Method for constructing SNP (Single Nucleotide Polymorphism) site set for identifying Shandai pigs and application of SNP site set

Abstract

The invention relates to a method for constructing a Shandao pig identification SNP site set and application thereof. The method comprises the following steps: S1, calculating SNP site variety score information of Shandao pigs; s2, selecting SNP loci of the Shandai pigs from the SNP loci, and dividing the SNP loci into three clusters by using a K-means clustering method; s3, further selecting the SNP loci of the three clusters to obtain a sequence of candidate loci of an SNP locus set for identifying the Shuai pigs; and S4, selecting the first 30-40 candidate sites as an identification SNP site set of the Shandai pigs. According to the method for constructing the SNP site set for identifying the Shandam pigs and the application of the SNP site set, the Shandam pigs can be rapidly identified from a plurality of pig strains, or the Shandam pork can be rapidly identified from a plurality of meat products to be detected, and the theoretical accuracy of identification is as high as 99.21%. The method has obvious Shandai pig breed specificity, is high in practicability and has good development and application prospects.

Description

Technical Field

[0001] This invention relates to the field of animal breed identification technology, specifically to a method for constructing a set of SNP loci for identifying Saba pigs and its application. Background Art

[0002] Saba pigs are a local breed of pig in Yunnan Province, mainly produced in Luquan Yi and Miao Autonomous County (under the jurisdiction of Kunming) and Wuding, Chuxiong, Nanhua, Lufeng, Yao'an, Dayao, and Shuangbai counties in Chuxiong Yi Autonomous Prefecture (in central Yunnan). They are named after Saba, the town of Sayingpan (formerly known as Saba) in Luquan County. Their advantages include high reproductive rate, tolerance to roughage, good disease resistance, and good meat quality, making them a superior local breed in Yunnan and one of the "Six Famous Pigs of Yunnan" recognized by the Yunnan Provincial Department of Agriculture. Saba pigs have a delicious flavor and command a higher market price than ordinary pig breeds, leading to instances where other breeds are passed off as Saba pigs. To maintain the unique and pure bloodline of Saba pigs and prevent other pig groups or pork products from substituting for Saba pigs in the market, rapid and accurate identification of the pig breed or pork is necessary. Therefore, developing a rapid and accurate identification method for Saba pigs is a current research direction. Developing a rapid and reliable method for identifying Saba pigs is beneficial for the protection, discovery, and breeding of their genetic resources.

[0003] Gene identification cards primarily utilize DNA fingerprinting technology, selecting several fixed loci for identification. Genes are inherent and unchanging genetic markers inherited by offspring from their parents; identification can be achieved by selecting only a few SNP loci, and the combination of these selected SNP loci is unique. Chinese patent 201810310363.3 discloses SNP loci for Guanzhuang flower pigs, SNP chips and their detection primer combinations, detection kits, their applications, and germplasm identification methods; Chinese patent 202110782383.2 discloses a kit and method for domestic pig pedigree division and parentage identification. This demonstrates that SNP loci possess very high specificity and are crucial for breed identification. However, current technologies commonly use PCR amplification followed by sequencing for identification, which is time-consuming and costly. Chinese patent 202211692300.1 discloses a method for identifying Jinhua pigs. This method involves collecting specific genetic tag combinations from Jinhua pigs, performing whole-genome sequencing, and then conducting genome-wide association analysis using a linear mixture model to select the top 25 significant SNP loci to form an identification dataset. A machine learning model is then constructed using this dataset for identification. While this method utilizes a mathematical model and is simple and quick to operate, it cannot be used to identify Saba pigs because the SNP locus datasets differ for different pig breeds. Constructing genetic identity cards for local pig breeds can effectively protect and develop them, but to date, no SNP locus datasets, their construction methods, or applications for identifying Saba pig breeds have been reported.

[0004] Zhao C, Wang D, Teng J, et al. published an article entitled "Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data[J]" in the Journal of Animal Science and Biotechnology, 2023, 14(1):85. The article discloses a method for detecting breed information and assigning breeds in cattle. It involves calculating breed information scores, ranking the scores, and selecting high-scoring loci for machine learning breed prediction. However, the quality of the results depends on the size of the original training set population and requires the selection of a large number of loci to improve its accuracy to over 99%.

[0005] There are currently no reports on the construction and application of accurate SNP locus sets for identification of Saba pigs. Summary of the Invention

[0006] To address the above problems, this invention proposes a method for constructing a set of SNP loci for identification in Saba pigs and its application.

[0007] This invention provides a method for constructing a set of SNP loci for identification in Saba pigs, the method comprising the following steps:

[0008] S1. Calculate the breed score information of SNP loci of Saba pigs: Collect blood samples of Saba pigs for whole genome sequencing, and summarize them with the SNP data of various pig breeds in the IAnimal database to calculate the breed score of SNP loci of all pig breeds, namely the genetic difference Delta, the genetic differentiation index between paired populations Fst, and the allocation information In.

[0009] S2. From the above SNP loci, select SNP loci of Saba pigs that have unique alleles and have Delta > 0.49252, Fst > 0.14251, or In > 0.77959. Standardize the score of each SNP locus and classify them into three clusters using the K-means clustering method based on the standardized score of each SNP locus. In cluster 1, the Fst score is higher than the mean, and the Delta and In scores are slightly higher than the mean. In cluster 2, the Fst score is higher than the mean, and the Delta and In scores are lower than the mean. In cluster 3, the Fst score is lower than the mean, and the Delta and In scores are higher than the mean.

[0010] S3. The SNP sites of the three clusters obtained in S2 are further selected using the following algorithm:

[0011] (1) Calculate the common centroid of SNP sites in the three clusters.

[0012]

[0013] in, As the central site, X ij This represents the coordinate of the i-th site in the j-th dimension, where n is the total number of sites.

[0014] j = 3;

[0015] (2) Calculate the distance D between each SNP site and the center site in each cluster:

[0016]

[0017] (3) Iterative selection of sites:

[0018] SNP sites are selected sequentially from cluster 1, cluster 2, and cluster 3 in descending order of D, and included in the site selection area in an ordered manner without replacement. In each round, three sites are selected sequentially from cluster 1, cluster 2, and cluster 3, and this process is repeated until all sites are selected, thereby reordering the original sites. This order is the candidate site ordering for the SNP site set for Saba pig identification.

[0019] S4. Reselection of candidate sites: Construct the LightGBM model, select the top 30-40 sites from the candidate sites of the above-mentioned SNP site set for Saba pigs, and use them as the identification SNP site set for Saba pigs.

[0020] Preferably, in step S4, the first 30 candidate sites of the above-mentioned SNP site set for Saba pig identification are selected as the SNP site set for Saba pig identification.

[0021] Furthermore, the K-means clustering uses the R package NbClust, as follows: sc.km <- kmeans(sc_nl, 3, nstart = 25), where sc.km is the final classification list; kmeans() is the K-means clustering function of the NbClust package; sc_nl is the scoring table of the SNP varieties; parameter 3 indicates clustering into three classes; and nstart = 25 indicates generating 25 preset centroid values ​​to ensure stable classification results.

[0022] This invention also provides a set of SNP loci for identification in Saba pigs. Constructed using the above method, the set contains 30 SNP loci, namely: SNP number 1116156, genotype TT, located at position 195559511 on chromosome 1; SNP number 897074, genotype CC, located at position 148210111 on chromosome 1; SNP number 4079088, genotype AA, located at position 75294722 on chromosome 4; SNP number 950547, genotype AA, located at position 160129325 on chromosome 1; and SNP number 897070, genotype GG, located at position 148210051 on chromosome 1. SNP number 4079094, genotype CC, located at position 75294931 on chromosome 4; SNP number 862065, genotype AA, located at position 141987098 on chromosome 1; SNP number 980377, genotype TT, located at position 165070451 on chromosome 1; SNP number 4082678, genotype GG, located at position 75639414 on chromosome 4; SNP number 950601, genotype TT, located at position 160136482 on chromosome 1; SNP number 897072, genotype CC, located at position 148210085 on chromosome 1; SNP number 4082685, genotype CC, located at position 75294931 on chromosome 4; SNP number 4082685, genotype AA, located at position 141987098 on chromosome 1; SNP number 980377, genotype TT, located at position 165070451 on chromosome 1; SNP number 4082678, genotype GG, located at position 75639414 on chromosome 4; SNP number 950601, genotype TT, located at position 160136482 on chromosome 1; SNP number 897072, genotype CC, located at position 148210085 on chromosome 1; SNP number 4082685, genotype AA, located at position 141987098 on chromosome 1; SNP number 4082685, genotype AA, located at position 141987098 on chromosome 1; SNP number 9803 The genotype is CC, located at position 75640471 on chromosome 4; SNP number 953025, genotype TT, located at position 160600275 on chromosome 1; SNP number 980650, genotype CC, located at position 165118874 on chromosome 1; SNP number 4083231, genotype CC, located at position 75755375 on chromosome 4; SNP number 953861, genotype CC, located at position 160708948 on chromosome 1; SNP number 977888, genotype GG, located at position 164637654 on chromosome 1; SNP number 4079656, genotype AA, located on chromosome 4. SNP at position 75343352; SNP number 11929687, genotype GG, located at position 116110241 on chromosome 13; SNP number 981803, genotype GG, located at position 165438294 on chromosome 1; SNP number 4082677, genotype TT, located at position 75639169 on chromosome 4; SNP number 953388, genotype TT, located at position 160641713 on chromosome 1; SNP number 935626, genotype GG, located at position 156644321 on chromosome 1; SNP number 4082666, genotype GG, located at position 75635325 on chromosome 4;SNP number 938563, genotype AA, located at position 157479708 on chromosome 1; SNP number 981840, genotype AA, located at position 165445915 on chromosome 1; SNP number 4079084, genotype AA, located at position 75294571 on chromosome 4; SNP number 727571, genotype TT, located at position 109395215 on chromosome 1; SNP number 896836, genotype AA, located at position 148184989 on chromosome 1; SNP number 4083278, genotype GG, located at position 75759399 on chromosome 4.

[0023] This invention provides an application of a SNP locus set for identifying Saba pig breeds. The application method involves collecting blood samples from the pigs to be tested and performing whole-genome sequencing. Based on the location information of the aforementioned SNP locus set, the genotype information of the corresponding loci for each pig is obtained, forming a test sample set. The test sample set is then compared with the genotypes of the 30 loci in the aforementioned SNP locus set. If the genotypes are completely identical, the pig breed can be identified as a Saba pig; otherwise, it is another breed.

[0024] The beneficial effects of the present invention are as follows:

[0025] This invention provides a method for constructing a set of SNP loci for identifying Saba pigs and its application, enabling rapid identification of Saba pigs from multiple pig breeds or from multiple meat products to be tested. The method of this invention systematically selects 30 loci as the identification SNP locus set for Saba pigs, achieving a theoretical accuracy of up to 99.21%. This invention exhibits significant species specificity for Saba pigs, demonstrates strong practicality, and possesses promising development and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a clustering effect diagram of Example 1;

[0027] Figure 2 This is a comparison chart of the standard score features of the three types of clusters in Example 1;

[0028] Figure 3 This is a line graph showing the number of loci versus accuracy in Example 1.

[0029] Figure 4 This is a distribution and density map of the first 30 loci on the pig chromosome in Example 1;

[0030] Figure 5 This is a line graph showing the number of sites versus accuracy as a comparative example. Detailed Implementation

[0031] The present invention will be further described below with reference to the examples.

[0032] Example 1: Constructing a set of SNP loci for identification in Saba pigs:

[0033] S1. Calculate the SNP breed score information for all pig populations:

[0034] S1.1 Group Composition: 100 Saba pigs were sampled from Yuanjia Village, Ziwu Town, Chuxiong City, Yunnan Province; 80 Dahe pigs were sampled from Fuyuan County, Yunnan Province; and 45 Diqing Tibetan pigs were sampled from Shixiang Village, Jinjiang Town, Shangri-La City, Yunnan Province. The other 45 pig breeds were: 69 Tibetan pigs, 48 ​​Meishan pigs, 23 Erhualian pigs, 20 Anqing Liubai pigs, 20 Wannan black pigs, 17 Min pigs, 14 Korean black pigs, 12 Laiwu pigs, 11 Rongchang pigs, 7 Wuzhishan pigs, 6 Hetao large-eared pigs, 6 Lulai black pigs, 6 Tongcheng pigs, 6 Bama fragrant pigs, 6 Luchuan pigs, 5 Jiangquhai pigs, 4 Jinhua pigs, 4 fragrant pigs, 3 Wujin pigs, 3 Neijiang pigs, 3 Enshi black pigs, 3 Penzhou mountain pigs, 2 Wannan spotted pigs, and 2 Songliao black pigs. Two Leping pigs, two Yunnan small-eared pigs, 188 Duroc pigs, 122 Yorkshire pigs, 56 Landrace pigs, 30 Pietrain pigs, 20 Miniature pigs, 14 Yucatan miniature pigs, 14 Berkshire pigs, 8 Curly-haired wild boars, 8 Iberian pigs, 5 Mangalica pigs, 5 Hampshire pigs, 3 Tamworth pigs, 2 Central White pigs, 2 Gloucestershire Old Spotted pigs, 2 Crioll pigs, 2 Spanish Murcia pigs, 2 Casertana pigs, 2 Wessex white-shouldered pigs, and 2 Angler saddle pigs were collected. Blood samples from the above-mentioned Saba pigs, Dahe pigs, and Diqing Tibetan pigs were collected for whole-genome sequencing. SNP data for the other 45 pig breeds were obtained from the IAnimal database: https: / / ianimal.pro / reception / genome / population.

[0035] S1.2 Data Processing:

[0036] (1) Whole-genome resequencing: Approximately 0.3-0.5 mL of blood samples were collected from each of the above-mentioned Saba pigs, Dahe pigs, and Diqing Tibetan pigs, and stored at -40℃. Nucleic acid was extracted from the DNA samples using methods such as the EasyPure Blood Genomic DNA Kit. The extracted DNA was analyzed for quality and concentration using a Nanodrop nucleic acid and protein analyzer. An A260 / 280 ratio of 1.8-2.0 and a concentration ≥50 ng / μL were considered acceptable. Samples meeting the concentration criteria were then subjected to whole-genome sequencing on the DNBSEQ platform.

[0037] (2) Genotyping and filling: Using Sscrofa11.1 as the reference genome, the sequencing results were genotyped using the GATK standard procedure. BCFtools was used to merge the loci of each pig breed in the IAnimal database. In order to obtain high confidence differential loci, VCFtools was used for filtering, shapeit5 was used for phasing, and impute5 was used to complete genotyping. Finally, data from 1016 samples were obtained, containing 16,167,605 loci.

[0038] (3) Calculate the variety score for all loci: Use TRES to calculate the genetic difference Delta, the genetic differentiation index Fst between paired populations, and the allocation information In score for all loci;

[0039] S2. Initial site selection and clustering:

[0040] S2.1. Acquisition of key loci: In order to shorten the data processing time, among all the above loci, we selected some loci with high breed scores, specific score thresholds of Delta>0.49252, Fst>0.14251 or In>0.77959, and unique alleles in Saba pigs, and standardized them according to the score value of each locus.

[0041] S2.2 Clustering: High-scoring sites were classified into three classes using the K-means clustering method. The K-means clustering used the R package NbClust, as follows: `sc.km <- kmeans(sc_nl, 3, nstart = 25)`. Here, `sc.km` is the final classification list; `kmeans()` is the K-means clustering function from the NbClust package; `sc_nl` is the scoring table of the SNP varieties; the parameter 3 indicates three clusters; and `nstart = 25` indicates 25 preset centroid values ​​to ensure stable clustering results. The clustering results are shown below. Figure 1 As shown.

[0042] The K-means clustering algorithm divided the selected sites into three different categories, such as Figure 2As shown, the standard score characteristics of these three clusters can be interpreted as follows: ① In cluster 1, the Fst score is higher than the mean of all selected sites, while the Delta and In scores are slightly higher than the mean; ② In cluster 2, the Fst score is higher than the mean, while the Delta and In scores are lower than the mean; ③ In cluster 3, the Fst score is lower than the mean, while the Delta and In scores are higher than the mean. Assuming that each cluster contributes equally to the identification of Saba pigs, sites need to be selected equally from these three clusters. That is, in each round, three sites are selected sequentially from clusters 1, 2, and 3, and this process is repeated until a sufficient combination of sites for identifying Saba pigs is selected.

[0043] S2.3 For the above three types of loci, the following algorithm is used to select the loci:

[0044] (1) Calculate the common centroid of SNP sites in the three clusters.

[0045]

[0046] in, As the central site, X ij This represents the coordinate of the i-th site in the j-th dimension, where n is the total number of sites.

[0047] j = 3;

[0048] (2) Calculate the distance D between each SNP site and the center site in each cluster:

[0049]

[0050] (3) Iterative selection of sites:

[0051] Following the descending order of D, SNP loci are selected sequentially from clusters 1, 2, and 3, and included in the selection region in an ordered, non-replaceable manner. This process is repeated in each round, selecting three loci from each cluster, until all loci have been selected. This reordering of the original loci is the candidate locus ranking for SNP identification in Saba pigs. In this embodiment, a total of 104 SNP loci were obtained.

[0052] Table 1. Ranking of candidate SNP loci obtained from Example 1 for Saba pig identification.

[0053]

[0054]

[0055]

[0056] S4. Reselection of Candidate Loci: A LightGBM model was constructed. For the aforementioned candidate loci, 3, 4, 5, 10, 15, 20, 25, 30, 35, and 40 loci were selected sequentially. Variety prediction was performed using a 10-fold cross-validation method. The model's output was compared with the reference results to calculate the accuracy. The model construction method is as follows:

[0057] The LightGBM model was trained and evaluated using the autogluon package in Python. The trained model was as follows:

[0058] predictor=

[0059] TabularPredictor(label=label).fit(train_data=df_train,hyperparameters={'GBM':{}}).

[0060] Here, `TabularPredictor` is the model trainer in the `autogluon` package, `label` is the breed name of the training set, and the `fit` method indicates the specific parameter settings: `train_data = df_train` represents the candidate set of Saba pig SNP loci mentioned above, and `hyperparameters = {'GBM': {}}` indicates that `LightGBM` is set as the machine learning training model. The accuracy calculation method is as follows:

[0061]

[0062] Where A represents accuracy and TP represents true cases, i.e. the number of samples that were correctly predicted as Saba pigs;

[0063] TN represents true negatives, which are the number of samples correctly predicted as other breeds; FP represents false positives, which are the number of samples incorrectly predicted as Saba pigs; FN represents false negatives, which are the number of samples incorrectly predicted as other breeds.

[0064] Table 2 Comparison of prediction accuracy with different numbers of loci

[0065] Number of loci Real example True negative example False positives False negatives Accuracy 3 100 888 28 0 97.24％ 4 100 888 28 0 97.24％ 5 100 888 28 0 97.24％ 10 100 896 20 0 98.03％ 15 100 896 20 0 98.03％ 20 100 902 14 0 98.62％ 25 100 905 11 0 98.92％ 30 100 908 8 0 99.21％ 35 100 908 8 0 99.21％ 40 100 908 8 0 99.21％

[0066] The results are shown in Table 2 and Figure 3 As shown, with the highest possible accuracy and the fewest possible number of loci, 30-40 loci were selected as the SNP locus set for identification of Saba pigs using the above method. The theoretical accuracy of the identification was as high as 99.21%, and the theoretical accuracy of 30 loci and 35 and 40 loci were both 99.21%.

[0067] Based on the above conclusions, the first 30 SNP loci for identification of Saba pigs were selected, as shown in Table 3:

[0068] Table 3 shows the set of SNP loci selected from the top 30 loci for identification in Saba pigs.

[0069]

[0070]

[0071] from Figure 4 The distribution and density map of the first 30 loci on pig chromosomes shows that the above 30 loci are mainly distributed on pig chromosomes 1, 4 and 13, with the majority distributed on chromosome 1.

[0072] Example 2: Application of the SNP locus set for Saba pig identification in practical identification

[0073] SNP data from another 10 Saba pig samples and 92 samples from other pig breeds were collected and combined. Based on the SNP locus set of the aforementioned 30 Saba pig identification sites, genotype information for the corresponding loci of each pig was obtained from the whole genome data. The most frequently occurring major alleles were selected to form the test sample set. By comparing the genotypes of the test sample set with the genotypes of the aforementioned 30 Saba pig identification SNP locus set, if the genotypes were completely identical, a sample could be identified as a Saba pig; otherwise, it was another breed. The comparison results are shown in Table 4.

[0074] The specific implementation steps are as follows:

[0075] The samples can be of any pig breed, but SNP information for that sample must be obtained. In this embodiment, the SNP information for the Saba pig, Dahe pig, and Diqing Tibetan pig samples in the test sample set was obtained from whole-genome sequencing of blood samples collected independently. Specifically, approximately 0.3-0.5 mL of blood sample was collected from each individual of Saba pig, Dahe pig, and Diqing Tibetan pig, frozen at -40℃, and nucleic acids were extracted from the DNA samples using methods such as the EasyPure Blood Genomic DNA Kit. The extracted DNA was then subjected to quality testing and concentration determination using a Nanodrop nucleic acid and protein analyzer. An A260 / 280 ratio of 1.8-2.0 and a concentration ≥50 ng / μL were considered acceptable. Samples meeting the concentration standard were then subjected to whole-genome sequencing on the DNBSEQ platform.

[0076] The SNP information of other pig breeds in the sample set to be tested was obtained from the IAnimal database. It was merged with the sequencing data of Saba pig, Dahe pig and Diqing Tibetan pig and genotyped to form the sample set to be tested. Specifically, the sequencing results of Saba pig, Dahe pig and Diqing Tibetan pig and the SNP information of other pig breeds in the IAnimal database were first merged into SNP files and genotype filled. The reference genome used was Sscrofa11.1. The GATK standard workflow was used for genotyping, bcftools was used for SNP site merging, shapeit5 was used for phasing, and impute5 was used for filling.

[0077] The genotyping results detected in each sample in the test sample set were compared with the genotypes of the 30 SNP loci identified in Example 1 for Saba pigs. If the former and the latter genotypes were completely identical, it indicated that the sample originated from a Saba pig; otherwise, it was from other individuals. Specifically:

[0078] (1) Use bcftools to extract genotype information for 30 loci from all samples to be tested: bcftools filtervcf.gz "-R 30_sites.txt>target.vcf; where filter is the filtering function of bcftools; vcf.gz is the previously merged and compressed vcf file; -R is the parameter for reading the file; 30_sites.txt contains information for 30 loci, with the first column being the chromosome number and the second column being the physical distance, separated by tabs; target.vcf contains the specific genotype information for the 30 loci generated.

[0079] (2) Use plink to convert target.vcf to PED format: plink--vcf target.vcf--recode--out target; where --vcf is the parameter for reading the vcf file; --recode--out is the output parameter; target is the filename prefix of the output file, and the output file contains target.ped and target_map;

[0080] (3) Use awk to extract the genotype information of each sample: awk'NR==FNR{printf$1"_"$4"\t"}NR>FNR{if(FNR==1){print""};for(i=1;i<=30;i++){printf"%s%s\t",$(i*2+5),$(i*2+6)};printf"%s\n",$1}'target_map target_ped>target_30.txt;target_30.txt is the genotype file for each sample. Each column except the last column is the locus genotype, and the last column is the sample variety;

[0081] (4) The information in target_30.txt was compared with the SNP locus set of 30 loci in Saba pigs. The results were summarized using Excel and Table 4 shows the constructed validation population and validation results:

[0082] Table 4 shows the results of identifying whether the test pigs were Saba pigs based on the SNP locus set for Saba pig identification.

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] As shown in Table 4, in this embodiment, the SNP locus set for Saba pig identification of the present invention can accurately identify Saba pigs with 100% accuracy in 102 samples from 31 pig breeds.

[0094] Comparative Example: Compared with existing technologies:

[0095] Zhao C, Wang D, Teng J, et al. published an article titled "Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data[J]" in the journal Journal of Animal Science and Biotechnology, 2023, 14(1):85. This article discloses a method for detecting and allocating breed information in cattle. It involves calculating breed information scores, ranking the scores, and selecting high-scoring loci for machine learning breed prediction. This method is most similar to the method in this invention, but when constructing the SNP locus set, the selection method involves calculating breed scores for all individuals of all breeds and then ranking them from highest to lowest to screen for high-scoring loci. It does not screen for the genotype of a single locus. Because the genotype is uncertain, the verification of the results requires inputting the genotype information of the sample to be tested into the pre-calculated model for prediction. The quality of the result depends on the population size of the original training set; and after population changes, further supplementary training is needed to ensure accuracy.

[0096] In this invention, a unique genotype was selected for each candidate breed. Furthermore, we believe that descending combinations of high-scoring loci are not effective for breed identification; instead, the dependencies between these loci should be identified, and these loci should be further classified. Loci are then selected from different categories. Therefore, this invention uses K-means clustering to select loci more suitable for identifying Saba pig breeds. Since a unique genotype is given, the identification result can be obtained by simply comparing the genotypes of the input samples. Moreover, the training set of this method basically includes pig breeds from various countries and regions, greatly increasing the applicability of the method.

[0097] We used the site selection method published in the above article to verify the site selection and identification results of the SNP site data obtained in Example 1 of this invention:

[0098] First, Delta, Fst, and In scores were calculated for the SNP locus data (SNP locus data from 1016 samples) obtained in Example 1. The scores were then sorted by size, and the intersection of the loci was obtained. The top 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 loci from the intersection were selected to form 10 locus sets.

[0099] Secondly, the LightGBM model was trained and evaluated using Python's autogluon package: The trained model was: predictor=

[0100] The `TabularPredictor(label = label).fit(train_data = df_fold_train, hyperparameters = {'GBM': {}})` method uses `TabularPredictor`, which is the model trainer from the `autogluon` package. `label` is the variety name of the training set. The `fit` method specifies the parameter settings: `train_data = df_fold_train` represents the training set for each fold, and `hyperparameters = {'GBM': {}}` indicates that `LightGBM` is set as the model used for training.

[0101] Next, using the Python Scikit-learn package, we performed 10-fold cross-validation to calculate accuracy: `StratifiedKFold(n_splits=10, shuffle=True, random_state=42)`. The `StratifiedKFold` class ensures that the proportion of each class in each fold is approximately the same as the proportion of the complete set; `n_splits=10` means dividing the dataset into 10 subsets, with 9 subsets used as training data and the remaining subset as test data in each subsequent cross-validation process; `shuffle=True` means randomly shuffling the data before splitting; `random_state=42` represents a seed for a pseudo-random number generator, ensuring code reproducibility; the accuracy calculation formula is:

[0102]

[0103] Where A represents accuracy, TP represents true positives, i.e. the number of samples correctly predicted as Saba pigs; TN represents true negatives, i.e. the number of samples correctly predicted as other breeds; FP represents false positives, i.e. the number of samples incorrectly predicted as Saba pigs; and FN represents false negatives, i.e. the number of samples incorrectly predicted as other breeds.

[0104] The results are shown in Table 5 and Figure 5 As shown:

[0105] Table 5 shows the number of loci and accuracy results selected based on existing technical methods.

[0106] SNP number Real example True negative example False positives False negatives Accuracy 10 38 905 11 62 92.81％ 20 97 876 40 3 95.77％ 30 99 893 23 1 97.64％ 40 97 897 19 3 97.83％ 50 99 898 18 1 98.13％ 60 99 900 16 1 98.33％ 70 100 901 15 0 98.52％ 80 97 910 6 3 99.11％ 90 97 908 8 3 98.92％ 100 98 910 6 2 99.21％

[0107] As shown in Table 5 and Figure 5As shown, existing site selection methods require at least 80 sites to achieve an accuracy of over 99%, which is 62.5% higher than the method of this invention. In summary, the method of this invention significantly reduces the number of sites required for identification, greatly improves identification accuracy, and generates significant benefits in guiding production practice.

[0108] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0109] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for constructing a SNP locus set for Saba pig identification, characterized in that: The method comprises the following steps: S1. Calculate the breed score information of Saba pigs' SNP sites: collect blood samples of Saba pigs for whole genome sequencing, and summarize the SNP data of various pig breeds in the IAnimal database to calculate the breed scores of all pig breeds' SNP sites, namely, the genetic difference (Delta), the genetic differentiation index between paired populations (Fst) and the distribution information (In); S2. From the above SNP loci, select SNP loci of Saba pig with unique allele type and Delta>0.49252 or Fst>0.14251 or In>0.77959, and standardize the score value of each SNP locus. Use K-means clustering method to classify according to the standardized score value of each SNP locus, and divide them into three clusters. Among them, in the loci of cluster 1, the Fst score is higher than the mean, and the Delta and In scores are slightly higher than the mean; in the loci of cluster 2, the Fst score is higher than the mean, and the Delta and In scores are lower than the mean; Among the sites in cluster 3, the scores of Fst were lower than the mean, while those of Delta and In were higher than the mean; S3. For the SNP sites of the three clusters obtained in S2, the following algorithm is used for further selection: (1) Calculate the common central site of SNP sites in three clusters in, is the central site, X ij represents the coordinate of the i-th site in the j-th dimension, n is the total number of sites, j＝3； (2) Calculate the distance D between each SNP site in each cluster and the central site: (3) Iterative selection of sites: According to the order of D from large to small, SNP sites are selected from cluster 1, cluster 2, and cluster 3 in turn, and are included in the site selection area in an orderly manner without replacement until all sites are selected, thereby re-ordering the original sites. This order is the candidate site order of the SNP site set for Saba pig identification; S4. Reselection of candidate sites: Construct a LightGBM model to identify candidate sites of the SNP site set for Saba pigs, and select the first 30-40 sites as the SNP site set for identification of Saba pigs.

2. The method for constructing a Saba pig identification SNP locus set according to claim 1, characterized in that: In S4, candidate sites of the SNP site set are identified for Saba pigs, and the first 30 sites are selected as the identification SNP site set for Saba pigs.

3. The method for constructing a Saba pig identification SNP locus set according to claim 1, characterized in that: The K-means clustering uses the R package NbClust, and the method is as follows: sc.km<-kmeans(sc_nl,3,nstart=25), where sc.km is the final classification list; The kmeans() function is the K-means clustering function of the NbClust package; sc_nl is the scoring table of the SNP variety scores; parameter 3 indicates clustering into three categories; and the nstart=25 parameter indicates generating 25 centroid preset values ​​to ensure stable classification results.

4. A SNP locus set for Saba pig identification, characterized in that: The site set obtained by the method of claim 2 is constructed to include 30 SNP sites, namely: SNP number 1116156, genotype TT, located at chromosome 1 at position 195559511; SNP number 897074, genotype CC, located at chromosome 1 at position 148210111; SNP number 4079088, genotype AA, located at chromosome 4 at position 75294722; SNP number 950547, genotype AA, located at chromosome 1 at position 160129325; SNP number 897070, genotype GG, located at chromosome 1 at position 148210051; SNP number 4079094, genotype CC, located at Chromosome 4, No. 75294931; SNP number 862065, genotype AA, located at chromosome 1, No. 141987098; SNP number 980377, genotype TT, located at chromosome 1, No. 165070451; SNP number 4082678, genotype GG, located at chromosome 4, No. 75639414; SNP number 950601, genotype TT, located at chromosome 1, No. 160136482; SNP number 897072, genotype CC, located at chromosome 1, No. 148210085; SNP number 4082685, genotype CC, located at chromosome 4, No. 75640471; SNP SNP number 953025, genotype TT, located at chromosome 1, position 160600275; SNP number 980650, genotype CC, located at chromosome 1, position 165118874; SNP number 4083231, genotype CC, located at chromosome 4, position 75755375; SNP number 953861, genotype CC, located at chromosome 1, position 160708948; SNP number 977888, genotype GG, located at chromosome 1, position 164637654; SNP number 4079656, genotype AA, located at chromosome 4, position 75343352; SNP number 11929687, genotype GG, located at chromosome 4, position at position 116110241 on chromosome 13; SNP number 981803, genotype is GG, located at position 165438294 on chromosome 1; SNP number 4082677, genotype is TT, located at position 75639169 on chromosome 4; SNP number 953388, genotype is TT, located at position 160641713 on chromosome 1; SNP number 935626, genotype is GG, located at position 156644321 on chromosome 1; SNP number 4082666, genotype is GG, located at position 75635325 on chromosome 4; SNP number 938563, genotype is AA, located at position 157479708 on chromosome 1;SNP number 981840, genotype AA, located at chromosome 1, position 165445915; SNP number 4079084, genotype AA, located at chromosome 4, position 75294571; SNP number 727571, genotype TT, located at chromosome 1, position 109395215; SNP number 896836, genotype AA, located at chromosome 1, position 148184989; SNP number 4083278, genotype GG, located at chromosome 4, position 75759399. ; 5. Application of a Saba pig identification SNP locus set in identifying Saba pig breeds, characterized in that: The application method is to collect blood samples of the pig breed to be tested for whole genome sequencing, obtain the genotype information of the corresponding sites of each pig breed to be tested according to the position information of the SNP site set identified in claim 4, form a sample set to be tested, and compare the genotypes of the sample set to be tested with the 30 sites of the above-mentioned identified SNP site set. If the genotypes are completely consistent, it can be determined that the pig breed to be tested is Saba pig, otherwise it is other breeds.

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