A sheep breeding risk analysis and prediction method based on gene detection chip

Abstract

The invention discloses a sheep breeding risk analysis and prediction method based on a gene detection chip, which relates to the technical field of sheep breeding. The method collects SNP sites and related gene characteristics of the sheep genome through a gene detection chip, and combines the health data, reproduction and environmental adaptability data collected by a sensor network to establish a sheep gene database and a sheep-related data set, and establish a sheep gene prediction model, extract and deeply analyze related gene characteristics and data such as growth, reproduction, and health stability, and then obtain the growth rate index Szsd, meat production index Rrl, wool quality index Ymz, reproductive capacity index Fzn, parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs; It provides an accurate prediction basis for sheep production capacity and health stability. This method realizes the refined management of sheep breeding and reduces breeding risks by combining multiple important indicators.

Description

Technical Field

[0001] The invention relates to the technical field of sheep breeding analysis, and in particular to a sheep breeding risk analysis and prediction method based on a gene detection chip. Background Art

[0002] With the development of global agricultural science and technology, animal husbandry, especially sheep breeding technology, has been continuously improving and has become an important means to improve the output and quality of livestock products. Traditional sheep breeding methods mainly rely on phenotypic selection and genetic pedigree analysis. Although these methods have improved the production performance of sheep to a certain extent, due to the complex genetic background, the genetic effect is difficult to predict, resulting in low breeding efficiency. In addition, the traditional method has a long breeding cycle and is difficult to quickly adapt to changes in market demand, which limits the further development of sheep breeding.

[0003] In recent years, the rapid development of genetic testing technology has brought new opportunities to the animal husbandry industry. Genetic testing chips can comprehensively and accurately analyze the genome information of sheep, thereby identifying genetic markers related to important economic traits such as growth, reproduction, and health. These genetic markers can be used for early screening, genetic evaluation, and disease prevention, significantly improving the efficiency and accuracy of sheep breeding. At the same time, the application of genetic testing technology makes the breeding process more scientific and systematic, providing technical support for the directional breeding of sheep varieties and the preservation of high-quality sheep resources.

[0004] However, although gene detection chips have shown great potential in sheep breeding, existing technologies still have some shortcomings. First, current genetic testing is mostly limited to the analysis of a few traits, making it difficult to comprehensively predict the comprehensive performance of sheep, such as growth, reproduction, and health. Secondly, traditional risk analysis methods often ignore the influence of external factors such as environmental adaptability and parasite resistance, resulting in increased uncertainty in breeding results. Finally, existing breeding methods lack in-depth analysis of the correlation between different traits, fail to make full use of genetic testing data for comprehensive decision-making, and limit the optimization of breeding strategies. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a sheep breeding risk analysis and prediction method based on a gene detection chip to solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a sheep breeding risk analysis and prediction method based on gene detection chip, comprising the following steps:

[0007] Step 1: acquiring SNP sites and related gene characteristics of sheep growth by gene detection chip, wherein the related gene characteristics include information related to bone development genes, muscle growth genes, growth hormone genes, metabolic genes, fat deposition genes, wool follicle development genes and anti-parasitic genes, and establishing a sheep genome database;

[0008] The sensor network is used to collect health data, reproduction data, and environmental adaptability data of sheep during their growth process, and to establish a sheep-related data set;

[0009] Step 2: Establish a sheep gene prediction model. After preprocessing the sheep gene database and sheep-related data sets, extract relevant gene characteristics, health data during growth, reproduction and environmental adaptability data characteristics, and conduct in-depth analysis to obtain the growth rate index Szsd, meat production index Rrl, wool quality index Ymz, reproductive capacity index Fzn, parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs;

[0010] Step 3: Correlate the growth rate index Szsd, the meat yield index Rrl, the wool quality index Ymz and the reproductive capacity index Fzn to calculate and obtain the first production capacity index Scxn; correlate the parasite resistance index Jsc, the gene diversity index Dyx and the environmental adaptability index HJs to calculate and obtain the second health stability index Jkfx;

[0011] Step 4: Analyze the first capacity index Scxn and the first production threshold X1 to obtain a first analysis result, compare and analyze the second health stability index Jkfx and the second health threshold Q2 to obtain a second analysis result, and generate a corresponding strategy based on the first analysis result and the second analysis result.

[0012] Preferably, the sensor network includes a weight sensor, an ultrasonic detector, a laser diameter measuring instrument, a hormone level monitor, a sperm analyzer, a feces analyzer, a flow cytometer, a water intake monitor and a feed intake monitor.

[0013] Preferably, the growth rate index Szsd is calculated by the following formula:

[0014] ;

[0015] ;

[0016] In the formula, represents the weight gain of the i-th sheep in the j-th region at the k-th stage, set to the juvenile stage and the adolescent stage; represents the weight of the i-th sheep at the end of the k-th stage in the j-th region, represents the weight of the i-th sheep at the beginning of the k-th stage in the j-th region, n represents the number of stages, and m represents the total number of sheep in the j-th region. represents the feed consumption of the i-th sheep in the j-th area at the k-th stage It represents the number of transcripts of the IGF-1 gene of the i-th sheep in the j-th region and the k-th stage.

[0017] Preferably, the meat yield index Rrl is calculated by the following formula:

[0018] ;

[0019] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the carcass weight of the i-th sheep at the k-th stage in the j-th region, set to the carcass weight at the final slaughter, represents the fat percentage of the i-th sheep in the j-th area and the k-th stage, It represents the number of Myostatin gene transcripts of the i-th sheep in the j-th region and the k-th stage.

[0020] Preferably, the wool quality index Ymz is calculated by the following formula:

[0021] ;

[0022] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the diameter of wool fiber of the i-th sheep in the j-th region and the k-th stage, represents the wool fiber length of the i-th sheep in the j-th region at the k-th stage, represents the wool fiber strength of the i-th sheep in the j-th region and the k-th stage, It represents the total mRNA expression of the protein of the i-th sheep in the j-th region and the k-th stage.

[0023] Preferably, the reproductive capacity index Fzn is calculated by the following formula: ;

[0024] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the average litter size of the i-th sheep in the j-th region at the k-th stage, represents the pregnancy rate of the i-th sheep in the j-th area at the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, It represents the number of polygyny genes expressed in the i-th sheep at the k-th stage in the j-th region.

[0025] Preferably, the growth rate index Szsd, the meat yield index Rrl, the wool quality index Ymz and the reproductive capacity index Fzn are dimensionlessly processed, and the first productivity index Scxn is calculated by the following associated formula;

[0026] ;

[0027] In the formula, , , and They are respectively expressed as the weight values ​​of growth rate index Szsd, meat production index Rrl, wool quality index Ymz and reproductive capacity index Fzn. Indicates the normal value of the first repair.

[0028] Preferably, the parasite resistance index Jsc is calculated by the following formula:

[0029] ;

[0030] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the parasite load of the i-th sheep at the k-th stage in the j-th area, represents the immune cell activity of the i-th sheep in the j-th region at the k-th stage, represents the amount of interferon encoded by the IFNG gene of the i-th sheep at the k-th stage in the j-th region;

[0031] The gene diversity index Dyx is calculated by the following formula:

[0032] ;

[0033] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the proportion of heterozygous sites in the genome of the i-th sheep at the j-th region and the k-th stage, represents the number of alleles in the genome of the i-th sheep in the j-th region and the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, represents the total number of SNP loci of the i-th sheep in the j-th region and the k-th stage;

[0034] The environmental adaptability index HJs is calculated by the following formula:

[0035] ;

[0036] In the formula, n represents the number of stages, m represents the total number of sheep in the jth region, represents the survival rate of the i-th sheep in the j-th area at the k-th stage, represents the water intake efficiency of the i-th sheep in the j-th area at the k-th stage, represents the number of heat shock protein transcripts of the i-th sheep in the j-th region and the k-th stage;

[0037] After the parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs were dimensionless, the second health stability index Jkfx was calculated by the following correlation formula;

[0038] ;

[0039] In the formula, , and They are respectively represented by the weight values ​​of parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs, Indicates the second normal value.

[0040] Preferably, analyzing the first capacity index Scxn and the first production threshold X1 to obtain a first analysis result includes:

[0041] If the first production capacity index Scxn is less than the first production threshold X1, it means that the sheep production capacity is unqualified, and the first production capacity prediction unqualified label is generated, and the first strategy is generated, including: increasing at least 10% of high-quality feed, conducting comprehensive health checks every quarter, and increasing the inspection frequency by 20% compared with the original plan; introducing at least 15% of new genes or breeds each year; and increasing the training time or frequency of breeder training by 25% each year;

[0042] If the first production threshold X1≤the first capacity index Scxn≤the first production threshold X1×130%, it means that the sheep capacity is qualified, and the first capacity prediction qualified label is generated, and the second strategy is generated, including: 5%-10% optimization adjustment for the production link, such as optimizing feed ratio or improving breeding management; increasing the production data monitoring frequency by 15% to ensure that the first capacity index Scxn can be maintained within the qualified range;

[0043] If the first capacity index Scxn> the first production threshold X1×130%, it means that the sheep capacity is excellent, and a second capacity prediction qualified label is generated, and a third strategy is generated, including: the goal is to expand the production scale by 15%-20%; increase the marketing budget by 10%-15% to expand the market share.

[0044] Preferably, the second health stability index Jkfx is compared and analyzed with the second health threshold Q2 to obtain a second analysis result, including: if the second health stability index Jkfx is less than the second health threshold Q2, it indicates that the health stability of the sheep is abnormal, a first predicted abnormal label is generated, and a fourth strategy is generated, including: increasing the number of health checks by 25% per month; increasing the number of vaccinations by 20% per year, increasing the environmental facility maintenance budget by at least 15%, and implementing 30% individual health interventions for sheep with abnormal risks;

[0045] If the second health threshold Q2≤the second health stability index Jkfx≤the second health threshold Q2×130%, it means that the health stability of the sheep is normal, and a first health qualified label is generated, and a fifth strategy is generated, including: increasing the health data monitoring frequency by 10% to continuously track the health status; optimizing and adjusting the health check frequency by 5%-10%;

[0046] If the second health stability index Jkfx>the second health threshold Q2×130%, it means that the sheep are in excellent health, and a second production capacity prediction qualified label is generated, and a sixth strategy is generated, including: expanding the current successful health management measures to other areas or units, with the goal of expanding the sheep activity range by 15%-20%, and continuing to implement existing health management measures.

[0047] The present invention provides a sheep breeding risk analysis and prediction method based on a gene detection chip. It has the following beneficial effects:

[0048] (1) This sheep breeding risk analysis and prediction method based on gene detection chip can comprehensively analyze the growth characteristics, reproductive ability and health stability of sheep through steps one to four, thereby providing a scientific basis for high-quality sheep breeding. The SNP sites and related gene characteristics of sheep growth are collected through the gene detection chip, and the health data, reproduction and environmental adaptability data collected by the sensor network are combined to establish a sheep gene database and a sheep-related data set. Based on the sheep gene database and the sheep-related data set, a sheep gene prediction model is established to extract and deeply analyze the relevant gene characteristics and growth, reproduction, health stability and other data, and then obtain the growth rate index Szsd, meat production index Rrl, wool quality index Ymz, reproductive ability index Fzn, parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs; providing an accurate prediction basis for sheep production capacity and health stability. This method realizes the refined management of sheep breeding by combining multiple important indicators, which can significantly improve breeding efficiency and quality and reduce breeding risks. In addition, the management strategies generated based on the analysis results are highly targeted and can effectively deal with various problems that may be encountered in the sheep breeding process, and ultimately achieve the goal of improving the quality and yield of sheep breeds.

[0049] (2) This sheep breeding risk analysis and prediction method based on gene detection chip can formulate targeted management strategies according to the evaluation results of the first productivity index Scxn, such as adjusting feed ratio, increasing health checks, introducing new genes, etc., to improve the production capacity of sheep. This optimization measure can effectively improve production efficiency and increase economic benefits. Through the analysis of the second health stability index Jkfx, different health management strategies can be formulated according to actual conditions, such as increasing the number of health checks, improving vaccination rates, and improving environmental facilities. These measures help to improve the health level of sheep, reduce the occurrence of diseases, and improve production stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a schematic diagram of the steps of a sheep breeding risk analysis and prediction method based on a gene detection chip. DETAILED DESCRIPTION

[0051] 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.

[0052] Example 1, please refer to Figure 1 The present invention provides a sheep breeding risk analysis and prediction method based on a gene detection chip, comprising the following steps:

[0053] Step 1: acquiring SNP sites and related gene characteristics of the sheep genome by gene detection chip, wherein the related gene characteristics include information related to bone development genes, muscle growth genes, growth hormone genes, metabolic genes, fat deposition genes, wool follicle development genes and anti-parasitic genes, and establishing a sheep gene database;

[0054] The sensor network is used to collect health data, reproduction data, and environmental adaptability data of sheep during their growth process, and to establish a sheep-related data set;

[0055] Step 2: Establish a sheep gene prediction model. After preprocessing the sheep gene database and sheep-related data sets, extract relevant gene characteristics, health data during growth, reproduction and environmental adaptability data characteristics, and conduct in-depth analysis to obtain the growth rate index Szsd, meat production index Rrl, wool quality index Ymz, reproductive capacity index Fzn, parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs;

[0056] The specific steps to establish the sheep gene prediction model are to collect and organize the sheep gene data from the gene detection chip, including genome information, SNP sites, gene characteristics, etc. Standardize these data to ensure the consistency and accuracy of the data. Collect the health data, reproduction data and environmental adaptability data of the sheep during their growth. These data include weight, feed intake, health indicators, reproduction records, environmental conditions, etc. Clean, denoise and standardize these data for subsequent analysis. Extract gene features related to growth rate, meat production, wool quality, reproductive capacity, parasite resistance, gene diversity and environmental adaptability from the gene database. These features include gene expression, transcript number, gene mutation information, etc. Extract key indicators from health data, such as weight gain, immune cell activity, disease burden, etc., to reflect the health status of sheep. Reproduction data feature extraction: Extract key features from reproduction data, such as litter size, conception rate, sperm motility, etc., to evaluate reproductive capacity. Environmental adaptability feature extraction: Extract key indicators from environmental adaptability data, such as survival rate, water intake value, heat shock protein expression, etc. The dataset is divided into training set, validation set and test set for model training, validation and evaluation.

[0057] Select appropriate machine learning or deep learning models, such as random forest RF and neural network NN, to establish a sheep gene prediction model for predicting and analyzing various sheep indexes. Use the training set to train the sheep gene prediction model and adjust the model parameters to improve the prediction accuracy of the model. Use the validation set to verify the trained model, evaluate the performance of the model, and make necessary adjustments and optimizations. Study the correlation between different features and explore how to combine genetic features, health data, reproduction data, and environmental adaptability data to improve the accuracy of predictions.

[0058] Step 3: Correlate the growth rate index Szsd, the meat yield index Rrl, the wool quality index Ymz and the reproductive capacity index Fzn to calculate and obtain the first production capacity index Scxn; correlate the parasite resistance index Jsc, the gene diversity index Dyx and the environmental adaptability index HJs to calculate and obtain the second health stability index Jkfx;

[0059] Step 4: Analyze the first capacity index Scxn and the first production threshold X1 to obtain a first analysis result, compare and analyze the second health stability index Jkfx and the second health threshold Q2 to obtain a second analysis result, and generate a corresponding strategy based on the first analysis result and the second analysis result.

[0060] In this embodiment, through steps one to four, the growth characteristics, reproductive capacity and health stability of sheep can be comprehensively analyzed, thereby providing a scientific basis for high-quality breeding of sheep. The SNP sites and related gene characteristics of the sheep genome are collected through the gene detection chip, and the health data, reproduction and environmental adaptability data collected by the sensor network are combined to establish a sheep gene database and a sheep-related data set. Based on the sheep gene database and the sheep-related data set, a sheep gene prediction model is established to extract and deeply analyze the relevant gene characteristics and data such as growth, reproduction, and health stability, and then obtain the growth rate index Szsd, meat production index Rrl, wool quality index Ymz, reproductive capacity index Fzn, parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs; It provides an accurate prediction basis for sheep production capacity and health stability. This method realizes the refined management of sheep breeding by combining a number of important indicators, which can significantly improve breeding efficiency and quality and reduce breeding risks. In addition, the management strategies generated based on the analysis results are highly targeted and can effectively deal with various problems that may be encountered in the sheep breeding process, and ultimately achieve the goal of improving the quality and yield of sheep breeds.

[0061] Embodiment 2, specifically, the sensor network includes a weight sensor, an ultrasonic detector, a laser diameter measuring instrument, a hormone level monitor, a sperm analyzer, a feces analyzer, a flow cytometer, a water intake monitor and a feed intake monitor.

[0062] The weight sensor is used to regularly measure the weight of sheep at different stages and obtain their growth rate and weight change;

[0063] The laser diameter measuring instrument is used to measure the wool fiber of the i-th sheep through a precise laser beam, and record its fiber diameter at the k-th stage in the j-th area; similarly, the laser measuring instrument is also used to detect the length of the wool fiber; through continuous measurement, the system can obtain the changes in the diameter and length of the wool fiber at each stage, so as to analyze the growth and quality of the sheep's wool;

[0064] Ultrasound is used to measure the backfat thickness of sheep and evaluate the fat deposition, which has an impact on the meat production index Rrl. Ultrasound is used to detect muscle thickness and area to evaluate muscle growth; hormone level monitors monitor the level data of sheep growth hormone, sex hormone, etc. Sperm analyzer analyzes the number and vitality data of sheep sperm. Fecal analyzer: analyzes the digestibility and health status of sheep through feces, as well as the parasite situation in feces; flow cytometer detects immune cell counts to evaluate the immune system status of sheep; water intake monitor and feed intake monitor are used to monitor the drinking water and feed consumption of sheep.

[0065] Embodiment 3, specifically, the growth rate index Szsd is calculated by the following formula:

[0066]

[0067] ;

[0068] In the formula, represents the weight gain of the i-th sheep in the j-th region at the k-th stage, set to the juvenile stage and the adolescent stage; represents the weight of the i-th sheep at the end of the k-th stage in the j-th region, represents the weight of the i-th sheep at the beginning of the k-th stage in the j-th region, n represents the number of stages, and m represents the total number of sheep in the j-th region. represents the feed consumption of the i-th sheep in the j-th area at the k-th stage Represents the number of transcripts of the IGF-1 gene of the i-th sheep in the j-th region and the k-th stage. The IGF-1 (Insulin-like Growth Factor 1) gene encodes a polypeptide hormone similar to insulin, called insulin-like growth factor 1. IGF-1 plays a key role in the growth and development of organisms, especially in regulating biological processes such as cell growth, differentiation, apoptosis and metabolism. The expression of IGF-1 is affected by many factors, including nutritional status, hormone levels, age and specific environmental conditions.

[0069] The growth rate index Szsd takes into account the weight changes of sheep at different growth stages by standardizing the weight gain at different stages. This method can accurately reflect the growth rate of sheep in the early and adolescent stages, thereby comprehensively evaluating their overall growth performance. By incorporating feed consumption and the number of IGF-1 gene transcripts into the calculation, the effects of feed efficiency and genes on growth rate can be more accurately evaluated. This comprehensive consideration helps to identify sheep with high feed utilization efficiency and good genetic performance, thereby optimizing breeding strategies.

[0070] Specifically, the meat yield index Rrl is calculated by the following formula:

[0071] ;

[0072] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the carcass weight of the i-th sheep at the k-th stage in the j-th region, set to the carcass weight at the final slaughter, represents the fat percentage of the i-th sheep in the j-th area and the k-th stage, It represents the number of Myostatin gene transcripts in the i-th sheep at the j-th region and the k-th stage. Myostatin gene (also known as GDF-8, Growth Differentiation Factor 8) is an important gene that regulates muscle growth. It belongs to the transforming growth factor-β (TGF-β) family, is expressed in muscle tissue, and its main function is to inhibit muscle growth and proliferation.

[0073] The meat yield index Rrl evaluates the meat quality yield of sheep by combining carcass weight, fat percentage and the number of Myostatin gene transcripts. The index not only takes into account the final carcass weight of the sheep, but also accurately evaluates meat quality yield by adjusting for fat percentage and genetic influence, thus providing a comprehensive meat production indicator.

[0074] Specifically, the wool quality index Ymz is calculated by the following formula:

[0075] ;

[0076] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the diameter of wool fiber of the i-th sheep in the j-th region and the k-th stage, represents the wool fiber length of the i-th sheep in the j-th region at the k-th stage, represents the wool fiber strength of the i-th sheep in the j-th region and the k-th stage, represents the total mRNA expression of the protein of the i-th sheep in the j-th region and the k-th stage. Wool is mainly composed of keratin protein, and the synthesis of keratin depends on the expression of specific genes. These genes generate mRNA during transcription, and the mRNA then instructs the ribosome to synthesize keratin.

[0077] The wool quality index Ymz comprehensively evaluates the quality of wool by combining the total expression of wool fiber diameter, length, strength and protein mRNA. This comprehensive index can effectively reflect the fineness, durability and overall quality of wool, and provide an accurate assessment of wool quality. Including the total expression of protein mRNA in the calculation can take into account the impact of genes on wool quality. Gene expression is closely related to wool growth and quality. By analyzing protein gene transcripts, the quality characteristics of wool can be predicted and improved.

[0078] Specifically, the reproductive capacity index Fzn is calculated by the following formula: ;

[0079] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the average litter size of the i-th sheep in the j-th region at the k-th stage, represents the pregnancy rate of the i-th sheep in the j-th area at the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, It represents the number of polygyny genes expressed in the i-th sheep at the k-th stage in the j-th region.

[0080] The reproductive capacity index Fzn comprehensively evaluates the reproductive capacity of sheep by combining multiple indicators such as average litter size, conception rate, sperm motility and gene expression of multi-birth. This can provide a comprehensive understanding of the performance and potential of sheep in the reproductive process.

[0081] Specifically, the growth rate index Szsd, the meat yield index Rrl, the wool quality index Ymz and the reproductive capacity index Fzn are dimensionlessly processed, and the first productivity index Scxn is calculated by the following correlation formula;

[0082] ;

[0083] In the formula, , , and They are respectively expressed as the weight values ​​of growth rate index Szsd, meat yield index Rrl, wool quality index Ymz and reproductive capacity index Fzn, which are adjusted and set by the user. , , , ,and , Indicates the normal value of the first correction.

[0084] In this embodiment, the first production capacity index Scxn comprehensively evaluates the four key production performance indicators of growth rate, meat production, wool quality and reproductive capacity to provide a comprehensive production capacity evaluation index. This helps to fully understand the overall production capacity of sheep. By calculating the first production capacity index, breeders can more accurately evaluate the comprehensive production capacity of each sheep, thereby optimizing resource allocation. For example, resources can be preferentially allocated to sheep with higher production capacity to improve overall production efficiency. The first production capacity index provides a quantitative evaluation basis to help breeders make scientific production decisions. According to the index results, breeding plans, feeding strategies and management measures can be adjusted to improve production efficiency. The calculation of the first production capacity index can help breeders better evaluate and improve the production performance of sheep, thereby increasing economic benefits. By optimizing production performance and improving product quality, higher economic returns can be achieved. Through the analysis of the first production capacity index, sheep with strong production capacity can be identified as the breeding objects of excellent genes. This helps to improve breeding efficiency and promote the cultivation of high-quality sheep varieties.

[0085] Example 4, specifically, the parasite resistance index Jsc is calculated by the following formula:

[0086] ;

[0087] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the parasite load of the i-th sheep at the k-th stage in the j-th area, represents the immune cell activity of the i-th sheep in the j-th region at the k-th stage, represents the amount of IFN-γ encoded by the IFNG gene of the i-th sheep in the j-th region and the k-th stage; Interferon-γ (IFN-γ) encoded by the IFNG gene is an important cytokine that plays a key role in the host's immune response, especially in fighting parasitic infections. IFN-γ is secreted by activated T cells (especially Th1 cells) and natural killer cells (NK cells). It plays a key role in activating macrophages, enhancing antigen presentation, and promoting Th1-type immune responses.

[0088] The parasite resistance index Jsc takes into account the parasite load, immune cell activity and IFNG gene expression levels and the amount of IFN-γ encoded by them, and can accurately assess the resistance of sheep to parasites. This helps to understand the parasite resistance level of different sheep individuals or groups.

[0089] The gene diversity index Dyx is calculated by the following formula:

[0090] ;

[0091] In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the proportion of heterozygous sites in the genome of the i-th sheep at the j-th region and the k-th stage, represents the number of alleles in the genome of the i-th sheep in the j-th region and the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, Represents the total number of SNP sites of the ith sheep in the jth region and the kth stage; Single Nucleotide Polymorphism (SNP) refers to the variation at a single nucleotide position in the genome. This variation may exist between different individuals or groups and is a form of genetic diversity. SNP is the simplest and most common form of genetic variation in the genome, referring to the difference of a single nucleotide between different individuals at a certain position in the genome. SNP can occur in the coding region (affecting the protein sequence) or the non-coding region (affecting gene expression or other regulatory mechanisms).

[0092] The genetic diversity index Dyx provides a quantitative assessment of the genetic diversity of the sheep population. By analyzing the proportion of heterozygous sites and the number of alleles, it can effectively assess the genetic diversity level of the sheep genome and thus understand the genetic structure of the population.

[0093] The environmental adaptability index HJs is calculated by the following formula:

[0094] ;

[0095] In the formula, n represents the number of stages, m represents the total number of sheep in the jth region, represents the survival rate of the i-th sheep in the j-th area at the k-th stage, represents the water intake efficiency of the i-th sheep in the j-th area at the k-th stage, It represents the number of heat shock protein transcripts of the i-th sheep in the j-th region and the k-th stage; Heat shock proteins (HSPs) are a class of proteins that are expressed in large quantities when cells are stressed (such as high temperature, oxidative stress, heavy metals, etc.). They play a key role in helping cells resist environmental stress, maintain the correct folding and function of proteins, and repair damaged proteins. The number of heat shock protein transcripts is closely related to environmental adaptation. The following is the specific relationship and mechanism:

[0096] The environmental adaptability index HJs provides an assessment of the adaptability of sheep under different environmental conditions. By combining survival rate, water intake efficiency and heat shock protein levels, a comprehensive understanding of the sheep's ability to respond to environmental stress can be obtained. The index calculation results can help identify the performance of sheep groups under specific environmental conditions, thereby optimizing environmental management measures. For individuals with poor adaptability under extreme weather conditions, appropriate management measures can be taken, such as improving drinking water conditions or adding summer shelter facilities.

[0097] After the parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs were dimensionless, the second health stability index Jkfx was calculated by the following correlation formula;

[0098] ;

[0099] In the formula, , and They are respectively expressed as the weight values ​​of the parasite resistance index Jsc, the gene diversity index Dyx and the environmental adaptability index HJs, which are adjusted and set by the user. , , ,and , Indicates the second normal value.

[0100] In this embodiment, the second health stability index (Jkfx) provides a comprehensive assessment of the health status of sheep by combining three key indicators: parasite resistance, genetic diversity, and environmental adaptability. It can comprehensively reflect the comprehensive resistance and stability of sheep to health risks. By analyzing Jkfx, individuals with higher health risks can be identified, and then feeding and management measures can be adjusted. For example, for sheep with low health stability, parasite control and environmental optimization measures can be strengthened to improve the overall health level. Jkfx provides a basis for breeding decisions. Giving priority to individuals with high health stability for breeding can improve the overall health level of the sheep population, thereby reducing the impact of diseases and parasites and improving production efficiency.

[0101] Embodiment 5, specifically, analyzing the first capacity index Scxn and the first production threshold X1 to obtain a first analysis result includes:

[0102] If the first production capacity index Scxn is less than the first production threshold X1, it means that the sheep production capacity is unqualified, and the first production capacity prediction unqualified label is generated, and the first strategy is generated, including: increasing at least 10% of high-quality feed, conducting comprehensive health checks every quarter, and increasing the inspection frequency by 20% compared with the original plan; introducing at least 15% of new genes or breeds each year; and increasing the training time or frequency of breeder training by 25% each year;

[0103] If the first production threshold X1≤the first capacity index Scxn≤the first production threshold X1×130%, it means that the sheep capacity is qualified, and the first capacity prediction qualified label is generated, and the second strategy is generated, including: 5%-10% optimization adjustment for the production link, such as optimizing feed ratio or improving breeding management; increasing the production data monitoring frequency by 15% to ensure that the first capacity index Scxn can be maintained within the qualified range;

[0104] If the first production threshold value X1>the first production threshold value X1×130%, it means that the sheep production capacity is excellent, and a second production capacity prediction qualified label is generated, and a third strategy is generated, including: the goal is to expand the production scale by 15%-20%; increase the marketing promotion budget by 10%-15% to expand the market share.

[0105] The second health stability index Jkfx is compared with the second health threshold Q2 to obtain a second analysis result, including: if the second health stability index Jkfx is less than the second health threshold Q2, it indicates that the health stability of the sheep is abnormal, a first predicted abnormal label is generated, and a fourth strategy is generated, including: increasing the number of health checks by 25% per month; increasing the number of vaccinations by 20% per year, increasing the environmental facility maintenance budget by at least 15%, and implementing 30% individual health interventions for sheep with abnormal risks;

[0106] If the second health threshold Q2≤the second health stability index Jkfx≤the second health threshold Q2×130%, it means that the health stability of the sheep is normal, and a first health qualified label is generated, and a fifth strategy is generated, including: increasing the health data monitoring frequency by 10% to continuously track the health status; optimizing and adjusting the health check frequency by 5%-10%;

[0107] If the second health stability index Jkfx>the second health threshold Q2×130%, it means that the sheep are in excellent health, and a second production capacity prediction qualified label is generated, and a sixth strategy is generated, including: expanding the current successful health management measures to other areas or units, with the goal of expanding the sheep activity range by 15%-20%, and continuing to implement existing health management measures.

[0108] In this embodiment, according to the evaluation results of the first production capacity index Scxn, targeted management strategies can be formulated, such as adjusting feed ratios, increasing health checks, introducing new genes, etc., to improve the production capacity of sheep. This optimization measure can effectively improve production efficiency and increase economic benefits. Through the analysis of the second health stability index Jkfx, different health management strategies can be formulated according to actual conditions, such as increasing the number of health checks, increasing vaccination rates, and improving environmental facilities. These measures help to improve the health level of sheep, reduce the occurrence of diseases, and improve production stability.

[0109] Multi-level assessment can identify potential production and health risks early. For example, when the first capacity index or the second health stability index is lower than the preset threshold, an early warning label can be generated in time, and necessary intervention measures can be taken to reduce the occurrence and impact of risks. According to the specific assessment results, precise intervention measures can be implemented. For unqualified production capacity, measures such as increasing high-quality feed and increasing the frequency of inspections can be taken; for abnormal health stability, health inspections can be increased, the number of vaccinations can be increased, etc. Such precise intervention measures can effectively reduce risks and improve breeding benefits. Multi-level assessment provides real-time production and health status information, which can help breeding managers dynamically adjust management measures. As production conditions and health conditions change, strategies can be adjusted in time to maintain optimal production and health.

[0110] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0111] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A sheep breeding risk analysis and prediction method based on gene detection chip, characterized in that: The following steps are involved: Step 1: Acquire SNP sites and related gene characteristics of the sheep genome by gene detection chip, wherein the related gene characteristics include bone development genes, muscle growth genes, growth hormone genes, metabolic genes, fat deposition genes, wool follicle development genes and anti-parasitic genes to establish a sheep gene database; The sensor network is used to collect health data, reproduction data, and environmental adaptability data of sheep during their growth process, and to establish a sheep-related data set; Step 2: Establish a sheep gene prediction model. After preprocessing the sheep gene database and sheep-related data sets, extract relevant gene characteristics, health data during growth, reproduction and environmental adaptability data characteristics, and conduct in-depth analysis to obtain the growth rate index Szsd, meat production index Rrl, wool quality index Ymz, reproductive capacity index Fzn, parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs; The growth rate index Szsd is calculated by the following formula: ； ； In the formula, represents the weight gain of the i-th sheep in the j-th area at the k-th stage; represents the weight of the i-th sheep at the end of the k-th stage in the j-th region, represents the weight of the i-th sheep at the beginning of the k-th stage in the j-th region, n represents the number of stages, and m represents the total number of sheep in the j-th region. represents the feed consumption of the i-th sheep in the j-th area at the k-th stage, represents the number of transcripts of the IGF-1 gene of the i-th sheep at the k-th stage in the j-th region; The meat yield index Rrl is calculated by the following formula: ； In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the carcass weight of the i-th sheep at the k-th stage in the j-th region, set to the carcass weight at the final slaughter, represents the fat percentage of the i-th sheep in the j-th area and the k-th stage, represents the number of Myostatin gene transcripts of the i-th sheep in the j-th region and the k-th stage; The wool quality index Ymz is calculated by the following formula: ； In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the diameter of wool fiber of the i-th sheep in the j-th region and the k-th stage, represents the wool fiber length of the i-th sheep in the j-th region at the k-th stage, represents the wool fiber strength of the i-th sheep in the j-th region and the k-th stage, represents the total mRNA expression of the protein of the i-th sheep in the j-th region and the k-th stage; The reproductive capacity index Fzn is calculated by the following formula: ； In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the average litter size of the i-th sheep in the j-th region at the k-th stage, represents the pregnancy rate of the i-th sheep in the j-th area at the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, represents the number of genes expressing polygyny in the i-th sheep at the k-th stage in the j-th region; Step 3: Correlate the growth rate index Szsd, the meat yield index Rrl, the wool quality index Ymz and the reproductive capacity index Fzn to calculate and obtain the first production capacity index Scxn; correlate the parasite resistance index Jsc, the gene diversity index Dyx and the environmental adaptability index HJs to calculate and obtain the second health stability index Jkfx; After the growth rate index Szsd, meat yield index Rrl, wool quality index Ymz and reproductive capacity index Fzn are dimensionless, the first productivity index Scxn is calculated by the following correlation formula; ； In the formula, , , and They are respectively expressed as the weight values ​​of growth rate index Szsd, meat yield index Rrl, wool quality index Ymz and reproductive capacity index Fzn, indicating the normal values ​​of the first correction; The parasite resistance index Jsc is calculated by the following formula: ； In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the parasite load of the i-th sheep at the k-th stage in the j-th area, represents the immune cell activity of the i-th sheep in the j-th region at the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, represents the amount of IFN-γ encoded by the IFNG gene of the i-th sheep at the k-th stage in the j-th region; The gene diversity index Dyx is calculated by the following formula: ； In the formula, n represents the number of stages, and m represents the total number of sheep in the jth region; represents the proportion of heterozygous sites in the genome of the i-th sheep at the j-th region and the k-th stage, represents the number of alleles in the genome of the i-th sheep in the j-th region and the k-th stage, represents the sperm motility of the i-th sheep at the k-th stage in the j-th region, represents the total number of SNP loci of the i-th sheep in the j-th region and the k-th stage; The environmental adaptability index HJs is calculated by the following formula: ； In the formula, n represents the number of stages, m represents the total number of sheep in the jth region, represents the survival rate of the i-th sheep in the j-th area at the k-th stage, represents the water intake efficiency of the i-th sheep in the j-th area at the k-th stage, represents the number of heat shock protein transcripts of the i-th sheep in the j-th region and the k-th stage; After the parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs were dimensionless, the second health stability index Jkfx was calculated by the following correlation formula; ； In the formula, , and They are respectively expressed as the weight values ​​of parasite resistance index Jsc, gene diversity index Dyx and environmental adaptability index HJs, indicating the second corrected normal value; Step 4: Analyze the first capacity index Scxn and the first production threshold X1 to obtain a first analysis result, compare and analyze the second health stability index Jkfx and the second health threshold Q2 to obtain a second analysis result, and generate a corresponding strategy based on the first analysis result and the second analysis result.

2. The sheep breeding risk analysis and prediction method based on gene detection chip according to claim 1, characterized in that: The sensor network includes weight sensors, ultrasonic detectors, laser diameter measuring instruments, hormone level monitors, sperm analyzers, feces analyzers, flow cytometers, water intake monitors, and feed intake monitors.

3. The sheep breeding risk analysis and prediction method based on gene detection chip according to claim 1, characterized in that: The first capacity index Scxn is analyzed with the first production threshold X1 to obtain a first analysis result, including: If the first production capacity index Scxn is less than the first production threshold X1, it means that the sheep production capacity is unqualified, and the first production capacity prediction unqualified label is generated, and the first strategy is generated, including: increasing high-quality feed by at least 10%, conducting comprehensive health checks every quarter, and increasing the inspection frequency by 20% compared with the original plan; introducing at least 15% new genes or breeds every year; If the first production threshold X1 ≤ the first capacity index Scxn ≤ the first production threshold X1 130%, it means that the sheep production capacity is qualified, and the first production capacity prediction qualified label is generated, and the second strategy is generated, including: 5%-10% feed ratio or improved breeding management optimization adjustment for the production link; increase the production data monitoring frequency by 15% to ensure that the first production capacity index Scxn can be maintained within the qualified range; If the first capacity index Scxn> the first production threshold X1 130%, it means that the sheep production capacity is excellent, and the second production capacity prediction qualified label is generated, and the third strategy is generated, including: the goal is to expand the production scale by 15%-20%; increase the marketing budget by 10%-15% to expand the market share.

4. The sheep breeding risk analysis and prediction method based on gene detection chip according to claim 3, characterized in that: The second health stability index Jkfx is compared with the second health threshold Q2 to obtain a second analysis result, including: If the second health stability index Jkfx < the second health threshold Q2, it means that the health stability of the sheep is abnormal, and the first predicted abnormal label is generated, and the fourth strategy is generated, including: increasing the number of health checks by 25% per month; increasing the number of vaccinations by 20% per year, increasing the environmental facility maintenance budget by at least 15%, and implementing 30% individual health interventions for sheep with abnormal risks; If the second health threshold Q2 ≤ the second health stability index Jkfx ≤ the second health threshold Q2 130%, it means that the health stability of the sheep is normal, and the first health qualified label is generated, and the fifth strategy is generated, including: increasing the frequency of health data monitoring by 10% to continuously track health status; If the second health stability index Jkfx> the second health threshold Q2 If it is 130%, it means that the sheep are in excellent health, and a second production capacity forecast qualified label is generated, and a sixth strategy is generated, including: expanding the current successful health management measures to other areas or units, with the goal of expanding the sheep's activity range by 15%-20%, and continuing to implement existing health management measures.

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