A method for estimating genetic economic trait parameters of a sea perch

Abstract

The application provides a genetic economic trait parameter estimation method of Epinephelus akaara, and relates to the technical field of Epinephelus akaara.The application firstly divides Epinephelus akaara into multiple genetic clusters based on SNP information through principal component analysis and spectral clustering algorithm;then obtains the half lethal temperature and disease resistance comprehensive score of each cluster through high-temperature tolerance experiment and disease resistance experiment;carries out breeding test under different environmental conditions, calculates growth resistance in combination with environmental fitness, and obtains growth evaluation index by comprehensively considering the growth rate under the standard environment;finally, the comprehensive genetic score is calculated by integrating the growth evaluation index, the half lethal temperature and the disease resistance score, and the top 20% excellent clusters are screened out.In the excellent clusters, the individuals are initially selected according to the growth rate, heat tolerance and disease resistance phenotype data, then the families are divided by SNP information to avoid inbreeding, and finally the BLUP method is used to calculate the comprehensive breeding value, and the top 20% individuals are selected as parents.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sea grouper, in particular to a sea grouper genetic economic trait parameter estimation method. BACKGROUND

[0002] As an important economic fish, sea grouper is widely farmed due to its rapid growth and delicious meat. In traditional breeding processes, breeders often rely on experience and intuition, lacking scientific evaluation methods. This approach not only makes it difficult to accurately identify superior individuals, but also may lead to a decline in genetic diversity, reducing overall farming efficiency. In addition, environmental factors such as water temperature, salinity, and oxygen content also have a significant impact on the growth and survival rate of sea grouper, further increasing the complexity of breeding.

[0003] In recent years, with the advancement of molecular biology and computing technology, genetics has gradually increased in aquaculture. Researchers have begun to focus on using genomics and genetic marker technologies, such as single nucleotide polymorphisms, to assess the genetic potential of individuals. However, existing methods mostly focus on correlation analysis between genetic markers and traits, lacking systematic evaluation of multiple economic trait parameters. This limitation makes it difficult to effectively select and breed superior individuals in actual breeding processes.

[0004] The existing technology has the following shortcomings:

[0005] In the sea grouper farming industry, accurately evaluating and selecting individuals with superior economic traits is a core problem in breeding work. Current sea grouper genetic evaluation methods have three main limitations: first, traditional methods often focus on a single trait indicator, such as growth rate or disease resistance, ignoring the synergistic effects between traits, resulting in poor performance in actual farming environments; second, existing technology lacks consideration of environmental factors, particularly failing to effectively quantify the impact of temperature, salinity, and dissolved oxygen on trait expression; third, conventional molecular marker analysis methods are difficult to fully capture the functional relationships between SNP sites, limiting the accuracy of genetic potential evaluation.

[0006] The above information disclosed in the background section is only intended to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0007] The present application aims to provide a sea grouper genetic economic trait parameter estimation method to solve the problems raised in the background technology.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0009] The application discloses a method for estimating genetic economic trait parameters of Epinephelus maculatus, and specifically comprises the following steps:

[0010] Step 1: obtaining SNP information of the Epinephelus maculatus, analyzing the SNP information through a clustering algorithm, dividing the Epinephelus maculatus into multiple clusters, selecting a plurality of Epinephelus maculatus from each cluster as sample Epinephelus maculatus, performing a high-temperature resistance experiment and a disease resistance experiment on the sample Epinephelus maculatus, fitting a survival rate curve of the Epinephelus maculatus through the high-temperature resistance experiment to obtain a median lethal temperature of each cluster, and generating a disease resistance comprehensive score of each cluster based on the disease resistance experiment;

[0011] Step 2: for the Epinephelus maculatus in each cluster, the Epinephelus maculatus is divided into three experimental groups and is bred based on different growth environments, the growth rate of the Epinephelus maculatus in each experimental group is calculated, the growth environment includes a standard environment, a high-temperature environment and a low-salt environment, environmental data of the growth environment is collected to generate corresponding environmental fitness, and the growth resistance of each cluster is determined in combination of the growth rate and the corresponding environmental fitness;

[0012] Step 3: obtaining a growth evaluation index of each cluster according to the growth resistance and the growth rate of each cluster in the standard environment;

[0013] Step 4: determining a comprehensive genetic score of each cluster according to the growth evaluation index, the median lethal temperature and the disease resistance score of each cluster, screening out excellent clusters, and selecting Epinephelus maculatus parents for breeding in the excellent clusters based on a parent selection method.

[0014] Further, the obtaining of the median lethal temperature and the disease resistance comprehensive score of the sample Epinephelus maculatus specifically comprises the following steps:

[0015] detecting 50,000 SNP sites of each Epinephelus maculatus, using 0 / 1 / 2 to represent a genotype, and representing genotype values of all Epinephelus maculatus in a matrix form as follows:

[0016]

[0017] wherein, X n×m represents a matrix of integrated SNP site information of n Epinephelus maculatus, a row represents each Epinephelus maculatus, a column represents each SNP site starting from the second row, and an element X ij represents a genotype of the i-th Epinephelus maculatus at the j-th SNP site, n represents the number of Epinephelus maculatus, and m represents the number of SNPs;

[0018] The SNP information of the Lateolabrax japonicus is pretreated, including filtering low-frequency SNPs, filling missing values, balance test and individual filtering; the pretreated SNP information is reduced dimensionally by using principal component analysis, clustered by using spectral clustering algorithm, a genetic similarity matrix between individuals is constructed, a normalized Laplacian matrix is calculated and feature analysis is performed, the first K eigenvectors are extracted to form a low-dimensional embedding, and then K-means clustering is performed in the feature space, the optimal cluster number is determined through the silhouette coefficient and genetic differentiation index, so that the Lateolabrax japonicus is divided into several clusters;

[0019] In each cluster, a plurality of Lateolabrax japonicus are selected as sample Lateolabrax japonicus, a temperature gradient is set, and the same number of sample Lateolabrax japonicus are cultured under each temperature gradient, and the survival rate of the Lateolabrax japonicus under each temperature gradient after the observation period is recorded, and according to the survival rate of the Lateolabrax japonicus at different temperatures, a survival rate curve of the Lateolabrax japonicus is fitted to obtain a median lethal temperature, and the fitting curve formula of the survival rate is:

[0020]

[0021] Wherein, S T (T) is the survival rate at temperature T in the high-temperature tolerance experiment, T is the temperature, ξ is the curve steepness, HT50 is the median lethal temperature of the cluster, indicating the temperature at which 50% of the experimental individuals die;

[0022] The sample Lateolabrax japonicus is injected with a standard amount of pathogen, and the survival rate and antibody titer of the sample Lateolabrax japonicus after the observation period are recorded to generate a comprehensive score of disease resistance corresponding to the cluster, and the calculation formula is as follows:

[0023] D=ω1·S p +ω2·A

[0024] Wherein, D is the comprehensive score of disease resistance, S p is the survival rate in the disease resistance test, A is the antibody titer, ω1 and ω2 are the weights of the survival rate and the antibody titer, respectively, 0<ω2<ω1<1, and ω1+ω2=1.

[0025] Further, the growth rate of the Lateolabrax japonicus is obtained, which specifically includes:

[0026] The environmental data includes temperature, salinity and dissolved oxygen concentration, and the environmental data under the standard environment is used as a reference, the high-temperature environment is that the temperature data is higher than the temperature of the standard environment, and the low-salinity environment is that the salinity data is lower than the salinity of the standard environment;

[0027] The growth rate of each Lateolabrax japonicus in the experimental group is calculated, and the formula is as follows:

[0028]

[0029] Wherein, G(t) represents the growth rate of the cobia in the culture test, W(t) represents the weight of the cobia at the end of the culture test, W(t0) represents the weight of the cobia at the beginning of the culture test, t represents the time at the end of the culture test, t0 represents the time at the beginning of the culture test, and Δt represents the length of the culture test.

[0030] Further, the acquiring the environment fitness specifically comprises:

[0031] The fitness of each environment data is calculated respectively, and specifically comprises:

[0032] The temperature fitness calculation formula is:

[0033]

[0034] Wherein, E T is the temperature fitness, T is the temperature mean value of the environment in the culture test, T opt is the optimal growth temperature, σ T 2 is the temperature variance of the environment in the culture test;

[0035] The salinity fitness calculation formula is:

[0036]

[0037] Wherein, E S is the salinity fitness, K S is the salinity influence coefficient, and S is the salinity mean value of the environment in the culture test;

[0038] The dissolved oxygen fitness calculation formula is:

[0039]

[0040] Wherein, E DO is the dissolved oxygen fitness, k DO is the dissolved oxygen influence coefficient, DO is the dissolved oxygen concentration mean value of the environment in the culture test, and DO crit is the dissolved oxygen concentration critical value;

[0041] The environment fitness is comprehensively analyzed in combination with the fitness of the temperature, the salinity and the dissolved oxygen concentration, and the calculation formula is:

[0042]

[0043] Wherein, E is the environment fitness, ω T is the temperature fitness weight coefficient, ω S is the salinity fitness weight coefficient, ω DO is the dissolved oxygen fitness weight coefficient, 0<ω DO <ω S<ω T <1, and ω T +ω S +ω DO =1.

[0044] Furthermore, obtaining the growth resistance specifically includes:

[0045] The formula for calculating growth resistance is as follows:

[0046]

[0047] Where R(k) represents the growth resistance of the k-th cluster, G(k,o) represents the average growth rate of all sea bass in the k-th cluster under standard conditions, G(k,p1) represents the average growth rate of all sea bass in the k-th cluster under high temperature conditions, G(k,p2) represents the average growth rate of all sea bass in the k-th cluster under low salinity conditions, E(k,o) represents the environmental fitness corresponding to the standard environment when conducting the breeding experiment on the k-th cluster, E(k,p1) represents the environmental fitness corresponding to the high temperature environment when conducting the breeding experiment on the k-th cluster, E(k,p2) represents the environmental fitness corresponding to the low salinity environment when conducting the breeding experiment on the k-th cluster, o is the standard environment index, p1 is the high temperature environment index, p2 is the low salinity environment index, and k is the cluster index.

[0048] Furthermore, obtaining the growth evaluation index for each cluster specifically includes:

[0049] Based on the growth resistance of each cluster and the growth rate under standard conditions, the growth evaluation index of each cluster is calculated, as follows:

[0050] Q(k)=λ1·G(k,o)-λ2·R(k)

[0051] Where Q(k) represents the growth evaluation index of the k-th cluster, G(k,o) represents the average growth rate of all sea bass in the k-th cluster under standard conditions, R(k) represents the growth resistance of the k-th cluster, λ1 and λ2 are the weight coefficients of the corresponding terms, and k is the index of the cluster.

[0052] Furthermore, the selection of superior clusters specifically includes:

[0053] Based on the growth evaluation index, median lethal temperature, and disease resistance score of spotted bass in each cluster, the corresponding comprehensive genetic score is calculated using the following formula:

[0054]

[0055] wherein S(k) represents the comprehensive genetic score of the kth cluster, HT50(k) represents the median lethal temperature of the kth cluster, D(k) represents the disease resistance comprehensive score of the kth cluster, and ω G , ω H , ω D , and ω D <1, respectively, and ω H <1, respectively, and ω G <1, respectively, and ω G <1, respectively, and ω H <1, respectively, and ω D <1, respectively, and ω

[0056] The comprehensive genetic scores of the clusters are sorted, and the clusters with the top 20% comprehensive genetic scores are selected as excellent clusters, and then a conventional parent selection method is used to further screen the Epinephelus akaara individuals in the excellent clusters; first, preliminary selection is performed based on the three phenotype data of the Epinephelus akaara individuals, that is, growth rate, median lethal temperature and disease resistance comprehensive score, a basic value of the phenotype data is set, and the Epinephelus akaara individuals with two phenotype data higher than the basic value are reserved, then the Epinephelus akaara individuals are divided into different families according to SNP information, and the Epinephelus akaara individuals of different families are preferentially selected for pairing, the comprehensive breeding value of each Epinephelus akaara individual is calculated by using the BLUP method according to the phenotype data and the SNP information, and the Epinephelus akaara individuals with the top 20% comprehensive breeding values are selected as parents.

[0057] In the above technical solution, the present application provides technical effects and advantages:

[0058] The sea area Epinephelus akaara genetic economic trait parameter estimation method provided by the present application effectively solves the problems of insufficient comprehensive performance and poor environmental adaptability caused by single trait selection in traditional breeding. By integrating SNP molecular marker analysis, multi-environment phenotype testing and genomic selection technology, the present application can systematically evaluate the growth performance, heat tolerance and disease resistance of the Epinephelus akaara population, and realize multi-trait coordinated selection. Specifically, the genetic grouping based on spectral clustering ensures the genetic diversity of the breeding basis, the environmental adaptability model accurately quantifies the influence of different environmental stresses on growth, and the comprehensive genetic score system scientifically balances the breeding weights of different traits. The parents selected by the BLUP method not only have excellent growth rate and stress resistance, but also can maintain the genetic diversity of the population, significantly improving the breeding efficiency and variety stability. The method is particularly suitable for addressing the challenges of high temperature and diseases faced by mariculture under the background of climate change, and provides reliable technical support for breeding new varieties of Epinephelus akaara with high yield and stable yield. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 The figure is a schematic diagram of the overall method of the present application;

[0060] Figure 2A temperature fitness and environmental fitness relationship diagram of the present application;

[0061] Figure 3 A salinity fitness and environmental fitness relationship diagram of the present application;

[0062] Figure 4 A dissolved oxygen fitness and environmental fitness relationship diagram of the present application. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with specific embodiments.

[0064] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present application should be understood as the common meanings understood by those skilled in the art to which the present application belongs. The terms "first", "second", and similar terms used in the present application do not represent any order, number, or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar terms mean that the elements or objects before the terms cover the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right", and the like only represent relative positional relationships, which may change accordingly when the absolute positions of the described objects change.

[0065] EMBODIMENT

[0066] Please refer to Figure 1 The present application provides a technical solution:

[0067] A genetic economic trait parameter estimation method for sea area Lateolabrax japonicus, the specific steps comprising:

[0068] Step 1: Obtain SNP information of Lateolabrax japonicus, analyze the SNP information by clustering algorithm, divide the Lateolabrax japonicus into multiple clusters, select a number of Lateolabrax japonicus from each cluster as sample Lateolabrax japonicus, perform high temperature resistance experiment and disease resistance experiment on the sample Lateolabrax japonicus, fit the survival rate curve of the Lateolabrax japonicus through the high temperature resistance experiment to obtain the median lethal temperature of each cluster, and generate a disease resistance comprehensive score of each cluster based on the disease resistance experiment;

[0069] In this embodiment, obtaining the median lethal temperature and the disease resistance comprehensive score of the sample Lateolabrax japonicus specifically comprises:

[0070] Detect 50,000 SNP sites of each Lateolabrax japonicus, use 0 / 1 / 2 to represent the genotype, and represent the genotype values of all Lateolabrax japonicus in matrix form as:

[0071]

[0072] wherein X n×m represents the integrated matrix of SNP site information of n collected Pagrus major, the rows represent each Pagrus major individual, the columns represent each SNP site from the second row, and the elements X ij represent the genotype of the ith Pagrus major at the jth SNP site, n represents the number of Pagrus major individuals, and m represents the number of SNPs;

[0073] The SNP information of Pagrus major was pretreated, and quality control standards were set for the collected SNP information to ensure data reliability. First, the minimum allele frequency (MAF) threshold was set to 0.05 to filter low-frequency variations to reduce false positives; each SNP site was required to be successfully typed in more than 95% of the individuals, and the missing genotypes were filled based on linkage disequilibrium using BEAGLE5.0 software; and sites with abnormal population structure or typing errors were excluded by Hardy-Weinberg equilibrium test (P value > 1 x 10 -6 For sample quality control, individuals with a genotype detection rate of less than 90% were removed. After quality control, the data needed to meet the density requirement of an average marker interval of ≤50 kb to ensure uniformity of whole genome coverage.

[0074] Spectral clustering algorithm was used for clustering, and the genotype matrix X n×mAs input, the genotype matrix is reduced to 100-500 dimensions by principal component analysis, retaining 95% of the variance. A genetic similarity matrix between individuals is constructed, including Jaccard similarity, cosine similarity and Gaussian kernel function. Jaccard similarity is suitable for binary SNPs, ignoring homozygous / heterozygous differences. Cosine similarity is suitable for retaining genotype numerical information. Gaussian kernel function is suitable for fine similarity and requires parameter bandwidth, so cosine similarity is selected to construct the genetic similarity matrix. Cosine similarity can retain genotype dosage information, is consistent with population genetics theory, and is computationally efficient and stable, suitable for high-throughput SNP data. The normalized Laplacian matrix is calculated and the normalized Laplacian matrix is analyzed, and the first K eigenvectors are extracted to form a low-dimensional embedding to obtain the normalized feature vector matrix. In the feature space, K-means clustering is performed, the input data is the normalized feature vector matrix, the cluster number is determined by the silhouette coefficient, and the cluster label of each individual is output. The initial center is initialized using k-means++, avoiding local optimization. Usually, 100-300 iterations are sufficient for convergence, thereby dividing the Lateolabrax japonicus into several clusters. The silhouette coefficient and genetic differentiation index can be used as evaluation indicators. The closer the value of the silhouette coefficient is to 1, the higher the intra-cluster tightness and the better the inter-cluster separation. The genetic differentiation index is the Fst value of the clustering reduction. If it is significantly greater than 0, such as Fst>0.05, it indicates that the genetic differentiation is obvious. If the value of the silhouette coefficient is <0.9 and Fst<0.02, the elbow method is used to determine the cluster number again.

[0075] In each cluster, select several Lateolabrax japonicus as sample Lateolabrax japonicus, set a temperature gradient, such as 24℃, 26℃, 28℃, 30℃, 32℃, and cultivate the same number of sample Lateolabrax japonicus under each temperature gradient. Record the survival rate of Lateolabrax japonicus under each temperature gradient after the observation period. Here, the cultivation period can be set to one month. According to the survival rate of Lateolabrax japonicus at different temperatures, the survival rate curve of Lateolabrax japonicus is fitted to obtain the median lethal temperature. The fitting curve formula of the survival rate is:

[0076]

[0077] where S T (T) is the survival rate at temperature T in the high temperature tolerance experiment, T is the temperature during the experiment, ξ is the curve steepness, and HT50 is the median lethal temperature of the cluster, indicating the temperature at which 50% of the experimental individuals die. The higher the value, the stronger the high temperature tolerance of Lateolabrax japonicus. The curve steepness and median lethal temperature are fitted using a nonlinear least squares method.

[0078] The survival rate statistics under multiple temperature gradients ensure that the data covers the sub-lethal to lethal temperature range, improving the reliability of curve fitting. The survival rate curve is fitted using the Logistic model to objectively quantify the median lethal temperature and temperature tolerance threshold. HT50 can directly compare the heat tolerance of different populations, while ξ reflects the temperature sensitivity, providing a basis for high-temperature stability breeding.

[0079] The sample snook is injected with a standard amount of pathogen, such as Aeromonas hydrophila, and the survival rate and antibody titer of the sample snook after the observation period are recorded. The observation period is also set to one month, and the corresponding cluster resistance score is generated, with the following formula:

[0080] D = ω1·S p + ω2·A

[0081] Where D is the comprehensive disease resistance score, S p is the survival rate in the disease resistance test, A is the antibody titer, ω1 and ω2 are the weights of survival rate and antibody titer, respectively, 0 < ω2 < ω1 < 1, and ω1 + ω2 = 1. Here, ω1 can be set to 0.7 and ω2 to 0.3.

[0082] The antibody titer in the comprehensive disease resistance score can be determined by ELISA to measure the antibody level, and the OD value can be directly used as the antibody titer, which can directly reflect the antibody concentration without additional conversion.

[0083] The comprehensive survival rate and antibody titer, which reflect the innate and acquired immune abilities, respectively, are set with different weights. The setting of the weights emphasizes the dominant position of the survival rate (direct economic value) and includes the antibody titer (long-term immune potential), avoiding the one-sidedness of a single indicator. The duration of the high-temperature experiment and the disease experiment balances the experimental efficiency and the stability of the results. The high weight of the survival rate reflects the fact that the survival rate is the ultimate embodiment of disease resistance and is directly related to the cost and income of aquaculture. The loss caused by the death of individuals is much higher than that of individuals with low antibody titers. The survival rate reflects the comprehensive disease resistance of fish, such as physical barriers and inflammatory responses, while the antibody titer only represents specific immunity, and the former is more critical for short-term survival. The low weight of the antibody titer reflects the fact that a high antibody titer indicates a low risk of secondary infection, but the protective effect needs time to build. The antibody titer is only for the experimental pathogen, while the survival rate may include resistance to other pathogens.

[0084] Step 2: For each cluster of snook, divide it into three experimental groups and conduct breeding experiments based on different growth environments, calculate the growth rate of snook in each experimental group, and the growth environments include standard environment, high temperature environment and low salt environment; collect environmental data of the growth environment to generate corresponding environmental fitness, and determine the growth resistance of each cluster based on the growth rate and corresponding environmental fitness;

[0085] In this embodiment, the growth rate of Epinephelus akaara is specifically obtained as follows:

[0086] The environmental data includes temperature, salinity and dissolved oxygen concentration. The environmental data in the standard environment is used as a reference. The high-temperature environment refers to a temperature data higher than that of the standard environment. The low-salt environment refers to a salinity data lower than that of the standard environment.

[0087] In the standard environment, the temperature is set to 26℃, the salinity is set to 25‰, and the dissolved oxygen concentration is set to 6 mg / L. The optimal growth temperature range of Epinephelus akaara is 24-28℃, and 26℃ is the average water temperature in summer in most aquaculture sea areas, and the metabolic efficiency is the highest. Epinephelus akaara is a euryhaline fish, and the ideal salinity range is 20-30‰, and 25‰ is close to the natural salinity of the coastal area. The critical value of dissolved oxygen is 3 mg / L, and 6 mg / L can ensure that there is no hypoxic stress, which meets the standard of intensive aquaculture. In the high-temperature environment, the temperature is set to 32℃, the salinity is set to 25‰, and the dissolved oxygen concentration is set to 5 mg / L. 30℃ or above is the sub-lethal temperature threshold of Epinephelus akaara, and 32℃ can simulate extreme high-temperature weather, such as the peak water temperature in summer. The dissolved oxygen saturation decreases in high temperature, and 5 mg / L simulates the oxygen decay in actual high-temperature water. In the low-salt environment, the temperature is set to 26℃, the salinity is set to 15‰, and the dissolved oxygen concentration is set to 6 mg / L. The lower limit of the salinity tolerance of Epinephelus akaara is 10‰, and 15‰ simulates the low-salt stress after the river mouth or heavy rain. When the salinity is less than 20‰, the energy consumption of osmoregulation increases, and the growth rate may decrease.

[0088] The growth rate of each Epinephelus akaara in the experimental group is calculated, and the formula is as follows:

[0089]

[0090] Wherein, G(t) represents the growth rate of Epinephelus akaara in the aquaculture experiment, W(t) represents the weight of Epinephelus akaara at the end of the aquaculture experiment, W(t0) represents the weight of Epinephelus akaara at the beginning of the aquaculture experiment, t represents the time at which the aquaculture experiment ends, t0 represents the time at which the aquaculture experiment starts, and Δt represents the length of the aquaculture experiment, which is consistent with the above-mentioned experimental duration, that is, one month.

[0091] In this embodiment, the environmental fitness is specifically obtained as follows:

[0092] The fitness of each environmental data is calculated, specifically including:

[0093] The temperature fitness calculation formula is:

[0094]

[0095] Wherein, E T is the temperature fitness, is the average temperature of the environment in the aquaculture experiment, T optFor the optimum temperature, you can refer to the relevant literature or experimental data of the same species, or set it to 26°C, σ T 2 For the temperature variance of the environment during the breeding experiment;

[0096] The Gaussian model is used to reflect the sensitivity of the temperature and the optimum growth temperature of the fish. When the temperature deviates from the optimum value, the temperature fitness index decreases, which conforms to the biological law of fish metabolism, such as low temperature inhibiting enzyme activity and high temperature increasing stress. The variance is used to dynamically adjust the curve. If the fish has strong tolerance to temperature fluctuations during the experiment, the variance will increase, and the curve will be smoother, avoiding overfitting.

[0097] The salinity fitness calculation formula is:

[0098]

[0099] Among them, E S is the salinity fitness, K S is the salinity influence coefficient, and S is the average salinity of the environment during the breeding experiment;

[0100] The critical range is determined: directly identify the safe breeding interval (such as the salinity of the offshore water is usually 25‰), and guide the water quality management. The salinity influence coefficient is used to control the attenuation rate, avoid the salinity fitness from dropping sharply when the salinity is slightly over the standard, and enhance the robustness of the model. Within the range of [20‰, 30‰], the salinity fitness is 1, and when the salinity exceeds the adaptive range, the salinity fitness decreases with the increase of the salinity, reflecting the inhibitory effect of the increase of the salinity on the growth of the fish. The salinity influence coefficient can be obtained by referring to the literature, and the range of seawater fish is usually [0.01, 0.05].

[0101] The dissolved oxygen fitness calculation formula is:

[0102]

[0103] Among them, E DO is the dissolved oxygen fitness, k DO is the dissolved oxygen influence coefficient, DO is the average dissolved oxygen concentration of the environment during the breeding experiment, and DO crit is the critical value of the dissolved oxygen concentration, which is usually 2-4 mg / L for seawater fish;

[0104] The Logistic function is used to simulate the mutation characteristics of anoxic stress, and the critical value DO critThe critical value reflects the suffocation point of Epinephelus akaara, and the DO fitness will decrease sharply when the DO concentration is lower than the critical value. When the DO concentration is lower than the critical value, the exponential term tends to positive infinity, and the DO fitness tends to 0; when the DO concentration is equal to the critical value, the exponential term is equal to 1, and the fitness value is 0.5; when the DO concentration is greater than the critical value, the exponential term tends to 0, and the DO fitness tends to 1. The influence coefficient is used to adjust the sensitivity and is suitable for different dissolved oxygen monitoring accuracy scenarios. The dissolved oxygen influence coefficient can be fitted according to the relevant experimental data, or the survival rate is substituted into the dissolved oxygen fitness value to fit the initial dissolved oxygen influence coefficient value.

[0105] The environmental fitness is comprehensively analyzed by combining the fitness of temperature, salinity and dissolved oxygen concentration, and the calculation formula is:

[0106]

[0107] wherein E is the environmental fitness, ω T is the temperature fitness weight coefficient, ω S is the salinity fitness weight coefficient, ω DO is the dissolved oxygen fitness weight coefficient, 0<ω DO <ω S <ω T <1, and ω T +ω S +ω DO =1.

[0108] The environmental fitness calculation formula quantifies the comprehensive adaptation ability of E. akaara to the farming environment by integrating the effects of temperature, salinity and dissolved oxygen, the three key environmental factors. The dependent variable of the formula is the environmental fitness E, which ranges from 0 to 1, directly reflecting the survival and growth potential of E. akaara under specific environmental conditions. When E is close to 1, it means that the current environment is completely suitable for the growth of E. akaara; when E is close to 0, it means that the environmental conditions have deteriorated to the point of endangering survival. This continuous quantitative index has important technical effects, overcoming the limitations of traditional binary judgments (suitable / unsuitable) and enabling more precise environmental quality assessment. The three independent variables (temperature, salinity and dissolved oxygen) in the formula are key environmental factors that directly affect fish physiological functions. Temperature determines the energy metabolism efficiency of fish by affecting the activity of metabolic enzymes; salinity is related to the energy consumption of osmotic pressure regulation in fish; dissolved oxygen directly restricts the progress of aerobic respiration. These three independent variables are designed as independent calculation modules, and are integrated into the comprehensive fitness E through geometric weighting. This processing method not only considers the independent effects of each environmental factor, but also reflects the importance differences of different factors on E. akaara through weight coefficients. In terms of specific mathematical relationships, the three environmental factors and fitness all show a positive correlation, and the temperature fitness adopts a Gaussian function form, which reaches the maximum value at the optimal temperature T optThe fitness value reaches a maximum of 1 at the optimum temperature, and decreases symmetrically as the temperature deviates from the optimum, which is consistent with the bell-shaped curve of fish metabolic rate with temperature. The salinity fitness is a piecewise function, taking a maximum of 1 within the ideal salinity range (20-30‰), and decreasing linearly outside this range, reflecting the tolerance limit of Lateolabrax japonicus to salinity changes. The dissolved oxygen fitness adopts a Sigmoid function, which rapidly increases when DO exceeds the critical value, capturing the nonlinear response characteristics under low oxygen stress. This diverse function form design enables the environmental fitness model to more realistically simulate the complex response mechanisms of Lateolabrax japonicus to different environmental factors.

[0109] Temperature is given the highest weight, reflecting its core influence on fish physiological functions. As ectothermic animals, fish's metabolic rate, feeding efficiency, immune capacity, and other key life activities are directly regulated by environmental temperature. Deviation from the optimal temperature range can significantly inhibit growth, and even lead to death. In contrast, although salinity and dissolved oxygen are important, fish can usually buffer the negative effects of their changes through short-term physiological regulation (such as osmotic pressure adjustment or behavioral adaptation). Therefore, temperature should dominate in environmental fitness assessment. The weight of salinity is higher than that of dissolved oxygen, which is consistent with the ecological adaptability of marine fish. As euryhaline fish, Lateolabrax japonicus has a certain tolerance to salinity changes, but long-term exposure to non-optimal salinity (such as freshwater or super-high salinity) will still increase the energy consumption of osmotic regulation, reducing growth efficiency. Although the influence of dissolved oxygen cannot be ignored in high-density aquaculture, modern aquaculture systems usually have oxygenation equipment to maintain a relatively stable level of dissolved oxygen concentration. In addition, fish's tolerance to low oxygen (such as reducing activity to reduce oxygen consumption) is usually stronger than their ability to adapt to extreme salinity. Therefore, the weight of salinity is slightly higher than that of dissolved oxygen, which is more consistent with the priority of environmental stress in actual aquaculture. DO +ω S +ω DO This constraint condition ensures that the environmental fitness is a normalized index, ensuring that the total contribution of the weighted combination of multiple factors is 100%.

[0110] In this embodiment, 20 sets of growth environment data are collected, and the corresponding temperature fitness, salinity fitness and dissolved oxygen fitness are calculated. By observing the relationship between temperature fitness, salinity fitness, dissolved oxygen fitness and environmental fitness data, the positive and negative proportional relationship between independent variables and dependent variables in the environmental fitness calculation formula is analyzed. Here, the temperature fitness weight is set to 0.5, the salinity fitness weight is set to 0.3, and the dissolved oxygen fitness weight is set to 0.2. The specific data is shown in the following table:

[0111] Table 1: Example of fitness value data corresponding to each growth environment data

[0112] Sample No. Temperature fitness Salinity fitness Dissolved oxygen concentration fitness Environmental fitness 1 0.1 0.1 0.1 0.1 2 0.2 0.2 0.2 0.19 3 0.3 0.3 0.3 0.3 4 0.4 0.4 0.4 0.4 5 0.5 0.5 0.5 0.5 6 0.6 0.6 0.6 0.6 7 0.65 0.65 0.65 0.65 8 0.7 0.7 0.7 0.7 9 0.75 0.75 0.75 0.75 10 0.8 0.8 0.8 0.8 11 0.85 0.85 0.85 0.85 12 0.88 0.88 0.88 0.88 13 0.9 0.9 0.9 0.9 14 0.92 0.92 0.92 0.92 15 0.94 0.94 0.94 0.94 16 0.96 0.96 0.96 0.96 17 0.97 0.97 0.97 0.97 18 0.98 0.98 0.98 0.98 19 0.99 0.99 0.99 0.99 20 1 1 1 1

[0113] With reference to Figures 2-4 As can be seen from the above table and data, with the increase of the fitness value of each growth environment data, the corresponding environment fitness value is also increasing, reflecting that the temperature fitness, salinity fitness and dissolved oxygen fitness are positively correlated with the environment fitness. The temperature fitness reflects the tolerance of Lateolabrax japonicus to temperature changes and the deviation degree of the most suitable growth temperature. The salinity fitness reflects the adaptability of Lateolabrax japonicus to salinity changes, especially the safe breeding range. The dissolved oxygen fitness reflects the tolerance of Lateolabrax japonicus to low oxygen environment. The environment fitness reflects the overall survival suitability under the joint action of temperature, salinity and dissolved oxygen.

[0114] In this embodiment, the growth resistance is obtained, and the obtaining specifically includes:

[0115] The formula for calculating the growth resistance is as follows:

[0116]

[0117] wherein, R(k) represents the growth resistance of the kth cluster, G(k,o) represents the average growth rate of all Lateolabrax japonicus in the kth cluster under standard environment for the cultivation test, G(k,p1) represents the average growth rate of all Lateolabrax japonicus in the kth cluster under high-temperature environment for the cultivation test, G(k,p2) represents the average growth rate of all Lateolabrax japonicus in the kth cluster under low-salinity environment for the cultivation test, E(k,o) represents the environment fitness corresponding to the standard environment when the kth cluster is subjected to the cultivation test, E(k,p1) represents the environment fitness corresponding to the high-temperature environment when the kth cluster is subjected to the cultivation test, E(k,p2) represents the environment fitness corresponding to the low-salinity environment when the kth cluster is subjected to the cultivation test, o is the index of the standard environment, p1 is the index of the high-temperature environment, p2 is the index of the low-salinity environment, and k is the index of the cluster.

[0118] Growth resistance is an index defined for each cluster, reflecting the ability of the cluster to maintain stable growth speed under different growth environments. The smaller the growth resistance value, the smaller the fluctuation of the growth speed of the cluster when the environment changes, i.e. the stronger the resistance, and the smaller the impact of environmental stress on growth. The larger the growth resistance value, the more significant the impact of environmental changes on growth rate, i.e. the weaker the resistance. Essentially, growth resistance is a stability index that evaluates the buffering capacity of the cluster to environmental stress. The numerator part respectively reflects the difference in growth rate under standard environment and high temperature environment and the difference in growth rate under standard environment and low salt environment, reflecting the decrease in growth rate caused by environmental stress; the denominator part respectively reflects the difference in fitness under standard environment and high temperature environment and the difference in fitness under standard environment and low salt environment, reflecting the strength of environmental stress, and the more significant the decrease in fitness, the stronger the stress; the ratio of the numerator to the denominator represents the change in growth rate caused by unit change in environmental fitness. If the ratio is small, it means that even if the environmental fitness decreases, the growth rate remains stable. Growth resistance is positively correlated with growth rate difference and negatively correlated with environmental fitness difference. Specifically, when environmental stress causes a significant decrease in growth rate, i.e. the numerator increases or the environmental fitness fluctuates slightly, i.e. the denominator decreases, the R(k) value will increase, indicating weak resistance; on the contrary, if the growth rate is stable, i.e. the numerator is small or the environmental fitness is significantly deteriorated, i.e. the denominator is large, then the R(k) value will decrease, reflecting strong resistance. This design ensures that the resistance score can capture both the sensitivity of growth performance and the actual strength of environmental pressure, avoiding the one-sidedness of a single index, such as only looking at growth rate.

[0119] Step 3: According to the growth resistance of each cluster and the growth rate under standard environment, obtain the growth evaluation index of each cluster;

[0120] In this embodiment, the growth evaluation index of each cluster is obtained specifically by:

[0121] According to the growth resistance of each cluster and the growth rate under standard environment, calculate the growth evaluation index of each cluster, and the formula is as follows:

[0122] Q(k) = λ1·G(k,o) - λ2·R(k)

[0123] Wherein, Q(k) represents the growth evaluation index of the kth cluster, G(k,o) represents the average growth rate of all cobia in the kth cluster under standard environment, R(k) represents the growth resistance of the kth cluster, λ1, λ 12 respectively are weight coefficients corresponding to the items, and k is the index of the cluster.

[0124] The dependent variable of the growth evaluation index is the weighted combination of the growth rate in the standard environment and the growth resistance, and its core meaning is to comprehensively evaluate the growth potential of the group of Lateolabrax maculatus in ideal conditions and the stability under environmental stress. This index avoids the limitation of only focusing on a single characteristic by integrating absolute growth performance and resistance, for example, a group with high growth rate but poor resistance may not perform stably in actual breeding. In terms of technical effects, the growth evaluation index provides a quantitative basis for breeding or breeding management, and preferentially selects clusters that grow fast in standard environments and are not sensitive to environmental fluctuations, thereby improving overall production efficiency and adaptability.

[0125] The independent variable G in the formula std (k,o) and R(k) represent growth potential and resistance, respectively, which together determine the overall performance of Lateolabrax maculatus in actual production. G std (k,o) reflects the growth efficiency of the cluster under optimal conditions and is a direct manifestation of production performance; R(k) measures the sensitivity of growth rate to environmental stress and reflects stability. The independent variables are combined by weighting coefficients, and their correlation depends on the research objective, for example, if high productivity is more important, then λ1 is higher; if environmental adaptability is more important, then λ2 needs to be adjusted higher. For example, in breeding areas with variable climates, clusters with strong resistance can be selected by increasing the weight of λ2.

[0126] The growth evaluation index is positively correlated with the standard environmental growth rate and negatively correlated with the growth resistance. Specifically, the greater the value of G std (k,o), the higher Q(k), indicating that the cluster has a clear growth advantage under ideal conditions; and the smaller the value of R(k), i.e., the stronger the resistance, the higher Q(k), because low R(k) means that environmental fluctuations have less impact on growth. This design ensures that Q(k) can balance growth speed and stability, for example, a cluster with high G std (k,o) and low R(k) will receive the highest evaluation, meeting the breeding goal of high yield and stability.

[0127] Step 4: According to the growth evaluation index, the median lethal temperature, and the disease resistance score of each cluster, determine the comprehensive genetic score of each cluster to screen out excellent clusters, and based on the parent selection method, select the Lateolabrax maculatus parents for breeding in the excellent clusters;

[0128] In this embodiment, screening the excellent clusters specifically includes:

[0129] According to the growth evaluation index, the median lethal temperature, and the disease resistance score of each cluster, calculate the corresponding comprehensive genetic score, and the formula is as follows:

[0130]

[0131] wherein S(k) represents the comprehensive genetic score of the kth cluster, HT50(k) represents the median lethal temperature of the kth cluster, D(k) represents the disease resistance comprehensive score of the kth cluster, ω G , ω H , and ω D are weight coefficients corresponding to the items, 0 < ω D < ω H < ω G < 1, and ω G + ω H + ω D = 1.

[0132] The comprehensive genetic score reflects the overall genetic advantage of the kth cluster of Epinephelus akaara populations. By integrating the growth performance Q(k), heat tolerance HT50(k), and disease resistance D(k) of the three key traits, the comprehensive performance of the cluster in growth potential, environmental adaptability, and health status is quantified. The score takes the growth evaluation index as the core, giving it the highest weight, emphasizing the priority of growth efficiency in breeding, while considering the synergistic effect of heat tolerance and disease resistance to ensure that the selected population not only has high yield, but also can withstand high temperature stress and resist pathogen invasion. By adjusting the weight coefficients, different breeding environments or breeding goals can be flexibly adapted, and ultimately excellent clusters with fast growth, strong stress resistance, and high survival rate can be selected.

[0133] The independent variables Q(k), HT50(k), and D(k) in the formula jointly determine the actual performance and value of E. akaara in aquaculture production. The growth evaluation index reflects the basic production performance and is a direct reflection of the breeding benefit; the median lethal temperature represents the heat tolerance, which is related to the selection of breeding areas and seasonal adaptability; and the disease resistance score affects the survival rate and drug cost. These independent variables are combined by weight coefficients ω G , ω H , and ω D , and their relevance is reflected in that they jointly constitute the main selection targets for genetic improvement of E. akaara, but the importance of each varies depending on the breeding demand and environmental conditions, which requires weight adjustment to reflect different breeding focuses.

[0134] In terms of correlation, the comprehensive genetic score is positively correlated with the growth evaluation index and the disease resistance score. The larger the values of these two indicators, the stronger the growth performance and disease resistance of the population, and the higher the comprehensive score. For the median lethal temperature HT50(k), since it is standardized by dividing by 40, it actually reflects the positive contribution of heat tolerance, so the larger the value of HT50(k) (the stronger the heat tolerance), the higher the comprehensive score S(k). This design ensures that all three traits can contribute positively to the score calculation, and the differential attention to different traits is achieved through the differential allocation of weight coefficients, ultimately selecting excellent populations with balanced development in all performance indicators.

[0135] The size relationship of each weight coefficient is set as ω G >ω H >ω D This allocation scheme is based on the dual consideration of economic benefits and biological characteristics of cobia breeding. The growth rate weight is given the highest priority, as it directly determines the length of the breeding cycle and feed conversion efficiency, which is the core variable affecting income. The heat tolerance weight is second, and its rationality stems from the risk prevention and control of breeding under climate change. For every 1℃ increase in the median lethal temperature of cobia, the survival rate during the summer high-temperature period can be increased by 15%-20%, especially in tropical sea areas. However, compared to the continuous income of growth rate, heat tolerance is a defensive trait, so the weight is slightly lower. The setting of disease resistance weight takes into account the probability of disease occurrence and the space for cost transfer. Although an outbreak of the disease can lead to some losses, the risk can be partially controlled through vaccination and water quality management, so its weight is lower than the first two. G +ω H +ω D = 1 This constraint condition ensures that the comprehensive genetic score is a normalized index, ensuring that the total contribution of the weighted combination of multiple factors is 100%.

[0136] The comprehensive genetic scores of each cluster are sorted, and the clusters with the top 20% comprehensive genetic scores are selected as excellent clusters. Then, the cobia individuals in the excellent clusters are further screened using the conventional parent selection method. First, based on the three phenotypic data of each cobia, preliminary selection is performed, i.e., growth rate, median lethal temperature, and disease resistance comprehensive score. The basic value of the phenotypic data is set, the mean value of the phenotypic data of all individuals in each cluster is set as the basic value of the corresponding phenotypic data, and the cobia individuals with two phenotypic data higher than the basic value are retained to balance the selection intensity of different traits and avoid degradation of other traits due to over-selection of a single trait. Then, according to the SNP information, the cobia individuals are divided into different families. Through cluster analysis, such as principal component analysis, neighbor-joining method, and STRUCTURE software, the individuals are divided into different families to ensure that the selected parents come from different genetic backgrounds. For example, first, the main genetic variation components are extracted through PCA dimension reduction, and individuals with similar genotypes are visualized and clustered in two-dimensional or three-dimensional space. Then, based on Nei's genetic distance, a neighbor-joining tree diagram is constructed, and the optimal family classification is determined using the dynamic cutting method to ensure that the genetic similarity of individuals within the same family (e.g., IBD ≥ 0.25) is significantly higher than the difference between families. At the same time, the Fst value between families (≥ 0.05) is calculated to verify the degree of population differentiation, and finally, family grouping with significant genetic structure differences is obtained to provide a reliable basis for avoiding inbreeding in subsequent parent pairing.

[0137] Different families of Epinephelus are selected to mate to maintain the genetic diversity of the population and reduce the risk of inbreeding depression. According to the phenotypic data and SNP information, the comprehensive breeding value of each Epinephelus individual is calculated by the BLUP method. First, a mixed linear model is constructed: the phenotypic data is used as the target trait, the SNP information is used as the random effect, and the family information is used as the fixed effect to correct the influence of environmental factors. The estimated breeding value: the estimated breeding value of each individual is calculated by the restricted maximum likelihood method or gene BLUP to reflect its genetic advantage. Finally, the comprehensive breeding value is calculated: according to the breeding goal, different traits are given different weights (such as growth 40%, heat tolerance 30%, and disease resistance 30%), and the final comprehensive breeding value is obtained by weighted summation. According to the comprehensive breeding value, all candidate individuals are sorted, and the top 20% of Epinephelus individuals with the highest comprehensive breeding value are selected as parents. This method ensures that the selected parents have excellent phenotypes, genetic diversity, and high genetic potential advantage through multi-stage screening, and can more efficiently breed varieties with excellent comprehensive performance.

[0138] The above formulas are dimensionless values calculated, and the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the actual situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0139] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0140] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0141] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for estimating genetic and economic trait parameters of spotted sea bass in marine areas, characterized in that, The specific steps include: Step 1: Obtain the SNP information of the spotted bass, analyze the SNP information through a clustering algorithm, divide the spotted bass into multiple clusters, select several spotted bass from each cluster as sample spotted bass, conduct high temperature resistance test and disease resistance test on the sample spotted bass, fit the survival rate curve of the spotted bass through the high temperature resistance test to obtain the median lethal temperature of each cluster, and generate a comprehensive disease resistance score for each cluster based on the disease resistance test; Step 2: For each cluster of spotted bass, it is divided into three experimental groups and cultured in different environments. The growth rate of spotted bass in each experimental group is calculated. The growth environment includes standard environment, high temperature environment and low salinity environment. Environmental data of the growth environment is collected to generate the corresponding environmental fitness. Combined with the growth rate and the corresponding environmental fitness, the growth resistance of each cluster is determined. Step 3: Obtain the growth evaluation index of each cluster based on its growth resistance and growth rate under standard conditions; Step 4: Determine the comprehensive genetic score of each cluster based on the growth evaluation index, median lethal temperature, and disease resistance score corresponding to each cluster, so as to screen out the superior clusters and select the spotted bass parents for breeding based on the parent selection method. Obtaining a comprehensive disease resistance score specifically includes: The sample sea bass were injected with a standard dose of pathogen, and the survival rate and antibody titer of the sample sea bass were recorded after the observation period to generate a comprehensive disease resistance score for the corresponding cluster. The calculation formula is as follows: Where D represents the overall disease resistance score, A represents the survival rate in the disease resistance test, and A represents the antibody titer. The weights for survival rate and antibody titer are respectively. ,and ; Obtaining the environmental adaptability specifically includes: The fitness of each environmental data point is calculated separately, including: The formula for calculating temperature adaptability is: in, For temperature adaptability, T represents the average environmental temperature during the breeding experiment. For the optimal growth temperature, The temperature variance of the environment during the aquaculture experiment; The formula for calculating salinity adaptability is: in, For salinity adaptation, denoted as the salinity influence coefficient, and S represents the average salinity of the environment during the aquaculture experiment. The formula for calculating dissolved oxygen fitness is: in, For dissolved oxygen adaptability, denoted as the dissolved oxygen influence coefficient, where DO is the average dissolved oxygen concentration in the environment during the aquaculture experiment. This is the critical value for dissolved oxygen concentration; Environmental adaptability is comprehensively analyzed by combining adaptability to temperature, salinity, and dissolved oxygen concentration. The calculation formula is as follows: Where E represents environmental adaptability. This is the temperature fitness weighting coefficient. This is the salinity fitness weighting coefficient. This is the dissolved oxygen fitness weighting coefficient. ,and ; Obtaining the growth resistance specifically includes: The formula for calculating growth resistance is as follows: in, This represents the growth resistance of the k-th cluster. This represents the mean growth rate of all spotted bass in the k-th cluster, under standard conditions during aquaculture trials. This represents the mean growth rate of all spotted bass in the k-th cluster that underwent aquaculture trials under high-temperature conditions. Let represent the mean growth rate of all spotted bass in the k-th cluster that underwent aquaculture trials in a low-salt environment. This represents the environmental fitness corresponding to the standard environment when conducting a breeding experiment on the k-th cluster. This represents the environmental adaptability corresponding to a high-temperature environment when conducting a breeding experiment on the k-th cluster. This represents the environmental adaptability corresponding to the low-salt environment when conducting a breeding experiment on the k-th cluster. o is the standard environment index, p1 is the high-temperature environment index, p2 is the low-salt environment index, and k is the cluster index. Obtaining the growth evaluation index for each cluster specifically includes: Based on the growth resistance of each cluster and the growth rate under standard conditions, the growth evaluation index of each cluster is calculated, as follows: Among them, Q This represents the growth evaluation index of the k-th cluster. This represents the mean growth rate of all spotted bass in the k-th cluster, under standard conditions during aquaculture trials. This represents the growth resistance of the k-th cluster. These are the weight coefficients for the corresponding items, and k is the cluster index; Based on the growth evaluation index, median lethal temperature, and disease resistance score of spotted bass in each cluster, the corresponding comprehensive genetic score is calculated using the following formula: in, This represents the overall genetic score of the k-th cluster. This represents the median lethal temperature of the k-th cluster. This represents the overall disease resistance score of the k-th cluster. These are the weight coefficients for the corresponding items. ,and .

2. The method for estimating genetic and economic trait parameters of spotted sea bass according to claim 1, characterized in that, The specific steps to obtain the median lethal temperature of the sample sea bass include: 50,000 SNP loci were analyzed for each spotted bass, and genotypes were represented by 0 / 1 / 2. The genotype values ​​of all spotted bass were represented in matrix form as follows: in, This matrix represents the integration of SNP locus information from n spotted bass. Rows represent each individual spotted bass, and starting from the second row, columns represent each SNP locus. Elements... Let represent the genotype of the i-th spotted bass at the j-th SNP locus, n represent the number of spotted bass individuals, and m represent the number of SNPs; The SNP information of spotted sea bass was preprocessed, including filtering low-frequency SNPs, imputing missing values, balance test, and individual filtering. Principal component analysis was used to reduce the dimensionality of the preprocessed SNP information. Spectral clustering algorithm was used for clustering to construct a genetic similarity matrix between individuals. The normalized Laplacian matrix was calculated and feature analysis was performed. The top K feature vectors were extracted to form a low-dimensional embedding. Then, K-means clustering was performed in the feature space. The optimal number of clusters was determined by the silhouette coefficient and the genetic differentiation index, thereby dividing the spotted sea bass into several clusters. In each cluster, several spotted bass were selected as sample spotted bass. A temperature gradient was set, and the same number of sample spotted bass were cultured at each temperature gradient. The survival rate of the spotted bass at each temperature gradient was recorded after the observation period. Based on the survival rate of the spotted bass at different temperatures, a survival rate curve of the spotted bass was fitted to obtain the median lethal temperature. The formula for the fitted curve of the survival rate is expressed as: in, This represents the survival rate at temperature T during a high-temperature resistance test, where T is the temperature. HT50 represents the steepness of the curve, and HT50 is the median lethal temperature for this cluster, indicating the temperature at which 50% of the experimental individuals die.

3. The method for estimating genetic and economic trait parameters of spotted sea bass according to claim 1, characterized in that, Obtaining the growth rate of the spotted bass specifically includes: The environmental data includes temperature, salinity, and dissolved oxygen concentration, with reference to environmental data under standard conditions. High-temperature environment means temperature data is higher than that of standard environment, and low-salinity environment means salinity data is lower than that of standard environment. The growth rate of each sea bass in the experimental group was calculated using the following formula: in, The value of W(t) represents the growth rate of the spotted bass in the culture experiment, and the value of W(t) represents the weight of the spotted bass at the end of the culture experiment. This indicates the weight of the spotted bass at the start of the breeding trial. Indicates the end time of the breeding trial. Indicates the start time of the breeding experiment. Indicates the duration of the breeding trial.

4. The method for estimating genetic and economic trait parameters of spotted sea bass according to claim 1, characterized in that, The selection of superior clusters specifically includes: The comprehensive genetic scores of each cluster were ranked, and the top 20% of the clusters were considered superior clusters. Then, conventional parent selection methods were used to further screen individuals from these superior clusters. Initial selection was performed based on three phenotypic data for each individual: growth rate, median lethal temperature, and disease resistance score. Base values ​​for these phenotypic data were set, and individuals with two phenotypic values ​​exceeding these base values ​​were retained. Next, individuals were divided into different families based on SNP information, and individuals from different families were preferentially paired. Based on the phenotypic data and SNP information, the BLUP method was used to calculate the comprehensive breeding value for each individual, and the top 20% of individuals with the highest comprehensive breeding values ​​were selected as parents.

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