A multi-trait collaborative decision support method and system for the breeding process of breeding hens

Abstract

The application provides a multi-trait collaborative decision support method and system for the breeding process of breeding hens, and relates to the technical field of computers, comprising: obtaining genomic sequence data of individual meat-breeding hens, production trait time series data at different physiological stages, and antagonistic trait data; performing genetic basis analysis according to the genomic sequence data and the antagonistic trait data to obtain a genetic antagonism atlas; performing modeling processing according to the genetic antagonism atlas to construct a trait expression potential surface with physiological balance as a constraint; performing individual whole-life-cycle potential trajectory optimization according to the trait expression potential surface and the production trait time series data to obtain an ideal trait development trajectory; performing cross-generation breeding scheme joint optimization processing according to the ideal trait development trajectory to obtain a cross-generation mating scheme; and performing action mapping according to the optimized cross-generation mating scheme to generate a decision result. The application significantly improves the precision, efficiency and comprehensive genetic progress of the breeding of breeding hens.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a multi-trait collaborative decision support method and system for the breeding process of breeder chickens. Background Technology

[0002] In the field of poultry breeding, the selection and breeding of high-quality broiler chickens (especially local breeds targeting specific consumer preferences, slow-growing yellow-feathered broilers, etc.) faces a unique core contradiction: the market value of their commercial offspring depends simultaneously on excellent meat production performance and considerable egg production capacity. This requires breeder chickens to maintain good meat production potential while possessing high reproductive efficiency to reduce chick costs. However, meat production and egg production traits have a significant antagonistic relationship genetically, and physiologically they share and compete for limited nutritional and energy resources, making it difficult to improve them simultaneously in conventional breeding.

[0003] Existing technologies mostly employ a fixed-weight comprehensive selection index method, attempting to linearly balance multiple traits. However, this method fails to analyze the antagonistic nature from a genetic mechanism perspective, cannot dynamically simulate the resource allocation strategies and potential boundaries of individuals throughout their life cycle, and does not fully consider the long-term stability of the genetic structure of their offspring population when selecting individuals for mating. This makes breeding decisions highly empirical and uncertain, making it difficult to achieve truly meaningful multi-trait synergistic genetic improvement.

[0004] Based on the shortcomings of the existing technologies, there is an urgent need for a multi-trait collaborative decision support method and system for the breeding process of breeder chickens. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-trait collaborative decision support method and system for the breeding process of breeder chickens, so as to improve the above-mentioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0006] Firstly, this application provides a multi-trait collaborative decision support method for the breeding process of breeder chickens, including:

[0007] Acquire genomic sequence data, time-series data of production traits at different physiological stages, and antagonistic trait data of individual broiler breeders, wherein the antagonistic trait data includes feed conversion ratio and egg production;

[0008] Genetic basis analysis was performed based on the genomic sequence data and the antagonistic trait data. Genetic loci with opposite effects on each antagonistic trait were located through genome-wide association analysis to obtain a genetic antagonism map.

[0009] Modeling was performed based on the genetic antagonism map. By integrating the effect values ​​of antagonistic alleles into an individual-level energy budget allocation model, a trait expression potential surface constrained by physiological homeostasis was constructed.

[0010] Based on the trait expression potential surface and the time series data of the production trait, the potential trajectory of an individual throughout its entire life cycle is optimized. By solving the dynamic Pareto front that conforms to the individual growth curve on the potential surface, the ideal trait development trajectory is obtained.

[0011] Based on the development trajectory of the ideal traits, a joint optimization process was performed on the intergenerational breeding scheme to obtain the optimized intergenerational mating scheme.

[0012] Action mapping is performed based on the optimized intergenerational mating scheme to generate decision results, which include recommendations for differentiated breeding and nutritional intervention timing for different family lines.

[0013] Secondly, this application also provides a multi-trait collaborative decision support method and system for the breeding process of breeder chickens, including:

[0014] The acquisition module is used to acquire the genome sequence data, time-series data of production traits at different physiological stages, and antagonistic trait data of individual broiler breeders, including feed conversion ratio and egg production.

[0015] The analysis module is used to perform genetic basis analysis based on the genomic sequence data and the antagonistic trait data, locate genetic loci with opposite effects on each antagonistic trait through genome-wide association analysis, and obtain a genetic antagonism map;

[0016] The modeling module is used to perform modeling processing based on the genetic antagonism map. By integrating the effect values ​​of antagonistic alleles into an individual-level energy budget allocation model, a trait expression potential surface constrained by physiological equilibrium is constructed.

[0017] The solution module is used to optimize the potential trajectory of an individual throughout its entire life cycle based on the trait expression potential surface and the time series data of the productive traits. By solving the dynamic Pareto front that conforms to the individual growth curve on the potential surface, the ideal trait development trajectory is obtained.

[0018] The optimization module is used to perform joint optimization of cross-generational breeding schemes based on the development trajectory of the ideal trait, so as to obtain an optimized cross-generational mating scheme;

[0019] The decision-making module is used to perform action mapping based on the optimized intergenerational mating scheme and generate decision results, which include recommendations for differentiated breeding and nutritional intervention timing for different families.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention constructs a genetic antagonism map, quantifies the trait expression potential surface, and performs full life cycle trajectory optimization and cross-generational joint optimization. It realizes integrated processing from genetic mechanism analysis to breeding decision generation, effectively overcomes the physiological resource competition problem between meat production and egg production traits, and significantly improves the accuracy, efficiency and comprehensive genetic progress of breeder chicken breeding. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a multi-trait collaborative decision support method for the breeding process of breeder chickens, as described in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the structure of a multi-trait collaborative decision support system for the breeding process of breeder chickens, as described in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of a multi-trait collaborative decision support device for the breeding process of breeder chickens, as described in an embodiment of the present invention.

[0026] The diagram is labeled as follows: 800, a multi-trait collaborative decision support device for the breeding process of chickens; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, parsing module; 903, modeling module; 904, solving module; 905, optimization module; 906, decision module. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Example 1:

[0030] This embodiment provides a multi-trait collaborative decision support method for the breeding process of breeder chickens.

[0031] See Figure 1 The figure shows that the method includes steps S100 to S600.

[0032] Step S100: Obtain genomic sequence data, time-series data of production traits at different physiological stages, and antagonistic trait data of individual broiler breeders. Antagonistic trait data include feed conversion ratio and number of eggs laid.

[0033] Understandably, this step is the data foundation building stage, the core of which lies in the systematic and targeted collection of multi-source heterogeneous data that can support subsequent in-depth analysis. Specifically, genomic sequence data is usually obtained through high-throughput sequencing technology, covering the whole genome single nucleotide polymorphism site information of individual breeder chickens, providing molecular-level raw data for analyzing the genetic basis of traits. Time-series data of production traits at different physiological stages need to be measured and recorded regularly at key growth and development nodes such as the brooding period, growth period, and laying period of breeder chickens. This mainly includes dynamic changes in body weight, body size, number of eggs laid, and egg weight at each stage, thereby depicting the production performance trajectory of an individual throughout its entire life cycle. Antagonistic trait data, specifically referring to feed conversion ratio (i.e., the ratio of feed consumption to weight gain or total egg production) and number of eggs laid, directly reflect the core contradiction between meat production efficiency and reproductive efficiency. These data need to be obtained through individual feeding record systems (such as automatic feed scales) and egg production monitoring equipment to ensure the accuracy and reliability of the data.

[0034] Step S200: Based on genomic sequence data and antagonistic trait data, perform genetic basis analysis, locate genetic loci with opposite effects on each antagonistic trait through genome-wide association analysis, and obtain a genetic antagonism map;

[0035] It should be noted that this step actively explores genomic regions with opposite genetic effects on the traits of feed conversion ratio and egg production through genome-wide association analysis, thereby transforming empirically observed negative correlations of traits into quantifiable genetic antagonism maps and achieving an understanding of the genetic structure level of complex trait relationships.

[0036] Step S300: Modeling is performed based on the genetic antagonism map. By integrating the effect values ​​of antagonistic alleles into an individual-level energy budget allocation model, a trait expression potential surface constrained by physiological equilibrium is constructed.

[0037] Understandably, this step transforms abstract genetic information into a physiological constraint model at the individual level. By introducing the perspective of energy budget allocation, it integrates the effect values ​​of antagonistic alleles into a dynamic physiological model, thereby constructing a potential boundary surface that represents all possible expression combinations of meat production and egg production traits under the inherent physiological constraints of an individual. This shifts the breeding goal from pursuing extreme values ​​of a single trait to finding the optimal balance point within the individual's physiological potential boundary.

[0038] Step S400: Optimize the individual's full life-cycle potential trajectory based on the trait expression potential surface and the time series data of productive traits. By solving the dynamic Pareto front that conforms to the individual's growth curve on the potential surface, the ideal trait development trajectory is obtained.

[0039] It should be noted that this step further dynamizes and personalizes the breeding problem, combining the actual growth curve data of each individual to optimize the trajectory of the entire life cycle on the aforementioned potential surface. The goal is to find a physiologically feasible development path for each individual from brooding to the end of egg production, which achieves Pareto optimality in terms of multi-trait synergy. This transforms the breeding decision from a static endpoint selection to a dynamic process optimization.

[0040] Step S500: Based on the development trajectory of the ideal traits, perform joint optimization of the intergenerational breeding program to obtain the optimized intergenerational mating program;

[0041] Understandably, this step takes the ideal development trajectory of multiple individuals as the breeding target at the population level. By comprehensively considering the genetic contribution potential of each candidate individual and its impact on the population genetic structure (such as the inbreeding coefficient) after mating, joint optimization calculations are performed. The final result is not a simple ranking of individuals, but a set of optimal mating schemes that can simultaneously take into account short-term genetic gains and long-term population health.

[0042] Step S600: Based on the optimized intergenerational mating program, conduct action mapping to generate decision results, which include recommendations for differentiated breeding and nutritional intervention timing for different family lines.

[0043] Finally, step S600 aims to transform genetic mating programs into actionable management recommendations in actual breeding practices. The idea is to analyze the genetic composition of superior combinations, associate specific genetic advantages with key physiological windows in the growth and development of breeder chickens, thereby generating differentiated nutritional intervention timing recommendations. This achieves seamless integration between breeding programs and feeding management, completing a full decision support loop from genetic information to production practice.

[0044] Further, step S200 includes steps S210 to S230.

[0045] Step S210: Based on the genomic sequence data and antagonistic trait data, perform association analysis. By calculating the association strength between each locus and the phenotype, select loci with p-values ​​lower than a preset threshold to obtain a set of candidate loci associated with a single trait.

[0046] Step S220: Conflict identification processing is performed based on the candidate site set. By comparing the signs of the regression coefficients of each candidate site for feed conversion ratio and pectoral muscle rate, sites with opposite signs of effects on the two traits are selected to obtain a subset of candidate sites with antagonistic effects.

[0047] Step S230: Gene function mapping is performed on a subset of candidate sites. By locating the sites to known gene regions in the chicken genome that are related to muscle protein deposition and basal metabolic rate, and confirming their physiological functions based on their expression differences in liver and pectoral muscle tissues, a genetic antagonism map is constructed.

[0048] Specifically, step S210 uses a statistical model (such as a mixed linear model) to calculate the association strength between each genomic locus and phenotypic data, and uses the p-value—an indicator used to measure statistical significance, where a lower value indicates that the association between the locus and the trait is less likely to be caused by random factors—as a screening criterion to identify loci significantly associated with a single trait such as feed conversion ratio or egg production, forming a preliminary set of candidate loci. This step achieves the first focusing from massive genomic information to loci associated with the target trait. Preferably, the mixed linear model represents:

[0049] ;

[0050] In the formula, y is the phenotypic value vector; X is the fixed-effects design matrix; β is the fixed-effects vector; M j G represents the genotype matrix of the j-th SNP locus; j Let represent the genetic effect of the j-th SNP; Z is the individual random effect design matrix; u is the individual random effect vector; W is the environmental random effect design matrix; v is the environmental random effect vector; and ε is the residual vector.

[0051] Subsequently, the conflict identification process in step S220 is further explored based on this. Its core lies in comparing the genetic effects of the candidate loci obtained in step S210 on the two traits of feed conversion ratio and egg production. Specifically, the method is to analyze the sign (positive or negative) of the regression coefficient (i.e., the average influence of allele changes at that locus on the phenotypic value) of each locus in different trait genetic assessment models. This allows for the precise screening of loci that have a positive effect on one trait and a negative effect on another. These loci constitute a subset of candidate loci with clear antagonistic effects. This step reveals the inherent genetic conflict of negative correlation between traits. Finally, the gene function mapping process in step S230 aims to provide a biological interpretation of the above statistical genetics findings. This is achieved by anchoring these antagonistic sites to the chicken reference genome and highlighting genes located in known pathways that regulate muscle protein deposition (related to meat production efficiency) and basal metabolic rate (related to energy allocation and reproductive efficiency). At the same time, tissue-specific gene expression data (such as comparing the expression levels of genes corresponding to the sites in the pectoral muscle and liver) are used to infer their possible physiological functions. This transforms abstract statistical signals into a biologically meaningful genetic antagonism map, laying a solid foundation for the subsequent construction of predictive models based on physiological mechanisms.

[0052] Further, step S300 includes steps S310 to S330.

[0053] Step S310: Perform parameter conversion processing based on the genetic antagonism map. By mapping the effect values ​​of the located antagonistic alleles to the theoretical impact values ​​on the individual's basal metabolic rate and protein deposition efficiency, an individualized physiological parameter vector is obtained.

[0054] Step S320: Based on the individualized physiological parameter vector, perform allocation constraint modeling. By constructing a system of differential equations that dynamically allocates daily net energy intake among maintenance needs, pectoral muscle growth, and follicle development, and using the physiological parameter vector as the adjustment variable of the allocation coefficient in the system of differential equations, a dynamic energy budget model is obtained.

[0055] Step S330: Based on the dynamic energy budget model, the model is processed by simulating all possible combinations of pectoral muscle rate and egg production under different energy intake strategies from the brooding period to the end of the laying period, and constructing a trait expression potential surface constrained by physiological balance.

[0056] It should be noted that step S310 transforms the abstract genetic effect values ​​revealed by the genetic antagonism map into more physiologically significant individual characteristic parameters. Specifically, this step maps the effect values ​​of specific alleles (i.e., different versions of genes) on traits into theoretical influence values ​​on two core physiological parameters: basal metabolic rate (reflecting the basic energy consumption level for maintaining life activities) and protein deposition efficiency (reflecting the efficiency of converting nutrients into muscle tissue). This generates a quantified individualized physiological parameter vector for each breeder chicken, containing its key physiological characteristic parameters, thus achieving a preliminary translation from "genotype" to "physiological phenotype". Based on this, the allocation constraint modeling process in step S320 aims to construct a dynamic physiological mechanism model. The technical means is to use the tool of differential equations in mathematics to quantitatively describe the dynamic allocation law of net daily energy intake among three competing physiological processes: maintaining basic bodily functions, promoting pectoral muscle tissue growth, and supporting follicle development (directly related to egg production capacity). In this model, the individualized physiological parameter vector obtained in step S310 is used as a key coefficient to regulate the energy allocation ratio. This allows the model to reflect the inherent differences in energy allocation strategies among individuals with different genetic backgrounds, ultimately resulting in a personalized dynamic energy budget model that can simulate energy flow and transformation. Finally, the deductive processing in step S330 utilizes this dynamic model for simulation calculations. The method involves setting the entire time range from the start of the brooding period to the end of the laying period in the computer, systematically changing the daily energy intake strategy, and running the energy budget dynamic model to predict all possible combinations of pectoral muscle rate and egg production under various nutritional programs. By aggregating all these simulation results, the trait expression boundary that the individual can achieve under physiological constraints can be outlined on a two-dimensional plane with pectoral muscle rate and egg production as coordinate axes. This is the trait expression potential surface, which intuitively depicts the maximum possible trade-off space between the two antagonistic traits of meat production and egg production in a specific individual, providing a direct scientific basis for subsequent personalized breeding decisions.

[0057] Further, step S400 includes steps S410 to S430.

[0058] Step S410: Based on the time series data of production traits, perform constraint extraction processing. By identifying the inflection point of weight gain and the starting point of the egg production cycle, divide the life cycle of broiler breeders into physiological stages with different nutritional priorities, and determine the boundary conditions of the trait performance of each stage to obtain staged growth constraints.

[0059] Step S420: Perform multi-stage dynamic optimization based on the trait expression potential surface and staged growth constraints. Decompose the entire life cycle trajectory optimization problem into continuous stage sub-problems and search for a set of candidate trajectories that satisfy the smooth transition between adjacent stages on the potential surface.

[0060] Step S430: Screening is performed based on the candidate trajectory set. By evaluating whether each trajectory conforms to the biological timeline of the coordinated development of the reproductive and muscular systems of the breeder chicken, ideal trajectories are selected from the Pareto solution set.

[0061] Understandably, step S410 transforms the continuous growth and development process into a discrete stage model with clear physiological significance. Specifically, it involves analyzing the data curve of body weight change with age to identify the inflection point where the growth rate changes significantly (such as the transition from a high-growth period to a slow-growth period), and accurately determining the starting point of the reproductive cycle by combining egg production records. This divides the life cycle of broiler breeders into different stages such as the brooding period, the rapid growth period, the physical maturity period, and the egg production period. Each stage corresponds to a unique priority of nutritional needs (for example, muscle deposition is prioritized during the rapid growth period, while follicle development needs to be considered after the start of egg production). It is also necessary to determine the reasonable upper and lower limits of traits such as body weight and egg production within each stage, ultimately forming a set of structured staged growth constraints, which defines the spatiotemporal framework and boundary constraints for subsequent optimization calculations. Based on this, the multi-stage dynamic optimization solution in step S420 aims to find an optimal development path that runs through the entire life cycle. It decomposes the complex global optimization problem into a series of continuous, stage-constrained sub-problems. Using optimization algorithms such as dynamic programming, it searches on the trait expression potential surface for a set of candidate development trajectories that not only satisfy the boundary conditions of their trait performance in each stage, but also ensure that the trait changes are smooth and in line with physiological laws during stage transitions. This method effectively avoids short-sighted decision-making that only considers the final trait performance and ignores the rationality of the development process. Finally, the screening process in step S430 makes a biologically based decision from the numerous candidate trajectories obtained by the optimization algorithm (which typically constitute a Pareto solution set, i.e., the set of solutions that cannot be further optimized for one trait without harming another trait). The screening criteria are not only mathematically optimal, but more importantly, to evaluate whether each candidate trajectory conforms to the inherent synergy and temporal patterns of the reproductive system (such as oviduct development and follicle wave occurrence) and muscular system (such as myofibril hyperplasia and hypertrophy) in the natural development of breeder chickens. For example, it ensures that the skeletal and muscular framework is fully developed before bearing a high egg production load. Through this screening that integrates quantitative optimization and biological verification, an ideal trait development trajectory that is physiologically feasible and close to optimal in terms of economic benefits is finally determined, providing a dynamic and personalized reference standard for the precise management and breeding of individual breeder chickens.

[0062] Further, step S500 includes steps S510 to S530.

[0063] Step S510: Evaluate the development trajectory of the ideal trait by calculating the degree of matching between the estimated breeding value of the candidate breeder chicken individual’s genome and the deviation of its trait development trajectory from the ideal trajectory, quantify the expected contribution of each individual as a parent to the achievement of the ideal trajectory of the offspring population, and obtain the parent breeding value vector.

[0064] Step S520: Based on the parent breeding value vector and pedigree data, optimize the modeling process. By constructing a complete graph model with candidate individuals as nodes and the average inbreeding coefficient and genetic complementarity of offspring after mating as edge weights, the problem of solving the mating scheme is transformed into finding the optimal subgraph under specific constraints in the graph.

[0065] Step S530: Solve the population genetic structure optimization model. With the goal of maximizing the weighted genetic gain of the offspring population and the constraint of controlling the growth of the inbreeding coefficient, solve for the optimal parent pairing combination and output the optimized intergenerational mating scheme.

[0066] Specifically, steps S510 to S530 together constitute a cross-generational decision-making process that transforms an individual's ideal breeding goal into an executable population genetic improvement plan. Step S510 uses the ideal trait development trajectory as a new standard for evaluating the individual breeding value of candidate breeder chickens. By calculating the degree of matching between each candidate individual's estimated genomic breeding value (a numerical value that predicts the genetic advantages that an individual can pass on to offspring as a parent based on genomic information) and this ideal trajectory, the expected probability or contribution of the offspring population in terms of meat and egg production traits as a whole closely approximating the ideal development trajectory is quantified. This transforms the abstract trajectory goal into a concrete and comparable parental breeding value vector for each individual, realizing the transformation of the selection standard from static trait values ​​to dynamic development paths. The formula for calculating the degree of matching is:

[0067] ;

[0068] In the formula, S i The trajectory matching score for individual i; T is the total number of time points in the life cycle of the breeder chicken; K is the number of target traits; w k G is the economic weight coefficient of trait k; i,k (t) represents the genomic estimated breeding value of individual i for trait k at time t; The target value of the ideal trajectory for phenotype k at time t; Let be the allowable deviation range of the ideal trajectory from phenotype k at time t; λ is the time decay factor. This is the critical peak time point for trait expression; For individual trajectory G i The geodesic distance between the trajectory and the ideal trajectory I is used to penalize trajectory shape deviation; α is the penalty weight for geometric distance.

[0069] Based on this, the optimization modeling in step S520 transforms the breeding planning problem into a graph theory optimization model. In this model, all candidate breeder individuals are regarded as nodes, and the possible mating relationships between any two individuals constitute edges. Each edge is assigned two weight indicators: one is the average inbreeding coefficient of the offspring expected to be produced after mating (used to measure the degree of blood similarity and control the risk of inbreeding depression), and the other is the genetic complementarity of the parents (i.e. whether the genetic advantages of one parent can effectively compensate for the shortcomings of the other parent, thereby producing offspring that exceed the average level). By constructing such a complete graph model containing all possible mating relationships, the problem of finding the optimal mating scheme is transformed into finding a subgraph structure in this complex graph that can satisfy specific constraints and optimize the overall goal. Finally, step S530 solves this highly structured model mathematically. Its core objective is to maximize the weighted genetic gain of the offspring population relative to the ideal trajectory. At the same time, it uses a hard constraint to control the growth of the average inbreeding coefficient of the population within a preset safety threshold. Using optimization algorithms such as integer programming, it searches for and determines the optimal set of parental pairing combinations that best balances short-term genetic progress and long-term population health from a massive number of possible pairing combinations. Finally, it outputs a specific and executable optimized intergenerational mating scheme, providing direct data support for breeding practices.

[0070] Further, step S600 includes steps S610 to S630.

[0071] Step S610: Perform complementarity analysis based on the optimized intergenerational mating scheme. By deconstructing the allele genotypes of each paired parent, identify the haplotype blocks provided by the allele genotypes in the offspring to achieve the ideal trait trajectory, and obtain a family-based genetic contribution blueprint.

[0072] Step S620: Perform association processing based on the genetic contribution blueprint. By associating the dominant haplotype controlling the target trait with its expression stage in the growth, development and reproductive cycle of breeder chickens, determine the physiological time window for intervention and obtain the association results.

[0073] Step S630: Generate differentiated feeding management strategies based on the correlation results. Based on the preset nutritional model, the genetic potential enhancement needs within a specific time window are transformed into specific dietary energy and protein level adjustment suggestions, generating decision results.

[0074] Specifically, the complementarity analysis in step S610 is essentially a deep genetic deconstruction of the optimal mating combination output in step S530. The method involves using bioinformatics tools to analyze the detailed allelic genotypes of the paired parents, identifying chromosomal segments (i.e., haplotype blocks) from the parents that are stably inherited in offspring and actively contribute to achieving ideal trait traits (such as good meat and egg production coordination). This creates a blueprint revealing the source of superior genetic makeup in a specific family. Based on this genetic contribution blueprint, the association processing in step S620 aims to determine the critical window period for converting genetic potential into phenotypic expression. The technique involves combining existing genome functional annotation databases and tissue development expression profiles to precisely correlate the dominant haplotype controlling the target trait (such as pectoral muscle development or ovulation count) with the specific physiological stage in the breeder's life cycle where it is most actively expressed (such as the rapid growth phase of pectoral muscles or the start of the egg-laying cycle). This identifies the key physiological time windows through which nutritional intervention may most effectively regulate gene expression and release genetic potential. Finally, in the differentiated feeding management strategy generation step S630, a pre-set mathematical model based on poultry nutrition research is used to quantitatively transform the qualitative demand for "enhancing genetic potential" within a specific physiological time window determined in step S620 into specific dietary nutrient parameter adjustment recommendations. For example, appropriately increasing dietary protein levels during the rapid growth period of pectoral muscles to support muscle deposition, or ensuring energy supply to meet reproductive needs during peak egg production. Ultimately, a set of differentiated breeding and nutritional intervention timing recommendations that are highly matched with the genetic background and physiological development rhythm of a specific family can be directly used to guide production, completing a complete decision support chain from genes to management measures.

[0075] Example 2:

[0076] like Figure 2 As shown, this embodiment provides a multi-trait collaborative decision support system for the breeding process of breeder chickens. The system includes:

[0077] The acquisition module 901 is used to acquire the genome sequence data of individual broiler breeders, the time series data of production traits at different physiological stages, and the antagonistic trait data, including feed conversion ratio and egg production.

[0078] The parsing module 902 is used to perform genetic basis analysis based on genomic sequence data and antagonistic trait data, locate genetic loci with opposite effects on each antagonistic trait through genome-wide association analysis, and obtain a genetic antagonism map;

[0079] Modeling module 903 is used for modeling based on the genetic antagonism map. By integrating the effect values ​​of antagonistic alleles into an individual-level energy budget allocation model, a trait expression potential surface constrained by physiological equilibrium is constructed.

[0080] Solver module 904 is used to optimize the potential trajectory of an individual throughout its entire life cycle based on the trait expression potential surface and the time series data of productive traits. By solving the dynamic Pareto front that conforms to the individual growth curve on the potential surface, the ideal trait development trajectory is obtained.

[0081] Optimization module 905 is used to perform joint optimization of cross-generational breeding programs based on the development trajectory of ideal traits, and obtain optimized cross-generational mating programs.

[0082] Decision module 906 is used to map actions based on the optimized intergenerational mating program and generate decision results, which include recommendations for differentiated breeding and nutritional intervention timing for different families.

[0083] In one specific embodiment of this application, the parsing module 902 includes:

[0084] The first parsing unit is used to perform association analysis based on genomic sequence data and antagonistic trait data. By calculating the association strength between each locus and the phenotype, it screens out loci with p-values ​​lower than a preset threshold to obtain a set of candidate loci associated with a single trait.

[0085] The second analysis unit is used to perform conflict identification processing based on the candidate site set. By comparing the signs of the regression coefficients of each candidate site for feed conversion ratio and pectoral muscle rate, sites with opposite signs of effects on the two traits are selected to obtain a subset of candidate sites with antagonistic effects.

[0086] The third analysis unit is used to perform gene function mapping based on a subset of candidate sites. By locating the sites to known gene regions in the chicken genome that are related to muscle protein deposition and basal metabolic rate, and confirming their physiological functions based on their expression differences in liver and pectoral muscle tissues, a genetic antagonism map is constructed.

[0087] In one specific embodiment of this application, the modeling module 903 includes:

[0088] The first modeling unit is used to perform parameter transformation processing based on the genetic antagonism map. By mapping the effect values ​​of the located antagonistic alleles to the theoretical impact values ​​on the individual's basal metabolic rate and protein deposition efficiency, an individualized physiological parameter vector is obtained.

[0089] The second modeling unit is used to perform allocation constraint modeling based on individualized physiological parameter vectors. It constructs a system of differential equations that dynamically allocates daily net energy intake among maintenance needs, pectoral muscle growth, and follicle development, and uses the physiological parameter vectors as adjustment variables for the allocation coefficients in the system of differential equations to obtain a dynamic energy budget model.

[0090] The third modeling unit is used to perform extrapolation based on the energy budget dynamic model. By simulating all possible combinations of pectoral muscle rate and egg production traits under different energy intake strategies from the brooding period to the end of the laying period, a trait expression potential surface constrained by physiological balance is constructed.

[0091] In one specific embodiment of this application, the solving module 904 includes:

[0092] The first solving unit is used to perform constraint extraction processing based on the time series data of production traits. By identifying the inflection point of weight gain and the starting point of the egg production cycle, the life cycle of broiler breeders is divided into physiological stages with different nutritional priorities, and the boundary conditions of the trait performance of each stage are determined to obtain staged growth constraints.

[0093] The second solution unit is used to perform multi-stage dynamic optimization based on the potential surface of trait expression and staged growth constraints. It decomposes the entire life cycle trajectory optimization problem into continuous stage sub-problems and searches for a set of candidate trajectories that satisfy the smooth transition between adjacent stages on the potential surface.

[0094] The third solution unit is used to screen candidates based on the set of candidate trajectories. By evaluating whether each trajectory conforms to the biological timeline of the coordinated development of the reproductive and muscular systems of the breeder chicken, it selects the ideal trajectories from the Pareto solution set.

[0095] In one specific embodiment of this application, the optimization module 905 includes:

[0096] The first optimization unit is used to evaluate the development trajectory of the ideal trait. It calculates the degree of matching between the estimated breeding value of the candidate breeder chicken individual's genome and the deviation of its trait development trajectory from the ideal trajectory, quantifies the expected contribution of each individual as a parent to the achievement of the ideal trajectory of the offspring population, and obtains the parent breeding value vector.

[0097] The second optimization unit is used to perform optimization modeling based on the parent breeding value vector and pedigree data. By constructing a complete graph model with candidate individuals as nodes and the average inbreeding coefficient and genetic complementarity of offspring after mating as edge weights, the problem of solving mating schemes is transformed into finding the optimal subgraph under specific constraints in the graph.

[0098] The third optimization unit is used to solve the population genetic structure optimization model. By maximizing the weighted genetic gain of the offspring population as the objective and controlling the growth of the inbreeding coefficient as the constraint, it solves the optimal parent pairing combination and outputs the optimized intergenerational mating scheme.

[0099] In one specific embodiment of this application, the decision module 906 includes:

[0100] The first output unit is used to perform complementarity analysis based on the optimized intergenerational mating scheme. By deconstructing the allele genotypes of each paired parent, it identifies the haplotype blocks provided by the allele genotypes in the offspring to achieve the ideal trait trajectory, and obtains a family-based genetic contribution blueprint.

[0101] The second output unit is used to perform association processing based on the genetic contribution blueprint. By associating the dominant haplotype of the target trait with its expression stage in the growth, development and reproductive cycle of breeder chickens, the physiological time window for intervention is determined, and the association results are obtained.

[0102] The third output unit is used to generate differentiated feeding management strategies based on the correlation results. Based on the preset nutritional model, it transforms the genetic potential enhancement needs within a specific time window into specific dietary energy and protein level adjustment suggestions, generating decision results.

[0103] Example 3:

[0104] Corresponding to the above method embodiments, this embodiment also provides a multi-trait collaborative decision support device for the breeding process of breeder chickens. The multi-trait collaborative decision support device for the breeding process of breeder chickens described below and the multi-trait collaborative decision support method for the breeding process of breeder chickens described above can be referred to in correspondence.

[0105] Figure 3 This is a block diagram illustrating a multi-trait collaborative decision support device 800 for the breeding process of breeder chickens, according to an exemplary embodiment. Figure 3 As shown, the multi-trait collaborative decision support device 800 for the breeding process of breeder chickens may include: a processor 801 and a memory 802. The multi-trait collaborative decision support device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0106] The processor 801 controls the overall operation of the multi-trait collaborative decision support device 800 for the breeding process of breeder chickens, to complete all or part of the steps in the multi-trait collaborative decision support method for the breeding process of breeder chickens described above. The memory 802 stores various types of data to support the operation of the multi-trait collaborative decision support device 800 for the breeding process of breeder chickens. This data may include, for example, instructions for any application or method operating on the multi-trait collaborative decision support device 800 for the breeding process of breeder chickens, as well as application-related data, such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the multi-trait collaborative decision support device 800 for the breeding process of breeder chickens and other devices. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or one or more combinations thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0107] In an exemplary embodiment, a multi-trait collaborative decision support device 800 for the breeding process of breeder chickens can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the aforementioned multi-trait collaborative decision support method for the breeding process of breeder chickens.

[0108] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the multi-trait collaborative decision support method for the breeding process of breeder chickens described above. For example, the computer-readable storage medium may be the memory 802 including the program instructions described above. These program instructions may be executed by a processor 801 of a multi-trait collaborative decision support device 800 for the breeding process of breeder chickens to complete the multi-trait collaborative decision support method for the breeding process of breeder chickens described above.

[0109] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-trait collaborative decision support method for the breeding process of breeder chickens, characterized in that, include: Acquire genomic sequence data, time-series data of production traits at different physiological stages, and antagonistic trait data of individual broiler breeders, wherein the antagonistic trait data includes feed conversion ratio and egg production; Genetic basis analysis was performed based on the genomic sequence data and the antagonistic trait data. Genetic loci with opposite effects on each antagonistic trait were located through genome-wide association analysis to obtain a genetic antagonism map. Modeling was performed based on the genetic antagonism map. By integrating the effect values ​​of antagonistic alleles into an individual-level energy budget allocation model, a trait expression potential surface constrained by physiological homeostasis was constructed. Based on the trait expression potential surface and the time series data of the production trait, the potential trajectory of an individual throughout its entire life cycle is optimized. By solving the dynamic Pareto front that conforms to the individual growth curve on the potential surface, the ideal trait development trajectory is obtained. Based on the development trajectory of the ideal trait, a joint optimization process of the intergenerational breeding scheme was carried out to obtain the optimized intergenerational mating scheme; Action mapping is performed based on the optimized intergenerational mating scheme to generate decision results, which include recommendations for differentiated breeding and nutritional intervention timing for different family lines.

2. The multi-trait collaborative decision support method for the breeding process of breeder chickens according to claim 1, characterized in that, Genetic basis analysis based on the genomic sequence data and the antagonistic trait data includes: Based on the genomic sequence data and the antagonistic trait data, association analysis is performed. By calculating the association strength between each locus and the phenotype, loci with p-values ​​lower than a preset threshold are screened out to obtain a set of candidate loci associated with a single trait. Conflict identification processing is performed based on the candidate site set. By comparing the signs of the regression coefficients of each candidate site for feed conversion ratio and pectoral muscle rate, sites with opposite signs of effects on the two traits are screened out to obtain a subset of candidate sites with antagonistic effects. Gene function mapping was performed on the subset of candidate sites. By locating the sites to known gene regions in the chicken genome that are related to muscle protein deposition and basal metabolic rate, and confirming their physiological functions based on their expression differences in liver and pectoral muscle tissues, a genetic antagonism map was constructed.

3. The multi-trait collaborative decision support method for the breeding process of breeder chickens according to claim 1, characterized in that, Modeling based on the genetic antagonism map includes: Based on the genetic antagonism map, parameter transformation processing is performed. By mapping the effect values ​​of the located antagonistic alleles to the theoretical impact values ​​on the individual's basal metabolic rate and protein deposition efficiency, an individualized physiological parameter vector is obtained. Based on the individualized physiological parameter vector, allocation constraint modeling is performed. By constructing a system of differential equations that dynamically allocates daily net energy intake among maintenance needs, pectoral muscle growth, and follicle development, and using the physiological parameter vector as the adjustment variable for the allocation coefficients in the system of differential equations, a dynamic energy budget model is obtained. Based on the aforementioned energy budget dynamic model, the potential expression surface of traits constrained by physiological equilibrium is constructed by simulating all possible combinations of pectoral muscle rate and egg production under different energy intake strategies from the brooding period to the end of the laying period.

4. The multi-trait collaborative decision support method for the breeding process of breeder chickens according to claim 1, characterized in that, Based on the trait expression potential surface and the time series data of the productive traits, the individual's full life-cycle potential trajectory is optimized, including: Constraint extraction processing is performed based on the time series data of the production traits. By identifying the inflection point of weight gain and the start point of the egg production cycle, the life cycle of broiler breeders is divided into physiological stages with different nutritional priorities, and the boundary conditions of the trait performance of each stage are determined to obtain staged growth constraints. Based on the trait expression potential surface and the staged growth constraints, a multi-stage dynamic optimization solution is performed. The entire life cycle trajectory optimization problem is decomposed into continuous stage sub-problems, and a set of candidate trajectories that satisfy the smooth transition between adjacent stages is searched on the potential surface. The candidate trajectory set is screened, and the ideal trait development trajectory is selected from the Pareto solution set by evaluating whether each trajectory conforms to the biological timeline of the coordinated development of the reproductive and muscular systems of the breeder chicken.

5. The multi-trait collaborative decision support method for breeding chicken selection process according to claim 1, characterized in that, Based on the development trajectory of the ideal trait, a joint optimization process for cross-generational breeding programs is performed, including: The evaluation process is carried out based on the ideal trait development trajectory. By calculating the degree of matching between the estimated genomic breeding value of the candidate breeder chicken individual and the deviation of its trait development trajectory from the ideal trajectory, the expected contribution of each individual as a parent to the achievement of the ideal trajectory of the offspring population is quantified, and the parent breeding value vector is obtained. Based on the parent breeding value vector and pedigree data, optimization modeling is performed. By constructing a complete graph model with candidate individuals as nodes and the average inbreeding coefficient and genetic complementarity of offspring after mating as edge weights, the problem of solving mating schemes is transformed into finding the optimal subgraph under specific constraints in the graph. The population genetic structure optimization model is used to solve the problem. The optimal parental pairing combination is solved by maximizing the weighted genetic gain of the offspring population and controlling the growth of the inbreeding coefficient. The optimized intergenerational mating scheme is then output.

6. A multi-trait collaborative decision support system for the breeding process of breeder chickens, characterized in that, include: The acquisition module is used to acquire the genome sequence data, time-series data of production traits at different physiological stages, and antagonistic trait data of individual broiler breeders, including feed conversion ratio and egg production. The analysis module is used to perform genetic basis analysis based on the genomic sequence data and the antagonistic trait data, locate genetic loci with opposite effects on each antagonistic trait through genome-wide association analysis, and obtain a genetic antagonism map; The modeling module is used to perform modeling processing based on the genetic antagonism map. By integrating the effect values ​​of antagonistic alleles into an individual-level energy budget allocation model, a trait expression potential surface constrained by physiological equilibrium is constructed. The solution module is used to optimize the potential trajectory of an individual throughout its entire life cycle based on the trait expression potential surface and the time series data of the productive traits. By solving the dynamic Pareto front that conforms to the individual growth curve on the potential surface, the ideal trait development trajectory is obtained. The optimization module is used to perform joint optimization of cross-generational breeding schemes based on the development trajectory of the ideal trait, so as to obtain an optimized cross-generational mating scheme; The decision-making module is used to perform action mapping based on the optimized intergenerational mating scheme and generate decision results, which include recommendations for differentiated breeding and nutritional intervention timing for different families.

7. The multi-trait collaborative decision support system for the breeding process of breeder chickens according to claim 6, characterized in that, The parsing module includes: The first parsing unit is used to perform association analysis processing based on the genomic sequence data and the antagonistic trait data. By calculating the association strength between each site and the phenotype, sites with p-values ​​lower than a preset threshold are screened out to obtain a set of candidate sites associated with a single trait. The second analysis unit is used to perform conflict identification processing based on the candidate site set. By comparing the signs of the regression coefficients of each candidate site for feed conversion ratio and pectoral muscle rate, sites with opposite signs of effects on the two traits are selected to obtain a subset of candidate sites with antagonistic effects. The third analysis unit is used to perform gene function mapping processing based on the subset of candidate sites. By locating the sites to known gene regions in the chicken genome that are related to muscle protein deposition and basal metabolic rate, and confirming their physiological functions based on their expression differences in liver and pectoral muscle tissues, a genetic antagonism map is constructed.

8. The multi-trait collaborative decision support system for the breeding process of breeder chickens according to claim 6, characterized in that, The modeling module includes: The first modeling unit is used to perform parameter transformation processing based on the genetic antagonism map, and obtains an individualized physiological parameter vector by mapping the effect values ​​of the located antagonistic alleles to the theoretical influence values ​​on the individual's basal metabolic rate and protein deposition efficiency. The second modeling unit is used to perform allocation constraint modeling based on the individualized physiological parameter vector. By constructing a system of differential equations that dynamically allocates daily net energy intake among maintenance needs, pectoral muscle growth, and follicle development, and using the physiological parameter vector as the adjustment variable for the allocation coefficients in the system of differential equations, a dynamic energy budget model is obtained. The third modeling unit is used to perform extrapolation processing based on the energy budget dynamic model. By simulating all possible combinations of pectoral muscle rate and egg production traits under different energy intake strategies from the brooding period to the end of the laying period, a trait expression potential surface constrained by physiological balance is constructed.

9. The multi-trait collaborative decision support system for the breeding process of breeder chickens according to claim 6, characterized in that, The solution module includes: The first solving unit is used to perform constraint extraction processing based on the time series data of the production traits. By identifying the inflection point of weight gain and the starting point of the egg production cycle, the life cycle of broiler breeders is divided into physiological stages with different nutritional priorities, and the boundary conditions of the trait performance of each stage are determined to obtain staged growth constraints. The second solution unit is used to perform multi-stage dynamic optimization based on the trait expression potential surface and the staged growth constraints. It decomposes the entire life cycle trajectory optimization problem into continuous stage sub-problems and searches for a set of candidate trajectories that satisfy the smooth transition between adjacent stages on the potential surface. The third solution unit is used to perform screening processing based on the candidate trajectory set. By evaluating whether each trajectory conforms to the biological timeline of the coordinated development of the reproductive and muscular systems of the breeder chicken, the ideal trait development trajectory is selected from the Pareto solution set.

10. The multi-trait collaborative decision support system for the breeding process of breeder chickens according to claim 6, characterized in that, The optimization module includes: The first optimization unit is used to evaluate the development trajectory of the ideal trait by calculating the degree of matching between the estimated breeding value of the genome of the candidate breeder chicken and the deviation of its trait development trajectory from the ideal trajectory, quantifying the expected contribution of each individual as a parent to the achievement of the ideal trajectory of the offspring population, and obtaining the parent breeding value vector. The second optimization unit is used to perform optimization modeling based on the parent breeding value vector and pedigree data. By constructing a complete graph model with candidate individuals as nodes and the average inbreeding coefficient and genetic complementarity of offspring after mating as edge weights, the problem of solving the mating scheme is transformed into finding the optimal subgraph under specific constraints in the graph. The third optimization unit is used to solve the population genetic structure optimization model. By maximizing the weighted genetic gain of the offspring population as the objective and controlling the growth of the inbreeding coefficient as the constraint, it solves the optimal parent pairing combination and outputs the optimized intergenerational mating scheme.

Images