Sheep variety breeding method combining phenotypic data and gene information

Abstract

The invention relates to the technical field of livestock breeding, and discloses a sheep variety breeding method combining phenotype data and gene information, and the method comprises the steps: building a data collection module, a data analysis module, a seed selection and reproduction module, a monitoring module, a genetic stability evaluation module and a feedback adjustment module; the data analysis module is used for analyzing data of the data acquisition module, calculating and evaluating growth, gene conditions and environmental correlation and providing a basis for breeding, a seed selection strategy is formulated in the seed selection and reproduction module, the monitoring module records multi-generation genetic data, and the genetic stability evaluation module is used for evaluating the genetic stability of the sheep flock subjected to multi-generation breeding. And comparing and analyzing the change conditions of gene frequencies and genotype distribution genetic parameters before and after multi-generation breeding, calculating a historical genetic stability coefficient As according to the gene frequencies before and after multi-generation breeding, evaluating a breeding effect and finding a problem improvement direction, and adjusting seed selection by a feedback adjustment module according to an evaluation result of a genetic stability evaluation module.

Description

Technical Field

[0001] The present invention relates to the technical field of livestock breeding, and particularly to a method for breeding sheep varieties by combining phenotypic data and genetic information. Background Art

[0002] The existing methods for breeding sheep varieties mainly focus on ordinary breeding, and can only measure and evaluate important economic traits such as growth rate, body weight, and feed conversion rate. For example, a step-by-step selection and elimination system is adopted during the weaning period, growing period, adult period, and fattening period. However, this traditional breeding method cannot accurately measure and evaluate important economic traits such as growth rate, body weight, wool length, fineness, and strength during the growth and development process of sheep, nor can it analyze and identify the genotypes related to these traits.

[0003] With the development of modern molecular biology technology, technologies such as gene chips and genome-wide association analysis have been widely applied in the field of animal breeding. Through these technologies, the genotypes of sheep can be analyzed in detail, and gene markers or candidate genes related to important economic traits can be determined. Thus, individuals carrying excellent genotypes can be preferentially considered as breeding sheep during seed selection, and marker-assisted selection or gene editing can be carried out during the breeding process to obtain offspring with better production performance and adaptability. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a method for breeding sheep varieties by combining phenotypic data and genetic information, which has the advantages of accurately measuring and evaluating important economic traits during the growth and development process of sheep, and analyzing and identifying genotypes related to important economic traits, and solves the problems that the traditional breeding method cannot accurately measure and evaluate important economic traits during the growth and development process of sheep, nor can it analyze and identify genotypes related to important economic traits.

[0006] (2) Technical Solutions

[0007] To achieve the above object, the present invention provides the following technical solution: A method for breeding sheep varieties by combining phenotypic data and genetic information, comprising the following steps:

[0008] Step 1: Establish a data acquisition module, a data analysis module, a seed selection and breeding module, a monitoring module, a genetic stability evaluation module, and a feedback adjustment module in sequence;

[0009] Step 2: The data acquisition module is responsible for collecting growth traits, gene detection, and environmental data of sheep at each growth stage;

[0010] Step 3: The data analysis module is used to analyze the data of the data collection module, calculate and evaluate the growth, gene conditions, and environmental correlations, providing a basis for breeding selection;

[0011] Step 4: The seed selection and reproduction module formulates a seed selection strategy based on the evaluation results of the data analysis module, conducts marker-assisted selection, and plans the reproduction plan to improve the quality of the sheep flock;

[0012] Step 5: The monitoring module monitors the growth performance and gene expression of the sheep flock in the long term and records multi-generation genetic data;

[0013] Step 6: The genetic stability evaluation module compares and analyzes the changes in genetic parameters such as gene frequencies and genotype distributions before and after multi-generation breeding based on the multi-generation genetic data of the sheep flock collected by the monitoring module, and calculates the historical genetic stability coefficient As based on the gene frequencies before and after multi-generation breeding, evaluates the breeding effect, and discovers the improvement direction of problems;

[0014] Step 7: The feedback adjustment module adjusts the seed selection, reproduction strategies, and parameter optimization according to the evaluation results of the genetic stability evaluation module.

[0015] Preferably, the data collection module includes a growth trait data collection unit, a gene detection data collection unit, and a growth environment data recording unit. The growth trait data collection unit collects growth trait data of sheep at different growth stages through measuring devices. The gene detection data collection unit detects gene information by collecting sheep tissue samples to obtain gene detection data. The growth environment data recording unit monitors and records the growth environment data and feeding data of sheep in real time.

[0016] Preferably, the growth trait data collection unit numbers the data of the wool length, fineness, and strength scores according to the characteristics of the growth trait data. The wool length, fineness, and strength scores are numbered as q1, q2, and q3 respectively.

[0017] Preferably, the gene detection data collection unit numbers the influence degree of gene markers on traits, the expression level of gene markers in individuals, and the expression quantity of gene markers according to the characteristics of the gene detection data. The influence degree of gene markers on traits is numbered as B1, B2, B3,... B n , the expression quantity of the gene marker is numbered as α1, α2, α3,... α n , the expression level of the gene marker in the individual is numbered as μ1, μ2, μ3,... μ m .

[0018] Preferably, the growth environment data recording unit numbers the influence degree of environmental factors on genes according to the characteristics of the growth environment data and feeding data of sheep. The influence degree of environmental factors on genes is numbered as F1, F2, F3,... Fm 。

[0019] Preferably, the data analysis module includes a phenotypic data analysis unit, a gene data analysis unit, and an environment-data correlation analysis unit.

[0020] Preferably, the phenotypic data analysis unit calculates the comprehensive wool quality index Vx based on the growth trait data, and its calculation formula is:

[0021] Vx = 0.4*q1 + 0.3*q2 + 0.4*q3

[0022] In the formula, Vx represents the comprehensive wool quality index, and q1, q2, and q3 respectively represent the wool length, fineness, and strength scores;

[0023] The seed selection and breeding module selects individuals with a high comprehensive wool quality index Vx as breeding sheep according to the comprehensive wool quality index Vx.

[0024] Preferably, the gene data analysis unit calculates the correlation degree Cr between the gene marker and the trait based on the gene detection data, and its calculation formula is:

[0025]

[0026] In the formula, Cr represents the correlation degree between the gene marker and the trait, B1, B2, B3,... B n represents the influence degree of the gene marker on the trait, B i represents the influence coefficient of the i-th gene marker on the trait, α1, α2, α3,... α n represents the expression level of the gene marker, α i represents the expression level of the i-th gene marker, and n represents the number of statistical gene markers;

[0027] The seed selection and breeding module selects individuals with a high correlation score as breeding sheep according to the correlation degree Cr between the gene marker and the trait.

[0028] Preferably, the environment-data correlation analysis unit calculates the environment-gene correlation Dz based on the gene detection data, the sheep growth environment data, and the feeding data, and its calculation formula is:

[0029]

[0030] In the formula, Dz represents the environment-gene correlation, F1, F2, F3,... F m represents the influence degree of the environmental factor on the gene, F i represents the influence degree of the j-th environmental factor on the gene, μ1, μ2, μ3,... μ m represents the expression level of the gene marker in the individual, μ jIt represents the expression level of the j-th gene marker in an individual, and m represents the number of gene markers to be statistically analyzed;

[0031] According to the environmental and gene correlation Dz, individuals with stable performance under different environmental conditions are selected as breeding sheep.

[0032] Preferably, the genetic stability evaluation module collects the genetic data of the flock after multiple generations of breeding through the monitoring module, compares and analyzes the changes in genetic parameters such as gene frequencies and genotype distributions before and after multiple generations of breeding, and calculates the historical genetic stability coefficient As based on the gene frequencies before and after multiple generations of breeding. Its calculation formula is:

[0033] Historical genetic stability coefficient As = (gene frequency of a certain gene in sheep after breeding - gene frequency of a certain gene in sheep before breeding) ÷ gene frequency of a certain gene before breeding

[0034] In the formula, the gene frequencies of a certain gene in sheep before and after breeding are selected from: the genotype frequency, allele frequency, polymorphic information content, heterozygosity, and effective number of alleles of the BMPR-IB gene determined by PCR amplification, enzyme digestion, and RFLP analysis methods;

[0035] The closer the historical genetic stability coefficient is to 0, the higher the genetic stability.

[0036] Compared with the prior art, the present invention provides a method for breeding sheep varieties that combines phenotypic data and gene information, and has the following beneficial effects:

[0037] 1. By comprehensively considering wool length, fineness, and strength in the calculation formula of the wool comprehensive quality index Vx, these three important growth trait indicators can more comprehensively reflect the comprehensive quality of wool, rather than solely relying on a single trait to select breeding sheep. This can ensure that the selected breeding sheep have advantages in multiple key traits, providing a basis for cultivating high-quality sheep varieties. The selection and breeding module selects individuals with a high wool comprehensive quality index as breeding sheep by setting a threshold for the excellent wool comprehensive quality index and based on the comparison data between the wool comprehensive quality index Vx and the excellent wool comprehensive quality index threshold. When the wool comprehensive quality index Vx is close to the excellent wool comprehensive quality index threshold, it indicates that the wool comprehensive quality of the current breed of sheep is excellent and can be used as a breeding object. Through this method, the most suitable breeding sheep can be accurately screened, gradually promoting the improvement and evolution of the flock in terms of wool quality.

[0038] 2. In the present invention, the gene data analysis unit calculates the association degree Cr between gene markers and traits based on gene detection data. The seed selection and breeding module selects individuals with high association scores as breeding sheep according to the magnitude of the association degree Cr between gene markers and traits, and sorts and screens the breeding sheep. When the association degree Cr between gene markers and traits approaches 1, it indicates that the gene markers have a strong decisive effect on the traits. At this time, individuals most closely associated with the target traits can be selected as breeding sheep, thereby increasing the selection intensity. High-intensity selection can change the gene frequency of the population more quickly, enabling excellent genes to spread rapidly in the population, accelerating the breeding process, and improving the breeding efficiency.

[0039] 3. In the present invention, the environment and data association analysis unit calculates the environmental and gene correlation Dz based on gene detection data, sheep growth environment data, and feeding data. The seed selection and breeding module selects individuals with stable performance under different environmental conditions as breeding sheep according to the environmental and gene correlation Dz. Since there are differences in the natural environment and feeding conditions in different regions, by analyzing the magnitude of the environmental and gene correlation Dz, when the environmental and gene correlation Dz approaches 1, it indicates that the performance of the genotype under different environmental conditions has a high degree of consistency. Selecting individuals with stable performance under different environmental conditions as breeding sheep can ensure that the cultivated sheep breeds or strains can better adapt to various complex environmental changes and reduce problems such as a decline in production performance caused by environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to Figure 1 , a method for breeding sheep breeds combining phenotypic data and gene information, comprising the following steps:

[0043] Step 1: Successively establish a data acquisition module, a data analysis module, a seed selection and breeding module, a monitoring module, a genetic stability evaluation module, and a feedback adjustment module, clarify the unique roles and key responsibilities of each module in the sheep breed breeding process, ensure close cooperation and efficient operation among the modules, and provide a strong guarantee for achieving precise and efficient sheep breed breeding.

[0044] Step 2: The data acquisition module is responsible for collecting growth traits, gene detection, and environmental data at various growth stages of sheep, ensuring accuracy and traceability;

[0045] Step 3: The data analysis module is used to analyze the data from the data acquisition module, calculate and evaluate growth, gene conditions, and environmental correlations, providing a basis for breeding selection;

[0046] Step 4: The seed selection and breeding module formulates a seed selection strategy based on the evaluation results of the data analysis module, conducts marker-assisted selection, and plans the breeding program to improve the quality of the sheep flock;

[0047] Step 5: The monitoring module monitors the growth performance and gene expression of the sheep flock in the long term and records multi-generation genetic data; the growth monitoring module monitors the variety indicators of the preliminarily selected sheep through online monitoring devices and regularly collects tissue samples to detect changes in gene expression, understanding the interaction between genes and the environment;

[0048] Step 6: The genetic stability evaluation module analyzes the changes in genetic parameters such as gene frequencies and genotype distributions before and after multi-generation breeding based on the multi-generation genetic data of the sheep flock collected by the monitoring module, and calculates the historical genetic stability coefficient As according to the gene frequencies before and after multi-generation breeding to evaluate historical genetic stability, analyze genetic stability, evaluate the breeding effect, and discover problem improvement directions to ensure the stability of the genetic characteristics of the sheep flock;

[0049] Step 7: The feedback adjustment module adjusts the seed selection, breeding strategies, and parameter optimization according to the evaluation results of the genetic stability evaluation module.

[0050] The data acquisition module includes a growth trait data acquisition unit, a gene detection data acquisition unit, and a growth environment data recording unit. The growth trait data acquisition unit collects growth trait data of sheep at different growth stages through measuring devices (during the weaning, growing, adult, and fattening stages of sheep, calibrated measuring devices are used to collect growth trait data such as growth rate (daily weight gain), body weight, wool length (cm), fineness (microns), and strength (N), and record the time, measurement value, and information on environmental conditions during each measurement). The gene detection data acquisition unit detects gene information by collecting sheep tissue samples to obtain gene detection data (collect tissue samples of sheep's blood and hair, use gene chip technology to detect a large number of gene loci, and through genome-wide association analysis, accurately determine gene markers or candidate genes related to important economic traits, and detailed record relevant gene information. The growth environment data recording unit monitors and records sheep growth environment data and feeding data in real time (monitors and records environmental parameters such as temperature, humidity, and light duration of the sheep growth environment, as well as feeding-related data such as feed type, nutritional composition, and feeding amount).

[0051] The growth trait data acquisition unit numbers the data of wool length (cm), fineness (μm), and strength (N) scores according to the characteristics of growth trait data. The wool length (cm), fineness (μm), and strength (N) scores are numbered as q1, q2, and q3 respectively.

[0052] The gene detection data acquisition unit numbers the degree of influence of gene markers on traits, the expression level of gene markers in individuals, and the expression quantity of gene markers according to the characteristics of gene detection data. The degree of influence of gene markers on traits is numbered as B1, B2, B3, … B n , and the expression quantity of gene markers is numbered as α1, α2, α3, … α n , and the expression level of gene markers in individuals is numbered as μ1, μ2, μ3, … μ m .

[0053] The growth environment data recording unit numbers the degree of influence of environmental factors on genes according to the characteristics of sheep growth environment data and feeding data. The degree of influence of environmental factors on genes is numbered as F1, F2, F3, … F m .

[0054] The data analysis module includes a phenotypic data analysis unit, a gene data analysis unit, and an environment and data correlation analysis unit.

[0055] The phenotypic data analysis unit calculates the wool comprehensive quality index Vx according to the growth trait data. The calculation formula is:

[0056] Vx = 0.4 * q1 + 0.3 * q2 + 0.4 * q3

[0057] In the formula, Vx represents the wool comprehensive quality index, and q1, q2, and q3 represent the scores of wool length (cm), fineness (μm), and strength (N) respectively;

[0058] By calculating the wool comprehensive quality index Vx, the seed selection and breeding module sets a threshold for the excellent wool comprehensive quality index, and based on the comparison data between the wool comprehensive quality index Vx and the excellent wool comprehensive quality index threshold, selects individuals with a high wool comprehensive quality index as breeding sheep. When the wool comprehensive quality index Vx is close to the excellent wool comprehensive quality index threshold, it indicates that the wool comprehensive quality of the current breed of sheep is excellent and can be used as a seed selection object. When the wool comprehensive quality index Vx is far from the excellent wool comprehensive quality index threshold, it indicates that the wool comprehensive quality of the current breed of sheep is poor and cannot be used as a seed selection object;

[0059] The advantages are as follows: In the calculation formula of the comprehensive wool quality index Vx, the wool length, fineness, and strength are comprehensively considered. These three important growth trait indicators can more comprehensively reflect the comprehensive quality of wool, rather than solely relying on a single trait to select breeding sheep. This ensures that the selected breeding sheep have advantages in multiple key traits, providing a basis for cultivating high-quality sheep breeds. Moreover, by using a specific calculation formula to obtain the comprehensive wool quality index Vx, the abstract trait performance is transformed into specific numerical values, making the breeding selection process more objective and accurate, reducing the interference of human subjective factors, and enabling the precise screening of the most suitable breeding sheep. By continuously selecting individuals with a high comprehensive wool quality index as breeding sheep, the improvement and evolution of the flock in terms of wool quality can be gradually promoted.

[0060] The gene data analysis unit calculates the correlation degree Cr between gene markers and traits based on gene detection data. Its calculation formula is:

[0061]

[0062] In the formula, Cr represents the correlation degree between gene markers and traits, B1, B2, B3, … B n represents the influence degree of gene markers on traits (indicating the influence degree of each gene marker on traits, which can be positive or negative. Positive indicates positive correlation, and negative indicates negative correlation), B i represents the influence coefficient of the i-th gene marker on traits, α1, α2, α3, … α n represents the expression level of gene markers (indicating the expression level of each gene marker in an individual, obtained through gene chip sequencing technology), α i represents the expression level of the i-th gene marker, and n represents the number of gene markers counted;

[0063] The advantages are as follows: Through the gene data analysis unit calculating the correlation degree Cr between gene markers and traits based on gene detection data, the breeding selection and reproduction module selects individuals with a high correlation score as breeding sheep according to the magnitude of the correlation degree Cr between gene markers and traits, and sorts and screens the breeding sheep. When the correlation degree Cr between gene markers and traits approaches 1, it indicates that the gene markers have a strong decisive effect on traits. At this time, individuals most closely associated with the target traits can be selected as breeding sheep, thereby increasing the selection intensity. And high-intensity selection can more quickly change the gene frequency of the population, enabling excellent genes to spread rapidly in the population, accelerating the breeding process, and improving the breeding efficiency.

[0064] The environment and data correlation analysis unit calculates the environmental and gene correlation Dz based on gene detection data, sheep growth environment data, and feeding data. Its calculation formula is:

[0065]

[0066] In the formula, Dz represents the environmental and genetic correlation, F1, F2, F3, … F m represents the degree of influence of environmental factors on genes, F i represents the degree of influence of the j-th environmental factor on genes, which can be positive or negative. A positive value indicates positive correlation, and a negative value indicates negative correlation. μ1, μ2, μ3, … μ m represents the expression level of gene markers in an individual, μ j represents the expression level of the j-th gene marker in an individual, obtained through gene high-throughput sequencing. m represents the number of statistical gene markers;

[0067] The advantages are as follows: The environmental and data correlation analysis unit calculates the environmental and genetic correlation Dz based on gene detection data, sheep growth environment data, and feeding data. The breeding selection and reproduction module selects individuals with stable performance under different environmental conditions as breeding sheep according to the environmental and genetic correlation Dz. Since there are differences in natural environments and feeding conditions in different regions, by analyzing the magnitude of the environmental and genetic correlation Dz, when the environmental and genetic correlation Dz is close to 1, it indicates that the performance of genotypes under different environmental conditions has a high degree of consistency. Selecting individuals with stable performance under different environmental conditions as breeding sheep can ensure that the cultivated sheep breeds or strains can better adapt to various complex environmental changes and reduce problems such as a decline in production performance caused by environmental changes.

[0068] The genetic stability evaluation module collects the genetic data of the flock after multiple generations of breeding selection through the monitoring module, compares and analyzes the changes in genetic parameters such as gene frequencies and genotype distributions before and after multiple generations of breeding selection, and calculates the historical genetic stability coefficient As based on the gene frequencies before and after multiple generations of breeding selection. The calculation formula is: Historical genetic stability coefficient As = Gene frequency of a certain gene in sheep after breeding selection (indicating the frequency of this gene in the flock after the breeding selection process) - Gene frequency of a certain gene in sheep before breeding selection (indicating the frequency of this gene in the flock before the start of the breeding selection) ÷ Gene frequency before breeding selection. The closer the historical genetic stability coefficient is to 0, the higher the genetic stability (this means that the change in gene frequency before and after breeding selection is smaller, and the genetic characteristics of the flock are more stable. This coefficient can help us evaluate the impact of the breeding selection process on the genetic stability of the flock, ensure that the breeding selection results can be stably inherited to future generations, and achieve the continuous improvement of the breed). In the formula, the gene frequencies of a certain gene in sheep before and after breeding selection are selected from one of the genotype frequencies, allele frequencies, polymorphic information content (PIC), heterozygosity (He), and effective number of alleles (Ne) of the BMPR-IB gene determined by PCR amplification, enzyme digestion, and RFLP analysis methods;

[0069] Through the long-term monitoring of the growth performance and gene expression of the flock by the monitoring module, gene frequency data before and after multiple generations of breeding are obtained. When calculating the historical genetic stability coefficient, the frequencies of the corresponding genes are selected from these monitoring data for calculation. For example, when calculating the historical genetic stability coefficient of a certain gene, the gene frequency values of this gene before and after breeding are found from the monitoring data and substituted into the formula for calculation. In this way, the change of the gene frequency among multiple generations can be analyzed, so as to evaluate the genetic stability of the flock for this gene during the breeding process.

[0070] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A sheep breed selection method combining phenotypic data and genetic information, characterized in that: The following steps are involved: Step 1: Establish a data collection module, a data analysis module, a seed selection and breeding module, a monitoring module, a genetic stability assessment module and a feedback adjustment module in sequence; Step 2: The data collection module is responsible for collecting growth traits, genetic testing and environmental data at each stage of sheep growth; Step 3: The data analysis module is used to analyze the data from the data collection module, calculate and evaluate the growth, genetic conditions and environmental correlation, and provide a basis for breeding; Step 4: The seed selection and breeding module formulates a seed selection strategy based on the evaluation results of the data analysis module, conducts marker-assisted selection, and plans a breeding plan to improve the quality of the flock; Step 5: The monitoring module monitors the growth performance and gene expression of the flock over a long period of time and records genetic data for multiple generations; Step 6: The genetic stability evaluation module collects the genetic data of the sheep flock after multiple generations of breeding through the monitoring module, compares and analyzes the changes in the genetic parameters of gene frequency and genotype distribution before and after multiple generations of breeding, and calculates the historical genetic stability coefficient As based on the gene frequency before and after multiple generations of breeding, evaluates the breeding effect and finds the direction of improvement of problems; Step 7: The feedback adjustment module adjusts the seed selection, breeding strategy and parameter optimization according to the evaluation results of the genetic stability evaluation module.

2. A sheep breed selection method combining phenotypic data and genetic information according to claim 1, characterized in that: The data acquisition module includes a growth trait data acquisition unit, a gene detection data acquisition unit and a growth environment data recording unit. The growth trait data acquisition unit collects growth trait data of sheep at different growth stages through measuring equipment, the gene detection data acquisition unit detects gene information by collecting sheep tissue samples to obtain gene detection data, and the growth environment data recording unit monitors and records sheep growth environment data and feeding data in real time.

3. A sheep breed selection method combining phenotypic data and genetic information according to claim 2, characterized in that: The growth trait data collection unit performs data numbering on wool length, fineness and strength scores according to the growth trait data characteristics, and the wool length, fineness and strength scores are numbered q1, q2 and q3 respectively.

4. A sheep breed selection method combining phenotypic data and genetic information according to claim 2, characterized in that: The gene detection data collection unit numbers the degree of influence of the gene marker on the trait, the expression level of the gene marker in the individual, and the expression amount of the gene marker according to the characteristics of the gene detection data. The degree of influence of the gene marker on the trait is numbered as B1, B2, B3, ... B n , the expression levels of the gene markers are numbered as α1, α2, α3, ... α n , the expression levels of the gene markers in individuals are numbered as μ1, μ2, μ3, …μ m .

5. The sheep breed selection method combining phenotypic data and genetic information according to claim 2, characterized in that: The growth environment data recording unit numbers the degree of influence of environmental factors on genes according to the sheep growth environment data and feeding data characteristics, and the degree of influence of environmental factors on genes is numbered as F1, F2, F3, ... F m .

6. The sheep breed selection method combining phenotypic data and genetic information according to claim 1, characterized in that: The data analysis module includes a phenotype data analysis unit, a gene data analysis unit and an environment and data association analysis unit.

7. A sheep breed selection method combining phenotypic data and genetic information according to claim 6, characterized in that: The phenotypic data analysis unit calculates the wool comprehensive quality index Vx according to the growth trait data, and the calculation formula is: Vx=0.4*q1+0.3*q2+0.4*q3 In the formula, Vx represents the comprehensive quality index of wool, q1, q2, and q3 represent the wool length, fineness, and strength scores respectively; The breeding and selection module selects individuals with high wool comprehensive quality index Vx as breeding sheep.

8. The sheep breed selection method combining phenotypic data and genetic information according to claim 6, characterized in that: The gene data analysis unit calculates the correlation degree Cr between the gene marker and the trait according to the gene detection data, and the calculation formula is: In the formula, Cr represents the degree of association between the gene marker and the trait, B1, B2, B3, ... B n Indicates the degree of influence of gene markers on traits, B i represents the influence coefficient of the i-th gene marker on the trait, α1, α2, α3, …α n represents the expression level of gene marker, α i represents the expression level of the ith gene marker, and n represents the number of statistical gene markers; The breeding and selection module selects individuals with high association scores as breeding sheep according to the degree of association Cr between gene markers and traits.

9. The sheep breed selection method combining phenotypic data and genetic information according to claim 6, characterized in that: The environment and data association analysis unit calculates the environment and gene association Dz according to the gene detection data and the sheep growth environment data and feeding data, and the calculation formula is: In the formula, Dz represents the correlation between environment and gene, F1, F2, F3, ... F m Indicates the degree of influence of environmental factors on genes, F i represents the influence of the jth environmental factor on the gene, μ1, μ2, μ3, …μ m represents the expression level of gene markers in individuals, μ j represents the expression level of the jth gene marker in an individual, and m represents the number of statistical gene markers; According to the correlation between environment and gene Dz, individuals with stable performance under different environmental conditions are selected as breeding sheep.

10. The sheep breed selection method combining phenotypic data and genetic information according to claim 1, characterized in that: The genetic stability evaluation module compares and analyzes the changes in the genetic parameters of gene frequency and genotype distribution before and after multi-generation breeding through the genetic data of the sheep flock collected by the monitoring module after multi-generation breeding, and calculates the historical genetic stability coefficient As based on the gene frequency before and after multi-generation breeding. The calculation formula is: Historical genetic stability coefficient As = frequency of a gene in sheep after breeding - frequency of a gene in sheep before breeding ÷ frequency of a gene before breeding In the formula, the frequency of a gene in sheep before and after breeding is selected from: the genotype frequency, allele frequency, polymorphic information content, heterozygosity, and effective allele number of the BMPR-IB gene determined by PCR amplification, enzyme digestion, and RFLP analysis; The closer the historical genetic stability coefficient is to 0, the higher the genetic stability.

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