Genetic marker and application thereof in South China tiger individual identification

Abstract

The invention provides a genetic marker and application thereof in South China tiger individual identification, and belongs to the technical field of molecular identification. The genetic marker provided by the invention comprises 300 SNP (Single Nucleotide Polymorphism) molecular markers which can be applied to South China tiger individual identification, and an individual identification method is provided. The problem that a traditional RFID chip is insufficient in individual identity recognition is solved. Meanwhile, the problems that existing molecular identification mainly depends on STR and SNP site amplification, a large number of specific primers need to be designed and repeatedly optimized in the early stage, and the process is tedious and time-consuming are solved. The South China tiger individual identity identification method which is convenient, reliable and low in cost is established, and powerful technical support is provided for genetic management and population protection.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular identification, and in particular to the technical field of individual animal identification. Background Art

[0002] The South China tiger is relatively small, with the male tiger's head and tail length of about 2.5 meters and a weight of about 150 kilograms. The female tiger's head and tail length is about 2.3 meters, the tail is 80-100 centimeters long, and the weight is about 120 kilograms. The South China tiger has a round head, short ears, thick and powerful limbs, a long tail, and a lot of milky white on the chest and abdomen. The whole body is orange-yellow and covered with black horizontal stripes. There are short and narrow stripes on the fur, and the spacing between the stripes is larger than that of the Bengal tiger and the Siberian tiger. Diamond-shaped stripes often appear on the side of the body. The South China tiger feeds on herbivorous animals such as wild boars, deer, and roe deer. It is one of the top ten endangered animals in China and a national first-class protected animal.

[0003] Accurate individual identification of captive populations of endangered animals is more conducive to scientific genetic management of the population, effectively avoiding inbreeding and maintaining the genetic diversity and health of the population.

[0004] There are currently two traditional technologies for identifying South China tigers: one is to rely on RFID radio frequency identification chips, which require the chip to be implanted in the animal when it is young and to accompany the animal as it grows. In actual applications, the chip may shift position or be damaged by physical collision during the growth of the animal, which limits the reliability of identification and affects subsequent genetic management. The other technology is to use molecular identification methods, which mainly rely on the amplification of STR and SNP sites. This method requires the design of a large number of specific primers in the early stage and repeated optimization to ensure stability. The experimental process is cumbersome and time-consuming.

[0005] Therefore, there is an urgent need for a simple, reliable and low-cost method for identifying individual South China tigers. Summary of the invention

[0006] The purpose of the present invention is to propose an individual identification method based on genome screening of SNP sites to solve the problem of the shortcomings of traditional RFID chips in individual identification. At the same time, it also solves the problem that the existing molecular identification mainly relies on STR and SNP site amplification, a large number of specific primers need to be designed and repeatedly optimized in the early stage, and a large number of site amplification PCR experiments need to be performed in the later stage, which is cumbersome and time-consuming.

[0007] A genetic marker, comprising 300 SNP molecular markers, wherein the 300 SNP molecular markers are:

[0008]

[0009]

[0010] Among them, the location and variation information of the site are expressed in the format of chromosome_physical location:reference genotype / variant allele type.

[0011] Application of a genetic marker in individual identification of South China tigers.

[0012] A method for identifying an individual South China tiger, the method comprising:

[0013] Step 1: Extract the DNA of the individual to be identified and perform genome sequencing to obtain sequencing data;

[0014] Step 2: Use BWA software to align the sequencing data to the AmyTig1.0 reference genome and generate a BAM file;

[0015] Step 3: Generate a VCF file for each individual to be identified using GATK 4.5 software;

[0016] Step 4: Use awk command and BCFtools1.10.1 software to extract the genotype data corresponding to the specified SNP site from the VCF file of the individual to be identified;

[0017] Step 5: Use the pandas library in Python to randomly sample the extracted SNP site list to obtain the SNP site and its chromosome and location information;

[0018] Step 6: Filter the corresponding genotype data according to the SNP site and its chromosome and position information to obtain the final comparison VCF file of the individual to be identified;

[0019] Step 7: Compare the final comparison VCF file of the individual to be identified with the VCF file containing the above 300 SNP molecular markers. The target individual corresponding to the SNP molecular marker with the highest similarity is the identity of the individual to be identified.

[0020] Furthermore, the sequencing depth in step 1 is 2X.

[0021] Furthermore, the number of SNP sites obtained in step five is 20.

[0022] Furthermore, the comparison method in step seven is:

[0023] Step 71: Based on the VCF file containing the above 300 SNP molecular markers, an index is created by chromosome and position information and stored in a dictionary;

[0024] Step 72: Extract chromosome and position information and their corresponding genotype data from the final comparison VCF file of the individual to be identified and compare them with the 300 SNP molecular markers in the dictionary;

[0025] Step 73: During the comparison process, the variant sites of the individual to be identified are scanned one by one, and the sites that match the chromosome and position information of the 300 SNP molecular markers are found, and the genotype of the individual to be identified is extracted, and the genotype data of all 300 SNP molecular markers at the site are updated synchronously;

[0026] Step 74: Count the genotype consistency between the individual to be identified and the 300 SNP molecular markers and calculate the similarity score. The target individual corresponding to the SNP molecular marker with the highest score is the identity of the individual to be identified.

[0027] Beneficial Effects

[0028] The present invention provides a genetic marker suitable for individual identification of South China tigers, and provides an individual identification method of South China tigers based on the genetic marker, that is, the identification method is implemented based on genome screening of SNP sites.

[0029] The genetic markers described in the present invention are obtained by resequencing the genome of the South China tiger population to obtain all SNP sites in its genome, and 300 highly polymorphic SNP sites are screened out through strict quality control as the individual's 'genetic identity'.

[0030] The identification method described in the present invention is to store the above-mentioned site data in a database for individual identification. When performing individual identification, it is only necessary to perform low-depth genome sequencing of the target individual at a minimum of 2X, extract all SNP sites that have been successfully sequenced based on the location information of the 300 sites that have been stored, and select 20 sites at random from them to compare them one by one with the known individuals in the database, and confirm the true identity of the individual to be identified based on the similarity of the comparison results. Compared with the traditional STR or SNP amplification method, this method does not require the design and optimization of a large number of specific primers, significantly simplifies the operational process of molecular identification, and reduces the complexity of the experiment. Individual identification is performed through low-depth sequencing, which effectively saves economic costs. High-precision identity confirmation can be achieved by randomly selecting 20 sites from 300 sites, which greatly reduces the computational burden, while effectively reducing the impact that may be caused by data loss, and improving the adaptability and reliability of the technology.

[0031] In summary, the present invention has established a convenient, reliable and low-cost method for identifying individual South China tigers by screening and preserving high-quality SNP sites, providing strong technical support for genetic management and population protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is the frequency distribution histogram of the number of chromosome SNP sites;

[0033] Figure 2This is the distribution diagram of the number of SNP sites on each chromosome;

[0034] Figure 3 It is the coupling probability diagram of the number of SNP sites and individual identification;

[0035] Figure 4 It is a heat map of similarity of 20 SNP sites in different individuals;

[0036] Figure 5 A graph showing the number of sites identified for different individuals at different sequencing depths;

[0037] Figure 6 Similarity score graph for identifying 20 SNP sites at different sequencing depths for different individuals. DETAILED DESCRIPTION

[0038] The sources of DNA used in this experiment are shown in Table 1.

[0039] Table 1

[0040]

[0041]

[0042]

[0043] Example 1: Screening of 300 highly polymorphic SNP sites.

[0044] DNA of 93 South China tigers was extracted and then subjected to 30X whole-genome resequencing. The sequencing data were then aligned with the AmyTig1.0 reference genome (https: / / ftp.cngb.org / pub / CNSA / data1 / CNP0001654 / CNS0348048 / CNA0019679) using BWA software to obtain a BAM file. GATK 4.5 software (HaplotypeCaller module) was then used to construct the GVCF file for each individual, and all GVCF files were merged using the CombineGVCFs module to generate VCF files for 93 samples. The VCF file was quality controlled. First, VariantFiltration was used to filter out sites with a quality score (minQ) less than 30. Then, Bcftools 1.10.1 software was used to filter out sites with too high and too low sequencing depth (specifically, the top and bottom 10% of sites were removed). Subsequently, BCFtools 1.10.1 software (parameters: -m2-M2-vsnps) was used to retain the dialleles. After the data were converted into bed, bim and fam files using PLINK1.9 software, minor allele filtering was performed (parameter: --maf 0.45). PLINK1.9 software was used for linkage disequilibrium (LD) filtering (parameter: --indep-pairwise 50kb 1 0.3). Finally, Hardy-Weinberg equilibrium filtering was performed (parameter: --hwe0.001) to obtain the SNP data set after quality control. Use the Pandas library in Python to read the SNP data set, count the number of SNP sites on each chromosome after screening, and draw a histogram of the frequency distribution of the number of chromosome SNP sites, such as Figure 1 As shown. Figure 1 The ratio of the number of SNPs on each chromosome was 300 SNP sites were screened from all chromosomes. The distribution of the number of SNPs on each chromosome is shown in Figure 2 The physical positions of the selected 300 loci are determined based on the comparison results of the AmyTig1.0 South China tiger reference genome. The position and variation information of the loci are represented in the format of chromosome_physical position: reference genotype / variant allele type. The position and variation information of the 300 SNP loci are shown in Table 2.

[0045] Table 2

[0046]

[0047]

[0048] Example 2: Calculation of coupling probability of individual identification of different numbers of SNP sites.

[0049] In this embodiment, in order to reduce the computational cost, we randomly selected different numbers of SNP sites from the 300 highly polymorphic SNP sites screened out to calculate the individual coupling probability to evaluate the impact of the number of SNP sites on individual recognition. Specifically set 13 kinds of quantity gradients, respectively: 2, 5, 10, 20, 30, 40, 50, 80, 100, 150, 200, 250, 300 SNP sites. For each gradient, a corresponding number of sites are randomly selected from 300 SNP sites, and random sampling is repeated 10 times, and the individual coupling probability of each group of sites is calculated using the Match_probability.py script. First, the script traverses the genotype data of each site and each sample in the VCF file, counts the frequency of each allele, and calculates the coupling probability (i.e., the sum of the squares of the allele frequencies) according to the frequency. The script stores the results of each site (including chromosome, position, allele frequency, and coupling probability) in a list. Then, the product of the coupling probabilities of all sites is calculated to obtain the total coupling probability. The calculation results show that there is a significant negative correlation between the number of SNP sites and the probability of individual identification coupling. Figure 3 As shown in Figure 2, the coupling probability is inversely proportional to the accuracy. When 20 sites are used for individual identification, the average coupling probability is 1×10 -6 , that is, the probability of two individuals having exactly the same genotype is one in a million, which can significantly distinguish different individuals and reflect their genetic differences. Therefore, the number of SNP sites is selected as 20. Further, the inter-individual site similarity heat map calculated based on 20 SNP sites (such as Figure 4 The results of the 20 SNPs showed that all individuals were significantly different and no individual had exactly the same genotype. Therefore, the use of 20 highly polymorphic SNP sites can achieve clear and efficient individual differentiation.

[0050] Example 3: Analysis of individual identification accuracy of 20 SNP sites at different individuals and different sequencing depths.

[0051] In order to save sequencing costs and improve the accuracy of individual identification, this example verifies the impact of different sequencing depths on individual identification accuracy.

[0052] The experiment collected DNA from 7 individuals (numbered HNH_677, HNH_367, HNH_437, HNH_514, HNH_552, HNH_555 and HNH_585), and sequenced them at four sequencing depths of 1X, 2X, 3X and 6X respectively.

[0053] The experimental results show that low-depth sequencing cannot cover all 300 known SNP sites; as the sequencing depth increases, the number of SNP sites detected gradually increases. Figure 5 Although it is impossible to fully detect all 300 known SNP sites, previous studies have shown that accurate individual identification can be achieved by randomly selecting only 20 of them. Therefore, this experiment collected sequencing data of 7 individuals at different sequencing depths for further verification.

[0054] The sequencing data were processed. First, the sequencing data were aligned to the AmyTig1.0 reference genome using the BWA software to generate a BAM file. Then, the SelectVariants module of the GATK 4.5 software was used to generate a VCF file for each individual to be identified. The chromosome (CHROM) and position (POS) information were extracted from the SNP site list using the awk command, and the results were saved in a temporary file. Next, the genotype data corresponding to the specified SNP site was extracted from the VCF file of the individual to be identified using the view module in the BCFtools1.10.1 software. Then, the pandas library in python was used to randomly sample the extracted SNP site list, select 20 random sites, and record the chromosome and position information of the site. Then, according to the sampled SNP site list, the corresponding genotype data was screened out from the extracted VCF file again and saved as a new VCF file. The annotate module in the BCFtools1.10.1 software was used to clean the extracted VCF file, delete all unnecessary format fields, and finally retain only the VCF file containing the genotype data.

[0055] Next, the SNP sites in the VCF file of the individual to be identified are compared with the 300 SNP sites of the known identity individuals in Example 1. The comparison process is as follows: by parsing the VCF files of the known individual and the individual to be identified, the genotype similarity between the individual to be identified and each known individual is calculated, and a result report is generated. First, the VCF file of the known individual is read, the sample name and the corresponding genotype data are extracted, and an index is established by chromosome and position (CHROM, POS), and stored in the dictionary for quick search. Subsequently, the VCF file of the individual to be identified is read, and the chromosome and position information and the corresponding genotype data are extracted. In the comparison process, the variant sites of the individual to be identified are scanned one by one, and the sites that match the chromosome and position information of the known individual are found, and the genotype of the individual to be identified is extracted, and the genotype data of all known individuals at the site are updated synchronously. For each known individual, the genotype consistency with the individual to be identified at the comparison site is counted, and the similarity score (i.e., the proportion of the number of similar sites to the total number of comparison sites) is calculated. Finally, the similarity results are written to the output file, reporting the similarity scores between the individual to be identified and all known individuals, and judging the true identity of the individual to be identified based on the similarity scores. After a large number of verifications, the site similarity scores between the individual to be identified and the target individual are usually significantly higher than the similarity scores with other non-target individuals. Therefore, the target individual with the highest score is the true identity of the individual to be identified.

[0056] In the genotype similarity comparison phase, in order to reduce the impact of random sampling errors, this example performed 10 repeated experiments on 20 randomly selected sites from each individual at each sequencing depth. The final results are as follows: Figure 6 As shown. The analysis shows that when the sequencing depth is 1X, the site similarity scores of the individual to be identified and the target individual may overlap with the scores of the non-target individual, resulting in some individuals not being able to achieve a 100% identification success rate. For example, the identification success rate of individual HNH_367 is 80%, that is, only 8 out of 10 repeated experiments were correctly identified; while the identification success rate of individual HNH_677 is only 60%. This shows that at a 1X sequencing depth, there is a certain probability of error in individual identification due to insufficient coverage.

[0057] When the sequencing depth was increased to 2X, the similarity score between the individual to be identified and the target individual was significantly higher than that with the non-target individual, and the identification success rate of all individuals in 10 repeated experiments reached 100%. When the sequencing depth was further increased to 3X or 6X, any 20 randomly selected sites were able to achieve a 100% identification success rate. This shows that a minimum sequencing depth of 2X can meet the accuracy requirements of individual identification.

[0058] Although low-depth sequencing may affect the accuracy of genotyping and lead to some genotyping errors, we cannot guarantee that the individual genotype obtained by low-depth sequencing is completely consistent with the true genotype in the database. However, the site similarity score between the individual to be identified and the target individual is still significantly different from the similarity score between the individual to be identified and the non-target individual, which is sufficient to support the accurate judgment of the individual's identity.

[0059] In summary, this embodiment has been verified through experiments that an efficient and low-cost individual identity recognition method can be achieved at a sequencing depth of 2X using 20 randomly selected sites.

[0060] Example 4. Setting a unique “genome ID card” through SNP sites.

[0061] Use 20 SNP sites to set up a unique "genomic ID card" for each individual. The rule for setting a unique "genomic ID card" is that since SNP is usually a dimorphic site, involving only two different bases, there are only 16 possible genotypes of SNP sites in the genome, namely AA, AT, AC, AG, TA, TT, TC, TG, CC, CA, CT, CG, GA, GT, GC, GG. The rules for defining genotype encoding are shown in Table 3. Extract 20 sites from the 300 high-quality sites of each individual, encode the genotypes of the 20 sites as described above, and you can get a unique "genomic ID card" for each individual.

[0062] Table 3

[0063]

[0064]

[0065] We extracted 20 SNP sites from 7 individuals (numbered HNH_367, HNH_437, HNH_514, HNH_552, HNH_555, HNH_585, HNH_677). The individual and site information is shown in Table 4. The location and variation information of the site are represented in the format of chromosome_physical location: reference genotype / variant allele type. According to the encoding method in Table 2, a unique "genomic ID card" was generated for each individual as shown in Table 5. The encoded genomic ID card is equivalent to a unique identifier based on genetic information, which can effectively distinguish individuals, facilitate display, and hide the individual's site and genotype information.

[0066] Table 4

[0067]

[0068]

[0069] Table 5

[0070] Individual number Genome ID card HNH_367 HIIMDDJFIGFAMIPFIPMP HNH_437 FFIMDDJGFIECMGPFOMMP HNH_514 FFLPDDIFGGECAGPGOMPA HNH_552 HKLPDPIFGGFCAGDFIMPP HNH_555 PKIAAAJFGIFAPIDGIMAP HNH_585 HFPPPPAIFFAIMFAIIMMA HNH_677 PFIPPPJIFFECPGDGIMMA

Claims

1. A genetic marker, characterized in that The genetic markers include 300 SNP molecular markers, and the 300 SNP molecular markers are: Among them, the location and variation information of the site are expressed in the format of chromosome_physical location:reference genotype / variant allele type.

2. Use of the genetic marker described in claim 1 in individual identification of South China tigers.

3. A method for identifying an individual South China tiger, characterized in that: The method is: Step 1: Extract the DNA of the individual to be identified and perform genome sequencing to obtain sequencing data; Step 2: Use BWA software to align the sequencing data to the AmyTig1.0 reference genome and generate a BAM file; Step 3: Generate a VCF file for each individual to be identified using GATK 4.5 software; Step 4: Use awk command and BCFtools1.10.1 software to extract the genotype data corresponding to the specified SNP site from the VCF file of the individual to be identified; Step 5: Use the pandas library in Python to randomly sample the extracted SNP site list to obtain the SNP site and its chromosome and location information; Step 6: Filter the corresponding genotype data according to the SNP site and its chromosome and position information to obtain the final comparison VCF file of the individual to be identified; Step 7: Compare the final comparison VCF file of the individual to be identified with the VCF file containing the 300 SNP molecular markers described in claim 1, and the target individual corresponding to the SNP molecular marker with the highest similarity is the identity of the individual to be identified.

4. The method according to claim 3, characterized in that The sequencing depth in step 1 is 2X.

5. The method according to claim 3, characterized in that: The number of SNP sites obtained in step 5 is 20.

6. The method according to claim 3, characterized in that The comparison method described in step 7 is Step 71: Based on the VCF file containing the 300 SNP molecular markers of claim 1, an index is established by chromosome and position information and stored in a dictionary; Step 72: Extract chromosome and position information and their corresponding genotype data from the final comparison VCF file of the individual to be identified and compare them with the 300 SNP molecular markers in the dictionary; Step 73: During the comparison process, the variant sites of the individual to be identified are scanned one by one, and the sites that match the chromosome and position information of the 300 SNP molecular markers are found, and the genotype of the individual to be identified is extracted, and the genotype data of all 300 SNP molecular markers at the site are updated synchronously; Step 74: Count the genotype consistency between the individual to be identified and the 300 SNP molecular markers and calculate the similarity score. The target individual corresponding to the SNP molecular marker with the highest score is the identity of the individual to be identified.

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