SNP (Single Nucleotide Polymorphism) molecular marker combination, primer, kit and method for identifying bamei mutton sheep variety and application

Abstract

The invention belongs to the technical field of animal variety identification, and relates to an SNP (Single Nucleotide Polymorphism) molecular marker combination, a primer, a kit and a method for identifying a bamei mutton sheep variety and application. The invention provides an SNP (Single Nucleotide Polymorphism) molecular marker for identifying a bamei mutton sheep variety, the SNP molecular marker comprises 17 SNP molecular markers, and the physical positions of the 17 SNP molecular markers are determined by sequence comparison based on a sheep reference genome Orambouiilletv1.0. By utilizing the 17 SNP molecular markers provided by the invention, rapid identification of the bamei mutton sheep variety can be realized in a high-throughput, high-accuracy and low-cost manner, and the requirements of variety resource protection, development and utilization and rapid breeding of new varieties at the present stage are met.

Description

Technical Field

[0001] This invention belongs to the field of animal breed identification technology, specifically relating to a combination of SNP molecular markers, primers, kits, methods, and applications for identifying the Bamei meat sheep breed. Background Art

[0002] The Bamei sheep is a meat sheep breed developed in Bayannur City, and it is my country's first new meat sheep breed with completely independent intellectual property rights. Bamei sheep are highly adaptable, genetically stable, have distinct meat characteristics, grow rapidly, have high feed conversion ratios, high reproductive rates, and strong disease resistance. Their various economic and technical indicators are among the leading levels in China. The development of the Bamei sheep breed has solved the problem of a severe shortage of breeding sheep in the region, providing a leading breed for the development of the meat sheep industry in Inner Mongolia Autonomous Region and even the whole country. Utilizing Bamei sheep for mutton production and for the development of new breeds is an important approach to mutton production in Inner Mongolia.

[0003] Currently, the methods for identifying the breed and purity of Bamei mutton sheep mainly rely on phenotypic identification (morphological markers) according to the People's Republic of China National Standard GB / T36396-2018 "Bamei Mutton Sheep". However, with the widespread adoption of crossbreeding in mutton production, the individual phenotypes of high-generation crossbred populations of Bamei mutton sheep (using paternal traits as the sire) are highly similar to their parents, making differentiation based on phenotypic characteristics impossible. Furthermore, phenotypic identification cannot be used to determine the breed origin of mutton and other sheep products. Therefore, relying solely on phenotypic characteristics for breed resource identification is neither accurate, comprehensive, nor scientific. Currently, there is no effective and accurate DNA molecular marker detection method for identifying the Bamei mutton sheep breed.

[0004] DNA molecular markers have been widely used in the precise identification of sheep breeds. Commonly used DNA molecular markers include SSRs and SNPs. While codominant SSR markers are highly stable, low-cost, and simple to apply, they suffer from "amplification loss" and disordered banding patterns, leading to a higher probability of misclassification and making them unsuitable for high-throughput detection. SNP markers, on the other hand, have advantages such as wide distribution in the genome, good genetic stability, and ease of high-throughput detection, and have rapidly replaced traditional molecular markers. Based on sheep genome SNPs, commercially available sheep SNP chips include the Ovine 50K Beadchip and Ovine HDBeadchip. The 50K SNP chip has been widely used for genetic structure analysis, genome-wide selection signal analysis, genome-wide association analysis, and sheep breed identification. However, interference from irrelevant sites in SNP chips reduces their accuracy in sheep breed identification, and the extremely high cost of SNP chip detection and analysis prevents its large-scale promotion and application in the precise identification of sheep and their animal products. Currently, there is a lack of a highly accurate and low-cost method for identifying the breed of Bameri sheep. Summary of the Invention

[0005] The purpose of this invention is to provide a combination of SNP molecular markers, primers, kits, methods, and applications for identifying Balmi meat sheep breeds. This invention develops a combination of 17 SNP molecular markers using Sequenom. SNP technology can achieve rapid identification of the Bamei meat sheep breed with high throughput, high accuracy, and low cost, meeting the current needs for breed resource protection, development and utilization, and rapid breeding of new breeds.

[0006] This invention provides a SNP molecular marker for identifying the breed of Balmi sheep, comprising 17 SNP molecular markers. The physical locations of these 17 SNP molecular markers were determined by sequence alignment based on the sheep reference genome Oar rambouillet v10. The specific site information is as follows:

[0007] SNP number chromosome physical location SNP 1_152059995 1 152059995 C / T 1_198361502 1 198361502 A / G 1_257632100 1 257632100 G / A 1_268865393 1 268865393 G / A 1_297687341 1 297687341 C / T 2_233390299 2 233390299 G / T 2_254130976 2 254130976 T / C 2_2568586 2 2568586 C / T 2_79866355 2 79866355 A / T 3_136564169 3 136564169 A / G 3_139659651 3 139659651 A / G 3_63786561 3 63786561 G / A 4_8683596 4 8683596 G / A 5_48606473 5 48606473 G / A 6_11534388 6 11534388 T / G 6_42386629 6 42386629 G / A 10_32787127 10 32787127 G / T .

[0008] The present invention also provides a primer for detecting the SNP molecular marker described in the above technical solution, wherein the nucleotide sequence of the primer is shown in SEQ ID NO.1 to 51.

[0009] The present invention also provides a kit for identifying the breed of Bamei meat sheep, the kit comprising the primers described in the above technical solution.

[0010] Preferably, the kit further includes dNTPs, Taq DNA polymerase, and Mg. 2+ PCR reaction buffer and SAP enzyme.

[0011] Preferably, the kit also includes standard positive template DNA.

[0012] This invention also provides a method using Sequenom The method for detecting single nucleotide types of SNP molecular markers described in the above technical solution using SNP technology includes the following steps:

[0013] 1) Extract genomic DNA from the sheep to be tested;

[0014] 2) Using the genomic DNA of the sheep to be tested as a template, PCR amplification reactions were performed using each pair of amplification primers 2nd-PCRP and 1st-PCRP in the primers described in claim 2.

[0015] 3) Digest the PCR amplification products with SAP enzyme;

[0016] 4) Using the digested PCR amplification product as a template, extension reactions are performed using the extension primers in claim 2.

[0017] 5) Analyze the extended products and determine the single nucleotide type of the SNP sites of the 17 SNP molecular markers.

[0018] This invention also provides the application of the SNP molecular markers, primers, kits, or methods described in the above-mentioned technical solutions in the identification of the Bamei meat sheep breed.

[0019] This invention also provides a method using Sequenom The method for identifying the breed of American meat sheep using SNP technology includes the following steps:

[0020] 1) Extract genomic DNA from the sheep to be tested;

[0021] 2) Using the genomic DNA of the sheep to be tested as a template, PCR amplification reactions were performed using each pair of amplification primers 2nd-PCRP and 1st-PCRP in the primers described in claim 2.

[0022] 3) Digest the PCR amplification products with SAP enzyme;

[0023] 4) Using the digested PCR amplification product as a template, extension reactions are performed using the extension primers in claim 2.

[0024] 5) Analyze the extended products and determine the genotypes of the 17 SNP markers;

[0025] 6) Based on the genotype results of 17 SNPs, the support vector machine method was used to determine whether the tested individual was a Bameri sheep.

[0026] Preferably, the method for determining whether an individual to be tested is a Bameri sheep using the support vector machine method includes the following steps:

[0027] 1) Using the background population database of the Bamei meat sheep breed as the training set, Bamei meat sheep are coded as 1 and other sheep are coded as 0. Different genotypes in each SNP are coded as 0, 1 and 2 respectively. SVM and 10-fold cross-validation are used to screen models and estimate model parameters. The training model with the highest average accuracy of 10-fold cross-validation and the corresponding parameters are obtained.

[0028] 2) Using the training model and corresponding parameters obtained in step 1), based on the genotypes corresponding to the 17 SNPs markers of the tested individuals, the genotype results are imported into the model to predict whether they belong to the Bamei meat sheep. A genotype prediction result of 1 indicates that they are Bamei meat sheep, and a genotype prediction result of 0 indicates that they are other meat sheep.

[0029] Preferably, steps 1) and 2) determine the probability of each sheep being a Bamei meat sheep by repeating the prediction 20 times; among the verified individuals, those individuals that are identified as Bamei meat sheep at least 18 times are significant Bamei meat sheep individuals.

[0030] This invention provides a combination of SNP molecular markers for identifying the Balmi sheep breed. The molecular marker combination described in this invention covers a wide range of chromosomal regions and eliminates interference from irrelevant SNPs on detection accuracy. This invention utilizes a limited combination of molecular markers to reveal the most extensive differences between sheep breeds. The SNP molecular marker combination of this invention for the precise identification of Balmi sheep can effectively distinguish Balmi sheep from other sheep breeds and crossbred sheep containing Balmi sheep lineage, exhibiting high stability, accuracy, and low cost.

[0031] Currently, there are no commercially available methods or kits for identifying Balmi sheep breeds. Identification requires using Illumina Ovine SNP50K chips or more expensive resequencing methods to detect over 50,000 SNPs in a single individual, and simultaneously identifying multiple sheep breeds, with at least 20 individuals of each breed tested. This invention, however, utilizes a SNP background library and an SVM method to complete breed identification by detecting only 17 SNPs. The detection of these 17 SNPs was performed using Sequenom. The SNP method can reduce the identification cost of a single sample from over 280 yuan (280 yuan for one individual detected by the Illumina Ovine SNP50K chip) to 25 yuan, offering a significantly lower cost. This invention is based on Sequenom. SNP technology has led to the development of a SNP combinatorial genotyping method, enabling precise biallelic identification of SNP sites in a wide range of DNA samples (including those with complex genomes). Furthermore, it can be coupled with high-throughput automated nucleic acid extraction, automated genomic DNA separation, and PCR system construction. It includes a water bath PCR instrument with 96-well, 384-well, and 1536-well plates for high-throughput PCR and DNA sample genotyping, which can improve the efficiency and accuracy of breed identification to a certain extent. Unaffected by environmental factors, it is suitable for large-scale application and can be further used to supplement molecular marker libraries for sheep breed identification. The data processing of the marker combinatorial method in this invention utilizes a simple and ingenious support vector machine approach, exhibiting high prediction accuracy, with an average exceeding 97.84% and an actual accuracy rate reaching 98.7%. It can be implemented using a free, license-free R language package, resulting in low cost. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 The distribution map of the 17 SNP sites in the genome provided by this invention;

[0034] Figure 2 The accuracy of identifying the Bamei meat sheep breed using the support vector machine method based on the 17 SNP combinations provided in this invention is shown in the figure.

[0035] Figure 3 The diagram shows the partitioning results of 384 populations using 17 effective SNP chip sites provided by this invention. Detailed Implementation

[0036] This invention provides a SNP molecular marker for identifying the breed of Balmi sheep, comprising 17 SNP molecular markers. The physical locations of these 17 SNP molecular markers were determined by sequence alignment based on the sheep reference genome Oar rambouillet v10. The specific site information is as follows:

[0037]

[0038]

[0039] The SNP molecular marker combination described in this invention can be used to identify the Bamei meat sheep breed. It contains 17 SNP loci, which are based on sheep genome sequence information version number Oar_rambouillet_v1.0 (GCA_002742125.1), https: / / ftp.ensembl.org / pub / release-109 / fasta / ovis_aries_rambouillet / . The specific SNP locus information corresponding to these loci is shown in Table 1, and their distribution in the genome is shown in [Table 1]. Figure 1 These SNP molecular marker combinations can effectively identify the Bamei meat sheep breed, promoting Bamei meat sheep breeding and germplasm resource management.

[0040] Table 1. Detailed information on SNP sites

[0041]

[0042] This invention also provides primers for detecting the SNP molecular markers described in the above-mentioned technical solutions, the nucleotide sequences of which are as follows (SEQ ID NO.1-51):

[0043]

[0044]

[0045] In this invention, the primers, specifically the 2nd-PCRP and 1st-PCRP corresponding to each SNP molecular marker, can amplify each SNP molecular marker through a PCR amplification reaction.

[0046] This invention also provides a kit for identifying the breed of Bameri sheep, the kit comprising the primers described in the above-mentioned technical solution. Preferably, the kit further comprises dNTPs, Taq DNA polymerase, and Mg... 2+ The kit includes PCR reaction buffer and SAP enzyme. Preferably, the kit also includes standard positive template DNA.

[0047] This invention also provides a method using Sequenom The method for detecting single nucleotide types of SNP molecular markers described in the above technical solution using SNP technology includes the following steps:

[0048] 1) Extract genomic DNA from the sheep to be tested;

[0049] 2) Using the genomic DNA of the sheep to be tested as a template, PCR amplification reactions were performed using each pair of amplification primers 2nd-PCRP and 1st-PCRP in the primers described in claim 2.

[0050] 3) Digest the PCR amplification products with SAP enzyme;

[0051] 4) Using the digested PCR amplification product as a template, extension reactions are performed using the extension primers in claim 2.

[0052] 5) Analyze the extended products and determine the single nucleotide type of the SNP sites of the 17 SNP molecular markers.

[0053] This invention involves extracting genomic DNA from sheep for testing. The method for extracting the genomic DNA is not specifically limited; conventional genomic DNA extraction methods in the art can be used.

[0054] After obtaining the genomic DNA, this invention uses the genomic DNA of the sheep to be tested as a template and performs PCR amplification using the amplification primers 2nd-PCRP and 1st-PCRP described in the above technical solution. In this invention, the preferred reaction system for the PCR amplification reaction (based on 5 wells) is: 10 ng / μL genomic DNA 1 well, 10× PCR reaction buffer 331.25 μL, 25 mmol / L MgCl2 172.25 μL, 25 μmol / L dNTPs 53 μL, 0.5 μmol / L PCR Primermix 530 μL, 5 U / μL Taq DNA polymerase 106 μL, and deionized water 927.5 μL. In this invention, the preferred PCR amplification program is: 94℃ for 2 min; 94℃ for 20 s, 56℃ for 30 s, 72℃ for 60 s, 45 cycles; 72℃ for 3 min; and storage at 4℃.

[0055] After obtaining the PCR amplification product, the present invention digests the PCR amplification product using SAP enzyme. In this invention, the preferred digestion system for the SAP enzyme (based on 2 wells) is: 90.1 μL of 10×SAP Buffer, 159 μL of 1.7 U / μL SAP Enzyme, and 810.9 μL of deionized water. In this invention, the preferred digestion conditions are 37℃ for 40 min, 85℃ for 5 min, and storage at 4℃.

[0056] After obtaining the digested PCR amplification product, this invention uses the digested PCR amplification product as a template and performs an extension reaction using the extension primers described in the above technical solution. In this invention, the preferred extension reaction system (based on 2 wells) is: 106 μL of 10×iplex Buffer Plus, 106 μL of iplex Terminator, 436.1 μL of 0.6–1.3 μmol / L primer mix, 21.7 μL of iplex Enzyme, and 400.2 μL of deionized water. In this invention, the preferred conditions for the extension reaction are: 94℃ for 30 s; 94℃ for 5 s, 52℃ for 5 s, 80℃ for 5 s, wherein (52℃ for 5 s, 80℃ for 5 s) is performed for 5 cycles, and [94℃ for 5 s, (52℃ for 5 s, 80℃ for 5 s)] is performed for 40 cycles; 72℃ for 3 min; and storage at 4℃.

[0057] After obtaining the extended product, the present invention analyzes the extended product and performs single nucleotide type determination on the SNP sites of the 17 SNP molecular markers.

[0058] This invention preferably uses single nucleotide type detection based on nucleotides from sheep genome sequence information version number Oar_rambouillet_v1.0 (GCA_002742125.1). SNP technology detects SNP molecular marker combinations where each SNP molecular marker includes two amplification primers and one extension primer.

[0059] This invention also provides the application of the SNP molecular markers, primers, kits, or methods described in the above-mentioned technical solutions in the identification of the Bamei meat sheep breed.

[0060] This invention also provides a method using Sequenom The method for identifying the breed of American meat sheep using SNP technology includes the following steps:

[0061] 1) Extract genomic DNA from the sheep to be tested;

[0062] 2) Using the genomic DNA of the sheep to be tested as a template, PCR amplification reactions were performed using each pair of amplification primers 2nd-PCRP and 1st-PCRP in the primers described in claim 2.

[0063] 3) Digest the PCR amplification products with SAP enzyme;

[0064] 4) Using the digested PCR amplification product as a template, extension reactions are performed using the extension primers in claim 2.

[0065] 5) Analyze the extended products and determine the genotypes of the 17 SNP markers;

[0066] 6) Based on the results of 17 SNP genotypes, the support vector machine method was used to determine whether the individual to be tested was a Bamei meat sheep.

[0067] The preferred limitations of steps 1) to 5) in this invention are the same as those described above, and will not be repeated here.

[0068] After obtaining the genotype of each SNP, this invention uses a support vector machine (SVM) method based on the 17 SNP genotype results to determine whether the individual being tested is a Pamir mutton sheep. Specifically, this invention preferably uses the R software packages e1071 and kernlab to perform an SVM method based on the 17 SNP genotype results to determine whether the individual being tested is a Pamir mutton sheep; wherein the function svm() in package e1071 provides an interface to the LIBSVM library, and package kernlab is an auxiliary package to package e1071, providing various SVM kernel functions.

[0069] In this invention, the preferred method for determining whether an individual to be tested is a Bameri sheep using the support vector machine method includes the following steps:

[0070] 1) Using the background population database of the Bamei meat sheep breed as the training set, Bamei meat sheep are coded as 1 and other sheep are coded as 0. Different genotypes in each SNP are coded as 0, 1 and 2 respectively. SVM and 10-fold cross-validation are used to screen models and estimate model parameters. The training model with the highest average accuracy of 10-fold cross-validation and the corresponding parameters are obtained.

[0071] 2) Using the training model and corresponding parameters obtained in step 1), based on the genotypes corresponding to the 17 SNPs markers of the tested individuals, the genotype results are imported into the model to predict whether they belong to the Bamei meat sheep. A genotype prediction result of 1 indicates that they are Bamei meat sheep, and a genotype prediction result of 0 indicates that they are other meat sheep.

[0072] Specifically, this invention preferably uses the background population database data containing the Bamei meat sheep breed (Table 2) as the training set, encodes Bamei meat sheep as 1 and other sheep as 0, and encodes different genotypes in each SNP as =0 (genotype 1), 1 (genotype 2) and 2 (genotype 3) respectively, to meet the data format requirements of the e1071 R software package. The e1071 and kernlab packages are loaded, and the svm() function of the e1071 package is used, where the kernel function parameters are set to "linear", "radial", "polynomial" and "sigmoid" to screen kernel functions and estimate model parameters cost (penalty factor, which can be paired with any kernel function) and gamma (selecting the radial kernel function as the built-in parameter after the kernel, which determines the distribution of data after mapping to the new feature space). The model and the accuracy of the corresponding parameters are trained through 10-fold cross-validation, and finally the optimal kernel function and model parameters cost and gamma with the highest average accuracy are obtained.

[0073] After obtaining the kernel function and model parameters cost and gamma with the highest average accuracy, this invention uses these parameters to detect the genotypes of 17 SNP loci in an individual, encoding them as 0, 1, and 2 respectively. These genotypes are then input into the R software package e1071, and the function svmfit() is used to predict whether the individuals belong to the Bamei meat sheep breed. The results are represented by 0 or 1, where 0 indicates that the individual is not a Bamei meat sheep and 1 indicates that it is.

[0074] Because 10x cross-validation is random, meaning that in a given cross-validation sample, some individuals cannot be accurately predicted due to the bias in the split between the training and prediction sets, repeating the model training-prediction process can overcome this problem. To overcome model randomness, this invention preferably repeats the above model training-validation process. In this invention, steps 1) and 2) are preferably repeated at least 20 times, and this number of repetitions is denoted as N. Rep Let N be the number of times the prediction result of 1 is obtained for each individual in each repetition using svmfit(). Res The accuracy of identifying each sheep as a Bameri mutton sheep is denoted as P. BM The calculation method is N Res / N Rep If P BM If the value is greater than 0.9, then this individual is identified as a Bamei meat sheep.

[0075] In this invention, steps 1) and 2) preferably determine the probability of each sheep being a Bameri sheep by repeating the prediction 20 times; among the validation individuals, those individuals who are identified as Bameri sheep at least 18 times are considered significant Bameri sheep individuals. For example, in the validation group in step 1), the number of times the prediction result of svmfit() is 1 in the 20 model training-validation processes is denoted as N. Res =18, then P BM An individual with a score of 0.9 is identified as a Bameri sheep. The average accuracy of the model obtained through screening in this embodiment of the invention is 97.84%.

[0076] This invention utilizes the aforementioned SNP molecular markers and employs machine learning (support vector machine) methods to identify the background population database information of the Bamei meat sheep breed. This invention also provides the background population database information for identifying the Bamei meat sheep breed. This database contains 12 sheep breeds and 3 hybrid populations. These 3 hybrid populations are a Bamei meat sheep × Hu sheep hybrid, a Bamei meat sheep × Mongolian sheep hybrid, and a Bamei meat sheep × Small-tailed Han sheep hybrid, totaling 417 sheep individuals. The genotype information for the aforementioned 17 SNPs is shown in Table 2. In Table 2, the different genotypes in each SNP are converted to numerical codes corresponding to genotypes in Table 1: 0 (genotype 1 in Table 1), 1 (genotype 2 in Table 1), and 2 (genotype 3 in Table 1).

[0077] Table 24 shows the genotypic information of the above 17 SNPs in 17 individual sheep.

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] NA stands for Not available, representing missing values. If using the R package e1071 for analysis, all missing values ​​must be labeled as NA.

[0084] To further illustrate the present invention, the following detailed description, in conjunction with the accompanying drawings and embodiments, describes a combination of SNP molecular markers, primers, kits, methods, and applications for identifying the Bamei meat sheep breed provided by the present invention. However, these descriptions should not be construed as limiting the scope of protection of the present invention.

[0085] Example 1

[0086] First, 30× depth second-generation resequencing was performed on 195 individuals of Bamei mutton sheep and 105 individuals of crossbred offspring of Bamei mutton sheep and other sheep breeds. Resequencing at approximately 5× depth was also performed on 110 individuals from 10 sheep populations. After obtaining the sequencing data, 4,861,294 SNPs were obtained. SNPs suitable for breed identification were then screened. These SNPs showed significant frequency distribution differences across the populations, allowing for the differentiation of different breeds through genotype clustering, and also distinguishing breeds from crossbred offspring.

[0087] The main approach involves calculating population allele frequencies and the differences in SNP allele frequencies between populations. For the 4,861,294 SNPs obtained, the allele frequencies of each SNP across different breeds were statistically analyzed. Then, the absolute values ​​of the differences between the SNP frequencies of each population and the SNP frequencies of all other populations (excluding the three hybrid sheep populations) were calculated, and the populations were sorted in descending order of this total. Next, population-specific SNPs were screened: based on the sum of frequencies of each population and all other populations, the top 3N SNPs (N = the target variant number = 24) were selected as candidate SNPs. To ensure that SNPs linked to the same LD interval were not selected repeatedly, the distance between different candidate SNPs was no less than 2.5 Mb. Finally, Sequenom was used as the final reference. The results of SNP primer design ultimately identified 17 SNPs, as shown in Table 1.

[0088] Example 2

[0089] A method for identifying Bameri sheep individuals in multiple breeds and crossbred mixed populations using SNP marker combinations.

[0090] 1. Experimental Materials

[0091] A total of 384 sheep were selected as the subjects of the test, including 96 Bamei mutton sheep, 48 Bamei mutton sheep and other crossbred sheep, Tibetan sheep, Cele black sheep, Hu sheep, Sunite sheep and small-tailed Han sheep.

[0092] 2. Reagents and Instruments

[0093] Reagent: Complete Genotyping Reagent Kit for Compact 196;

[0094] Gene amplification: 9700196Dual;

[0095] Mass spectrometry spotting: MassARRAY Nanodispenser RS1000;

[0096] Mass spectrometry analysis: MassARRAY Compact System;

[0097] All reagents and instruments were purchased from Beijing Yinuo Zhongda Biotechnology Co., Ltd.

[0098] 3. Extraction of genomic DNA

[0099] Whole sheep blood was collected, and tissue DNA was extracted using a DNA extraction kit.

[0100] 4. Sequenom SNP technology for genotyping

[0101] The DNA of each sheep was genotyped using 17 SNPs. The nucleotide sequences of the PCR amplification primers and extension primers are shown in SEQ ID NO.1-51.

[0102] The primers mentioned above were synthesized by Beijing Yinuo Zhongda Biotechnology Co., Ltd.

[0103] The testing process is as follows:

[0104] 1. Genomic DNA was extracted from 384 sheep;

[0105] 2. Using genomic DNA from 384 sheep as templates, PCR amplification reactions were performed on the above 17 SNPs using the corresponding amplification primers 2nd-PCRP and 1st-PCRP.

[0106] 3. The PCR amplification products of 17 SNPs were digested with SAP enzyme;

[0107] 4. Using the PCR amplification products of the 17 digested SNPs as templates, extension reactions were performed using the extension primers corresponding to the 17 SNPs.

[0108] 5. Analyze the extended products separately to determine the genotypes of the 17 SNPs in each sheep.

[0109] The PCR amplification reaction system, in 5 wells, consisted of: 10 ng / μL genomic DNA 1 well, 10× PCR reaction buffer 331.25 μL, 25 mmol / L MgCl2 172.25 μL, 25 μmol / L dNTPs 53 μL, 0.5 μmol / L PCR Primer mix 530 μL, 5 U / μL Taq DNA polymerase 106 μL, and deionized water 927.5 μL.

[0110] The PCR amplification program was as follows: 94℃ for 2 min; 94℃ for 20 s, 56℃ for 30 s, 72℃ for 60 s, 45 cycles; 72℃ for 3 min; and stored at 4℃.

[0111] The PCR amplification products were digested using the following SAP enzyme digestion system (2 wells): 90.1 μL of 10×SAP Buffer, 159 μL of 1.7 U / μL SAP Enzyme, and 810.9 μL of deionized water.

[0112] The reaction conditions were: 37℃ for 40 min, 85℃ for 5 min, and stored at 4℃.

[0113] The extended reaction conditions were: 94℃ for 30 s; [94℃ for 5 s, (52℃ for 5 s, 80℃ for 5 s)]; of which (52℃ for 5 s, 80℃ for 5 s) was performed for 5 cycles, and [94℃ for 5 s, (52℃ for 5 s, 80℃ for 5 s)] was performed for 40 cycles; 72℃ for 3 min.

[0114] The resin-purified extended products were transferred to a 196-well SpectroCHIP (Sequenom, San Diego, CA) chip for MALDI-TOF-MS (matrix-assisted laser desorption / ionization time-of-flight mass spectrometry) reaction. The mass spectral peaks were detected using Typer 4.0 software, and the genotypes of the target loci in each sample were determined based on the mass spectral peak diagrams. The genotypic results of 17 SNPs in 384 individuals were obtained.

[0115] 4. Identification of experimental individuals based on 17 SNP genotypes.

[0116] The background population database of the Bamei meat sheep breed (Table 2) was used as the training set. Bamei meat sheep was coded as 1 and other sheep were coded as 0. In each SNP, genotype 1 was coded as 0, genotype 2 was coded as 1, and genotype 3 was coded as 2. SVM and 10-fold cross-validation were used to screen models and estimate model parameters. The model with the highest average accuracy of 10-fold cross-validation and its corresponding parameters were used for subsequent validation.

[0117] The accuracy of the variety identification method adapted to this SNP combination is shown in the figure. Figure 2 And Table 3. Figure 2 The graph shows the accuracy of identifying the Bamei sheep breed using 10-fold cross-validation with different kernel functions selected by support vector machine for 17 SNP combinations. The horizontal axis represents different kernel function types, and the vertical axis represents the kernel function prediction accuracy.

[0118] Table 3. Prediction accuracy (%) of 17 SNP combinations using the support vector machine method under 10-fold cross-validation.

[0119] SVM_kernal fold.1 fold.2 fold.3 fold.4 fold.5 fold.6 fold.7 fold.8 fold.9 fold.10 linear 97.56 97.62 95.24 92.68 97.62 95.24 95.12 95.24 100.00 95.24 radial 100.00 97.62 97.62 95.12 97.62 100.00 97.56 97.62 95.24 100.00 polynomial 100.00 100.00 92.86 95.12 100.00 97.62 95.12 95.24 97.62 100.00 sigmoid 97.56 100.00 100.00 97.56 97.62 100.00 95.12 97.62 92.86 97.62

[0120] according to Figure 2 As shown in Table 3, the data processing method of the label combination of the present invention using support vector machines is simple, ingeniously designed, and has a high prediction accuracy with an average value of over 97.84%. It can be implemented using a free, license-free R language open package, which is low in cost.

[0121] Step 2. Prediction using the trained model: The model and corresponding parameters selected in Step 1 were used to predict whether 384 individuals belonged to the Bamei meat sheep breed based on their genotypes. The model training-validation process was repeated 20 times, achieving an accuracy of 98.7% (only 5 Bamei meat sheep and other breeds of crossbred sheep were significantly identified as Bamei meat sheep). Results are as follows... Figure 3 As shown: the first column from the left represents the actual group status, the second column represents the predicted group results, red represents Bameri sheep, and blue represents other individuals that are not Bameri sheep. Figure 3 The figure shows the results of dividing 384 populations using 17 valid SNP chip loci.

[0122] This invention selects markers that cover a wide range of chromosomal regions and eliminates interference from irrelevant SNPs on detection accuracy, using a limited combination of markers to reveal the differences between sheep breeds as broadly as possible. The SNP combinations used in this invention for the precise identification of Pamir sheep can effectively distinguish Pamir sheep from other sheep breeds and crossbred sheep containing Pamir sheep lineage. They are characterized by high stability, accuracy, and low cost, and are based on Sequenom. The SNP genotyping method developed using SNP technology can accurately determine biallelic loci for SNPs in a wide range of DNA samples (including DNA samples with complex genomes). Furthermore, it can be coupled with high-throughput automated nucleic acid extraction, automated genomic DNA separation, and PCR system construction. It includes a water bath PCR instrument with 96-well, 384-well, and 1536-well plates for high-throughput PCR and DNA sample genotyping, which can improve the efficiency and accuracy of breed identification to a certain extent. It is unaffected by environmental factors, making it suitable for large-scale application and further useful for supplementing molecular marker libraries for sheep breed identification. The data processing of the marker combinations in this invention utilizes a simple and ingenious support vector machine method, achieving a high prediction accuracy of over 97.84% on average. It can be implemented using a free, license-free R language package, resulting in low cost.

[0123] Although the above embodiments have provided a detailed description of the present invention, they are only some embodiments of the present invention, and not all embodiments. People can obtain other embodiments based on these embodiments without creative effort, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A SNP molecular marker for identifying the breed of Bameri sheep, characterized in that, This includes 17 SNP molecular markers. The physical locations of these 17 SNP molecular markers were determined by sequence alignment based on the sheep reference genome Oar_rambouillet_v1.

0. The specific site information is as follows: 。 2. A primer for detecting the SNP molecular marker of claim 1, characterized in that, The nucleotide sequence of the primer is as follows:

3. A kit for identifying the breed of Bameri sheep, characterized in that, The kit includes the primers as described in claim 2.

4. The reagent kit according to claim 3, characterized in that, The kit also includes dNTPs, Taq DNA polymerase, and Mg. 2+ PCR reaction buffer and SAP enzyme.

5. The reagent kit according to claim 3, characterized in that, The kit also includes standard positive template DNA.

6. A method utilizing Sequenom A method for detecting single nucleotide types of the SNP molecular marker described in claim 1 using SNP technology, characterized in that... Includes the following steps: 1) Extract genomic DNA from the sheep to be tested; 2) Using the genomic DNA of the sheep to be tested as a template, PCR amplification reactions were performed using each pair of amplification primers 2nd-PCRP and 1st-PCRP in the primers described in claim 2. 3) Digest the PCR amplification products using SAP enzyme; 4) Using the digested PCR amplification product as a template, extension reactions are performed using the extension primers in claim 2. 5) Analyze the extended products and determine the single nucleotide type of the SNP sites of the 17 SNP molecular markers.

7. The application of the SNP molecular marker of claim 1, the primer of claim 2, the kit of any one of claims 3 to 5, or the method of claim 6 in the identification of the Bamei meat sheep breed.

8. A method utilizing Sequenom The method for identifying the breed of Brazilian meat sheep using SNP technology is characterized by... Includes the following steps: 1) Extract genomic DNA from the sheep to be tested; 2) Using the genomic DNA of the sheep to be tested as a template, PCR amplification reactions were performed using each pair of amplification primers 2nd-PCRP and 1st-PCRP in the primers described in claim 2. 3) Digest the PCR amplification products with SAP enzyme; 4) Using the digested PCR amplification product as a template, extension reactions are performed using the extension primers in claim 2. 5) Analyze the extended products and determine the genotypes of the 17 SNP markers; 6) Based on the results of 17 SNP genotypes, the support vector machine method was used to determine whether the individual to be tested was a Bamei meat sheep.

9. The method according to claim 8, characterized in that, The method for determining whether an individual is a Bameri sheep using the support vector machine method includes the following steps: 1) Using the background population database of the Bamei meat sheep breed as the training set, Bamei meat sheep are coded as 1 and other sheep are coded as 0. Different genotypes in each SNP are coded as 0, 1 and 2 respectively. SVM and 10-fold cross-validation are used to screen models and estimate model parameters. The training model with the highest average accuracy of 10-fold cross-validation and the corresponding parameters are obtained. 2) Using the training model and corresponding parameters obtained in step 1), based on the genotypes corresponding to the 17 SNPs markers of the tested individuals, the genotype results are imported into the model to predict whether they belong to the Bamei meat sheep. A genotype prediction result of 1 indicates that they are Bamei meat sheep, and a genotype prediction result of 0 indicates that they are other meat sheep.

10. The method according to claim 9, characterized in that, Steps 1) and 2) determine the probability of each sheep being a Bamei meat sheep by 20-fold repeated prediction; among the verified individuals, those that are identified as Bamei meat sheep at least 18 times are significant Bamei meat sheep individuals.

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