Method for optimizing chicken genome selection and cross combinations

Abstract

The application provides a chicken genomic selection and cross combination optimization method, comprising the following steps: S1: obtaining genomic data and corresponding phenotype data of a target chicken population, and performing SNP site detection and chromosome linkage segment division on the genomic data to obtain a genetic unit set; S2: constructing a genetic path constraint graph based on the genetic unit set, wherein the genetic path constraint graph comprises nodes representing SNP sites or linkage segments, connecting edges representing genetic transmission relationships, and corresponding genetic weight parameters, and the genetic weight parameters at least include trait contribution weights and recombination risk weights; S3: based on the genetic weight parameters in the genetic path constraint graph, calculating the genetic path contribution stability score of a target trait in each candidate parent individual, and performing hierarchical screening on the candidate parent individuals according to the stability score to obtain a preferred parent individual set.

Description

Technical Field

[0001] This invention relates to the field of poultry breeding technology, and in particular to a method for optimizing chicken genome selection and hybridization combinations. Background Technology

[0002] With the development of poultry breeding technology, genomic selection methods have been gradually applied to the genetic improvement of chickens. By analyzing the association between SNP loci and target traits, individuals with superior traits can be screened and propagated. However, existing technologies usually use single SNP loci or simple weighted models as the basis for trait prediction, which makes it difficult to effectively characterize the structural relationships between multiple linked segments in the process of genetic transmission. This is especially true in the control of complex quantitative traits (such as meat quality, egg production performance, and growth rate), where there is a problem of fragmented genetic information.

[0003] Furthermore, traditional hybrid combination optimization relies heavily on experience or linear models based on statistical correlation, lacking a systematic characterization of potential conflicts between paternal and maternal parents at the multi-genetic module level, resulting in insufficient stability of optimized combinations in actual generational breeding. Simultaneously, existing methods for modeling phenotypic fluctuations in offspring typically employ fixed distribution assumptions, failing to reflect the non-deterministic expression changes caused by different genetic pathway combinations, thus affecting the reliability of breeding decisions. Summary of the Invention

[0004] The present invention aims to solve the above-mentioned technical problems by providing a method for chicken genome selection and hybridization combination optimization.

[0005] The technical solution of this invention is a method for chicken genome selection and hybridization combination optimization, comprising the following steps: S1: Obtain the genomic data and corresponding phenotypic data of the target chicken population, and perform SNP site detection and chromosome linkage segmentation on the genomic data to obtain a set of genetic units; S2: Construct a genetic path constraint graph based on the set of genetic units, wherein the genetic path constraint graph includes nodes representing SNP sites or linkage segments, connection edges representing genetic transmission relationships, and corresponding genetic weight parameters, wherein the genetic weight parameters include at least trait contribution weight and recombination risk weight; S3: Based on the genetic weight parameters in the genetic pathway constraint diagram, calculate the genetic pathway contribution stability score of the target trait in each candidate parent individual, and classify and screen the candidate parent individuals according to the stability score to obtain the preferred parent individual set; S4: Based on the preferred set of parent individuals, construct candidate hybridization combinations and calculate the genetic pathway conflict index and trait expression uncertainty entropy value of each candidate hybridization combination in terms of target trait expression; S5: Based on the genetic pathway conflict index and the trait expression uncertainty entropy value, screen the optimal hybridization pairing combination that meets the preset threshold conditions, and determine the corresponding paternal and maternal pairing relationship; S6: Perform actual mating operations on the optimal hybrid pairing combination to obtain the first generation of hybrid offspring population; S7: Perform phenotypic detection and genotyping analysis on the first generation of hybrid offspring population, and update the genetic weight parameters in the genetic pathway constraint diagram in reverse according to the detection results; S8: Based on the updated genetic pathway constraint graph, repeat S3 to S7 to form a dynamic optimization breeding process for consecutive generations.

[0006] As one implementation method, the construction of the genetic path constraint graph in step S2 includes: Different SNP sites or chromosomal linkage segments in the genetic unit set are clustered according to the continuity of chromosomal physical location to form linked genetic modules with continuous genetic transmission relationships; The linked genetic modules are used as the basic building blocks of the graph nodes, and the connection edges between the nodes are established based on the genetic transmission relationship of different modules in historical generations. The genetic weight parameter is assigned a value by simultaneously introducing the consistency index of the phenotypic contribution of the target trait and the frequency of recombination between modules, so as to characterize the stability of different genetic modules in the process of cross-generational transmission.

[0007] In one implementation, the partitioning of the linked genetic module is based on consecutive chromosomal segments that exhibit low recombination frequencies during meiosis.

[0008] As one implementation method, the calculation of the genetic pathway contribution stability score in step S3 includes: Based on the integrity of the genetic pathway corresponding to the target trait in the genetic pathway constraint diagram, the degree of preservation of each genetic unit set in the candidate parent individuals is evaluated; The stability score is increased when the set of genetic units carried by the candidate parent individuals is consistently transmitted genetically across generations; the stability score is decreased when there is structural breakage or recombination of the set of genetic units.

[0009] As one implementation method, the calculation of the genetic pathway conflict index in step S4 includes: Matching analysis was performed on the linked genetic modules carried by the candidate paternal and maternal parents; When two parents carry linked genetic modules that produce opposite phenotypic contribution trends to the same target trait, they are identified as genetic pathway conflict modules. The genetic path conflict index is determined based on the number of conflicting modules and their weight distribution in the genetic path constraint graph.

[0010] In one implementation, the genetic pathway conflict module includes different linked genetic modules that have a competitive expression relationship for the same trait.

[0011] As one implementation method, the calculation of the trait expression uncertainty entropy value in step S4 includes: Based on the distribution of all possible genetic pathways in the genetic pathway constraint diagram of candidate hybridization combinations, the distribution of possible phenotypic categories of the target trait is statistically analyzed. Increase the uncertainty entropy value when the target trait exhibits multiple distribution results under different combinations of genetic pathways; decrease the uncertainty entropy value when the target trait exhibits a single stable expression result under different combinations of genetic pathways.

[0012] As one implementation, the updating of the genetic weight parameters in step S7 includes: Based on the deviation between the actual phenotypic distribution and the predicted phenotypic distribution of the first-generation hybrid offspring population, the phenotypic contribution weights of the corresponding linked genetic modules in the genetic pathway constraint diagram are corrected. When the actual phenotypic stability is higher than the predicted phenotypic stability, the weight of the corresponding genetic module is increased; when the actual phenotypic deviates from the prediction, the weight of the corresponding genetic module is decreased.

[0013] The advantages of this invention compared to existing technologies are that the chicken genome selection and hybridization combination optimization method provided by this invention detects SNP sites and divides linkage segments in genomic data, and constructs a genetic pathway constraint map to uniformly express the transmission relationships between different genetic units and their influence on traits. Then, based on the genetic pathway constraint map, candidate parents are screened by stability scoring, and genetic pathway conflicts and trait expression uncertainties are comprehensively evaluated at the hybridization combination level, thereby achieving optimized selection of hybridization pairings. Through the above technical solution, the randomness caused by relying on experience-based screening in traditional breeding can be reduced, the accuracy of selecting optimal parents can be improved, and the fluctuation of offspring traits can be reduced, improving the genetic stability of target traits, thereby enhancing overall breeding efficiency and result reliability. Attached Figure Description

[0014] Figure 1 A flowchart illustrating the chicken genome selection and hybridization combination optimization method provided in this embodiment of the invention. Detailed Implementation

[0015] The above and other embodiments and advantages of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0016] like Figure 1 As shown, this embodiment provides a method for optimizing chicken genome selection and hybridization combinations. This method is applied to the molecular breeding optimization process of chicken populations, and is particularly suitable for the genetic improvement of complex quantitative traits such as meat quality, egg production performance, or growth rate. In practice, this method relies on a gene sequencing platform, a phenotypic acquisition system, and a computer analysis system to complete the process collaboratively. Genomic data can be obtained through high-throughput SNP chip detection or whole-genome resequencing, while phenotypic data can be derived from slaughter measurement indicators, muscle physicochemical indicator test results, and production performance record data.

[0017] In step S1, after acquiring the genomic data and corresponding phenotypic data of the target chicken population, SNP loci detection and chromosome linkage segmentation are performed on the genomic data. Specifically, SNP loci on each chromosome are sorted according to their physical location, and segments are divided based on the linkage disequilibrium coefficient (LD) value. When the LD value between adjacent SNP loci is higher than a preset threshold, they are grouped into the same linkage segment, thereby forming a set of genetic units with shared genetic transmission characteristics. This set of genetic units includes not only single SNP loci but also linkage block structures composed of multiple SNPs, for subsequent genetic pathway modeling.

[0018] In step S2, a genetic pathway constraint graph is constructed based on the set of genetic units. This graph visualizes the structural relationships of the genetic unit set during cross-individual genetic transmission. It is a directed weighted network structure composed of nodes and connecting edges. Nodes represent SNP loci or chromosomal linkage segments, each corresponding to a genetic unit with a defined physical genomic location. Different nodes form a basic topological order based on a linear arrangement on the chromosome. Connecting edges represent the linkage or common genetic transmission relationships formed between different genetic units during parent-to-offspring transmission, and the direction of the connecting edges indicates the direction of genetic information transmission. Each node and connecting edge in this genetic pathway constraint graph is assigned a genetic weight parameter. This weight parameter characterizes the strength of the influence of the genetic unit or genetic transmission relationship on the formation of the target trait and the probability of maintaining stable inheritance during recombination. The relative importance of different genetic units in the overall genetic pathway is reflected by the weight differences. By combining the aforementioned nodes, connecting edges, and genetic weight parameters, multiple genetic units related to the target trait can form constrained path expressions within the same structural framework, thus providing a unified data structure foundation for subsequent path contribution stability scoring and conflict analysis. In specific implementation, each linked genetic module is treated as a node in the graph, and each node is assigned a unique identifier corresponding to its chromosomal location range and functional annotation information. Connecting edges between nodes represent the co-heritage relationships or recombination segregation relationships of different genetic modules in successive generations of the population. The establishment of connecting edges is based on population genetic tracking data, specifically, counting the frequency of two genetic modules being simultaneously inherited by offspring during multiple generations of reproduction. When this frequency is higher than a set threshold, a strong connecting edge is established; when the frequency is low but occasional recombination exists, a weak connecting edge is established.

[0019] In a genetic pathway constraint graph, each node is assigned a genetic weight parameter, which includes at least a trait contribution weight and a recombination risk weight. The trait contribution weight is obtained by statistically analyzing the correlation between the genetic module and the target trait phenotype in historical populations, for example, through stratified regression or phenotypic mean difference analysis. The recombination risk weight is determined by statistically analyzing the frequency of recombination segregation of the genetic module during meiosis; the higher the frequency, the greater the risk weight. In this way, the genetic pathway constraint graph not only expresses structural relationships but also genetic stability and the direction of trait contribution.

[0020] In step S3, a genetic pathway contribution stability score is calculated based on the genetic pathway constraint graph. Specifically, for each candidate parent individual, the entire set of genetic units it carries is extracted and mapped to the corresponding node in the genetic pathway constraint graph. If the genetic module carried by the individual has been continuously transmitted across generations without significant breaks, its genetic pathway integrity is considered high, and the stability score is increased accordingly; conversely, if its key linkage modules are split or frequent recombination occurs in the historical population, the score is decreased. Simultaneously, a pathway weighting mechanism is introduced, whereby the trait contribution weight of the node and the stability of the path connection edge are both included in the score calculation, allowing the score to simultaneously reflect both "trait contribution capacity" and "genetic stability capacity." In this step, the genetic pathway contribution stability score is calculated using a weighted summation model: the trait contribution weight and recombination risk weight of each genetic unit are used as basic weight coefficients, combined with the integrity detection result of the individual's corresponding path in the genetic pathway constraint graph as a correction factor, and a comprehensive score is obtained through linear weighting. This score is used to quantify the individual's ability to completely transmit its superior genetic modules to its offspring.

[0021] In step S4, candidate hybridization combinations are constructed based on the preferred set of parental individuals, and the genetic pathway conflict index and trait expression uncertainty entropy are calculated. The genetic pathway conflict index is calculated based on the node matching relationship between the paternal and maternal parents in the genetic pathway constraint graph. Specifically, when the paternal and maternal parents show opposite phenotypic contribution directions in the same trait-related linkage module, for example, one parent tends to increase muscle fat content while the other parent tends to decrease this index, then the module is determined to have a conflict relationship. The conflict index is jointly determined by the number of conflicting modules, the weight value of the conflicting modules, and their hierarchical position in the genetic pathway graph. Conflicting modules located at critical path nodes will be given higher weights, thereby amplifying their impact on combination risk. In this step, the genetic pathway conflict index is calculated using a model that accumulates the number of conflicting modules and the weight of critical paths: first, the number of conflicting modules with opposite contribution trends between the candidate paternal and maternal parents is counted, and then the conflict index is obtained by weighted accumulation based on the hierarchical position of each conflicting module in the genetic pathway constraint graph and the trait contribution weight. Conflicting modules located on the main path and with high weights are assigned higher coefficients in the accumulation, enabling the index to reflect the inherent incompatibility of combinations on key genetic pathways.

[0022] The calculation of the trait expression uncertainty entropy is based on the expansion of the state space of genetic pathway combinations. In practice, all possible combinations of paternal and maternal genetic pathways are simulated and expanded to obtain multiple possible phenotypic distribution states of offspring, and the dispersion of the target trait under these states is statistically analyzed. When the phenotypic distribution of offspring is concentrated in a single stable interval, the entropy value is low; when the phenotypic distribution of offspring exhibits a multi-peaked distribution or large fluctuations, the entropy value increases. This entropy value is used to characterize the uncertainty of the hybridization result, thus forming a basis for combination evaluation together with the conflict index. In this step, the trait expression uncertainty entropy value is established based on the information entropy theory: the phenotypic distribution of offspring resulting from all possible combinations of genetic pathways of the candidate hybridization combination is used as the input space, and its dispersion is calculated according to the probability distribution of different phenotypic categories. When the probability distribution is concentrated in a single category, the entropy value tends to zero; when the distribution is dispersed across multiple categories, the entropy value increases, thereby quantifying the expected stable expression of the target trait in the hybrid offspring.

[0023] In step S5, the optimal hybridization combination is selected based on the conflict index and entropy value. By setting a double threshold constraint, only the pairing combination with low conflict index and small entropy value is retained to ensure genetic pathway compatibility and trait stability.

[0024] In step S6, the optimal hybrid combination obtained through screening is subjected to actual mating. In practice, artificial insemination or controlled mating can be used to ensure the uniqueness and controllability of the parental pairing relationship, thereby obtaining the first generation of hybrid offspring.

[0025] In step S7, phenotypic detection and genotyping analysis are performed on the first-generation hybrid offspring population, and the genetic weight parameters in the genetic pathway constraint diagram are updated in reverse based on the detection results. Specifically, the actual phenotypic mean of the offspring population is compared with the predicted phenotypic. When a genetic module shows higher consistency in phenotypic contribution in the offspring, its phenotypic contribution weight is increased; when the predicted result deviates significantly from the actual result, its weight value is decreased. Simultaneously, based on newly emerging recombination phenomena in the offspring population, the recombination risk weight is dynamically adjusted, enabling the genetic pathway diagram to continuously and adaptively update as generations progress.

[0026] In step S8, the screening and mating process is repeated based on the updated genetic pathway constraint graph, thereby forming a multi-generational dynamic optimization breeding closed-loop system. As generations progress, the genetic pathway constraint graph gradually converges to a stable structural region, causing the genetic modules related to desirable traits to continuously accumulate in the population, achieving the goal of targeted breeding.

[0027] As can be seen from the above implementation methods, the core of this method lies in replacing traditional single-point SNP association analysis with a genetic pathway constraint map, thus elevating genetic information from a "point-like statistical model" to a "path structure model." Furthermore, it uses a genetic pathway conflict index and phenotypic expression uncertainty entropy to perform dual-dimensional constraint screening of hybrid combinations, thereby reducing phenotypic expression fluctuations while ensuring genetic stability and improving breeding prediction accuracy. In addition, through a cross-generational weight dynamic update mechanism, the model can continuously adapt to changes in population genetic structure, achieving a closed-loop optimization breeding process and significantly improving the efficiency of hybrid combination screening and the ability to directionally improve target traits.

[0028] Based on the above implementation method, as a further preferred technical solution, step S5 of the chicken genome selection and hybridization combination optimization method, which involves screening for the optimal hybridization combination that meets the preset threshold conditions, further includes an active dissociation step for conflict modules: when the genetic path conflict index of a candidate hybridization combination exceeds the preset conflict threshold, the conflict-linked genetic module corresponding to the combination is marked in the genetic path constraint diagram; in the next round of parental screening, individuals with historical recombination and exchange events on the chromosome segment where the marked conflict-linked genetic module is located are preferentially selected as candidate parents to re-enter step S4 for candidate hybridization combination construction; wherein, the historical recombination and exchange events are used to break the linkage genetic relationship between opposing phenotypic contribution trends within the conflict-linked genetic module, so as to reduce the genetic path conflict index of the reconstructed candidate hybridization combination.

[0029] When the genetic pathway conflict index mentioned in step S5 exceeds the preset conflict threshold, this method does not simply eliminate or reduce the weight of candidate hybridization combinations, but further introduces an active dissociation mechanism for conflict modules to reduce the continuous impact of conflict-linked genetic modules on the subsequent hybridization combination optimization process from the genetic structure level, thereby improving the reconfigurability and convergence efficiency of the entire breeding path.

[0030] Specifically, when the genetic pathway conflict index corresponding to a candidate hybridization pairing is higher than a preset conflict threshold, the conflict-linked genetic modules involved in the candidate combination are first marked in the genetic pathway constraint diagram. The mark is used to indicate that there is at least one set of genetic units within the linked genetic module that show opposite contribution trends in the direction of target trait expression. That is, there are at least two or more genetic subunits within the module that show competitive or inhibitory expression relationships in the target trait, thereby causing a decrease in the overall genetic pathway stability.

[0031] After marking, this implementation does not directly discard such individuals. Instead, in the next round of parental selection, a priority selection mechanism based on historical recombination and exchange events is introduced to achieve the structural dissociation of conflicting modules. Specifically, the system searches the candidate parental individual set for records of historical recombination and exchange events on the chromosomal segments where the corresponding conflicting linked genetic modules are located. These historical recombination and exchange events include, but are not limited to, chromosomal crossing over events during meiosis, linkage segment breakage and recombination events, or genetic segment exchange events within low-frequency recombination hotspot regions.

[0032] When a candidate individual is detected to have a historical recombination exchange event within the chromosomal segment corresponding to the conflict-linked genetic module, that individual is preferentially selected into the candidate parent set and re-participates in the candidate hybridization pairing combination construction process in step S4. In this way, conflicting genetic units that originally exhibited strong linkage in the genetic pathway constraint diagram have the opportunity to re-segregate through historical recombination sites during subsequent hybridization, thereby weakening the genetic binding effect of the original conflict structure.

[0033] Furthermore, in this embodiment, the historical recombination and exchange event is defined as a genetic process capable of altering the allele combination structure within a linked genetic module. Essentially, it involves the rearrangement of different functional genetic units originally within the same linked genetic module through natural or induced chromosome exchange, thereby breaking the linkage dependency between previously opposing phenotypic contribution trends. Under this mechanism, previously incompatible genetic pathways within the conflicting module are redistributed, allowing the genetic contributions related to the target trait to be co-expressed again in the new combination structure.

[0034] By introducing the aforementioned active dissociation mechanism for conflict modules, this method achieves a technological upgrade. It not only evaluates and controls genetic pathway conflicts but also utilizes historical recombination events as a structural genetic tool to actively disassemble and optimize conflict-linked genetic modules, thereby achieving dynamic topological adjustment at the genetic pathway constraint graph level.

[0035] Therefore, in practical applications, this mechanism can significantly reduce the cumulative effect of high-conflict combinations in subsequent generations, allowing the genetic path constraint map to gradually evolve from a high-conflict-density structure to a low-conflict, high-stability structure over multiple generations. Simultaneously, by preferentially introducing individuals with historical recombination and exchange backgrounds into the recombination process, genetic diversity is maintained while controlling conflict, thus avoiding the genetic potential loss problem caused by premature convergence in traditional screening methods.

[0036] Furthermore, this preferred embodiment works synergistically with the dynamic update mechanism of genetic weights in step S7. When the conflict module is successfully dissociated in subsequent generations and exhibits more stable phenotypic expression, the conflict weight of its corresponding genetic path in the constraint graph will gradually decrease, while the trait contribution weight will increase accordingly. This allows the entire genetic path constraint graph to continuously tend towards a stable convergence state during the iteration process, realizing a closed-loop optimization breeding process driven by conflict dissociation.

[0037] The specific embodiments described above further illustrate the inventive purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for chicken genome selection and hybridization combination optimization, characterized in that, Includes the following steps: S1: Obtain the genomic data and corresponding phenotypic data of the target chicken population, and perform SNP site detection and chromosome linkage segmentation on the genomic data to obtain a set of genetic units; S2: Construct a genetic path constraint graph based on the set of genetic units, wherein the genetic path constraint graph includes nodes representing SNP sites or linkage segments, connection edges representing genetic transmission relationships, and corresponding genetic weight parameters, wherein the genetic weight parameters include at least trait contribution weight and recombination risk weight; S3: Based on the genetic weight parameters in the genetic pathway constraint diagram, calculate the genetic pathway contribution stability score of the target trait in each candidate parent individual, and classify and screen the candidate parent individuals according to the stability score to obtain the preferred parent individual set; S4: Based on the preferred set of parent individuals, construct candidate hybridization combinations and calculate the genetic pathway conflict index and trait expression uncertainty entropy value of each candidate hybridization combination in terms of target trait expression; S5: Based on the genetic pathway conflict index and the trait expression uncertainty entropy value, screen the optimal hybridization pairing combination that meets the preset threshold conditions, and determine the corresponding paternal and maternal pairing relationship; S6: Perform actual mating operations on the optimal hybrid pairing combination to obtain the first generation of hybrid offspring population; S7: Perform phenotypic detection and genotyping analysis on the first generation of hybrid offspring population, and update the genetic weight parameters in the genetic pathway constraint diagram in reverse according to the detection results; S8: Based on the updated genetic pathway constraint graph, repeat S3 to S7 to form a dynamic optimization breeding process for consecutive generations.

2. The method for chicken genome selection and hybridization combination optimization according to claim 1, characterized in that, The construction of the genetic pathway constraint graph in step S2 includes: Different SNP sites or chromosomal linkage segments in the genetic unit set are clustered according to the continuity of chromosomal physical location to form linked genetic modules with continuous genetic transmission relationships; The linked genetic modules are used as the basic building blocks of the graph nodes, and the connection edges between the nodes are established based on the genetic transmission relationship of different modules in historical generations. The genetic weight parameter is assigned a value by simultaneously introducing the consistency index of the phenotypic contribution of the target trait and the frequency of recombination between modules, so as to characterize the stability of different genetic modules in the process of cross-generational transmission.

3. The method for chicken genome selection and hybridization combination optimization according to claim 2, characterized in that, The division of the linked genetic modules is based on consecutive chromosomal segments that exhibit low recombination frequencies during meiosis.

4. The method for chicken genome selection and hybridization combination optimization according to claim 1, characterized in that, The calculation of the genetic pathway contribution stability score in step S3 includes: Based on the integrity of the genetic pathway corresponding to the target trait in the genetic pathway constraint diagram, the degree of preservation of each genetic unit set in the candidate parent individuals is evaluated; The stability score is increased when the set of genetic units carried by the candidate parent individuals is consistently transmitted genetically across generations; the stability score is decreased when there is structural breakage or recombination of the set of genetic units.

5. The method for chicken genome selection and hybridization combination optimization according to claim 2, characterized in that, The calculation of the genetic pathway conflict index in step S4 includes: Matching analysis was performed on the linked genetic modules carried by the candidate paternal and maternal parents; When two parents carry linked genetic modules that produce opposite phenotypic contribution trends to the same target trait, they are identified as genetic pathway conflict modules. The genetic path conflict index is determined based on the number of conflicting modules and their weight distribution in the genetic path constraint graph.

6. The method for chicken genome selection and hybridization combination optimization according to claim 5, characterized in that, The genetic pathway conflict module includes different linked genetic modules that compete for the expression of the same trait.

7. The method for chicken genome selection and hybridization combination optimization according to claim 1, characterized in that, The calculation of the trait expression uncertainty entropy value in step S4 includes: Based on the distribution of all possible genetic pathways in the genetic pathway constraint diagram of candidate hybridization combinations, the distribution of possible phenotypic categories of the target trait is statistically analyzed. Increase the uncertainty entropy value when the target trait exhibits multiple distribution results under different combinations of genetic pathways; decrease the uncertainty entropy value when the target trait exhibits a single stable expression result under different combinations of genetic pathways.

8. The method for chicken genome selection and hybridization combination optimization according to claim 2, characterized in that, The updating of the genetic weight parameters in step S7 includes: Based on the deviation between the actual phenotypic distribution and the predicted phenotypic distribution of the first-generation hybrid offspring population, the phenotypic contribution weights of the corresponding linked genetic modules in the genetic pathway constraint diagram are corrected. When the actual phenotypic stability is higher than the predicted phenotypic stability, the weight of the corresponding genetic module is increased; when the actual phenotypic deviates from the prediction, the weight of the corresponding genetic module is decreased.

Images