaagtgc ggggaagtta acaact 46

<210> 51

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 51

ctccaagtct tgtggattga aaggtc 26

<210> 52

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 52

gaaggtgacc aagttcatgc tacttaggcc cactcaaccg cag 43

<210> 53

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 53

gaaggtcgga gtcaacggat tacttaggcc cactcaaccg caa 43

<210> 54

<211> 23

<212> DNA

<213> Artificial Sequence

<400> 54

aaccaactct ccctctccct tat 23

<210> 55

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 55

gaaggtgacc aagttcatgc tcgctgcagt gatgctgtta caac 44

<210> 56

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 56

gaaggtcgga gtcaacggat tcgctgcagt gatgctgtta caat 44

<210> 57

<211> 22

<212> DNA

<213> Artificial Sequence

<400> 57

taatttcaat ccccgcctcg tt 22

<210> 58

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 58

gaaggtgacc aagttcatgc tacataggtg ccatcacgac tcc 43

<210> 59

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 59

gaaggtcgga gtcaacggat tacataggtg ccatcacgac tcg 43

<210> 60

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 60

cccaagcttc accttctaag aacct 25

<210> 61

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 61

gaaggtgacc aagttcatgc tggccctccc tacttcaatt cct 43

<210> 62

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 62

gaaggtcgga gtcaacggat tggccctccc tacttcaatt ccc 43

<210> 63

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 63

gagtttcgaa ttctcctcac gcct 24

<210> 64

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 64

gaaggtgacc aagttcatgc tgtcacttga agatcgtgtg tgtca 45

<210> 65

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 65

gaaggtcgga gtcaacggat tgtcacttga agatcgtgtg tgtcc 45

<210> 66

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 66

ttcatgactc attggggact cacaag 26

<210> 67

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 67

gaaggtgacc aagttcatgc tcgaatcgga ccggacgaaa acg 43

<210> 68

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 68

gaaggtcgga gtcaacggat tcgaatcgga ccggacgaaa aca 43

<210> 69

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 69

tgacacaagc agatgcacag agac 24

<210> 70

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 70

gaaggtgacc aagttcatgc tgttcatagg aatccagaca caccg 45

<210> 71

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 71

gaaggtcgga gtcaacggat tgttcatagg aatccagaca cacca 45

<210> 72

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 72

cggtcaccgt gtagactatt gtagtc 26

<210> 73

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 73

gaaggtgacc aagttcatgc tcaaatacaa ccggaccaac tcctc 45

<210> 74

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 74

gaaggtcgga gtcaacggat tcaaatacaa ccggaccaac tcctt 45

<210> 75

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 75

gtttgggttg gagcaaggat tacgc 25

<210> 76

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 76

gaaggtgacc aagttcatgc taggacttgg caatcctgtt agtgctt 47

<210> 77

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 77

gaaggtcgga gtcaacggat taggacttgg caatcctgtt agtgcta 47

<210> 78

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 78

caaagttctc atccatcggt tctcgt 26

<210> 79

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 79

gaaggtgacc aagttcatgc tagtaacgag gttgttgctc tgtg 44

<210> 80

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 80

gaaggtcgga gtcaacggat tagtaacgag gttgttgctc tgtc 44

<210> 81

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 81

ccttctaggt gtcccatcaa gactcc 26

<210> 82

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 82

gaaggtgacc aagttcatgc tcctgattgc cacgtcaagg gac 43

<210> 83

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 83

gaaggtcgga gtcaacggat tcctgattgc cacgtcaagg gat 43

<210> 84

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 84

tacaaggaag cctgtaaaga ctgc 24

<210> 85

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 85

gaaggtgacc aagttcatgc tgttggatct ctagtattag gtccccg 47

<210> 86

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 86

gaaggtcgga gtcaacggat tgttggatct ctagtattag gtcccca 47

<210> 87

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 87

acaattctag ctccttcttg gaaccc 26

<210> 88

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 88

gaaggtgacc aagttcatgc tatgatcctt cccatggttc cactcg 46

<210> 89

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 89

gaaggtcgga gtcaacggat tatgatcctt cccatggttc cactct 46

<210> 90

<211> 22

<212> DNA

<213> Artificial Sequence

<400> 90

gagcacggca caacacatta ca 22

<210> 91

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 91

gaaggtgacc aagttcatgc tcctagaacc gaataaggta tccccca 47

<210> 92

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 92

gaaggtcgga gtcaacggat tcctagaacc gaataaggta tccccct 47

<210> 93

<211> 22

<212> DNA

<213> Artificial Sequence

<400> 93

gatgcttttc ttcaggcttg gc 22

<210> 94

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 94

gaaggtgacc aagttcatgc tgaaagtctg ccatacttgg ggtc 44

<210> 95

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 95

gaaggtcgga gtcaacggat tgaaagtctg ccatacttgg ggtt 44

<210> 96

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 96

ggtccataca aggcctcact agatg 25

<210> 97

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 97

gaaggtgacc aagttcatgc ttataacccg gggacaagtg atgg 44

<210> 98

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 98

gaaggtcgga gtcaacggat ttataacccg gggacaagtg atga 44

<210> 99

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 99

ccaacccaaa atttttcccc gttgga 26

<210> 100

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 100

gaaggtgacc aagttcatgc tggcgtcacg atagaggaag tccaa 45

<210> 101

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 101

gaaggtcgga gtcaacggat tggcgtcacg atagaggaag tccag 45

<210> 102

<211> 23

<212> DNA

<213> Artificial Sequence

<400> 102

aagcctgctg atctgttatc ccc 23

<210> 103

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 103

gaaggtgacc aagttcatgc tggaagattg gagtccgagt tagtgtc 47

<210> 104

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 104

gaaggtcgga gtcaacggat tggaagattg gagtccgagt tagtgta 47

<210> 105

<211> 27

<212> DNA

<213> Artificial Sequence

<400> 105

cctccccttt catcacaaca tctatgc 27

<210> 106

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 106

gaaggtgacc aagttcatgc tggacatctc attggccacc attg 44

<210> 107

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 107

gaaggtcgga gtcaacggat tggacatctc attggccacc attt 44

<210> 108

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 108

aaaactttgt ggtgtttagg gccc 24

<210> 109

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 109

gaaggtgacc aagttcatgc ttaaccttga atccttgggg aagcc 45

<210> 110

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 110

gaaggtcgga gtcaacggat ttaaccttga atccttgggg aagca 45

<210> 111

<211> 23

<212> DNA

<213> Artificial Sequence

<400> 111

atctgagtag cgtgagggta aga 23

<210> 112

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 112

gaaggtgacc aagttcatgc tccccacgtt tccaaaaccc tagg 44

<210> 113

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 113

gaaggtcgga gtcaacggat tccccacgtt tccaaaaccc taga 44

<210> 114

<211> 28

<212> DNA

<213> Artificial Sequence

<400> 114

gattcctcgg gagaaatgtc gtaagtca 28

<210> 115

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 115

gaaggtgacc aagttcatgc tggctccaaa agtcagctca aaggag 46

<210> 116

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 116

gaaggtcgga gtcaacggat tggctccaaa agtcagctca aaggaa 46

<210> 117

<211> 28

<212> DNA

<213> Artificial Sequence

<400> 117

tgagatgtgg actccttata aggcttgg 28

<210> 118

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 118

gaaggtgacc aagttcatgc tctcgaagac tggctcgtaa gacg 44

<210> 119

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 119

gaaggtcgga gtcaacggat tctcgaagac tggctcgtaa gaca 44

<210> 120

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 120

aaacggcccg tcataattac ctcca 25

<210> 121

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 121

gaaggtgacc aagttcatgc tgcttaggaa ccatgtgatc gtcg 44

<210> 122

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 122

gaaggtcgga gtcaacggat tgcttaggaa ccatgtgatc gtca 44

<210> 123

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 123

tctactgtgc atctatcagc gtct 24

<210> 124

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 124

gaaggtgacc aagttcatgc tgtgtccagc ttgcttgctc ctc 43

<210> 125

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 125

gaaggtcgga gtcaacggat tgtgtccagc ttgcttgctc ctt 43

<210> 126

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 126

ttgatgatgg gaggtgacaa ttaccc 26

<210> 127

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 127

gaaggtgacc aagttcatgc tccaccgtca gaaacaaatt gcg 43

<210> 128

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 128

gaaggtcgga gtcaacggat tccaccgtca gaaacaaatt gca 43

<210> 129

<211> 23

<212> DNA

<213> Artificial Sequence

<400> 129

gctccacgca caaaaacaag cag 23

<210> 130

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 130

gaaggtgacc aagttcatgc ttggatgact gcaggaagag gag 43

<210> 131

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 131

gaaggtcgga gtcaacggat ttggatgact gcaggaagag gaa 43

<210> 132

<211> 23

<212> DNA

<213> Artificial Sequence

<400> 132

tttgaattcc ctcagcagct ggc 23

<210> 133

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 133

gaaggtgacc aagttcatgc taaccagcat ttgtcgtgga cta 43

<210> 134

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 134

gaaggtcgga gtcaacggat taaccagcat ttgtcgtgga ctg 43

<210> 135

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 135

ggcacacgat gatggattag tggt 24

<210> 136

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 136

gaaggtgacc aagttcatgc tggcgaccat gttctagacc tcac 44

<210> 137

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 137

gaaggtcgga gtcaacggat tggcgaccat gttctagacc tcaa 44

<210> 138

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 138

gaaatgtggt cccaggggctt acac 24

<210> 139

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 139

gaaggtgacc aagttcatgc tggggtatga tgagggtggt gtg 43

<210> 140

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 140

gaaggtcgga gtcaacggat tggggtatga tgagggtggt gta 43

<210> 141

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 141

acgcccactt gttaacactc aaag 24

<210> 142

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 142

gaaggtgacc aagttcatgc ttagggggaa tgagtgcaac agaag 45

<210> 143

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 143

gaaggtcgga gtcaacggat ttagggggaa tgagtgcaac agaaa 45

<210> 144

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 144

ctgtaaaggc agtcattcct actacg 26

<210> 145

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 145

gaaggtgacc aagttcatgc ttgatccatg gtgtgtgcac atcg 44

<210> 146

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 146

gaaggtcgga gtcaacggat ttgatccatg gtgtgtgtgcac atct 44

<210> 147

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 147

cccaaagctc acatttcctt acag 24

<210> 148

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 148

gaaggtgacc aagttcatgc tggaaatgag tgtcccagcc atctatg 47

<210> 149

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 149

gaaggtcgga gtcaacggat tggaaatgag tgtcccagcc atctatt 47

<210> 150

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 150

agtcccaacc ctactctcat gtcct 25

<210> 151

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 151

gaaggtgacc aagttcatgc tgaccacaac atccaggttc acc 43

<210> 152

<211> 43

<212> DNA

<213> Artificial Sequence

<400> 152

gaaggtcgga gtcaacggat tgaccacaac atccaggttc aca 43

<210> 153

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 153

gcttgggcac tctattggta ctccta 26

<210> 154

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 154

gaaggtgacc aagttcatgc tggcgtagat tggtgtacac actg 44

<210> 155

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 155

gaaggtcgga gtcaacggat tggcgtagat tggtgtacac acta 44

<210> 156

<211> 26

<212> DNA

<213> Artificial Sequence

<400> 156

gaatttacta aaccacccct cgcttg 26

<210> 157

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 157

gaaggtgacc aagttcatgc tcattagtgt cctcgatggc tgatg 45

<210> 158

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 158

gaaggtcgga gtcaacggat tcattagtgt cctcgatggc tgata 45

<210> 159

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 159

agcatctctt cgggtggttt agtac 25

<210> 160

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 160

gaaggtgacc aagttcatgc tcgcaacgaa tattggtcac cagg 44

<210> 161

<211> 44

<212> DNA

<213> Artificial Sequence

<400> 161

gaaggtcgga gtcaacggat tcgcaacgaa tattggtcac caga 44

<210> 162

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 162

cgattgactt tgttggcctc caaa 24

<210> 163

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 163

gaaggtgacc aagttcatgc tggttgtaga tgcggaagtg agtgc 45

<210> 164

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 164

gaaggtcgga gtcaacggat tggttgtaga tgcggaagtg agtgg 45

<210> 165

<211> 24

<212> DNA

<213> Artificial Sequence

<400> 165

accaccttcg tattgagttg gaca 24

<210> 166

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 166

gaaggtgacc aagttcatgc tccggaaatg atcaatgcga accctc 46

<210> 167

<211> 46

<212> DNA

<213> Artificial Sequence

<400> 167

gaaggtcgga gtcaacggat tccggaaatg atcaatgcga accctt 46

<210> 168

<211> 22

<212> DNA

<213> Artificial Sequence

<400> 168

aacactagac gcccttgttg ga 22

<210> 169

<211> 48

<212> DNA

<213> Artificial Sequence

<400> 169

gaaggtgacc aagttcatgc tgcctggtaa gatataccga cccttgtc 48

<210> 170

<211> 48

<212> DNA

<213> Artificial Sequence

<400> 170

gaaggtcgga gtcaacggat tgcctggtaa gatataccga cccttgtg 48

<210> 171

<211> 23

<212> DNA

<213> Artificial Sequence

<400> 171

gtggaggtag agaggctgtt tct 23

<210> 172

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 172

gaaggtgacc aagttcatgc tgcagatgct catcggactg atttg 45

<210> 173

<211> 45

<212> DNA

<213> Artificial Sequence

<400> 173

gaaggtcgga gtcaacggat tgcagatgct catcggactg attta 45

<210> 174

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 174

gcaaaatagt gaaatcgccg cgaga 25

<210> 175

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 175

gaaggtgacc aagttcatgc tgcctaccag accttttcta tgccttc 47

<210> 176

<211> 47

<212> DNA

<213> Artificial Sequence

<400> 176

gaaggtcgga gtcaacggat tgcctaccag accttttcta tgcctta 47

<210> 177

<211> 27

<212> DNA

<213> Artificial Sequence

<400> 177

ccagcctttt ggtttatcct ctatgcc 27

Claims (10)

1. A method for typing an SNP marker, comprising the steps of:

(1) extracting the genomic DNA of the tomato to be detected;

(2) based on site set information consisting of a plurality of SNP sites, detecting the SNP sites of the tomato genome to be detected by reducing the amplification temperature in a programmed manner, wherein the site set comprises the following sites: SNP01-3, SNP02-1, SNP02-3, SNP03-1, SNP03-3, SNP04-1, SNP04-2, SNP05-3, SNP06-3, SNP06-6, SNP07-4, SNP07-5, SNP07-8, SNP07-9, SNP08-1, SNP09-1, SNP10-1, SNP11-3, SNP12-4, SNP12-6 and SNP 12-8;

(3) and carrying out the genotyping and the genotyping result analysis of the tomato to be detected.

2. The method for typing a SNP marker according to claim 1, wherein the set of sites further comprises:

SNP01-1, SNP01-2, SNP02-2, SNP02-4, SNP03-2, SNP03-4, SNP03-5, SNP03-6, SNP04-3, SNP04-4, SNP04-5, SNP05-1, SNP05-2, SNP05-4, SNP05-5, SNP05-6, SNP05-7, SNP06-1, SNP06-2, SNP06-4, SNP06-5, SNP07-1, SNP07-2, SNP07-3, SNP07-6, SNP07-7, SNP07-10, SNP07-11, SNP07-12, SNP07-13, SNP11-1, SNP11-2, SNP12-1, SNP12-2, SNP12-3, SNP12-5, SNP 12-7-599-12 and SNP 599-5.

3. The method of typing an SNP marker according to claim 1 or 2, wherein the information of each site in the genomic DNA site set is detected using a primer set consisting of three primers, i.e., a first forward primer, a second forward primer and a reverse primer.

4. The method of typing an SNP marker according to claim 1, wherein the detection of the set of sites in the genomic DNA is carried out using primers shown in SEQ ID Nos. 1 to 63.

5. The method of typing an SNP marker according to claim 2, wherein the detection of the set of sites in the genomic DNA is carried out using primers shown in SEQ ID Nos. 64 to 177.

6. The method of typing a SNP marker according to claim 3, wherein the step (2) comprises the step of constructing a reaction system comprising a buffer and a plurality of primer sets dissolved in the buffer, wherein the concentrations of the first forward primer, the second forward primer and the reverse primer of each primer set in the reaction system are 50 to 200. mu. mol/L, respectively.

7. The method for typing an SNP marker according to claim 6, wherein the step (2) of detecting the SNP site by lowering the amplification temperature in a programmed manner comprises:

denaturation step, denaturation at 94 ℃ for 15 min; denaturation at 94 ℃ for 20 s;

the first circulation step, annealing at 61 ℃ for 60s at first, and then annealing at 0.6 ℃ for 60s in each circulation for 10 circulations;

the second cycle, denaturation at 94 ℃ for 20s and renaturation at 55 ℃ for 60s, is 26-32 cycles.

8. A composition or a kit for typing a SNP marker, comprising primers for detecting information of a site set consisting of a plurality of SNP sites, wherein the site set comprises SNP01-3, SNP02-1, SNP02-3, SNP03-1, SNP03-3, SNP04-1, SNP04-2, SNP05-3, SNP06-3, SNP06-6, SNP07-4, SNP07-5, SNP07-8, SNP07-9, SNP08-1, SNP09-1, SNP10-1, SNP11-3, SNP12-4, SNP12-6, and SNP 12-8.

9. The composition or kit for typing a SNP marker according to claim 1, wherein the set of sites further comprises SNP-1, SNP-2, SNP-4, SNP-5, SNP-6, SNP-3, SNP-4, SNP-5, SNP-1, SNP-2, SNP-4, SNP-5, SNP-6, SNP-7, SNP-1, SNP-2, SNP-4, SNP-5, SNP-1, SNP-2, SNP-3, SNP-6, SNP-7, SNP-10, SNP-11, SNP-12, SNP-13, SNP-1, SNP-2, SNP-3, SNP-5, SNP-1, SNP-2, SNP-3, SNP-5, SNP12-7 and SNP 12-9.

10. Use of a composition or kit according to claim 8 or 9 in the identification of tomato varieties.

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