CN112143829A - Typing and application of universal SNP (single nucleotide polymorphism) markers - Google Patents
Typing and application of universal SNP (single nucleotide polymorphism) markers Download PDFInfo
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Abstract
The invention discloses typing and application of a group of universal SNP markers. The typing method comprises the following steps: extracting the genomic DNA of the tomato to be detected; detecting SNP sites by reducing amplification temperature in a programming manner based on site set information consisting of a plurality of SNP sites; and carrying out genotyping on the tomatoes to be detected based on the detection result and analyzing the genotyping result. The SNP marker has good polymorphism and good stability and repeatability, and the detection process with higher flux can be realized by detecting the SNP marker through competitive allele specificity PCR. In addition, the present invention provides a core SNP marker set. The authenticity of tomato varieties can be efficiently and accurately identified by utilizing the set of marker sets, the reliability of tomato germplasm resource collection is ensured, and meanwhile, the detection cost is remarkably reduced.
Description
Technical Field
The invention relates to the technical field of germplasm resources and molecular breeding, in particular to a universal SNP marker composition, a typing method and application thereof.
Background
The tomato belongs to the genus Lycopersicon (Lycopersicon) of the family Solanaceae, is rich in lycopene and a plurality of vitamins, has short growth cycle and higher economic value, is not only one of the important vegetable crops with the largest planting area in China, but also is a model plant for researching fleshy fruits. With the rapid development of breeding technology, the number of tomato varieties is increasing day by day, and the improvement of living standard promotes the demand of variety diversification to be continuously improved. In production, phenomena of counterfeit varieties, impure varieties, confusion of varieties and the like generally exist, the benefits of breeders and seed companies are greatly damaged, and the confusion is brought to seed demanders when selecting seeds. The variety identification is an important link in the agricultural development process, how to scientifically identify and analyze huge tomato germplasm resources, avoid repeated collection of the same tomato germplasm, protect the intellectual property of new plant varieties, promote healthy development of tomato variety markets and is one of the problems which are urgently needed to be solved in tomato production.
The traditional tomato variety identification method mainly depends on a morphological marking method to visually identify the relative differences of characters such as stems, leaves, flowers, plant heights, fruits and the like, or depends on certain characters which are easy to test, such as typical disease and insect resistance and physiological characteristics. With the development of molecular biology technology, the identification of varieties by using molecular marker technology has become a trend, the influence of environmental factors is eliminated, and the result is more reliable. The international new-variety-right plant protection association (UPOV) has adopted ssr (single sequence repeat) markers and SNP (single-nucleotide polymer-phism) markers as molecular marker methods recommended for plant variety identification in BMT molecular test guidelines (Button P, 2007). The SNP marker has the advantages of rich variation, high accuracy, easy realization of automation and the like, can break through the limitations of limited SSR marker quantity and time-consuming operation, and is recognized as a molecular marker with great application prospect (Rasheet Awais et al, 2017). Meanwhile, the completion of tomato genome sequencing provides a good basis for the development of tomato SNP markers (Lin et al, 2014).
At present, the SNP marker technology is widely applied to variety identification of crops such as rice, corn, soybean and the like, and has positive effects in variety management and protection. The tomato variety identification mainly adopts an InDel marking method (Gaojianchang et al, 2013) and utilizes polyacrylamide gel electrophoresis to perform genotyping and analysis, and the method has the advantages of low flux, complex steps and difficult data integration. In addition, the problems of low efficiency, easy influence of environmental factors and the like in the identification of variety authenticity in the traditional method still need to be further improved.
Disclosure of Invention
In order to solve at least part of technical problems in the prior art, the invention provides a set of universal SNP marker typing methods and applications. The composition and the genotyping method can identify commercial varieties of tomatoes which widely exist on the market, not only can realize high-throughput detection, but also can efficiently and accurately identify the authenticity of the varieties of the tomatoes, ensure the reliability of collection of germplasm resources of the tomatoes and simultaneously obviously reduce the detection cost. Specifically, the present invention includes the following.
In a first aspect of the present invention, there is provided a SNP marker-based typing method, which comprises 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 (5) carrying out genotyping and genotyping result analysis on the tomato to be detected.
According to the SNP marker combination-based typing method 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, SNP 12-7-599-12 and SNP 599-5.
According to the SNP marker combination-based typing method of the present invention, information of each site in a genomic DNA site set is preferably detected using a primer set consisting of three primers, a first forward primer, a second forward primer and a reverse primer.
According to the SNP marker combination-based typing method of the present invention, it is preferable to perform detection of a set of sites in genomic DNA using primers shown in SEQ ID NOS: 1 to 63.
According to the SNP marker combination-based typing method of the present invention, it is preferable to perform detection of a set of sites in genomic DNA using primers shown in SEQ ID NOS: 1-177.
According to the SNP marker combination-based typing method of the present invention, preferably, the step (2) includes the step of constructing a reaction system comprising a buffer and a plurality of primer sets dissolved in the buffer, wherein the final concentration of the first forward primer, the second forward primer and the reverse primer in each primer set in the reaction system is generally 50 to 200. mu. mol/L, preferably 80 to 150. mu. mol/L, for example 100. mu. mol/L.
According to the SNP marker combination-based typing method of the present invention, preferably, the step (2) of detecting SNP sites by programmatically lowering the amplification temperature 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 5-20 circulations;
the second cycle step, denaturation at 94 ℃ for 20s and renaturation extension at 55 ℃ for 60s, comprises 26-32 cycles, and is preferably added by 2-5 cycles, preferably 3-5 cycles on the basis of 26 cycles.
In a second aspect of the present invention, there is provided a composition for SNP marker-based typing, which comprises 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.
The composition for typing based on SNP markers according to the present invention, preferably, the set of sites further includes 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, SNP07-1, SNP07-2, SNP07-1, SNP07-2, SNP07-3, SNP 07-07, SNP 07-07, and SNP 847.
In a third aspect of the invention, there is provided the use of a composition according to the second aspect in the identification of tomato cultivars.
In a fourth aspect of the present invention, there is provided a kit for typing based on SNP markers, which comprises a primer set comprising the sequences shown as SEQ ID NOS: 1 to 177.
The invention designs and screens a set of SNP marker sets related to tomato variety identification based on the SNP generated by comparing tomato re-testing data with a tomato variety whole genome sequence, wherein the set of marker sets comprises 59 SNP markers. These SNP markers have good polymorphism and good stability and reproducibility. The SNP markers are 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 marker sets, which contains 21 SNP markers. The authenticity of tomato varieties can be efficiently and accurately identified by utilizing the set of marker sets, the reliability of tomato germplasm resource collection is ensured, and meanwhile, the detection cost is remarkably reduced. The SNP marker set of the invention is particularly suitable for identifying varieties with indistinguishable morphological characteristics or indistinguishable morphological characteristics.
Drawings
FIG. 1 is a boxplot of the comparison of the marker random combinatorial discrimination before and after MAF screening.
FIG. 2 shows an example of the fluorescent reading of marker SNP04-1 (the result of a Matrix Scanner scan from HC Scientific).
FIG. 3 shows the number of SNPs corresponding to the MAF interval.
FIG. 4 shows the number of SNPs corresponding to the deletion rate region.
Detailed Description
Reference will now be made in detail to various exemplary embodiments of the invention, the detailed description should not be construed as limiting the invention but as a more detailed description of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Further, for numerical ranges in this disclosure, it is understood that the upper and lower limits of the range, and each intervening value therebetween, is specifically disclosed. Every smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in a stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless defined otherwise, 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 belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference herein for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control. Unless otherwise indicated, "%" is percent by weight.
Unless otherwise specified, the gene position or site in the present invention refers to the position relative to the tomato variety Heinz 1706 (version number SL 3.0).
[ method of genotyping Using SNP marker ]
The genotyping method of the invention comprises the following steps:
(1) extracting the genomic DNA of the tomato to be detected;
(2) detecting the SNP sites of the tomato genome to be detected by reducing the amplification temperature in a programmed manner based on site set information consisting of a plurality of SNP sites;
(3) and (5) carrying out genotyping and genotyping result analysis on the tomato to be detected.
In the step (1), the method for extracting genomic DNA of tomato to be tested is not particularly limited, and can be performed by a method known in the art, and the method for extracting genomic DNA can be referred to a known textbook, for example, a publication such as molecular cloning instruction of Cold spring harbor, fourth edition, etc. The tomato sample used for extraction can be tomato cotyledon or true leaf, root and other parts of tomato plant.
The specific mode for obtaining the tomato DNA sequence polymorphic site in step (2) is not particularly limited. Preferably, the invention uses an Illumina Genome Analyzer-based sequencing system platform for re-sequencing of samples. Further comprises the step of processing the sequencing data to obtain the polymorphism data containing the SNP locus, wherein the data processing comprises the step of obtaining the SNP variation locus by using PopSeq2Geno by taking the tomato variety Heinz 1706 as a reference genome.
Preferably, in step (2), the following parameters are set to obtain reliable SNP sites: the Minimal Allele Frequency (MAF) is filtered out to be less than 0.01, the genotype deletion Rate (Miss Rate) is greater than 0.2, and no other variant sites exist within 50bp near the SNP. Further preferably, the SNP site set is obtained based on an average minimum allele frequency of 0.379 and an average deletion rate of 0.002. In certain embodiments, the set of SNP sites is set forth in table 1.
Preferably, the SNP sites are mapped to each chromosome of tomato, and then R of each SNP site present in each chromosome is calculated using the formula R (minimum allele frequency) 0.8- (genotype deletion rate) 0.2, and the R values of each SNP site in each chromosome are sorted by size. The R-value of the present invention only takes into account the physical location and genetic distance location of the molecular marker on the chromosome, and does not relate to the functional or phenotypic characteristics of the molecular marker. The functional traits or phenotypes referred to herein include, but are not limited to, for example, plant height, leaf size, fruit color, seed or characteristics that are classified according to physiology, distribution area or some other biological characteristic associated with a certain ecosystem process and with a species. The method is particularly suitable for solving the problem that germplasm resources cannot be effectively identified and analyzed due to the fact that tomato seeds develop to seedlings and cultivated or semi-cultivated tomato seedlings cannot be correctly classified.
Preferably, the core SNP site set of the invention is obtained by carrying out size sorting through R values, 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 SNP 12-8.
The report of tomato variety identification and analysis based on SNP marker is less common. The detection methods of SNP markers are various, and the invention adopts competitive allele specific PCR for related detection, and a plurality of detection platforms with high automation degree have been provided at home and abroad. Therefore, the tomato SNP marker with common representativeness is developed, technical support can be provided for authenticity identification of tomato varieties, collection of germplasm and large-scale management, time and cost are saved, and working efficiency is improved.
Preferably, the present invention detects information of each site in a genomic DNA site set using a primer set consisting of three primers, a first forward primer, a second forward primer and a reverse primer. Further preferably, the detection of the set of sites in the genomic DNA is performed using the primers shown in SEQ ID NO 1-63. Also preferably, the detection of the set of sites in the genomic DNA is carried out using the primers shown in SEQ ID NO 64-177.
Preferably, the step (2) includes the step of constructing a reaction system including 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, but is generally 50 to 200. mu. mol/L, preferably 80 to 150. mu. mol/L, for example 100. mu. mol/L. The concentration is the initial concentration, when the primer is dry powder, the dry powder can be diluted to 100 mu mol/L to be used as a stock solution. Before use, the stock solution is diluted according to a specified proportion and then mixed with other components to obtain a reaction system as a working solution. 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. mu. mol/L as a stock solution. The three primers can be prepared into three different storage solutions, and the three primers can also be prepared into the same storage solution containing the three primers at the same time. In constructing the reaction system, the first forward primer solution, the second forward primer solution and the reverse primer solution may be mixed in a specific volume ratio, for example, a volume ratio of (10-15): (28-32). Further preferably, the volume ratio of the solutions of the first forward primer, the second forward primer and the reverse primer is (11-14): (29-31). Also preferably, the volume ratio of the solutions of the first forward primer, the second forward primer and the reverse primer is (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 amount is about 5. mu.L, specifically 2.5. mu.L (60 ng. mu.L) of genomic DNA-1) The primer mixture was 0.07. mu.L, KASP V4.02 XMaster Mix 2.5. mu.L from LGC, and was applied to 384-well plates. In another exemplary reaction, the total reaction volume is 10. mu.L, specifically 5. mu.L (60 ng. mu.L) of genomic DNA-1) The primer mixture was 0.14. mu.L, KASP V4.02 XMaster Mix 5. mu.L from LGC, and was applied to a 96-well plate. The primer mixture comprises a first forward primer A1, a second forward primer A2 and a reverse primer C, ddH2O was mixed at a volume ratio of 12:12:30:46 (initial concentration of each primer was 100. mu. mol/L). A water blank may be set for the test.
The invention carries out the detection step of the SNP locus of the tomato genome to be detected by programmatically reducing the amplification temperature. High-temperature amplification is firstly used before starting, the amplification rigidness is ensured, and after the abundance of the target gene is increased, the amplification temperature is reduced, so that the amplification efficiency is improved. In addition, the occurrence of non-specific PCR products is avoided, especially when complex genomic DNA templates are used, and non-specific annealing is likely to occur. Preferably, it comprises a 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 25 to 32 cycles, preferably 25 to 30 cycles, and more preferably 25 to 27, for example 26 cycles. 3-5 cycles can be added properly according to the actual typing result.
[ 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 set of sites consisting of a plurality of SNP sites, wherein the set of sites 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 SNP 12-8.
Preferably, the set of sites further includes 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, SNP07-1, SNP07-2, SNP07-3, SNP 07-07, and SNP 07-07.
[ use ]
In a third aspect of the invention, there is provided the use of a composition according to the second aspect in the identification of tomato cultivars.
In a fourth aspect of the present invention, there is provided a kit for typing based on SNP markers, which comprises a primer set comprising the sequences shown as SEQ ID NOS: 1 to 177.
Examples
1. Development of SNP (single nucleotide polymorphism) marker related to tomato variety identification
The SNP markers of the invention are obtained by aligning 38 parts of tomato resequencing data with the whole genome sequence of tomato variety Heinz 1706 (version number is SL 3.0). Specifically, 38 tomato genomic DNAs were extracted at a concentration range of 900-1200ng/ul, and 10ng of each sample was re-sequenced on the platform of the Illumina Genome Analyzer sequencing System from sequencing company.
Firstly, filtering sequencing data by using fastp software, filtering out sequences with quality values less than 10% and more than 30%, containing joints and having base proportion more than 10%. Tomato variety Heinz 1706 (version number is SL3.0) is taken as a reference genome, PopSeq2Geno is utilized to obtain SNP variation sites, the minimum allele frequency is initially filtered out and is less than 0.01, the genotype deletion rate is more than 0.2, and sites with no other variation within 50bp near the SNP are initially filtered out, so that more than ten thousand reliable SNP sites are obtained.
The SNP sites were mapped to each chromosome of tomato, and then R of each SNP site present in each chromosome was calculated using the formula R (minimum allele frequency) × 0.8- (genotype deletion rate) × 0.2, and the R values of each SNP site in each chromosome were sorted by size. The sites with the largest R value distributed on each tomato chromosome were selected, and a total of 120 sites were converted into competitive allele-specific PCR markers.
Next, the marker random combinatorial region contrast before and after MAF screening was analyzed. Specifically, the discrimination of the random combination of 100 markers corresponding to the gradient of the number of markers before and after the MAF screening calculated by the Python script is drawn into a box line graph by using R. There were 83 markers before MAF screening and 59 markers after MAF screening.
Before MAF filtering, when the number of labels is 50, the 100 possible average discrimination degrees are 99.17% of 241 samples, when the number of labels is 40, the average discrimination degree is 96.03%, when the number of labels is 30, the average discrimination degree is 93.9%, when the number of labels is 20, the average discrimination degree is 90.3%, and when the number of labels is 10, the average discrimination degree is 75.04%. After MAF filtering, when the number of markers is 80, the 100 possible average discrimination degrees are 98.96% of 241 samples, when the number of markers is 70, the average discrimination degree is 98.34%, when the number of markers is 60, the average discrimination degree is 97.51%, when the number of markers is 50, the average discrimination degree is 96.39%, when the number of markers is 40, the average discrimination degree is 94.68%, when the number of markers is 30, the average discrimination degree is 92.15%, when the number of markers is 20, the average discrimination degree is 88.81%, and when the number of markers is 10, the average discrimination degree is 69.97%.
The results are shown in fig. 1, and the discrimination of 59 markers after MAF screening is the same as the discrimination of 83 markers before MAF screening, and both are 99.59%. Meanwhile, the discrimination of the random combination of the marks after the MAF screening is higher than that of the random combination of the marks before the MAF screening. The 59 markers are shown in table 1 below:
TABLE 1
The average Minimum Allele Frequency (MAF) was 0.379 and the average deletion Rate (misrate) was 0.002.
Based on the analysis of the results of competitive allele-specific PCR typing of 241 tomato material, the 59 SNP markers were randomly combined by bioinformatics, and 100 possible combinations of 50, 40, 30, 20, and 10 markers were randomly output. The 100 possible average discrimination degrees were 99.17% for 241 samples with a marker number of 50, 96.03% for 100 possible average discrimination degrees for 241 samples with a marker number of 40, 93.9% for 100 possible average discrimination degrees for 241 samples with a marker number of 30, 90.3% for 100 possible average discrimination degrees for 241 samples with a marker number of 20, and 75.04% for 100 possible average discrimination degrees for 241 samples with a marker number of 10. In order to ensure that the discrimination of the varieties is more than 90%, and the physical position and the genetic distance position of the markers on the chromosome are considered, a core marker set consisting of 21 SNP markers with excellent effect is finally selected. The core mark of the set was differentiated to 92.53% of the 241 samples.
3. The genotyping method based on the SNP marker of the invention comprises the following steps:
3.1 extracting tomato genome DNA.
3.2 construction of PCR reaction System:
the total system is about 5 μ L, specifically 5ng of genomic DNA, 0.07 μ L of primer mixture, 4.02 × 2.5 μ L of KASP V4.02 × Master Mix from LGC company, and ddH2O is supplemented to 5 mu L, and the method is suitable for 384-hole plates.
The total reaction volume is 10. mu.L, specifically 10ng of genomic DNA, 0.14. mu.L of primer mixture, 4.02 times of KASP V4.02 times of Master Mix 5. mu.L from LGC company, plus ddH2O is supplemented to 10 mu L, and the method is suitable for a 96-well plate. The primer mixture comprises a forward primer A1, a forward primer A2 and a reverse primer C, ddH2O was mixed at a volume ratio of 12:12:30:46 (initial concentration of each primer was 100. mu. mol/L). A water blank may be set for the test.
The invention provides a primer pair for PCR amplification of tomato SNP markers, which comprises 59 primer groups, wherein each primer group is used for amplifying a corresponding SNP marker and consists of two forward primers (A1/A2) and a reverse primer, the 5 'end of the forward primer A1 is added with a FAM fluorescent joint (GAAGGTGACCAAGTTCATGCT), and the 5' end of the forward primer A2 is added with a VIC fluorescent joint (GAAGGTCGGAGTCAACGGATT). Specifically shown as a sequence SEQ ID NO 1-177.
Procedure for PCR amplification
Denaturation at 94 deg.C for 15 min; denaturation at 94 ℃ for 20s, annealing at 61 ℃ (0.6 ℃ per cycle) for 60s for 10 cycles;
denaturation at 94 ℃ for 20s and renaturation at 55 ℃ for 60s for 25-32 cycles, preferably 25-30 cycles, further preferably 25-27, e.g.26 cycles.
The fluorescence data can be read and analyzed by using Scientific QuantStaudio of Applied BiosystemsTMThe 12K Flex real-time PCR system is used, the reading temperature of fluorescence data is 30 ℃, the reading time is 60s, and the fluorescence is FAM and HEX fluorescence. The GeneMatrix genotyping system from HC Scientific, or other PCR systems capable of performing competitive allele-specific PCR experiments can also be used. FIG. 2 shows the fluorescent reading of marker SNP04-1 (Matrix Scanner scanning of HC Scientific). FIG. 3 shows MAF intervalsThe number of SNPs. FIG. 4 shows the number of SNPs corresponding to the deletion rate region.
The marker set of the present invention comprises 59 SNP markers. The 59 SNP markers have good polymorphism and good stability and repeatability, and are detected by a competitive allele specific PCR technology, so that the detection process has higher throughput. The authenticity of tomato varieties can be efficiently and accurately identified by utilizing the set of marker sets, and the reliability of tomato germplasm resource collection is ensured.
Sequence listing
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<400> 6
ggaaaacttg gccgattcat tgg 23
<210> 7
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 7
gaaggtgacc aagttcatgc tggggtcgaa atctgtcatc caatg 45
<210> 8
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 8
gaaggtcgga gtcaacggat tggggtcgaa atctgtcatc caatt 45
<210> 9
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 9
gttgagttca tcgttgctga tcgtg 25
<210> 10
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 10
gaaggtgacc aagttcatgc tcttcccgtg ccgtctgatc tgataa 46
<210> 11
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 11
gaaggtcgga gtcaacggat tcttcccgtg ccgtctgatc tgatat 46
<210> 12
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 12
cgagatgctt gtagagactc gac 23
<210> 13
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 13
gaaggtgacc aagttcatgc tcaccaatct aaggactcgg cgg 43
<210> 14
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 14
gaaggtcgga gtcaacggat tcaccaatct aaggactcgg cga 43
<210> 15
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 15
gcactccatg aagcatagcg tagta 25
<210> 16
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 16
gaaggtgacc aagttcatgc taccccatgt ttcctccaat ccttagc 47
<210> 17
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 17
gaaggtcgga gtcaacggat taccccatgt ttcctccaat ccttaga 47
<210> 18
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 18
cagaacaaga ccactatttc tgcg 24
<210> 19
<211> 49
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 19
gaaggtgacc aagttcatgc tgtaacgctc taaaggacca ctcgatctc 49
<210> 20
<211> 49
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 20
gaaggtcgga gtcaacggat tgtaacgctc taaaggacca ctcgatcta 49
<210> 21
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 21
gtcatttggt tggtcaaagg atgc 24
<210> 22
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 22
gaaggtgacc aagttcatgc tctaactgat acgcaaccct acttggc 47
<210> 23
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 23
gaaggtcgga gtcaacggat tctaactgat acgcaaccct acttggt 47
<210> 24
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 24
tcatttttgg gcgggcttac tttg 24
<210> 25
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 25
gaaggtgacc aagttcatgc ttctcgggcg tgactcagtt ttag 44
<210> 26
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 26
gaaggtcgga gtcaacggat ttctcgggcg tgactcagtt ttac 44
<210> 27
<211> 27
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 27
catggtttct ggcatggaaa agtggac 27
<210> 28
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 28
gaaggtgacc aagttcatgc tgtgcaaact ttggagacta gggt 44
<210> 29
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 29
gaaggtcgga gtcaacggat tgtgcaaact ttggagacta gggc 44
<210> 30
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 30
ggtgcttgtt gcctcttact gg 22
<210> 31
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 31
gaaggtgacc aagttcatgc taaacaagtt gggatcgggt aagcg 45
<210> 32
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 32
gaaggtcgga gtcaacggat taaacaagtt gggatcgggt aagca 45
<210> 33
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 33
tgtggactaa cgatttcacg gg 22
<210> 34
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 34
gaaggtgacc aagttcatgc tcattacagg agtggtacgt ctgac 45
<210> 35
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 35
gaaggtcgga gtcaacggat tcattacagg agtggtacgt ctgaa 45
<210> 36
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 36
aagaacaaca cgtcctttag tgcc 24
<210> 37
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 37
gaaggtgacc aagttcatgc tgctggttaa gtgccaagaa catggac 47
<210> 38
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 38
gaaggtcgga gtcaacggat tgctggttaa gtgccaagaa catggat 47
<210> 39
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 39
gtagccctct catcttccat ggac 24
<210> 40
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 40
gaaggtgacc aagttcatgc tgcgctcaac atgttctgtt cctcatc 47
<210> 41
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 41
gaaggtcgga gtcaacggat tgcgctcaac atgttctgtt cctcatt 47
<210> 42
<211> 28
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 42
cgaaaaacca ctcttatcaa aggctcgc 28
<210> 43
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 43
gaaggtgacc aagttcatgc tcccattttc ttttagccga cgggtc 46
<210> 44
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 44
gaaggtcgga gtcaacggat tcccattttc ttttagccga cgggtt 46
<210> 45
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 45
taattccaca ggtgaggtct aggg 24
<210> 46
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 46
gaaggtgacc aagttcatgc tgttgtgtcc gtgaccagag agagttt 47
<210> 47
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 47
gaaggtcgga gtcaacggat tgttgtgtcc gtgaccagag agagttc 47
<210> 48
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 48
tcgttacttg tccctgttca cacc 24
<210> 49
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 49
gaaggtgacc aagttcatgc tcctaagtgc ggggaagtta acaacc 46
<210> 50
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 50
gaaggtcgga gtcaacggat tcctaagtgc ggggaagtta acaact 46
<210> 51
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 51
ctccaagtct tgtggattga aaggtc 26
<210> 52
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 52
gaaggtgacc aagttcatgc tacttaggcc cactcaaccg cag 43
<210> 53
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 53
gaaggtcgga gtcaacggat tacttaggcc cactcaaccg caa 43
<210> 54
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 54
aaccaactct ccctctccct tat 23
<210> 55
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 55
gaaggtgacc aagttcatgc tcgctgcagt gatgctgtta caac 44
<210> 56
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 56
gaaggtcgga gtcaacggat tcgctgcagt gatgctgtta caat 44
<210> 57
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 57
taatttcaat ccccgcctcg tt 22
<210> 58
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 58
gaaggtgacc aagttcatgc tacataggtg ccatcacgac tcc 43
<210> 59
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 59
gaaggtcgga gtcaacggat tacataggtg ccatcacgac tcg 43
<210> 60
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 60
cccaagcttc accttctaag aacct 25
<210> 61
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 61
gaaggtgacc aagttcatgc tggccctccc tacttcaatt cct 43
<210> 62
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 62
gaaggtcgga gtcaacggat tggccctccc tacttcaatt ccc 43
<210> 63
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 63
gagtttcgaa ttctcctcac gcct 24
<210> 64
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 64
gaaggtgacc aagttcatgc tgtcacttga agatcgtgtg tgtca 45
<210> 65
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 65
gaaggtcgga gtcaacggat tgtcacttga agatcgtgtg tgtcc 45
<210> 66
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 66
ttcatgactc attggggact cacaag 26
<210> 67
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 67
gaaggtgacc aagttcatgc tcgaatcgga ccggacgaaa acg 43
<210> 68
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 68
gaaggtcgga gtcaacggat tcgaatcgga ccggacgaaa aca 43
<210> 69
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 69
tgacacaagc agatgcacag agac 24
<210> 70
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 70
gaaggtgacc aagttcatgc tgttcatagg aatccagaca caccg 45
<210> 71
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 71
gaaggtcgga gtcaacggat tgttcatagg aatccagaca cacca 45
<210> 72
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 72
cggtcaccgt gtagactatt gtagtc 26
<210> 73
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 73
gaaggtgacc aagttcatgc tcaaatacaa ccggaccaac tcctc 45
<210> 74
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 74
gaaggtcgga gtcaacggat tcaaatacaa ccggaccaac tcctt 45
<210> 75
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 75
gtttgggttg gagcaaggat tacgc 25
<210> 76
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 76
gaaggtgacc aagttcatgc taggacttgg caatcctgtt agtgctt 47
<210> 77
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 77
gaaggtcgga gtcaacggat taggacttgg caatcctgtt agtgcta 47
<210> 78
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 78
caaagttctc atccatcggt tctcgt 26
<210> 79
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 79
gaaggtgacc aagttcatgc tagtaacgag gttgttgctc tgtg 44
<210> 80
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 80
gaaggtcgga gtcaacggat tagtaacgag gttgttgctc tgtc 44
<210> 81
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 81
ccttctaggt gtcccatcaa gactcc 26
<210> 82
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 82
gaaggtgacc aagttcatgc tcctgattgc cacgtcaagg gac 43
<210> 83
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 83
gaaggtcgga gtcaacggat tcctgattgc cacgtcaagg gat 43
<210> 84
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 84
tacaaggaag cctgtaaaga ctgc 24
<210> 85
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 85
gaaggtgacc aagttcatgc tgttggatct ctagtattag gtccccg 47
<210> 86
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 86
gaaggtcgga gtcaacggat tgttggatct ctagtattag gtcccca 47
<210> 87
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 87
acaattctag ctccttcttg gaaccc 26
<210> 88
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 88
gaaggtgacc aagttcatgc tatgatcctt cccatggttc cactcg 46
<210> 89
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 89
gaaggtcgga gtcaacggat tatgatcctt cccatggttc cactct 46
<210> 90
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 90
gagcacggca caacacatta ca 22
<210> 91
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 91
gaaggtgacc aagttcatgc tcctagaacc gaataaggta tccccca 47
<210> 92
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 92
gaaggtcgga gtcaacggat tcctagaacc gaataaggta tccccct 47
<210> 93
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 93
gatgcttttc ttcaggcttg gc 22
<210> 94
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 94
gaaggtgacc aagttcatgc tgaaagtctg ccatacttgg ggtc 44
<210> 95
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 95
gaaggtcgga gtcaacggat tgaaagtctg ccatacttgg ggtt 44
<210> 96
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 96
ggtccataca aggcctcact agatg 25
<210> 97
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 97
gaaggtgacc aagttcatgc ttataacccg gggacaagtg atgg 44
<210> 98
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 98
gaaggtcgga gtcaacggat ttataacccg gggacaagtg atga 44
<210> 99
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 99
ccaacccaaa atttttcccc gttgga 26
<210> 100
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 100
gaaggtgacc aagttcatgc tggcgtcacg atagaggaag tccaa 45
<210> 101
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 101
gaaggtcgga gtcaacggat tggcgtcacg atagaggaag tccag 45
<210> 102
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 102
aagcctgctg atctgttatc ccc 23
<210> 103
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 103
gaaggtgacc aagttcatgc tggaagattg gagtccgagt tagtgtc 47
<210> 104
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 104
gaaggtcgga gtcaacggat tggaagattg gagtccgagt tagtgta 47
<210> 105
<211> 27
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 105
cctccccttt catcacaaca tctatgc 27
<210> 106
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 106
gaaggtgacc aagttcatgc tggacatctc attggccacc attg 44
<210> 107
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 107
gaaggtcgga gtcaacggat tggacatctc attggccacc attt 44
<210> 108
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 108
aaaactttgt ggtgtttagg gccc 24
<210> 109
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 109
gaaggtgacc aagttcatgc ttaaccttga atccttgggg aagcc 45
<210> 110
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 110
gaaggtcgga gtcaacggat ttaaccttga atccttgggg aagca 45
<210> 111
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 111
atctgagtag cgtgagggta aga 23
<210> 112
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 112
gaaggtgacc aagttcatgc tccccacgtt tccaaaaccc tagg 44
<210> 113
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 113
gaaggtcgga gtcaacggat tccccacgtt tccaaaaccc taga 44
<210> 114
<211> 28
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 114
gattcctcgg gagaaatgtc gtaagtca 28
<210> 115
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 115
gaaggtgacc aagttcatgc tggctccaaa agtcagctca aaggag 46
<210> 116
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 116
gaaggtcgga gtcaacggat tggctccaaa agtcagctca aaggaa 46
<210> 117
<211> 28
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 117
tgagatgtgg actccttata aggcttgg 28
<210> 118
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 118
gaaggtgacc aagttcatgc tctcgaagac tggctcgtaa gacg 44
<210> 119
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 119
gaaggtcgga gtcaacggat tctcgaagac tggctcgtaa gaca 44
<210> 120
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 120
aaacggcccg tcataattac ctcca 25
<210> 121
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 121
gaaggtgacc aagttcatgc tgcttaggaa ccatgtgatc gtcg 44
<210> 122
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 122
gaaggtcgga gtcaacggat tgcttaggaa ccatgtgatc gtca 44
<210> 123
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 123
tctactgtgc atctatcagc gtct 24
<210> 124
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 124
gaaggtgacc aagttcatgc tgtgtccagc ttgcttgctc ctc 43
<210> 125
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 125
gaaggtcgga gtcaacggat tgtgtccagc ttgcttgctc ctt 43
<210> 126
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 126
ttgatgatgg gaggtgacaa ttaccc 26
<210> 127
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 127
gaaggtgacc aagttcatgc tccaccgtca gaaacaaatt gcg 43
<210> 128
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 128
gaaggtcgga gtcaacggat tccaccgtca gaaacaaatt gca 43
<210> 129
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 129
gctccacgca caaaaacaag cag 23
<210> 130
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 130
gaaggtgacc aagttcatgc ttggatgact gcaggaagag gag 43
<210> 131
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 131
gaaggtcgga gtcaacggat ttggatgact gcaggaagag gaa 43
<210> 132
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 132
tttgaattcc ctcagcagct ggc 23
<210> 133
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 133
gaaggtgacc aagttcatgc taaccagcat ttgtcgtgga cta 43
<210> 134
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 134
gaaggtcgga gtcaacggat taaccagcat ttgtcgtgga ctg 43
<210> 135
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 135
ggcacacgat gatggattag tggt 24
<210> 136
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 136
gaaggtgacc aagttcatgc tggcgaccat gttctagacc tcac 44
<210> 137
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 137
gaaggtcgga gtcaacggat tggcgaccat gttctagacc tcaa 44
<210> 138
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 138
gaaatgtggt cccagggctt acac 24
<210> 139
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 139
gaaggtgacc aagttcatgc tggggtatga tgagggtggt gtg 43
<210> 140
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 140
gaaggtcgga gtcaacggat tggggtatga tgagggtggt gta 43
<210> 141
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 141
acgcccactt gttaacactc aaag 24
<210> 142
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 142
gaaggtgacc aagttcatgc ttagggggaa tgagtgcaac agaag 45
<210> 143
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 143
gaaggtcgga gtcaacggat ttagggggaa tgagtgcaac agaaa 45
<210> 144
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 144
ctgtaaaggc agtcattcct actacg 26
<210> 145
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 145
gaaggtgacc aagttcatgc ttgatccatg gtgtgtgcac atcg 44
<210> 146
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 146
gaaggtcgga gtcaacggat ttgatccatg gtgtgtgcac atct 44
<210> 147
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 147
cccaaagctc acatttcctt acag 24
<210> 148
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 148
gaaggtgacc aagttcatgc tggaaatgag tgtcccagcc atctatg 47
<210> 149
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 149
gaaggtcgga gtcaacggat tggaaatgag tgtcccagcc atctatt 47
<210> 150
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 150
agtcccaacc ctactctcat gtcct 25
<210> 151
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 151
gaaggtgacc aagttcatgc tgaccacaac atccaggttc acc 43
<210> 152
<211> 43
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 152
gaaggtcgga gtcaacggat tgaccacaac atccaggttc aca 43
<210> 153
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 153
gcttgggcac tctattggta ctccta 26
<210> 154
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 154
gaaggtgacc aagttcatgc tggcgtagat tggtgtacac actg 44
<210> 155
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 155
gaaggtcgga gtcaacggat tggcgtagat tggtgtacac acta 44
<210> 156
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 156
gaatttacta aaccacccct cgcttg 26
<210> 157
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 157
gaaggtgacc aagttcatgc tcattagtgt cctcgatggc tgatg 45
<210> 158
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 158
gaaggtcgga gtcaacggat tcattagtgt cctcgatggc tgata 45
<210> 159
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 159
agcatctctt cgggtggttt agtac 25
<210> 160
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 160
gaaggtgacc aagttcatgc tcgcaacgaa tattggtcac cagg 44
<210> 161
<211> 44
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 161
gaaggtcgga gtcaacggat tcgcaacgaa tattggtcac caga 44
<210> 162
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 162
cgattgactt tgttggcctc caaa 24
<210> 163
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 163
gaaggtgacc aagttcatgc tggttgtaga tgcggaagtg agtgc 45
<210> 164
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 164
gaaggtcgga gtcaacggat tggttgtaga tgcggaagtg agtgg 45
<210> 165
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 165
accaccttcg tattgagttg gaca 24
<210> 166
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 166
gaaggtgacc aagttcatgc tccggaaatg atcaatgcga accctc 46
<210> 167
<211> 46
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 167
gaaggtcgga gtcaacggat tccggaaatg atcaatgcga accctt 46
<210> 168
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 168
aacactagac gcccttgttg ga 22
<210> 169
<211> 48
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 169
gaaggtgacc aagttcatgc tgcctggtaa gatataccga cccttgtc 48
<210> 170
<211> 48
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 170
gaaggtcgga gtcaacggat tgcctggtaa gatataccga cccttgtg 48
<210> 171
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 171
gtggaggtag agaggctgtt tct 23
<210> 172
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 172
gaaggtgacc aagttcatgc tgcagatgct catcggactg atttg 45
<210> 173
<211> 45
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 173
gaaggtcgga gtcaacggat tgcagatgct catcggactg attta 45
<210> 174
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 174
gcaaaatagt gaaatcgccg cgaga 25
<210> 175
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 175
gaaggtgacc aagttcatgc tgcctaccag accttttcta tgccttc 47
<210> 176
<211> 47
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 176
gaaggtcgga gtcaacggat tgcctaccag accttttcta tgcctta 47
<210> 177
<211> 27
<212> DNA
<213> Artificial Sequence (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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| CN115094155A (en) * | 2022-05-24 | 2022-09-23 | 沧州市农林科学院 | SNP (single nucleotide polymorphism) and haplotype influencing salt tolerance of wheat |
| WO2024212386A1 (en) * | 2023-04-14 | 2024-10-17 | 华智生物技术有限公司 | Kasp marker primer group for detecting purity of solanum lycopersicom variety, kit thereof and use thereof |
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| CN115094155A (en) * | 2022-05-24 | 2022-09-23 | 沧州市农林科学院 | SNP (single nucleotide polymorphism) and haplotype influencing salt tolerance of wheat |
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| WO2024212386A1 (en) * | 2023-04-14 | 2024-10-17 | 华智生物技术有限公司 | Kasp marker primer group for detecting purity of solanum lycopersicom variety, kit thereof and use thereof |
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| CN112143829B (en) | 2021-04-30 |
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