Beef cattle genome matching method integrating genetic advantages and defects and application

Abstract

The invention relates to the technical field of animal breeding, and particularly discloses a beef cattle genome matching method integrating genetic advantages and defects and application, the method comprises the following steps: S1, respectively extracting beef cattle individual DNA samples from to-be-matched breeding bulls and to-be-matched cows for genetic typing, and obtaining individual SNP genotype data; s2, calculating an additive genetic relationship matrix according to a VanRaden algorithm to obtain genome estimation breeding values of beef cattle individuals on important economic characters, and calculating comprehensive selection indexes of the individuals; s3, according to the comprehensive selection index value of the individual, the SNP genotype data, the inbreeding coefficient and the economic value corresponding to each part, constructing a beef cattle economic scoring model; s4, based on the beef cattle economic scoring model, calculating economic scores of all possible pairing combinations between the to-be-mated breeding bulls and the to-be-mated cows, and performing maximum solution to generate an optimal mating scheme; through genome breeding value evaluation and economic scoring model construction, beef cattle genome matching with genetic advantages and defects is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of animal breeding technology, and more particularly to a method for integrating genetic advantages and defects in beef cattle genome matching and application. BACKGROUND

[0002] The beef cattle industry has developed rapidly in recent years, but there are still many deficiencies. The main breeds of beef cattle currently raised include imported Simmental, Angus, Charolais, etc., as well as native breeds such as Huaxi cattle and Qinchuan cattle. Although these breeds meet market demand to some extent, further improvements are needed in growth rate, feed conversion rate, meat quality, and slaughter rate, etc.

[0003] In terms of genetic improvement, some key advantage genes have been proven to significantly improve beef cattle production performance. For example, MSTN mutation of the gene (myostatin) can promote muscle development and increase slaughter rate; IGF2 the gene is closely related to growth rate and feed efficiency; and the multi-rib gene can increase the number of ribs and improve meat production. Rational use of these advantage genes can greatly accelerate the breeding process and further increase the added economic value of each cow.

[0004] In addition, there are also various genetic defects in beef cattle populations. Since the 1960s, a variety of genetic diseases related to calf birth defects and metabolic disorders have been found in Simmental cattle, such as Thrombopathy (TP) and Syndactyly disease (SY). In recent years, with the rapid development of bovine genome SNP chip and whole genome sequencing technology, more new genetic defects have been identified. These include embryonic lethal defect - Brown Swiss haplotype 2 (BH2), bovine male subfertility (BMS), Fleckvieh haplotype 2 (FH2) and FH4, Montbéliarde haplotype 1 (MH1), and Simmental haplotype 9 (SH9). These genetic defects not only affect the growth and development of individuals, but also may have long-term negative effects on the health of the population.

[0005] The current beef cattle mating system mainly relies on traditional methods: selection based on phenotypic data and pedigree information, combined with artificial insemination technology to expand excellent individuals. Although some large breeding farms have begun to apply genomic selection technology, there are still obvious deficiencies: first, the use of dominant genes is not systematic enough, making it difficult to achieve multi-gene coordinated improvement; second, the screening and prevention of genetic defects are insufficient, leading to the spread of harmful mutations in the population; third, there is a lack of precise mating programs integrating genomic information, and the breeding efficiency needs to be improved. To address these issues, a new genomic mating method that integrates the use of dominant genes and the prevention of genetic defects is urgently needed to transform beef cattle breeding from an empirical to a precise approach. SUMMARY

[0006] The purpose of the present application is a beef cattle genomic mating method integrating genetic advantages and defects and its application, which realizes the genomic mating of beef cattle with genetic advantages and defects through genomic breeding value evaluation and economic score model construction.

[0007] To achieve the above purpose, the present application provides the following technical solutions: A beef cattle genomic mating method integrating genetic advantages and defects, comprising the following steps: S1. Extracting beef cattle individual DNA samples from the to-be-mated sire and to-be-mated female for genotyping and quality control, and obtaining individual SNP genotype data; wherein the SNP genotype data includes the carrying status of genetic advantages and defects; S2. Calculating the additive kinship matrix according to the VanRaden algorithm, obtaining the genomic estimated breeding value of beef cattle individuals on important economic traits using GBLUP, and calculating the comprehensive selection index of individuals according to the genomic estimated breeding value of each trait; S3. Constructing a beef cattle economic score model for evaluating the economic value of paired combinations according to the comprehensive selection index value of individuals, SNP genotype data, inbreeding coefficient, and the economic value of each part; S4. Based on the beef cattle economic score model, calculating the economic score of all possible paired combinations between the to-be-mated sire and the to-be-mated female, and using linear programming to maximize the solution to generate the optimal mating scheme.

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

[0009] Further, in S1, the quality control is as follows: Only autosomal sites are retained, sites with a typing success rate less than 90% and a minimum allele frequency less than 0.05 are excluded, and individuals with a detection rate less than 90% are excluded.

[0010] Further, in the S2, the additive genetic relationship matrix is calculated according to the VanRaden algorithm, and the genomic estimated breeding value of the beef cattle individual on the important economic traits is obtained by GBLUP, and the function expression is:

[0011] wherein, represents a phenotype observation value vector; is a correlation matrix of dimension n x n; is a fixed effect vector of dimension p x 1; is the number of fixed effects; Z is a structure matrix associated with a ; represents an additive effect vector, which is subject to a normal distribution of N(0, ); wherein G is an additive genomic relationship matrix, is an additive genetic variance, is a residual vector, which is subject to a normal distribution of N(0, ).

[0012] Further, in the S2, the comprehensive selection index of the individual is a function expression of:

[0013] wherein, is a calving ease genomic estimated breeding value; is a weaning weight genomic estimated breeding value, which is corrected to the 6-month weight; is a average daily gain during the fattening period genomic estimated breeding value; is a carcass weight genomic estimated breeding value; is a dressing percentage genomic estimated breeding value.

[0014] Further, in the S3, the function expression of the beef cattle economic score model is

[0015] wherein, is used to calculate the score of the mating of the first i female and the second j male, and are the respective comprehensive selection index values, is a penalty value of 1% increase in inbreeding degree, is the inbreeding coefficient between the paired individuals, is the number of considered advantageous genes, is the probability of offspring carrying the advantageous gene, is the economic value of offspring carrying the advantageous mutation, is the probability of offspring expressing the advantageous gene, is the additional economic value brought by expression of the advantageous gene, is the number of considered recessive genetic defects, is the probability of expression of the genetic defect, 、 and are the weights of the breeding value index of mating combinations, the probability of carrying the advantageous mutation and the genetic defect in the model, respectively.

[0016] Further, in the S4, the function expression for which the linear programming is solved to maximize is:

[0017] wherein, represents the expected genetic value of offspring of the current mating combination, is a binary decision variable; when is 0, it indicates that the mating is not adopted, and when is 1, it indicates that the mating is retained; the usage frequency constraints of the sire and the dam are:

[0018] wherein, a and b respectively represent the maximum usage frequency limit of the sire and the dam: , represents that for the sire , the maximum usage frequency is ; , represents that for the dam , the maximum usage frequency is .

[0019] The application also provides a specific application of the beef cattle genome mating method integrating genetic advantages and defects in beef cattle breeding.

[0020] According to the specific embodiments provided by the application, the following technical effects are disclosed: 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 beef cattle individual DNA samples from the to-be-mated male and to-be-mated female for genotyping, and perform processing and quality control to obtain individual SNP genotype data; wherein the SNP genotype data includes genetic advantages and defect carrying conditions; Among them, the quality control is specifically: only autosomal sites are retained, sites with a typing success rate less than 90% and a minimum allele frequency less than 0.05 are excluded, and individuals with a detection rate less than 90% are excluded.

[0027] S2, calculate the additive kinship matrix according to the VanRaden algorithm, obtain the genomic estimated breeding value of the beef cattle individual on the important economic traits by GBLUP, and calculate the comprehensive selection index of the individual according to the genomic estimated breeding value of each trait; wherein the important economic traits include: carcass weight, calving difficulty, weaning weight, average daily gain during the fattening period, and dressing percentage; According to the VanRaden algorithm, the additive kinship matrix is calculated, and the genomic estimated breeding value of the beef cattle individual on the important economic traits is obtained by GBLUP, and the function expression is:

[0028] Among them, represents the phenotype observation vector; is a correlation matrix; is a fixed effect vector; is the number of fixed effects; is a structure matrix associated with a ; represents an additive effect vector, which is normally distributed as N(0, ); wherein G is the additive genomic relationship matrix, is the additive genetic variance, is a residual vector, which is normally distributed as N(0, ).

[0029] S3, according to the comprehensive selection index value of the individual, the SNP genotype data, the inbreeding coefficient and the economic value corresponding to each part, a beef cattle economic score model for evaluating the economic value of the mating combination is constructed; wherein the function expression of the comprehensive selection index of the beef cattle individual is:

[0030] Among them, is the genomic estimated breeding value of calving difficulty; is the genomic estimated breeding value of weaning weight, which is corrected to 6-month-old weight; is the genomic estimated breeding value of average daily gain in the finishing period; is the genomic estimated breeding value of carcass weight; is the genomic estimated breeding value of dressing percentage.

[0031] wherein the function expression of the beef cattle economic score model is:

[0032] wherein, is used to calculate the score of mating the i th female parent with the j th male parent, and are the respective comprehensive selection index values, is the penalty value of 1% increase in inbreeding degree, is the inbreeding coefficient between the paired individuals, is the number of considered dominant genes, is the probability of carrying the dominant gene offspring, is the economic value of the offspring carrying the dominant mutation, is the probability of offspring dominant gene expression, is the additional economic value brought by the expression of the dominant gene, is the number of considered recessive genetic defects, is the probability of genetic defect expression, , and are the breeding value index of the mating combination, the weight of carrying the dominant mutation and genetic defect in the model, respectively.

[0033] S4, based on the beef cattle economic score model, calculate the economic score of all possible pairing combinations between the to-be-mated bulls and the to-be-mated cows, and use linear programming to maximize the solution to generate the optimal mating scheme.

[0034] wherein the function expression for maximizing the solution by using linear programming is:

[0035] wherein, represents the expected genetic value of the offspring of the current mating combination, is a binary decision variable; when is 0, it indicates that the pairing is not adopted, and when is 1, it indicates that the pairing is retained; the usage frequency constraint condition of the male and female animals is:

[0036] wherein, aand b respectively represent the maximum number of times of use limit of the male and female livestock: , represents for the male livestock , the maximum number of times of use is ; , represents for the female livestock , the maximum number of times of use is .

[0037] Example 2 The application process of the beef cattle genome matching method integrating genetic advantages and defects in Example 1 is provided: (1) Blood is taken from each beef cattle in the population to be matched and stored, and DNA is extracted for genotyping, and the genotypes AA, Aa and aa of genetic advantages and defects are recoded into a 0, 1, 2 format file. The PLINK 1.0 software is used to process and quality control the genotyped data, and the quality control standards are: ① located on autosomes; ② the minimum allele frequency (MAF) is greater than 0.05; ③ the detection rate (Call rate) of each SNP marker is greater than 0.9; ④ Hardy-Weinberg equilibrium test (HW) P > 1×10 6 .

[0038] (2) The PLINK 1.0 is used to numerically process the genotyped data, and the genotypes AA, Aa and aa are recoded into a 0, 1, 2 raw format file; the VanRaden model is used to construct an additive kinship matrix by using the A.mat function in the rrBLUP software package. The Asreml-R software package is used to predict the genomic estimated breeding values of the five important economic traits (carcass weight, ease of calving, weaning weight, average daily gain during the fattening period, and dressing percentage) of the population to be tested according to the genotype and phenotype data of the beef cattle genome selection reference population in China, and the Chinese beef cattle genome selection comprehensive index (GCBI) is calculated.

[0039]

[0040] (3) The PLINK 1.0 is used to extract the genotype of the genetic advantages and defects of the male and female cattle in the above formula, and the male cattle with a GCBI value in the top 10% and carrying no more than one genetic defect or a GCBI value in the top 30% and carrying the target advantage gene are selected. The R language is used to construct a beef cattle economic scoring model according to the GCBI value, the genotype of genetic advantages and defects, the inbreeding coefficient of the beef cattle to be matched, and the breeding value index of the mating combination, the economic value corresponding to the carrying of the advantage mutation and genetic defects.

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

[0042] (4) Calculate the economic score of all possible combinations of male and female cattle using R language, and use the Lp_solve software package to solve the maximum economic score based on linear programming. The constraints of the number of times of using male and female cattle are: 1) for each male cattle j , the maximum number of times is a, that is ; 2) for female cattle i , there must be and only one mating number , that is . The linear programming model is as follows:

[0043] (5) Give the optimal candidate male cattle mating list for each female cattle to be mated.

[0044] The application process is as follows: integrating genetic advantage and defect genome in Huaxi cattle breeding Experimental materials: 150 Huaxi cattle from Tongliao Jingyuan Cattle Breeding Co., Ltd., Henan Dingyuan Cattle Breeding Co., Ltd., and other stud bulls, and 600 Huaxi cattle from Jilin Aojin Agriculture and Pasture Technology Development Co., Ltd.

[0045] The specific steps of the application process are as follows: (1) All Huaxi cattle are collected by intravenous blood, stored in 5ml EDTA vacuum anticoagulant blood collection tubes, and typed by 10x genome resequencing to identify genetic advantage and defect genotypes. The advantage gene selected in this embodiment is myostatin (Myostatin, MSTNChr2:6279278 C>A); genetic defects are Fleckvieh haplotype 4 (FH4; Chr12: 11102143 A>G), embryo lethal defect - Brown Swiss haplotype 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), Thrombopathy (TP; Chr29: 42978791 A>G), with reference genome version ARS-UCD1.2. The above genetic advantage and defect genotypes AA, Aa and aa are recoded as 0, 1, 2, and the recoded genotype is output to the file genetic_adv_defect_file (Table 1).

[0046] Table 1. Example of genetic advantage and defect genotype recoding file

[0047] (2) Before analysis, quality control of genotype data is required, and PLINK 1.0 is used to remove unqualified individuals and SNPs. The quality control standards and codes in this study are: plink --cow --file filename --geno0.1 --maf 0.05 --hwe 0.000001 --mind 0.1 --recode --out filename. The obtained genotype file is recoded into 0, 1, 2 typing format data, using PLINK 1.0 software, and the running command is: plink --cow --vcf filename --recode A --out filename. The transformed genotype data is used for subsequent genome matching of Huaxi cattle.

[0048] (3) The additive relationship matrix (G matrix) was constructed by using the A.mat function in the rrBLUP software package based on the VanRaden model. The genetic evaluation was performed by using the Asreml-R software package, and the GBLUP model was adopted to calculate the genomic estimated breeding values of the five traits of the evaluated Huaxi cattle, including carcass weight, calving ease, weaning weight, average daily gain during the fattening period, and dressing percentage. The reference population was the Huaxi cattle reference population established by the Beijing Institute of Animal Science of the Chinese Academy of Agricultural Sciences, which included 3,928 individuals. The comprehensive selection index was calculated based on the genomic estimated breeding values of the five traits of the candidate population, and the individuals were ranked according to the 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) The evaluated Huaxi cattle were selected according to the GCBI size and the carrying of genetic advantages and defects. The selection criteria were as follows: the GCBI was greater than 150 and the number of genetic defects carried was not more than one, or the GCBI value was greater than 130 and the individual carried MSTN advantageous genes. A total of 15 Huaxi cattle were selected for genomic mating, and the detailed information of the selected Huaxi cattle is shown in Table 3.

[0051] Table 3 GCBI values and genetic advantages and defects of the selected Huaxi cattle

[0052] (5) In this embodiment, the economic value of one unit of GCBI index was defined as 39 yuan, and the economic penalty for increasing the inbreeding coefficient by 1% was set as 240 yuan. The economic loss of genetic defects such as early embryonic lethality or abortion (FH4, MH1, SH9) was set as 500 yuan, and the economic loss of genetic defects such as chronic respiratory disease and abnormal wound clotting (BH2, BMS, TP) was set as 200 yuan. For advantageous genes, the additional economic value of offspring carrying the gene was set as 300 yuan, and the additional economic value of gene expression was set as 600 yuan. For the weight of each part, priority was given to eliminating the expression probability of genetic defects and increasing the carrying frequency of advantageous genes without reducing the average GCBI value of offspring. Based on this, the weight ratio of MSTN MSTN 、 and was finally determined as in this embodiment to construct the beef cattle economic score model.

[0053] ​​(6) Using R language to input the male and female cattle individual ID and GCBI value file, the genotype file of genetic advantages and defects extracted in step (1) genetic_adv_defect_file, and according to the beef cattle economic score model constructed in step (5), the economic score of all possible combinations of the to-be-mated male and female cattle is calculated.

[0054] (7) The economic score of each combination calculated in step (6) is input into the Lp_solve software package, and linear programming algorithm is used for mating. The constraint condition of linear programming is set as: the maximum number of use of each bull is 100, and each female needs to be used once, and the solving direction is to maximize the economic score.

[0055] (8) In the output result of linear programming, the scheme with the maximum economic score of mating combination is selected to obtain the optimal candidate bull mating list of the to-be-mated female cattle in this field (see Table 4).

[0056] Table 4 Optimal candidate bull mating list of to-be-mated female cattle

[0057] (9) Genomic mating effect: In order to further illustrate the superiority of the genomic mating method of integrating genetic advantages and defects of beef cattle, in this embodiment, the offspring GCBI, inbreeding level, genetic advantage carrying frequency and genetic defect expression probability of genomic mating, homogeneous mating and random mating are evaluated. Compared with homogeneous mating and random mating, the offspring of genomic mating MSTN The carrying frequency is increased by 7.0% and 16.7% respectively, and the average GCBI level is not reduced (Table 5). In addition, compared with the parent female cattle population, the offspring population obtained by genomic mating of integrating genetic advantages and defects is increased from 115.6 to 153.0, MSTN The carrying frequency is increased from 0.3% to 53.4%, and the carrying frequency of each genetic defect in the population can be reduced (Table 6).

[0058] The above results show that the economic score model constructed can effectively control the inbreeding level of the population, realize the maximization of the average GCBI At the same time, the probability of genetic defect expression is completely eliminated, and the proportion of mutation carriers in the offspring can be significantly improved in one generation. MSTN

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

[0060] Table 6 Comparison results of parent female cattle population and offspring population obtained by genomic mating ​

[0061] The application can perfect the beef cattle genome matching system, accelerate the efficient diffusion of dominant genes in the population, and effectively avoid the accumulation and spread of harmful mutations. The technology provides an efficient and accurate breeding method for the beef cattle industry, significantly speeds up the breeding process, effectively promotes the high-quality development of the beef cattle industry, and has important application value and broad popularization prospect.

[0062] The above description of the disclosed embodiments enables one skilled in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended 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 of genomic selection of beef cattle integrating genetic advantages and defects, characterized in that, Comprise the following steps: S1, extract beef cattle individual DNA samples from the to be mated bull and the to be mated cow respectively for genotyping, and perform quality control, and obtain individual SNP genotype data; wherein the SNP genotype data includes genetic advantage and defect carrying condition; S2, calculate the additive relationship matrix according to the VanRaden algorithm, obtain the genomic estimated breeding value of the beef cattle individual on important economic traits by GBLUP, and calculate the comprehensive selection index of the individual according to the genomic estimated breeding value of each trait; S3, according to the individual comprehensive selection index value, SNP genotype data, inbreeding coefficient and the corresponding economic value of each part, a beef cattle economic score model for evaluating the economic value of the pairing combination is constructed; S4, based on the beef cattle economic score model, the economic score of all possible pairing combinations between the to be mated bull and the to be mated cow is calculated, and linear programming is used for maximum solution to generate the optimal mating scheme.

2. The integrated genetic advantage and defect beef cattle genome-assisted mating method according to claim 1, characterized in that, In the S2, the important economic traits include: carcass weight, calving difficulty, weaning weight, average daily gain in the fattening period and dressing percentage.

3. The integrated genetic advantage and defect beef cattle genome-assisted mating method according to claim 1, wherein, In the S1, the quality control is specifically: Only autosomal sites are retained, sites with a typing success rate less than 90% and a minimum allele frequency less than 0.05 are excluded, and individuals with a detection rate less than 90% are excluded.

4. The integrated genetic advantage and defect beef cattle genome-assisted mating method according to claim 2, wherein, In the S2, the additive relationship matrix is calculated according to the VanRaden algorithm, and the genomic estimated breeding value of the beef cattle individual on important economic traits is obtained by GBLUP, and the function expression is: ； wherein represents a vector of phenotypic observations; is a matrix of associations of dimension is a vector of fixed effects of dimension is the number of fixed effects is a matrix of structure associated with ; represents a vector of additive effects, normally distributed as N(0, ); wherein G is an additive genomic relationship matrix, is an additive genetic variance, is a residual vector, normally distributed as N(0, ).

5. The integrated genetic advantage and defect beef cattle genome-assisted mating method according to claim 1, wherein, In the S2, the comprehensive selection index of the individual The function expression is: ； wherein, is the genomic estimated breeding value for calving ease; is the genomic estimated breeding value for weaning weight, which is uniformly corrected to 6-month weight; is the genomic estimated breeding value for average daily gain during the finishing period; is the genomic estimated breeding value for carcass weight; is the genomic estimated breeding value for dressing percentage.

6. The integrated genetic advantage and defect beef cattle genome-assisted mating method according to claim 1, wherein, In the S3, the function expression of the beef cattle economic score model is: ； wherein, the score for mating the i th female with the j th male, and are the respective overall selection index values, is the penalty value for an increase of 1% in inbreeding, is the inbreeding coefficient between the pair of individuals, is the number of advantageous genes considered, is the probability of a progeny carrying an advantageous gene, is the economic value of a progeny carrying an advantageous mutation, is the probability of a progeny expressing an advantageous gene, is the additional economic value brought by the expression of an advantageous gene, is the number of recessive genetic defects considered, is the probability of expression of a genetic defect, , and are respectively the breeding value index of the mating combination, the weight of the carrying of advantageous mutations and genetic defects in the model.

7. The integrated genetic advantage and defect beef cattle genome-assisted mating method according to claim 1, wherein, In the S4, the function expression of the linear programming for maximum solution is: ； wherein, represents the expected genetic value of the current mating combination offspring, is a binary decision variable; when is 0, it means that the pair is not accepted, when is 1, it means that the pair is kept; the usage constraint for the sire and dam is: ； wherein, a and b respectively represent the maximum number of uses limit for male and female animals: represents for male animals , the maximum number of uses is ; represents for female animals i , the maximum number of uses is .

8. The application of the integrated genetic advantage and defect beef cattle genome mating method of any one of claims 1-7 in beef cattle breeding.

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