A set of SNP sites for rapid identification of bayingbuluke sheep
By screening 250 SNP loci through genome-wide association analysis and preparing liquid-phase microarrays, the problem of Bayinbuluke sheep breed identification was solved, achieving efficient and accurate identification of Bayinbuluke sheep and promoting the healthy development of the population.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XINJIANG ACADEMY OF ANIMAL SCI QUALITY STANDARDS INST OF ANIMAL HUSBANDRY XINJIANG UYGUR AUTONOMOUS REGION SHEEP & WOOL CASHMERE QUALITY SAFETY SUPERVISION & INSPECTION CENT
- Filing Date
- 2024-09-12
- Publication Date
- 2026-07-10
AI Technical Summary
The lack of effective SNP loci for the identification of Bayinbuluke sheep in existing technologies increases the difficulty of breeding Bayinbuluke sheep populations and lacks scientific breeding methods, which affects the healthy development of the population.
Through genome-wide association analysis, 250 specific SNP loci were screened, and liquid-phase microarrays were prepared for specific detection. By combining random forest model and support vector machine learning, the accuracy of identification was improved.
This has enabled efficient and accurate identification of Bayinbuluke sheep, with an accuracy rate of 97.76%, promoting the establishment of Bayinbuluke sheep germplasm resources and the healthy development of the population.
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Figure CN119220693B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of molecular marker technology and relates to a set of SNP sites for rapid identification of Bayinbuluke sheep. Background Technology
[0002] Bayinbuluke sheep is an important local sheep breed in Xinjiang. Through long-term selective breeding, it has developed characteristics such as early maturity, tolerance to roughage, strong adaptability, and strong disease resistance. It can adapt to harsh, cold climates and is one of the excellent local breeds suitable for both meat, fat, and wool production. However, there are many problems in the breeding and raising of Bayinbuluke sheep that cannot be ignored. For example, the lack of scientific breeding methods and the neglect of ram feeding have led to breed degeneration, large differences in sheep weight, and inconsistent meat quality in offspring. In addition, poor infrastructure, uneven forage supply, and a lack of scientific feeding and management methods further increase the difficulty of breeding Bayinbuluke sheep. Therefore, it is necessary to establish a sound breeding system for Bayinbuluke sheep and cultivate high-quality breeds with excellent production performance.
[0003] Single nucleotide polymorphisms (SNPs), as widely distributed genetic molecular markers, are increasingly used due to their high density, strong genetic stability, and ease of automated analysis. Depending on their different functions, SNPs can be used not only for breeding superior traits but also for breed identification and genetic diversity analysis. Currently, research on SNP loci that can be used for breed identification of Bayinbuluke sheep is still relatively scarce, limiting the healthy development of the Bayinbuluke sheep population. Summary of the Invention
[0004] To accelerate the breeding process of Bayinbuluke sheep and efficiently and accurately identify the genotypes of batch samples, this invention performs genome-wide association analysis on Bayinbuluke sheep based on resequencing data, screening out 250 specific SNP loci present in Bayinbuluke sheep. These SNP loci are listed as NO.1 to NO.250.
[0005]
[0006]
[0007]
[0008]
[0009]
[0010] The SNP site is located in the sheep reference genome ARS-UI_Ramb_v2.0.
[0011] Furthermore, the SNP sites are screened using the following steps:
[0012] 1) Sequencing: The genome of blood samples from Bayinbuluke sheep was resequencing to obtain fastq.gz data for each sample.
[0013] 2) Format conversion: The fastq.gz data is processed by fastqc software for quality control, bwa software for sequence alignment, samtools software for sorting and removing duplicates, and gatk software for mutation calling, 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 deletion 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 following conditions: window size of 50 SNPs, step size of 10 SNPs and LD threshold of 0.2.
[0015] 4) Initial screening: The population fixation index (Fst) of Bayinbuluke sheep and other different breeds of sheep was calculated using the plink software, and the top 10,000 SNP loci with the highest Fst of Bayinbuluke sheep were screened out.
[0016] 5) Secondary screening: The best loci are selected using the random forest model and support vector machine learning method in Python. The 10,000 SNP loci are divided into training and test sets in an 8:2 ratio using stratified sampling. The most important SNP features are selected by utilizing the feature importance of the random forest model.
[0017] 6) Site Preservation: The output random forest model and support vector machine learning yield the 250 SNP sites under the optimal model.
[0018] On the other hand, the present invention also provides the application of the above-mentioned SNP sites in the preparation of liquid phase chips, wherein the liquid phase chips contain molecular probes for specifically detecting the above-mentioned SNP sites.
[0019] In the above application, the molecular probe is prepared by extending SNP sites NO.1 to NO.250 by 60 bp before and after the sheep reference genome ARS-UI_Ramb_v2.0 to obtain a 121 bp base sequence including the SNP site. The reverse complementary sequence of the base sequence is used as the molecular probe of the SNP site.
[0020] Furthermore, this invention also claims protection for a SNP liquid phase chip containing molecular probes for specifically detecting the aforementioned SNP sites.
[0021] Finally, this invention also seeks protection for the application of the above-mentioned SNP sites in the genotyping detection, breed identification, genetic breeding, and genetic diversity analysis of Bayinbuluke sheep.
[0022] Compared with existing technologies, the present invention, "a set of SNP loci for rapid identification of Bayinbuluke sheep," has the following beneficial effects:
[0023] Based on resequencing data, this invention performs genome-wide association analysis on Bayinbuluke sheep and screens out 250 specific SNP loci present in Bayinbuluke sheep. The SNP loci provided by this invention have an accuracy rate of 97.76% when used for Bayinbuluke sheep identification. They can be used for breed identification, genetic breeding and other work of Bayinbuluke sheep, help to establish and improve Bayinbuluke sheep germplasm resources and promote the healthy development of the population. Attached Figure Description
[0024] Figure 1 The distribution of 250 SNP loci on the chromosome. Detailed Implementation
[0025] The present invention will now be described in conjunction with embodiments, providing a clear and complete description of the technical solutions in these embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0026] Example 1
[0027] This embodiment provides the screening process for 250 SNP loci in Bayinbuluke sheep.
[0028] The Bayinbuluke sheep SNP sites were screened using the following method:
[0029] 1) Sequencing: The genome of blood samples from Bayinbuluke sheep was resequencing (Xinjiang Compson Biotechnology Co., Ltd.), and fastq.gz data for each sample were obtained.
[0030] 2) Format conversion: The fastq.gz data is processed by fastqc software for quality control, bwa software for sequence alignment, samtools software for sorting and removing duplicates, and gatk software for mutation calling, 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 deletion 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 following conditions: window size of 50 SNPs, step size of 10 SNPs and LD threshold of 0.2.
[0032] 4) Initial screening: The population fixation index (Fst) of Bayinbuluke sheep and other different breeds of sheep was calculated using the plink software, and the top 10,000 SNP loci with the highest Fst of Bayinbuluke sheep were screened out.
[0033] 5) Secondary screening: The best loci are selected using the random forest model and support vector machine learning method in Python. The 10,000 SNP loci are divided into training and test sets in an 8:2 ratio using stratified sampling. The most important SNP features are selected by utilizing the feature importance of the random forest model.
[0034] 6) Site Preservation: The output random forest model and support vector machine learning yield the 250 SNP sites under the optimal model.
[0035] Filtering results:
[0036] Following the above screening steps, the locations of 250 specific SNP loci in Bayinbuluke sheep are shown in Table 1. Their distribution on the chromosomes is shown in [see table below]. Figure 1 .
[0037] Table 1. Locations and mutation types of 250 SNP sites.
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[0043] In the table above, SNP positions are numbered in the format A:B, where A represents the corresponding chromosome and B represents the position on the corresponding chromosome. For example, NO.1 is 1:4099461, meaning that SNP numbered 1 is located at the 4099461st base on the first chromosome of the sheep reference genome ARS-UI_Ramb_v2.0.
[0044] Example 2
[0045] This embodiment provides a procedure for genotyping sheep using the aforementioned 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 DNA extraction kit (Tiangen Biotech Co., Ltd., Beijing).
[0047] 2) DNA sample quality testing: Agarose gel electrophoresis with a mass fraction of 1-1.5% was used for detection, and the electrophoresis results were judged using a gel imaging system (GelDocXRSystem, Bio-Rad, USA) to ensure genome integrity; 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 a working concentration of 10-50 ng / μL;
[0048] 3) Liquid phase chip testing: Operate according to the standard procedure for liquid phase chip testing;
[0049] 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 genotype-genotype correspondence for the 250 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 4099461 on chromosome 1, with reference base C and mutant base T. The labeling method for NO.1 is: Genotype 0 corresponds to CC; Genotype 1 corresponds to CT; Genotype 2 corresponds to 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 embodiment provides an accuracy test for genotyping results.
[0061] The accuracy of the genotyping results in Example 2 was tested using Python software. The accuracy test included the following steps:
[0062] 1. Import the 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 splitting, model evaluation, missing value imputation, feature selection, cross-validation, model training and evaluation.
[0066] II. Reading and Preprocessing 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] III. Missing value imputation:
[0071] 1) Use algorithms such as KNN to fill in missing values.
[0072] 2) Save the filler (such as KNNImputer) for later use.
[0073] IV. Dataset Partitioning:
[0074] 1) Use train_test_split to split the data into training and test sets.
[0075] 2) Ensure that the class distribution in the training and test sets is the same as that in the original data.
[0076] V. Feature Selection:
[0077] 1) Use models (such as RandomForestClassifier) for feature selection.
[0078] 2) Retain the important features after selection.
[0079] 3) Save the feature selector for later use.
[0080] VI. Model Training and Hyperparameter Optimization:
[0081] 1) Use methods such as GridSearchCV to perform 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] VII. Model Evaluation:
[0085] 1) Calculate the scores for the test set, such as accuracy, confusion matrix, classification report, etc.
[0086] 2) Save the model evaluation results for subsequent analysis.
[0087] VIII. New Data Processing and Forecasting:
[0088] 1) Read the new data and ensure it is aligned with the original data.
[0089] 2) Use the saved filler to fill missing values in the 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] IX. Saving and Outputting Results:
[0094] Save the prediction results as a CSV file for easy subsequent analysis and application.
[0095] Test results:
[0096] The 250 SNP loci provided by this invention were tested on 1486 sheep of 15 breeds, including blood samples from 134 Bayinbuluke sheep. The results are shown in Table 3.
[0097] Table 3. Theoretical and Detection Results of 134 Bayinbuluke Sheep
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[0099]
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[0102]
[0103] Note: In Table 3, "0" represents Bayinbuluke sheep, and "1" represents non-Bayinbuluke sheep.
[0104] In this embodiment, since all blood samples used in the test were taken from Bayinbuluke sheep, the theoretical result should be "0". However, three samples showed a result of "1", which contradicts the theoretical result, and no other breeds of sheep were identified as Bayinbuluke sheep. The identification accuracy rate is: (134-3) / 134×100%=97.76%. In summary, the accuracy rate of the SNP locus used to identify Bayinbuluke sheep is 97.76%, demonstrating superior specificity and accuracy.
[0105] The embodiments described above are only some, not all, of the embodiments of the present invention. The detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments. All other embodiments obtained by those skilled in the art through related deductions and substitutions based on the inventive concept, without inventive effort, are within the scope of protection of the present invention.
Claims
1. An SNP liquid phase chip for identifying Bayinbuluke sheep in Xinjiang, characterized in that, The SNP liquid-phase chip contains molecular probes for the specific detection of SNP sites as described in NO.1 to NO.
250. These SNP sites are located in the sheep reference genome ARS-UI_Ramb_v2.
0. The location, reference genotype, and mutant genotype of each SNP site are shown in the table below. 。 2. The SNP liquid phase chip according to claim 1, characterized in that, The SNP sites described in NO.1 to NO.250 were obtained by screening using the following steps: 1) Sequencing: The genome of blood samples from Bayinbuluke sheep was resequencing to obtain fastq.gz data for each sample; 2) Format conversion: The fastq.gz data is processed by fastqc software for quality control, bwa software for sequence alignment, samtools software for sorting and removing duplicates, and gatk software for mutation calling, to obtain a vcf.gz format file; 3) Filtering: Use gatk, bcftools and plink software to filter and obtain loci with allele frequencies greater than 0.1, deletion rates less than 0.1 and Hardy-Weinberg equilibrium test p-values greater than 0.001; use plink software to remove linked loci under the following conditions: window size of 50 SNPs, step size of 10 SNPs, and LD threshold of 0.
2. 4) Initial screening: The population fixation index (Fst) of Bayinbuluke sheep and other different breeds of sheep in Xinjiang was calculated using plink software, and the top 10,000 SNP loci with the highest Fst of Bayinbuluke sheep were screened out. 5) Secondary screening: The best loci are selected using the random forest model and support vector machine learning method in Python. The 10,000 SNP loci are divided into training and test sets in an 8:2 ratio using stratified sampling. The most important SNP features are selected using the feature importance of the random forest model. 6) Site Preservation: The output random forest model and support vector machine learning yield the 250 SNP sites under the optimal model.
3. The SNP liquid phase chip according to claim 1, characterized in that, The molecular probe is prepared by extending SNP sites NO.1 to NO.250 by 60 bp before and after the sheep reference genome ARS-UI_Ramb_v2.0 to obtain a 121 bp base sequence including the SNP site. The reverse complementary sequence of the base sequence is used as the molecular probe of the SNP site.
4. The application of the SNP liquid phase chip according to claim 1 in the genotyping detection of Bayinbuluke sheep in Xinjiang.
5. The application of the SNP liquid phase chip as described in claim 1 in the identification of Bayinbuluke sheep breed in Xinjiang.