SNP (Single Nucleotide Polymorphism) marker combination for identifying Gaoligong pig variety and application of SNP marker combination

Abstract

The invention provides an SNP (Single Nucleotide Polymorphism) marker combination for identifying a Gaoligong Mountain pig variety. The sites of the SNP marker are selected from a combination of more than two sites in 28 listed sites. The invention also provides application of the SNP marker combination and a method for rapidly identifying the Gaoligong pig variety by using the SNP marker combination. According to the method, group differentiation index analysis is carried out on the Gaoligong pigs, linkage imbalance analysis is further carried out on the screened SNP markers, deep mining is carried out in combination with a random forest algorithm, 28 SNPs markers are successfully determined, and the markers can clearly distinguish the Gaoligong pigs from other representative varieties. According to the invention, a target variety characteristic SNP marker combination is successfully constructed, and powerful technical support is provided for low-cost and rapid variety identification of Gaoligong pigs and other varieties of pigs.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to SNP markers and their applications, especially SNP marker combinations for identifying the Gaoligong Mountain pig breed and their applications. Background Art

[0002] In the field of pig genetic research, accurately identifying breed-specific genetic markers plays a decisive role in breed identification, proper protection of genetic resources, and efficient utilization. In the early days, traditional pedigree analysis methods relied on tracing the family pedigrees of pigs in detail and inferring potential genetic markers from the trait performances of individuals in different generations. However, for local pig breeds such as the Gaoligong Mountain pig with a long breeding cycle and a complex population structure, it is extremely difficult to construct a long-term and complete pedigree record, which not only consumes a large amount of manpower, material resources, and time, but also the traits of pigs are extremely vulnerable to external factors such as breeding environment and feed nutrition, resulting in a significant reduction in the accuracy of pedigree analysis results. For example, in some mountainous breeding environments, due to the lack of standardized breeding conditions, the traits such as growth rate and meat quality of pigs are greatly affected by the environment, making the error in inferring genetic markers based on pedigree analysis obvious.

[0003] Subsequently, the emerging microsatellite marker technology, relying on the polymorphism of short tandem repeat sequences in the genome, analyzes the differences between individuals through PCR amplification and electrophoresis detection, and has been widely used in pig genetic research for a period of time. However, the detection process of this technology is complicated, has strict requirements for experimental equipment and experimental environment, requires operators to have high professional skills, and is prone to introducing errors due to factors such as PCR amplification efficiency and electrophoresis conditions during the detection process. For example, in actual operation, a slight deviation in the annealing temperature during PCR amplification may lead to inaccurate amplification products, thereby affecting the microsatellite marker analysis results.

[0004] With the revolutionary breakthrough of sequencing technology, single nucleotide polymorphism (SNP) markers have become the focus of current research due to their unique advantages. SNPs are widely distributed and have a large number in the genome, and have good stability. Although the traditional direct sequencing method can accurately determine SNP sites, the cost is extremely high, and the sequencing cost of each sample can reach several thousand yuan or even higher, which greatly limits its application in large-scale sample research. Whole genome sequencing, as a method for comprehensively analyzing the genome sequence of a species, although it can obtain rich genetic information, also faces a cost bottleneck. Its high requirements for sequencing depth and data volume result in high costs, which is an excessive economic burden for large-scale pig population research.

[0005] Chinese patent application document CN 117431325A discloses a SNP locus combination for identifying the Rongchang pig breed and its application.

[0006] The Chinese patent application document CN 115011699A discloses a specific molecular identity card for identifying the Nanyang black pig breed and its application. The SNP loci are a set of SNP loci with relatively high allele frequencies among Nanyang black pig breeds.

[0007] The Chinese patent application document CN 118207340A discloses a set of SNP loci for identifying the Xidu black pig breed and its application.

[0008] None of the above prior arts disclose the SNP-related research content of the Gaoligongshan pig. As a rare local pig breed in China with unique genetic characteristics and excellent regional adaptability, it is urgent to deeply analyze its genetic code. This not only helps to protect this precious genetic resource and prevent its genetic characteristics from gradually disappearing under the impact of modern breeding models, but also provides a solid theoretical basis for the scientific and reasonable development and utilization of the Gaoligongshan pig. For example, through marker-assisted selection, excellent pig breeds that are more adaptable to market demands can be cultivated. Summary of the Invention

[0009] To solve the above problems, the present invention provides a combination of SNP markers for identifying the Gaoligongshan pig breed. The loci of the SNP markers are selected from the combination of two or more loci among the following 28 loci:

[0010] SNP1: Chromosome 16, physical position: 76598300, genotype: G-C;

[0011] SNP2: Chromosome 7, physical position: 39640638, genotype: A-C;

[0012] SNP3: Chromosome 2, physical position: 25773832, genotype: C-T;

[0013] SNP4: Chromosome 2, physical position: 140390429, genotype: T-C;

[0014] SNP5: Chromosome 31, physical position: 39288479, genotype: G-T;

[0015] SNP6: Chromosome 4, physical position: 25910939, genotype: A-G;

[0016] SNP7: Chromosome 11, physical position: 18345833, genotype: T-C;

[0017] SNP8: Chromosome 14, physical position: 1738222, genotype: T-C;

[0018] SNP9: Chromosome 14, physical position: 69199892, genotype: C-T;

[0019] SNP10: Chromosome 4, physical position: 22839998, genotype: G-A;

[0020] SNP11: Chromosome 10, physical position: 18211790, genotype: G-T;

[0021] SNP12: Chromosome 14, physical position: 130815537, genotype: A-G;

[0022] SNP13: Chromosome 13, physical position: 12280581, genotype: A-G;

[0023] SNP14: Chromosome 5, physical position: 94784784, genotype: TA-T;

[0024] SNP15: Chromosome 7, physical position: 32881624, genotype: A-G;

[0025] SNP16: Chromosome 11, physical position: 22269181, genotype: A-G;

[0026] SNP17: Chromosome 2, physical position: 10080235, genotype: *-T;

[0027] SNP18: Chromosome 11, physical position: 10616144, genotype: T-C;

[0028] SNP19: Chromosome 14, physical position: 63583304, genotype: G-A;

[0029] SNP20: Chromosome 31, physical position: 27186004, genotype: A-G.

[0030] SNP21: Chromosome 14, physical position: 10822761, genotype: T-C.

[0031] SNP22: Chromosome 13, physical position: 22866197, genotype: A-G.

[0032] SNP23: Chromosome 2, physical position: 122057639, genotype: G-A.

[0033] SNP24: Chromosome 1, physical position: 8010876, genotype: A-T.

[0034] SNP25: Chromosome 6, physical position: 147967616, genotype: C-T.

[0035] SNP26: Chromosome 6, physical position: 140241229, genotype: ATAT - A.

[0036] SNP27: Chromosome 8, physical position: 26772919, genotype: G - A.

[0037] SNP28: Chromosome 2, physical position: 103134655, genotype: A - G.

[0038] Preferably, the specific information of the 28 loci is shown in Table 1.

[0039] The present invention also provides a gene chip for identifying the breed of Gaoligongshan pigs, and the chip includes probes using the above SNP marker combination.

[0040] Preferably, the chip is a 60K SNP chip.

[0041] The present invention also provides the application of the above SNP marker combination or gene chip in identifying the breed of Gaoligongshan pigs.

[0042] The present invention also provides a method for quickly identifying the breed of Gaoligongshan pigs, which is carried out using the above SNP marker combination or gene chip.

[0043] Preferably, it includes the following steps: 1) Collect pig samples; 2) Perform gene chip genotyping on the pig samples; 3) Exclude low-quality data through quality control; 4) Perform population differentiation index analysis to locate characteristic SNP loci; 5) Extract SNP marker loci without linkage disequilibrium; 6) Further determine the SNP marker combination through the random forest algorithm; 7) Compare with the above SNP marker combination to obtain the identification result.

[0044] More preferably, the quality control parameters in step 3) are "--mind 0.1 --geno 0.1".

[0045] Further preferably, the parameters for population differentiation index analysis in step 4) are "--fst-window-size 10 --fst-window-step 10", and MEAN_FAST≥0.65 is used as the key screening threshold.

[0046] Most preferably, the linkage disequilibrium analysis parameter in step 5) is "-blocks no-pheno-req".

[0047] Beneficial effects:

[0048] Through intuitive visualization methods such as principal component analysis (PCA) plots and genotype heatmaps, the present invention clearly demonstrates the significant genetic differences between Gaoligongshan pigs and other pig breeds. The present invention successfully identified 28 SNP markers from the sequencing data of 7 pig breeds / lines that can accurately distinguish Gaoligongshan pigs from other representative breeds, and constructed a characteristic SNP marker combination for Gaoligongshan pigs. This achievement provides strong technical support for the low-cost and rapid breed identification of Gaoligongshan pigs and other pig breeds. In actual production and breeding work, it can quickly and accurately identify the breeds of pigs, avoid breed mixing, and improve breeding efficiency.

[0049] The characteristic SNP marker combination for Gaoligongshan pigs constructed in the present invention also paves a solid path for in-depth exploration of the genetic characteristics of Gaoligongshan pigs. By analyzing these markers, researchers can further understand the genetic structure, evolutionary history of Gaoligongshan pigs, and their genetic relationships with other pig breeds. This helps to excavate the unique genetic resources of Gaoligongshan pigs, provides a theoretical basis for their application in the field of modern genetic breeding, and is expected to promote the vigorous development of local pig breeds in the field of modern genetic breeding.

[0050] With the help of deep sequencing technology, the present invention uses high-quality sequencing data of more than 10x as the basis and applies PLINK (V1.90) software to strictly control the quality of SNP data. By simply setting the parameters of "--mind 0.1 --geno 0.1", individuals with a missing rate > 0.1 and related SNPs can be accurately screened out. This operation effectively avoids a large amount of invalid data from entering the subsequent analysis process, thus greatly saving computing resources and time costs. Without this strict quality control step, invalid data will not only occupy valuable computing resources, resulting in low computing efficiency, but may also interfere with the accuracy of research results, causing researchers to draw wrong conclusions based on wrong data.

[0051] In the stage of population differentiation index analysis, the present invention uses VCFTools (V0.1.13) software and applies the 1-to-N analysis strategy to finely calculate the Fst value of each SNP with a window and step size of 10 kb. This method not only ensures the accuracy of the analysis but also significantly improves the analysis efficiency. Through this refined analysis, researchers can more deeply understand the genetic differences between different pig populations, providing an important basis for screening representative SNP loci subsequently. Then, the present invention uses PLINK software again to screen out SNP loci with MEAN_FST ≥ 0.65, construct a marker combination and conduct linkage disequilibrium analysis. The entire analysis process is closely connected, without the need for repeated sequencing or complex cross-experiments, greatly improving the research efficiency.

[0052] In the genotyping step, the present invention adopts the 60K SNP chip technology, demonstrating a huge advantage of low cost. Compared with whole-genome sequencing, the SNP chip does not need to perform undifferentiated sequencing on the entire genome, but targets a large number of pre-selected SNP sites. Taking the pig population involved in this study as an example, if whole-genome sequencing is used, the cost of each sample may soar to several thousand yuan due to the sequencing depth and huge data volume. Each 60K SNP chip can detect multiple samples simultaneously. When purchasing a whole box of chips, the average cost per sample can be controlled within 100 - 300 yuan, which is only one fortieth of the cost of whole-genome sequencing. This significant reduction in cost enables more research institutions and researchers to carry out relevant research, promoting the popularization and development of pig population genetics research.

[0053] In addition to the cost advantage, the 60K SNP chip technology also greatly simplifies the experimental process. The present invention does not require complicated pre-experimental steps such as library construction, reducing experimental consumables and labor input. Technicians can quickly complete the detection by preparing DNA samples and performing chip hybridization according to the standardized process. Usually, it takes 2 - 3 weeks from sample preparation to obtaining preliminary data for whole-genome sequencing, while for the 60K SNP chip experiment, with skilled operation, the sample detection can be completed within 1 - 2 days. The significant shortening of the experimental cycle enables researchers to obtain research results faster, timely adjust the research direction, and greatly improves the timeliness of research.

[0054] Generally speaking, the various technologies used in the present invention cooperate with each other, forming a complete technical system from data acquisition, analysis to variety identification and genetic characteristic exploration, bringing significant benefits and broad development prospects to pig population genetics research and genetic breeding work. Brief Description of the Drawings

[0056] Figure 1 Shows the PCA plot drawn based on the specific SNP markers of the Gaoligong Mountain pigs of the present invention.

[0057] Figure 2 Shows the heat map drawn based on the specific SNPs of the Gaoligong Mountain pigs of the present invention. Among them, yellow represents the heterozygous genotype, and other colors represent the homozygous genotype. Detailed Description of the Invention

[0058] The following is a further detailed description through specific embodiments. It should be noted that the embodiments cited below are only for better explaining the content of the present invention, but do not represent that the content of the present invention is limited to the cited embodiments. Therefore, those skilled in the art who make non-essential improvements and adjustments to the implementation plan based on the above invention content still fall within the protection scope of the present invention, subject to the protection scope of the appended claims.

[0059] The term SNP involved in the present invention, namely Single Nucleotide Polymorphism, refers to DNA sequence polymorphisms caused by variations of single nucleotides at the genomic level. Essentially, SNP is a kind of variation triggered by the substitution, transversion, insertion or deletion of a single base. Among them, substitution refers to the replacement between purines and purines, pyrimidines and pyrimidines, while transversion is the replacement between purines and pyrimidines. These two types are the most common in SNPs. SNPs are characterized by a large number and wide distribution. They exist not only in gene coding regions but also are abundantly distributed in non-coding regions such as gene regulatory regions, introns, etc. This enables SNPs to potentially affect aspects such as gene expression regulation, protein structure and function, and thus affect biological traits and disease susceptibility. In addition, SNPs usually have biallelic nature, that is, in a population, there are generally only two alleles at a certain SNP locus, and this characteristic brings great convenience to genotyping and genetic analysis.

[0060] SNP technology has extensive applications in many aspects. The occurrence of many diseases is closely related to gene variations. As the main type of gene variations, SNPs play a key role in disease association studies. By performing genome-wide association analysis of SNPs in a large number of case and control samples, researchers can identify SNP loci significantly associated with specific diseases. Based on the SNP information carried by individuals and combined with big data and machine learning algorithms, a disease risk prediction model can be constructed. SNPs also affect an individual's metabolism and response to drugs. Due to the presence of specific SNPs in different individuals, the same drug may produce different therapeutic effects and adverse reactions. By detecting SNPs in drug metabolism-related genes, doctors can formulate personalized medication plans for patients, improve treatment effects, and reduce drug adverse reactions.

[0061] In the field of agricultural breeding, SNP markers can serve as reliable tools for variety identification. By analyzing specific SNP loci, different varieties can be quickly and accurately distinguished, the purity of seeds or livestock can be determined, variety mixing can be avoided, and the genetic stability of superior varieties can be ensured. Through SNP analysis of genes related to important traits in crops and livestock, researchers have screened out SNP markers that are closely related to superior traits such as high yield, disease resistance, and stress tolerance. With the help of these markers, marker-assisted selection breeding is carried out, greatly shortening the breeding cycle, improving breeding efficiency, and cultivating new varieties that better meet market demands. SNP data provides key information for studying the genetic structure, genetic diversity, and evolutionary relationships of populations. By analyzing the differences in SNP frequencies among different populations, researchers can understand the migration history and gene flow of populations and reveal the evolutionary process of species. Under the influence of natural selection, the SNP frequencies related to environmental adaptability will change. By comparing the SNP data of populations in different environments, gene regions affected by natural selection can be identified, and the adaptation mechanism of species to the environment can be deeply understood.

[0062] The Gaoligong Mountain pig involved in the present invention is an excellent local pig breed in China. It is named after being mainly produced in the Gaoligong Mountain area of Nujiang Prefecture, Yunnan. With its unique genetic characteristics and economic value, it occupies an important position in the livestock field. The central production area of the Gaoligong Mountain pig is located in Lushui County, Nujiang Prefecture. Due to the local special natural, geographical, and social and economic conditions, there are few foreign pig breeds. The Gaoligong Mountain pig has long been in a semi-wild state of being closed, naturally inbred, and free-range, forming characteristics adapted to the harsh natural environment and the extensive feeding methods of ethnic minorities. As an excellent breed included in the "National Livestock and Poultry Genetic Resources List (2021 Edition)", the Gaoligong Mountain pig has received key protection. To implement the third national livestock and poultry genetic resources census, relevant expert groups have measured the slaughter performance and meat quality of the Gaoligong Mountain pig and collected samples for preservation, research, and utilization in the national livestock gene bank. Nevertheless, affected by the introduction of foreign pig breeds, the breeding quantity of the Gaoligong Mountain pig still shows a downward trend, and the protection work has a long way to go. Through scientific protection and reasonable development and utilization, the Gaoligong Mountain pig industry is expected to achieve sustainable development and create greater economic and social value.

[0063] The 60K genotyping technology used in the present invention refers to the process of performing genotyping on approximately 60,000 loci in the biological genome with the help of specific technologies and tools. Genotyping can determine the gene form of an individual at specific gene loci and plays a key role in fields such as disease research, animal and plant breeding, and population genetics. In 60K genotyping, chip technology is relatively commonly used. Taking the maize high-density chip Maize6H-60K as an example, this chip can detect more than 60,000 SNP (single nucleotide polymorphism) and InDel (insertion or deletion mutation) fixed loci for each sample in one scan.

[0064] The PLINK software used in the present invention is a software for genetic data analysis and processing. PLINK, short for "Genome-wide Association Analysis Toolset", is a free and open-source tool for genome-wide association analysis. It was developed by researchers from Harvard University, the Massachusetts Institute of Technology, and the Broad Institute, aiming to help researchers efficiently analyze large-scale genetic data. After years of continuous updates and improvements, the currently widely used versions are PLINK 1.90 and PLINK 2.0, and these new versions have been optimized in terms of functions and performance, and can better meet the growing needs of genetic research.

[0065] The analysis of the population differentiation index used in the present invention occupies a key position in population genetics research and is an important method for deeply exploring the genetic differences between populations. It is widely used in fields such as species evolution, biodiversity research, and plant and animal breeding. The population differentiation index is represented by Fst, which is a key indicator for measuring the degree of genetic differentiation between populations, and its value range is between 0 and 1. When the Fst value is 0, it indicates that there is no genetic differentiation between populations and the gene frequencies are exactly the same; when the Fst value is 1, it means that the populations are completely differentiated and there is no gene exchange. In actual research, the larger the Fst value, the greater the genetic differences between populations and the higher the degree of population differentiation; on the contrary, the smaller the Fst value, the higher the genetic similarity between populations and the lower the degree of differentiation. Among many calculation methods of the population differentiation index, the algorithm derived based on the Hardy-Weinberg equilibrium principle is the most commonly used. This algorithm calculates the gene diversity within and between populations by means of the allele frequencies of different populations, and then obtains the Fst value. The present invention uses the VCFTools software for the analysis of the population differentiation index.

[0066] The VCFTools used in the present invention is an open-source software for analyzing genetic variant data stored in the VCF (Variant Call Format). It has a wide range of applications in the fields of genetics and bioinformatics research. With the continuous in-depth development of life science research, the scale and complexity of genetic variant data continue to increase. Due to its powerful functions, efficient data processing capabilities, and simple and easy-to-use features, VCFTools has become increasingly important in the fields of genetics and bioinformatics, providing strong support for scientific researchers to explore the mysteries of life.

[0067] In the field of genetics, linkage disequilibrium (LD) analysis is a key technology for studying the relationships between genes and exploring the genetic laws of species. It complements the analysis of the population differentiation index and is widely used in various aspects such as disease gene mapping, plant and animal breeding, and population genetics research. Linkage disequilibrium refers to the phenomenon that the frequency of the simultaneous occurrence of two alleles at different gene loci in a population is higher or lower than the random occurrence frequency. Suppose there are two gene loci A and B, with alleles A1, A2 at locus A and alleles B1, B2 at locus B. Under the ideal random state, the frequency of the A1B1 combination should be the product of the frequencies of A1 and B1. However, in the actual population, if the frequency of the A1B1 combination deviates from this expected value, it indicates that there is linkage disequilibrium between these two loci. This invention uses PLINK software for linkage disequilibrium analysis.

[0068] The principal component analysis (PCA) plot of the population genetic structure used in this invention is a widely used visualization tool in population genetics research. It can visually display the genetic relationships and differences between populations, helping researchers deeply analyze the genetic structure of populations and providing key information for understanding the evolutionary history, genetic diversity, and gene flow between populations of species. Principal component analysis is a dimensionality reduction technique aimed at transforming multiple related variables into a few uncorrelated composite variables, namely principal components. In population genetics research, these variables are usually the gene frequencies or genotype data of different individuals, such as SNP locus information. By performing principal component analysis on a large amount of genetic data, researchers can simplify the complex multi-dimensional data into a few principal components, and each principal component represents a major source of genetic variation. These principal components are arranged in descending order of variance contribution rate. The first principal component can explain the largest proportion of the variation in the data, followed by the second principal component, and so on. The PCA plot is drawn based on these principal components, and by projecting individuals or populations into two-dimensional or three-dimensional space, it visually displays the genetic relationships between them. This invention conducts in-depth mining through the R language plotting function combined with the random forest algorithm (based on the R package: randomForest(version 4.7 - 1.2)) for PCA visualization processing.

[0069] The randomForest used in this invention is a highly influential machine learning package in R language. It is developed based on the random forest algorithm, providing a comprehensive solution for data mining and predictive modeling, and has been widely applied in genetic research and many other fields. The random forest algorithm belongs to the category of ensemble learning. It constructs multiple decision trees and combines the prediction results of these decision trees to obtain the final prediction. When constructing each decision tree, the randomForest package randomly samples with replacement from the original dataset to form multiple bootstrap sample sets for training each decision tree. In addition, when splitting each node of the decision tree, the algorithm does not use all features but randomly selects a part of the features to find the best split point. This dual randomization strategy effectively reduces the correlation between decision trees, enhances the generalization ability of the model, and improves the accuracy and stability of prediction.

[0070] The genotype heatmap used in this invention, as a commonly used visualization tool in genetic research, and the principal component analysis (PCA) of population genetic structure Figure 1 Similarly, it plays a key role in mining the value of genetic data. By intuitively presenting the distribution and patterns of genotype data, it helps researchers gain insights into the genetic relationships, population structures, and gene variation characteristics among samples, providing clear ideas and bases for genetic research. The genotype heatmap shows the genotype information of multiple samples at multiple gene loci in matrix form. In the heatmap, each row represents a gene locus, and each column represents a sample. Each cell in the matrix corresponds to the genotype of a sample at a specific gene locus, and different genotypes are distinguished by different colors. For example, red represents the homozygous dominant genotype, blue represents the homozygous recessive genotype, and the heterozygous genotype is represented by an intermediate transitional color. The intensity change of the color intuitively reflects the distribution of different genotypes in the samples, enabling researchers to quickly identify the differences and regularities of genotypes.

[0071] All the above tools used in this invention are commercial tools or open-source tools, which can be easily obtained by those skilled in the art. This invention is based on the reasonable combination and use of these tools, and by selecting optimized parameters, the SNP marker combination for identifying the Gaoligongshan pig breed of this invention is obtained.

[0072] For those technologies, instruments, equipment, and materials not specifically specified in this invention, any other technologies, instruments, equipment, and materials well-known to those skilled in the art that can achieve the same purpose can be used; even if the technologies, instruments, equipment, and materials specifically specified in this invention are mentioned, it does not mean that this invention can only use these technologies, instruments, equipment, and materials. It only represents the preferred solutions of this invention, and those skilled in the art can still use any other technologies, instruments, equipment, and materials well-known to those skilled in the art that can achieve the same purpose.

[0073] Example

[0074] 1. Sample collection and SNP chip genotyping

[0075] To ensure the representativeness of the samples, this study selected Basa pigs (Group A, n = 66), Gaoligong Mountain pigs (Group B, n = 136), Saba pigs (Group C, n = 59), Chuan - Saba pigs (Group D, n = 5), Yorkshire pigs (Group E, n = 99), Berkshire pigs (Group F, n = 14), and Chuan - Xiang boars (Group G, n = 3). The sample collection site was ear tissue, which was immediately stored in a - 20°C refrigerator for later use to ensure the quality and integrity of the samples.

[0076] Genomic DNA was extracted from ear tissue samples using 60K. The DNA concentration and quality were detected using a Nanodrop spectrophotometer and agarose gel electrophoresis. Qualified DNA samples were selected and genotyped using 60K.

[0077] 2. Data analysis quality control

[0078] This invention relies on deep - sequencing technology and uses sequencing data of more than 10x as the analysis basis. The PLINK (V1.90) software was used to strictly control the quality of single - nucleotide polymorphism (SNP) data. During this process, individuals with a missing rate > 0.1 were accurately identified and deleted, and at the same time, individuals with SNPs having a missing rate > 0.1 were excluded. This operation was achieved by setting the parameters "--mind 0.1 --geno 0.1". Through this step, the interference of low - quality data was effectively excluded, providing a reliable data foundation for subsequent analysis.

[0079] 3. Population differentiation index analysis

[0080] Gaoligong Mountain pigs (Group B) were selected as the target breed / strain from multiple pig breeds / lines. With the help of VCFTools (V0.1.13) software, Fst analysis was carried out using a 1 - to - N analysis strategy. With a window size of 10kb and a step size of 10kb, the Fst value of each SNP single point was calculated one by one, and the specific parameter settings were "--fst - window - size 10 --fst - window - step 10". Through this refined calculation method, the genetic differentiation degree between Gaoligong Mountain pigs and other pig breeds was deeply analyzed, and SNP loci possibly related to the unique genetic characteristics of Gaoligong Mountain pigs were accurately located.

[0081] 4. Linkage disequilibrium analysis

[0082] Using the PLINK (V1.90) software, SNP loci with MEAN_FST≥0.65 were screened from the results of population differentiation index analysis, and these loci were integrated to form SNP marker combinations. For the SNP marker loci in this set, linkage disequilibrium (LD) analysis was carried out with the parameter setting of "--blocks no-pheno-req". After the analysis was completed, all SNP marker loci without linkage disequilibrium were extracted, and 1 SNP marker locus was selected in each region with linkage disequilibrium. These loci together constituted independent SNP marker loci, providing core materials for the subsequent construction of characteristic SNP marker combinations of Gaoligongshan pigs.

[0083] The dataset of the SNP marker combinations obtained in the present invention contains rich information, including SNP_id for accurately locating each SNP; chromosome position (chr) and physical position (Pos) to clarify the specific coordinates of the SNP in the genome; population information (Group) of specific SNPs to record in detail the distribution of this SNP in different pig populations; genotypes of SNP alleles (allele1 and allele2) to intuitively display the polymorphism of SNPs; and genotype frequencies of the A-G group to provide quantitative data for analyzing genotype differences between different populations. These data constitute an important basis for deeply mining characteristic SNPs of Gaoligongshan pigs.

[0084] A total of 20 specific SNP loci were obtained in the present invention, and their specific information is shown in Table 1 below.

[0085] Table 1 Results of population-specific SNP data of the present invention

[0086]

[0087]

[0088] Table 1 (continued) Results of population-specific SNP data of the present invention

[0089]

[0090]

[0091] 5. Visualization

[0092] In order to visually display complex analysis data, this study used the powerful R language plotting function for visualization. Whether it is the principal component analysis (PCA) graph showing population genetic structure or the heat map reflecting genotype distribution, they are all drawn by writing specific codes in the R language. These graphs present data characteristics in an intuitive form, helping researchers quickly understand and interpret analysis results.

[0093] Figure 1 It shows the PCA plot drawn based on the specific SNPs of the Gaoligong Mountain pigs of the present invention. This plot clearly shows that the Gaoligong Mountain pig samples show an obvious separation trend from the samples of other populations in the principal component space. This result intuitively reveals that there are significant differences in genetic characteristics between Gaoligong Mountain pigs and pigs of other breeds, indicating that the screened SNP markers can effectively capture the unique genetic information of Gaoligong Mountain pigs.

[0094] Figure 2 It shows the heat map drawn based on the SNPs specific to the Gaoligong Mountain pig population of the present invention. This map presents distinct characteristics. In this heat map, yellow represents the heterozygous genotype, and other colors represent the homozygous genotypes. Through the intuitive distinction of colors, it can be clearly seen that the genotype distribution pattern of the Gaoligong Mountain pig population is significantly different from that of other populations. This difference further confirms the uniqueness of the genetic composition of Gaoligong Mountain pigs, and also shows that the screened SNP markers are highly effective in reflecting the genetic characteristics of Gaoligong Mountain pigs.

[0095] In summary, through a rigorous analysis process, the present invention has successfully determined 28 SNP markers, which can clearly distinguish Gaoligong Mountain pigs from other representative breeds. Through this series of work, a characteristic SNP marker combination of the target breed has been successfully constructed, providing strong technical support for the low-cost and rapid breed identification of Gaoligong Mountain pigs and pigs of other breeds.

[0096] The above are only the embodiments of the present invention. The well-known technical common sense in the solution is not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the application date, can know all the existing technologies in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given by this application and combined with their own abilities, improve and implement this solution. Some typical well-known technologies should not become an obstacle for those of ordinary skill in the art to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several adjustments and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners and the like described in the specification can be used to explain the content of the claims.

Claims

1. A SNP marker combination for identifying Gaoligong Mountain pig breeds, characterized in that: The site of the SNP marker is selected from a combination of two or more sites among the following 28 sites: SNP1: chromosome 16, physical location: 76598300, genotype: GC; SNP2: chromosome 7, physical location: 39640638, genotype: AC; SNP3: chromosome 2, physical location: 25773832, genotype: CT; SNP4: chromosome 2, physical location: 140390429, genotype: TC; SNP5: chromosome 31, physical location: 39288479, genotype: GT; SNP6: chromosome 4, physical location: 25910939, genotype: AG; SNP7: chromosome 11, physical location: 18345833, genotype: TC; SNP8: chromosome 14, physical location: 1738222, genotype: TC; SNP9: chromosome 14, physical location: 69199892, genotype: CT; SNP10: chromosome 4, physical location: 22839998, genotype: GA; SNP11: chromosome 10, physical location: 18211790, genotype: GT; SNP12: chromosome 14, physical location: 130815537, genotype: AG; SNP13: chromosome 13, physical location: 12280581, genotype: AG; SNP14: chromosome 5, physical location: 94784784, genotype: TA-T; SNP15: chromosome 7, physical location: 32881624, genotype: AG; SNP16: chromosome 11, physical location: 22269181, genotype: AG; SNP17: chromosome 2, physical location: 10080235, genotype: *-T; SNP18: chromosome 11, physical location: 10616144, genotype: TC; SNP19: chromosome 14, physical location: 63583304, genotype: GA; SNP20: chromosome 31, physical location: 27186004, genotype: AG; SNP21: chromosome 14, physical location: 10822761, genotype: TC; SNP22: chromosome 13, physical location: 22866197, genotype: AG; SNP23: chromosome 2, physical location: 122057639, genotype: GA; SNP24: chromosome 1, physical location: 8010876, genotype: AT; SNP25: chromosome 6, physical location: 147967616, genotype: CT; SNP26: chromosome 6, physical location: 140241229, genotype: ATAT-A; SNP27: chromosome 8, physical location: 26772919, genotype: GA; SNP28: chromosome 2, physical location: 103134655, genotype is AG.

2. A SNP marker combination for identifying Gaoligong Mountain pig breeds according to claim 1, characterized in that: The specific information of the 28 sites is shown in Table 1.

3. A gene chip for identifying Gaoligong Mountain pig breeds, characterized in that: The chip comprises probes using the SNP marker combination according to claim 1 or 2.

4. A gene chip for identifying Gaoligong Mountain pig breeds as claimed in claim 3, characterized in that: The chip is a 60K SNP chip.

5. Use of the SNP marker combination according to claim 1 or 2 or the gene chip according to claim 3 or 4 in identifying Gaoligong Mountain pig breeds.

6. A method for rapid identification of Gaoligongshan pig breeds, characterized in that: The method is carried out using the SNP marker combination according to claim 1 or 2 or the gene chip according to claim 3 or 4.

7. A method for rapid identification of Gaoligong Mountain pig breeds as claimed in claim 6, characterized in that: The following steps are involved: 1) Collecting pig samples; 2) Performing gene chip typing on the pig samples; 3) Excluding low-quality data through quality control; 4) Performing population differentiation index analysis to locate characteristic SNP sites; 5) Extracting SNP marker sites without linkage disequilibrium; 6) Further determining the SNP marker combination through a random forest algorithm; 7) Comparing with the SNP marker combination described in claim 1 or 2 to obtain an identification result.

8. A method for rapid identification of Gaoligong Mountain pig breeds as claimed in claim 7, characterized in that: In step 3), the quality control parameters are "--mind 0.1 --geno 0.1".

9. A method for rapid identification of Gaoligong Mountain pig breeds as claimed in claim 7 or 8, characterized in that: The parameters for population differentiation index analysis in step 3) are "--fst-window-size 10 --fst-window-step 10", and MEAN_FAST ≥ 0.65 is used as the key screening threshold.

10. A method for rapid identification of Gaoligong Mountain pig breeds as claimed in any one of claims 7 to 9, characterized in that: The linkage disequilibrium analysis parameter in step 5) is "-blocks no-pheno-req".

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