A SNP site combination for rapid identification of chinese merino sheep

Abstract

This invention belongs to the field of biodetection technology and discloses a combination of SNP loci for rapid identification of Chinese Merino sheep. This invention provides a combination of 10 SNP loci for rapid identification of Chinese Merino sheep, exhibiting high specificity and high predictive accuracy, thereby enabling breed identification of Chinese Merino sheep. By integrating molecular marker technology and bioinformatics analysis, this invention simplifies the identification process, improves identification efficiency and reliability, and lays a solid foundation for the effective protection, scientific management, and rational utilization of Chinese Merino sheep genetic resources.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology and discloses a combination of SNP sites for rapid identification of Chinese Merino sheep. Background Technology

[0002] The Chinese Merino sheep, as one of the important livestock breeds, is widely praised for its excellent meat quality, high wool yield, and good adaptability. However, with the rapid development of animal husbandry and the diversification of market demands, the requirements for breed purity are increasing. Traditional breed identification methods, such as morphological observation and production performance assessment, while effective to some extent, are easily affected by external factors such as environment and feeding management, and are time-consuming, making it difficult to meet the needs of the rapid development of modern animal husbandry. In recent years, molecular marker technology, especially SNP (single nucleotide polymorphism) markers, has shown great potential in animal breed identification. As the most common type of genetic variation in the genome, SNPs have advantages such as wide distribution, high stability, and simple detection, and have become an important tool for breed identification and genetic resource assessment. Developing a rapid identification system based on SNP loci for the Chinese Merino sheep is of great significance for improving breed purity, protecting genetic diversity, and promoting breeding improvement. However, there is currently a lack of SNP identification combinations specifically for the Chinese Merino sheep on the market, and existing research mostly focuses on exploring a small number of SNP loci, which cannot comprehensively reflect breed characteristics and affects the accuracy and reliability of identification results. Therefore, developing an efficient and accurate SNP identification combination for Chinese Merino sheep is of urgent need and great significance for promoting the protection and rational utilization of the breed's genetic resources. Summary of the Invention

[0003] The purpose of this invention is to provide a combination of SNP loci for rapid identification of Chinese Merino sheep. This combination consists of 10 carefully selected SNP loci that are highly specific in the Chinese Merino sheep genome and can accurately distinguish this breed from other similar breeds or hybrid offspring.

[0004] On one hand, this invention relates to a combination of SNP loci for rapid identification of Chinese Merino sheep, characterized by consisting of 10 SNP loci located in the sheep reference genome ARS-UI_Ramb_v2.0, numbered NO.1 to NO.10, as detailed below:

[0005] Number (NO.) Nth chromosome Location Reference base Mutant bases 1 3 43521750 A G 2 6 28030087 T A 3 6 29100317 T C 4 6 29772568 T C 5 8 63572173 C T 6 8 63576026 G A 7 8 63615387 A G 8 12 57099456 C T 9 15 10951762 A G 10 15 37756467 A G

[0006] On the other hand, the present invention relates to a method for identifying Chinese Merino sheep breeds, comprising: collecting the gene sequence of a sample to be tested, detecting the mutation status of SNP sites in the SNP site combination for rapid identification of the Chinese Merino sheep, and determining the breed of the sample to be tested.

[0007] Furthermore, in the method for identifying Chinese Merino sheep breeds provided by the present invention, based on the location of the SNP site, a 60bp extension is made before and after the SNP site on the sheep reference genome ARS-UI_Ramb_v2.0 to obtain a 121bp base sequence. The reverse complementary sequence of the 121bp base sequence is used as the probe sequence of the SNP site.

[0008] Furthermore, in the method for identifying Chinese Merino sheep breeds provided by this invention, genotyping is performed based on the mutation status.

[0009] Furthermore, in the method for identifying Chinese Merino sheep breeds provided by the present invention, the genotyping includes: extracting genomic DNA from the sample to be tested to obtain a DNA sample, testing the quality of the DNA sample, performing liquid phase chip detection, and performing quality control, comparison, and SNP detection on the raw data obtained from the detection to obtain the genotyping result.

[0010] Furthermore, in the method for identifying Chinese Merino sheep breeds provided by this invention, the quality control uses FastP software, the comparison uses BWA software, and the SNP detection uses GATK software.

[0011] Furthermore, in the method for identifying Chinese Merino sheep breeds provided by this invention, breed identification is performed based on a classifier algorithm according to the results of the genotyping.

[0012] On the other hand, the present invention relates to the application of the SNP locus combination for rapid identification of Chinese Merino sheep in distinguishing sheep breeds, and to distinguish Chinese Merino sheep breeds from multiple sheep genomic DNA.

[0013] On the other hand, the present invention relates to a gene chip for the identification of Chinese Merino sheep breeds, which consists of sites that detect the combination of SNP sites for the rapid identification of Chinese Merino sheep.

[0014] Compared with the prior art, the technical solution provided by the present invention has at least the following beneficial effects or advantages.

[0015] This invention, based on chromosomes 1-26 and chromosome X (or sex chromosome) disclosed in the sheep reference genome ARS-UI_Ramb_v2.0, locates 10 SNP loci closely associated with the Chinese Merino sheep breed. When applied to breed identification of the Chinese Merino sheep, the SNP locus combination of this invention achieves a prediction accuracy of 99.34%. Compared to traditional solid-phase microarrays, this invention fabricates the Chinese Merino sheep SNP loci into a liquid-phase microarray, offering greater flexibility and allowing for the addition of marker loci as needed. Based on targeted capture sequencing technology, this invention's Chinese Merino sheep liquid-phase microarray not only genotypes the target locus but also accurately genotypes SNPs within a certain range around the target locus, providing more SNP genotyping information than marker loci. Compared to whole-genome resequencing, the Chinese Merino sheep liquid-phase microarray provided by this invention relies on a next-generation sequencing platform, resulting in lower genotyping costs and a significant price advantage. This enables large-scale genotyping of the Chinese Merino sheep, thereby promoting breeding efforts. The liquid phase chip for Chinese Merino sheep of the present invention can be used for breed identification, kinship identification and germplasm resource improvement of Chinese Merino sheep. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 Map showing the location of the SNP locus combinations for rapid identification of Chinese Merino sheep provided by this invention on the sheep reference genome ARS-UI_Ramb_v2.0. Detailed Implementation

[0018] The technical solution of the present invention will be described below with reference to embodiments. However, the present invention is not limited to the following embodiments. Unless otherwise specified, the experimental methods and detection methods described in each embodiment are conventional methods; unless otherwise specified, the reagents and materials can be purchased commercially. Unless otherwise specified, the percentages in the following embodiments refer to mass percentages.

[0019] Example 1

[0020] This embodiment provides a process for screening SNP sites to identify Chinese Merino sheep.

[0021] (1) Sample data acquisition: Blood samples from 15 local sheep breeds in Xinjiang (Lop Nur sheep, Dolan sheep, Kazakh sheep, Turpan black sheep, Altay sheep, Chinese Merino sheep, Bashibai sheep, Balchuk sheep, Kyrgyz sheep, Yecheng sheep, Bayinbuluke sheep, Cele black sheep, Hotan sheep, Karakul sheep, and Chinese Merino sheep) were collected and sent to Xinjiang Compson Biotechnology Co., Ltd. for genome resequencing to obtain fastq.gz data for each sample.

[0022] (2) Data format conversion: The fastq.gz data of each breed of sheep were quality controlled by fastqc software (default parameters, adapters are r1-adapter: AGATCGGAAGAGCACACGTCTGAACTCCAGTCA, r2-adapter: AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT). Then, the sequencing data were aligned to the sheep reference genome ARS-Ul Ramb v2.0 using BWA software. The data were sorted and duplicates were removed using SAMtools software. Finally, the vcf.gz format file was obtained by calling the variants in GATK software.

[0023] (3) Filtering: Using GATK, BCFtools and PLINK software, allele frequencies greater than 0.1 (--maf 0.1), site deletion rates less than 0.1 (--geno 0.1) and Hardy-Weinberg equilibrium test P values ​​greater than 0.001 (--hwe0.001) were filtered. Linked sites were removed using PLINK software with the following conditions: window size of 50 SNPs, step size of 10 SNPs, and LD threshold of 0.2.

[0024] (4) Initial screening: The genetic differentiation index (Fst) between Chinese Merino sheep and other breeds was calculated using PLINK software. The top 10,000 SNP loci with the highest Fst values ​​in Chinese Merino sheep were selected as the breed selection loci.

[0025] (5) Secondary screening: The best loci were selected using the random forest model and support vector machine learning method in Python. The dataset of 10,000 SNP loci of Chinese Merino sheep was divided into training set and test set in an 8:2 ratio, and stratified sampling was performed to ensure that the distribution ratio of each locus was consistent in the training set and test set. The most important SNP features were selected using the feature importance of the random forest model. The parameters controlled the number of features selected, and 40 to 60 features were selected in a loop. The number of decision trees was set to 1000. The random number seed was equal to 0. The parameters of SVC (Support Vector Classifier) ​​were set as follows: C = [0.01, 0.1, 1, 5, 10, 100]; the number of folds for cross-validation was set to 5.

[0026] (6) Site Preservation: Candidate SNP sites were obtained from the output Random Forest (RF) model and Support Vector Machine Learning (SVG) to achieve optimal model results. These candidate SNP sites were then processed by removing duplicate sites, SNP sites that could not be uniquely aligned on the genome, and SNP sites containing repetitive sequences in their flanking sequences. The spacing between SNP sites was then evaluated to ensure that the distance between any two adjacent SNP sites was greater than 60 bp, thus enabling probe synthesis. Finally, 10 SNP sites were obtained for the identification of Chinese Merino sheep breeds (Table 1). The distribution of these SNP sites on the sheep reference genome ARS-Ul Ramb v2.0 is shown below. Figure 1 As shown.

[0027] Table 1: SNP sites and corresponding mutations

[0028] Number (NO.) Nth chromosome Location Reference base Mutant bases 1 3 43521750 A G 2 6 28030087 T A 3 6 29100317 T C 4 6 29772568 T C 5 8 63572173 C T 6 8 63576026 G A 7 8 63615387 A G 8 12 57099456 C T 9 15 10951762 A G 10 15 37756467 A G

[0029] Example 2

[0030] This embodiment provides a procedure for genotyping sheep using the aforementioned 10 SNP loci.

[0031] (1) Extraction of sheep genomic DNA: Blood was collected from the jugular vein of sheep, and DNA was extracted using the phenol-chloroform method or a blood genomic DNA extraction kit (Tiangen Biotech Co., Ltd., Beijing).

[0032] (2) DNA sample quality detection: Agarose gel electrophoresis with a mass fraction of 1-1.5% was used for detection. The electrophoresis results were judged using a gel imaging system (GelDocXRSystem, Bio-Rad, USA) to ensure the integrity of the genome. The concentration of genomic DNA was measured using a micro-volume ultraviolet spectrophotometer (Q5000, Quawell, USA) or a similar nucleic acid and protein analyzer, and the DNA concentration was adjusted to the working concentration of 10-50 ng / μL.

[0033] (3) Liquid phase chip testing: Operate according to the standard procedure for liquid phase chip testing;

[0034] (4) Data Analysis: The raw data were quality controlled using FastQC software. Then, BWA software was used to align the sequencing data to the sheep reference genome ARS-Ul Ramb v2.0. SNPs were detected using the standard GATK software procedure for genotyping. The correspondence between genotypes and genotypes for the 10 SNP loci was as follows: Genotype 0 corresponds to reference base + reference base; Genotype 1 corresponds to reference base + mutant base; Genotype 2 corresponds to mutant base + mutant base; Genotype NA indicates missing sequencing data. For example, NO.1 is located at position 43521750 on chromosome 3, with reference base A and mutant base G. The labeling method for NO.1 is: Genotype 0 corresponds to AA; Genotype 1 corresponds to AG; Genotype 2 corresponds to GG. The genotyping results of Chinese Merino sheep are shown in Table 2.

[0035] Table 2: Correspondence between SNP loci genotypes and genotyping

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Based on the genotyping results in Table 2, the varieties were classified using Python software. The test included the following steps:

[0043] 1. Import the necessary libraries:

[0044] 1) Use NumPy and Pandas for data processing.

[0045] 2) Use joblib to save and load models and other objects.

[0046] 3) Use various modules in sklearn for data splitting, model evaluation, missing value imputation, feature selection, cross-validation, model training and evaluation.

[0047] II. Reading and Preprocessing Data:

[0048] 1) Read the raw data file (such as CSV or TSV format).

[0049] 2) Replace missing values ​​(such as 'NA') in the data with NaN for subsequent processing.

[0050] 3) Extract feature values ​​and labels (i.e., target variables).

[0051] III. Missing value imputation:

[0052] 1) Use algorithms such as KNN to fill in missing values.

[0053] 2) Save the filler (such as KNNImputer) for later use.

[0054] IV. Dataset Partitioning:

[0055] 1) Use train_test_split to split the data into training and test sets.

[0056] 2) Ensure that the class distribution in the training and test sets is the same as that in the original data.

[0057] V. Feature Selection:

[0058] 1) Use models (such as RandomForestClassifier) ​​for feature selection.

[0059] 2) Retain the important features after selection.

[0060] 3) Save the feature selector for later use.

[0061] VI. Model Training and Hyperparameter Optimization:

[0062] 1) Use methods such as GridSearchCV to perform hyperparameter search and model optimization.

[0063] 2) Train the model and select the best model and parameters.

[0064] 3) Save the best model for later use.

[0065] VII. Model Evaluation:

[0066] 1) Calculate the scores for the test set, such as accuracy, confusion matrix, classification report, etc.

[0067] 2) Save the model evaluation results for subsequent analysis.

[0068] VIII. New Data Processing and Forecasting:

[0069] 1) Read the new data and ensure it is aligned with the original data.

[0070] 2) Use the saved filler to fill missing values ​​in the new data.

[0071] 3) Use the saved feature selector to select important features in the new data.

[0072] 4) Use the saved model to make predictions on new data.

[0073] 5) Save and output the prediction results.

[0074] IX. Saving and Outputting Results:

[0075] Save the prediction results as a CSV file for easy subsequent analysis and application.

[0076] The prediction results are shown in Table 3. In Table 3, 0 indicates that it is identified as a Chinese Merino sheep, and 1 indicates that it is identified as not belonging to the Chinese Merino sheep.

[0077] Table 3: Prediction Results for Merino Sheep in China

[0078]

[0079]

[0080]

[0081] As shown in Tables 2 and 3, the 10 SNP loci provided by this invention were tested on 1486 sheep, including 15 breeds. Among them, 232 were Chinese Merino sheep, of which 231 were identified as Chinese Merino sheep, 1 was not identified as a Chinese Merino sheep, and no other breeds were identified as Chinese Merino sheep. In summary, the SNP locus combination of this invention for rapid identification of Chinese Merino sheep has an accuracy rate of 99.34% and exhibits superior specificity.

[0082] As described above, the basic principles, main features, and advantages of the present invention have been well described. The above embodiments and specifications are merely descriptions of preferred embodiments of the present invention, and the present invention is not limited to the above embodiments. Various changes and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit and scope of the present invention should fall within the protection scope defined by the present invention.

Claims

1. A method of identifying a Chinese Merino breed, characterized by, The test samples were selected from Lop Nur sheep, Dolan sheep, Kazakh sheep, Turpan black sheep, Altay sheep, Chinese Merino sheep, Bashibai sheep, Barchuk sheep, Kyrgyz sheep, Yecheng sheep, Bayinbuluke sheep, Cele black sheep, Hotan sheep, and Karakul sheep; This includes: collecting the gene sequence of the sample to be tested, detecting the mutation status of SNP sites in the SNP site combination for rapid identification of Chinese Merino sheep, and determining whether the breed of the sample to be tested is Chinese Merino sheep; The SNP locus combination consists of 10 SNP loci located in the sheep reference genome ARS-UI_Ramb_v2.0, numbered NO.1 to NO.10 respectively; 2. The method of the Chinese Merino breed according to claim 1, characterized by the fact that, Based on the location of the SNP site, the sequence is extended by 60 bp before and after the SNP site in the sheep reference genome ARS-UI_Ramb_v2.0, resulting in a 121 bp sequence. The reverse complementary sequence of the 121 bp sequence is then used as the probe sequence for the SNP site.

3. The method for breeding Chinese Merino sheep according to claim 1, characterized in that, Genotyping is performed based on the mutations described.

4. The method for breeding Chinese Merino sheep according to claim 3, characterized in that, The genotyping process includes: extracting genomic DNA from the sample to be tested to obtain a DNA sample, testing the quality of the DNA sample, performing liquid-phase microarray detection, and performing quality control, comparison, and SNP detection on the raw data obtained from the detection to obtain the genotyping result.

5. The method for breeding Chinese Merino sheep according to claim 4, characterized in that, The quality control uses FastP software, the comparison uses BWA software, and the SNP detection uses GATK software.

6. The method for breeding Chinese Merino sheep according to claim 4, characterized in that, Based on the genotyping results, variety identification is performed using a classifier algorithm.

7. Application of SNP locus combinations for rapid identification of Chinese Merino sheep in distinguishing sheep breeds, characterized by: Chinese Merino sheep breeds were distinguished from multiple sheep genomic DNA sources, including Lop Nur sheep, Dolan sheep, Kazakh sheep, Turpan black sheep, Altay sheep, Chinese Merino sheep, Bashibai sheep, Barchuk sheep, Kyrgyz sheep, Yecheng sheep, Bayinbuluke sheep, Cele black sheep, Hotan sheep, and Karakul sheep. The SNP locus combination consists of 10 SNP loci located in the sheep reference genome ARS-UI_Ramb_v2.0, numbered NO.1 to NO.10 respectively.

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