Group of SNP (Single Nucleotide Polymorphism) loci for rapid identification of Barbrook sheep

Abstract

The invention belongs to the technical field of molecular markers, and discloses a group of SNP (Single Nucleotide Polymorphism) loci for rapid identification of Bambucker sheep. According to the invention, based on re-sequencing data, whole genome association analysis is carried out on the Bombubruk sheep, 250 specific SNP loci existing in the Bombubruk sheep are screened, the SNP loci can be used for variety identification of the Bombubruk sheep, and the identification accuracy is 97.76%. The method has important contribution significance in the aspects of genetic breeding, variety identification and the like of the Bambubrook sheep.

Description

Technical Field

[0001] The invention belongs to the technical field of molecular markers and relates to a group of SNP sites for rapid identification of Bayinbuluke sheep. Background Art

[0002] Bayinbuluke sheep is an important local sheep breed in Xinjiang. After long-term selection and breeding, it has the characteristics of early maturity, tolerance to roughage, strong adaptability and disease resistance. It can adapt to the harsh climate of high altitude and cold weather, and is one of the excellent local breeds for both meat, fat and wool. However, there are many problems that cannot be ignored in the breeding and reproduction of Bayinbuluke sheep, such as the lack of scientific breeding methods and the neglect of the feeding of rams, which have led to the degeneration of the breed, large differences in the weight of sheep, and uneven meat quality in the offspring. In addition, the poor infrastructure conditions, unbalanced forage supply, and lack of scientific feeding and management methods have further increased the difficulty of breeding the Bayinbuluke sheep population. This requires the establishment of a sound breeding system for Bayinbuluke sheep and the cultivation of high-quality breeds with excellent production performance.

[0003] Single nucleotide polymorphism (SNP) is a widely distributed genetic molecular marker with the characteristics of high density, strong genetic stability and easy automated analysis, so it is increasingly used. According to the different functions of SNP, it can not only be used for breeding of excellent traits, but also for research such as variety identification and genetic diversity analysis. At present, the relevant research results of SNP sites that can be used for the identification of Bayinbuluke sheep varieties are still scarce, which limits the healthy development of the Bayinbuluke sheep population. Summary of the invention

[0004] In order to speed up the breeding process of Bayinbuluke sheep and identify the genotypes of batch samples efficiently and accurately, the present invention conducted genome-wide association analysis on Bayinbuluke sheep based on resequencing data and screened out 250 specific SNP sites existing in Bayinbuluke sheep. The SNP sites are shown as NO.1 to NO.250:

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[0010] , the SNP site is located in the sheep reference genome ARS-UI_Ramb_v2.0.

[0011] Further, the SNP site is screened by the following steps:

[0012] 1) Sequencing: The blood samples of Bayinbuluke sheep were resequenced to obtain the fastq.gz data of each sample.

[0013] 2) Format conversion: The fastq.gz data was subjected to quality control by fastqc software, sequence alignment by bwa software, sorting and duplication removal by samtools software, and variant calling by gatk software to obtain a vcf.gz format file.

[0014] 3) Filtering: Use gatk, bcftools and plink software to filter and obtain sites with allele frequency greater than 0.1, site missing rate less than 0.1 and Hardy-Weinberg equilibrium test P value greater than 0.001; use plink software to remove linked sites, with the conditions: window size of 50SNPs, step size of 10SNPs and LD threshold of 0.2.

[0015] 4) Preliminary screening: The population fixation index (Fst) of Bayinbuluke sheep and other different breeds of sheep was calculated using plink software, and the top 10,000 SNP loci with the highest Fst of Bayinbuluke sheep were screened out.

[0016] 5) Rescreening: The random forest model and support vector machine learning method in Python were used to screen the best sites. The 10,000 SNP sites were divided into training set and test set in a ratio of 8:2 using the stratified sampling method. The feature importance of the random forest model was used to select the most important SNP features.

[0017] 6) Site retention: Output the random forest model and support vector machine learning to obtain the 250 SNP sites under the best model.

[0018] On the other hand, the present invention also provides the use of the above-mentioned SNP site in the preparation of a liquid phase chip, wherein the liquid phase chip contains a molecular probe, and the molecular probe is used to specifically detect the above-mentioned SNP site.

[0019] In the above application, the method for preparing the molecular probe is: extending SNP sites NO.1 to NO.250 by 60 bp before and after on the sheep reference genome ARS-UI_Ramb_v2.0 to obtain a base sequence with a length of 121 bp including the SNP site, and using the reverse complementary sequence of the base sequence as the molecular probe of the SNP site.

[0020] In addition, the present invention also seeks to protect a SNP liquid phase chip, wherein the SNP liquid phase chip contains a molecular probe, and the molecular probe is used to specifically detect the above-mentioned SNP site.

[0021] Finally, the present invention also requests protection for the application of the above-mentioned SNP loci in Bayinbuluke sheep genotyping detection, variety identification, genetic breeding, and genetic diversity analysis.

[0022] Compared with the prior art, the present invention "a group of SNP sites for rapid identification of Bayinbuluke sheep" has the following beneficial effects:

[0023] Based on resequencing data, the present invention conducted a whole genome association analysis on the Bayinbuluke sheep and screened out 250 specific SNP sites existing in the Bayinbuluke sheep. The SNP sites provided by the present invention had an accuracy rate of 97.76% when used for Bayinbuluke sheep identification. They can be used for variety identification, genetic breeding and other work of Bayinbuluke sheep, help establish and improve the germplasm resources of Bayinbuluke sheep, and promote the healthy development of the population. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The figure shows the distribution of 250 SNP sites on chromosomes. DETAILED DESCRIPTION

[0025] The present invention is explained below in conjunction with the embodiments, and the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example 1

[0027] This embodiment provides a screening process for 250 Bayinbuluke sheep SNP loci.

[0028] The Bayinbuluke sheep SNP loci were screened by the following method:

[0029] 1) Sequencing: The blood samples of Bayinbuluke sheep were subjected to genome resequencing (Xinjiang Compson Biotechnology Co., Ltd.) to obtain fastq.gz offline data for each sample.

[0030] 2) Format conversion: The fastq.gz data was subjected to quality control by fastqc software, sequence alignment by bwa software, sorting and duplication removal by samtools software, and variant calling by gatk software to obtain a vcf.gz format file.

[0031] 3) Filtering: Use gatk, bcftools and plink software to filter and obtain sites with allele frequency greater than 0.1, site missing rate less than 0.1 and Hardy-Weinberg equilibrium test P value greater than 0.001; use plink software to remove linked sites under the conditions of window size of 50SNPs, step size of 10SNPs and LD threshold of 0.2.

[0032] 4) Preliminary screening: The population fixation index (Fst) of Bayinbuluke sheep and other different breeds of sheep was calculated using plink software, and the top 10,000 SNP loci with the highest Fst of Bayinbuluke sheep were screened out.

[0033] 5) Rescreening: The random forest model and support vector machine learning method in Python were used to screen the best sites. The 10,000 SNP sites were divided into training set and test set in a ratio of 8:2 using the stratified sampling method. The feature importance of the random forest model was used to select the most important SNP features.

[0034] 6) Site retention: Output the random forest model and support vector machine learning to obtain the 250 SNP sites under the best model.

[0035] Filter results:

[0036] After the above screening steps, the locations of 250 specific SNP loci in Bayinbuluke sheep are shown in Table 1, and their distribution on chromosomes is shown in Table 2. Figure 1 .

[0037] Table 1. Location and mutation type of 250 SNP sites

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[0043] In the above table, the SNP positions are numbered in the format of A:B, where A is the corresponding chromosome and B is the position on the corresponding chromosome. For example, NO.1 is 1:4099461, that is, the SNP numbered 1 is located at the 4099461th base of the 1st chromosome in the sheep reference genome ARS-UI_Ramb_v2.0.

[0044] Example 2

[0045] This example provides a process for genotyping sheep using the above 250 SNP loci.

[0046] 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 extraction kit (Tiangen Biotechnology Co., Ltd., Beijing);

[0047] 2) DNA sample quality detection: Use agarose gel electrophoresis with a mass fraction of 1-1.5% to detect, and use a gel imaging system (GelDocXRSystem, Bio-Rad, USA) to judge the electrophoresis results to ensure the integrity of the genome; use a micro-ultraviolet spectrophotometer (Q5000, Quawell, USA) or a similar nucleic acid protein analyzer to measure the concentration of genomic DNA and adjust the DNA concentration to a working concentration of 10-50 ng / μL;

[0048] 3) Liquid chip detection: follow the standard process of liquid chip detection;

[0049] 4) Data analysis: The obtained raw data were quality controlled using fastqc software, and then the sequencing data were aligned to the sheep reference genome ARS-Ul Ramb v2.0 using BWA software. The standard process of GATK software was used to detect SNPs and perform genotyping. The corresponding relationship between the genotypes and genotyping of the 250 SNP sites was marked as follows: genotyping is 0, the corresponding genotype is reference base + reference base; genotyping is 1, the corresponding genotype is reference base + mutant base; genotyping is 2, the corresponding genotype is mutant base + mutant base; genotyping is NA, indicating that the sequencing data is missing. For example, NO.1 is located at the 4099461st position of the 1st chromosome, the reference base is C, and the mutant base is T. The marking method for NO.1 is: genotyping is 0, the corresponding genotype is CC; genotyping is 1, the corresponding genotype is CT; genotyping is 2, and the corresponding genotype is TT. The genotyping results of Bayinbuluke sheep are shown in Table 2 (Table 2 shows the genotyping results of 7 Bayinbuluke sheep).

[0050] Table 2. Genotyping results of SNP loci (partial)

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[0059] Example 3

[0060] This example provides a test of the accuracy of genotyping results.

[0061] The genotyping results of Example 2 were tested for accuracy using Python software. The accuracy test included the following steps:

[0062] 1. Import necessary libraries:

[0063] 1) Use numpy and pandas for data processing.

[0064] 2) Use joblib to save and load models and other objects.

[0065] 3) Use various modules in sklearn for data segmentation, model evaluation, missing value filling, feature selection, cross validation, model training and evaluation.

[0066] 2. Read and preprocess data:

[0067] 1) Read the raw data file (such as CSV or TSV format).

[0068] 2) Replace missing values ​​(such as 'NA') in the data with NaN for subsequent processing.

[0069] 3) Extract feature values ​​and labels (i.e. target variables).

[0070] 3. Missing value filling:

[0071] 1) Use KNN and other algorithms to fill in missing values.

[0072] 2) Save the filler (such as KNNImputer) for later use.

[0073] 4. Dataset Division:

[0074] 1) Use train_test_split to split the data into training and test sets.

[0075] 2) Ensure that the category distribution in the training set and test set is the same as the original data.

[0076] 5. Feature Selection

[0077] 1) Use a model (such as RandomForestClassifier) ​​for feature selection.

[0078] 2) Keep the important features after selection.

[0079] 3) Save the feature selector for later use.

[0080] 6. Model training and hyperparameter optimization:

[0081] 1) Use methods such as GridSearchCV for hyperparameter search and model optimization.

[0082] 2) Train the model and select the best model and parameters.

[0083] 3) Save the best model for later use.

[0084] 7. Model Evaluation

[0085] 1) Calculate the scores of the test set, such as accuracy, confusion matrix, classification report, etc.

[0086] 2) Save the results of model evaluation for subsequent analysis.

[0087] 8. New data processing and prediction:

[0088] 1) Read the new data and make sure it is aligned with the original data.

[0089] 2) Use the saved filler to fill missing values ​​in new data.

[0090] 3) Use the saved feature selector to select important features in the new data.

[0091] 4) Use the saved model to make predictions on new data.

[0092] 5) Save and output the prediction results.

[0093] 9. Result preservation and output:

[0094] Save the prediction results as a CSV file for subsequent analysis and application.

[0095] Identification results:

[0096] When identifying the 250 SNP sites provided by the present invention, a total of 1486 sheep of 15 breeds were tested, including blood samples of 134 Bayinbuluke sheep. The results are shown in Table 3.

[0097] Table 3. Theoretical results and test results of 134 Bayinbuluke sheep

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[0103] Note: In Table 3, “0” represents Bayinbuluke sheep and “1” represents non-Bayinbuluke sheep.

[0104] In this embodiment, since it is known that the blood samples involved in the test are all taken from Bayinbuluke sheep, the theoretical results are all "0"; in the test results, 3 samples are "1", which is inconsistent with the theoretical results, and no other breeds of sheep are identified as Bayinbuluke sheep. The identification accuracy is: (134-3) / 134×100%=97.76%. In summary, the accuracy of the SNP site for identifying Bayinbuluke sheep is 97.76%, which has better specificity and accuracy.

[0105] The embodiments described above are only some embodiments of the present invention, not all embodiments. The detailed description of the embodiments of the present invention is not intended to limit the scope of the invention claimed for protection, but only represents selected embodiments of the present invention. All other embodiments obtained without creative work and related deductions and substitutions made by ordinary technicians in the field under the conditions of the concept of the present invention belong to the scope of protection of the present invention.

Claims

1. A group of SNP loci of Bayinbuluke sheep, characterized in that: The SNP sites are shown as NO.1 to NO.250: The SNP site is located in the sheep reference genome ARS-UI_Ramb_v2.

0.

2. The Bayinbuluke sheep SNP locus according to claim 1, characterized in that: Filter by the following steps: 1) Sequencing: The blood samples of Bayinbuluke sheep were resequenced to obtain the fastq.gz data of each sample; 2) Format conversion: The fastq.gz data is subjected to quality control by fastqc software, sequence alignment by bwa software, sorting and duplication removal by samtools software, and variant calling by gatk software to obtain a vcf.gz format file; 3) Filtering: Use gatk, bcftools and plink software to filter and obtain sites with allele frequency greater than 0.1, site missing rate less than 0.1 and Hardy-Weinberg equilibrium test P value greater than 0.001; use plink software to remove linked sites, with the conditions: window size of 50SNPs, step size of 10SNPs, and LD threshold of 0.2; 4) Preliminary screening: Plink software was used to calculate the population fixation index (Fst) of Bayinbuluke sheep and other different breeds of sheep, and the top 10,000 SNP loci with the highest Fst of Bayinbuluke sheep were screened; 5) Rescreening: Use the random forest model and support vector machine learning method in Python to screen the best sites. Use the stratified sampling method to divide the 10,000 SNP sites into training set and test set in a ratio of 8:

2. Use the feature importance of the random forest model to select the most important SNP features. 6) Site retention: Output the random forest model and support vector machine learning to obtain the 250 SNP sites under the best model.

3. The use of the SNP site according to claim 1 in preparing a liquid phase chip, characterized in that: The liquid phase chip contains a molecular probe, and the molecular probe is used to specifically detect the SNP site described in claim 1.

4. The use according to claim 3, characterized in that: The preparation method of the molecular probe is as follows: SNP sites NO.1 to NO.250 are extended by 60 bp before and after on the sheep reference genome ARS-UI_Ramb_v2.0 to obtain a base sequence with a length of 121 bp including the SNP site, and the reverse complementary sequence of the base sequence is used as the molecular probe of the SNP site.

5. A SNP liquid phase chip, characterized in that: The SNP liquid phase chip contains a molecular probe, and the molecular probe is used to specifically detect the SNP site described in claim 1.

6. Use of the SNP site described in claim 1 in genotyping detection.

7. Use of the SNP site described in claim 1 in variety identification.

8. Use of the SNP site according to claim 1 in genetic breeding.

9. Use of the SNP site described in claim 1 in genetic diversity analysis.

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