A method and application of genomic mate selection for Huaxi cattle based on whole-genome SNP information

Abstract

The present invention provides a genomic mating method and application of Huaxi cattle based on whole-genome SNP information, belonging to the technical field of animal breeding. The problem to be solved is the increase in inbreeding level and the decrease in genetic progress caused by multi-generation genomic selection in the existing beef cattle breeding population. The specific steps of the present invention are as follows: Step 1, extract DNA from the Huaxi cattle individuals to be mated for genotyping, and process and quality control the data; Step 2, perform genotype data imputation to obtain high-density chip data; Step 3, calculate the additive relationship matrix, use GBLUP to obtain the genomic estimated breeding values of five important economic traits of the Huaxi cattle population to be mated, and then calculate the comprehensive selection index of individuals; Step 4, use a genetic algorithm to construct a list of optimal mating combinations for the population. The present invention greatly saves breeding costs, reduces the inbreeding level of the offspring population, shortens the breeding process, and has good application prospects and economic benefits.

Description

Technical Field

[0001] The present invention belongs to the technical field of animal breeding, and particularly relates to a Huaxi cattle genome matching method and application based on whole-genome SNP information. Background Art

[0002] Huaxi cattle is a specialized beef cattle new breed painstakingly cultivated by the Beijing Institute of Animal Science and Veterinary Medicine, Chinese Academy of Agricultural Sciences over more than 40 years. It has characteristics such as fast growth rate, high feed conversion rate, high net meat rate, good meat production and reproductive performance, and strong stress resistance. Compared with international beef cattle breeds of the same type, the daily weight gain, slaughter rate, and net meat rate of Huaxi cattle are at the international advanced level. However, the performance of this breed still needs to be continuously selected and improved to achieve sustainable genetic progress. In the breeding of Huaxi cattle, an excellent breeding bull will be mated with hundreds or thousands of cows through artificial insemination. Although this method can quickly improve the population genetic progress, after multiple generations of cultivation, it will cause the homozygous accumulation of harmful genes and the loss of rare genes, resulting in inbreeding depression and being unfavorable to the breeding process. Therefore, multiple aspects need to be considered when breeding Huaxi cattle.

[0003] With the development and application of high-throughput sequencing technology and chip technology, genomic mating, as a more reliable and accurate mating method, is a technological innovation for carrying out precise breeding and accelerating the cultivation of high-quality beef cattle populations. In the late 20th century, domestic and foreign researchers proposed methods for optimizing mating using pedigree relationships, such as sequential programming, optimal contribution selection (OCS), etc. With the wide application of genomic molecular markers (such as SNPs), optimizing mating using genomic information has become the focus of breeding research. Currently, domestic and foreign researchers have used various methods to integrate genomic information for mating optimization, such as linear programming (LP), genomic optimal contribution selection (GOCS), minimum coancestry mating (MC), minimizing the covariance of genetic contributions between ancestors (MCAC), etc., which have effectively controlled the inbreeding level of the population while accelerating genetic progress. Compared with genomic selection, genomic mating (GM) uses genomic information to trace the inheritance of chromosomal segments, thereby improving the accuracy of estimating Mendelian sampling of parents and its relationship with the genetic contributions of parents. It shifts the focus of the problem to mating and is more conducive to achieving the goal of efficient breeding. So far, there has been no report on the genomic mating method of Huaxi cattle. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to propose a Huaxi cattle genome matching method and application based on whole-genome SNP information in view of the deficiencies of the above-mentioned prior art, so as to accelerate the breeding process of Huaxi cattle.

[0005] To achieve its purpose, the present invention adopts the following technical solution: a genome selection method for Huaxi cattle based on whole genome SNP information, specifically comprising the following steps:

[0006] Step 1: DNA is extracted from individuals of Huaxi cattle to be mated, genotyping is performed using Cattle110K gene chips, and data is processed and quality controlled;

[0007] Step 2: Fill the genotype data to obtain 770K high-density chip data, perform numerical processing on the filled genotype data, and use PLINK software to convert the genotypes AA, Aa, and aa into 0, 1, and 2 respectively;

[0008] Step 3: Calculate the additive kinship matrix (G matrix) according to the VanRaden algorithm, use GBLUP to obtain the genomic estimated breeding values ​​of the five important economic traits of the Huaxi cattle population to be mated, and calculate the individual comprehensive selection index according to the genomic estimated breeding value of each trait;

[0009] Step 4: Construct all possible mating combinations. Based on the genotype data of the bulls and cows in each mating pair and the additive kinship matrix, use a genetic algorithm to calculate the expected comprehensive selection index value and inbreeding coefficient of each mating pair's offspring group while considering mutations. Based on these two indicators, optimize the mating combinations between the cows to be mated and the candidate bulls, and finally give the optimal candidate bull mating list for the cows to be mated.

[0010] As a further solution of the present invention: the quality control in step one is to retain only autosomal loci, eliminate loci with typing success rate less than 90%, minimum allele frequency less than 0.05, and Hardy-Weinberg equilibrium test less than 0.000001, and the biochip is Cattle110K gene chip.

[0011] As a further solution of the present invention: the five important economic traits in step 3 include carcass weight, calving difficulty, weaning weight, average daily weight gain, and slaughter rate. The additive kinship matrix (G matrix) is calculated according to the VanRaden algorithm, and the genomic estimated breeding value is calculated using the GBLUP model. The model is as follows:

[0012] y=Xb+Za+e

[0013] Where y represents the phenotypic observation vector; X is the n×f-dimensional association matrix; b is the f-dimensional fixed effect vector; f is the number of fixed effects; Z is the structure matrix associated with a; a represents the additive effect vector, which obeys Normal distribution of; where G is the additive genomic relationship matrix, is the additive genetic variance, e is the residual vector, and The normal distribution of .

[0014] As a further solution of the present invention: In step four, an optimized genetic algorithm model is adopted. Considering mutation, the expected comprehensive selection index value and inbreeding coefficient of each mating pair for the offspring population are calculated. According to these two indicators, the mating combination between the cows to be mated and the candidate bulls is optimized, and finally an optimal candidate bull mating list for the cows to be mated is given. The optimized genetic algorithm model is as follows:

[0015]

[0016] Among them, λ2≥0 is a parameter controlling the inbreeding degree in the offspring, and λ1 is a parameter controlling the allelic heterozygosity. The specific calculation models for genetic progress and inbreeding coefficient are as follows:

[0017]

[0018]

[0019] Among them, P is an N C ×N mating matrix, where N is the number of parents, and N C is the number of offspring; G is the additive genomic relationship matrix; D is the Mendelian sampling deviation; M is the genotype matrix; a is the marker effect.

[0020] As a further solution of the present invention: In step four, optimization pairing is carried out according to the comprehensive selection index of the parents to be mated. The formula for the comprehensive selection index (GCBI) is as follows:

[0021]

[0022] Among them, GEBV CE is the genomic estimated breeding value for calving ease; GEBV WWT is the genomic estimated breeding value for weaning weight, and the weaning weight is uniformly corrected to the weight at 6 months of age; GEBV DGF is the genomic estimated breeding value for daily weight gain during the fattening period; GEBV CW is the genomic estimated breeding value for carcass weight; GEBV DP is the genomic estimated breeding value for dressing percentage.

[0023] The object of the present invention also lies in the application of the method in the genomic mating of Huaxi cattle in the above technical solution.

[0024] Advantages of the present invention:

[0025] The present invention establishes a method for genome selection of Huaxi cattle based on whole genome SNP information, which greatly saves breeding costs and maintains long-term sustainable genetic progress; the present invention can also fill the gap in genome selection of beef cattle in my country, solve the problem of how to perform pairing and combination after genome selection in the beef cattle breeding process, provide technical means for efficient and high-quality beef cattle breeding for my country's beef cattle industry, accelerate my country's beef cattle breeding process, and promote the rapid development of my country's beef cattle industry, which has great application value and promotion prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] For ease of explanation, the present invention is described in detail with reference to the following specific embodiments and the accompanying drawings.

[0027] Figure 1 A flow chart of a method for genome selection of West China cattle based on whole genome SNP information provided in an embodiment of the present invention.

[0028] Figure 2 This is a schematic diagram comparing the GCBI of offspring groups under different mating schemes. DETAILED DESCRIPTION

[0029] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments; in the following description, specific details such as specific configurations are provided only to help fully understand the embodiments of the present invention. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention.

[0030] Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. The chemical reagents used in the examples are all commercially available.

[0031] The method of the present invention for genome selection of Huaxi cattle population based on whole genome SNP information is as follows:

[0032] like Figure 1 The figure shows the process of genome selection of Huaxi cattle population based on whole genome SNP data of the present invention.

[0033] (1) Blood was collected and frozen from each cow of the Huaxi cattle population to be tested, and DNA was extracted for genotyping using the Cattle110K gene chip. The genotyping data were processed and quality controlled using PLINK 1.0 software. The quality control criteria were: ① located on the autosome; ② the minimum allele frequency (MAF) was greater than 0.05; ③ the call rate of each SNP marker was greater than 0.9; ④ Hardy-Weinberg equilibrium test (HW) P>1×10 -6 .

[0034] (2) Based on the 770K chip data of 3928 heads of Huaxi cattle reference group established earlier, the 110K chip data of the test group were filled into 770K high-density chip data (774,660 SNPs) using Beagle software. The filled genotype data were digitized using PLINK 1.0, and the genotypes AA, Aa, and aa were recoded as 0, 1, and 2.

[0035] (3) Using the data obtained in step 2, the additive kinship matrix was constructed using the VanRaden model using the A.mat function in the rrBLUP software package. Based on the genotype and phenotypic data of the Huaxi cattle reference group, the Asreml-R software package was used to perform GBLUP to predict the genomic estimated breeding values ​​of five important economic traits (carcass weight, calving difficulty, weaning weight, average daily gain, and slaughter rate) of the test group, and the comprehensive selection index was calculated.

[0036]

[0037] (4) The top 10% of bulls and top 90% of cows in GCBI values ​​were selected, and the corresponding genotype data were extracted using PLINK 1.0. The TrainSel software package was used to calculate the expected GCBI values ​​and inbreeding coefficients of all possible pairings based on the optimized genetic algorithm model to solve the optimal pairing that maximizes the expected GCBI value of the offspring while minimizing the inbreeding coefficient.

[0038] (5) Provide a list of optimal candidate bulls for mating for each cow to be mated.

[0039] Example 1 Genome selection of Huaxi cattle based on whole genome SNP information

[0040] Experimental materials: A total of 137 West China cattle bulls from Tongliao Jingyuan Cattle Breeding Co., Ltd., Henan Dingyuan Cattle Breeding Co., Ltd. and other bull stations, and a total of 213 West China cattle cows from Jilin Okins Agriculture and Animal Husbandry Technology Development Co., Ltd. were selected.

[0041] The specific steps are as follows:

[0042] (1) All Huaxi cattle were blooded by venous sampling, stored in 5 ml EDTA vacuum anticoagulation blood collection tubes (Boruidi special blood collection tubes), frozen and mailed to Shijiazhuang Boruidi Biotechnology Co., Ltd., and the samples were registered through the Boruidi sample delivery management system. The genotype information of 110K genomic SNPs (112, 180 SNPs) was obtained.

[0043] (2) Before analysis, quality control of genotype data is required. PLINK 1.0 is used to remove unqualified SNPs. The quality control criteria and code in this study are: plink --cow --file filename --geno 0.1 --maf 0.05 --hwe 0.000001 --recode --out filename. After quality control, 350 Huaxi cattle and 106,658 SNPs remained. After quality control, the reference panel established using the Illumina BovineHD 770K high-density chip data of 5,099 Huaxi cattle was used to impute the 110K chip data of the Huaxi cattle population. The Beagle software was first used to impute the missing SNPs after quality control. The running command of the Beagle software is: java -Xmx1000m -jar unphased=file.bgl out=output niterations=100. Then, the Beagle software was used to impute it into the 770K high-density chip data (774,660 SNPs). The running command is: java -Xmx1000m -jar gt=filename.vcf ref-imputation_ref.vcf.gz out=filename. The obtained genotype file was recoded into data with three genotyping formats of 0, 1, and 2 using PLINK1.0 software. The running command is: plink --cow --vcf filename --recode A --out filename. The transformed genotype data was used for subsequent genomic mate selection of Huaxi cattle.

[0044] (3) The VanRaden model was used to construct the additive relationship matrix (G matrix) using the A.mat function in the rrBLUP software package. Genetic evaluation was carried out using the Asreml-R software package, adopting the GBLUP model. The genomic estimated breeding values were calculated for five traits of the Huaxi cattle to be mated, namely carcass weight, calving ease, weaning weight, average daily gain, and slaughter rate. The reference population selected was the Huaxi cattle reference population previously established by the Institute of Animal Science and Veterinary Medicine, Chinese Academy of Agricultural Sciences, with a total of 3,928 heads. According to the genomic estimated breeding values of each individual in the population to be mated for the five traits calculated, the comprehensive selection index GCBI was calculated and sorted by male and female cattle respectively. The genetic parameters of each trait are shown in Table 1.

[0045] Table 1 Genetic parameters of each trait

[0046]

[0047] (4) Select Huaxi cattle bulls and cows according to their GCBI values. The selection criteria are as follows: for bulls, GCBI > 150; for cows, GCBI > 80. The individual IDs and GCBI values of the selected animals are shown in Table 2.

[0048] Table 2 Bull and cow numbers and GCBI values after selection

[0049]

[0050]

[0051] (5) A total of 13 breeding bulls and 200 breeding cows for genomic mating selection are obtained. Use PLINK 1.0 to extract the genotype data of the selected bulls and cows respectively. The running commands are: plink --cow --file filename --keep dam.txt --recode A --out hx_dam, plink --cow --filename --keep sire.txt --recode A --out hx_sire. Here, dam.txt and sire.txt are the ID lists of the selected cows and bulls respectively.

[0052] (6) Input the genotypes and corresponding GCBI values of the bulls and cows selected in step (4) into the TrainSel software package, and perform mating selection using the optimized genetic algorithm. The parameter settings of the genetic algorithm are: npop = 200, nelite = 10, mutprob = 0.01, niterations = 800, niterSANN = 200, stepSANN = 0.01, minitbefstop = 200, tolconv = 1e - 7, nislands = 1, mc.cores = 1.

[0053] (7) Select the mating plan with the maximum expected GCBI value of the offspring from the output results of the genetic algorithm to obtain the optimal candidate bull mating list for the cows to be mated (see Table 3).

[0054] Table 3 Optimal candidate bull mating list for cows to be mated

[0055] Candidate cow number Candidate cow GCBI value Candidate bull number Candidate bull GCBI value 1429 200.77 15217191 152.68 1779 197.75 15419623 254.81 1492 169.50 15219124 190.54 2078 165.65 15217181 302.06 1230 164.22 15219174 155.06 1497 158.12 15419619 192.61 20120701 157.94 15420645 214.85 1236 155.92 15420611 154.98 1071 153.15 15219124 190.54 1753 152.54 15420611 154.98 A051 150.73 15420613 210.20 2843 150.28 15217181 302.06 A120 150.11 15217181 302.06 A047 148.69 15420611 154.98 21010902 147.66 15219124 190.54 1469 147.47 15420616 197.65 1965 145.91 15420635 196.29 1374 145.65 15421638 245.75 … … … …

[0056] (8) Genomic mating selection effect: To further illustrate the superiority of the genomic mating selection method, in this dataset, we evaluated the GCBI and inbreeding levels of the offspring of genomic mating selection, homogeneous mating, and random mating. The results show that the genomic mating selection plan obtains the maximum expected GCBI value of the offspring, with the largest genetic progress. Figure 2) At the same time, the average inbreeding level of the offspring population is lower than that of the traditional mating scheme (Table 4). The implementation of genomic mating can rapidly improve the genetic progress of the Huaxi cattle population, highlighting the importance of genomic information.

[0057] Table 4 Comparison of the average GCBI and inbreeding level of the offspring population under different mating schemes

[0058]

[0059] The present invention can fill the gap in genomic mating of beef cattle in China, solve the problem of how to perform pairing and combination after genomic selection in the process of beef cattle breeding, provide an efficient and high-quality technical means for beef cattle breeding in China, accelerate the beef cattle breeding process in China, and promote the rapid development of the beef cattle industry in China, having great application value and popularization prospects.

[0060] Those skilled in the art to which this application pertains can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, but will not deviate from the inventive concept of this application or exceed the scope defined by the appended claims.

Claims

1. A genomic matching method for Huaxi cattle based on whole-genome SNP information, characterized in that, The specific steps are as follows: Step 1: DNA is extracted from individuals of Huaxi cattle to be mated, genotyping is performed using Cattle110K gene chips, and data is processed and quality controlled; Step 2: Fill the genotype data to obtain 770K high-density chip data, and perform numerical processing on the filled genotype data. Use PLINK software to convert the genotypes AA, Aa, and aa into 0, 1, and 2, respectively; Step 3: Calculate the additive kinship matrix according to the VanRaden algorithm, use GBLUP to obtain the genomic estimated breeding values ​​of the five important economic traits of the Huaxi cattle population to be bred, and calculate the individual comprehensive selection index according to the genomic estimated breeding value of each trait; Step 4: Use genetic algorithms to construct a list of optimal mating combinations for the population. Based on the genotype data of the bulls and cows in each mating pair and the additive kinship matrix, use genetic algorithms to calculate the expected comprehensive selection index value and inbreeding coefficient of each mating pair offspring population while considering mutations. Based on these two indicators, the mating combinations between the cows to be mated and the candidate bulls are optimized, and finally a mating list of the optimal candidate bulls for the cows to be mated is given.

2. The genomic mating method of Huaxi cattle based on whole-genome SNP information according to claim 1, wherein, The quality control in step 1 is to retain only autosomal loci, and eliminate loci with typing success rate less than 90%, minimum allele frequency less than 0.05, and Hardy-Weinberg equilibrium test less than 0.000001. Genotyping is performed using Cattle110K chip.

3. The genomic matching method for Huaxi cattle based on whole-genome SNP information according to claim 1, characterized in that, The five important economic traits in step 3 include carcass weight, calving difficulty, weaning weight, average daily weight gain, and slaughter rate. The additive kinship matrix is ​​calculated according to the VanRaden algorithm, and the genomic estimated breeding value is calculated using the GBLUP model. The model is as follows: y=Xb+Za+e Among them, y represents the vector of phenotypic observation values; X is an n×f dimensional association matrix; b is a v dimensional fixed effect vector; f is the number of fixed effects; Z is the structural matrix associated with a; a represents the additive effect vector, which follows N of the normal distribution; where G is the additive genomic relationship matrix, is the additive genetic variance, e is the residual vector, which follows N of the normal distribution.

4. A method for genomic matching of Huaxi cattle based on whole-genome SNP information according to claim 1, characterized in that In step 4, the optimized genetic algorithm model is used to calculate the expected comprehensive selection index value and inbreeding coefficient of each mating pair offspring group under the condition of considering mutation, and the mating combination between the cows to be mated and the candidate bulls is optimized according to these two indicators, and finally the mating list of the best candidate bulls for the cows to be mated is given. The optimized genetic algorithm model is as follows: Among them, λ2≥0 is a parameter that controls the degree of inbreeding in the offspring, λ1 is a parameter that controls the heterozygosity of alleles, and the specific calculation model of genetic progress and inbreeding coefficient is as follows: where P is an N C × N mating matrix, where N is the number of parents, and N C is the number of offspring; G is the additive genomic relationship matrix; D is the Mendelian sampling deviation; M is the genotype matrix; and a is the marker effect.

5. The genomic matching method for Huaxi cattle based on whole-genome SNP information according to claim 4, wherein Optimize pairing based on the comprehensive selection index of the individual parents to be paired. The comprehensive selection index formula is as follows: Among them, GEBV CE is the genomic estimated breeding value for calving ease; GEBV WWT is the genomic estimated breeding value for weaning weight, and the weaning weight is uniformly corrected to the weight at 6 months of age; GEBV DGF is the genomic estimated breeding value for daily weight gain during the fattening period; GEBV CW is the genomic estimated breeding value for carcass weight; GEBV DP is the genomic estimated breeding value for dressing percentage.

6. Use of the method according to any one of claims 1 to 5 in genome selection of Huaxi cattle.

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