Application of single SNP and multi-SNP marker combination in identification of seven grass carp geographical populations

Abstract

The invention relates to the technical field of grass carp geographic population identification, in particular to application of a single SNP and multi-SNP marker combination in identification of seven grass carp geographic populations. According to the application, the SNP locus genotype of the grass carp is associated with the geographic information of the grass carp, the geographic information of the grass carp is judged through the SNP locus genotype, and the geographic population of the grass carp is determined. The method provided by the invention can accurately and efficiently identify seven grass carp geographical populations. The method provided by the invention provides a new grass carp new germplasm identification technical means for the requirements of grass carp geographical population right identification, germplasm resource protection and utilization, genetic evolution research and the like.

Description

Technical Field

[0001] The present application relates to the technical field of identification of geographical populations of grass carp, and in particular to the application of a combination of single SNP and multi-SNP markers in the identification of seven geographical populations of grass carp. Background Art

[0002] Grass carp (Ctenopharyngodon idella) is farmed in 30 provinces (municipalities, autonomous regions) in my country, with the Yangtze River, Pearl River and Heilongjiang River being the main distribution areas of grass carp in China. Due to environmental selection, grass carp populations in different river sections differ in growth performance, nutritional quality and other traits, providing rich breeding materials for the creation of new grass carp germplasm. However, different grass carp geographical populations do not have typical appearance characteristics and cannot be distinguished intuitively and accurately. Without accurate geographical population information, the identification of grass carp germplasm resources and the determination of geographical population rights cannot be carried out at all, and the creation of new grass carp germplasm will also be hindered. Therefore, the development of accurate identification methods for grass carp geographical populations is of great significance to the protection and utilization of seedlings. Summary of the invention

[0003] The inventors of this application creatively used the SNP site genotype of grass carp to associate with its geographical information, judged its geographical information through the SNP site genotype, and determined its geographical group. The method provided in this application can accurately and efficiently identify 7 grass carp geographical populations. The method provided in this application provides a new technical means for identifying new grass carp germplasm for the needs of grass carp geographical population rights identification, germplasm resource protection and utilization, genetic evolution research, etc.

[0004] To this end, the embodiments of the present application disclose at least the following technical solutions:

[0005] The embodiment discloses a method for identifying a geographical population of grass carp. The method comprises: obtaining a genotype of a grass carp sample to be tested at a first SNP site; dividing the grass carp population into a first population and a second population according to the genotype of the first SNP site; obtaining a genotype of a second SNP site combination and a genotype of a third SNP site combination of the grass carp sample to be tested; identifying a North China population, a Huaihe River population, and an upper Yangtze River population from the first population according to the genotype of the second SNP site combination, and identifying a Xiangjiang River population, a middle Yangtze River population, a lower Yangtze River population, and a Pearl River population from the second population according to the genotype of the third SNP site combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 This is a Manhattan plot of the genome-wide association analysis of the first population provided in the embodiment, wherein the red line is the molecular marker screening threshold value P<10E-05.

[0007] Figure 2This is a Manhattan plot of the genome-wide association analysis of the second group provided in the embodiment, wherein the red line is the molecular marker screening threshold P<10E-08.

[0008] Figure 3 A schematic flow chart of a method for identifying geographical populations of grass carp provided in an embodiment.

[0009] Figure 4 This is a schematic diagram of the method flow of step S40 provided in an embodiment.

[0010] Figure 5 A schematic flow chart of a method for obtaining an optimal splitting point step provided in an embodiment. Figure 6 This is a schematic diagram of the method flow of step S42 provided in an embodiment. Figure 7 A schematic diagram of the training steps of the random forest learning model provided in the embodiment. DETAILED DESCRIPTION

[0011] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. The reagents not described separately in detail in the present application are all conventional reagents and can be obtained from commercial channels; the methods not described in detail are all conventional experimental methods and can be obtained from the prior art.

[0012] In order to identify the geographical population of grass carp, the embodiment of the present application uses the SNP site genotype of grass carp to associate with its geographical information, judges its geographical information through the SNP site genotype, and determines its geographical population. The method provided in the present application can accurately and efficiently identify 7 geographical populations of grass carp. The method provided in the present application provides a new technical means for identifying new germplasm of grass carp for the needs of identifying geographical population rights of grass carp, protecting and utilizing germplasm resources, and genetic evolution research.

[0013] The embodiments disclose methods for identifying geographical populations of grass carp. Figure 3 As shown, the method includes:

[0014] S10, obtaining the genotype of the grass carp sample to be tested at the first SNP site;

[0015] S20, dividing the grass carp population into a first population and a second population according to the genotype of the first SNP site;

[0016] S30, obtaining the genotype of the second SNP site combination and the genotype of the third SNP site combination of the grass carp sample to be tested;

[0017] S40. According to the genotype of the second SNP site combination, the North China population, the Huaihe River population, and the upper reaches of the Yangtze River population are identified from the first population; according to the genotype of the third SNP site combination, the Xiangjiang River population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population are identified from the second population.

[0018] In some embodiments, the first SNP site is a single nucleotide mutation formed by A>T found at position 17682028 nt of chromosome 1 of the grass carp genome.

[0019] In some embodiments, the second SNP site combination is as shown in Table 1.

[0020] Table 1 Second SNP site combination

[0021]

[0022]

[0023]

[0024]

[0025] In some embodiments, the third SNP site combination is as shown in Table 2.

[0026] Table 2 Combination of the third SNP locus

[0027]

[0028]

[0029]

[0030]

[0031] The registration accession number of the grass carp genome in the NCBI database is HZGC01, reference Wu, CS; ZYMa; GDZheng; SMZou; XJZhangY.A.Zhang Chromosome-level genome assembly ofgrass carp (Ctenopharyngodon idella) provides insights into its genomeevolution.BMC Genomics, 2022, 23, 271.10.1186 / s12864-022-08503-x.

[0032] In addition, the embodiment also discloses the development process of the first SNP site, the second SNP site combination and the third SNP site combination. Specifically including:

[0033] (1) Combining the collected grass carp samples, a total of 726 samples were constructed, which included seven grass carp geographical populations: North China, Huaihe River, Xiangjiang River, middle reaches of the Yangtze River, lower reaches of the Yangtze River, Pearl River, and upper reaches of the Yangtze River.

[0034] (2) For the collected grass carp samples, genomic DNA was extracted using the conventional CTAB method, and whole genome resequencing was performed using the DNBSEQ-T7 platform after library construction. The main band of genomic DNA gel electrophoresis showed no obvious degradation, the concentration should be greater than 70 ng / μL, the light absorption ratio at OD260 / 280 should be in the range of 1.7-2.0, and the light absorption ratio at OD260 / 230 should be in the range of 1.8-2.2. After filtering low-quality sequences, sequences containing N or adapters, the sequencing reads were aligned to the grass carp reference genome (NCBI registration number: HZGC01), and genomic variations were detected using GATK. Sites with minor allele frequencies below 0.01 and SNP sites with 20% sample deletions were filtered, and the remaining SNP molecular markers were used for subsequent association analysis.

[0035] (3) The 7 grass carp geographic populations were divided into the first population and the second population by the first SNP locus (1:17682028, A / T). The first population included the North China population, the Huaihe River population, and the upper reaches of the Yangtze River population, and the genotypes of the first SNP locus were all AA homozygous. The second population included the Xiangjiang River population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population, and the genotypes of the first SNP locus were mostly AT heterozygous.

[0036] Table 1 Genotyping statistics of the first SNP locus in seven different grass carp geographical populations

[0037] Grass carp geographic population 1:17682028(A / T) genotype Genotype ratio North China AA 157 / 157(100％) Huaihe River AA 51 / 51(100％) Upper Yangtze River AA 11 / 11(100％) Xiangjiang River AT 25 / 26(96％) Middle Yangtze River AT 194 / 207(94％) Lower Yangtze River AT 157 / 169(93％) Pearl River AT 24 / 28(86％)

[0038] (4) Each grass carp geographic population was distinguished from the first group of AA genotype and the second group of AT genotype, and the geographic information of each sample in the two groups was converted into a continuous natural number as the phenotype. The genetic loci after quality control filtering were used as genotypes, and the mixed linear model of GEMMA was used for genome-wide association analysis. Figure 1 As shown in the figure, the SNP marker screening threshold of the genome-wide association analysis of the geographical code values ​​of each grass carp individual in the first population (North China, Huaihe River, and the upper reaches of the Yangtze River are coded as natural numbers 1, 2, and 3, respectively) is P<10E-05, and 158 SNP markers are obtained. Figure 2As shown in the figure, the threshold value of the genome-wide association analysis of significant SNP markers for the second population (Xiangjiang River, middle reaches of the Yangtze River, lower reaches of the Yangtze River, and Pearl River are coded as natural numbers 1, 2, 3, and 4, respectively) was P < 10E-08, and 150 SNP markers were obtained. The different natural numbers converted from the geographic information of the two major populations are independent of each other.

[0039] In some embodiments, Figure 4 As shown, step S20 includes: S21. If the grass carp sample to be tested is of AA genotype at position 17682028 nt of chromosome 1, the grass carp sample to be tested belongs to the first population.

[0040] In some embodiments, Figure 4 As shown, step S20 also includes: S22, if the grass carp sample to be tested has an AT genotype at position 17682028 nt of chromosome 1, then the grass carp sample to be tested belongs to the second population.

[0041] In some embodiments, Figure 4 As shown, step S30 includes: S31, obtaining the genotype of the second SNP site combination of the grass carp sample to be tested.

[0042] In some embodiments, Figure 4 As shown, step S30 includes: S32, obtaining the genotype of the third SNP site combination of the grass carp sample to be tested.

[0043] In some embodiments, Figure 4 As shown, step S40 includes: S41, identifying the North China population, the Huaihe River population, and the upper reaches of the Yangtze River population from the first population based on the genotype of the second SNP site combination.

[0044] In some embodiments, Figure 4 As shown, step S40 includes: S42, identifying the Xiangjiang population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population from the second population based on the genotype of the third SNP site combination.

[0045] In some embodiments, Figure 5 As shown, step S41 specifically includes:

[0046] S411, obtaining a first training set of the first population, wherein the first training set includes second SNP site combination genotype information and a first geographic code value of a plurality of grass carp samples from the first population;

[0047] S412, performing uniform sampling with replacement (bootstrap sampling) from the first training set to construct a plurality of first sub-datasets, configuring a decision tree for each of the first sub-datasets, and constructing a random forest learning model from the decision tree;

[0048] S413, obtaining the second SNP site combination genotype information and the first geocode value of each of the first sub-datasets to train the random forest learning model, so as to obtain the optimal splitting point of the multiple feature splits of each of the decision trees, and obtain the trained random forest learning model;

[0049] S414, obtaining the second SNP site combination genotype information of the grass carp sample to be tested;

[0050] S415, inputting the second SNP site combination genotype information into the trained random forest learning model to obtain a first prediction value of the grass carp sample to be tested;

[0051] S416. Identify the geographical information of the grass carp population to be tested in the first population according to the first prediction value.

[0052] In some embodiments, in step S411, a total of 220 grass carp samples are obtained in the first training set.

[0053] In some embodiments, the second SNP site combined genotype information is the record information of the genotype of each SNP site as shown in Table 1. For example, for the G>A mutation, the homozygous genotype such as GG is recorded as "1", the homozygous mutant genotype such as AA is recorded as "-1", and the heterozygous genotype such as GA is recorded as "0".

[0054] In some embodiments, the first geocoding value is a natural number of geocoding values ​​corresponding to three grass carp geographical populations in North China, Huaihe River, and the upper reaches of the Yangtze River: 1, 2, 3.

[0055] In step S413 provided in some embodiments, the second SNP site combined genotype information is used as the input of each decision tree, and the first geocoding value is used as the output of each decision tree, so as to train each decision tree.

[0056] In step S416 provided in some embodiments, the first prediction value is a value that is relatively close to any of the first geocoding values. The higher the degree of closeness, the more it represents that the grass carp sample comes from a geographical location in North China, Huaihe River and the upper reaches of the Yangtze River in the first group.

[0057] In some embodiments, Figure 6 As shown, step S42 specifically includes:

[0058] S421, obtaining a second training set of the second population, wherein the second training set includes third SNP site combination genotype information and second geocoding values ​​of a plurality of grass carp samples from the second population;

[0059] S422, constructing a plurality of second sub-datasets by uniform sampling with replacement (bootstrap sampling) from the second training set, configuring a decision tree for each of the second sub-datasets, and constructing a random forest learning model by the decision tree;

[0060] S423, obtaining the third SNP site combination genotype information and the second geocode value of each of the second sub-datasets to train the random forest learning model, so as to obtain the optimal splitting point of the multiple feature splits of each of the decision trees, and obtain the trained random forest learning model;

[0061] S424, obtaining the third SNP site combination genotype information of the grass carp sample to be tested;

[0062] S425, inputting the third SNP site combined genotype information into the trained random forest learning model to obtain a second predicted value of the grass carp sample to be tested;

[0063] S426. Identify the geographical information of the grass carp population to be tested in the second population according to the second prediction value.

[0064] In some embodiments, in step S411, a total of 506 grass carp samples are obtained in the first training set.

[0065] In some embodiments, the third SNP site combined genotype information is the record information of the genotype of each SNP site as shown in Table 2. For example, for the G>A mutation, the homozygous genotype such as GG is recorded as "1", the homozygous mutant genotype such as AA is recorded as "-1", and the heterozygous genotype such as GA is recorded as "0".

[0066] In some embodiments, the second geocoding value is a natural number of geocoding values ​​corresponding to the Xiangjiang River group, the middle reaches of the Yangtze River group, the lower reaches of the Yangtze River group, and the Pearl River group: 1, 2, 3, 4.

[0067] In step S423 provided in some embodiments, the third SNP site combined genotype information is used as the input of each decision tree, and the second geocoding value is used as the output of each decision tree, so as to train each decision tree.

[0068] In step S426 provided in some embodiments, the first prediction value is a value that is relatively close to any of the first geocoding values. The higher the degree of closeness, the more it represents that the grass carp sample comes from a geographical location in the Xiangjiang River, the middle reaches of the Yangtze River, the lower reaches of the Yangtze River and the Pearl River in the first group.

[0069] In some embodiments, the random forest model consists of multiple decision trees (n t ), each decision tree (T b ) are trained independently. For a given input sample x, each tree will give a corresponding prediction value

[0070] In some embodiments, such as Figure 7 As shown, the training steps of the random forest learning model include:

[0071] S501, randomly obtain any sub-data set from the training set, construct a decision tree, and for each node of the decision tree, randomly select one or more features from the sub-data set for splitting;

[0072] S502, obtaining a first mean square error before splitting each feature at each node of each decision tree and a second mean square error after splitting each feature;

[0073] S503, taking the difference between the first mean square error and the second mean square error as a gain;

[0074] S504, taking the detected splitting point with the maximum gain number as the best splitting point, and configuring the corresponding splitting feature and splitting threshold at the best splitting point;

[0075] S505, repeating steps S501 to S504, respectively training each decision tree of the random forest model to obtain the best splitting point of each decision tree, and configuring the trained splitting feature and splitting threshold for each best splitting point;

[0076] S506: Determine the training degree of the random forest learning model according to the training error of the random forest learning model.

[0077] Steps S501 to S506 are applicable to the training process of the random forest learning model in step S41 and step S42, and will not be described in detail here.

[0078] In step S415 provided in some embodiments, the recorded value of the second SNP site combined genotype information of the grass carp sample to be tested is compared with the split threshold. When the recorded value of the second SNP site combined genotype information of the sample to be tested is less than or equal to the split threshold, the left child node is used to continue searching. When it is greater than the split threshold, the right child node is used to continue searching until the leaf node is reached. When the split threshold is "NA", the split threshold of the corresponding node is directly read as the predicted value.

[0079] In step S425 provided in some embodiments, the recorded value of the third SNP site combined genotype information of the grass carp sample to be tested is compared with the split threshold. When the recorded value of the third SNP site combined genotype information of the sample to be tested is less than or equal to the split threshold, the left child node is used to continue searching. When it is greater than the split threshold, the right child node is used to continue searching until the leaf node is reached. When the split threshold is "NA", the split threshold of the corresponding node is directly read as the predicted value.

[0080] Among them, the first mean square error can be expressed as: Among them, n t is the number of samples in the decision tree node t, y i is the target value of the i-th sample, is the average value of all sample targets in decision tree node t.

[0081] Among them, the second mean square error can be expressed as: Among them, n tL and n tR They are decision tree nodes tL and t Number of samples, MSE in R tL and MSE tR is the mean square error of the nodes.

[0082] The gain can be expressed as: Gain = MSE t -MSE t,split

[0083] Among them, the error of the trained random forest learning model can be expressed as Where n is the number of samples, and yi is the actual observed value of the i-th sample; is the predicted value of the i-th sample. The smaller the RMSE value, the higher the prediction accuracy of the model.

[0084] Table 2 shows the prediction results of the first and second groups of grass carp by the method provided in this application. Among them, the accuracy of 158 SNP markers and the corresponding random forest machine learning model in the first group predicting the corresponding geographic information of the North China group, the Huaihe River group, and the upper reaches of the Yangtze River group is 0.842; the accuracy of 150 SNP markers and the corresponding random forest machine learning model in the second group predicting the corresponding geographic information of the Xiangjiang River group, the middle reaches of the Yangtze River group, the lower reaches of the Yangtze River group, and the Pearl River group is 0.9327.

[0085] Table 2

[0086] Classification based on single marker genotype The first group Second group SNP markers 158 second SNP loci combination 150 third SNP loci combination Grass carp geographic population North China, Huaihe River, Upper Yangtze River Xiangjiang River, middle reaches of Yangtze River, lower reaches of Yangtze River, Pearl River Prediction Accuracy 0.842 0.9327 Standard deviation of forecast accuracy 0.0058 0.0018 Root mean square error 0.0001 0.0069

[0087] In some embodiments, 30 grass carp samples with 3 AA homozygous types (grass carp geographical populations in North China, Huaihe River, and the upper reaches of the Yangtze River) and 30 grass carp samples with 4 AT heterozygous types (grass carp geographical populations in Xiangjiang River, the middle reaches of the Yangtze River, the lower reaches of the Yangtze River, and the Pearl River) were collected as independent test sets.

[0088] The genomic DNA was extracted by conventional CTAB method, and the whole genome resequencing was carried out by DNBSEQ-T7 platform of Huazhi Biotechnology Co., Ltd., and the sequencing amount of each sample was at least 10 times the genome coverage. For the three grass carp geographic populations of North China, Huaihe River, and the upper reaches of the Yangtze River with AA homozygous type, the genotyping detection of 158 SNP markers in Example 1 was carried out; for the Xiangjiang population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population with AT heterozygous type, the genotyping detection of 150 SNP markers in Example 1 was carried out. The genotypes obtained from each sample were directly introduced into the random forest regression model constructed in Example 1, and the predicted value was calculated according to the relationship between the classification value of the classification variable and the sample genotype coding value, and the geographical information of the test sample was identified.

[0089] Table 3 Identification results of the second SNP locus combination in three grass carp geographical populations in North China, Huaihe River and the upper reaches of the Yangtze River

[0090]

[0091]

[0092] Table 4 Identification results of the third SNP locus combination in the Xiangjiang population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population

[0093] Sample No. Recording geographic information Second geocode value Second prediction value Identification results XB-2342 Xiangjiang River 1 1.464 correct XB-2359 Xiangjiang River 1 1.39 correct XBR-10 Xiangjiang River 1 1.309 correct XBR-12 Xiangjiang River 1 1.092 correct XF2332 Xiangjiang River 1 1.574 mistake XM-12367 Xiangjiang River 1 1.551 mistake XM-22376 Xiangjiang River 1 1.291 correct 10121 Middle Yangtze River 2 2 correct 10148 Middle Yangtze River 2 2 correct 10173 Middle Yangtze River 2 2 correct 10193 Middle Yangtze River 2 2.008 correct CAS-13 Middle Yangtze River 2 2.031 correct CAS-38 Middle Yangtze River 2 2 correct CBR-20 Middle Yangtze River 2 1.992 correct CCS-29 Middle Yangtze River 2 2.068 correct 20231106-106 Lower Yangtze River 3 2.985 correct 20231106-118 Lower Yangtze River 3 2.99 correct 20231106-13 Lower Yangtze River 3 2.992 correct 20231106-159 Lower Yangtze River 3 2.975 correct 20231106-168 Lower Yangtze River 3 2.997 correct 20231106-176 Lower Yangtze River 3 2.951 correct 20231106-185 Lower Yangtze River 3 2.964 correct 20231106-194 Lower Yangtze River 3 2.993 correct ZhJ01 Pearl River 4 3.869 correct ZhJ03 Pearl River 4 3.965 correct ZhJ05 Pearl River 4 3.875 correct ZhJ Pearl River 4 3.824 correct ZhJ09 Pearl River 4 3.976 correct ZhJ11 Pearl River 4 3.909 correct ZhJ Pearl River 4 3.922 correct

[0094] The method for identifying a grass carp geographic population provided in the embodiment of the present application can be performed by a device for identifying a grass carp geographic population, or a control unit in the device for identifying a grass carp geographic population for performing the method for identifying a grass carp geographic population. In the embodiment of the present application, the device for identifying a grass carp geographic population performing the method for identifying a grass carp geographic population is taken as an example to illustrate the device for identifying a grass carp geographic population provided in the embodiment of the present application.

[0095] Optionally, an embodiment of the present application also provides an electronic device, including a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, each process of the above-mentioned method embodiment for identifying the geographical population of grass carp is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0096] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0097] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned method embodiment for identifying the geographical population of grass carp is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0098] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0099] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned method embodiment for identifying the geographical population of grass carp, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0100] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. Methods for identifying geographical populations of grass carp, including: Obtaining the genotype of the grass carp sample to be tested at the first SNP site; Dividing the grass carp population into a first population and a second population according to the genotype of the first SNP site; Obtaining the genotype of the second SNP site combination and the genotype of the third SNP site combination of the grass carp sample to be tested; According to the genotype of the second SNP site combination, the North China population, Huaihe River population, and upper Yangtze River population are identified from the first population, and according to the genotype of the third SNP site combination, the Xiangjiang River population, middle Yangtze River population, lower Yangtze River population, and Pearl River population are identified from the second population.

2. According to the method of claim 1, the first SNP site is a single nucleotide mutation formed by A>T found at the 17682028nt position of chromosome 1 of the grass carp genome, the SNP sites in the second SNP site combination are shown in Table 1, and the third SNP site combination is shown in Table 2.

3. The method according to claim 1, further comprising: If the grass carp sample to be tested is found to have an AA genotype at position 17682028 nt of chromosome 1, the grass carp sample to be tested is a first population; Obtaining the genotype of the second SNP site combination of the grass carp sample to be tested, the SNP sites in the second SNP site combination are shown in Table 1; The North China population, the Huaihe River population, and the upper Yangtze River population are identified from the first population based on the genotype of the second SNP site combination.

4. The method according to claim 3, "identifying the North China population, the Huaihe population, and the upper Yangtze River population from the first population according to the genotype of the second SNP locus combination" comprises: Obtaining a first training set of the first population, wherein the first training set includes second SNP site combination genotype information and a first geocoding value of a plurality of grass carp samples from the first population; Uniform sampling with replacement is performed from the second training set to construct multiple first sub-datasets, and a decision tree is configured for each of the first sub-datasets, and a random forest learning model is constructed by the decision tree Obtaining the second SNP site combination genotype information and the first geocode value of each of the first sub-datasets to train the random forest learning model, so as to obtain the optimal splitting point of the multiple feature splits of each of the decision trees, and obtain a trained random forest learning model; Obtaining the second SNP site combination genotype information of the grass carp sample to be tested; Inputting the second SNP site combined genotype information into the trained random forest learning model to obtain a first predicted value of the grass carp sample to be tested; The geographical information of the grass carp sample to be tested in the first population is identified according to the first prediction value.

5. According to the method of claim 3, the second SNP site combined genotype information is the record information of the genotype of each SNP site as shown in Table 1, the homozygous genotype information is recorded as "1", the homozygous mutant genotype information is recorded as "-1", and the heterozygous genotype information is recorded as "0".

6. The method according to claim 3, wherein the first geocoding value is a natural number of geocoding values ​​corresponding to three grass carp geographical groups in North China, Huaihe River, and upper reaches of the Yangtze River: 1, 2, 3.

7. The method according to claim 1, further comprising: If it is detected that the grass carp sample to be tested has an AT genotype at position 17682028 nt of chromosome 1, the grass carp sample to be tested belongs to the second population; Obtaining the genotype of the third SNP site combination of the grass carp sample to be tested, the SNP sites in the third SNP site combination are shown in Table 2; The Xiangjiang population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population are identified from the second population based on the genotype of the third SNP site combination.

8. The method according to claim 7, "identifying the Xiangjiang population, the middle reaches of the Yangtze River population, the lower reaches of the Yangtze River population, and the Pearl River population from the second population according to the genotype of the third SNP locus combination" specifically comprises: Obtaining a second training set of the second population, wherein the second training set includes third SNP site combination genotype information and second geocoding values ​​of a plurality of grass carp samples from the second population; Uniformly sampling with replacement from the second training set to construct a plurality of second sub-datasets, configuring a decision tree for each of the second sub-datasets, and constructing a random forest learning model from the decision tree; Obtaining the third SNP site combination genotype information and the second geocode value of each of the second sub-datasets to train the random forest learning model, so as to obtain the optimal splitting point of the multiple feature splits of each of the decision trees, and obtain a trained random forest learning model; Obtaining the third SNP site combination genotype information of the grass carp sample to be tested; Inputting the third SNP site combined genotype information into the trained random forest learning model to obtain a second predicted value of the grass carp sample to be tested; The geographical information of the grass carp sample to be tested in the second population is identified according to the second prediction value.

9. According to the method of claim 8, the third SNP site combined genotype information is the record information of the genotype of each SNP site as shown in Table 2, the homozygous genotype information is recorded as "1", the homozygous mutant genotype information is recorded as "-1", and the heterozygous genotype information is recorded as "0".

10. According to the method described in claim 8, the second geocoding value is the natural number of the geocoding values ​​corresponding to the Xiangjiang River group, the middle reaches of the Yangtze River group, the lower reaches of the Yangtze River group, and the Pearl River group: 1, 2, 3, 4.

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