SNP molecular marker for identifying germplasm resources of macrobrachium rosenbergii and application of SNP molecular marker

Abstract

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker for identifying germplasm resources of macrobrachium rosenbergii and application of the SNP molecular marker, the SNP molecular marker combination is selected from 40 SNP molecular markers, and 40 primer pairs are developed on the basis of the 40 SNP molecular markers; a random forest classification model constructed on the basis of the 40 SNP loci screened by the invention achieves an accurate identification rate of 90% or more in four varieties / lines of Buddhida, XinMyanmar, Thailand and Huanfeng No.1 macrobrachium rosenbergii, the problem that variety / line identification is difficult to carry out in production practice is solved, and a powerful technical support is provided for subsequent germplasm identification and management. According to the invention, 40 primer pairs or detection kits are developed, so that germplasm identification of macrobrachium rosenbergii can be quickly completed at low cost; the problem that variety / line identification is difficult to carry out in production practice is solved, and powerful technical support is provided for follow-up germplasm identification and management.

Description

Technical Field

[0001] The invention relates to the technical field of Macrobrachium rosenbergii molecular markers, and in particular to a SNP molecular marker for identifying Macrobrachium rosenbergii germplasm resources and an application thereof. Background Art

[0002] Macrobrachium rosenbergii belongs to the class Crustacea, order Decapoda, family Macrobrachium, genus Macrobrachium. As one of the important freshwater economic shrimps in my country, it has the characteristics of fast growth, strong adaptability, and high nutritional value, and is very popular among consumers. In terms of breeding, Macrobrachium rosenbergii has a short growth cycle and fast growth rate, and has broad breeding prospects. It is currently widely farmed in provinces such as Guangdong, Zhejiang, and Jiangsu. As of 2022, my country's breeding output has reached nearly 180,000 tons, accounting for more than half of the global breeding output. At present, there are many varieties / lines of Macrobrachium rosenbergii that are mainly cultivated in my country, and most of them are derived strains. These different varieties / lines of Macrobrachium rosenbergii do not have typical appearance characteristics, so they cannot be accurately distinguished in appearance. Without accurate variety information, the identification of Macrobrachium rosenbergii germplasm resources and the recognition of variety rights cannot be carried out, and the creation of new varieties of Macrobrachium rosenbergii will inevitably be affected. Therefore, it is particularly important to establish an efficient Macrobrachium rosenbergii germplasm resource identification platform.

[0003] With the continuous development of genotyping technology, molecular marker-assisted breeding has been increasingly applied to genetic breeding of aquatic animals. Among them, SNP molecular markers have gradually replaced other molecular markers with their high throughput, high stability, high compatibility and low cost, and have become the main molecular markers currently used. For example, Zhang et al. established an identification platform based on 26 SNP loci to distinguish the populations of mantis shrimp in the South China Sea and Bohai Sea. Wang Yanyun obtained a marker combination consisting of 27 SNP loci through screening, which can meet the identification of more than 80% of the main production areas of Yellow River carp in my country. Wang Qi et al. obtained a marker combination consisting of 144 SNP loci through screening, which can be used to distinguish 16 carp varieties including Yellow River carp, super carp and Fu'an carp, providing an effective technical means for the identification of carp variety rights and the protection of germplasm resources. Therefore, the application of molecular markers to the genetic background investigation of Macrobrachium rosenbergii is expected to identify different strains at the molecular level, which is helpful for the authenticity and purity identification of varieties.

[0004] In summary, there is an urgent need to establish a reliable and efficient SNP molecular marker-based DNA fingerprint of Macrobrachium rosenbergii, so that it can play a more important role in variety breeding, germplasm protection, anti-counterfeiting, variety identification, etc., thereby promoting the rapid development of my country's high-quality Macrobrachium rosenbergii industry. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an application of a whole-genome SNP molecular marker combination in the identification of Macrobrachium rosenbergii varieties / lines. The present invention constructs a random forest classifier based on a combination of 40 SNP molecular markers through genotyping and core site screening, which can be used for the identification of Macrobrachium rosenbergii varieties / lines in Florida, New Myanmar, Thailand and "Shufeng No. 1".

[0006] To achieve the above purpose, the technical solution designed by the present invention is as follows:

[0007] The present invention provides a SNP molecular marker combination for identifying Macrobrachium rosenbergii germplasm resources, wherein the SNP molecular marker combination is selected from 40 SNP molecular markers, and in the SNP molecular marker combination, the physical position information and nucleotide sequence of the SNP molecular markers are shown in Table 1 below.

[0008] The present invention also provides an application of the above SNP locus combination in the identification of Macrobrachium rosenbergii varieties / lines

[0009] The present invention also provides a primer combination for amplifying the above-mentioned SNP molecular marker using PCR technology, wherein the primer combination includes at least one pair of 40 pairs of primers, wherein the nucleotide sequences of the 40 pairs of primers are shown in Table 5 below.

[0010] The present invention also provides an application of the above primer combination in preparing a detection kit.

[0011] The present invention also provides a detection kit, characterized in that: the detection kit comprises the above-mentioned primer combination.

[0012] The present invention also provides an application of the primer combination or the detection kit in identifying species / lines of Macrobrachium rosenbergii.

[0013] Beneficial effects of the present invention:

[0014] 1) The 40 SNP sites provided by the present invention were screened out from the whole genome resequencing data of 203 samples of Macrobrachium rosenbergii from 10 populations, with wide population coverage and representativeness.

[0015] 2) Compared with the method of germplasm identification based on second-generation sequencing data, the present invention utilizes fixed and limited SNP sites to quickly and cost-effectively complete the germplasm identification of Macrobrachium rosenbergii.

[0016] 3) The random forest classification model constructed based on the 40 SNP sites screened by the present invention achieved an accurate identification rate of more than 90% in four varieties / lines of Florida, New Myanmar, Thailand and "Shufeng No. 1" Macrobrachium rosenbergii, solving the problem of difficulty in variety identification in production practice and providing strong technical support for subsequent germplasm identification and management.

[0017] 4) The present invention develops 40 pairs of primers or detection kits that can quickly and inexpensively complete the identification of Macrobrachium rosenbergii germplasm; solves the problem of difficulty in carrying out variety identification in production practice, and provides strong technical support for subsequent germplasm identification and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the simulated identification rates of different SNP site combinations in Example 1;

[0019] Figure 2 Schematic diagram of the cross-validation curve based on different SNP site combinations in Example 1;

[0020] Figure 3 is a graph showing the relationship between the model error and the number of features and the number of decision trees in Example 1;

[0021] Figure 4 This is a diagram showing the results of the population structure analysis based on 40 SNP sites in Example 2. Specific embodiments

[0022] Example 1 SNP site combination screening for identification of Macrobrachium rosenbergii germplasm resources

[0023] 1. Genome resequencing and SNP site screening

[0024] 1. Sample Collection and DNA Extraction

[0025] Tentacle tissues of 203 Macrobrachium rosenbergii from 10 cultured populations in Florida (FD, n=20), Guangxi (GX, n=20), Hefu (HF, n=19), Myanmar (MD, n=20), New Myanmar (NMD, n=27), "Nan Taihu No. 2" (NT, n=20), Thailand (TG, n=19), Taiwan (TW, n=20), "Shufeng No. 1" (SF, n=18), and Zhengda (ZD, n=20) were collected and stored in 95% ethanol. Genomic DNA was extracted using a DNA extraction kit (Aidlab, Beijing). The quality and integrity of the DNA samples were determined using a NanoDrop2000 spectrophotometer (Thermo Scientific, USA) and 1% agarose gel electrophoresis.

[0026] 2. Library Construction and Genome Resequencing

[0027] The genomic DNA samples were digested with restriction endonucleases and the sequencing library was constructed using a paired-end DNA sample preparation kit (Illumina Inc., San Diego, CA, USA). The constructed library was sequenced using the Illumina NovaSeq6000 platform (Illumina, San Diego, CA) to obtain 150 bp (PE150) paired-end read data.

[0028] 3. Data filtering and comparison

[0029] The raw data were filtered using fastp (v 0.20.0) to remove adapter sequences, low-quality sequences, and sequences that were too short, and a total of 6,430,808,165 clean reads were obtained. The quality-controlled data were then aligned to the Macrobrachium rosenbergii reference genome using BWA-MEM (v 0.7.1563) software, and the deduplicated BAM files were output and indexed using smtools (v 1.10) software.

[0030] 4.SNP site search and filtering

[0031] FreeBayes (v 1.2.0) and vcflib software were used for mutation detection and site screening, respectively, and a total of 704,709 SNPs were obtained. VCFtools (v 0.1.13) software was used to perform further quality control on the obtained SNP sites with the following parameters: (1) --max-missing 0.8; (2) --maf0.05; (3) --min-alleles 2; (4) --max-alleles 2, and 121,971 SNP sites were screened for subsequent analysis. Plink (v1.90) software was used to eliminate SNP sites with a missing rate greater than 5%, a minimum allele frequency less than 0.2, and a polymorphism information content less than 0.3, and finally 6,178 SNP sites were obtained for subsequent core site screening.

[0032] 2. SNP site combination screening

[0033] 1. Screening of core SNP combinations and construction of random forest classifier

[0034] Since there is almost no genetic differentiation among the artificially bred Macrobrachium rosenbergii populations in my country, the "Shufeng No. 1" Macrobrachium rosenbergii variety was selected as the representative of the artificially bred population, and together with the Florida, New Myanmar, and Thailand Macrobrachium rosenbergii populations introduced in recent years, they were used as target populations for SNP site combination screening and random forest classifier construction. The specific steps are as follows:

[0035] (1) Genotype coding

[0036] A total of 84 individuals from four populations were genetically encoded for 6,178 SNP loci obtained after quality control, among which the categorical variable 0 / 0 of the homozygous genotype identical to the reference Macrobrachium rosenbergii genome was recorded as 0, the categorical variable 0 / 1 was recorded as 1, the categorical variable 1 / 0 was recorded as 2, and the categorical variable 1 / 1 was recorded as 3.

[0037] (2) Simulated classification analysis of different site combinations based on the random forest model

[0038] The R script was used to hierarchically select site combinations from SNP sites according to the conditional random selection (CRS) algorithm. The preliminary screening results showed that 20 core sites were sufficient for identification and analysis of the four target groups. Subsequently, the 20 core SNP sites were used to construct a decision tree using the random forest algorithm, and the randomly classified training set (accounting for 0.75 of the total data) was used for identification. The results showed that the identification rate between groups was 69.84% (Table 1), among which the simulated classification of the Thai population in the training set would be misclassified as the Florida population. Therefore, the CRS algorithm was used to increase the core SNP sites to improve the identification rate. When the number of sites was less than 100, 5 sites were added each time. When the number of sites exceeded 100, 20 sites were added each time to observe the changing trend of the identification rate with the increase of core SNP sites. The results are shown in the figure below. Figure 1 As shown in Table 2, when the number of sites increased to 40, the simulation identification result reached the highest value of 79.37%. When the number of sites continued to increase, the identification rate did not increase, but showed a downward trend.

[0039] Table 1 Simulated classification of 20 core SNP loci combinations for 4 population training sets

[0040]

[0041] Table 2 Simulated classification of 40 core SNP loci combinations for 4 population training sets

[0042]

[0043]

[0044] In order to finally determine the number of SNP sites and site information to be used, we used two important indicators in the random forest model, "Mean Decrease Accuracy" and "Mean Decrease Gini", to sort the 100 sites screened by the CRS algorithm from high to low, and then performed 5 repeated ten-fold cross validations for different numbers of sites. The cross validation curve results show that the model error will first decrease with the increase of SNP sites, and the decrease is large at the beginning, but after reaching a certain range, the curve shows an upward trend first and then a downward trend, and the decline gradually decreases ( Figure 2 A). According to the cross-validation curve results, the model error is kept within 0.1 when the number of SNP sites is in the range of 25-100. Adhering to the principle of parsimony, we selected the top 40 variables for cross-validation again. The results show that when the number of SNPs is 40, the model error is the smallest ( Figure 2 B).

[0045] 2. Optimization of Random Forest Model

[0046] After determining to select 40 core SNP sites, we further reduced the model misjudgment rate by adjusting two important parameters ntree and mtry in the randomForest() function. ntree is the number of base classifiers included, which defaults to 500; mtry is the number of variables included in each decision tree, which defaults to logN (N is the number of objects). The results show that when mtry is selected as 30, rate is the minimum value, indicating that mtry is equal to 30 is the optimal solution ( Figure 3 A). We use the optimal solution of mtry to simulate the optimal number of base categories of the decision tree. The results show that when the number of trees exceeds 270, the model error rate always remains at the lowest value (error<0.2) ( Figure 3 B). Considering the generalization ability of the model and the risk of overfitting, we finally chose the number of trees to be 500. After optimization, the out-of-bag error (OOB error) of the model dropped from 22.22% to 19.05%. Finally, the top 40 SNP loci ranked by "Mean Decrease Accuracy" were determined as the core SNP combination to distinguish between Florida, New Myanmar, Thailand, and "Shufeng No. 1" Macrobrachium rosenbergii populations, and an optimized random forest classifier was constructed on this basis. The detailed information of the 40 SNP loci is shown in Table 3.

[0047] Table 3 Location information of 40 SNP loci

[0048]

[0049]

[0050]

[0051]

[0052] 3. Actual evaluation of identification effect

[0053] In order to evaluate the identification ability of the random forest classifier constructed based on 40 core SNP loci, we conducted germplasm identification on 84 individuals of Macrobrachium rosenbergii from Florida, New Myanmar, Thailand, and "Shufeng No. 1". The genotype data of these 84 individuals at 40 SNP loci were directly imported into the constructed random forest classification model to identify the variety / line of the test samples, and the identification rate was calculated based on the classification prediction results and true values ​​of each sample. The results showed that there were 83 samples that could correctly identify the variety / line, accounting for about 98.81% (Table 4), and the classification error came from the samples of the TG population being misclassified as the FD population.

[0054] Table 4 Identification analysis of 40 core SNP loci for 84 individuals in 4 populations

[0055]

[0056] Example 2 Genotyping and germplasm identification based on PCR technology

[0057] According to the 40 core SNP sites in Example 1, the genotype of Macrobrachium rosenbergii was detected and the germplasm was identified in combination with PCR technology. The specific method is as follows:

[0058] 1. PCR amplification primers were designed according to the flanking sequences of the 40 SNP sites. The detailed information of the primers is shown in Table 5.

[0059] Table 5 Amplification primer information of 40 SNP loci

[0060]

[0061]

[0062]

[0063] 2. Collect muscle samples of Macrobrachium rosenbergii, including 7 from the New Myanmar group, 4 from the "Shufeng No. 1" group, 5 from the Thailand group, and 5 from the Florida group, extract genomic DNA, perform PCR amplification reaction, and directly sequence the amplified products to obtain the genotype data of each individual. The genotype data of the four groups are shown in the following table:

[0064] Table 6 PCR detection results of 40 SNP sites in 4 populations

[0065]

[0066]

[0067]

[0068] 3. The typing data was imported into the optimized random forest classifier in Example 1. The results showed that among the 21 samples from the four populations, 20 samples were able to correctly identify the varieties / lines, accounting for about 95.24% (Table 4). Only one sample had a classification prediction error, and the main classification error came from the TG population being easily misjudged as the FD population.

[0069] 4. Phylogenetic tree construction and principal component analysis (PCA) analysis were performed using the typing data of 21 individuals. The phylogenetic tree showed that the new Burmese population and the "Shufeng 1" population were clustered into one branch, while the Thai population and Florida population were clustered into one branch ( Figure 4 A), PCA analysis results showed similar results ( Figure 4 B), Overall, the results of population structure analysis are consistent with the classification results of the random forest model.

[0070] Although the above embodiments have been described in detail, they are only a part of the embodiments of the present invention, not all of them. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all belong to the protection scope of the present invention.

Claims

1. A SNP molecular marker combination for identifying Macrobrachium rosenbergii germplasm resources, characterized in that: The SNP molecular marker combination is selected from 40 SNP molecular markers. In the SNP molecular marker combination, the physical location information and nucleotide sequence of the SNP molecular markers are as follows:

2. An application of the SNP locus combination according to claim 1 in the identification of Macrobrachium rosenbergii varieties / lines.

3. A primer combination for amplifying the SNP molecular marker according to claim 1 using PCR technology, characterized in that: The primer combination includes at least one of the 40 primer pairs, wherein the nucleotide sequences of the 40 primer pairs are as follows:

4. Use of the primer combination according to claim 3 in preparing a detection kit.

5. A detection kit, characterized in that: The detection kit comprises the primer combination according to claim 3.

6. Use of the primer combination according to claim 3 or the detection kit according to claim 5 in identifying species / lines of Macrobrachium rosenbergii.

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