CN115305289A - Genome selection method for reducing chicken abdominal fat rate by integrating SNP point set prior information - Google Patents
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Abstract
The invention discloses a genome selection method for reducing chicken abdominal fat rate by integrating SNP point set prior information. According to the method, a 55K genome chip of Jingxin I is utilized, through whole genome association analysis and selection accuracy verification analysis, 8647 SNPs related to chicken abdominal fat percentage traits are finally mined and can be added into a breeding value estimation model as prior information, and the prediction accuracy can be improved by 2.57%.
Description
Technical Field
The invention relates to the technical field of molecular breeding, in particular to a group of SNP (Single nucleotide polymorphism) point sets influencing the abdominal fat rate of chickens and application of a genome selection method thereof.
Background
Chinese broiler can grow in 105 hundred million years and produce 1580 million tons of meat. Chicken is the second largest meat product in the world, china. Accumulation of excess abdominal fat in chickens has become a significant problem, and the large deposition of abdominal fat reduces feed conversion efficiency and the yield of cut meat and causes environmental pollution due to slaughter waste. Low abdominal fat is an important goal in the broiler breeding field worldwide.
With the development and application of molecular markers such as Single Nucleotide Polymorphism (SNP), the molecular marker assisted selection of calculating a breeding value by combining a part of functionally verified candidate markers with BLUP can not only improve the selection accuracy, but also realize early selection and shorten the generation interval. However, most of livestock and poultry economic traits are quantitative traits controlled by micro-effect polygenes, and the acquisition of a mutation site with a large effect is difficult, which is also a limitation existing in molecular marker-assisted selection.
Genome-wide SNP variation detection is the basis for developing genome breeding (GS) and accurately measuring the genetic diversity of populations. After 60K and 600K chicken SNP chips are developed abroad, a commercial detection chip with high cost performance of 55K SNP (see Chinese patent: 201780023241. X) is independently developed by units such as Beijing animal husbandry and veterinary institute of Chinese academy of agricultural sciences aiming at the current situation and the demand of domestic chicken breeding and local germplasm resource protection. The chip is characterized by comprising: (1) the genetic variation information specific to local chicken breeds in China is contained, and the foreign commercialized chicken breed genome information is considered; (2) integrating a large number of SNP sites related to functional genes; (3) uniformly distributed on the genome; (4) moderate density, high cost performance and the like. Application practices prove that the chicken genome SNP chip can play an important role in aspects of genome selective breeding, germplasm resource diversity analysis, genetic relationship identification, genome association research, gene positioning and the like.
The genome selection method mainly includes a direct method represented by GBLUP and an indirect method represented by a bayesian method. Although the indirect method has the advantage of higher calculation accuracy, the application of the indirect method in production is limited due to the problems of long calculation time and large occupied calculation resources. The GBLUP has the advantages of short calculation time and simple use, and can be widely applied to the actual breeding of pigs, cattle, sheep, chickens and other livestock.
In the GBLUP model, it is clearly unreasonable to default to all SNP sites that respond effectively and equally to a trait. There is therefore room for improvement in the GBLUP process. Some learners use the QTL with larger effect as a fixed effect, and the residual markers are added into the model as random effects, so that the strategy can improve the prediction accuracy, but has certain difficulty in selecting the QTL with larger effect for quantitative traits. Researchers have also proposed GS methods that can integrate biological priors with genomic features such as BLUP, bayes rc, etc. The genome relation matrix is optimized, a character specific relation matrix, namely TABLUP, is constructed, different weights are distributed to all the markers based on indirect Bayes and rrBLUP methods, and compared with GBLUP, the method is remarkably improved, but Bayes B is long in calculation time, high in complexity and high in prediction accuracy, and the significance of improving GBLUP accuracy based on Bayes B is not large. And calculating the effect value of each mark based on rrBLUP, sequencing according to the absolute value, respectively constructing two different matrixes, and adding, thereby remarkably improving the prediction accuracy. Meanwhile, the prediction accuracy can also be improved by assigning weights to the markers by using genome-wide association analysis.
Genome Wide Association Study (GWAS) is an important means for mining economic trait candidate sites of livestock and poultry, and is the most common prior information acquisition mode on the Genome level. Research results of human complex characters, important economic characters of livestock and poultry and simulation data show that the accuracy of GS can be improved by integrating GWAS prior information. The acquisition group and the verification group of the prior information are different groups, so that false positive results are avoided.
In the quantitative genetics, quantitative traits refer to traits regulated by a small number of genes with a large effect, and the heritability of the traits is low, so that QTLs with a large effect are difficult to locate. Most economic characters of livestock and poultry belong to quantitative characters, and abdominal fat percentage characters of broilers also belong to quantitative characters. Therefore, significant SNP sites were rarely located in GWAS results. Therefore, the SNP collection with the most significant P value is extracted and used as prior information to be added into a genome selection model.
Based on the Beijing-core-I55K gene detection chip, the breeding effect of abdominal fat ratio is further improved, and the use strategy of chip genotyping is optimized. The genomic loci obtained from the conventional breeding value calculation default chip have the same function, SNP (single nucleotide polymorphism) related to abdominal fat percentage phenotype can be obtained through whole genome association analysis, the obtained significantly related SNP loci are used as prior information in a weighting mode, and the accuracy of breeding value calculation can be improved.
Disclosure of Invention
The invention aims to perform whole genome association analysis to obtain an optimal SNP locus set based on a 'Jingxin I' 55K genome chip, add the optimal SNP locus set as prior information into a breeding value calculation model, apply to chicken low abdominal fat rate genome breeding and improve chicken abdominal fat rate breeding progress.
Aiming at breeding and production practice requirements, fast large yellow-feathered broilers are taken as materials, and SNP (single nucleotide polymorphism) site sets which obviously influence the abdominal fat rate of the yellow-feathered broilers are screened and obtained through abdominal fat phenotype and whole genome SNP (single nucleotide polymorphism) determination. The accuracy of the breeding value estimation is improved by a method of adding a significant SNP weight in GBLUP method estimation. Provides technical support for realizing early selection of abdominal fat rate and rapid pure sum of relative alleles of traits and accelerating genetic selection progress.
Specifically, the technical scheme of the invention is as follows:
the invention provides a method for selecting a low abdominal fat ratio genome by using an integrated molecular marker, which is characterized by comprising the following steps:
1. selection of individuals from the reference group:
uniformly covering each family individual according to the selection of the half-sib families, and preventing the genetic relationship of the reference group from being too close;
2. blood collection and preservation:
collecting blood coagulation resistance of the wing vein, and storing at-20 ℃ for later use; or directly and uniformly smearing the fin vein anticoagulation blood on a blood card, and storing in a dry environment for later use after the blood card is dried.
3. DNA extraction:
extracting blood card DNA by a magnetic bead method, shearing blood spot cards with proper amount of 5mm by using scissors sterilized by 75% alcohol, and putting the blood spot cards into a 2.0ml cracking tube with a corresponding number, wherein the scissors are wiped by 75% alcohol when one sample is taken;
4. agarose gel electrophoresis for gDNA integrity detection
Electrophoresis: add 6.5. Mu.L of 1 XLodding buffer to the disposable gel plate and add the corresponding volume of DNA solution. Transferring the liquid of the dispensing plate into a glue hole of 0.8% agarose gel, and after the liquid adding is finished, setting electrophoresis parameters to be 170V, 14min and 400mA for electrophoresis.
5. Genotyping
Genotype detection was performed using the "Jingxin No. one" chicken 55K genomic chip based on the Illumina sequencing platform.
6. Genotype quality control and filling
Quality control processing is carried out on the data by using PLINK v1.9 software, and the quality control standard is as follows: the site detection rate is more than 0.90; a Minimum Allele Frequency (MAF) greater than 0.05; the individual detection rate is more than 0.90, and the deleted SNP is filled in by using Beagle v5.0 software.
The invention has the beneficial effects that:
the invention adopts chicken abdominal fat rate character improved molecular breeding technology, relates to a genome selection method by integrating SNP molecular markers, and reduces abdominal fat rate through breeding.
Compared with the conventional GEBV estimation result without setting weight for top25% (8647) SNP, the method for selecting abdominal fat percentage by using the genome selection method for integrating top25% (8647) SNP of the whole genome association analysis result can obviously improve the prediction accuracy. the optimal weight ratio of the constructed G matrix with top25% SNP to the conventional G matrix is 0.49, the prediction accuracy of the GBLUP method is improved by 2.57 percent
The breeding with low abdominal fat rate is carried out by the method, the feeding cost in production is saved, the production slaughtering waste is reduced, and the method has great guiding significance for the production and breeding of the broiler chickens.
Drawings
FIG. 1 Manhattan plots obtained by GWAS analysis of 1-3 generations based on the AFP trait determined.
Figure 2 QQ diagram obtained by GWAS analysis
Detailed Description
Example 1: obtaining prior information selected by low abdominal fat rate through whole genome correlation analysis
1. Genome-wide association analysis (GWAS) yielded the presence of SNP markers on different chromosomes that are significantly associated with abdominal fat fraction (AFP).
Test animals and determination of target traits:
the method uses 1-3 generation groups of Jinling spotted chickens E as materials, takes the abdominal fat rate of 56 days old as target characters, and carries out GWAS analysis;
phenotypic data determination the formula for calculating abdominal fat fraction (AFP) is as follows:
abdominal fat ratio = abdominal fat weight/total bore weight x 100, wherein total bore is after slaughter of the broiler chicken, exsanguination, dehairing, and removal of trachea, oesophagus, crop, intestine, spleen, pancreas, heart, liver, glandular stomach, myostomach, abdominal fat and reproductive organs.
Abdominal fat rate table type descriptive statistics
And (3) merging genotype data and controlling quality:
combining genotype data of 1-3 generations of E series of Jinling chicks by using a PLINK v1.9 software conventional method to carry out genotype numerical control, and after combination, carrying out quality control processing on the data by using PLINK v1.9 software, wherein the quality control standard is as follows: the site detection rate is more than 0.90; a Minimum Allele Frequency (MAF) greater than 0.05; the individual detection rate is more than 0.90, the deleted SNP is filled by using Beagle v5.0 software, and 35337 sites are finally reserved.
And (3) taking the generation 1-3 of the test population as prior marker information to discover the population for GWAS analysis. The model used is a mixed linear model (LMM), specifically:
y＝Xα+Zp+Wμ+e#
wherein y is a researched character, namely AFP, X alpha is a Fixed Effect (Fixed Effect), and other factors influencing y mainly refer to a group structure; z β is Marker Effect (Marker Effect SNP); mu is the random effect (random effect), which here generally refers to the genetic relationship of individuals; e is the residual error. Performing regression analysis by taking each SNP as a fixed factor, performing significance test, obtaining the P value of each SNP, and respectively extracting SNP loci of 5%, 10%, 15%, 20%, 25% and 30% of top with smaller P values as prior marker information of 4 generations.
Results of genome-wide association analysis:
based on the measured AFP traits, 1-3 generations were analyzed for GWAS and a Manhattan plot was plotted (FIG. 1). The most significant SNP sites of top 5%, top 10%, top 15%, top 20%, top25%, and top 30% were obtained as the prior marker information of the 4 generations.
Example 2: evaluation of genome selection effect of yellow-feathered broiler abdominal fat rate by integrating different gradient SNP markers
In this example, the trait of interest for genomic selection was AFP. Blood collection and storage are carried out on 4-generation chickens of the Jinling spotted chickens, DNA is extracted, a 55K SNP chip is adopted for whole genome SNP typing, different gradient prior information in the whole genome correlation analysis result is extracted, genetic relationship matrixes are respectively constructed, and different weights are given to fit into a new weight matrix. And (3) obtaining accuracy by adopting a 5-time cross-validation method, namely randomly dividing a GS analysis group into 5 groups, combining 4 groups into a reference group, taking the rest group as a validation group, dividing the Pearson correlation coefficient of the obtained genome breeding value and the phenotypic value by the heritability evolution as prediction accuracy, and finally taking the average value obtained by repeating for 10 times as a standard for evaluating the prediction accuracy. And comparing the prediction accuracy weighted by different gradient prior information, and acquiring an SNP point set with higher prediction accuracy for calculating the abdominal fat percentage GEBV, thereby providing theoretical and technical support for the selection and breeding of the abdominal fat percentage.
Phenotypic statistics of the trait, results as in example 1
Adopting 55K SNP chip to carry out complete genome typing and gene marking quality control
The quality control of genome-wide SNPs was performed using common criteria: the site detection rate is more than 0.90; a Minimum Allele Frequency (MAF) greater than 0.05; the individual detection rate is more than 0.90, the missing SNP is filled by using Beagle v5.0 software, and finally 34916 sites are reserved, so that the statistical accuracy and the effectiveness are ensured.
Weight G matrix construction
The method for constructing the genetic relationship matrix used by the GS in combination with GWAS prior information is as follows, and the genetic relationship matrix G is constructed by using the prior mark information in the GWAS result 1 And constructing a genetic relationship matrix G by using the rest sites 2 Separately calculate G 1 And G 2 The genetic variance and the heritability for the explanation of the abdominal fat rate are fitted to form a genetic relationship matrix G by taking the proportion of the explained genetic variance as the weight t ：
G t ＝λG 1 +(1-λ)G 2 #
Wherein
Is G 1 The genetic variance of the matrix is determined,
is G 2 Genetic variance of the matrix. Using a genetic relationship matrix G and a genetic relationship matrix G incorporating different a priori label information t GS analysis is carried out, and genetic parameters of abdominal fat rate calculated by different methods are compared with prediction accuracy results.
Genetic parameters and prediction accuracy results:
genetic and environmental variances calculated using the 4-generation genotype building genetic relationship G matrix were 0.336 and 0.261, respectively, and the heritability was calculated to be 0.563. The prediction accuracy result of quintupling cross validation was 0.364.
Screening the most significant SNP sites with top 5% to construct a genetic relationship matrix G 1 The genetic variance and the environmental variance obtained by calculation are respectively 0.240 and 0.356, the heritability is 0.402, and the prediction accuracy is 0.327; remaining 95% of SNPs construct matrix G 2 The explained genetic and environmental variances were 0.329 and 0.268, respectively, the heritability was 0.552, and the prediction accuracy was 0.361. Calculating G 1 The weight is 0.42, G 2 The weight is 0.58. Genetic relationship matrix G using combined GWAS prior labeling information t The calculated genetic variance and the environmental variance are respectively 0.333 and 0.265, the calculated heritability is 0.557, the prediction accuracy is 0.365, and the accuracy is improved by 0.41%.
Screening the most significant SNP sites with top 10% to construct a genetic relationship matrix G 1 The genetic variance and the environmental variance obtained by calculation are respectively 0.274 and 0.322, the heritability is 0.460, and the prediction accuracy is 0.353; remaining 90% of SNPs construct matrix G 2 The explained genetic and environmental variances were 0.329 and 0.268, respectively, the heritability was 0.550, and the prediction accuracy was 0.360. Calculation of G 1 The weight is 0.45, G 2 The weight is 0.55. Genetic relationship matrix G using combined GWAS prior labeling information t The genetic variance and the environmental variance are respectively calculated to be 0.333 and 0.264, the heritability is calculated to be 0.558, the prediction accuracy is 0.367, and the accuracy is improved by 0.95%.
Screening the most significant SNP sites with top 15% to construct a genetic relationship matrix G 1 The genetic variance and the environmental variance obtained by calculation are respectively 0.298 and 0.297, the heritability is 0.501, and the prediction accuracy is 0.361; remaining 85% of SNPs construct matrix G 2 The explained genetic and environmental variances were 0.324 and 0.273, respectively, the heritability was 0.543, and the prediction accuracy was 0.359. Calculation of G 1 The weight is 0.48 g 2 The weight is 0.52. Genetic relationship matrix G using combined GWAS prior labeling information t The calculated genetic variance and the environmental variance are respectively 0.340 and 0.257, the calculated heritability is 0.569, the prediction accuracy is 0.369, and the accuracy is improved by 1.30%.
Screening the most remarkable SNP sites with top 20% to construct a genetic relationship matrix G 1 The genetic variance and the environmental variance obtained by calculation are respectively 0.309 and 0.285, the heritability is 0.521, and the prediction accuracy is 0.370; remaining 80% of SNPs construct matrix G 2 The explained genetic and environmental variances were 0.320 and 0.277, respectively, the heritability was 0.536, and the prediction accuracy was 0.355.Calculation of G 1 The weight is 0.49, G 2 The weight is 0.51. Genetic relationship matrix G using combined GWAS prior labeling information t The calculated genetic variance and the environmental variance are respectively 0.342 and 0.255, the calculated heritability is 0.572, the prediction accuracy is 0.371, and the accuracy is improved by 2.01%.
Screening the most significant SNP sites with top25% to construct a genetic relationship matrix G 1 The genetic variance and the environmental variance obtained by calculation are respectively 0.324 and 0.271, the heritability is 0.545, and the prediction accuracy is 0.377; remaining 75% of SNPs construct matrix G 2 The explained genetic and environmental variances were 0.312 and 0.284, respectively, the heritability was 0.524, and the prediction accuracy was 0.350. Calculation of G 1 The weight is 0.49, G 2 The weight is 0.51. Genetic relationship matrix G using combined GWAS prior labeling information t The calculated genetic variance and the environmental variance are 0.346 and 0.252 respectively, the calculated heritability is 0.578, the prediction accuracy is 0.373, and the accuracy is improved by 2.57%.
Screening the most significant SNP sites with top 30% to construct a genetic relationship matrix G 1 The genetic variance and the environmental variance obtained by calculation are respectively 0.324 and 0.272, the heritability is 0.544, and the prediction accuracy is 0.368; remaining 70% of SNPs construct matrix G 2 The explained genetic and environmental variances were 0.312 and 0.284, respectively, the heritability was 0.524, and the prediction accuracy was 0.351. Calculating G 1 The weight is 0.49, G 2 The weight is 0.51. Genetic relationship matrix G using combined GWAS prior labeling information t The calculated genetic variance and the environmental variance are 0.342 and 0.255 respectively, the calculated heritability is 0.573, the prediction accuracy is 0.369, and the accuracy is improved by 1.33%.
Genetic parameters
Note: g is a genetic relationship matrix constructed by all SNPs; g 1 Establishing a genetic relationship matrix for prior marking information; g 2 Establishing a genetic relationship matrix for the SNPs except the prior marker; g t Is G 1 And G 2 Matrix combined according to genetic variance
By comparing the prediction accuracy results of adding different amounts of prior information, the fact that the prediction accuracy is improved most remarkably by adding a top25% SNP (see attached table 1 in the end of text) serving as the prior information into a GS (general knowledge base) model is found, and compared with the conventional GEBV estimation accuracy without setting weights for SNP markers of the prior information, the accuracy is improved. For abdominal fat percentage, the optimal weight ratio of the G matrix constructed by top25% SNP to the conventional G matrix is 0.49, and the prediction accuracy of the GBLUP method is improved by 2.57%.
Example 3: breeding method for selecting low-abdominal fat rate genome by using integrated significant SNP as prior information
(1) Establishment of reference population, phenotypic trait determination and genotypic determination
Each line establishes an independent reference group, and the source of the reference group is required to cover all the existing families of the line.
When the reference group of chickens were raised near the age of the commercial chickens, a group of 1500-2000 chickens was established as the reference group.
The reference group has definite phenotype record and genealogy record
Collecting anticoagulation blood of wing vein of individual with reference group, storing blood sample at-20 deg.C or using blood card, extracting blood DNA by magnetic bead method, and detecting individual genotype
The chicken 55K SNP commercial chip of Jingxin I (see Chinese patent: 201780023241. X) is used for genotyping to obtain the genotype information of the individual of the reference group. And processing and quality control are carried out on the data after genotyping; the quality control standard is as follows: the site detection rate is more than 0.90; a Minimum Allele Frequency (MAF) greater than 0.05; the individual detection rate is more than 0.90, and the deleted SNP is filled in by using Beagle v5.0 software. For the next step of estimating the breeding value.
Slaughtering the reference group individuals to obtain reference group phenotype information, determining abdominal fat weight and total bore weight phenotype in slaughtering, and calculating abdominal fat rate according to the calculation formula: abdominal fat ratio = abdominal fat weight/total bore weight x 100, wherein total bore is after slaughter of the broiler chicken, exsanguination, dehairing, and removal of trachea, oesophagus, crop, intestine, spleen, pancreas, heart, liver, glandular stomach, myostomach, abdominal fat and reproductive organs.
(2) Establishment of population to be tested and whole genome genotype collection
The test population is a candidate breeding hen population which has no phenotypic character record and is prepared for breeding the next generation.
The test group requires an affinity within 5 generations of the reference group.
On the premise of not influencing the survival rate and growth and development of chickens, a group to be detected needs to collect a blood sample as early as possible, extract DNA, and carry out genotyping by using a Jing core I chicken 55K SNP commercial chip to obtain the individual genotype information of the group to be detected. And processing and quality control are carried out on the data after genotyping; the quality control standard is as follows: the site detection rate is more than 0.90; a Minimum Allele Frequency (MAF) greater than 0.05; the individual detection rate is more than 0.90, and the deleted SNP is filled in by using Beagle v5.0 software.
(3) Calculating breeding values of individual of reference group and candidate group by fitting weighting matrix
The breeding value calculation needs to utilize the reference group and the pedigree record of all the individuals to be reserved
Referring to the abdominal fat surface phenotype value of the group individual and the genotype information of the whole genome SNP site, the acquisition mode is shown in the step (1)
The whole genome SNP genotype information of the population to be tested is obtained in the step (2)
Obtaining prior information:
through whole genome association analysis, SNP sites with high significance are obtained as prior information, the patent uses 1-3 generations of Jinling spotted chickens to carry out whole genome association analysis, and the prior information of low abdominal fat rate breeding is mined
And (3) verifying the prior information:
the obvious sites obtained by the whole genome association analysis are added into a genome breeding model to verify the site effect, the verification group is prevented from being the same as the prior information acquisition group, and the generation of a false positive result is avoided.
Constructing an A matrix:
the matrix A is constructed as a common method, and the genetic relationship matrix between individuals is constructed based on pedigree information
And (3) constructing a weight G matrix:
extracting SNP markers with high significance as prior information to construct a weight genetic relationship matrix according to a whole genome association analysis result, wherein G1 represents a genetic relationship matrix constructed based on 8647 molecular markers, G2 represents a genome genetic relationship matrix constructed based on a chicken whole genome SNP chip, c is G1 genetic relationship matrix weight, d is G2 genetic relationship matrix weight, and the weights are set by respectively calculating the ratio of abdominal fat percentage genetic variance through G1 and G2
The relative weight formula for G1 and G2 is set as:
Gt＝c*G1+d*G2
in the formula, gt represents a weight G matrix, c =0.49, d =0.51;
h, matrix construction:
the H matrix is constructed into a common method, an A matrix constructed based on pedigree and a G matrix constructed based on genotype information are fitted to generate an H matrix, and asreml packages in R software are used for calculating breeding values
(4) Selection method of chicken low-abdominal-fat strain
Selecting according to breeding values calculated by a weight matrix constructed by combining prior information, evaluating and sequencing breeding values of candidate breed reserving groups, carrying out low abdominal fat rate strain breeding aiming at reducing individual abdominal fat rate, selecting candidate group individuals with low breeding values as parents, selecting 100-500 common cocks, selecting 1000-2000 hens and reserving the breeds to establish families.
Appendix Table 1 top25%
Claims (9)
1. A genome selection method for improving selection accuracy to reduce chicken abdominal fat rate based on prior information of an integrated SNP (Single nucleotide polymorphism) point set of an existing chicken 55K chip is characterized by comprising the following steps:
1) Collecting blood and storing 4 generation group samples, extracting DNA, carrying out whole genome SNP typing by adopting a 55K SNP chip, extracting different gradient prior information in a whole genome association analysis result, respectively constructing a genetic relationship matrix, and giving different weights to fit into a new weight matrix;
2) Obtaining accuracy by adopting a 5-time cross-validation method, namely randomly dividing a GS analysis group into 5 groups, combining 4 groups into a reference group, taking the rest group as a validation group, dividing a Pearson correlation coefficient of an obtained genome breeding value and a phenotypic value by a heritage power evolution as prediction accuracy, and finally taking a mean value obtained by repeating for 10 times as a standard for evaluating the prediction accuracy;
3) And comparing the prediction accuracy weighted by different gradient prior information, obtaining an SNP point set with higher prediction accuracy for calculating the abdominal fat percentage GEBV, selecting according to a breeding value calculated by a weight matrix constructed by combining the prior information, evaluating and sequencing the breeding values of the candidate seed reserving groups, and breeding the strains with low abdominal fat percentage.
2. The method as claimed in claim 1, wherein the method for obtaining the a priori information in step 1 comprises the steps of:
1) Selection of individuals from the reference group: aiming at the 1-3 generation group of the sample, each family individual is uniformly covered according to the selection of the half-sib family, and the close relationship of the reference group is prevented;
2) Blood collection and preservation: collecting blood coagulation resistance of the wing vein, and storing at-20 ℃ for later use; or directly and uniformly smearing the anticoagulation blood of the wing vein on a blood card, and storing in a dry environment for later use after the blood card is dried;
3) DNA extraction: extracting blood card DNA by a magnetic bead method, shearing blood spot cards with proper amount of 5mm by using scissors sterilized by 75% alcohol, and putting the blood spot cards into a 2.0ml cracking tube with a corresponding number, wherein the scissors are wiped by 75% alcohol when one sample is taken;
4) Agarose gel electrophoresis was used to detect the integrity of gDNA;
5) Electrophoresis: adding 6.5 mu L of 1 XLodding buffer into the disposable gel plate, and adding DNA solution with corresponding volume to the corresponding hole position according to the dispensing position table; transferring the liquid of the glue dispensing plate into a 0.8% glue hole, confirming whether the electrophoresis apparatus is 170V, 14min or 400mA after the liquid adding is finished, and carrying out electrophoresis after no error is confirmed.
6) Genotyping: carrying out genotype detection by using a 'Jingxin No. one' chicken 55K genome chip based on an Illumina sequencing platform;
7) Genotype quality control and filling: quality control processing is carried out on the data by using PLINK v1.9 software, and the quality control standard is as follows: the site detection rate is more than 0.90; a Minimum Allele Frequency (MAF) greater than 0.05; the individual detection rate is more than 0.90, and the missing SNP is filled by using Beagle v5.0 software;
8) Data analysis was performed using GWAS analysis method, the model used was a mixed linear model (LMM):
y＝Xα+Zβ+Wμ+e#
wherein y is a researched character, namely AFP, X alpha is a Fixed Effect (Fixed Effect), and other factors influencing y mainly refer to a group structure; z beta is Marker Effect (Marker Effect SNP); w μ is the random effect (random effect), which here generally refers to the genetic relationship of individuals; e is the residual error. Performing regression analysis by taking each SNP as a fixed factor, performing significance test, obtaining the P value of each SNP, and respectively extracting SNP loci of 5%, 10%, 15%, 20%, 25% and 30% of top with smaller P values as prior marker information of 4 generations.
3. The method of claim 2, wherein the genetic relationship matrix is constructed by using the a priori label information to construct the genetic relationship matrix G 1 And constructing a genetic relationship matrix G by using the rest sites 2 Separately calculate G 1 And G 2 The genetic variance and the heritability for the explanation of the abdominal fat rate are fitted to form a genetic relationship matrix G by taking the proportion of the explained genetic variance as the weight t ：
G t ＝λG 1 +(1-λ)G 2 #
Wherein
Is G 1 The genetic variance of the matrix is used,
is G 2 Genetic variance of the matrix.
4. The method of claim 3, wherein the top25% SNP is selected as prior information to be added to the GS model, the optimal weight ratio of the top25% SNP to the conventional G matrix after the G matrix is constructed is 0.49:0.51 for the abdominal fat rate, and the prediction accuracy of the method of GBLUP is improved by 2.57%.
5. The method of claims 1 and 2, wherein a genome selection method for improving specific trait breeding effect is provided based on a commercial chicken genome breeding chip optimization use strategy.
6. Use of the method of claim 2 in chicken breeding, wherein the population of chickens includes broiler chickens and laying hens.
7. Use according to claim 6 in chicken breeding, wherein the provided method strategy is applied to traits not limited to the abdominal fat rate of chicken, but also to other quantitative traits of chicken.
8. Use of the method of claim 6 in chicken breeding, wherein the provided method strategy is applicable to both quantitative traits, either as a specific quantitative trait or as a balanced breeding of both traits.
9. The method of claim 4, wherein the a priori information used is as set forth in Table 1 appended to the description.
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