阅读说明：本技术 一种评估香猪表型性状的个体基因组育种值方法 (Individual genome breeding value method for evaluating phenotypic characters of fragrant pigs ) 是由 王嘉福 王志勇 犹龙江 陈芳 孙镘熹 刘畅 黄世会 牛熙 冉雪琴 于 2021-01-22 设计创作，主要内容包括：本发明提供一种香猪表型性状的个体基因组育种值评估方法,涉及动物育种技术领域,为香猪的选种、配种提供理论依据。该发明采用GBLUP(基因组最佳线性无偏估计)模型,结合表型、基因型和谱系信息进行基因组育种值估测,采用自编写的Python程序,计算个体的育种值(即种畜个体遗传潜力的相对值)。(The invention provides an individual genome breeding value evaluation method for fragrant pig phenotypic characters, relates to the technical field of animal breeding, and provides a theoretical basis for seed selection and hybridization of fragrant pigs. The invention adopts a GBLUP (optimal linear unbiased estimation of genome) model, combines phenotype, genotype and pedigree information to estimate the breeding value of the genome, and adopts a self-written Python program to calculate the breeding value of an individual (namely, the relative value of the genetic potential of an individual breeding stock).)

1. An individual genome breeding value method for evaluating the phenotypic characters of fragrant pigs is characterized by comprising the following steps: the method comprises the following steps:

(1) screening and determining candidate gene markers related to the breeding traits of fragrant pigs, and establishing a detection technology;

(2) collecting phenotype data of each candidate fragrant pig;

(3) extracting genome DNA and genotyping;

(4) constructing a GBLUP breeding model, and calculating the litter size breeding value of an individual genotype;

(5) according to the breeding value, the breeding pigs are selected, and individuals with low breeding value are eliminated.

2. The method for evaluating the individual genome breeding value of the fragrant pig phenotypic trait according to claim 1, wherein the individual genome breeding value is as follows: the method specifically comprises the following steps: step 1: the genotype is digitalized, namely, the genotype is assigned, the genotypes DD, DI and II are respectively assigned to-1, 0 and 1, wherein, -1 represents the DD genotype, 0 represents the ID genotype and 1 represents the II genotype, a genotype relationship matrix is constructed, and a genetic relationship matrix between individuals is obtained, wherein the G matrix is defined as:

G＝ZZ'/k

wherein, the Z array elements are: 0-2p_j、1-2p_j、2-2p_jRespectively corresponding to genotypes DD, DI, II; p is a radical of_jIs the frequency of the allele I, and Z' is the inverse matrix corresponding to the Z matrix; k is defined as:

k＝2∑P_j(1-P_j)；

step 2: substituting the G matrix model constructed in the step 1 into a GBLUP model equation set, and calculating the genome breeding value of each individual, wherein the basic principle of genome selection under the GBLUP model is as follows:

wherein G is the genetic relationship matrix G matrix obtained in step 1, and G is^-1Is an inverse matrix of the genome relation matrix G, and lambda is a heritability correlation value:

cov (u, e') -0, where I is the identity matrix, i.e. the number of each individual is recorded, V (μ) is the μ random additive genetic effect vector variance, V (e) is the variance of the e random residual effect vector, Cov denotes the covariance, h²Evaluating the fixation effect of the animal for genetic additive variance, namely the transmission capacity of the animal variation, in a mixed model equation system by X' X; z' Z assessing the stochastic effect of the animal; z 'X, X' Z is the relation between fixed effect and random effect of the animal, lambda G^-1Is the product of the heritability correlation value and the genetic relationship,calculating a value for the fixation effect;calculating a value for the random effect, i.e., the genomic breeding value of the individual;and (3) accumulating phenotype observed values, calculating breeding values after matrix construction is completed, wherein the obtained breeding values represent relative values of individual genetic potentials in the evaluated animal population, and the breeding values are larger to indicate that the genetic potentials are better and can be used as breeding pigs, otherwise, the breeding values are eliminated.

3. The method for evaluating the individual genome breeding value of the fragrant pig phenotypic trait according to claim 1, wherein the individual genome breeding value is as follows: after the matrix is constructed, the self-written Python program code is used as a script, and the genotype breeding value corresponding to each individual can be calculated by inputting data.

4. The method for evaluating the individual genome breeding value of the fragrant pig phenotypic trait according to claim 1, wherein the individual genome breeding value is as follows: and (4) screening 18 breeding character structure variation sites as molecular marker sites.

Technical Field

The invention relates to the technical field of animal molecular breeding, in particular to a method for evaluating individual genome breeding value of fragrant pig phenotypic characters.

Background

The Zhongjiang fragrant pig is a precious local pig breed in China, and the producing area is located in Zhongzhou province, Zhongjiang county. The fragrant pig has small body, pure gene, fresh and tender meat, no mutton smell and fishy smell, and high nutritive value, and is a raw material for processing and manufacturing high-quality meat products. With the improvement of living standard of people, the requirement on meat quality is higher and higher, the fragrant pig becomes the first choice of more and more people, but the farrowing rate of the fragrant pig is lower, and the supply is not in demand frequently, so that how to improve the litter size becomes the key point of the current fragrant pig breeding industry.

The traditional breeding is time-consuming and labor-consuming, and the GBLUP method can not only shorten the generation interval, but also rapidly and accurately breed individuals carrying excellent genotypes as breeding pigs. Principle of GBLUP method: the method is a marker-assisted selection technology in the whole genome range, which aims to obtain higher breeding value and estimation accuracy by detecting molecular markers covering the whole genome and utilizing genetic information at the genome level to perform genetic evaluation on individuals.

Disclosure of Invention

The invention aims to provide a method for evaluating the individual genome breeding value of the phenotypic character of the fragrant pig, which is used for breeding and selecting the fragrant pig.

The method comprises the following steps: the method is simple and quick, omits a fussy calculation process, and can be used for calculating the individual genome breeding values of other animal phenotypic traits.

In order to solve the problems of time and labor waste, large capital consumption and the like in the traditional breeding of the fragrant pig, the technical scheme adopted by the invention is as follows: an individual genome breeding value method for evaluating fragrant pig phenotype traits, which uses phenotype, genotype and pedigree information to predict genome breeding value and evaluates the genetic value (i.e. breeding value) of an individual, and specifically comprises the following steps:

an individual genome breeding value method for evaluating fragrant pig phenotype character adopts GBLUP (genome optimal linear unbiased estimation) model, uses phenotype, genotype and pedigree information to predict genome breeding value, and calculates estimated individual breeding value (genetic value), and concretely comprises the following steps:

step 1: the genotype is digitalized, namely, the genotype is assigned, the genotypes DD, DI and II are respectively assigned to-1, 0 and 1, wherein, -1 represents the DD genotype, 0 represents the ID genotype and 1 represents the II genotype, a genotype relationship matrix is constructed, and a genetic relationship matrix between individuals is obtained, wherein the G matrix is defined as:

G＝ZZ'/k

wherein, the Z array elements are: 0-2p_j、1-2p_j、2-2p_jRespectively corresponding to genotypes DD, DI, II; p is a radical of_jAnd Z' is the frequency of the allele I and is the inverse matrix corresponding to the Z matrix. k is defined as:

k＝2∑P_j(1-P_j)

step 2: building a BLUP (optimal linear unbiased estimation) breeding model, namely building a relation matrix among individuals by using genetic markers, putting the relation matrix into a mixed model equation set, and calculating the genome estimated breeding value of the individuals. Animal phenotypic observations of the BLUP breeding model consist of environmental and genetic factors. The general mathematical formula of the BLUP model is:

y＝Xb+Zμ+e

wherein y is a phenotypic trait observation; b is a fixed effect vector (e.g., environmental and physiological factors); mu is a random additive genetic effect vector, namely a breeding value; x and Z are incidence matrixes of a fixed effect b and a random effect mu respectively; e is the random residual effect vector.

And step 3: the above equation is a general mathematical expression of the mixed model equation set, which needs to be further converted into the mixed model equation set. GBLUPs can more realistically describe the genetic relationship between individuals using molecular markers instead of phenotypic pedigrees, compared to methods that calculate BLUP from phenotypic pedigrees. The genome selection technology uses the marked genotypes to construct a relationship matrix (G matrix) among individuals, and realizes the direct estimation of genome breeding values. And (3) substituting the G matrix model constructed in the step 1 into a mixed model equation set, and calculating the genome breeding value of each individual. The rationale for genome selection under the GBLUP model is:

wherein G is the genetic relationship matrix G matrix obtained in step 1, and G is^-1Is an inverse matrix of the genome relation matrix G, and lambda is a heritability correlation value:

cov (u, e') -0, where I is the identity matrix, i.e. the number of each individual is recorded, V (μ) is the μ random additive genetic effect vector variance, V (e) is the variance of the e random residual effect vector, Cov denotes the covariance, h²Is the genetic additive variance, i.e., the transmissibility of the animal variation. In the mixed model equation set, X' X evaluates the fixation effect of the animal; z' Z assessing the stochastic effect of the animal; z 'X, X' Z is the relation between fixed effect and random effect of the animal, lambda G^-1Is the product of the heritability correlation value and the genetic relationship,calculating a value for the fixation effect;calculating a value for the random effect, i.e., the genomic breeding value of the individual;is the accumulation of phenotypic observations. After the matrix is constructed, the Python program is used to write codes to convert numerical values and calculate breeding values, and the data size is large. The obtained breeding value represents the relative value of individual genetic potential in the evaluated animal population, so that the larger breeding value indicates that the genetic potential is better, and the breeding can be used as a boar, otherwise (such as a negative value) the breeding value is eliminated.

And after the matrix is constructed, writing codes by using Python language for numerical value conversion. Because the data volume is large, the phenotype data and the genotype data need to be converted by using codes written by Python language, and the breeding value of the individual is calculated by adopting the methods described in the step 1 and the step 3. The breeding values solved represent the relative values of the individual genetic potential in the population of animals evaluated. Therefore, the larger the breeding value is, the better the genetic potential is, and the breeding pig can be used as a breeding pig; if the value is negative, the individual is not favorable for improving the target traits, and is not suitable for breeding pigs, and the individual needs to be eliminated or degraded for use as fattening pigs.

The invention has the beneficial effects that: 18 breeding character structure variation sites screened out in a laboratory at the earlier stage are used as molecular marker sites, a breeding value model is constructed, a Python program is used for compiling codes and used as a script, the genotype breeding value of each individual can be solved, breeding value with large breeding value can be selected as a boar, otherwise, the boar is eliminated and sold as a commercial boar, the breeding and mating method can be used as a breeding and mating method of fragrant boars, and can also be used for calculating the genotype breeding values of other animals, and the method is simple and convenient.

Drawings

FIG. 1 is a flow chart of an individual genome breeding value method for a fragrant pig phenotypic trait provided by an embodiment of the invention.

Detailed Description

The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.

As shown in fig. 1, the method of the present embodiment is as follows.

On the basis of genome re-sequencing data analysis and bioinformatics analysis, the structural variation sites (Table 1) contained in 18 genes were selected and primers were designed.

Table 1 shows the structural variation sites contained in 18 genes as molecular markers and primers

Collecting 116 fragrant pig ear tissue samples, extracting genome DNA of the fragrant pig ear tissue samples, performing genotyping by using the extracted genome DNA as a template, then counting pedigree information and phenotype information of the fragrant pig samples, wherein specific data are shown in tables 2 and 3, the litter size (third fetus) of the fragrant pig is used for phenotypic characters in the embodiment, and the litter size condition of replacement sows is counted.

TABLE 2

TABLE 3

Step 1: the genotypes were assigned and the genotypes DD, DI and II were assigned to-1, 0, 1, respectively, where-1 represents DD, 0 represents ID, and 1 represents II, as shown below.

And constructing a genome relation matrix to obtain an affinity relation matrix G matrix among individuals, and constructing each matrix in the GBLUP mixed model, wherein codes for constructing each matrix are shown below.

After each matrix is constructed, the matrices are combined to construct a hybrid model equation set, as shown by the following codes.

And finding an output file in the target folder, wherein the file contains the breeding value of each individual, and the files can be opened and sorted by an EXCEL program to select the individual with higher breeding value as a boar. Shown in table 4.

TABLE 4

In this embodiment, according to the calculated breeding value, the numbered sows corresponding to the serial numbers 1 to 37 can be selected as replacement pigs, and the relative values of the individual genetic potentials of the other sows are relatively low, i.e., the utilization value of the breeding stock is relatively low, and the breeding stock can be eliminated and sold as commercial pigs.

Sequence listing

<110> Guizhou university

<120> method for evaluating individual genome breeding value of fragrant pig phenotypic traits

<160>36

<210>1

<211>22

<212>DNA

<213> Artificial Synthesis

<400>1

CCCCACCTCGATAACACTTGAT

<210>2

<211>22

<212>DNA

<213> Artificial Synthesis

<400>2

CTGGGCTGCAGTGGTGAAAATA

<210>3

<211>20

<212>DNA

<213> Artificial Synthesis

<400>3

GTGGAGCGTCTGGGGAACAT

<210>4

<211>20

<212>DNA

<213> Artificial Synthesis

<400>4

TGGGAGATGGAAGGTGGAGG

<210>5

<211>22

<212>DNA

<213> Artificial Synthesis

<400>5

TTGGCACCATTTTCTGACAGCA

<210>6

<211>22

<212>DNA

<213> Artificial Synthesis

<400>6

GGGTGTGGGATAGACTGGGATT

<210>7

<211>22

<212>DNA

<213> Artificial Synthesis

<400>7

CTTTCCCCTCCACTGACTCCCT

<210>8

<211>22

<212>DNA

<213> Artificial Synthesis

<400>8

CTTTCCCCTCCACTGACTCCCT

<210>9

<211>22

<212>DNA

<213> Artificial Synthesis

<400>9

GGCTAAACATTGCCGAGTGACT

<210>10

<211>23

<212>DNA

<213> Artificial Synthesis

<400>10

GGAATACAGAAGGCAGAGAGGGT

<210>11

<211>21

<212>DNA

<213> Artificial Synthesis

<400>11

TGGATATGCGTGCACAGTCAG

<210>12

<211>21

<212>DNA

<213> Artificial Synthesis

<400>12

TAATTGGGCTCTTGGGTCAGG

<210>13

<211>23

<212>DNA

<213> Artificial Synthesis

<400>13

CCCATGCGTAACAAAAGACACT

<210>14

<211>24

<212>DNA

<213> Artificial Synthesis

<400>14

GTCCAGAACTGTACCTCCAAGAGT

<210>15

<211>24

<212>DNA

<213> Artificial Synthesis

<400>15

CATCACAACATCAGCGATAGTCTG

<210>16

<211>23

<212>DNA

<213> Artificial Synthesis

<400>16

TGTTGAGGGGAAATATGTCTGGT

<210>17

<211>23

<212>DNA

<213> Artificial Synthesis

<400>17

AGCCACAACAAGAACTCCAACAT

<210>18

<211>24

<212>DNA

<213> Artificial Synthesis

<400>18

CCTAGAACGCTAAACCCTCAGAAT

<210>19

<211>22

<212>DNA

<213> Artificial Synthesis

<400>19

CATCAGGTCATCAGGCAAAGGA

<210>20

<211>22

<212>DNA

<213> Artificial Synthesis

<400>20

TCTGTGAGGTTCAAAACGGTGG

<210>21

<211>25

<212>DNA

<213> Artificial Synthesis

<400>21

TGAGTCTGCCAGTAGTTTTCAGTTT

<210>22

<211>24

<212>DNA

<213> Artificial Synthesis

<400>22

TATATGGGAAGATGCAAGTGTCTG

<210>23

<211>23

<212>DNA

<213> Artificial Synthesis

<400>23

TGTTCCTTTCCACTGTGTCAATG

<210>24

<211>21

<212>DNA

<213> Artificial Synthesis

<400>24

CCCCTCTAAACTTTTCAGCGG

<210>25

<211>24

<212>DNA

<213> Artificial Synthesis

<400>25

ACTCAGTTTTCCTAGGTCTCTCCC

<210>26

<211>24

<212>DNA

<213> Artificial Synthesis

<400>26

ACGGCTGAGTAATATTCCATTGTG

<210>27

<211>22

<212>DNA

<213> Artificial Synthesis

<400>27

TTAGCAGATCTGGCCCTTGATG

<210>28

<211>24

<212>DNA

<213> Artificial Synthesis

<400>28

ACAAGAGGGAGAATAGTGACTGGC

<210>29

<211>23

<212>DNA

<213> Artificial Synthesis

<400>29

GACCTCCAACTTCAGCATTTTCA

<210>30

<211>21

<212>DNA

<213> Artificial Synthesis

<400>30

ATGCCTTCCTCCTTGTGTCCA

<210>31

<211>20

<212>DNA

<213> Artificial Synthesis

<400>31

TACATCTCAGGCGTCGTGGG

<210>32

<211>22

<212>DNA

<213> Artificial Synthesis

<400>32

GTAAAGCATTTGGTCAGGCAGG

<210>33

<211>22

<212>DNA

<213> Artificial Synthesis

<400>33

CCCCAGCTCCCTTACCTAATAG

<210>34

<211>23

<212>DNA

<213> Artificial Synthesis

<400>34

GGTGGTGTAAATGCAGTTATCGG

<210>35

<211>22

<212>DNA

<213> Artificial Synthesis

<400>35

GTCTTTGGCAGGCATTTTCTCA

<210>36

<211>23

<212>DNA

<213> Artificial Synthesis

<400>36

TAGGCTTGCGTTTCCACTCTTAG