Genome optimization configuration analysis system and method for molecular marker-assisted breeding

Abstract

The invention discloses a genome optimization configuration analysis system and method for molecular marker-assisted breeding, and relates to the technical field of biological information. The method is used for solving the problems of genetic interaction distortion, environmental response lag and breeding execution disjunction in multi-gene collaborative optimization. The system comprises a conflict resolution module, an environment adaptation module, an interaction compression module and a configuration generation module, wherein the conflict resolution module calculates and corrects an interaction effect value through phenotypic deviation and environment fluctuation, generates a linkage burdens intensity index and dynamically allocates a character weight; the environment adaptation module is used for fusing the stress data and a historical threshold value to identify a stress period and lock a key gene interval; the interaction compression module constructs a radial interaction network taking a major gene as a core and generates a linkage vector; and the configuration generation module analyzes key vector dimensions, locates recombination hotspots, matches complementary parents and outputs a structured breeding instruction, so that a breeding optimization closed loop from interaction recognition to parent combination recommendation is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bioinformatics, in particular to a genome optimization configuration analysis system and method for molecular marker assisted selection. BACKGROUND

[0002] With the rapid development of molecular biology, genome sequencing and high-throughput data analysis technology, the field of poultry breeding is gradually transforming from the traditional phenotype selection model to the precision breeding based on genomic information. By analyzing the genetic information such as single nucleotide polymorphism (SNP), structural variation and chromatin conformation in the whole genome, researchers can deeply reveal the key genetic factors affecting poultry meat performance, growth rate, disease resistance and environmental adaptability. Molecular marker assisted selection (MAS) and genome selection (GS) have become the core tools in modern poultry breeding system, which mainly predict the performance of complex traits through marker loci, and then screen parents and optimize combinations to improve the genetic progress speed of breeding stock and the scientificity of selection decision.

[0003] Although the current molecular breeding method has been applied in the poultry industry to some extent, in actual operation, there are still some key problems that restrict the selection efficiency and recombination stability. First, the existing analysis system generally models the relationship between major genes and target traits as a linear action model, which is difficult to reflect the non-linear interaction effects that may occur between multiple genes in different genetic backgrounds and stress environments, leading to the neglect of recessive interaction sites or regulatory modifiers in the selection process, thereby affecting the true prediction of target traits. Second, in dealing with linkage regions, most methods only evaluate the linkage effect of markers based on a single genetic distance parameter, without considering complex structural factors such as chromatin openness and recombination potential of gene regions, resulting in negative interference of linkage inhibition on excellent alleles in the combination process, thereby limiting the recombination efficiency of breeding materials.

[0004] Further, in the combination of stress response and trait analysis, the existing system usually uses static environmental factors as the stress determination standard, which cannot accurately capture the time window misalignment and reaction lag between the dynamic changes of poultry house environment and animal phenotype response, leading to poor stability in identifying stress-sensitive gene sites. In addition, in the process of generating breeding stock configuration strategy, traditional methods mostly rely on single-dimensional sorting of marker information, lacking a mechanism to compress key features from the global interaction network for matching parents, which easily causes blind selection of recombination intervals, unreasonable configuration structure of breeding stock, and further affects the stable transmission and recombination efficiency of offspring traits. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a genome optimization configuration analysis system and method for molecular marker assisted selection, which solves the problems in the above background art.

[0006] To achieve the above object, the application is implemented by the following technical solutions: a molecular marker assisted selection genome optimization configuration analysis system, comprising the following modules: a conflict resolution module, an environment adaptation module, an interaction compression module, and a configuration generation module; the conflict resolution module is used to calculate and correct interaction effect values by the phenotypic deviation degree of poultry target trait haplotype combination and the standard deviation of environmental fluctuation, to integrate genetic distance and chromatin openness data to generate linkage load intensity index, and to dynamically allocate trait weight according to the linkage load intensity index; the environment adaptation module is used to fuse poultry house environment stress data and historical disease sensitivity threshold, to identify the intensity of continuous stress cycle, and to lock the physical position interval of the target gene according to the linkage load intensity index and the stress cycle intensity; the interaction compression module is used to intercept high-frequency molecular marker data in the locked genomic interval, to construct a radial interaction network with a major gene as the center, to adjust the neighbor node aggregation priority by dynamic weight, and to generate a low-dimensional vector representing linkage strength; and the configuration generation module is used to analyze the key dimension values in the low-dimensional vector, to locate recombination hotspots with dense repeat sequences according to chromatin interaction data, to match complementary genotypes parents by combining linkage load intensity index and dynamic weight, and to output molecular marker names and cross combination instructions of breeding birds.

[0007] Further, the specific process of calculating and correcting interaction effect values by the phenotypic deviation degree of poultry target trait haplotype combination and the standard deviation of environmental fluctuation is as follows: extract the group phenotype data set of poultry target haplotype combination under the stress environment of poultry house, take the phenotype mean value of the control group in the optimal growth environment as the benchmark, calculate the phenotypic deviation of the target haplotype carrying group under stress environment, and eliminate the interference of breed-specific growth baseline difference and feather color variation; dynamically calculate the standard deviation of phenotypic fluctuation according to the data flow of poultry house temperature and humidity sensors, comprehensively process the phenotypic deviation and environmental fluctuation intensity, and generate environment corrected interaction effect values.

[0008] Further, the specific process of integrating genetic distance and chromatin openness data to generate linkage load intensity index, and dynamically allocating trait weight according to the linkage load intensity index is as follows: obtain the nuclease hypersensitive signal intensity value of the accessible region of chromatin in the linkage segment, calculate the linkage disequilibrium coefficient combining the physical linkage distance and base length of poultry chromosomes, comprehensively process the absolute value of the corrected interaction effect value and the linkage disequilibrium coefficient to generate the linkage load intensity index; when the linkage load intensity index exceeds the preset threshold, the weight is transferred from the negative linkage trait to the target trait according to the proportion of the index exceeding the threshold, wherein the negative linkage trait refers to a trait that is genetically exclusive with the target trait.

[0009] Further, the specific process of fusing the poultry house environment stress data with the historical disease sensitivity threshold to identify the intensity of the continuous stress cycle is as follows: set the stress sensitivity threshold parameters for different growth stages of poultry, based on the high-frequency time series data collected by the poultry house temperature and humidity sensors, analyze the effective cumulative duration of continuous exceeding of the threshold of each stage, and filter out transient abnormal fluctuation noise signals; combined with the regional high incidence records in the historical disease database, extract the sensitivity percentile value within a specific period, construct the statistical mapping relationship between stress events and disease response, and grade the intensity level of the current stress cycle.

[0010] Further, the specific process of locking the physical location interval of the target gene according to the linkage cumulative intensity index and the stress cycle intensity is as follows: taking the chromosome physical coordinates of the major gene with the largest linkage cumulative intensity index value as the anchor point, calculating the interval expansion coefficient according to the weight amplitude ratio of the major site and the current stress intensity level; referring to the physical length of poultry species chromosome, recombination rate distribution and cross-chromosome structure characteristics, length control is performed on the expanded genomic interval to generate a standardized machine-readable positioning instruction containing chromosome number, starting and ending physical location.

[0011] Further, the specific process of constructing a radial interaction network with the major gene as the center by intercepting high-frequency molecular marker data in the locked genomic interval is as follows: extract molecular markers within the locked physical interval from the poultry genome, and screen marker sites with allele frequency exceeding the set polymorphism threshold in the population; taking the physical coordinates of the major gene with the highest linkage cumulative intensity index value as the network core node, dividing the surrounding area into multiple concentric layer regions according to fixed physical distance intervals, and classifying each marker according to its physical distance from the center node to the corresponding level; assign a basic connection weight to each level, and the weight decreases by a preset decay ratio layer by layer; based on the poultry phenotype observation data and candidate marker genotype data in the stress cycle, perform multivariate partial least squares regression analysis, and screen key marker nodes with regression coefficient absolute value greater than the significance threshold; establish a direct edge relationship between the key marker node and the major gene node, and increase the weight to a fixed multiple of the basic value; non-key markers are connected through layer-by-layer decreasing paths and the weight decreases layer by layer.

[0012] Further, the specific process of generating a low-dimensional vector representing the linkage strength by dynamically adjusting the neighbor node aggregation priority is as follows: extract the key marker nodes assigned with trait promotion weight in the dynamic weight allocation result, and regard them as target nodes, assign aggregation weighting coefficients to the direct adjacent nodes of each target node, where the weighting amplitude is positively correlated with the trait weight increase amplitude; weight and aggregate the gene effects of the neighbor nodes at each level through the hierarchical feature propagation algorithm, forming a gene interaction representation with major genes as the core; vectorize the neighbor relationship of all key nodes in the interaction network, and output a fixed-dimensional low-dimensional vector for representing the local linkage strength.

[0013] Further, the specific process of locating the recombination hotspots with dense repeat sequences based on the chromatin interaction data according to the key dimension value in the low-dimensional vector is as follows: identify the key dimension value representing negative linkage strength in the low-dimensional vector, and when the value is lower than the strength threshold, locate the anchor point region of the chromatin loop where the central gene is located in the chromatin conformation capture data; scan the microsatellite repeat sequences in the anchor point region, detect specific repeat unit types with continuous repeat number exceeding the set standard, and map the physical coordinates of the microsatellite sequences meeting the repeat standard to the chromosome segment number, and mark them as potential recombination hotspot sites in the poultry genome.

[0014] Further, the specific process of combining the linkage drag strength index with the dynamic weight matching complementary genotype parents, and outputting the molecular marker name and crossbreeding combination instruction is as follows: based on the linkage drag strength index, screen poultry parent individuals that are homozygous for the target haplotype at the major gene site, and according to the dynamic weight aggregation result, identify compensatory trait parents carrying key complementary alleles; standardize the functional molecular markers located in the recombination hotspot segment, generate marker names containing chromosome number, physical coordinates and functional annotation information, output structured poultry configuration instructions, and the instruction content includes: the breed name of the target parent and the complementary parent, the recommended mating combination mode, the recombination marker name and the screening strategy condition.

[0015] The genome optimization configuration analysis method of molecular marker assisted selection comprises the following steps: S1. The correction interaction effect value is calculated by the phenotypic deviation degree of the poultry target trait haplotype combination and the standard deviation of environmental fluctuation, the linkage cummulative intensity index is generated by integrating genetic distance and chromatin openness data, and the trait weight is dynamically distributed according to the linkage cummulative intensity index; S2. The stress cycle intensity of continuous stress is identified by fusing the poultry house environmental stress data and the historical disease sensitivity threshold, the physical position interval of the target gene is locked according to the linkage cummulative intensity index and the stress cycle intensity; S3. The high-frequency molecular marker data in the locked genomic interval is intercepted, the dominant gene is taken as the center to construct a radial interaction network, the neighbor node aggregation priority is adjusted by dynamic weight, and a low-dimensional vector representing linkage intensity is generated; S4. The key dimension value in the low-dimensional vector is analyzed, the recombination hot spot with dense repeat sequences is located according to the chromatin interaction data, the complementary genotype parent is matched by combining the linkage cummulative intensity index and the dynamic weight, and the molecular marker name and the cross combination instruction of the poultry are output.

[0016] The present application has the following beneficial effects:

[0017] (1) The genome optimization configuration analysis system of molecular marker assisted selection significantly improves the ability of complex trait genetic analysis and target gene precise positioning by constructing conflict resolution module and environment adaptation module. On the one hand, by introducing the phenotypic deviation degree of the poultry target trait haplotype combination and the standard deviation of environmental fluctuation to jointly correct the interaction effect, the information loss and distortion problem of traditional static linear model in multi-trait gene interaction modeling is avoided; at the same time, the linkage cummulative intensity index is generated by combining chromatin openness data and genetic distance information, which provides a scientific quantitative basis for dynamic distribution of trait weight, and effectively alleviates the interference of linkage negative effect on breeding selection. On the other hand, the environment adaptation module integrates the poultry house environmental stress data and the historical disease sensitivity threshold, establishes a stress intensity identification mechanism based on the growth cycle of poultry, realizes the dynamic locking of the physical interval of the continuous stress related gene, and improves the environmental adaptability and regional specificity of target gene screening.

[0018] (2) The genome optimization configuration analysis method of marker-assisted selection realizes efficient conversion of molecular marker data to recombination optimization breeding scheme by constructing interaction compression module and configuration generation module. Based on the radial interaction network constructed with major genes as the center, and by adjusting the aggregation priority of adjacent nodes through dynamic weight, not only the high-dimensional gene interaction feature space is effectively compressed, but also the key linkage information is retained, a low-dimensional feature vector representing linkage intensity is generated, and the data expression efficiency and downstream analysis accuracy are significantly improved. Further, by analyzing the low-dimensional vector and chromatin interaction data to accurately locate recombination hotspots, combining linkage load intensity index and dynamic weight, the intelligent matching of complementary genotypes of parents is realized, and the structured molecular marker and crossbreeding combination instructions are output, which enhances the pertinence and recombination efficiency of crossbreeding combination, and promotes the intelligent and precise animal breeding process.

[0019] Of course, implementing any product of the present application does not necessarily require achieving all the advantages described above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The genome optimization configuration analysis system flow chart of the present application molecular marker-assisted selection.

[0021] Figure 2 The genome optimization configuration analysis method flow chart of the present application molecular marker-assisted selection. DETAILED DESCRIPTION

[0022] The genome optimization configuration analysis system and method of the present application solve the problems of target trait weight configuration distortion, environmental response lag, marker interaction dimension disaster and low recombination parent combination efficiency in the existing molecular marker-assisted breeding.

[0023] The general idea of the scheme in the present application embodiment is as follows:

[0024] The phenotype-environment deviation effect of poultry target trait haplotype combination is analyzed by the conflict resolution module, the linkage load intensity index is constructed by integrating genetic distance and chromatin openness data, and the dynamic allocation of trait weight is realized, which effectively breaks through the limitations of traditional linear model in allelic linkage exclusion.

[0025] The environment adaptation module fuses real-time environmental stress data and historical disease sensitivity threshold of poultry house to construct an environmental stress intensity grading model, dynamically locks the physical interval of the target gene by combining the linkage load index, realizes the minute-level response and precise adaptation to environmental abnormalities.

[0026] The interaction compression module intercepts high-frequency molecular markers in the locked genomic interval, constructs a radial interaction topology network with the major gene as the center, optimizes the aggregation priority of adjacent nodes through a dynamic weight mechanism, generates a low-dimensional linkage intensity vector, and breaks through the computational bottleneck in whole-genome interaction analysis.

[0027] The configuration generation module analyzes the key dimensions of the linkage intensity vector, accurately locates the physical coordinates of recombination hotspots combined with chromatin interaction data, matches complementary genotypes based on linkage load intensity and dynamic weight, and outputs structured execution instructions containing standardized molecular marker names and parent combination conditions, improving the efficiency and accuracy of poultry cross combinations.

[0028] Please refer to Figure 1 The embodiments of the present application provide a technical solution: a genome optimization configuration analysis system for molecular marker assisted selection, comprising the following modules: conflict resolution module, environment adaptation module, interaction compression module, configuration generation module; the conflict resolution module is used to calculate and correct interaction effect value by phenotypic deviation degree of poultry target trait haplotype combination and environmental fluctuation standard deviation, integrate genetic distance and chromatin openness data to generate linkage load intensity index, and dynamically allocate trait weight according to the linkage load intensity index; the environment adaptation module is used to fuse poultry house environmental stress data and historical disease sensitivity threshold, identify the intensity of continuous stress cycle, and lock the physical position interval of the target gene according to the linkage load intensity index and the stress cycle intensity; the interaction compression module is used to intercept high-frequency molecular marker data in the locked genomic interval, construct a radial interaction network with the major gene as the center, adjust the aggregation priority of neighbor nodes through dynamic weight, and generate a low-dimensional vector representing linkage intensity; the configuration generation module is used to analyze the key dimension values in the low-dimensional vector, locate the recombination hotspots with dense repetitive sequences according to the chromatin interaction data, match complementary genotypes combined with linkage load intensity index and dynamic weight, and output molecular marker names and poultry cross combination instructions.

[0029] In this embodiment, the conflict resolution module: for correcting the phenotypic prediction bias in poultry target traits due to gene interaction, linkage interference and environmental variation, is the basic module for the system to realize accurate trait weight allocation. Phenotypic deviation degree refers to the difference between the measured performance and the theoretical expectation of a specific haplotype combination individual in the target trait, which is used to measure the stability of the influence of a certain allele combination on the trait. The standard deviation of environmental fluctuation reflects the variation degree caused by non-genomic level in the breeding environment, which is combined with the phenotypic deviation degree to eliminate the interference of environmental noise on genetic evaluation. The linkage load intensity index is a comprehensive index for representing the severity of linkage disadvantage structure in a certain genomic interval. Its calculation is based on two core parameters: genetic distance: representing the possibility of recombination between two markers; chromatin openness: reflecting the accessibility of DNA to transcription factors in this region, which is usually obtained by ATAC-seq or DNase-seq data to infer recombination potential and functional activity. According to the above index, the system can dynamically allocate relative weights for different traits, solving the problem of linear weighting failure caused by interference between major genes and modifier genes, and antagonistic relationship between alleles. The environmental adaptation module: aiming at the influence of environmental fluctuations (such as temperature, humidity, ammonia concentration) in poultry breeding field on gene expression regulation, dynamically identifies the stress period and accurately locates the target gene under stress. Poultry house environmental stress data include but are not limited to continuous monitoring data such as temperature and humidity changes, ventilation conditions, feed ingredients, water quality parameters, etc., which are known to significantly affect poultry growth, immunity and reproductive traits. Historical disease sensitivity threshold refers to the key environmental parameter threshold summarized from the records of past poultry disease outbreaks, which is used as a reference to judge whether the stress reaches the biological response level. The intensity of continuous stress stress period is based on the total intensity score of continuous threshold exposure per unit time, which is used to distinguish between physiological reversible fluctuations and stress events that cause significant phenotype changes. The module can dynamically lock the target gene physical interval (i.e. specific Mb level coordinate range on the chromosome) with key regulatory function under stress conditions by integrating the linkage load intensity index and the stress period intensity, enhancing the situational adaptability of gene screening. The interaction compression module: constructs a network and dimensionally compresses a large amount of molecular marker information in the locked gene interval, extracts the most genetically significant linkage relationship features, reduces data redundancy and enhances computational efficiency. High-frequency molecular marker data refers to SNP, InDel or SSR markers with high polymorphism (such as allele frequency > a certain threshold) in breeding populations, representing variation sites that may have selection advantages. Major genes refer to genes that have a significant dominant effect on target traits, with large effect size and stable effect, which are often the core nodes of the interaction network constructed by the selection system. The radial interaction network is constructed based on physical distance and marker interaction strength, with major genes as the core and molecular markers with phenotypic linkage as the periphery.The dynamic weight adjustment dynamically determines the contribution value of each node (i.e. marker) in the network to the main effect by aggregating the biological statistical parameters such as effect coefficient, marker-phenotype regression coefficient, etc. The neighbor node aggregation priority reflects the propagation path and effect transmission efficiency of each marker in the network structure. The final output low-dimensional vector (such as 128-dimensional or 256-dimensional) is a numerical representation of the complex gene interaction relationship, which can be directly used for machine learning modeling or principal component analysis (PCA), avoiding the curse of dimensionality. The configuration generation module: based on the vector representation results of the interaction network and the chromatin conformation data, locates the genomic regions that can promote recombination, and designs parent configuration schemes with optimized combination potential, supporting actual breeding decisions. The key dimension values in the low-dimensional vector reflect the linkage tightness, marker synergy strength and other implicit genetic structure characteristics in a certain region, which can be used to identify potential recombination regulatory sites; chromatin interaction data (such as Hi-C) is used to restore the spatial conformation of the genome, and to locate the chromatin loop anchor region, which is usually rich in sequence features (such as microsatellite repeat sequences) that can regulate recombination; repeat sequence dense regions are often considered as recombination hotspots, which are beneficial to break unfavorable linkage; parent genotype matching combined with the aforementioned linkage drag index and key marker weight selects breeding individuals that can complement alleles; standardized marker naming follows the rule of "chromosome number + physical coordinate + functional tag" encoding, which is convenient for cross-platform marker management; the output of the crossbreeding combination instruction is structured information, including recommended mating method, screening standard, target trait and visualization parameters, which provides clear operation path and selection basis for breeders.

[0030] Specifically, the specific process of calculating the corrected interaction effect value by the phenotypic deviation degree of the target haplotype combination of poultry target traits and the standard deviation of environmental fluctuations is as follows: extract the population phenotype data set of the target haplotype combination of poultry target traits under the stress environment of the poultry house, take the phenotypic mean of the control group in the optimal growth environment as the benchmark, calculate the phenotypic deviation of the target haplotype carrying population under the stress environment, and eliminate the interference of breed-specific growth baseline difference and feather color variation; dynamically calculate the standard deviation of the phenotype fluctuation according to the data flow of the temperature and humidity sensor of the poultry house, and comprehensively process the phenotypic deviation and the environmental fluctuation intensity to generate the environment-corrected interaction effect value.

[0031] In this embodiment, the phenotypic data of the target haplotype combination is extracted from the poultry with the haplotype combination of specific target traits (including daily weight gain, feed conversion ratio, and egg production rate) in a real poultry house stress environment, and the population trait data formed in the stress environment are taken as the research object. Note: The "haplotype combination" referred to in this example refers to the combination of one or more continuous segments of alleles identified in the poultry genome through molecular marker comparison analysis, which can determine the expression trend of a specific phenotype. Based on the control group, the phenotypic deviation is selected, and the population in the most suitable environment (such as moderate temperature and humidity, reasonable feeding density) in the same breed is taken as the control group to obtain the phenotypic mean of the target trait. Take this value as the benchmark to calculate the deviation of the target haplotype population in the stress environment. And in this process, the following interference factors are removed: breed-specific growth baseline difference: use strain correction coefficient; interference of feather color related traits on light induction reaction: use spectral adaptation coefficient for normalization adjustment. The formula is as follows: ; wherein: : target haplotype combination phenotypic deviation of the target haplotype combination : haplotype phenotypic mean of the population in the stress environment : phenotypic mean of the same haplotype in the control group environment : growth baseline deviation correction term corresponding to the breed genotype : photosensitive adjustment coefficient of feather color interference. Photosensitive adjustment coefficient of feather color interference Obtained by carrying out light intensity-phenotype response experiments in poultry with different feather color genotypes (such as E / E, e+ / e+, and E / e+), record their behavior activity, growth rate, and target trait (such as daily weight gain) change trend under different light conditions, and use regression analysis to extract the interference amplitude of different feather colors on photosensitive trait expression; calculate the environmental fluctuation intensity (standard deviation of phenotype fluctuation) using the temperature and humidity sensors deployed in the poultry house to collect data streams (such as recording every minute), calculate the standard deviation of the environmental parameters during the stress period, and use it to measure the influence intensity of environmental fluctuation on phenotype expression. The formula is as follows: ; wherein: : environmental fluctuation intensity of the poultry house : number of sampling time points (for example, 1440 means sampling every minute for one day) : temperature at minute : temperature at minute : humidity at minute : humidity at minute : daily average of temperature and humidity. Step 4: generate environment-corrected interaction effect value comprehensive phenotype deviation and environmental fluctuation intensity, construct the environment-corrected interaction effect value , which is used as the input basis for subsequent dynamic weight allocation feedback to the conflict resolution module. The formula is as follows: ; wherein: haplotype combination environmental correction interaction effect value phenotype deviation environmental fluctuation intensity environmental disturbance adjustment factor, environmental disturbance adjustment factor Then, based on historical multi-batch poultry house sensor data and phenotype stability indicators, a multiple linear regression model is established, and cross-validation is performed under representative environmental stress conditions to ultimately determine the response sensitivity of the interaction effect to environmental fluctuations, taking The correction method can dynamically weaken the excessive exaggeration of phenotype deviation caused by severe environmental fluctuations, purify the actual genetic regulatory signal, and provide stable input for subsequent genetic network modeling. It is suitable for genome configuration optimization for breeding poultry (such as broilers and laying hens) under high-density cage feeding conditions, especially for screening haplotype combinations that still have stable growth performance under different environmental conditions in various stress environments (such as heat stress, humidity stress, and poor ventilation). Traditional static models can easily ignore the nonlinear effects of environmental fluctuations on trait expression, leading to poor stability of breeding programs in actual poultry house applications. This program improves the authenticity and robustness of gene-environment interaction modeling by building an environmental dynamic correction mechanism. Haplotype combination: refers to the combination of alleles on a continuous genetic segment, usually obtained by genotype clustering or sliding window method; for example: the 32.3Mb~34.7Mb interval on chromosome GGA02; interaction effect value: reflects the interactive contribution of different allele combinations to the phenotype under a specific environmental background; phenotype deviation: the difference between the target phenotype value of individuals / groups under stress environment and the ideal condition, used to identify unstable or deteriorating traits; environmental fluctuation standard deviation: an indicator to measure the intensity of environmental disturbance outside the poultry house, dynamically calculated based on temperature and humidity sensor data; corrected interaction effect value: genetic interaction signal representation after removing environmental interference based on the comprehensive phenotype deviation and environmental intensity.

[0032] Specifically, the linkage load intensity index is generated by integrating genetic distance and chromatin openness data, and the specific process of dynamically allocating trait weight according to the linkage load intensity index is as follows: obtain the nuclease hypersensitive signal intensity value of the accessible region of the chromatin within the linkage segment, calculate the linkage disequilibrium coefficient combining the physical linkage distance and base length of poultry chromosomes, and comprehensively process the absolute value of the corrected interaction effect value and the linkage disequilibrium coefficient to generate the linkage load intensity index; when the linkage load intensity index exceeds the preset threshold, the weight is transferred from the negative linkage trait to the target trait according to the proportion of the index exceeding the threshold, wherein the negative linkage trait refers to a trait that has genetic exclusion with the target trait.

[0033] In this implementation scheme, the acquisition and processing of chromatin-accessible regions first involves extracting chromatin accessibility data from linkage regions (i.e., near alleles associated with the target trait). This data is derived from poultry tissue-specific nuclease hypersensitive regions (DNaseseq) or ATACseq experiments, where chromatin accessibility is reflected by measuring cleavability. Higher accessibility indicates a greater susceptibility to transcriptional regulation or recombination events in the region. The nuclease hypersensitive signal intensity values ​​at each site within the linkage region are recorded and denoted as: ;in: : No. Chromatin accessibility score for each marker site; : The index number of the high-frequency molecular marker located within the linkage segment. The linkage disequilibrium coefficient is calculated by considering the influence of chromatin openness. It represents the adverse structural effect caused by the mismatch between physical distance and recombination probability between markers. The definition is as follows: ;in: : No. Linkage disequilibrium coefficients of each marker site; : No. The physical distance between a marker and a target marker (e.g., linear distance measured in base pairs); : No. The base length of the recombinant block in the region where each marker is located is used to normalize the contribution of distance to the chain effect; The aforementioned chromatin openness score; The chromatin openness regulation parameter controls the sensitivity of chromatin structure to linkage tightness. It expresses the biological fact that the more open the chromatin, the greater the probability of recombination and the weaker the linkage barrier. The linkage baggage strength index generation combines the linkage disequilibrium coefficient obtained in the previous step with the absolute value of the interaction effect (after environmental correction) obtained in the previous module to measure the inhibitory strength of a linkage segment on the expression of the target trait. The linkage baggage strength index is defined as: ;in: :No. Chaining redundancy strength index for each labeled pair; :No. The absolute value of the environmental correction interaction effect of each label pair is used to measure its ability to interfere with the target trait. :No. 4. Dynamically assign trait weights to all Calculate the weighted average to obtain the total chain burden strength index of the target chain segment. If the index exceeds an empirically set threshold... If the trait weight exceeds the allowable proportion, the weight of the trait will be transferred accordingly, and the amount of transfer will be calculated as follows: ; wherein: : the weight adjustment amount transferred from the negative linkage trait to the target trait; : the base weight value of the target trait, reflecting its initial breeding priority in the multi-trait breeding system; : the empirically set linkage drag intensity critical threshold; : the average linkage drag intensity index within the linkage segment. When , the breeding weight is proportionally deducted from the traits that have genetic incompatibility with the target trait, and the weight is compensated to the target trait, thereby improving the weight priority of the trait in subsequent parent matching and recombination configuration. The trait inhibition problem caused by negative linkage is solved. Through fine positioning of the target trait genetic locus and dynamic adjustment of the weight in the high-density molecular marker background, accurate support is provided for subsequent parent combination optimization.

[0034] Specifically, the specific process of identifying the intensity of the continuous stress cycle by fusing the poultry house environmental stress data and the historical disease sensitivity threshold is as follows: the stress sensitivity threshold parameters for different growth stages of poultry are set, the effective cumulative duration continuously exceeding the threshold of each stage is analyzed based on the high-frequency time series data collected by the poultry house temperature and humidity sensors, and transient abnormal fluctuation noise signals are filtered out; the sensitivity percentile value in a specific period is extracted in combination with the regional high incidence records in the historical disease database, and a statistical mapping relationship between stress events and disease responses is constructed to determine the intensity level of the current stress cycle.

[0035] In the present embodiment, the phase stress sensitivity threshold model is established. According to the physiological response differences of poultry to environmental changes in different growth stages (such as the brooding period, the growth period, the laying period, etc.), the phase stress threshold of temperature, humidity and other parameters is set, denoted as: ; wherein: : the growth stage ; wherein: : the stress threshold of the th environmental factor (such as temperature, relative humidity, etc.) in the growth stage : the identifier of the growth stage of poultry; : the environmental factor type number corresponding to temperature, humidity, ammonia concentration, etc. The model is determined based on the existing breeding standards and empirical research, and is the baseline parameter for subsequent stress determination. , the total duration continuously exceeding the corresponding threshold is calculated, and the effective stress duration is defined after eliminating short-time noise fluctuations: ; wherein: : the growth stage effective stress accumulation length of an environmental factor; : time : real-time value of the th environmental factor collected; : threshold over-limit indicator function, taking value 1 when , otherwise 0; : noise filtering function, used to eliminate short-term abnormal fluctuations less than a set time window, taking value 1 indicating effective continuous stress. This step ensures that the statistics are "continuous" stress periods, excluding abnormal pulse interference. Avian disease sensitivity percentile extraction obtains avian disease records from the regional historical disease database, extracts historical disease records matching the current environmental conditions, and constructs a sensitivity percentile value matrix: ; wherein: : the th environmental factor in the th percentile value in the disease response; : set of environmental values related to the th environmental factor in the historical disease record; : historical record index; : set sensitivity percentile (such as 90%, indicating response under extreme conditions). This value is used to reflect the statistical mapping strength between historical stress and disease occurrence. Stress cycle intensity determination compares the current effective stress length with the disease sensitivity percentile to normalize the comprehensive stress response index: ; wherein: : the th environmental factor in the th stress response index; : the aforementioned effective stress length; : the th percentile sensitivity value in the historical disease database. According to the interval distribution of the response index , set the grading rules (such as: : mild stress, : moderate stress, : severe stress), to obtain the multi-dimensional intensity level label of the current poultry house stress cycle. Realize the structured interpretation of high-frequency, multi-source environmental data, improve the sensitivity and accuracy of early identification of stress events through historical data-driven statistical modeling. Suitable for application in the environmental response positioning scenario of target traits in precision poultry breeding.

[0036] Specifically, the process of locking the physical location interval of the target gene based on the linkage redundancy strength index and the stress cycle intensity is as follows: using the chromosome physical coordinates of the major gene with the largest linkage redundancy strength index as the anchor point, the interval expansion coefficient is calculated based on the weight increase ratio of the major site and the current stress intensity level; referring to the chromosome physical length, recombination rate distribution and cross-chromosomal structural characteristics of poultry species, the length of the expanded genomic interval is regulated to generate standardized machine-resolvable positioning instructions containing chromosome number, start and end physical locations.

[0037] In this implementation plan, the identification and coordinate extraction of anchor point major genes first involves screening the loci corresponding to the major genes with the highest linkage redundancy indices obtained in the previous stage, and recording their chromosome numbers as follows: The corresponding physical coordinate interval center point (principal effect point) is represented as: ;in: : No. The chromosome number represents the chromosome where the target major gene is located; :chromosome The physical coordinate center point of the major gene (unit: base position); This represents the unique index of the selected major gene. This major locus serves as a genomic region anchor, providing a physical reference for subsequent expansion. The region expansion coefficient is calculated based on the linkage slack strength index value corresponding to this major gene. Compared with the current stress intensity level Jointly determine an interval expansion coefficient Used to dynamically control the physical span of genomic regions: ;in: : No. The major effect site is at the [number]th major effect site. The range expansion coefficient under level-one stress intensity; The basic expansion ratio constant is the statistical result of the minimum effective regulatory interval of the major gene regions of multiple target traits under different stress levels under controlled experimental conditions. The optimal expansion coefficient range is fitted by measured data (such as 0.05–0.15), and the median value with the highest robustness is determined as a constant to control the initial window size. : Linkage burden strength index of major sites; : Normalized standard reference value for chain burden strength (set as empirical average); : The intensity level of the current stress cycle (integer level, such as 0, 1, 2); : Normalized reference value of stress intensity (percentile processing of the calculated linkage disequilibrium intensity value distribution in the 5% genomic region with the highest linkage disequilibrium intensity in the known poultry population, usually taking the value ranked in the 95% position as the upper limit reference value in the normalization processing, so as to ensure that the dynamic range of weight regulation can still be maintained under the extreme disequilibrium segment). The expansion coefficient reflects the influence of the main effect trait and environmental stress interaction on the interval width. The chromosome segment length control is based on the expansion coefficient , combined with the upper limit of the physical length of the chromosome and the local recombination rate , to calculate the final segment radius (i.e. the physical distance expanded from the anchor point to both ends): ; wherein: : the expansion radius (physical distance) on the th chromosome under stress level ; : base length constant for converting the coefficient to physical scale; : the maximum physical length of the chromosome ; : the local recombination rate function of the coordinate point on the chromosome ; : recombination rate control function, the higher the value, the tighter the expansion needs to be controlled to prevent interference from crossing recombination hotspots. This step realizes the biological reasonable regulation of the chromosome interval through the joint action of physical length limitation and recombination rate. Based on the anchor point position and the expansion radius , the starting and ending coordinates of the chromosome interval are finally determined as follows: ; ; The standard format of the generated positioning instruction is: sql[Chromosome:χ_k;Start:P_k^start;End:P_k^end]; wherein, Chromosome:χ_k represents the chromosome number of the positioned gene interval, the kth chromosome; Start:P_k^start represents the starting physical position coordinate of the interval; End:P_k^end represents the ending physical position coordinate of the interval; this format is uniformly used for system identification of target gene interval, and serves as the standard input interface for downstream marker screening and interaction network construction. The instruction will be delivered to the downstream modules (interaction compression module, configuration generation module) in a machine-resolvable format for further analysis and recombination configuration.

[0038] Specifically, the process of extracting high-frequency molecular marker data within the locked genomic region and constructing a radial interaction network centered on the major gene is as follows: Molecular markers located within the locked physical region are extracted from the poultry genome, and marker sites with allele frequencies exceeding a set polymorphism threshold in the population are screened; the physical coordinates of the major gene with the highest linkage carryover strength index are used as the core node of the network, and the surrounding area is divided into multiple concentric circle hierarchical regions at fixed physical distance intervals, and each marker is classified into the corresponding level according to its physical distance from the center point; basic connection weights are assigned to each level, and the weights decrease layer by layer according to a preset decay ratio as the level becomes farther apart; multiple partial least squares regression analysis is performed based on poultry phenotypic observation data and candidate marker genotypic data during the stress cycle to screen key marker nodes with absolute regression coefficient values ​​greater than the significance threshold; direct connections are established between key marker nodes and major gene nodes, and their weights are increased to a fixed multiple of the basic value, while non-key markers are connected through hierarchical decreasing paths with weights decreasing layer by layer.

[0039] In this implementation plan, the extraction and screening of marker sites are performed within the locked physical region determined in the previous stage. Within the corresponding region, all molecular marker sites are extracted and subjected to population polymorphism analysis. Let the set of all extracted markers be: Screening for allele frequencies exceeding a preset polymorphism threshold. The mark: ;in: :No. One molecular marker; :No. Secondary allele frequencies of each marker; : The set population polymorphism threshold (e.g., 0.05 or 0.1, depending on the breeding project). The selected set of highly polymorphic markers. A radial hierarchical structure (concentric circle layering) was constructed, using the physical coordinates of the major gene with the highest linkage carryover strength index. Centered on, according to fixed physical intervals Divide its surrounding area into Concentric circle levels: ;in: :No. a mark Physical coordinates; : The physical coordinates of the major gene; Fixed physical spacing for each level (e.g., 50kb); :No. The set of tags for the layer; The total number of levels, based on the interval length and... Adaptive determination. The initial connection weight is set as ω0, and the base connection weight of the h-th layer is given to all markers: ; wherein: : the base edge weight of the h-th layer; δ: the weight decay factor (0 < δ < 1, such as 0.8); : the initial central layer weight reference value. Each marker node establishes a subordinate connection with the main effective gene, and the weight is determined by the level to which it belongs. The key marker screening (based on multiple partial least squares regression) uses the collected poultry phenotype data Yobs and the genotype data Gobs of each candidate marker in the current stress period to construct a multiple partial least squares (PLS) regression model: ; wherein: Yobs: the phenotype observation matrix of the sample individual (sample number x target trait number); Gobs: the marker genotype matrix of the corresponding individual (sample number x marker number); B: the PLS regression coefficient matrix (marker number x trait number); ε: the residual matrix. For each candidate marker Mq, the absolute value of its regression coefficient on the target trait is calculated and a significance threshold θb is set: ; wherein: Bq,t: the regression coefficient of marker Mq on target trait t; Mkey: the set of key significant markers. Network edge and weight optimization For each key marker , a direct edge connection is established with the main effective gene node , and the weight is enhanced to a multiple of the weight of the level it belongs to: ; wherein: : the connection edge weight between the key marker and the main effective gene; : the level number to which the marker belongs; : the key node weight amplification coefficient (for example, 2.5-5.0 times). For non-key markers, the original level edge weight is maintained, and an indirect interaction structure with the central node is formed through layer-by-layer decreasing connection paths. The spatial center is constructed using the main effective gene, which significantly improves the structural order and visualization ability of the marker interaction network; the concentric circle structure combined with physical coordinate information makes the edge weight distribution more consistent with the topological properties of chromosomes; the introduction of multiple PLS screening of significant markers focuses on nodes with actual phenotype influence, improving the information density of the interaction network; the decay and weighting mechanism considers both data dimensionality reduction and interaction information preservation, effectively avoiding the dimension disaster problem; the constructed network provides a high-quality structural input basis for subsequent low-dimensional vector generation and reconfiguration.

[0040] Specifically, the process of generating a low-dimensional vector representing the linkage strength by dynamically adjusting the aggregation priority of neighbor nodes is as follows: Key marker nodes assigned trait enhancement weights in the dynamic weight allocation results are extracted and treated as target nodes. An aggregation weighting coefficient is assigned to the direct neighbor nodes of each target node, where the weighting magnitude is positively correlated with the increase in trait weight. The gene effects of neighbor nodes at each level are weighted and aggregated using a hierarchical feature propagation algorithm to form a gene interaction representation centered on the major gene. The relationships between all key nodes and their neighbors in the interaction network are vectorized, outputting a fixed-dimensional low-dimensional vector representing the local linkage strength.

[0041] In this implementation scheme, target node identification involves: selecting key molecular marker nodes that are assigned trait enhancement weights from the dynamic weight allocation results, and denoting them as the target node set. Each node in this set represents a marker site that significantly influences the target trait. Adjacency node weighting: For each target node... Extract all first-order adjacent nodes in its network. and for each adjacent node Assign aggregation weighting coefficients: ;in: Target node To adjacent nodes Assigned aggregate weights; Target node The trait weighting factor; :node With nodes The structural compactness coefficient between nodes reflects the strength of their interactions (such as gene co-expression values ​​or chromatin interaction frequencies). Hierarchical feature propagation and aggregation: A hierarchical propagation mechanism based on graph neural structures is introduced to integrate the effects of neighboring nodes layer by layer, forming a major gene-centered interaction representation. Layer aggregation features are: ;in: : No. Layer nodes Aggregated feature vectors; : Neighbor nodes in the upper layer eigenvectors; : No. Depth adjustment factor for layer propagation; Nonlinear activation functions, often ReLU or sigmoid, are used to modulate nonlinear expressive power. Interaction network vectorization encoding: After aggregation, an ordered concatenation strategy is used to generate the final representation vectors of all target nodes and their neighboring nodes. Encode and compress to generate a low-dimensional vector representing the strength of local linkages: ; wherein: : the final generated fixed-dimension low-dimensional vector (e.g., 32-dimension, 64-dimension, etc.); : linear compression projection matrix, used to reduce dimensionality and preserve local interaction features; : concatenating vectors in node order. Key marker node: refers to a molecular marker (e.g., SNP site, InDel, etc.) that has a significant contribution to the performance of the target trait in the stress response model. Aggregated weighting coefficient: determines the proportion of information received by a node in the network from its surroundings, reflecting its "speaking power" in the interaction network. Structural tightness coefficient : determined by interaction scores calculated from HiC experimental data or RNAseq, such as chromatin three-dimensional contact frequency or gene co-expression coefficient. Depth propagation coefficient : used to control the attenuation ratio of information transmission from peripheral nodes to target nodes, which can be obtained by combining empirical weights or training data. Nonlinear function : such as ReLU (Rectified Linear Unit), to enable the model to have nonlinear expression capability.

[0042] Specifically, the specific process of analyzing the key dimension values in the low-dimensional vector and locating the recombination hot spot with dense repeat sequences according to the chromatin interaction data is as follows: identifying the key dimension values in the low-dimensional vector representing negative linkage strength, and when the value is lower than the strength threshold, locating the anchor point region of the chromatin loop where the central gene is located in the chromatin conformation capture data; scanning the microsatellite repeat sequences in the anchor point region, detecting specific repeat unit types with continuous repeat numbers exceeding the set standard, and mapping the physical coordinates of the microsatellite sequences meeting the repeat standard to the chromosome segment number, and marking as potential recombination hot spot sites in the poultry genome.

[0043] In this embodiment, low-dimensional vector key dimension identification: analyzing the input low-dimensional vector , wherein some dimensions are specifically used to represent negative linkage strength. Selecting a dimension index set , which corresponds to the key dimensions representing negative linkage signals. Threshold judgment: performing threshold judgment on each key dimension value : ; wherein: : the value of the th key dimension in the low-dimensional vector, representing negative linkage strength; : negative linkage strength threshold, below which it indicates strong negative linkage signal, suggesting a potential recombination hot spot region. Anchor point region positioning: for the dimensions that meet the conditions, use chromatin conformation capture (HiC or ChIAPET) data to locate the anchor point region of the chromatin loop where the major gene is located, denoted as: ; wherein: represents the start physical coordinate of the chromosome; represents the end physical coordinate of the chromosome; interval represents the anchor point physical range of the chromatin loop. Microsatellite repeat sequence scanning: within interval , scan microsatellite (SSR) repeat sequences to detect the number of consecutive repeats whether the set criteria are met: ; wherein: is the number of consecutive repeats of repeat unit ; and is the microsatellite repeat number threshold, and the repeat unit type exceeding the value is considered to be associated with recombination hotspots. Recombination hotspot mapping: for microsatellite sequences meeting the repeat number threshold, extract their physical coordinates , , : the start and end physical coordinates of the microsatellite repeat sequence meeting the conditions; map the interval to the chromosome segment number , and mark it as a potential recombination hotspot. The low-dimensional vector is obtained by aggregating different interaction features, and part of the dimension is specialized as a quantitative indicator of negative linkage strength. When the indicator is lower than the preset threshold, it indicates that there is significant genetic exclusion or linkage blocking phenomenon in the region; using chromatin conformation data to accurately locate the three-dimensional space anchor point where the major gene is located, combined with physical genome sequence information, scan the microsatellite repeat sequences in the anchor point region; microsatellite sequences are important biomarkers of genomic recombination hotspots, and the quantitative standard threshold of the number of repeats is set according to the characteristics of poultry genomes to exclude false judgments caused by low-frequency random repeats; the mapped hotspot sites can be used to guide the optimization of parent genotype combinations to improve recombination efficiency and the stability of target trait expression.

[0044] Specifically, combining the linkage drag intensity index and the dynamic weight matching complementary genotype parents, the specific process of outputting the molecular marker name and the crossbreeding combination instruction of the poultry is as follows: based on the linkage drag intensity index, select poultry parent individuals that are homozygous for the target haplotype at the major gene site, and according to the aggregation results of the dynamic weight, identify compensatory trait parents carrying key complementary alleles; standardize the naming of functional molecular markers located in the recombination hotspot segment, generate marker names containing chromosome number, physical coordinates and functional annotation information, output structured poultry configuration instructions, and the instruction content includes: the breed name of the target parent and the complementary parent, the recommended mating combination method, the recombination marker name and the screening strategy condition.

[0045] In this embodiment, the major gene homozygous parent screening is based on the linkage drag intensity index to screen parents that are homozygous for the target haplotype at the major gene site. The homozygous state guarantees the stable genetic basis of the target trait. The calculation steps are as follows: define the parent major gene homozygosity index : ; wherein: : homozygosity score of the parent ; : number of major gene loci : genotype of the parent at the th locus : genotype of the target haplotype at the th locus : consistency function, 1 if , otherwise 0. The screening condition is: ; wherein is the homozygosity threshold, ensuring that the parent genotype is highly matched to the target haplotype. The identification of the complementary parent is based on the dynamic weight vector , identifying the compensatory trait parent carrying the key complementary allele, achieving genetic complementation. The calculation steps are: define the complementary matching score of the parent : ; wherein: : complementary matching score of the parent ; : number of key complementary gene loci : weight of the th locus in the dynamic weight, reflecting the contribution of the locus to the target trait : genotype of the parent at the th locus : complementary allele genotype : complementary matching function, 1 if is the complementary allele of , otherwise 0. The parent that meets is screened as the compensatory trait parent, is the complementary matching threshold. Functional molecular marker standardized naming performs unified naming on functional molecular markers within the locked recombination hotspot segment, with the format: ; wherein: : chromosome number : marker physical coordinate Functional annotation tags (e.g. coding region, regulatory region, etc.). This naming method ensures the uniqueness and information integrity of the marker name, facilitating computer automatic recognition and mating strategy application. The output of the structured configuration instructions of the breeding birds, the instruction generation process summarizes the basic information of the target parent (such as number, breed, major locus genotype); summarize the basic information of the complementary parent (such as number, breed, complementary locus genotype); extract functional markers in the recombination hot spot region and name them; integrate cross method recommendations (such as single cross, backcross, multiple cross, etc.) and molecular screening strategy conditions; generate structured instructions according to the pre-defined template. Instruction example (standard format) [Breed_Target: Langshan]; [Breed_Complement: White Leghorn]; [Cross_Type: SingleCross]; [Markers: Chr3:2743051Enhancer, Chr5:987221-Coding, Chr7:1887309-Promoter]; [Filter_Strategy: Q_n≥0.92, W_m≥1.15, Haplotype=AAGGTT]. Among them, Breed_Target: The target parent breed is Langshan chicken; Breed_Complement: The complementary parent breed is White Leghorn; Cross_Type: The recommended cross method is single cross; Markers: The output of three functional markers, including chromosome number, physical location and functional annotation; Filter_Strategy: Molecular screening strategy, including major gene homozygosity threshold, complementary score threshold, and required target haplotype.

[0046] See Figure 2 , the genomic optimization configuration analysis method of molecular marker assisted selection, comprising the following steps: S1. Calculate the correction interaction effect value by the phenotypic deviation degree of the target trait haplotype combination of poultry and the standard deviation of environmental fluctuation, integrate genetic distance and chromatin openness data to generate linkage complexity index, and dynamically allocate trait weight according to the linkage complexity index; S2. Fuse the data of poultry house environmental stress and historical disease sensitivity threshold to identify the intensity of continuous stress cycle, and lock the physical location interval of the target gene according to the linkage complexity index and stress cycle intensity; S3. Extract high-frequency molecular marker data in the locked genomic interval, construct a radial interaction network with the major gene as the center, adjust the neighbor node aggregation priority through dynamic weight, and generate a low-dimensional vector representing linkage strength; S4. Analyze the key dimension values in the low-dimensional vector, locate the recombination hot spot with dense repeat sequences according to the chromatin interaction data, match the complementary genotype parent combined with the linkage complexity index and dynamic weight, and output the molecular marker name and breeding bird cross combination instructions.

[0047] In this embodiment, step S1: construct linkage drag intensity index and allocate dynamic trait weight, this step corrects the interaction between the deviation of haplotype combination of target traits at the phenotype level and the environmental standard deviation, obtains the corrected interaction effect value reflecting the true genetic contribution; and then integrates genetic distance and chromatin openness data to generate linkage drag intensity index. This index is used to guide the dynamic trait weight allocation mechanism to realize the genomic precise regulation of trait selection pressure. This step combines chromatin openness with phenotype deviation to correct the traditional genetic contribution calculation method, breaking through the bottleneck of "uncoupling of phenotype selection and structural genetic layer" in existing molecular breeding. At the same time, linkage drag modeling is introduced for the first time to dynamically allocate trait weight, realizing the quantitative dynamic allocation of trait weight, and solving the problem of low adaptability caused by fixed trait weight in traditional models. Step S2: lock candidate gene interval based on stress cycle intensity, fuse poultry house environment sensor data (such as temperature and humidity, ammonia concentration, etc.) and historical disease sensitive threshold to identify the start and end time of stress cycle and its fluctuation intensity, and then use linkage drag index and stress intensity to determine interval expansion coefficient to determine the upstream and downstream boundary range of the chromosome where the major gene is located. Combined with species-specific chromosome structure and recombination rate regulation information, standard candidate gene interval positioning instructions are generated. Compared with traditional gene positioning methods relying on static QTL or GWAS results, this step provides a real-time coupling mechanism of dynamic environmental stress and linkage genetic structure for gene segment locking. Step S3: construct radial interaction network and generate linkage intensity low-dimensional vector, extract molecular markers with allele frequency higher than the polymorphism threshold in the locked interval, take the major gene as the core node, and construct a multi-level radial interaction network; use multivariate partial least squares regression (PLSR) on stress cycle phenotype and molecular data to screen key nodes and define dynamic weighting rules based on network hierarchy structure, and further generate fixed-dimensional vector representation representing local linkage intensity through feature propagation algorithm. This step establishes a new mechanism for embedding statistical genotype-phenotype regression results into graph neural network structure modeling, which breaks through the deep interaction path between phenotype selection signal, linkage intensity spatial structure and marker density. At the same time, the genetic drag structure in the complex network is represented in the form of low-dimensional vector, which is convenient for downstream recombination hotspot identification and parent matching algorithm calling, and is a breakthrough in structure compression and generalization modeling of existing linkage analysis methods. Step S4: locate recombination hotspots and output hybrid combination instructions, analyze the dimension value representing negative linkage in the low-dimensional vector to locate potential genetic bottleneck sites. Combine chromatin conformation capture data (Hi-C, ChIA-PET, etc.) to lock chromatin loop anchors, scan microsatellite structures that meet the repetition number standard in them, and mark them as recombination hotspots. Then, combined with the linkage index and weight aggregation results, identify the optimal complementary combination from the target parent and compensation parent, and output structured poultry hybridization instructions including functional marker naming, parent breed, mating method and screening strategy.This step establishes a complete path from linkage load to accurate mapping of recombination hotspots, overcoming the technical obstacles that traditional genotype analysis cannot resolve structural variation drag; At the same time, through the structured output standard breeding instruction, the complex graph calculation and selection logic are embedded into the executable engineering instruction, which greatly improves the breeding practicability and deployment efficiency.

[0048] In summary, the present application has at least the following effects:

[0049] The genome optimization configuration analysis system and method of molecular marker assisted selection, by fusing phenotype deviation and chromatin openness data to calculate linkage load intensity index, and dynamically adjusting trait selection weight based on the index, realizes real-time regulation of dynamic response of trait importance to environmental stress, and overcomes the low adaptability problem caused by fixed weighting of traits in traditional methods. By introducing the poultry house environment monitoring data and historical disease sensitivity curve, combined with linkage load intensity, the chromosome segment of the major gene under specific stress background is accurately determined, which provides high reliability physical coordinate basis for subsequent function site extraction and recombination optimization. The radial gene interaction network is constructed and the linkage intensity low-dimensional vector is generated by feature propagation algorithm, which effectively compresses the high-dimensional space information of molecular markers, realizes structure perception and linkage load modeling, and improves the identification accuracy of potential genetic bottleneck area. The recombination hot spot annotation based on chromatin conformation and repeat sequence recognition improves the reconstruction efficiency of hybrid combination. The standardized molecular marker naming method containing chromosome number, physical coordinate and function annotation is generated, and the structured configuration instruction containing target parent, complementary parent, recombination marker and screening condition is output, which significantly improves the automation of breeding decision.

[0050] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems, or computer program products. Accordingly, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) having computer-usable program code embodied in the medium.

[0051] The present application is described with reference to flowcharts and / or block diagrams that illustrate the architecture, systems, and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocksFigure 1 means for performing the function specified in the block or blocks.

[0052] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 flow or flows and / or blocks Figure 1 means for performing the function specified in the block or blocks.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 flow or flows and / or blocks Figure 1 steps for performing the function specified in the block or blocks.

[0054] Although preferred embodiments of the application have been described herein, changes and modifications can be suggested to one skilled in the art and are intended to be encompassed within the scope of the appended claims. It is the intent, therefore, to be limited only as indicated by the scope of the claims appended hereto.

[0055] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A genome optimization configuration analysis system for molecular marker-assisted breeding, characterized in that, It includes the following modules: conflict resolution module, environment adaptation module, interoperability compression module, and configuration generation module; The conflict resolution module is used to calculate the corrected interaction effect value by using the phenotypic deviation of haplotype combinations of target traits in poultry and the standard deviation of environmental fluctuations, integrate genetic distance and chromatin openness data to generate a linkage burden intensity index, and dynamically allocate trait weights according to the linkage burden intensity index. The environmental adaptation module is used to integrate poultry house environmental stress data with historical disease sensitivity thresholds, identify the intensity of continuous stress cycles, and lock the physical location range of target genes based on the linkage carryover intensity index and stress cycle intensity. The interaction compression module is used to extract high-frequency molecular marker data within the locked genomic region, construct a radial interaction network centered on the major gene, and adjust the aggregation priority of neighboring nodes through dynamic weights to generate a low-dimensional vector representing the linkage strength. The configuration generation module is used to parse the key dimension values ​​in the low-dimensional vector, locate recombination hotspots with dense repetitive sequences based on chromatin interaction data, and combine linkage redundancy intensity index and dynamic weight to match complementary genotype parents, outputting molecular marker names and breeding poultry hybridization combination instructions.

2. The molecular marker-assisted breeding genome optimization configuration analysis system according to claim 1, characterized in that: The specific process for calculating the corrected interaction effect value using the phenotypic deviation of haplotype combinations of target traits in poultry and the standard deviation of environmental fluctuations is as follows: Phenotypic datasets of target haplotype combinations in poultry under stress were extracted. Using the mean phenotype of the control group in the optimal growth environment as a benchmark, the phenotypic shift of the target haplotype-carrying population under stress was calculated, and the interference of breed-specific growth baseline differences and feather color variations was removed. The standard deviation of phenotypic fluctuation is dynamically calculated based on the data stream from the poultry house temperature and humidity sensors. The phenotypic offset and the intensity of environmental fluctuations are then combined to generate the interaction effect value for environmental correction.

3. The genome optimization configuration analysis system for molecular marker-assisted breeding according to claim 2, characterized in that: The specific process of integrating genetic distance and chromatin openness data to generate a linkage baggage strength index, and then dynamically allocating trait weights based on the linkage baggage strength index, is as follows: The intensity values ​​of nuclease hypersensitive signals in chromatin-accessible regions within linkage segments are obtained. The linkage disequilibrium coefficient is calculated by combining the physical linkage distance and base length of poultry chromosomes. The absolute value of the correction interaction effect value and the linkage disequilibrium coefficient are combined to generate the linkage cumber intensity index. When the linkage burden strength index exceeds a preset threshold, the weight is transferred from the negative linkage trait to the target trait according to the proportion of the index exceeding the threshold. The negative linkage trait refers to the trait that has genetic mutual exclusion with the target trait.

4. The molecular marker-assisted breeding genome optimization configuration analysis system according to claim 1, characterized in that: The specific process for identifying the intensity of continuous stress cycles by integrating poultry house environmental stress data with historical disease sensitivity thresholds is as follows: Stress sensitivity threshold parameters are set for different growth stages of poultry. Based on the high-frequency time-series data collected by poultry house temperature and humidity sensors, the effective cumulative duration of continuous exceedance of each stage threshold is analyzed to filter out instantaneous abnormal fluctuation noise signals. By combining regional high incidence records in historical disease databases, sensitivity percentile values ​​for specific time periods are extracted to construct a statistical mapping relationship between stress events and disease responses, and the intensity level of the current stress cycle is determined by classification.

5. The genome optimization configuration analysis system for molecular marker-assisted breeding according to claim 4, characterized in that: The specific process of locating the physical location range of the target gene based on the linkage redundancy strength index and stress cycle strength is as follows: Using the chromosomal physical coordinates of the major gene with the largest linkage carryover strength index as the anchor point, the interval expansion coefficient is calculated based on the weight increase ratio of the major site and the current stress intensity level. Based on the physical length of chromosomes, recombination rate distribution, and cross-chromosomal structural characteristics of poultry species, the length of the expanded genome region is regulated to generate standardized machine-resolvable positioning instructions containing chromosome numbers, start and end physical locations.

6. The molecular marker-assisted breeding genome optimization configuration analysis system according to claim 1, characterized in that: The specific process of extracting high-frequency molecular marker data within a locked genomic region and constructing a radial interaction network centered on the major gene is as follows: Molecular markers located within locked physical regions were extracted from poultry genomes, and marker sites with allele frequencies exceeding a set polymorphism threshold in the population were screened. Using the physical coordinates of the major gene with the highest chain cumber strength index as the core node of the network, the surrounding area is divided into multiple concentric circle hierarchical regions at fixed physical distance intervals, and each marker is classified into the corresponding level according to its physical distance from the center point. Assign basic connection weights to each level, with the weights decreasing layer by layer according to a preset decay ratio as the levels become more distant. Multiple partial least squares regression analysis was performed based on poultry phenotypic observation data and candidate marker genotypic data during the stress cycle to screen key marker nodes with absolute regression coefficient values ​​greater than the significance threshold. Establish direct connections between key marker nodes and major gene nodes, and increase their weights to a fixed multiple of the base value. Non-key markers are connected through hierarchical decreasing paths with their weights decreasing layer by layer.

7. The molecular marker-assisted breeding genome optimization configuration analysis system according to claim 6, characterized in that: The specific process of generating a low-dimensional vector representing the linkage strength by dynamically adjusting the aggregation priority of neighbor nodes is as follows: Extract the key marker nodes that are assigned trait enhancement weights in the dynamic weight allocation results and regard them as target nodes. Assign aggregate weighting coefficients to the direct adjacent nodes of each target node, where the weighting magnitude is positively correlated with the increase in trait weight. The gene effects of neighboring nodes at each level are weighted and aggregated using a hierarchical feature propagation algorithm to form a gene interaction representation with the major gene as the core. The relationships between all key nodes and their neighbors in the interaction network are vectorized, and the output is a fixed-dimensional low-dimensional vector used to represent the local linkage strength.

8. The molecular marker-assisted breeding genome optimization configuration analysis system according to claim 7, characterized in that: The specific process of analyzing the key dimension values ​​in a low-dimensional vector and locating dense recombination hotspots of repeating sequences based on chromatin interaction data is as follows: Identify the key dimension values ​​representing the strength of negative linkage in low-dimensional vectors, and when the value is below the strength threshold, locate the anchor region of the chromatin loop where the central gene is located in the chromatin conformation capture data. The microsatellite repeat sequences within the scanning anchor region are detected, and specific repeat unit types with consecutive repeat counts exceeding a set standard are identified. The physical coordinates of microsatellite sequences that meet the repeat criteria are mapped to chromosome segment numbers and marked as potential recombination hotspots in the poultry genome.

9. The molecular marker-assisted breeding genome optimization configuration analysis system according to claim 8, characterized in that: The specific process of combining linkage redundancy strength index and dynamic weighted matching of complementary genotype parents to output molecular marker names and poultry hybridization instructions is as follows: Based on the linkage carryover strength index, poultry parents who are homozygous for the target haplotype at the major gene locus are screened, and parents carrying the compensatory trait of key complementary alleles are identified based on the dynamic weight aggregation results. Functional molecular markers located in recombination hotspot regions are standardized and named, generating marker names that include chromosome number, physical coordinates, and functional annotation information. Structured poultry configuration instructions are output, including: the breed names of the target parent and complementary parent, the recommended mating combination, the name of the recombination marker, and the screening strategy conditions.

10. A method for genome optimization configuration analysis of molecular marker-assisted breeding, applied to the genome optimization configuration analysis system of molecular marker-assisted breeding as described in any one of claims 1-9, characterized in that, Includes the following steps: S1. The corrected interaction effect value is calculated by the phenotypic deviation of haplotype combinations of target traits in poultry and the standard deviation of environmental fluctuations. Genetic distance and chromatin openness data are integrated to generate the linkage burden strength index, and the trait weights are dynamically allocated according to the linkage burden strength index. S2. By integrating poultry house environmental stress data with historical disease sensitivity thresholds, the intensity of continuous stress cycles is identified, and the physical location range of target genes is determined based on the linkage carryover intensity index and stress cycle intensity. S3. Extract high-frequency molecular marker data within the locked genomic region, construct a radial interaction network centered on the major gene, and generate a low-dimensional vector representing the linkage strength by dynamically adjusting the aggregation priority of neighboring nodes. S4. Parse the values ​​of key dimensions in the low-dimensional vector, locate recombination hotspots with dense repetitive sequences based on chromatin interaction data, and combine linkage redundancy strength index and dynamic weight to match complementary genotype parents, outputting molecular marker names and breeding poultry hybridization combination instructions.

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