Genomic selection method for integrating genetic advantages and defects of beef cattle and application thereof

Abstract

The application relates to the field of animal breeding technology, and particularly discloses a beef cattle genome matching method integrating genetic advantages and defects and application, which comprises the following steps: S1, extracting beef cattle individual DNA samples from to-be-matched sire and to-be-matched dam for gene typing, and obtaining SNP genotype data of the individuals; S2, calculating an additive kinship matrix according to a Van Raden algorithm, obtaining a genomic estimated breeding value of the beef cattle individuals on important economic traits, and calculating a comprehensive selection index of the individuals; S3, constructing a beef cattle economic score model according to the comprehensive selection index value of the individuals, the SNP genotype data, an inbreeding coefficient and corresponding economic values of each part; and S4, calculating economic scores of all possible matching combinations between the to-be-matched sire and the to-be-matched dam based on the beef cattle economic score model, maximizing solving and generating an optimal matching scheme; the beef cattle genome matching integrating genetic advantages and defects is realized through the genomic breeding value evaluation and the economic score model construction.

Description

Technical Field

[0001] This invention relates to the field of animal breeding technology, and more specifically to a method and application for selecting and matching beef cattle genomes that integrates genetic advantages and disadvantages. Background Technology

[0002] The beef cattle industry has developed rapidly in recent years, but many shortcomings still exist. Currently, the main beef cattle breeds raised include imported breeds such as Simmental, Angus, and Charolais, as well as locally bred breeds like Huaxi and Qinchuan. While these breeds have met market demand to some extent, further improvements are needed in areas such as growth rate, feed conversion ratio, meat quality, and slaughter rate.

[0003] In terms of genetic improvement, some key superior genes have been proven to significantly improve the production performance of beef cattle. For example, MSTN Mutations in the gene (myosin) can promote muscle development and increase slaughter rate; IGF2 Genes are closely related to growth rate and feed efficiency; multi-rib genes can increase the number of ribs and improve meat yield. The rational use of these advantageous genes can significantly accelerate the breeding process and further enhance the added economic value of each cow.

[0004] In addition, various genetic defects exist in beef cattle populations. Since the 1960s, several genetic diseases associated with calf birth defects and metabolic disorders have been identified in Simmental cattle, such as thrombopathic disease (TP) and syndactyly disease (SY). In recent years, with the rapid development of bovine genome SNP chips and whole-genome sequencing technologies, more new genetic defects have been identified. These include embryonic lethal defects—Brownswiss haplotype 2 (BH2), Bovine male subfertility (BMS), Fleckvieh haplotype 2 (FH2) and FH4, Montbéliardehaplotype 1 (MH1), and Simmental haplotype 9 (SH9). These genetic defects not only affect individual growth and development but may also have long-term negative impacts on the health of the herd.

[0005] The current beef cattle breeding system mainly relies on traditional methods: selection based on phenotypic data and pedigree information, combined with artificial insemination to propagate superior individuals. Although some large-scale breeding farms have begun to apply genomic selection technology, there are still significant shortcomings: first, the utilization of dominant genes is not systematic enough, making it difficult to achieve multi-gene synergistic improvement; second, the screening and control of genetic defects are insufficient, leading to the spread of harmful mutations in the population; and third, there is a lack of precise breeding schemes that integrate genomic information, resulting in lower breeding efficiency. To address these issues, there is an urgent need to establish a new genomic selection method that integrates the utilization of dominant genes with the control of genetic defects, in order to achieve a shift from experience-based to precision-based beef cattle breeding. Summary of the Invention

[0006] The purpose of this invention is to integrate the genetic advantages and disadvantages of beef cattle genome selection and application, and to achieve the genetic advantages and disadvantages of beef cattle genome selection through genome breeding value evaluation and economic scoring model construction.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for selecting beef cattle genomes that integrates genetic advantages and disadvantages includes the following steps: S1. Extract individual DNA samples from the bulls and cows to be bred for genotyping and quality control, and obtain individual SNP genotype data; the SNP genotype data includes genetic advantages and defect carrier status. S2. Calculate the additive sex kinship matrix according to the VanRaden algorithm, use GBLUP to obtain the estimated genomic breeding values ​​of beef cattle individuals for important economic traits, and calculate the comprehensive selection index of individuals based on the estimated genomic breeding values ​​of each trait. S3. Based on the individual's comprehensive selection index value, SNP genotype data, inbreeding coefficient, and the economic value corresponding to each part, construct a beef cattle economic scoring model for evaluating the economic value of paired combinations. S4. Based on the beef cattle economic scoring model, calculate the economic scores of all possible pairing combinations between the bull and the cow to be bred, and use linear programming to maximize the solution to generate the optimal selection scheme.

[0008] Furthermore, in S2, important economic traits include: carcass weight, calving difficulty, weaning weight, average daily weight gain during the fattening period, and dressing percentage.

[0009] Furthermore, in S1, the quality control specifically includes: Only autosomal loci are retained, while loci with a typing success rate of less than 90% and a minimum allele frequency of less than 0.05 are removed, as are individuals with a detection rate of less than 90%.

[0010] Further, in S2, the step of calculating the additive sex kinship matrix according to the VanRaden algorithm and obtaining the estimated genomic breeding values ​​of individual beef cattle for important economic traits using GBLUP is expressed as follows:

[0011] in, A vector of phenotypic observations; yes 3D correlation matrix; yes 3D fixed effects vector; It is the number of fixed effects; Z Is with a Related structural matrices; Represents the additive effect vector, following the order N(0, The normal distribution of ); where G An additive genomic relationship matrix, For additive inheritance variance, Let N be the residual vector, which follows the expression N(0, The normal distribution of ).

[0012] Furthermore, in S2, the individual's comprehensive selection index The function expression is:

[0013] in, It is a genomic estimation breeding value for the ease or difficulty of calving; It is the breeding value estimated by the genome of weaning weight, and the weaning weight is uniformly corrected to the weight at 6 months of age; It is the genomically estimated breeding value of the average daily weight gain during the fattening period; It is the estimated breeding value of carcass weight based on genome; It is the slaughter rate genomic estimation breeding value.

[0014] Furthermore, in S3, the functional expression of the beef cattle economic scoring model is:

[0015] in, Used to calculate the first i The first parent and the first j The score of mating between the male parents. and For their respective comprehensive selection index values, It is a penalty value for a 1% increase in inbreeding. It is the inbreeding coefficient between paired individuals. It is the number of dominant genes being considered. It is the probability of offspring carrying advantageous genes. The economic value of offspring carrying advantageous mutations. It is the probability of dominant gene expression in offspring. It is the additional economic value brought about by the expression of advantageous genes. It is the number of recessive genetic defects under consideration. It is the probability of expressing a genetic defect. , and These are the weights of the breeding value index of the mating combination, the carrying of advantageous mutations, and the genetic defects in the model, respectively.

[0016] Furthermore, in S4, the function expression for maximizing the linear programming problem is:

[0017] in, This indicates the expected genetic value of the offspring of the currently selected combination. It is a binary decision variable; when When the value is 0, it indicates that the pairing is not accepted. A value of 1 indicates that the pairing is retained; the constraint on the number of times male and female animals can be used is:

[0018] in, a and b These represent the maximum number of times male and female animals can be used: This indicates the status of male livestock. The maximum number of times it can be used is ; This indicates that for female animals The maximum number of times it can be used is .

[0019] This invention also provides a specific application of the method for selecting beef cattle genomes that integrates genetic advantages and disadvantages in beef cattle breeding.

[0020] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention utilizes high-density SNP genotyping technology to achieve in-depth analysis of the genetic material of breeding bulls and cows, from macroscopic phenotypes to microscopic genotypes. It precisely captures genetic advantages that determine their production potential and defect-carrying information that may harm offspring, laying an unprecedented data foundation for scientific breeding. Subsequently, using the VanRaden algorithm, massive amounts of genotype data are transformed into quantifiable and comparable genome-estimated breeding values, integrated into a comprehensive selection index, enabling an objective and efficient assessment of individual genetic levels. The beef cattle economic scoring model in this invention not only considers genetic value but also proactively incorporates the economic value weights of inbreeding risk control and marketization, elevating the goal of breeding decisions from simple genetic improvement to a strategic level of maximizing economic benefits. Finally, through the powerful mathematical optimization tool of linear programming, a global and rapid optimization calculation is performed on massive pairing combinations to automatically generate the optimal breeding scheme. This entire process completely overturns the traditional breeding model that relies on phenotypes and experience, achieving intelligent, precise, and forward-looking breeding decisions. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] The following description, in conjunction with the accompanying drawings, further illustrates the method for selecting and matching beef cattle genomes that integrates genetic advantages and disadvantages, and its application. Figure 1 This is a schematic diagram of the process of the beef cattle genome selection method that integrates genetic advantages and disadvantages according to the present invention. Detailed Implementation

[0023] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0024] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0025] Example 1

[0026] like Figure 1 As shown, this invention provides a method for selecting beef cattle genomes that integrates genetic advantages and disadvantages, comprising the following steps: S1. Extract individual DNA samples from the bulls and cows to be bred for genotyping, process and control the quality, and obtain individual SNP genotype data; the SNP genotype data includes genetic advantages and defect carrier status. The quality control measures specifically include: retaining only autosomal loci, removing loci with a typing success rate of less than 90% and a minimum allele frequency of less than 0.05, and removing individuals with a detection rate of less than 90%.

[0027] S2. Calculate the additive sex kinship matrix using the VanRaden algorithm, obtain the estimated genomic breeding values ​​of individual beef cattle for important economic traits using GBLUP, and calculate the comprehensive selection index of individuals based on the estimated genomic breeding values ​​of each trait. Important economic traits include: carcass weight, calving difficulty, weaning weight, average daily weight gain during the fattening period, and dressing percentage. The additive sex kinship matrix was calculated using the VanRaden algorithm, and the estimated genomic breeding values ​​of beef cattle for important economic traits were obtained using GBLUP. The functional expression is as follows:

[0028] in, A vector of phenotypic observations; yes 3D correlation matrix; yes 3D fixed effects vector; It is the number of fixed effects; Is with a Related structural matrices; Represents the additive effect vector, following the order N(0, The normal distribution of ); where G An additive genomic relationship matrix, For additive inheritance variance, Let N be the residual vector, which follows the expression N(0, The normal distribution of ).

[0029] S3. Based on the individual's comprehensive selection index value, SNP genotype data, inbreeding coefficient, and the corresponding economic value of each component, construct a beef cattle economic scoring model to evaluate the economic value of paired combinations; the functional expression of the beef cattle individual's comprehensive selection index is:

[0030] in, It is a genomic estimation breeding value for the ease or difficulty of calving; It is the breeding value estimated by the genome of weaning weight, and the weaning weight is uniformly corrected to the weight at 6 months of age; It is the genomically estimated breeding value of the average daily weight gain during the fattening period; It is the estimated breeding value of carcass weight based on genome; It is the slaughter rate genomic estimation breeding value.

[0031] The functional expression for the beef cattle economic scoring model is as follows:

[0032] in, Used to calculate the first i The first parent and the first j The score of mating between the male parents. and For their respective comprehensive selection index values, It is a penalty value for a 1% increase in inbreeding. It is the inbreeding coefficient between paired individuals. It is the number of dominant genes being considered. It is the probability of offspring carrying advantageous genes. The economic value of offspring carrying advantageous mutations. It is the probability of dominant gene expression in offspring. It is the additional economic value brought about by the expression of advantageous genes. It is the number of recessive genetic defects under consideration. It is the probability of expressing a genetic defect. , and These are the weights of the breeding value index of the mating combination, the carrying of advantageous mutations, and the genetic defects in the model, respectively.

[0033] S4. Based on the beef cattle economic scoring model, calculate the economic scores of all possible pairing combinations between the bull and the cow to be bred, and use linear programming to maximize the solution to generate the optimal selection scheme.

[0034] The function expression for maximization using linear programming is:

[0035] in, This indicates the expected genetic value of the offspring of the currently selected combination. It is a binary decision variable; when When the value is 0, it indicates that the pairing is not accepted. A value of 1 indicates that the pairing is retained; the constraint on the number of times male and female animals can be used is:

[0036] in, aand b These represent the maximum number of times male and female animals can be used: This indicates the status of male livestock. The maximum number of times it can be used is ; This indicates that for female animals The maximum number of times it can be used is .

[0037] Example 2 This invention provides an application process for the beef cattle genome selection method that integrates genetic advantages and disadvantages as described in Example 1: (1) Blood samples were collected from each head of the cattle to be bred and frozen. DNA was extracted for genotyping, and the genotypes AA, Aa, and aa, representing genetic advantages and disadvantages, were recoded into files in 0, 1, and 2 formats. PLINK 1.0 software was used to process and perform quality control on the genotyped data. The quality control criteria were: ① located on an autosome; ② minimum allele frequency (MAF) greater than 0.05; ③ call rate of each SNP marker greater than 0.9; ④ Hardy-Weinberg equilibrium test (HW). P > 1×10 6 .

[0038] (2) The quality-controlled genotype data were numerically processed using PLINK 1.0, and the genotypes AA, Aa, and aa were re-encoded into raw format files of 0, 1, and 2. The VanRaden model was used to construct an additive kinship matrix using the A.mat function in the rrBLUP software package. Based on the genotype and phenotypic data of the Chinese beef cattle genomic selection reference group, the Asreml-R software package was used to perform GBLUP prediction of the genomic estimated breeding values ​​of five important economic traits (carcass weight, calving difficulty, weaning weight, average daily weight gain during the fattening period, and dressing percentage) of the test population, and the Chinese beef cattle genomic selection composite index (GCBI) was calculated.

[0039]

[0040] (3) Using PLINK 1.0, extract the genotypes of the genetic advantages and defects of the bulls and cows in the above formula, and select the bulls with the top 10% GCBI values ​​and carrying no more than 1 genetic defect, or the bulls with the top 30% GCBI values ​​and carrying the target advantageous gene. Using R language, construct a beef cattle economic scoring model based on the GCBI value of the individual beef cattle to be bred, the genotypes of genetic advantages and defects, the inbreeding coefficient, the breeding value index of the mating combination, and the economic value corresponding to the carrying of advantageous mutations and genetic defects.

[0041] The economic scoring model for beef cattle is as follows:

[0042] (4) Calculate the economic score of all possible combinations of male and female cattle using R language, and solve the problem of maximizing the economic score using the Lp_solve software package based on linear programming. The constraint on the number of times male and female cattle are used is: 1) For each male animal j The maximum number of times it can be used is 'a', that is... ;2) For female animals i It requires exactly one and only one pairing count. ,Right now The linear programming model is as follows:

[0043] (5) Provide the optimal candidate bull for mating for each cow to be bred.

[0044] The specific application process involves: genomic matching that integrates genetic advantages and disadvantages in the breeding of West China cattle. Experimental materials: 150 Huaxi cattle bulls from breeding bull stations such as Tongliao Jingyuan Breeding Cattle Co., Ltd. and Henan Dingyuan Breeding Cattle Co., Ltd., and 600 Huaxi cattle cows from Jilin Augens Agricultural and Animal Husbandry Technology Development Co., Ltd. were selected.

[0045] The specific steps of this application process are as follows: (1) All West China cattle were venipunctured and stored in 5ml EDTA vacuum anticoagulation blood collection tubes. Genotyping was performed using 10× genome resequencing to identify genetically dominant and defective genotypes. In this embodiment, the dominant gene selected was myostatin. MSTNThe genetic defects include Fleckviehhaplotype 4 (FH4; Chr12:11102143 A>G), embryo lethality defect—Brown swisshaplotype 2 (BH2; Chr19:10833921 ​​T>C), bovine male subfertility (BMS; Chr19:27056843 C>A), Montbéliarde haplotype 1 (MH1; Chr19:27895397 C>T), and Simmental haplotype 9 (SH9; Chr21:42154344). C>A), Thrombobopathy (TP; Chr29:42978791 A>G), with ARS-UCD1.2 as the reference genome version. The genetic advantage and defect genotypes AA, Aa, and aa were recoded as 0, 1, and 2, respectively, and the recoded genotypes were output to the file genetic_adv_defect_file (Table 1).

[0046] Table 1 Examples of Genetic Advantage and Defect Genotype Recoding Files

[0047] (2) Before analysis, quality control of the genotype data is required. PLINK 1.0 is used to remove unqualified individuals and SNPs. The quality control standard and code for this study are: plink --cow --file filename --geno0.1 --maf 0.05 --hwe 0.000001 --mind 0.1 --recode --out filename. The obtained genotype files are re-encoded into 0, 1, and 2 genotyping formats using PLINK 1.0 software. The command is: plink --cow --vcf filename --recode A --out filename. The transformed genotype data is used for subsequent West China cattle genome selection.

[0048] (3) The VanRaden model was used to construct an additive sex-linked matrix (G matrix) using the A.mat function in the rrBLUP software package. Genetic evaluation was performed using the Asreml-R software package, and the GBLUP model was adopted to calculate the genomic estimation breeding values ​​for five traits of the Huaxi cattle to be evaluated: carcass weight, calving difficulty, weaning weight, average daily weight gain during the fattening period, and dressing percentage. The reference population was the Huaxi cattle reference population previously established by the Beijing Institute of Animal Husbandry and Veterinary Medicine, Chinese Academy of Agricultural Sciences, consisting of 3928 head. The comprehensive selection index was calculated based on the calculated genomic estimation breeding values ​​of the five traits of the candidate population individuals, and they were ranked separately by male and female cattle. The genetic parameters of each trait are shown in Table 2.

[0049] Table 2 Genetic parameters of each trait

[0050] (4) After evaluation, bulls from Huaxi cattle will be selected based on GCBI size, genetic advantage, and defect carrier status. The selection criteria are: GCBI greater than 150 and the number of genetic defect carriers not exceeding 1, or GCBI value greater than 130 and the number of carriers not exceeding 1. MSTN Individuals with dominant genes. A total of 15 bulls were selected for genomic selection. Details of the selected bulls are shown in Table 3.

[0051] Table 3. GCBI values, genetic advantages, and carrier status of selected bulls.

[0052] (5) In this embodiment, the economic value of one unit of GCBI index is defined as 39 yuan, and the economic penalty for a 1% increase in the inbreeding coefficient is set at 240 yuan. The economic loss for early embryonic lethality or miscarriage due to genetic defects (FH4, MH1, SH9) is set at 500 yuan, and the economic loss for carrying genetic defects such as chronic respiratory disease and abnormal wound coagulation (BH2, BMS, TP) is set at 200 yuan. For dominant genes, the economic loss for carrying them is set at 200 yuan. MSTN The offspring of the gene have an additional economic value of 300 yuan. MSTN The additional economic value of gene expression is 600 yuan. For the weighting of each component, priority is given to eliminating the probability of genetic defect expression and increasing the frequency of dominant gene carriers without reducing the average GCBI value of offspring. Based on this, the final weighting in this embodiment is determined as follows: 、 and The weight ratio is This is used to construct an economic scoring model for beef cattle.

[0053] (6) Using R language, input the individual IDs and GCBI value files of male and female cattle, the genotype file of genetic advantages and defects extracted in step (1), and calculate the economic scores of all possible combinations of male and female cattle to be bred according to the beef cattle economic scoring model constructed in step (5).

[0054] (7) Input the economic score of each combination calculated in step (6) into the Lp_solve software package and use the linear programming algorithm for selection. The constraints of the linear programming are set as follows: the maximum number of times each bull can be used is 100, each cow needs to be used once, and the solution direction is to maximize the economic score.

[0055] (8) Select the scheme that maximizes the economic score of the mating combination from the output results of the linear programming to obtain the optimal candidate bull mating list of the cows to be bred in this farm (see Table 4).

[0056] Table 4. List of Optimal Candidate Bulls for Mating Cows

[0057] (9) Genomic Selection Effect: To further illustrate the superiority of the genomic selection method for beef cattle that integrates genetic advantages and defects, this embodiment evaluated the GCBI, inbreeding level, frequency of carrying genetic advantages, and expression probability of genetic defects in the offspring of genomic selection, homogeneous selection, and random mating. Compared with homogeneous selection and random mating, the offspring of genomic selection showed better results. MSTN The carrier frequencies increased by 7.0% and 16.7% respectively, without decreasing the average GCBI level (Table 5). Furthermore, compared to the parent cow population, the offspring population obtained through genomic selection integrating genetic advantages and disadvantages saw an increase in average GCBI from 115.6 to 153.0. MSTN The carrier frequency increased from 0.3% to 53.4%, and the carrier frequency of various genetic defects in the population was reduced (Table 6).

[0058] The above results demonstrate that the constructed economic scoring model can effectively control the level of inbreeding in a population, achieving an average... GCBI While maximizing the expression of genetic defects, it completely eliminates the probability of their expression and can significantly improve the expression of genetic defects in offspring within one generation. MSTN The proportion of mutation carriers.

[0059] Table 5 Comparison of offspring populations obtained from different mating schemes

[0060] Table 6. Comparison of parental cow populations and offspring populations obtained through genomic selection.

[0061] This invention improves the beef cattle genome selection system, accelerates the efficient diffusion of advantageous genes in the population, and effectively avoids the accumulation and spread of harmful mutations. This technology provides the beef cattle industry with an efficient and precise breeding method, significantly accelerates the breeding process, and powerfully promotes the high-quality development of the beef cattle industry, possessing significant application value and broad prospects for promotion.

[0062] The above description of the disclosed embodiments enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for selecting beef cattle genomes that integrates genetic advantages and disadvantages, characterized in that, Includes the following steps: S1. Extract individual DNA samples from the bulls and cows to be bred for genotyping and quality control, and obtain individual SNP genotype data; the SNP genotype data includes genetic advantages and defect carrier status. S2. Calculate the additive sex kinship matrix according to the VanRaden algorithm, use GBLUP to obtain the estimated genomic breeding values ​​of beef cattle individuals for important economic traits, and calculate the comprehensive selection index of individuals based on the estimated genomic breeding values ​​of each trait. In step S2, the additive sex kinship matrix is ​​calculated using the VanRaden algorithm, and the estimated genomic breeding values ​​of individual beef cattle for important economic traits are obtained using GBLUP. The functional expression is as follows: in, A vector of phenotypic observations; yes 3D correlation matrix; yes 3D fixed effects vector; It is the number of fixed effects; Is with Related structural matrices; Represents the additive effect vector, following the order N(0, The normal distribution of ); where An additive genomic relationship matrix, For additive inheritance variance, Let N be the residual vector, which follows the expression N(0, The normal distribution of ) In S2, the individual's comprehensive selection index The function expression is: in, It is a genomic estimation breeding value for the ease or difficulty of calving; It is the breeding value estimated by the genome of weaning weight, and the weaning weight is uniformly corrected to the weight at 6 months of age; It is the genomically estimated breeding value of the average daily weight gain during the fattening period; It is the estimated breeding value of carcass weight based on genome; It is the genomic estimation breeding value of slaughter rate; S3. Based on the individual's comprehensive selection index value, SNP genotype data, inbreeding coefficient, and the economic value corresponding to each part, construct a beef cattle economic scoring model for evaluating the economic value of paired combinations. In S3, the functional expression of the beef cattle economic scoring model is: in, Used to calculate the first The first parent and the first The score of mating between the male parents. and For their respective comprehensive selection index values, It is a penalty value for a 1% increase in inbreeding. It is the inbreeding coefficient between paired individuals. It is the number of dominant genes being considered. It is the probability of offspring carrying advantageous genes. The economic value of offspring carrying advantageous mutations. It is the probability of dominant gene expression in offspring. It is the additional economic value brought about by the expression of advantageous genes. It is the number of recessive genetic defects under consideration. It is the probability of expressing a genetic defect. , and These are the breeding value index of the mating combination, the weights of carrying advantageous mutations and genetic defects in the model, respectively; S4. Based on the beef cattle economic scoring model, calculate the economic scores of all possible pairing combinations between the bull and the cow to be bred, and use linear programming to maximize the solution to generate the optimal selection scheme. In S4, the function expression for maximizing the linear programming problem is: in, This indicates the expected genetic value of the offspring of the currently selected combination. It is a binary decision variable; when When the value is 0, it indicates that the pairing is not accepted. A value of 1 indicates that the pairing is retained; the constraint on the number of times male and female animals can be used is: in, and These represent the maximum number of times male and female animals can be used: + + This indicates the status of male livestock. The maximum number of times it can be used is ; + + This indicates that for female animals The maximum number of times it can be used is .

2. The method for selecting beef cattle genomes that integrates genetic advantages and disadvantages according to claim 1, characterized in that, In S2, important economic traits include: carcass weight, calving difficulty, weaning weight, average daily weight gain during the fattening period, and dressing percentage.

3. The method for selecting beef cattle genomes that integrates genetic advantages and disadvantages according to claim 1, characterized in that, In S1, the quality control specifically includes: Only autosomal loci are retained, while loci with a typing success rate of less than 90% and a minimum allele frequency of less than 0.05 are removed, as are individuals with a detection rate of less than 90%.

4. The application of the beef cattle genome selection method integrating genetic advantages and defects as described in any one of claims 1-3 in beef cattle breeding.

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