A multi-trait weighted group selection and assortative mating breeding method for leopard coral grouper

Abstract

The application provides a multi-trait weighted group selection and mating breeding method for Plectropomus leopardus, and relates to the technical field of group selection and mating breeding. The method comprises the following steps: obtaining candidate SNP markers through multi-trait GWAS analysis, verifying and merging effect values through a genetically independent verification population, and constructing a targeted typing Panel; performing genotyping on candidate parents, combining multi-effect marker processing and trait antagonistic correction to calculate a multi-trait marker weighted total score MWS and a reproductive performance independent score RS, and performing double-dimensional grouping; further screening feasible pairing combinations based on expected inbreeding coefficients of offspring, a reproductive synchronization index RSI and expected MWS of offspring, combining a favorable allele complement index FACI and trait coverage balance correction to determine a mating combination, and realizing the multi-trait collaborative breeding of Plectropomus leopardus.

Description

Technical Field

[0001] This invention relates to the field of selection and mating breeding technology, and in particular to a multi-trait weighted selection and mating breeding method for leopard gill spiny perch. Background Technology

[0002] Leopard sea bass, commonly known as the Eastern Star Grouper, is an important marine fish species artificially cultured in southern my country. With the gradual establishment of broodstock cultivation, artificial spawning induction, seedling breeding, and factory farming techniques, the production method of leopard sea bass has shifted from primarily relying on wild resources to artificial breeding and sustainable utilization of cultured populations. Song Yu et al., in their paper "Research Progress and Prospects on Artificial Breeding and Culture of Leopard Sea Bass and Breeding Technology," systematically summarized the artificial breeding, seedling cultivation, culture models, genomic resources, and molecular breeding techniques of this species, and documented the application of SNP chips and genomic selection technologies in leopard sea bass breeding research. Li Wensheng et al., in their paper "Factory-based Indoor Breeding and Early Growth and Development Characteristics of Leopard Sea Bass," further reported on the factory-based indoor breeding and early growth and development of leopard sea bass. These studies indicate that leopard sea bass already possesses the production conditions for artificial breeding and genetic improvement, but the growth, body color, and reproductive performance of cultured populations still require continuous selection and improvement.

[0003] Current methods for multi-trait breeding in aquatic animals typically employ phenotypic, pedigree, or molecular marker-based parent selection. For example, Chinese patent application CN1957684A discloses a method for developing selection indices in multi-trait breeding of Chinese shrimp. This method uses the best linear unbiased prediction method to calculate the breeding values ​​for each trait, standardizes the breeding values, and then weights them according to trait importance to form selection indices. Chinese patent application CN103070119B discloses a group-based selection breeding method for aquatic animals, which uses group selection indices to screen breeding groups and individuals, and establishes mating groups accordingly. For leopard-gill sea bass, Chinese patent application CN116694785A discloses 75 growth-related SNP molecular markers and related genotype combinations, which can be used for marker-assisted breeding of rapid growth traits. Chinese patent application CN115992265A discloses a whole-genome liquid phase chip for grouper, which can be used for grouper genome selection, genome-wide association analysis, and germplasm resource improvement. As a result, existing technologies have developed into multi-trait selection based on phenotypic breeding values, parental pairing based on groups, marker-assisted selection based on trait-related SNPs, and genomic typing based on high-density SNP loci.

[0004] However, the aforementioned technologies primarily address one aspect of multi-trait phenotypic weighting, group selection, single-trait marker screening, or high-density genotyping. When using linear weighting of breeding values ​​to form selection indices, it's difficult to handle duplicate scoring caused by the same SNP marker simultaneously associated with multiple traits, and it cannot reflect potential genetic antagonism after the aggregation of favorable alleles for different traits. Published SNP markers for leopard-gill perch mainly target growth traits; growth, body color, and reproductive traits are not included in the molecular weighted scoring, nor are genetically independent validation populations used to sequentially validate the effect direction, significance reproducibility, and effect value stability of candidate markers. While grouper whole-genome liquid microarrays can provide high-density genotypic data, they do not further address how to convert validated markers into low-cost targeted panels, how to balance inbreeding levels and reproductive synchronicity in offspring, and how to adjust parental complementarity based on differences in favorable allele coverage for different traits. Therefore, existing methods still struggle to simultaneously achieve balanced improvement of multiple traits, cost control of molecular detection, parental genetic complementarity, and practical reproductive feasibility. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a multi-trait weighted selection and breeding method for leopard gill spiny perch. This invention solves the problems that existing methods still cannot simultaneously achieve balanced improvement of multiple traits, cost control of molecular detection, genetic complementation of parents, and feasibility of actual breeding.

[0006] To achieve the above objectives, the present invention provides the following solution: A multi-trait weighted selection breeding method for leopard-gill spiny perch includes: Multitrait GWAS analysis was performed on the GWAS-targeted population to obtain candidate SNP markers and effect values; The candidate SNP markers were validated in a genetically independent validation population, and the effect values ​​of the two populations were combined to obtain the validated markers and the combined additive effect values, so as to construct a targeted typing panel; Genotyping of candidate parent populations is performed using the targeted genotyping panel to obtain genotype data, and pleiotropic markers are identified. The effects of the pleiotropic markers are combined into a single contribution value. Based on the genotype data, the combined additive effect value, and the target trait weight, the weighted total score (MWS) and the reproductive trait marker group score are calculated to perform trait antagonism correction on the MWS. The reproductive performance independent score RS is calculated based on the reproductive trait marker group scores and reproductive phenotype values, and the parents participating in mating are determined by two-dimensional grouping based on the MWS corrected for trait antagonism and the RS. Based on the identified parents participating in the selection, the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of the offspring of the male-female pairing were calculated according to the genotype data, historical data on peak days of egg laying or ejaculation, and the MWS of both males and females, respectively, and feasible pairing combinations were screened. The favorable allele complementation index (FACI) and the favorable allele coverage of each target trait in the offspring are calculated based on the genotype data. The FACI is then adjusted based on the coverage differences to determine the optimal pairing combination and proceed with pairing and breeding.

[0007] A multi-trait weighted selection breeding system for leopard-gill spiny perch includes: The GWAS analysis module is used to perform multi-trait GWAS analysis on GWAS-located populations to obtain candidate SNP markers and effect values. The marker validation and panel construction module is used to validate the candidate SNP markers in a genetically independent validation population, merge the effect values ​​of the two populations, obtain the validated markers and the merged additive effect values, and construct a targeted typing panel. The parental scoring module is used to perform genotyping on the candidate parental population using the targeted genotyping panel to obtain genotype data, identify pleiotropic markers, combine the effects of the pleiotropic markers into a single contribution value, and calculate the multi-trait marker weighted total score (MWS) and reproductive trait marker group score based on the genotype data, the combined additive effect value, and the target trait weight, so as to perform trait antagonism correction on the MWS. The two-dimensional clustering module is used to calculate the reproductive performance independent score RS based on the reproductive trait marker group score and reproductive phenotype value, and to perform two-dimensional clustering based on the MWS corrected for trait antagonism and the RS to determine the parents participating in mating; The pairing combination screening module is used to screen feasible pairing combinations based on the determined parents participating in the selection, according to the genotype data, historical oviposition or spermation peak day data and the MWS of both male and female parents, to calculate the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of the offspring of the male and female pairing combinations. The selection and matching determination module is used to calculate the favorable allele complementation index (FACI) and the favorable allele coverage of each target trait in the offspring based on the genotype data, and to correct the FACI based on the coverage difference, thereby determining the selection and matching combination and carrying out pairing and breeding.

[0008] The present invention discloses the following technical effects: This invention provides a multi-trait weighted selection breeding method for leopard-gill spiny perch. Through a two-stage design of "one-time high-density GWAS + independent validation → low-cost targeted panel transformation," this invention reduces the cost of single-sample molecular testing per breeding iteration from 200-800 yuan (whole genome selection) to 10-80 yuan (targeted panel genotyping). For a breeding farm with 1000 parent stock, the cost of testing per generation is reduced from 200,000-800,000 yuan to 10,000-80,000 yuan, making the molecular marker-driven precision breeding method truly economically feasible for widespread application in breeding farms. This invention introduces an independent validation population to perform triple screening of GWAS candidate markers, establishing a quality control barrier from "statistical association" to "breeding usability" at the methodological level, which significantly reduces the risk of false positive markers and population-specific markers entering breeding applications; This invention innovatively introduces a three-item synergistic optimization mechanism at the molecular marker scoring level, which solves the imbalance problem that may occur in the traditional weighted index method, where "some traits improve significantly while others stagnate or even regress", from the root of the scoring formula design.

[0009] Mechanism one involves the redistribution of weights for pleiotropic markers. Existing comprehensive selection index methods (such as Comparative Example 1) are based on the assumption of "linear summation after independent weighting of each trait," which faces a structural flaw at the molecular level: if a SNP simultaneously affects both growth and body color (pleiotropic markers), in a system where scores are based on independent trait evaluation, the effect of that marker will be repeatedly included in the total score, effectively artificially increasing the weight of that locus. This invention corrects this structural bias at the scoring formula level by identifying pleiotropic markers and combining their effects into a single contribution value according to weights, making the weight allocation of individual scores more accurately reflect the relative importance of each trait in the breeding objectives.

[0010] Mechanism two involves correction for the trait antagonism coefficient (TAC). Existing methods assume that the genetic relationship between traits can be fully expressed through a linear combination of economic weights, neglecting the genetic antagonism at the molecular level. That is, when certain advantageous alleles aggregate, the gains are not additive but cancel each other out. This invention introduces TAC to pre-estimate the degree of antagonism between trait pairs in the validation population. When TAC < 0.8, a product correction is applied to the MWS, enabling the score to capture and reflect the negative genetic correlation between traits.

[0011] Mechanism three involves trait coverage balance correction. Most existing mating methods prioritize maximizing the overall genetic value of offspring, potentially leading to biased pairings where offspring have over 90% coverage of favorable alleles for one trait but only 50% for another. This invention introduces the trait coverage variation coefficient into the FACI calculation. Trait coverage is used as a penalty term, which puts pairs with large differences in coverage of traits at a disadvantage in the optimization ranking, thus ensuring balanced improvement of multiple traits from the algorithm level. This invention incorporates the reproductive traits of the leopard gill sea bass (female egg production and egg diameter, male sperm motility and sperm density) into a weighted scoring system as formal breeding target traits, making genetic improvement of reproductive traits possible through method design. This invention introduces the Reproductive Synchronization Index (RSI) as a hard constraint in the mating model, so that the output mating scheme not only pursues the optimal solution in genetics, but also ensures its feasibility in actual artificial breeding operations. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of a multi-trait weighted selection and mating breeding method for leopard gill spiny perch provided in an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown, this invention provides a multi-trait weighted selection breeding method for leopard-gill spiny perch, comprising: Step 100: Perform multi-trait GWAS analysis on the GWAS-locating population to obtain candidate SNP markers and effect values; Step 200: Validate the candidate SNP markers in a genetically independent validation population, merge the effect values ​​of the two populations, obtain the validated markers and the merged additive effect values, and construct a targeted typing panel; Step 300: Genotyping the candidate parent population using the targeted genotyping panel to obtain genotype data, identifying pleiotropic markers, merging the effects of the pleiotropic markers into a single contribution value, and calculating the multi-trait marker weighted total score (MWS) and reproductive trait marker group score based on the genotype data, the merged additive effect value, and the target trait weight, so as to perform trait antagonism correction on the MWS. Step 400: Calculate the reproductive performance independent score RS based on the reproductive trait marker group scores and reproductive phenotype values, and perform two-dimensional grouping based on the MWS corrected for trait antagonism and the RS to determine the parents participating in mating; Step 500: Based on the identified parents participating in the selection, calculate the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of the offspring of the male-female pairing combination according to the genotype data, historical data on peak days of egg laying or sperm ejaculation, and the MWS of both males and females, and screen feasible pairing combinations. Step 600: Calculate the favorable allele complementation index (FACI) and the favorable allele coverage of each target trait in the offspring based on the genotype data, and correct the FACI according to the coverage difference to determine the selected pairing and carry out pairing and breeding.

[0017] Specifically, the panel design consists of: The panel consists of detection primers or probes with 30-300 SNP markers, wherein the SNP markers are selected from significantly associated markers of the following traits: growth traits (body weight, body length), body color traits (body surface redness a). Values, color uniformity) and reproductive traits (female egg production, egg diameter, male sperm motility, sperm density).

[0018] Source marked: All SNP markers were screened through triple validation in independent validation populations (consistency of effect direction, significance recurrence, and effect size stability of 0.5-2.0), and effect sizes were combined using the inverse variance weighting method.

[0019] Marker structural features: Each trait retains 5-10 markers; pleiotropic markers (significantly associated with ≥2 traits) account for no more than 15% of the total number of panel markers; markers are evenly distributed on chromosomes, with the number of markers on a single chromosome not exceeding 20% ​​of the total number of panels.

[0020] Fractal platform: KASP, PACE, multiplex PCR amplicon sequencing, MassARRAY, or targeted capture sequencing.

[0021] Application method: Genomic DNA was extracted from the individuals of the leopard gill spiny perch to be tested, and genotyping was performed using the panel to obtain genotype data.

[0022] Furthermore, a multi-trait GWAS analysis was performed on the GWAS-targeted population to obtain candidate SNP markers and effect sizes, including: The phenotypic values ​​of the target traits in each individual in the GWAS-mapped population were determined, and whole-genome resequencing was performed on each individual to obtain whole-genome SNP genotype data. The target traits include: growth traits, body color traits, and reproductive traits. Using the target phenotypic values ​​and the whole-genome SNP genotype data as the analysis objects, a mixed linear model was used to perform single-trait GWAS analysis and pleiotropic multi-trait GWAS analysis to screen SNP markers that reached the whole-genome significance level. Based on the effect direction of the screened SNP markers, favorable alleles are determined, the screened SNP markers are identified as candidate SNP markers, and the effect value of the candidate SNP markers is obtained.

[0023] Specifically, GWAS localization population construction and multi-trait GWAS analysis: Requirements for constructing a GWAS target population: The GWAS target population should meet the following conditions: ① The sample size should be no less than 500 individuals to ensure that the detection power for intermediate-effect markers (explaining phenotypic variance ≥ 2%) reaches more than 80%; ② The individuals should come from at least two different geographical populations or farmed populations to ensure the representativeness of the genetic background; ③ There should be no full-sibling relationship among individuals in the population (kinship coefficient < 0.25) to improve the statistical power of the association analysis.

[0024] Multi-trait phenotypic determination: The following target trait phenotypic values ​​were measured in all individuals of the target population: Growth traits: ①Weight: Weigh using an electronic balance, accurate to 0.1g; ② Body length: Measure the straight-line distance from the snout to the base of the caudal fin using a measuring board, accurate to 0.1 cm; ③ Daily weight gain: Under standardized breeding conditions, record the weight gain per unit time.

[0025] Body color characteristics: ① Red chromaticity a value on the body surface: Using a portable colorimeter, the a value in the CIELAB color space was measured at three fixed points (the starting point of the first dorsal fin, the center of the dorsal fin, and the end of the dorsal fin) above the lateral line on both sides of the fish's body, and the average value of the three points was taken. Among them, the a value is a channel parameter in the CIELAB color space defined by the International Commission on Illumination, which represents the chromaticity unit. The +a direction represents red. The larger the value, the deeper / more saturated the red. ② Color spot uniformity score: Under standard light source, the uniformity of the distribution of color spots on the fish body is subjectively scored from 1 to 5 points (1 point = extremely uneven, with a large number of color spots missing or clustered; 5 points = very uniform, with color spots of consistent size and even distribution). The median score is taken as the color spot uniformity score of the individual.

[0026] Reproductive traits (female): ① Single spawning quantity: Under artificial spawning conditions, all eggs from a single female fish spawned in one go are collected, weighed, and divided by the female fish's body weight, expressed in g / kg body weight, accurate to 0.1g / kg; ② Average egg diameter: Randomly select no less than 50 eggs, measure the egg diameter under a stereomicroscope, and take the average value, accurate to 1μm; ③ Number of days of spawning: Record the number of days the female fish continues to spawn within a breeding cycle.

[0027] Reproductive traits (male): ① Semen volume: Gently press the male fish's genital opening, collect the semen with a micropipette, and measure the volume to an accuracy of 0.01 mL; ② Sperm motility: Fresh semen was collected and observed under a microscope to calculate the percentage of rapidly progressively motile sperm in the total sperm count; ③ Semen density: Measured using a hematology analyzer or sperm density analyzer, expressed as ×10⁻⁶. 9 Quantities / mL are expressed as follows; ④ Induction response rate: Record the proportion of male fish that successfully ejaculated after artificial induction out of the total number of male fish that were induced to spawn.

[0028] Stress resistance traits (optional): Properties include high temperature resistance, low temperature resistance, and disease resistance.

[0029] Among them, growth traits, body color and reproductive traits do not need to be completed at the same time. For example, growth traits can be collected at several key growth time points (6 months of age, harvest weight, etc.), while the phenotype of reproductive traits can be recorded after entering the reproductive period.

[0030] Whole genome resequencing and SNP genotyping: Genomic DNA was extracted from all individuals in the target population and whole-genome resequencing was performed to obtain genotype data.

[0031] Genome-wide association analysis: Using the phenotypic value of each target trait as the dependent variable and the SNP genotype (0 / 1 / 2 encoding) as the independent variable, a mixed linear model was used for GWAS. The software used was GEMMA (v0.98.3 or later, combining single-trait GWAS analysis and pleiotropic multi-trait GWAS analysis) or GCTA (v1.94.1 or later). The significance threshold was Bonferroni corrected for P < 0.05 (i.e., 0.05 / number of effective SNPs). For SNP markers that reached genome-wide significance, the following were recorded: ① Marker ID and physical location (chromosome number, base position); ② Favorable alleles: determined by the direction of the effect, alleles that increase the phenotypic value in the direction of the breeding target were considered favorable alleles; ③ Additive effect value 'a': the average change in phenotypic value when the marker replaced one allele; ④ Effect standard error (SE).

[0032] Furthermore, the candidate SNP markers are validated in a genetically independent validation population, and the effect sizes of the two populations are combined to obtain the validated markers and the combined additive effect size, in order to construct a targeted typing panel, including: The phenotypic values ​​of the target traits in the genetically independent validation population were determined, and the candidate SNP markers were targeted and genotyped. Determine the favorable allele orientation of the candidate SNP marker in the genetically independent validation population, and remove candidate SNP markers that are inconsistent with the favorable allele orientation in the GWAS mapping population. Single-label linear regression association analysis was performed on the retained candidate SNP markers and their corresponding target traits, and candidate SNP markers with nominal P-values ​​greater than or equal to the Bonferroni threshold were removed. Calculate the ratio of the effect size of the remaining candidate SNP markers in the genetically independent validation population to the effect size in the GWAS mapping population, and retain candidate SNP markers with an effect size ratio of 0.5 to 2.0 to obtain the validated markers; The effect values ​​of the validated markers in the GWAS mapping population and the genetically independent validation population were combined using the inverse variance weighting method to obtain the combined additive effect value. The validated markers were then selected from largest to smallest based on the absolute value of the combined additive effect value corresponding to each target trait to construct a targeted typing panel.

[0033] Furthermore, the targeted genotyping panel consists of detection primers or probes corresponding to 30 to 300 SNP markers, with each target trait corresponding to 3 to 15 SNP markers; The proportion of pleiotropic markers in the total number of SNP markers in the targeted typing panel shall not exceed 15%, and the number of SNP markers on a single chromosome shall not exceed 20% of the total number of SNP markers in the targeted typing panel.

[0034] Specifically, label verification by independent verification groups: Construction of the independent validation population: Construct a genetically independent validation population from the GWAS mapping population: ① Sample size: no less than 300 individuals; ② Independent origin: ③ Do not share parents with the mapping population, and come from different geographical populations or different batches of breeding populations; ④ Phenotypic determination: Measure the phenotypic values ​​of all target traits using the same methods as the mapping population.

[0035] Targeting of candidate markers: Design targeted genotyping primers for GWAS significant markers (multiplex PCR + Sanger sequencing or small-scale KASP can be used first) to genotype all individuals in the validation population.

[0036] Triple verification screening: For each candidate marker, the following validations were performed: ① Validation 1: Consistency test of effect direction. The favorable allele direction for the marker was calculated in the validation population and compared with that of the localization population. Markers with inconsistent directions were directly eliminated. ② Validation 2: Significance recurrence test. In the validation population, a single-marker linear regression association analysis was performed between the marker and the corresponding trait, and the nominal P-value was calculated. Markers with P ≥ Bonferroni threshold were eliminated. ③ Validation 3: Effect size stability assessment. The effect size of the validation population was calculated. With the positional group effect value The ratio. Markers with a ratio outside the range of 0.5-2.0 are removed.

[0037] Effect size pooling: For markers that passed triple validation, the effect sizes of the discovery and validation populations were combined using the inverse variance weighting (IVW) method. The standard error of the combined effect size is: In the formula, This is the combined additive effect value; This represents the additive effect value of the marker in the GWAS-located population; This represents the additive effect value of the marker in the validation population; The standard error for g_ is the location of the effect size in the population and reflects the accuracy of the effect size estimation. It is output by the GWAS mixed linear model. , which is the standard error of g_validation, reflects the accuracy of effect size estimation in the validation population, and is output by the regression model during the validation phase; is the square of the standard error, and statistically, it is the variance of the effect estimate.

[0038] Low-cost targeted typing panel construction: Tag filtering and Panel design: From the validated markers, they are sorted in descending order of the absolute value of the combined effect of each trait, with 5-10 markers retained for each trait (preferably the top 5-8 markers by effect value) to form a targeted genotyping panel. Panel parameter ranges: ① Total number of markers in the panel: 30-300, preferably 50-200, optimal 80-150; ② Number of markers per trait: 3-15, preferably 5-10, optimal 5-8; ③ Markers are evenly distributed across chromosomes, avoiding concentration of all markers in a few chromosomal regions.

[0039] Fractal platform selection: The targeted genotyping platform is selected from one of the following: ① KASP (Kompetitive Allele Specific PCR): suitable for scenarios with 30-100 markers, costing 30-60 yuan per sample, with low equipment requirements, suitable for small and medium-sized breeding farms; ② PACE (PCR Allele Competitive Extension): a KASP-like technology with comparable costs; ③ Multiplex PCR amplicon sequencing: suitable for scenarios with 50-300 markers, costing 50-100 yuan per sample, providing more information; ④ MassARRAY nucleic acid mass spectrometry genotyping: suitable for scenarios with 30-50 markers, offering extremely high genotyping accuracy; ⑤ Targeted capture sequencing: suitable for scenarios with 100-500 markers, costing 80-150 yuan per sample. After the panel is constructed, at least 100 individuals with known genotypes should be used to verify the consistency between panel genotyping and high-density genotyping results. Only when the consistency rate is ≥95% can it be used for breeding applications.

[0040] Furthermore, the candidate parent population is genotyped using the targeted genotyping panel to obtain genotype data, and pleiotropic markers are identified. The effects of the pleiotropic markers are combined into a single contribution value. Based on the genotype data, the combined additive effect value, and the target trait weights, the multi-trait marker weighted total score (MWS) and the reproductive trait marker group score are calculated to perform trait antagonism correction on the MWS, including: Based on the genotype data, the copy numbers of the favorable alleles corresponding to the homozygous, heterozygous, and non-homozygous favorable alleles are determined to be 2, 1, and 0, respectively. The target trait weights are set according to the economic importance of the breeding objectives; SNP markers that are significantly associated with two or more target traits in both the multi-trait GWAS analysis and the genetically independent validation population are identified as pleiotropic markers. Based on the absolute value of the combined additive effect of the pleiotropic markers on each associated target trait and the corresponding target trait weights, the effects of the pleiotropic markers are combined into a single contribution value. The effect contribution of markers within each target trait group is determined based on the favorable allele copy number, the combined additive effect value, and the single contribution value. The effect contribution of markers within each target trait group is normalized according to the sum of the effect values ​​within the group, and the weighted total score (MWS) of multi-trait markers and the score of reproductive trait marker groups are calculated in combination with the corresponding target trait weights. The multi-effect markers are classified according to the target trait with the highest weight among the associated target traits. The verified markers are subjected to pairwise interaction effect analysis, and the MWS is corrected for trait antagonism based on the results of the pairwise interaction effect analysis.

[0041] Furthermore, trait antagonism correction is performed on the MWS, including: In the genetically independent validation population, a full factorial model including interaction terms is established for each pair of validated markers. Based on the full factorial model, the gain when the advantageous markers corresponding to the two target traits are aggregated separately and the total gain when aggregated simultaneously are determined. The ratio of the total gain to the sum of the gains when the advantageous markers of the two target traits are aggregated separately is determined as the trait antagonism coefficient (TAC). When the trait antagonism coefficient TAC is less than 0.8, the two target traits are identified as antagonistic trait pairs, and the MWS of candidate parents carrying the favorable alleles corresponding to the antagonistic trait pairs is corrected using the trait antagonism coefficient TAC. When there are multiple antagonistic trait pairs, the trait antagonism coefficients (TAC) corresponding to each antagonistic trait pair are multiplied together to obtain the MWS after trait antagonism correction.

[0042] Specifically, parental population targeted typing and multi-trait marker weighted scoring: Parental population targeted typing: For the current breeding candidate parent population, fin DNA was extracted, and genotyping was performed using a constructed low-cost targeted genotyping panel to obtain the genotype of each individual at all panel marker sites (2 points for homozygous favorable alleles, 1 point for heterozygous alleles, and 0 points for homozygous non-favorable alleles).

[0043] Trait weight setting: Weighting coefficients are assigned to each target trait based on its economic importance as a breeding objective: ; Weight parameter range: : 0.20-0.50, preferred 0.30-0.40, optimal 0.35; : 0.15-0.45, preferred 0.25-0.35, optimal 0.30; : 0.10-0.40, preferred 0.20-0.30, optimal 0.25; : 0.05-0.20, preferred 0.08-0.15, optimal 0.10.

[0044] Multi-effect marker recognition and processing: For each marker in the panel, pleiotropic identification is performed: if a SNP marker is significantly associated with two or more target traits in both the GWAS analysis and the validation population analysis (P < 0.05 in the validation population), then the marker is considered a pleiotropic marker. For pleiotropic markers, their effect contributions are not repeatedly counted in the MWS after being split by trait, but are instead combined into a single contribution value in the following manner: ; In the formula, represents the copy number of the favorable allele carried by an individual at marker m, taking values ​​of 0, 1, or 2. 0: The individual is homozygous for the non-favorable allele at that marker locus, meaning both alleles are non-favorable; 1: The individual is heterozygous, carrying one favorable allele and one non-favorable allele; 2: The individual is homozygous for the favorable allele, meaning both alleles are favorable. In the formula for calculating the contribution value of pleiotropic markers... Its function is to multiply the pooled effect value of the marker by the individual's actual genotype dose; For the set of traits associated with label m, To label the absolute value of the combined additive effect of m on trait t, is the weighting coefficient for trait t.

[0045] Calculation of weighted total score for multiple trait markers: For each individual, calculate the multi-trait marker weighted total score (MWS): In the formula, : Weighted total score of multiple trait markers for individual j; T: Total number of target traits; : Weighting coefficient of trait t; : Number of markers in the Panel associated with trait t (pleiotropic markers are classified according to the highest weight among the associated traits); : The number of copies of the favorable allele carried by individual j on marker m (0, 1 or 2); : The absolute value of the combined additive effect of marker m (for multi-effect markers, the combined effect value is used). The contribution of each marker within each trait group is normalized by the sum of the within-group effect values ​​to ensure that the difference in the number of different trait markers does not affect the substantive meaning of the weight allocation.

[0046] Trait antagonism correction: Perform pairwise interaction effect analysis on all validated markers: (1) In the validation population, a full factorial model including interaction terms is built for each pair of labels: (2) Define the trait antagonism coefficient (TAC): The numerical meanings of TAC are: ① TAC > 1: Synergistic gain (when two advantageous markers of two traits aggregate, the total gain is greater than the sum of their individual gains); ② TAC ≈ 1: The effects are independently additive; ③ TAC < 1: Antagonism exists (gains cancel each other out during aggregation).

[0047] When TAC < 0.8, a significant antagonistic relationship is considered to exist between the two traits. For individuals carrying the favorable alleles for both traits (x_j ≥ 1 on both marker groups), corrections are applied on MWS: ; In the formula, This is an indicator function, taking the value 1 if individual j carries the favorable alleles for traits t1 and t2 simultaneously, and 0 otherwise. When multiple antagonistic pairs exist, the TACs are multiplied together.

[0048] Furthermore, the reproductive performance independent score RS is calculated based on the reproductive trait marker group scores and reproductive phenotype values, and the parents participating in mating are determined by two-dimensional grouping based on the MWS corrected for trait antagonism and the RS, including: The scores of the reproductive trait marker group are Z-score standardized to obtain the standardized score of the reproductive trait marker group, and the reproductive phenotype values ​​are standardized to obtain the standardized value of the reproductive phenotype. The standardized score of the reproductive trait marker group is multiplied by the marker weight α, the standardized value of the reproductive phenotype is multiplied by 1-α, and the two products are added together to obtain the reproductive performance independent score RS, where the marker weight α is 0.5 to 0.9; Using the median of the MWS (moderately adjusted for trait antagonism) and the median of the RS (independent reproductive performance score) as the dividing lines, the candidate parent population was divided into four categories: Category A core propagation parents with both high MWS and RS, Category B genetic reserve parents with high MWS and low RS, Category C production reserve parents with low MWS and high RS, and Category D culled parents with both low MWS and RS. The Category A core propagation parents and the Category B genetic reserve parents were then selected as the parents to participate in the mating selection.

[0049] Independent score for reproductive performance: For each candidate parent, calculate the reproductive performance independent score RS: In the formula, ① α: label weight, with a value range of 0.5-0.9, preferably 0.6-0.8, and optimally 0.7; ② Reproductive standardization: The Z-score standardized (mean 0, standard deviation 1) of an individual's score on the reproductive trait marker group; ③ Phenotypic standardization: The standardized value of the reproductive phenotype for this individual. For females, the reproductive phenotype value = weighted sum of spawning score + egg diameter score (each weighted at 0.5); for males, the reproductive phenotype value = sperm motility score × 0.6 + semen density score × 0.4. Each component is standardized before weighting.

[0050] Two-dimensional clustering: Using MWS (or MWS correction, if antagonistic correction) as the horizontal axis and RS as the vertical axis, and the median of the two (or the quantile threshold set according to the breeding objectives) as the dividing line, the candidate parents are divided into four categories, as shown in Table 1: Table 1

[0051] Furthermore, based on the genotype data, historical peak spawning or ejaculation day data, and the mean time between spawning (MWS) of both male and female partners, the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of offspring for each paired couple are calculated to screen feasible pairing combinations, including: Based on the genotype data of the parents participating in the selection, the VanRaden method is used to calculate the kinship coefficient matrix. Based on the kinship coefficient matrix, the expected inbreeding coefficient of each male-female pairing combination is calculated. The upper limit of the expected inbreeding coefficient of the offspring is 0.05 to 0.20. The peak breeding day is determined based on the historical spawning or ejaculation peak day data of the parent plants participating in the selection process within at least two complete breeding cycles. A breeding peak day interval is formed by extending forward and backward by 3 days from the peak breeding day. The ratio of the number of overlapping days between the breeding peak day intervals of the male and female to the total span of the two breeding peak day intervals is determined as the reproductive synchronization index (RSI). Pairing combinations with a reproductive synchronization index (RSI) less than 0.5 are eliminated. The average value of the MWS of both male and female parents is determined as the expected MWS of the offspring. Pairings of male and female parents whose expected MWS of the offspring is lower than the average MWS of the candidate parent population are excluded. The male-female pairings that simultaneously satisfy the constraints of the expected inbreeding coefficient of offspring, the reproductive synchronization index (RSI), and the expected MWS of offspring are determined as feasible pairings.

[0052] Specifically, multiple constraints are considered when selecting configurations: Constraint calculation for optional combinations: For all male-female pairings within and between parents of type A and type B, calculate the following three constraint indicators one by one: Constraint 1: Expected inbreeding coefficient F of offspring. Based on panel marker genotype data, the VanRaden method was used to calculate the kinship coefficient matrix, and the expected inbreeding coefficient of offspring was calculated for each pair (i,j). Constraints: . The value range is 0.05-0.20, with a preferred range of 0.08-0.15 and an optimal value of 0.10.

[0053] Constraint 2: Reproductive Synchronization Index (RSI). Based on historical spawning or spermation peak days data from at least two complete reproductive cycles for each parent, determine its peak reproductive day. Extend the peak day by 3 days before and after, forming a 7-day range for the individual's peak reproductive days. For a female-male pair (i,j), calculate: ; RSI values ​​range from 0 to 1. 0 indicates no overlap, and 1 indicates complete overlap. Constraints: . The value range is 0.3-0.8, with a preferred range of 0.5-0.7 and an optimal value of 0.5. Pairs with an RSI < 0.5 are directly eliminated and will not proceed to further optimization.

[0054] Constraint 3: Expected MWS of Offspring. The midparental MWS of both parents (i.e., their mean) is used as a preliminary estimate of the offspring MWS, requiring that this value not be lower than the average level of the candidate parent population (or the mean MWS of A+B parental lines minus one standard deviation). Pairs with values ​​below the threshold are eliminated.

[0055] Calculation of favorable allele complementation index: For each pair (i,j), calculate the favorable allele complementation index (FACI): ; In the formula, the numerator is the number of loci in which the offspring of the pairing (i,j) acquire at least one favorable allele (i.e., the offspring genotype is heterozygous or the favorable allele is homozygous) among all marker loci in the Panel. Pleiotropic markers are counted as one locus in both the numerator and denominator of FACI, and are not counted repeatedly according to the number of traits they are associated with.

[0056] Trait Cover Balance Correction: Calculate the favorable allele coverage of each target trait (growth, body color, reproduction, etc.) in the offspring (i.e., the proportion of offspring that inherit at least one favorable allele from the associated marker for that trait), and calculate the coefficient of variation (CV) of each trait coverage. ; In the formula, μ trait coverage: the average coverage of favorable alleles for each target trait; σ trait coverage: the standard deviation of favorable allele coverage for each target trait.

[0057] Apply trait cover balance correction to FACI: ; The effect of this correction is that when the coverage of favorable alleles for different traits in the offspring of a pair varies significantly (e.g., 90% growth coverage, 85% body color coverage, 50% reproduction coverage, CV=0.23), the FACI is reduced by approximately 23%, putting the pair at a disadvantage in subsequent optimization and ranking. This ensures that the selection results progress evenly across multiple traits, avoiding unbalanced pairings where one trait is superior while another is severely deficient.

[0058] Furthermore, based on the genotype data, the favorable allele complementarity index (FACI) and the favorable allele coverage of each target trait in the offspring are calculated for the feasible pairing combinations. The FACI is then adjusted based on the coverage differences to determine the optimal pairing combinations and proceed with pairing and reproduction, including: Based on the genotype data, determine the number of sites in which the offspring of each feasible pairing combination obtain at least one favorable allele from all marker sites in the targeted typing panel. The ratio of the number of sites to the total number of markers in the targeted typing panel is determined as the favorable allele complementation index (FACI), wherein pleiotropic markers are counted as one marker site. The ratio of the number of markers for each target trait whose offspring acquire at least one favorable allele to the number of markers for the corresponding target trait is calculated to obtain the favorable allele coverage rate of each target trait in the offspring. Calculate the mean and standard deviation of the favorable allele coverage corresponding to each target trait, determine the ratio of the standard deviation to the mean as the trait coverage coefficient of variation, and multiply the difference obtained by subtracting the trait coverage coefficient of variation from 1 by the favorable allele complementation index FACI to obtain the corrected FACI; Sort the feasible pairing combinations in descending order according to the corrected FACI, determine the mating combination by using a greedy algorithm or the Hungarian algorithm, and perform artificial induced spawning and paired spawning according to the mating combination.

[0059] Specifically, three constraints are solved simultaneously for optimal pairing: Method process: Step 1: Eliminate combinations that do not satisfy any hard constraint ( or or the expected offspring MWS is lower than the threshold), and retain the feasible combination set S.

[0060] Step 2: Calculate the FACI corrected value for all combinations in S, and sort them in descending order.

[0061] Step 3: Use the greedy algorithm or the Hungarian algorithm to solve for the optimal pairing: (1) Greedy algorithm: Sequentially select the combination with the largest FACI corrected value, remove the matched female fish and male fish from the to-be-paired set, and continue the selection until all female fish are paired or there are no optional male fish left; (2) Hungarian algorithm (when the number of females and males is approximately equal): Take the FACI corrected value as the cost matrix (converted to a minimization problem) to solve for the global optimal one-to-one matching.

[0062] Step 4: When the number of functional male fish is less than the number of female fish N_f, one male fish is allowed to mate with multiple female fish. The upper limit of the pairing number for a single male fish is ceil(N_f / N_m)+1, preferably 3 pairs.

[0063] Step 5: Male scarcity weight adjustment (optional). When N_m<N_f / 3, assign a weighting coefficient to the breeding trait contribution of the male fish (i.e., the breeding trait marker component score and breeding phenotypic value of the male fish itself) in the solving algorithm . The value ranges from 1.2 to 2.0, preferably 1.3 to 1.8, and most preferably 1.5. This adjustment ensures that scarce male fish with excellent reproductive performance get more pairing opportunities during the solving process.

[0064] Step 6: Output a mating schedule, which includes the optimal male fish number corresponding to each female fish, the FACI corrected value, the pairing priority (Class A pairing takes precedence over Class B pairing), and suggestions for production batch arrangement.

[0065] Optional implementation and offspring tracking verification: Artificial breeding procedures: Artificial ovulation and pairing for egg laying were carried out according to the selection and mating plan.

[0066] Genotyping verification of offspring: Randomly sample the offspring population, with a sample size of no less than 100 tails, and perform genotyping using the same low-cost targeted genotyping panel. Calculate the following metrics: (1) The Pearson correlation coefficient between the actual favorable allele aggregation rate and the predicted FACI should be ≥0.70; (2) The Spearman rank correlation coefficient between the sub-representative values ​​and the MWS should reach the statistical significance level (P<0.05).

[0067] Panel iteration update trigger conditions: The Panel is triggered to update iteratively when any of the following conditions are met: (1) The correlation coefficient between the sub-representative value and MWS decreases significantly for two consecutive generations (the decrease is >20%), or the P value is no longer significant, indicating that the predictive power of the marker combination has declined. (2) After 3-5 generations of selection, the frequency of favorable alleles marked in the panel increased significantly (>0.80). The genetic variation at these marker loci within the population was basically fixed, the differences between individuals were small, the distinguishing ability of the markers decreased significantly, and the selection response approached the plateau period. (3) New target traits are incorporated into breeding objectives.

[0068] Update method: Perform GWAS analysis on the current population again (using historical phenotypic records and newly acquired resequencing data) to identify new significant markers, and supplement or replace the markers in the panel after validation and screening.

[0069] This embodiment also provides a multi-trait weighted selection and mating breeding system for leopard-gill spiny perch, including: The GWAS analysis module is used to perform multi-trait GWAS analysis on GWAS-located populations to obtain candidate SNP markers and effect values. The marker validation and panel construction module is used to validate the candidate SNP markers in a genetically independent validation population, merge the effect values ​​of the two populations, obtain the validated markers and the merged additive effect values, and construct a targeted typing panel. The parental scoring module is used to perform genotyping on the candidate parental population using the targeted genotyping panel to obtain genotype data, identify pleiotropic markers, combine the effects of the pleiotropic markers into a single contribution value, and calculate the multi-trait marker weighted total score (MWS) and reproductive trait marker group score based on the genotype data, the combined additive effect value, and the target trait weight, so as to perform trait antagonism correction on the MWS. The two-dimensional clustering module is used to calculate the reproductive performance independent score RS based on the reproductive trait marker group score and reproductive phenotype value, and to perform two-dimensional clustering based on the MWS corrected for trait antagonism and the RS to determine the parents participating in mating; The pairing combination screening module is used to screen feasible pairing combinations based on the determined parents participating in the selection, according to the genotype data, historical oviposition or spermation peak day data and the MWS of both male and female parents, to calculate the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of the offspring of the male and female pairing combinations. The selection and matching determination module is used to calculate the favorable allele complementation index (FACI) and the favorable allele coverage of each target trait in the offspring based on the genotype data, and to correct the FACI based on the coverage difference, thereby determining the selection and matching combination and carrying out pairing and breeding.

[0070] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0071] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch, characterized in that, include: Multitrait GWAS analysis was performed on the GWAS-targeted population to obtain candidate SNP markers and effect values; The candidate SNP markers were validated in a genetically independent validation population, and the effect values ​​of the two populations were combined to obtain the validated markers and the combined additive effect values, so as to construct a targeted typing panel; Genotyping of candidate parent populations is performed using the targeted genotyping panel to obtain genotype data, and pleiotropic markers are identified. The effects of the pleiotropic markers are combined into a single contribution value. Based on the genotype data, the combined additive effect value, and the target trait weight, the weighted total score (MWS) and the reproductive trait marker group score are calculated to perform trait antagonism correction on the MWS. The reproductive performance independent score RS is calculated based on the reproductive trait marker group scores and reproductive phenotype values, and the parents participating in mating are determined by two-dimensional grouping based on the MWS corrected for trait antagonism and the RS. Based on the identified parents participating in the selection, the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of the offspring of the male-female pairing were calculated according to the genotype data, historical data on peak days of egg laying or ejaculation, and the MWS of both males and females, respectively, and feasible pairing combinations were screened. The favorable allele complementation index (FACI) and the favorable allele coverage of each target trait in the offspring are calculated based on the genotype data. The FACI is then adjusted based on the coverage differences to determine the optimal pairing combination and proceed with pairing and breeding.

2. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 1, characterized in that, A multi-trait GWAS analysis was performed on the GWAS-targeted population to obtain candidate SNP markers and effect sizes, including: The phenotypic values ​​of the target traits in each individual in the GWAS-mapped population were determined, and whole-genome resequencing was performed on each individual to obtain whole-genome SNP genotype data. The target traits include: growth traits, body color traits, and reproductive traits. Using the target phenotypic values ​​and the whole-genome SNP genotype data as the analysis objects, a mixed linear model was used to perform single-trait GWAS analysis and pleiotropic multi-trait GWAS analysis to screen SNP markers that reached the whole-genome significance level. Based on the effect direction of the screened SNP markers, favorable alleles are determined, the screened SNP markers are identified as candidate SNP markers, and the effect value of the candidate SNP markers is obtained.

3. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 1, characterized in that, The candidate SNP markers are validated in a genetically independent validation population. The effect sizes of the two populations are combined to obtain the validated markers and the combined additive effect sizes, in order to construct a targeted typing panel, including: The phenotypic values ​​of the target traits in the genetically independent validation population were determined, and the candidate SNP markers were targeted and genotyped. Determine the favorable allele orientation of the candidate SNP marker in the genetically independent validation population, and remove candidate SNP markers that are inconsistent with the favorable allele orientation in the GWAS mapping population. Single-label linear regression association analysis was performed on the retained candidate SNP markers and their corresponding target traits, and candidate SNP markers with nominal P-values ​​greater than or equal to the Bonferroni threshold were removed. Calculate the ratio of the effect size of the remaining candidate SNP markers in the genetically independent validation population to the effect size in the GWAS mapping population, and retain candidate SNP markers with an effect size ratio of 0.5 to 2.0 to obtain the validated markers; The effect values ​​of the validated markers in the GWAS mapping population and the genetically independent validation population were combined using the inverse variance weighting method to obtain the combined additive effect value. The validated markers were then selected from largest to smallest based on the absolute value of the combined additive effect value corresponding to each target trait to construct a targeted typing panel.

4. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 3, characterized in that, The targeted genotyping panel consists of detection primers or probes corresponding to 30 to 300 SNP markers, with each target trait corresponding to 3 to 15 SNP markers; The proportion of pleiotropic markers in the total number of SNP markers in the targeted typing panel shall not exceed 15%, and the number of SNP markers on a single chromosome shall not exceed 20% of the total number of SNP markers in the targeted typing panel.

5. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 1, characterized in that, Genotyping of the candidate parent population is performed using the targeted genotyping panel to obtain genotype data. Pleiotropic markers are identified, and their effects are combined into a single contribution value. Based on the genotype data, the combined additive effect value, and the target trait weights, a multi-trait marker weighted score (MWS) and reproductive trait marker group scores are calculated. Trait antagonism correction is then applied to the MWS, including: Based on the genotype data, the copy numbers of the favorable alleles corresponding to the homozygous, heterozygous, and non-homozygous favorable alleles are determined to be 2, 1, and 0, respectively. The target trait weights are set according to the economic importance of the breeding objectives; SNP markers that are significantly associated with two or more target traits in both the multi-trait GWAS analysis and the genetically independent validation population are identified as pleiotropic markers. Based on the absolute value of the combined additive effect of the pleiotropic markers on each associated target trait and the corresponding target trait weights, the effects of the pleiotropic markers are combined into a single contribution value. The effect contribution of markers within each target trait group is determined based on the favorable allele copy number, the combined additive effect value, and the single contribution value. The effect contribution of markers within each target trait group is normalized according to the sum of the effect values ​​within the group, and the weighted total score (MWS) of multi-trait markers and the score of the reproductive trait marker group are calculated in combination with the corresponding target trait weights. The multi-effect markers are classified according to the target trait with the highest weight among the associated target traits. The verified markers are subjected to pairwise interaction effect analysis, and the MWS is corrected for trait antagonism based on the results of the pairwise interaction effect analysis.

6. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 5, characterized in that, Trait antagonism correction is performed on the MWS, including: In the genetically independent validation population, a full factorial model including interaction terms is established for each pair of validated markers. Based on the full factorial model, the gain when the advantageous markers corresponding to the two target traits are aggregated separately and the total gain when aggregated simultaneously are determined. The ratio of the total gain to the sum of the gains when the advantageous markers of the two target traits are aggregated separately is determined as the trait antagonism coefficient (TAC). When the trait antagonism coefficient TAC is less than 0.8, the two target traits are identified as antagonistic trait pairs, and the MWS of candidate parents carrying the favorable alleles corresponding to the antagonistic trait pairs is corrected using the trait antagonism coefficient TAC. When there are multiple antagonistic trait pairs, the trait antagonism coefficients (TAC) corresponding to each antagonistic trait pair are multiplied together to obtain the MWS after trait antagonism correction.

7. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 1, characterized in that, The reproductive performance independent score (RS) is calculated based on the scores of the reproductive trait marker groups and the reproductive phenotype values. Parents participating in mating are then determined through two-dimensional grouping using the MWS corrected for trait antagonism and the RS, including: The scores of the reproductive trait marker group are Z-score standardized to obtain the standardized score of the reproductive trait marker group, and the reproductive phenotype values ​​are standardized to obtain the standardized value of the reproductive phenotype. The standardized score of the reproductive trait marker group is multiplied by the marker weight α, the standardized value of the reproductive phenotype is multiplied by 1-α, and the two products are added together to obtain the reproductive performance independent score RS, where the marker weight α is 0.5 to 0.9; Using the median of the MWS (moderately adjusted for trait antagonism) and the median of the RS (independent reproductive performance score) as the dividing lines, the candidate parent population was divided into four categories: Category A core propagation parents with both high MWS and RS, Category B genetic reserve parents with high MWS and low RS, Category C production reserve parents with low MWS and high RS, and Category D culled parents with both low MWS and RS. The Category A core propagation parents and the Category B genetic reserve parents were then selected as the parents to participate in the mating selection.

8. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 1, characterized in that, Based on the genotype data, historical peak spawning or ejaculation day data, and the mean time between ovulation (MWS) of both male and female partners, the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of offspring for each paired couple are calculated to screen feasible pairings, including: Based on the genotype data of the parents participating in the selection, the VanRaden method is used to calculate the kinship coefficient matrix. Based on the kinship coefficient matrix, the expected inbreeding coefficient of each male-female pairing combination is calculated. The upper limit of the expected inbreeding coefficient of the offspring is 0.05 to 0.

20. The peak breeding day is determined based on the historical spawning or ejaculation peak day data of the parent plants participating in the selection process within at least two complete breeding cycles. A breeding peak day interval is formed by extending forward and backward by 3 days from the peak breeding day. The ratio of the number of overlapping days between the breeding peak day intervals of the male and female to the total span of the two breeding peak day intervals is determined as the reproductive synchronization index (RSI). Pairing combinations with a reproductive synchronization index (RSI) less than 0.5 are eliminated. The average value of the MWS of both male and female parents is determined as the expected MWS of the offspring. Pairings of male and female parents whose expected MWS of the offspring is lower than the average MWS of the candidate parent population are excluded. The male-female pairings that simultaneously satisfy the constraints of the expected inbreeding coefficient of offspring, the reproductive synchronization index (RSI), and the expected MWS of offspring are determined as feasible pairings.

9. The method for multi-trait weighted selection and mating breeding of leopard-gill spiny perch according to claim 1, characterized in that, Calculate the favorable allele complementarity index (FACI) and the favorable allele coverage of each target trait in the offspring based on the genotype data, and adjust the FACI according to the coverage differences to determine the selected pairings and carry out pairing and breeding, including: Based on the genotype data, determine the number of sites in which the offspring of each feasible pairing combination obtain at least one favorable allele from all marker sites in the targeted typing panel. The ratio of the number of sites to the total number of markers in the targeted typing panel is determined as the favorable allele complementation index (FACI), wherein pleiotropic markers are counted as one marker site. The ratio of the number of markers for each target trait whose offspring acquire at least one favorable allele to the number of markers for the corresponding target trait is calculated to obtain the favorable allele coverage rate of each target trait in the offspring. Calculate the mean and standard deviation of the coverage of the favorable alleles for each target trait. The ratio of the standard deviation to the mean is determined as the coefficient of variation of the trait coverage. The difference obtained by subtracting the coefficient of variation of the trait coverage from 1 is multiplied by the favorable allele complementation index (FACI) to obtain the corrected FACI. The feasible pairing combinations are sorted from largest to smallest according to the modified FACI, and the selected pairing combination is determined by a greedy algorithm or a Hungarian algorithm. Artificial spawning and pairing are then carried out according to the selected pairing combination.

10. A multi-trait weighted selection and mating breeding system for leopard-gill spiny perch, characterized in that, include: The GWAS analysis module is used to perform multi-trait GWAS analysis on GWAS-located populations to obtain candidate SNP markers and effect values. The marker validation and panel construction module is used to validate the candidate SNP markers in a genetically independent validation population, merge the effect values ​​of the two populations, obtain the validated markers and the merged additive effect values, and construct a targeted typing panel. The parental scoring module is used to perform genotyping on the candidate parental population using the targeted genotyping panel to obtain genotype data, identify pleiotropic markers, combine the effects of the pleiotropic markers into a single contribution value, and calculate the multi-trait marker weighted total score (MWS) and reproductive trait marker group score based on the genotype data, the combined additive effect value, and the target trait weight, so as to perform trait antagonism correction on the MWS. The two-dimensional clustering module is used to calculate the reproductive performance independent score RS based on the reproductive trait marker group score and reproductive phenotype value, and to perform two-dimensional clustering based on the MWS corrected for trait antagonism and the RS to determine the parents participating in mating; The pairing combination screening module is used to screen feasible pairing combinations based on the determined parents participating in the selection, according to the genotype data, historical oviposition or spermation peak day data and the MWS of both male and female parents, to calculate the expected inbreeding coefficient, reproductive synchronization index (RSI), and expected MWS of the offspring of the male and female pairing combinations. The selection and matching determination module is used to calculate the favorable allele complementation index (FACI) and the favorable allele coverage of each target trait in the offspring based on the genotype data, and to correct the FACI based on the coverage difference, thereby determining the selection and matching combination and carrying out pairing and breeding.

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