A sheep breeding gene data analysis method and system

Abstract

The present invention relates to the field of gene analysis technology, and discloses a sheep breeding gene data analysis method and system, which realizes the full chain optimization from gene data to breeding decision through systematic data acquisition, processing, screening, matching and breeding steps. This method not only improves the recognition accuracy of high-quality genes, but also optimizes gene matching and pairing strategies, making the breeding process more scientific and efficient. Ultimately, this improvement improves the breeding rate, improves the quality of offspring, reduces genetic risks, speeds up the breeding process, and achieves a comprehensive improvement in the sheep breeding effect. By setting a clear comparison threshold Y, it can intuitively judge whether the sheep is a high-quality species. This method realizes the classification of high-quality species by simply comparing the #imgabs0# value with the threshold Y, making the screening process clearer and easier to operate. By comparing #imgabs1# with the preset threshold Y, high-quality populations and non-high-quality populations can be quickly screened out.

Description

Technical Field

[0001] The present invention relates to the technical field of gene analysis, and in particular to a sheep breeding gene data analysis method and system. Background Art

[0002] In animal breeding, people hope to achieve the purpose of early selection and improve the accuracy of breeding values ​​by selecting DNA markers that are closely related to traits such as growth and reproduction and closely linked to quantitative traits, thereby achieving greater genetic progress in livestock and poultry breeding.

[0003] In the existing sheep breeding practice, traditional methods mainly rely on phenotypic data and empirical selection, which have certain limitations. These methods often lack in-depth analysis at the genetic level, making the screening of excellent genes less accurate, resulting in insufficient stability and predictability of breeding results. Specifically, traditional methods may ignore the complex interaction between genes and the environment and fail to fully evaluate the actual impact of each gene on the trait, which makes the transmission of excellent genes and breeding efficiency unable to be fully utilized; therefore, it does not meet the existing needs. In this regard, we propose a sheep breeding genetic data analysis method. Summary of the invention

[0004] The present invention provides the following technical solution: a sheep breeding gene data analysis method, the specific steps are as follows:

[0005] S1. Data acquisition: blood biological samples are collected from different sheep species to extract multi-source data of biological characteristics of different sheep;

[0006] S2, data processing, processing the extracted multi-source data and reorganizing them into a first data set, a second data set, and a third data set;

[0007] S3, DNA screening, integrating and calculating the first data set and the second data set to generate the gene screening coefficient , and the gene screening coefficient Compare with a preset first threshold value Y to generate a first comparison result, and determine whether the current gene is a high-quality gene according to the first comparison result;

[0008] S4, DNA matching, calculate the obtained gene screening coefficient The gene matching coefficient is generated by integrating and calculating with the preset first threshold Y and the third data set. , multiple sets of gene matching coefficients Compare with the preset R to generate a second comparison result, and determine the matching degree of the two sets of DNA according to the second comparison result;

[0009] S5. Breeding sheep with compatible DNA according to the second matching result.

[0010] Preferably, in step S1, the multi-source data includes a phenotype data set, an immune data set, and a breeding data set;

[0011] The representational data set includes SNP gene frequency, genotype diversity index and gene marker association coefficient;

[0012] The immune data set includes the MHC gene expression intensity, PRNP gene expression intensity, and SP110 gene expression intensity;

[0013] The breeding data set includes BMPR1B gene expression intensity, GDF9 gene expression intensity, and ESR1 gene expression intensity.

[0014] Preferably, in step S2, data preprocessing is performed on the phenomenological data set, the immune data set and the breeding data set collected in step S1, and the preprocessed multi-source data sets are reintegrated into the first data set, the second data set and the third data set according to the numbers;

[0015] The first data set includes SNP gene frequency, genotype diversity index, and gene marker association coefficient;

[0016] The SNP gene frequencies are recorded according to the numbers: , , ,..., ;

[0017] The genotype diversity index is recorded as follows: , , ,..., ;

[0018] The gene marker association coefficients are recorded according to the numbers: , , ,..., ;

[0019] The second data set includes the MHC gene expression intensity, the PRNP gene expression intensity, and the SP110 gene expression intensity;

[0020] The MHC gene expression intensities are recorded according to the numbers: , , ,..., ;

[0021] According to the expression intensity of PRNP gene, they are recorded as follows: , , ,..., ;

[0022] According to the expression intensity of SP110 gene, they are recorded as follows: , , ,..., ;

[0023] The third data set includes BMPR1B gene expression intensity, GDF9 gene expression intensity, and ESR1 gene expression intensity;

[0024] The expression intensity of BMPR1B gene is recorded as follows: , , ,..., ;

[0025] According to the expression intensity of GDF9 gene, they are recorded as follows: , , ,..., ;

[0026] According to the expression intensity of ESR1 gene, they are recorded as follows: , , ,..., .

[0027] Preferably, the gene screening coefficient in step S3 Calculated by the following formula:

[0028]

[0029] Wherein: a1 and a2 are weight values, the values ​​of a1 and a2 are adjusted and set by the user, e is the base function, S1 and S2 are the first reference value and the second reference value respectively, wherein the first reference value S1 is obtained by integrating the first data set and the second reference value S2 is obtained by integrating the second data set and calculated.

[0030] Preferably, the first reference value S1 and the second reference value S2 are calculated and obtained by the following formulas respectively:

[0031] ;

[0032]

[0033] In the formula, , , , , as well as are the SNP gene frequency, genotype diversity index, gene marker association coefficient, MHC gene expression intensity, PRNP gene expression intensity and SP110 gene expression intensity of sheep with sample number n, respectively. b1, b2, c1 and c2 are all weight values, and the values ​​of b1, b2, c1 and c2 are adjusted and set by the user.

[0034] Preferably, the first comparison result is as follows:

[0035] when When , it means that the sheep with the current number n is not of high-quality breed;

[0036] when , it means that the sheep currently numbered n is of high-quality breed.

[0037] Preferably, in step S4, the gene matching coefficient Calculated by the following formula:

[0038]

[0039] In the formula, As the reference difference, the gene screening coefficient The value is obtained by integrating and calculating with the preset first threshold value Y. The adjustment value is obtained by integrating the third data set.

[0040] Preferably, the reference difference value and the adjustment value are calculated and obtained by the following formulas respectively;

[0041]

[0042]

[0043] Where: , as well as They are the BMPR1B gene expression intensity, GDF9 gene expression intensity and ESR1 gene expression intensity of the sheep with sample number n, is the gene screening coefficient coded as sheep, Y is the first threshold, d1, d2 and d3 are all weight values, and the values ​​of d1, d2 and d3 are adjusted and set by the user.

[0044] Preferably, the second comparison result is as follows;

[0045] when When, it means the two breeds of sheep are breeding compatible;

[0046] when or When , it means that the two breeds of sheep are not compatible for breeding;

[0047] is the genetic matching coefficient of the sheep with code number n-1, is the genetic matching coefficient of the sheep code n.

[0048] The present application also includes a sheep breeding gene data analysis system, including a data acquisition module, a data processing module, a data calculation module and a data analysis module;

[0049] The data collection module collects blood biological samples from different sheep species, thereby extracting multi-source data of biological characteristics of different sheep;

[0050] A data processing module processes the extracted multi-source data and reorganizes them into a first data set, a second data set, and a third data set;

[0051] Data calculation module, generates gene screening coefficients through calculation and genetic matching coefficient ;

[0052] Data analysis module, and gene screening coefficient The first comparison result is generated by comparing the first threshold value Y with the preset first threshold value Y, and the first comparison result is generated by comparing the first threshold value Y with the preset first threshold value Y. The DNA sequence is compared with the preset R to generate a second comparison result, and the matching degree of the two groups of DNA is determined according to the second comparison result. According to the second matching result, sheep with compatible DNA are bred.

[0053] The present invention has the following beneficial effects:

[0054] 1. This sheep breeding gene data analysis method achieves full-chain optimization from gene data to breeding decisions through systematic data acquisition, processing, screening, matching and breeding steps. This method not only improves the recognition accuracy of high-quality genes, but also optimizes gene matching and pairing strategies, making the breeding process more scientific and efficient. Ultimately, this improvement increases the breeding rate, improves the quality of offspring, reduces genetic risks, speeds up the breeding process, and achieves a comprehensive improvement in sheep breeding results.

[0055] 2. This sheep breeding gene data analysis method can intuitively determine whether the sheep is a high-quality breed by setting a clear comparison threshold Y. This method uses a simple comparison The value and threshold Y are used to classify high-quality species, making the screening process clearer and easier to operate. By comparing with the preset threshold value Y, high-quality populations and non-high-quality populations can be quickly screened out. This standardized method reduces the complexity of manual screening, improves the efficiency and accuracy of screening, and sets the threshold value Y and makes judgments based on it, providing a scientific basis for breeding decisions. Through quantitative standards, the screening of high-quality species is more practical and targeted. The setting of the threshold value Y can be adjusted according to different breeding needs and goals, increasing the flexibility of the method. Users can optimize the threshold value Y according to actual conditions to better meet breeding goals. The comparison with the threshold value Y can not only effectively distinguish high-quality from non-high-quality varieties, but also accurately identify high-quality genotypes based on actual data, thereby optimizing breeding strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a diagram of the method steps of the present invention;

[0057] Figure 2 The figure is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0059] Example 1

[0060] See also Figure 1 , a sheep breeding gene data analysis method, the specific steps are as follows:

[0061] S1. Data acquisition: blood biological samples are collected from different sheep species to extract multi-source data of biological characteristics of different sheep;

[0062] S2, data processing, processing the extracted multi-source data and reorganizing them into a first data set, a second data set, and a third data set;

[0063] S3, DNA screening, integrating and calculating the first data set and the second data set to generate the gene screening coefficient , and the gene screening coefficient Compare with a preset first threshold value Y to generate a first comparison result, and determine whether the current gene is a high-quality gene according to the first comparison result;

[0064] S4, DNA matching, calculate the obtained gene screening coefficient The gene matching coefficient is generated by integrating and calculating with the preset first threshold Y and the third data set. , multiple sets of gene matching coefficients Compare with the preset R to generate a second comparison result, and determine the matching degree of the two sets of DNA according to the second comparison result;

[0065] S5. Breeding sheep with compatible DNA according to the second matching result.

[0066] In this embodiment: In step S1, by collecting blood biological samples from different types of sheep, multi-source data of biological characteristics of sheep can be extracted. This process ensures the acquisition of rich individual genome and phenotypic information, laying a data foundation for subsequent analysis. By collecting blood samples and extracting biological characteristic data, the genetic background and physiological characteristics of sheep can be fully understood, providing the necessary raw data for the screening and matching of high-quality genes. This step not only provides high-quality data support, but also lays a solid foundation for further data processing and analysis.

[0067] In step S2, the extracted multi-source data are sorted and re-summarized into a first data set, a second data set, and a third data set. This sorting makes the data clearer and more orderly in structure, and provides a data format that is easy to operate for subsequent analysis. By dividing the data into different data sets, each type of data can be processed in a targeted manner to improve the operability and analysis efficiency of the data. This process enables the data to more accurately reflect the genetic characteristics and phenotypic performance of sheep in subsequent analysis, thereby improving the efficiency of data processing and the accuracy of analysis.

[0068] In step S3, the gene screening coefficient is generated by integrating the first data set and the second data set. , and compare it with the preset first threshold value Y to generate a first comparison result. This process can effectively screen out whether it is a high-quality gene. Through this screening method, high-quality genes with significant effects can be accurately identified, thereby improving breeding results. The accuracy of the screening results directly affects the selection of high-quality genes, ensures the transmission of high-quality genes in the breeding process, and improves the scientificity and effectiveness of breeding.

[0069] In step S4, the obtained gene screening coefficient is calculated The gene matching coefficient is generated by integrating and calculating with the preset first threshold Y and the third data set. , and multiple sets of gene matching coefficients Compare with the preset R to generate the second comparison result. Through this process, the matching degree of the two sets of DNA can be judged to ensure the correct pairing of excellent genes. The accuracy of gene matching is crucial to improving breeding efficiency. Through scientific matching analysis, it can ensure that high-quality genes are effectively inherited in the next generation, improving the accuracy and success rate of breeding.

[0070] In step S5, the sheep with DNA adaptation are bred according to the second matching result. By applying the matching result to the actual breeding process, the effective transmission of high-quality genes is ensured. This process optimizes gene pairing and makes the breeding strategy more scientific and efficient. By selecting suitable sheep for breeding based on the matching analysis results, the high-quality performance of the offspring can be maximized, thereby improving the production performance and economic value of the overall population.

[0071] This method achieves full chain optimization from genetic data to breeding decisions through systematic data acquisition, processing, screening, matching and breeding steps. This method not only improves the identification accuracy of high-quality genes, but also optimizes gene matching and pairing strategies, making the breeding process more scientific and efficient. Ultimately, this improvement increases the breeding rate, improves the quality of offspring, reduces genetic risks, speeds up the breeding process, and achieves a comprehensive improvement in sheep breeding results.

[0072] Example 2

[0073] See also Figure 1 ,In step S1, the multi-source data includes a phenotype data set, an immune data set, and a breeding data set;

[0074] The representational data set includes SNP gene frequency, genotype diversity index and gene marker association coefficient;

[0075] The immune data set includes the MHC gene expression intensity, PRNP gene expression intensity, and SP110 gene expression intensity;

[0076] The breeding data set includes BMPR1B gene expression intensity, GDF9 gene expression intensity, and ESR1 gene expression intensity.

[0077] In this example: By analyzing the SNP (single nucleotide polymorphism) gene frequency, the distribution of different gene variations in the sheep population can be revealed. This helps to identify genetic variations associated with target traits and provides basic data for the screening of high-quality genes. High-frequency SNPs may be associated with excellent traits, thereby supporting subsequent precise screening and breeding decisions.

[0078] Measuring the diversity index of genotypes can assess the genetic variation of a population. A high diversity index generally indicates rich genetic resources, which helps increase the adaptability and disease resistance of the population. This data provides a reference for ensuring the genetic diversity and long-term health of the population.

[0079] By calculating the association coefficient between genetic markers, gene marker combinations associated with target traits can be identified. This information is crucial for understanding the interactions between different genes and selecting gene combinations with synergistic effects, which helps optimize breeding strategies.

[0080] The intensity of MHC (major histocompatibility complex) gene expression reflects the immune capacity of sheep. High-intensity MHC genes are usually associated with strong immune responses and can improve the sheep's resistance to diseases. Analysis of the intensity of MHC gene expression helps to select individuals with strong disease resistance and enhance the health level of the population.

[0081] PRNP (universal variable protein gene) is associated with scrapie in sheep. Analysis of the PRNP gene can help screen out individuals that are resistant to scrapie, thereby reducing the impact of this infectious disease on sheep populations and improving overall immunity and reproductive stability.

[0082] The SP110 gene is associated with the regulation of the immune system. By analyzing the SP110 gene, it is possible to assess the sheep's immune system's ability to respond to different environmental stresses. This helps select individuals with strong adaptability and enhance the population's survival and reproductive capacity.

[0083] The BMPR1B gene is associated with multiparity in sheep. High levels of the BMPR1B gene are usually associated with a higher number of fetuses, which can improve the reproductive capacity of sheep. By measuring the expression intensity of the BMPR1B gene, individuals with high reproductive potential can be selected, thereby improving reproductive efficiency.

[0084] The GDF9 (Growth Differentiation Factor 9) gene plays an important role in sheep reproduction. By analyzing the expression intensity of the GDF9 gene, individuals with a positive impact on reproductive performance can be identified. This helps to optimize breeding plans and improve sheep reproductive success and production benefits.

[0085] ESR1 (estrogen receptor 1 gene) is related to the reproductive cycle and reproductive performance of sheep. High-intensity ESR1 gene can enhance the reproductive ability of sheep and ensure a higher reproductive success rate. Analysis of the expression intensity of ESR1 gene helps to select suitable breeding individuals and improve reproductive performance.

[0086] In step S1, through the above-mentioned collection and analysis of these multi-source data, the genetic characteristics and reproductive capacity of sheep can be effectively evaluated comprehensively. The phenotype dataset helps to identify gene markers related to traits, the immune dataset provides genetic information for fighting diseases, and the breeding dataset is directly related to the reproductive capacity of sheep. This comprehensive data collection and analysis not only provides a solid foundation for the screening of high-quality genes, but also optimizes reproduction and health management, and ultimately realizes the scientific and efficient sheep breeding process.

[0087] Example 3

[0088] See also Figure 1 In step S2, data preprocessing is performed on the appearance data set, the immune data set and the breeding data set collected in step S1, and the preprocessed multi-source data sets are reintegrated into the first data set, the second data set and the third data set according to the numbers;

[0089] The first data set includes SNP gene frequency, genotype diversity index, and gene marker association coefficient;

[0090] The SNP gene frequencies are recorded according to the numbers: , , ,..., ;

[0091] The genotype diversity index is recorded as follows: , , ,..., ;

[0092] The gene marker association coefficients are recorded according to the numbers: , , ,..., ;

[0093] The second data set includes the MHC gene expression intensity, the PRNP gene expression intensity, and the SP110 gene expression intensity;

[0094] The MHC gene expression intensities are recorded according to the numbers: , , ,..., ;

[0095] According to the expression intensity of PRNP gene, they are recorded as follows: , , ,..., ;

[0096] According to the expression intensity of SP110 gene, they are recorded as follows: , , ,..., ;

[0097] The third data set includes BMPR1B gene expression intensity, GDF9 gene expression intensity, and ESR1 gene expression intensity;

[0098] The expression intensity of BMPR1B gene is recorded as follows: , , ,..., ;

[0099] According to the expression intensity of GDF9 gene, they are recorded as follows: , , ,..., ;

[0100] According to the expression intensity of ESR1 gene, they are recorded as follows: , , ,..., .

[0101] In this embodiment: Through the systematic preprocessing and integration of sheep breeding data, this method significantly improves the scientificity and efficiency of data processing. The multi-source data set is integrated into a clear first, second, and third data set, making the analysis of genotype frequency, immune gene expression intensity, and breeding gene expression intensity more accurate. This method not only optimizes the operability of the data, but also improves the accuracy of high-quality gene screening, and improves the scientificity and efficiency of breeding decisions. Compared with traditional methods, this improvement can achieve the screening of high-quality genes more quickly, improve the breeding success rate, and thus improve the reproductive capacity and overall quality of sheep.

[0102] Example 4

[0103] See also Figure 1 , gene screening coefficient in step S3 Calculated by the following formula:

[0104]

[0105] Wherein: a1 and a2 are weight values, the values ​​of a1 and a2 are adjusted and set by the user, e is the base function, S1 and S2 are the first reference value and the second reference value respectively, wherein the first reference value S1 is obtained by integrating the first data set and the second reference value S2 is obtained by integrating the second data set and calculated.

[0106] The first reference value S1 and the second reference value S2 are calculated and obtained by the following formulas respectively;

[0107] ;

[0108]

[0109] In the formula, , , , , as well as are the SNP gene frequency, genotype diversity index, gene marker association coefficient, MHC gene expression intensity, PRNP gene expression intensity and SP110 gene expression intensity of sheep with sample number n, respectively. b1, b2, c1 and c2 are all weight values, and the values ​​of b1, b2, c1 and c2 are adjusted and set by the user.

[0110] In this embodiment: the gene screening coefficient calculated by the formula The weight values ​​a1 and a2 are introduced, which allows users to adjust the weights according to specific breeding needs and the importance of data, thereby improving the flexibility and accuracy of screening. The calculation method of the first reference value S1 and the second reference value S2 integrates multiple data into one indicator, making genetic screening more comprehensive. S1 is calculated through multiple parameters of the representation data set, and S2 integrates the relevant gene information of the immune data set. This comprehensive calculation can more comprehensively reflect the genetic characteristics of sheep. The settings of weight values ​​b1, b2, c1, and c2 allow users to adjust according to specific experimental conditions or breeding goals, thereby enhancing the applicability and flexibility of the formula. The use of the base function e to process data is helpful for modeling nonlinear relationships, so that the gene screening coefficient can more accurately reflect the complex relationship between genes and traits. Factors such as gene frequency, genotype diversity index, and gene marker association coefficient are integrated in a calculation formula, providing a comprehensive indicator for evaluating gene quality. This systematic integration improves the efficiency and accuracy of data analysis.

[0111] By adopting the above-mentioned gene screening coefficient calculation method, accurate gene screening coefficient calculation and flexible weight adjustment make breeding decisions more scientific and personalized. By integrating reference values ​​from different data sources, the method not only improves the accuracy of gene screening, but also ensures the comprehensiveness of the screening process. This improvement makes gene screening more adaptable to actual needs, improves breeding efficiency and the quality of offspring. Compared with traditional methods, this method provides a more accurate and comprehensive data analysis method, which can significantly improve breeding results and scientificity.

[0112] Example 5

[0113] See also Figure 1 ,The first comparison results are as follows;

[0114] when When , it means that the sheep with the current number n is not of high-quality breed;

[0115] when , it means that the sheep currently numbered n is of high-quality breed.

[0116] In this embodiment, by setting a clear comparison threshold Y, it is possible to intuitively determine whether the sheep is of high quality. The value and threshold Y are used to classify high-quality species, making the screening process clearer and easier to operate. By comparing with the preset threshold value Y, high-quality populations and non-high-quality populations can be quickly screened out. This standardized method reduces the complexity of manual screening, improves the efficiency and accuracy of screening, and sets the threshold value Y and makes judgments based on it, providing a scientific basis for breeding decisions. Through quantitative standards, the screening of high-quality species is more practical and targeted. The setting of the threshold value Y can be adjusted according to different breeding needs and goals, increasing the flexibility of the method. Users can optimize the threshold value Y according to actual conditions to better meet breeding goals. The comparison with the threshold value Y can not only effectively distinguish high-quality from non-high-quality varieties, but also accurately identify high-quality genotypes based on actual data, thereby optimizing breeding strategies.

[0117] The above-mentioned improvement in the criteria for judging high-quality breeds has significantly improved the accuracy and efficiency of sheep genetic data analysis methods. This method provides a standardized and efficient screening method based on the value of high-quality seeds. This improvement not only improves the efficiency of high-quality seed screening, but also enhances the scientificity and reliability of breeding decisions. Compared with traditional breeding methods, this analysis method based on quantitative standards makes the screening process simpler and more intuitive, and can more accurately identify and select high-quality genotypes, thereby improving the overall effect and success rate of breeding.

[0118] Example 6

[0119] See also Figure 1 , in step S4, the gene matching coefficient Calculated by the following formula:

[0120]

[0121] In the formula, is the reference difference, which is obtained by integrating the gene screening coefficient with the preset first threshold value Y. The adjustment value is obtained by integrating the third data set.

[0122] The reference difference value and adjustment value are calculated and obtained by the following formulas respectively;

[0123]

[0124]

[0125] Where: , as well as They are the BMPR1B gene expression intensity, GDF9 gene expression intensity and ESR1 gene expression intensity of the sheep with sample number n, is the gene screening coefficient coded as sheep, Y is the first threshold, d1, d2 and d3 are all weight values, and the values ​​of d1, d2 and d3 are adjusted and set by the user.

[0126] In this embodiment: by using the combined gene screening coefficient and adjustment value Calculate the gene matching coefficient The calculation is more accurate and can effectively reflect the matching degree between different genes. This method provides a relatively simple and intuitive way to judge the quality of genes, which is convenient for rapid screening and decision-making. It combines the influencing factors of different gene expression intensities together. Through weighting and integration, the calculation of gene matching coefficient can more comprehensively consider the contribution of each gene. By adjusting the weight values ​​d1, d2 and d3, the adjustment value can be flexibly adjusted. The calculation method of genetic matching coefficient can be adapted to different breeding goals and needs. This flexibility allows users to optimize the calculation method of genetic matching coefficient according to actual conditions.

[0127] The gene matching coefficient calculation method in step S4 significantly improves the accuracy and reliability of sheep breeding gene data analysis through precise formulas and flexible parameter settings. Compared with the traditional gene pairing analysis method, this new method makes the calculation of the gene matching coefficient more comprehensive and scientific by combining reference differences and adjustment values. The quantification of reference differences provides an intuitive way to evaluate the effect of gene screening, while the integration of adjustment values ​​ensures that the combined effects of multiple genetic factors are taken into account. This improvement makes the evaluation of the effect of gene pairing more accurate and can better support breeding decisions, thereby improving the efficiency and accuracy of high-quality gene screening throughout the breeding process.

[0128] Example 7

[0129] The second comparison results are as follows;

[0130] when When, it means the two breeds of sheep are breeding compatible;

[0131] when or When , it means that the two breeds of sheep are not compatible for breeding;

[0132] is the genetic matching coefficient of the sheep with code number n-1, is the genetic matching coefficient of the sheep code n.

[0133] In this embodiment: the second comparison result method in step S4, by setting a percentage range to judge the breeding suitability of the sheep breed, provides a more scientific and flexible adaptation evaluation standard compared to the traditional absolute value judgment method. This method can dynamically evaluate the changes in the gene matching coefficient, and by flexibly adjusting the matching range, ensure the accuracy and reliability of the adaptation judgment. Compared with the traditional method, this improvement better adapts to the complexity of the actual breeding environment, reduces the misjudgment caused by data fluctuations, and improves the scientific nature of the overall breeding decision. Through accurate and flexible matching evaluation, this method effectively improves the quality of sheep breeding and ensures the effective transmission of high-quality genes.

[0134] This application also includes a sheep breeding gene data analysis system, see Figure 2 , including a data acquisition module, a data processing module, a data calculation module and a data analysis module;

[0135] The data collection module collects blood biological samples from different sheep species, thereby extracting multi-source data of biological characteristics of different sheep;

[0136] A data processing module processes the extracted multi-source data and reorganizes them into a first data set, a second data set, and a third data set;

[0137] Data calculation module, generates gene screening coefficients through calculation And the genetic matching coefficient ;

[0138] Data analysis module, and gene screening coefficient The first comparison result is generated by comparing the first threshold value Y with the preset first threshold value Y, and the first comparison result is generated by comparing the first threshold value Y with the preset first threshold value Y. The DNA sequence is compared with the preset R to generate a second comparison result, and the matching degree of the two groups of DNA is determined according to the second comparison result. According to the second matching result, sheep with compatible DNA are bred.

[0139] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0140] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A sheep breeding gene data analysis method, characterized in that: The specific steps are as follows: S1. Data acquisition: blood biological samples are collected from different sheep species to extract multi-source data of biological characteristics of different sheep. The multi-source data includes appearance data set, immune data set and breeding data set; The representational data set includes SNP gene frequency, genotype diversity index and gene marker association coefficient; The immune data set includes the MHC gene expression intensity, PRNP gene expression intensity, and SP110 gene expression intensity; The breeding data set includes BMPR1B gene expression intensity, GDF9 gene expression intensity, and ESR1 gene expression intensity; S2, data processing, performing data preprocessing on the phenotype data set, immune data set and breeding data set collected in step S1, and reintegrating the preprocessed multi-source data sets into a first data set, a second data set and a third data set according to the numbers; The first data set includes SNP gene frequency, genotype diversity index, and gene marker association coefficient; The SNP gene frequencies are recorded according to the numbers: , , ,..., ; The genotype diversity index is recorded as follows: , , ,..., ; The gene marker association coefficients are recorded according to the numbers: , , ,..., ; The second data set includes the MHC gene expression intensity, the PRNP gene expression intensity, and the SP110 gene expression intensity; The MHC gene expression intensity is recorded according to the number: , , ,..., ; According to the expression intensity of PRNP gene, they are recorded as follows: , , ,..., ; According to the expression intensity of SP110 gene, they are recorded as follows: , , ,..., ; The third data set includes BMPR1B gene expression intensity, GDF9 gene expression intensity, and ESR1 gene expression intensity; The expression intensity of BMPR1B gene is recorded as follows: , , ,..., ; According to the expression intensity of GDF9 gene, they are recorded as follows: , , ,..., ; According to the expression intensity of ESR1 gene, they are recorded as follows: , , ,..., ; S3, DNA screening, integrating and calculating the first data set and the second data set to generate the gene screening coefficient , and the gene screening coefficient Compare with a preset first threshold value Y to generate a first comparison result, and determine whether the current gene is a high-quality gene according to the first comparison result; Gene screening coefficient in step S3 Obtained by the following formula: ； Wherein: a1 and a2 are weight values, and the values ​​of a1 and a2 are adjusted and set by the user, e is the base function, S1 and S2 are the first reference value and the second reference value, respectively, wherein the first reference value S1 is obtained by integrating the first data set, and the second reference value S2 is obtained by integrating the second data set; The first comparison results are as follows: when When , it means that the sheep with the current number n is not of high-quality breed; when When , it means that the sheep with the current number n is of high-quality breed; S4, DNA matching, calculate the obtained gene screening coefficient The gene matching coefficient is generated by integrating and calculating with the preset first threshold Y and the third data set. , multiple sets of gene matching coefficients Compare with the preset R to generate a second comparison result, and determine the matching degree of the two sets of DNA according to the second comparison result; S5. Breeding sheep with compatible DNA according to the second matching result.

2. The sheep breeding gene data analysis method according to claim 1, characterized in that: The first reference value S1 and the second reference value S2 are calculated and obtained by the following formulas respectively; ； ； In the formula, , , , as well as are the SNP gene frequency, genotype diversity index, gene marker association coefficient, MHC gene expression intensity, PRNP gene expression intensity and SP110 gene expression intensity of sheep with sample number n, respectively. b1, b2, c1 and c2 are all weight values, and the values ​​of b1, b2, c1 and c2 are adjusted and set by the user.

3. The sheep breeding gene data analysis method according to claim 1, characterized in that: In step S4, the gene matching coefficient Calculated by the following formula: ； In the formula, As the reference difference, the gene screening coefficient The value is obtained by integrating and calculating with the preset first threshold value Y. The adjustment value is obtained by integrating the third data set.

4. The sheep breeding gene data analysis method according to claim 3, characterized in that: The reference difference value and adjustment value are calculated and obtained by the following formulas respectively; ； ； Where: , as well as They are the BMPR1B gene expression intensity, GDF9 gene expression intensity and ESR1 gene expression intensity of the sheep with sample number n, is the gene screening coefficient coded as sheep, Y is the first threshold, d1, d2 and d3 are all weight values, and the values ​​of d1, d2 and d3 are adjusted and set by the user.

5. The sheep breeding gene data analysis method according to claim 4, characterized in that: The second comparison results are as follows; when When, it means the two breeds of sheep are breeding compatible; when or When , it means that the two breeds of sheep are not compatible for breeding; is the genetic matching coefficient of the sheep with code number n-1, is the genetic matching coefficient of the sheep code n.

6. A sheep breeding gene data analysis system, comprising the sheep breeding gene data analysis method according to any one of claims 1 to 5, characterized in that: It includes a data acquisition module, a data processing module, a data calculation module and a data analysis module; The data collection module collects blood biological samples from different sheep species, thereby extracting multi-source data of biological characteristics of different sheep; A data processing module processes the extracted multi-source data and reorganizes them into a first data set, a second data set, and a third data set; Data calculation module, generates gene screening coefficients through calculation and genetic matching coefficient ; Data analysis module, and gene screening coefficient The first comparison result is generated by comparing the first threshold value Y with the preset first threshold value Y, and the first comparison result is generated by comparing the first threshold value Y with the preset first threshold value Y. The DNA sequence is compared with the preset R to generate a second comparison result, and the matching degree of the two groups of DNA is determined according to the second comparison result. According to the second matching result, sheep with compatible DNA are bred.

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