Method for molecular breeding of apricot flower chicken with optimized meat quality

Abstract

The application discloses a kind of molecular breeding methods of apricot flower chicken for optimizing meat quality, it is related to molecular assisted selection, the method comprises: the index correlation analysis is carried out to the chicken of selected variety;Statistical meat quality trait target characteristic value is inconsistent with the first occurrence frequency ratio of the first selected chicken trait record data of variety in the index of feeding condition;Statistical meat quality trait target characteristic value is consistent with the second occurrence frequency ratio of the first selected chicken trait record data of variety in the index of feeding condition, the average of second occurrence frequency ratio is third occurrence frequency ratio;When the first occurrence frequency ratio or third occurrence frequency ratio is greater than or equal to occurrence frequency ratio threshold value, add variety chicken into SNP genotyping chicken;According to SNP genotyping variety chicken, apricot flower chicken molecular breeding is carried out.Solve the problem that the existing breeding method carries out genotyping detection to each variety of apricot flower chicken, determines the correlation of trait and genotype, selects variety and then optimizes apricot flower chicken meat quality, resulting in low efficiency.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of molecular assisted selection, and particularly relates to a molecular breeding method for optimizing meat quality of apricot flower chickens. BACKGROUND

[0002] With the improvement of people's living standards, the requirements for poultry meat quality are increasingly strict. Apricot flower chickens are high-quality local chicken breeds in China, and are favored due to delicious meat and rich nutrition. However, the traditional breeding method of apricot flower chickens mainly relies on phenotype selection, which requires long-term observation and a large number of breeding generations, consumes a large amount of time and resources, and has limited accuracy, and it is difficult to precisely improve meat quality traits. SUMMARY

[0003] The embodiment of the application provides a molecular breeding method for optimizing meat quality of apricot flower chickens, which solves the technical problem that the existing breeding method needs to detect the genotypes of apricot flower chickens of each breed, determine the correlation between traits and genotypes, and then optimize the meat quality of apricot flower chickens after selecting the breed, resulting in low efficiency.

[0004] The technical scheme for solving the above technical problem of the application is as follows:

[0005] The application provides a molecular breeding method for optimizing meat quality of apricot flower chickens, which comprises the following steps:

[0006] Based on meat quality trait indexes, the correlation between the feeding condition indexes of the selected breed chickens and the environment is analyzed to obtain an environmental correlation degree.

[0007] When the environmental correlation degree is less than or equal to a correlation degree threshold value, the first occurrence frequency ratio of the meat quality trait target characteristic value in the historical trait record data of the first selected breed chickens that are inconsistent with the feeding condition indexes is counted.

[0008] When the environmental correlation degree is greater than the correlation degree threshold value, the second occurrence frequency ratios of the meat quality trait target characteristic value in the historical trait record data of a plurality of first selected breed chickens that are consistent with a plurality of feeding condition indexes are counted, the average of the second occurrence frequency ratios is counted, and a third occurrence frequency ratio is obtained.

[0009] When the first occurrence frequency ratio or the third occurrence frequency ratio is greater than or equal to an occurrence frequency ratio threshold value, the selected breed chickens are added to the SNP genotyping breed chickens.

[0010] According to the SNP genotyping breed chickens, the molecular breeding of apricot flower chickens is performed.

[0011] The application provides one or more technical schemes, which have at least the following technical effects or advantages:

[0012] The embodiment of the present application provides a kind of molecular breeding method of apricot flower chicken of optimizing meat quality, first, the influence degree of environmental factor to meat quality character is determined by correlation analysis, and different statistical methods are used to determine the occurrence proportion of meat quality character target characteristic value based on environmental correlation degree, and then the chicken of SNP gene typing variety to be screened out.Avoid the cumbersome process of carrying out comprehensive genotype detection to each variety apricot flower chicken, reduce unnecessary detection workload.Second, according to the SNP gene typing variety to be executed apricot flower chicken molecular breeding, more accurately, the gene related to meat quality is operated using molecular assisted selection technology.In the breeding process, by SNP gene typing to apricot flower chicken and chicken to be selected, obtain genotyping result, and the desired genome of apricot flower chicken is obtained by the aid of molecular breeding simulation optimization, so as to carry out breeding pertinently.And, by setting correlation degree threshold, occurrence frequency proportion threshold and similarity threshold and other parameters, the whole breeding process is quantitatively controlled, and the reliability and stability of breeding are further improved.

[0013] Through the above technical solution, the breeding process is more scientific and efficient, can improve the meat quality of apricot flower chicken in a shorter time, improve the breeding efficiency and accuracy, meet the demand of people for high-quality poultry meat. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating labor.

[0015] Figure 1 It is a flowchart of the molecular breeding method of apricot flower chicken of optimizing meat quality provided by the embodiment of the present application;

[0016] Figure 2 It is a flowchart of obtaining apricot flower chicken desired genome in the molecular breeding method of apricot flower chicken of optimizing meat quality provided by the embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a kind of molecular breeding method of apricot flower chicken of optimizing meat quality, for solving the technical problem that the existing breeding method is to carry out genotype detection to each variety of apricot flower chicken, determine the correlation of character and genotype, select variety, then optimize apricot flower chicken meat quality, resulting in low efficiency.

[0018] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0019] In the description of the present application, the terms "first", "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0020] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. In order to enable any person skilled in the art to implement and use the present application, the following description is given. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope in accordance with the principles and characteristics disclosed in the present application.

[0021] Embodiment one, as Figure 1 shown, the present application provides a method for optimizing the meat quality of apricot flower chicken, comprising:

[0022] S10: based on the meat quality trait index, the breeding condition index correlation analysis of the selected chicken is carried out, and the environmental correlation degree is obtained;

[0023] In the embodiments of the present application, first, based on the meat quality trait index, the breeding condition index correlation analysis of the selected chicken is carried out, and the environmental correlation degree is obtained. The meat quality trait index includes muscle tenderness, fat content, meat color and the like, and the breeding condition index includes environmental factors such as feed type, breeding density, illumination time and the like.

[0024] By correlation analysis on the above indexes, the influence degree of environmental factors on the meat quality trait of apricot flower chicken is understood. For example, the correlation between the protein content in feed and chicken tenderness, or the influence of breeding density on chicken fat content, etc.

[0025] Specifically, the meat quality trait data of the candidate breed chicken under different feeding conditions is collected, and the environmental correlation is calculated by using a statistical method, which serves as a basis for screening the candidate breed chicken and provides support for subsequent breeding work.

[0026] Specifically, step S10 in the method specifically includes:

[0027] The first feeding condition attribute is extracted from the feeding condition indicators;

[0028] The historical SNP genotyping results of the candidate breed chicken are consistent, and only the first feeding condition attribute in the feeding condition indicators is inconsistent, and several pairs of meat quality trait indicator detection values are detected;

[0029] The proportion of the number of inconsistencies in the several pairs of meat quality trait indicator detection values is counted and set as the first feeding condition attribute correlation;

[0030] Until the Nth feeding condition attribute correlation is obtained;

[0031] The average of the first feeding condition attribute correlation to the Nth feeding condition attribute correlation is extracted and set as the environmental correlation.

[0032] In the embodiments of the present application, first, the first feeding condition attribute is extracted from the feeding condition indicators, which include the composition of the feed, the density of the breeding, the lighting time, etc. The first feeding condition attribute is any specific attribute, for example, the content of protein in the feed.

[0033] Secondly, by searching the database or relevant records, the historical SNP genotyping results of the candidate breed chicken are obtained, which are consistent, and only the first feeding condition attribute in the feeding condition indicators is inconsistent, that is, under the condition that other conditions are the same, only the first feeding condition attribute is changed, and the change of the meat quality trait indicators is observed.

[0034] Thirdly, the proportion of the number of inconsistencies in the several pairs of meat quality trait indicator detection values is counted and set as the first feeding condition attribute correlation. For example, 100 pairs of meat quality trait indicator detection values are counted, of which 20 pairs are inconsistent, and the first feeding condition attribute correlation is 20%. Then, according to the same method, other feeding condition attributes are analyzed in turn until the Nth feeding condition attribute correlation is obtained.

[0035] Finally, the average of the first feeding condition attribute correlation to the Nth feeding condition attribute correlation is extracted as the environmental correlation. The environmental correlation reflects the influence degree of the feeding condition indicators on the meat quality traits, which provides a reference basis for subsequent judgment of whether the candidate breed chicken is suitable for SNP genotyping and molecular breeding.

[0036] For example, if the correlation degree of the protein content in the feed is 15%, the correlation degree of the breeding density is 25%, and the correlation degree of the light time is 20%, the environmental correlation degree is (15% + 25% + 20%) ÷ 3 = 20%.

[0037] wherein the historical SNP genotyping results of the candidate chicken breeds are consistent, and only the first feeding condition attribute in the feeding condition indicators is inconsistent, and the detection values of the several pairs of meat quality trait indicators include:

[0038] If the first feeding condition attribute is a type attribute, when the first feeding condition attribute characteristic values are different, it is considered inconsistent, otherwise, it is considered consistent.

[0039] If the first feeding condition attribute is a quantitative attribute, when the first feeding condition attribute characteristic value deviation is greater than or equal to a predefined first feeding condition attribute characteristic value deviation threshold, it is considered inconsistent, otherwise, it is considered consistent.

[0040] In the embodiments of the present application, different judgment criteria are used to determine whether the meat quality trait indicator detection values are consistent according to different types of first feeding condition attributes. For type attributes, such as feed types, when the first feeding condition attribute characteristic values are different, for example, different types of feed are used, it is considered inconsistent; if the characteristic values are the same, it is considered consistent.

[0041] For quantitative attributes, such as the content of protein in the feed, when the first feeding condition attribute characteristic value deviation is greater than or equal to a predefined first feeding condition attribute characteristic value deviation threshold, such as when the protein content deviation exceeds the set threshold, it is considered inconsistent; if the deviation is less than the threshold, it is considered consistent.

[0042] The above judgment method can more accurately analyze the correlation between the feeding condition attributes and the meat quality trait indicators, thereby providing more reliable data for subsequent calculation of the environmental correlation degree. Through detailed analysis, the feeding condition attributes closely related to the meat quality traits are screened out, providing strong support for optimizing apricot chicken molecular breeding, and further improving the efficiency and accuracy of breeding.

[0043] S20: When the environmental correlation degree is less than or equal to the correlation degree threshold, the first occurrence frequency ratio of the meat quality trait target characteristic value in the first selected breed chicken historical trait record data with inconsistent feeding condition indicators is counted.

[0044] In the embodiments of the present application, when the environmental correlation degree is less than or equal to the correlation degree threshold, it means that the influence of the feeding condition on the meat quality trait is relatively small. At this time, the first occurrence frequency ratio of the meat quality trait target characteristic value in the first selected breed chicken historical trait record data with inconsistent feeding condition indicators is counted. Selecting the feeding condition attribute with greater influence as the inconsistent selection reference attribute can improve the efficiency of data collection.

[0045] Specifically, first, the historical trait record data of the first candidate breed chicken under different and inconsistent feeding conditions is collected. Then, the records meeting the target characteristic value of the meat quality trait are screened, the number of occurrences is counted, and the number of occurrences is divided by the total number of historical trait record data to obtain the first occurrence ratio.

[0046] Exemplarily, 500 pieces of historical trait record data of the first candidate breed chicken under different feeding conditions are collected, of which 150 records meet the target characteristic value of the meat quality trait, and then the first occurrence ratio is 150÷500=30%. The first occurrence ratio reflects the probability of the occurrence of the target characteristic value of the meat quality trait under the condition that the feeding conditions differ greatly, thereby providing a basis for subsequent screening of the SNP genotyping breed chicken.

[0047] Specifically, the step S20 in the method comprises:

[0048] Based on the first feeding condition attribute correlation degree to the Nth feeding condition attribute correlation degree, a selected feeding condition attribute set with a feeding condition attribute correlation degree greater than or equal to the correlation degree threshold is extracted from the feeding condition index;

[0049] The historical trait record data of the first candidate breed chicken with any one attribute of the selected feeding condition attribute set being inconsistent is loaded;

[0050] The first occurrence ratio of the target characteristic value of the meat quality trait in the historical trait record data of the first candidate breed chicken is counted.

[0051] In the embodiment of the application, first, the feeding condition attribute correlation degrees are screened based on the first feeding condition attribute correlation degree to the Nth feeding condition attribute correlation degree. The feeding condition attributes with higher correlation degrees often have a more important influence on the meat quality trait, so the attributes with a feeding condition attribute correlation degree greater than or equal to the correlation degree threshold are extracted to form a selected feeding condition attribute set. For example, if the correlation degree threshold is set to 20%, and the protein content in the feed has a correlation degree of 25% and the breeding density has a correlation degree of 30%, then the protein content in the feed and the breeding density will be included in the selected feeding condition attribute set.

[0052] Secondly, the historical trait record data of the first candidate breed chicken with any one attribute of the selected feeding condition attribute set being inconsistent is loaded. It is explained that as long as there is one attribute in the selected feeding condition attribute set that differs in different records, the corresponding historical trait record data will be loaded.

[0053] Exemplarily, in a certain historical record, the breeding density of part of chickens is 5 per square meter, and that of another part is 8 per square meter, and other conditions are the same, and the above record is loaded for subsequent analysis.

[0054] Finally, the proportion of the first occurrence frequency of the target characteristic value of the meat quality trait in the loaded historical trait record data of the first candidate breed of chicken is counted. The proportion reflects the frequency of the occurrence of the target characteristic value of the meat quality trait under the condition that there are differences in feeding conditions. If the first occurrence frequency proportion obtained by counting is high, it means that the target characteristic value of the meat quality trait is less affected by the difference in feeding conditions, and may be more determined by genetic factors; otherwise, it means that the feeding conditions have a greater impact on the target characteristic value of the meat quality trait.

[0055] The first occurrence frequency proportion data provides a key reference for subsequent judgment of whether the candidate breed of chicken is suitable for SNP genotyping and molecular breeding, and helps to more accurately select a breed of chicken with excellent meat quality trait genetic potential, and further improve the efficiency and quality of Xinghua chicken molecular breeding.

[0056] S30: When the environmental correlation degree is greater than the correlation degree threshold, the second occurrence frequency proportions of the target characteristic value of the meat quality trait in the historical trait record data of the first candidate breed of chicken under the same feeding condition index are counted, the average of the second occurrence frequency proportions is counted, and a third occurrence frequency proportion is obtained.

[0057] In the embodiment of the application, when the environmental correlation degree is greater than the correlation degree threshold, it means that the feeding conditions have a significant impact on the meat quality trait. At this time, the second occurrence frequency proportions of the target characteristic value of the meat quality trait in the historical trait record data of the first candidate breed of chicken under the same feeding condition index are counted, and the average of the proportions is calculated to obtain the third occurrence frequency proportion. That is, when the environment has a greater impact, multiple groups of data with the same environment are selected, the occurrence frequency proportion of each group is calculated, and if the environment is stable, it means that the environment has a small impact.

[0058] Specifically, the historical trait record data of the first candidate breed of chicken under the same feeding condition index is collected first. The feeding condition index consistent means that the feed type, breeding density, illumination time and other feeding conditions are the same. After collecting a sufficient amount of historical trait record data, for each group of data, the record meeting the target characteristic value of the meat quality trait is selected, the occurrence frequency is counted, and then the occurrence frequency is divided by the total number of historical trait record data of the group to obtain the second occurrence frequency proportion corresponding to each group of data.

[0059] Exemplarily, historical trait record data of 3 groups of first candidate breed chickens under the same feeding conditions is collected, the first group has 400 records, of which 120 records meet the target characteristic value of the meat quality trait, and the second occurrence ratio is 120÷400=30%; the second group has 350 records, of which 105 records meet the target characteristic value, and the ratio is 105÷350=30%; the third group has 450 records, of which 135 records meet the target characteristic value, and the ratio is 135÷450=30%. Then, the three second occurrence ratios are added and divided by 3, that is, (30%+30%+30%)÷3=30%, to obtain the third occurrence ratio.

[0060] The third occurrence ratio reflects the average probability of the target characteristic value of the meat quality trait under relatively stable feeding conditions. The third occurrence ratio under specific feeding conditions reflects the stability of the target characteristic value of the meat quality trait.

[0061] Further, if the third occurrence ratio is high, it means that the target characteristic value of the meat quality trait is more likely to appear under the feeding conditions, and it may be more stable under the positive influence of the feeding conditions; on the contrary, if the ratio is low, it may indicate that the target characteristic value of the meat quality trait is greatly affected by other unknown factors, or the current feeding conditions are not conducive to the performance of the target characteristic value of the meat quality trait.

[0062] S40: When the first occurrence ratio or the third occurrence ratio is greater than or equal to the occurrence ratio threshold, the candidate breed chicken is added to the SNP genotyped breed chicken;

[0063] In the embodiment of the application, when the first occurrence ratio or the third occurrence ratio is greater than or equal to the occurrence ratio threshold, it means that the candidate breed chicken has certain stability and heredity in the performance of the target characteristic value of the meat quality trait. The candidate breed chicken is added to the SNP genotyped breed chicken for further genetic analysis.

[0064] Among them, SNP genotyping is a technology that can accurately detect single nucleotide variations in the gene sequence of a chicken. By SNP genotyping the candidate breed chicken, the gene locus closely related to the target characteristic value of the meat quality trait can be found. After determining the key gene locus, targeted selection and breeding can be carried out in the subsequent breeding process.

[0065] Further, in SNP genotyping, the sample of the candidate breed chicken, such as blood, feather and other tissues containing DNA, is first collected, and then gene detection technology and equipment are used to extract, amplify and sequence the DNA in the sample to obtain the genotyping data of the chicken.

[0066] Then, the data is analyzed by bioinformatics methods to find SNP sites related to the target characteristic value of meat quality traits. For the determined SNP sites, they are applied as molecular markers in subsequent breeding work. In selecting breeding chickens, individuals with favorable SNP sites are preferentially selected for breeding, which can increase the probability of offspring chickens having excellent meat quality traits.

[0067] Through continuous screening and breeding, the meat quality traits of apricot flower chickens are gradually optimized, and a new breed of apricot flower chickens with more tender meat, more reasonable fat content, and better meat color is bred.

[0068] S50: performing apricot flower chicken molecular breeding on the SNP-genotyped breed chickens.

[0069] In the embodiments of the present application, a scheme is formulated according to the genotyping data of the SNP-genotyped breed chickens. First, a molecular marker-assisted selection system is established according to the SNP sites determined to be related to the target characteristic value of meat quality traits. In selecting breeding chickens, the system is used to accurately screen the SNP-genotyped breed chickens, and individuals with favorable SNP site combinations are preferentially selected as breeding chickens to ensure that excellent genes can be passed on to offspring.

[0070] In the breeding process, advanced breeding techniques such as artificial insemination are used to improve breeding efficiency and genetic quality of offspring. At the same time, the breeding process is strictly monitored and managed to ensure the stability and suitability of the breeding environment and reduce the impact of external factors on the breeding effect.

[0071] Further, when breeding offspring chickens, feeding and management schemes are provided according to the characteristics of different growth stages. The nutritional components of feed are reasonably allocated to meet the needs of chicken growth and development and promote the optimization of meat quality traits. At the same time, disease prevention and control work is strengthened, and regular health checks and vaccinations are carried out on the chicken population to prevent and control the occurrence of diseases and ensure the healthy growth of the chicken population.

[0072] During the growth of the chicken population, meat quality traits are continuously monitored and evaluated. Samples of the chicken population are regularly collected to detect meat quality trait indicators such as meat tenderness, fat content, and meat color, and compared and analyzed with breeding targets. Based on the monitoring results, breeding programs and feeding management measures are adjusted in a timely manner to ensure that breeding work progresses towards the expected target.

[0073] Specifically, step S50 in the method includes:

[0074] SNP genotyping of apricot flower chickens for meat quality trait indicators to obtain a first genotyping result;

[0075] SNP genotyping of the SNP-genotyped breed chickens for meat quality trait indicators based on the genetic mutation sites to obtain a second genotyping result;

[0076] performing apricot chicken molecular breeding simulation optimization according to the first genotyping result, to obtain an apricot chicken desired genome;

[0077] performing apricot chicken molecular breeding based on the apricot chicken desired genome, the second genotyping result and the first genotyping result.

[0078] In the embodiments of the present application, first, SNP genotyping of meat quality trait indicators is performed on apricot chickens to understand the overall genotyping of apricot chickens, and a first genotyping result is obtained. The first genotyping result is the collection of basic data of the genetic characteristics of the apricot chicken population.

[0079] Secondly, SNP genotyping of meat quality trait indicators is performed on the chicken varieties to be SNP genotyped based on the identified genetic mutation sites, thereby obtaining a second genotyping result. The genetic mutation sites are sites closely related to the target characteristic values of meat quality traits screened in the previous study. By genotyping the genetic mutation sites, the characteristics of the chicken varieties to be selected on the key genes can be more accurately understood.

[0080] Thirdly, apricot chicken molecular breeding simulation optimization is performed according to the first genotyping result. Computer simulation technology is used to simulate and optimize various possible breeding schemes in a virtual environment in combination with the first genotyping result and breeding goals. By continuously adjusting the gene combination and breeding strategy, the gene combination most likely to achieve excellent meat quality traits is found out, and then an apricot chicken desired genome is obtained. The desired genome represents the gene combination that can theoretically enable apricot chickens to have the best meat quality traits.

[0081] Finally, apricot chicken molecular breeding is performed based on the apricot chicken desired genome, the second genotyping result and the first genotyping result. In the actual breeding process, the desired genome is used as a target to compare the second genotyping result of the chicken varieties to be SNP genotyped with the first genotyping result of the apricot chicken population, and individuals with characteristics close to the desired genome are selected for breeding. By continuously screening and breeding, the genetic composition of the offspring chicken population is gradually made close to the desired genome, thereby realizing the optimization of the meat quality traits of apricot chickens.

[0082] wherein apricot chicken molecular breeding simulation optimization is performed according to the second genotyping result and the first genotyping result, to obtain an apricot chicken desired genome, as shown in Figure 2 , which comprises:

[0083] SNP genotyping is performed on apricot chickens to obtain a first meat quality trait indicator and a first mutation site;

[0084] Based on the first mutation site, collect the genomic record data, feeding condition record data and first meat quality trait record data of apricot flower chicken, train the first meat quality trait index apricot flower chicken molecular breeding simulator, and add it into the apricot flower chicken molecular breeding simulator set;

[0085] Based on the apricot flower chicken molecular breeding simulator set, according to the second genotyping result and the first genotyping result, perform apricot flower chicken molecular breeding simulation optimization, and obtain apricot flower chicken expected genome.

[0086] In the embodiment of the application, first, the apricot flower chicken is SNP genotyped, and the gene sequence is analyzed, so as to obtain the first meat quality trait index and the first mutation site. The first meat quality trait index covers meat tenderness, fat content, meat color and other meat-related indexes, and the first mutation site is a specific position where variation occurs in the gene sequence.

[0087] Then, based on the first mutation site, the genomic record data, feeding condition record data and first meat quality trait record data of apricot flower chicken are collected. The genomic record data contains the complete genetic information of apricot flower chicken, the feeding condition record data involves various feeding factors such as feed type, breeding density and lighting time, and the first meat quality trait record data is the actual measurement value of each meat quality index. The collected data is used to train the first meat quality trait index apricot flower chicken molecular breeding simulator. The simulator learns the internal relationship between the data through complex algorithms and models, such as neural networks, to simulate the meat quality trait performance of apricot flower chicken under different gene combinations and feeding conditions. After training, it is added into the apricot flower chicken molecular breeding simulator set, which contains multiple simulators for different meat quality trait indexes, providing support for subsequent simulation optimization.

[0088] Exemplarily, the first meat quality trait index apricot flower chicken molecular breeding simulator is built and trained based on neural network, and the specific steps are as follows:

[0089] First, data preparation, the collected genomic record data, feeding condition record data and first meat quality trait record data of apricot flower chicken are preprocessed, including data cleaning, normalization and other operations, to improve data quality and model training effect. Then, the training set, validation set and test set are divided according to the ratio of 7:2:1. The training set is used for parameter learning of the model, the validation set is used for adjusting the hyperparameters of the model, and the test set is used for evaluating the final performance of the model.

[0090] Then, the model is constructed. The number of nodes of the input layer is equal to the dimension of the input features, such as the genomic record data, the feeding condition record data, and the first meat quality trait record data, which are three features, so the input layer contains three nodes; 1-3 hidden layers are set, the number of nodes of each layer is adjusted through experiments, such as 64, 32, etc., and the activation function is selected as ReLU; the number of nodes of the output layer is equal to the number of simulated Xinghua chicken molecular breeding, such as one node for predicting time-consuming, and the output layer generally does not use the activation function, and directly outputs continuous values.

[0091] Then, the model is trained, an Adam optimizer and a mean square error (MSE) loss function are used to construct a training framework, a batch size of 32, a total training round of 50, and an early stopping mechanism (patience = 5) are set, when the validation set loss does not appear for 5 consecutive rounds, the training process is automatically terminated, and the first meat quality trait Xinghua chicken molecular breeding simulator is obtained.

[0092] Finally, based on the Xinghua chicken molecular breeding simulator set, combined with the second genotyping result and the first genotyping result, the Xinghua chicken molecular breeding simulation optimization is performed. In the optimization process, the simulator set simulates various possible breeding schemes and their corresponding meat quality trait performances according to different genotyping results. Through continuous comparison and screening, the most likely to achieve excellent meat quality trait gene combination is found, and then the Xinghua chicken desired genome is obtained. The desired genome represents the theoretical genome combination that can make Xinghua chicken have the best meat quality trait, and provides a clear target and direction for actual breeding work.

[0093] Further, based on the Xinghua chicken molecular breeding simulator set, according to the first genotyping result, the Xinghua chicken molecular breeding simulation optimization is performed to obtain the Xinghua chicken desired genome, including:

[0094] According to the first genotyping result, the Xinghua chicken genome is randomly configured;

[0095] The Xinghua chicken molecular breeding simulator set is used to process the Xinghua chicken genome to obtain the Xinghua chicken predicted trait;

[0096] When the Xinghua chicken predicted trait meets the meat quality trait target characteristic value, the Xinghua chicken genome is added to the Xinghua chicken desired genome.

[0097] In the embodiments of the present application, according to the first genotyping result, the Xinghua chicken genome is randomly configured. Random configuration is a random combination within the gene characteristics presented by the first genotyping result, to produce diversified genome schemes.

[0098] Subsequently, the randomly configured genome of the apricot chicken is processed by the apricot chicken molecular breeding simulator set. Each simulator in the simulator set simulates different genome schemes based on the data relationship learned from the previous training, thereby obtaining the predicted traits of the apricot chicken. The predicted traits include meat tenderness, fat content, meat color, and other aspects, which are the prediction of the meat performance of the randomly configured genome in actual breeding.

[0099] Further, when the predicted traits of the apricot chicken meet the target characteristic value of the meat quality traits, it means that the randomly configured genome of the apricot chicken has the potential to achieve excellent meat quality traits. At this time, the apricot chicken genome is added to the apricot chicken desired genome. With the continuous random configuration and simulation processing, the apricot chicken desired genome will continue to expand and contain more and more genome schemes with potential excellent traits.

[0100] Further, based on the apricot chicken molecular breeding simulator set, the apricot chicken molecular breeding simulation optimization is performed according to the first genotyping result, and the apricot chicken desired genome is obtained.

[0101] When the predicted traits of the apricot chicken do not meet the target characteristic value of the meat quality traits, the genome with a genome similarity greater than or equal to the similarity threshold value with the apricot chicken genome is set as a taboo genome.

[0102] The taboo genome is avoided, and the apricot chicken genome is randomly configured and subjected to cycle analysis.

[0103] In the embodiments of the present application, when the predicted traits of the apricot chicken do not meet the target characteristic value of the meat quality traits, it means that the currently randomly configured genome of the apricot chicken is difficult to achieve excellent meat quality traits. At this time, in order to avoid the subsequent random configuration from being trapped in ineffective repetition, the genome with a genome similarity greater than or equal to the similarity threshold value with the apricot chicken genome is set as a taboo genome. The similarity threshold value is a pre-set standard for measuring the similarity between two genomes.

[0104] Exemplarily, the cosine similarity is used to calculate the similarity between genomes.

[0105] Secondly, after avoiding the taboo genome, the operation of randomly configuring the apricot chicken genome is performed again. During the random configuration process, the combination of genomes in the taboo genome is strictly excluded to ensure that the newly generated genome has different characteristics. Then, the newly configured genome is subjected to cycle analysis, that is, the apricot chicken molecular breeding simulator set is used again to process the new apricot chicken genome, and the new predicted traits of the apricot chicken are obtained. Then, it is judged whether the new predicted traits meet the target characteristic value of the meat quality traits. If yes, the genome is added to the apricot chicken desired genome; if not, the process of setting the taboo genome, avoiding and re-randomly configuring, and cycle analysis is repeated.

[0106] Through continuous cyclical processes, more genomes that meet the target characteristics of meat quality are gradually screened out, continuously refining the desired genome for Xinghua chickens. With an enriched desired genome, more high-quality gene combinations are available for selection during actual Xinghua chicken molecular breeding, allowing for more targeted selection and breeding of breeder chickens. This increases the success rate of breeding Xinghua chickens with more tender meat, a more balanced fat content, and superior meat color. Simultaneously, this cyclical optimization approach effectively improves breeding efficiency, reduces unnecessary resource waste, and makes breeding work more scientific, precise, and efficient.

[0107] In summary, the embodiments of this application have at least the following technical effects:

[0108] This application provides a molecular breeding method for optimizing the meat quality of Xinghua chickens. First, correlation analysis clarifies the influence of environmental factors on meat quality traits. Based on environmental correlation, different statistical methods are used to determine the proportion of occurrence of target characteristic values ​​for meat quality traits, thereby screening out chicken breeds for SNP genotyping. This avoids the cumbersome process of comprehensive genotyping for every Xinghua chicken breed, reducing unnecessary testing workload. Second, molecular breeding of Xinghua chickens is performed based on the breeds for SNP genotyping, utilizing molecular-assisted selection technology to more precisely target genes related to meat quality traits. During the breeding process, SNP genotyping is performed on Xinghua chickens and the selected breeds to obtain genotyping results. Molecular breeding simulation is used for optimization to obtain the desired Xinghua chicken genome, enabling targeted breeding. Furthermore, by setting parameters such as correlation threshold, occurrence frequency threshold, and similarity threshold, the entire breeding process is quantitatively controlled, further improving the reliability and stability of the breeding. Through the above technical solutions, the breeding process becomes more scientific and efficient, enabling the improvement of Xinghua chicken meat quality in a shorter time, increasing breeding efficiency and accuracy, and meeting people's demand for high-quality poultry meat.

[0109] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0110] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0111] The specification and drawings are, of course, to be regarded in an illustrative rather than a restrictive sense. It is to be understood that any such modifications, variations, combinations or equivalents that fall within the scope of the application are intended to be embraced herein.

Claims

1. A molecular breeding method for optimizing the meat quality of Xinghua chicken, characterized in that, include: Based on meat quality trait indicators, a correlation analysis of feeding condition indicators was conducted on the selected chicken breeds to obtain the environmental correlation degree. When the environmental correlation is less than or equal to the correlation threshold, the percentage of the first occurrence of the target feature value of meat quality trait in the historical trait record data of the candidate breed chickens that are inconsistent with the feeding condition indicators is counted. When the environmental correlation is greater than the correlation threshold, the percentage of the second occurrence of the target feature value of meat quality trait is statistically analyzed in the historical trait record data of several candidate chicken breeds that are consistent with several feeding condition indicators. The average of the percentage of the second occurrence is calculated to obtain the percentage of the third occurrence. When the proportion of the first occurrence or the proportion of the third occurrence is greater than or equal to the threshold of the proportion of occurrence, the candidate breed chicken is added to the breed chicken for SNP genotyping; Molecular breeding of Xinghua chickens was carried out based on the chicken breeds to be genotyped by SNP.

2. The method as described in claim 1, characterized in that, Based on meat quality traits, a correlation analysis of feeding condition indicators was conducted on the selected chicken breeds to obtain environmental correlations, including: Extract the first feeding condition attribute from the feeding condition indicators; The historical SNP genotyping results of the candidate chicken breeds are consistent, and only a few pairs of meat quality trait indicators among the feeding condition indicators are inconsistent with the first feeding condition attribute. The percentage of inconsistent values ​​among the detected values ​​of several pairs of meat quality trait indicators is set as the correlation degree of the first feeding condition attribute. Until the correlation degree of the Nth feeding condition attribute is obtained; Extract the average of the correlation scores of the first feeding condition attribute up to the Nth feeding condition attribute, and set it as the environmental correlation score.

3. The method as described in claim 2, characterized in that, The historical SNP genotyping results of the candidate chicken breeds are consistent, and the detection values ​​of several pairs of meat quality trait indicators among the feeding condition indicators are only inconsistent with the first feeding condition attribute, including: If the first feeding condition attribute is a type attribute, when the characteristic values ​​of the first feeding condition attribute are different, it is considered inconsistent; otherwise, it is considered consistent. If the first feeding condition attribute is a quantitative attribute, when the deviation of the first feeding condition attribute feature value is greater than or equal to the predefined first feeding condition attribute feature value deviation threshold, it is considered inconsistent; otherwise, it is considered consistent.

4. The method as described in claim 2, characterized in that, The percentage of the first occurrence of the target meat quality trait value in the historical trait records of candidate chicken breeds where the feeding condition indicators are inconsistent with those of the target trait values ​​includes: Based on the correlation degree of the first feeding condition attribute up to the correlation degree of the Nth feeding condition attribute, a selected set of feeding condition attributes whose correlation degree of feeding condition attribute is greater than or equal to the correlation degree threshold is extracted from the feeding condition index. Load historical trait records of the candidate breed chickens where any attribute of the selected feeding condition attribute set is inconsistent; The percentage of the first occurrence of the target meat quality trait value in the historical trait records of the candidate chicken breeds is statistically analyzed.

5. The method as described in claim 1, characterized in that, Molecular breeding of Xinghua chickens was carried out based on the chicken breeds to be genotyped by the SNPs, including: SNP genotyping of meat quality traits in Xinghua chicken was performed to obtain the first genotyping results; Based on the gene mutation sites, the chicken breed to be genotyped by SNP was genotyped for meat quality traits to obtain the second genotyping result. Based on the first genotyping results, molecular breeding simulation optimization of Xinghua chicken was performed to obtain the desired genome of Xinghua chicken; Based on the expected genome of Xinghua chicken, the second genotyping result, and the first genotyping result, molecular breeding of Xinghua chicken is carried out.

6. The method as described in claim 5, characterized in that, Based on the second genotyping result and the first genotyping result, molecular breeding simulation optimization of Xinghua chicken was performed to obtain the desired Xinghua chicken genome, including: SNP genotyping was performed on Xinghua chickens to obtain the first meat quality trait index and the first mutation site. Based on the first mutation site, collect the genome record data, feeding condition record data and first meat quality trait record data of Xinghua chicken, train the Xinghua chicken molecular breeding simulator for the first meat quality trait index, and add it to the Xinghua chicken molecular breeding simulator set. Based on the Xinghua chicken molecular breeding simulator set, and according to the second genotyping result and the first genotyping result, Xinghua chicken molecular breeding simulation optimization is performed to obtain the desired Xinghua chicken genome.

7. The method as described in claim 6, characterized in that, Based on the Xinghua chicken molecular breeding simulator set, and according to the first genotyping result, a Xinghua chicken molecular breeding simulation optimization is performed to obtain the desired Xinghua chicken genome, including: Based on the first genotyping result, the Xinghua chicken genome was randomly configured; The Xinghua chicken genome was processed using the Xinghua chicken molecular breeding simulator set to obtain predicted traits of Xinghua chicken. When the predicted traits of Xinghua chicken meet the target characteristic value of the meat quality trait, the Xinghua chicken genome is added to the expected Xinghua chicken genome.

8. The method as described in claim 7, characterized in that, Also includes: When the predicted traits of Xinghua chicken do not meet the target feature value of the meat quality trait, the genomes with a genome similarity greater than or equal to the similarity threshold of the Xinghua chicken genome are set as forbidden genomes. By circumventing the taboo genome, the Xinghua chicken genome was randomly configured, and cyclic analysis was performed.

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