A genetic evaluation method for genomic breeding values ​​of high-yielding yolk-feather broiler chickens

Abstract

The present invention discloses a genetic evaluation method for genomic breeding values ​​of high-yielding yolk-feathered broilers, and relates to the technical field of poultry breeding. The method comprises the steps of determining the weight and egg production of a chicken group, genomic marker typing, estimating the genomic marker effect value, calculating the genomic marker evaluation index, calculating the individual genomic evaluation index, selecting individuals for breeding, and the like. Based on the genomic marker effect value of the weight and egg production of yellow-feathered broilers, the present invention analyzes the influence of genomic markers on antagonistic effects, formulates a marker effect evaluation index, and then calculates the individual genomic evaluation index for determining the selection of individuals for breeding. The present invention meets the breeding requirements of egg-laying traits of yellow-feathered broilers, can increase the egg production of a chicken group on the basis of maintaining the growth rate, and provides support for the selection of antagonistic traits.

Description

Technical Field

[0001] The present invention relates to the technical field of genetic evaluation of livestock and poultry genome big data, and in particular to a genetic evaluation method for genome breeding values ​​of high-yield egg yolk-feather broilers. Background Art

[0002] Growth and reproductive traits are of great significance to the breeding of yellow-feathered broilers. Growth traits ensure the meat production performance of yellow-feathered broilers, and reproductive traits ensure the seed supply performance of yellow-feathered broilers. However, growth and reproductive traits are antagonistic traits. Selecting one trait alone will lead to a decrease in the production level of the other trait.

[0003] At present, the method commonly used in livestock and poultry breeding is to assign different weights to two antagonistic traits to achieve the purpose of selecting two traits at the same time. The traditional selection of egg-laying traits is prone to two problems: on the one hand, there is a bottleneck in weight selection, and even a reduction in growth rate, resulting in unstable meat production performance. On the other hand, after ensuring weight gain, the improvement of egg-laying performance is ignored, resulting in a decrease in the efficiency of chick supply.

[0004] With the rise of disciplines such as molecular biology and genomics, the theory and practice of breeding and selection have undergone tremendous changes, and molecular breeding has emerged. Molecular breeding combines molecular biology and traditional breeding, and is based on molecular quantitative genetics, including molecular marker-assisted selection breeding, transgenic breeding, and molecular design breeding. QTL (Quantitative Trait Locus) is the location of genes that control quantitative traits in the genome. QTL is an auxiliary tool for breeding. By locating the correlation between QTL and traits and marking QTL molecules, genetic traits can be tracked, thereby improving the efficiency of screening for excellent traits. Growth and reproductive traits are both complex traits that are controlled by multiple genes at the same time. Antagonistic expression is due to the fact that some of the QTL sites that control growth and reproductive traits at the same time show a positive effect on one trait and a negative effect on the other trait. Direct selection of these QTL markers will inevitably cause the traits to develop in opposite directions.

[0005] Traditional selection methods evaluate the performance of individual traits as a whole, without optimizing the QTL that causes antagonistic effects, and the selection efficiency of antagonistic traits is low. The genomic selection method is a new generation of genetic evaluation method that can use broiler genomic big data to estimate the genetic effects of genomic markers, distinguish specific antagonistic effect sites, and make the selection of antagonistic QTL sites possible. Summary of the invention

[0006] In order to overcome the shortcomings and deficiencies of the prior art, the purpose of the present invention is to propose a method for optimizing and improving gene markers directly based on gene marker effect values, to perform genetic evaluation on broilers, and to provide a method for genetic evaluation of genomic breeding values ​​of high-yielding yolk-feathered broilers. The present invention calculates the effect values ​​of gene markers for growth and reproductive traits by genomic methods, and then establishes an evaluation model for antagonistic gene loci to formulate individual genetic evaluation indexes, so as to achieve the purpose of simultaneously improving the growth and reproductive performance of yellow-feathered broilers. The present invention provides a necessary method for the breeding of reproductive performance of yellow-feathered broilers, and the present invention can be promoted and applied in the breeding of egg-laying traits of yellow-feathered broilers and the cultivation of egg-laying strains.

[0007] To achieve the above technical objectives, the technical solution of the present invention is as follows:

[0008] A method for genetic evaluation of genomic breeding values ​​of high-yield egg yolk broiler chickens, the method comprising the following steps:

[0009] Step 1, determine the weight and egg production of the chicken group: at the age of listing, measure the weight of the chicken group to be evaluated, and record the total number of eggs produced in 65 weeks;

[0010] Step 2, estimate the genetic effect value of gene markers: based on the genomic marker information of the flock, estimate the genetic marker effect values ​​of body weight and egg production at the same time;

[0011] Step 3, establish the gene marker effect evaluation index: based on the weight and egg production gene marker effect values ​​estimated in step 2, calculate the individual evaluation index through the following model:

[0012]

[0013]

[0014]

[0015] In the formula, κ represents the genetic evaluation index of the individual genome, m represents the number of gene markers, and a i and b i Respectively represent the genetic effect value of the ith gene marker for egg production and body weight traits, a' i and b' i represents the genetic evaluation index of the ith gene marker for egg production and body weight traits, c i is the genotype of the i-th gene marker, taking values ​​of 0, 1, and 2 (where 0 and 2 represent homozygous types, and 1 represents heterozygous types), and α is the egg-laying weight control parameter.

[0016] The α value described in step 3 can be changed according to the heritability of egg production and body weight, as well as breeding needs, and the value range is 0.5 to 1. According to the current breeding needs, that is, to maximize the genetic progress of egg production while maintaining a small increase in individual body weight, the α value is preferably 0.8.

[0017] Preferably, the α value in step 3 can be obtained by the following steps:

[0018] According to current breeding needs, the genetic progress of egg production is maximized while maintaining a small increase in individual body weight;

[0019]

[0020] Among them, α is the egg-laying weight control parameter, G a and G b Respectively represent the breeding improvement target values ​​of egg production and body weight, σ a and σ b represent the standard deviation of genetic variation in egg number and body weight, respectively.

[0021] Step 4, select individuals for seed retention: sort the genetic evaluation index of the individual genome in descending order by gender, determine the number of individuals to be retained based on the seed retention rate, and determine the individuals to be retained for seed retention from large to small.

[0022] The mechanism of the present invention is:

[0023] According to the fundamental cause of the antagonism between growth and egg-laying traits and based on the genetic effect values ​​of genomic markers of these two traits, the present invention formulates a genetic evaluation index for egg-laying performance of yellow-feathered broilers from a genomic level, thereby ensuring weight gain while improving egg-laying performance, thereby overcoming the deficiency that traditional yellow-feathered broiler breeding methods only focus on individuals and do not optimize the selection of genomic antagonistic markers, thereby resulting in low efficiency in improving the genetic progress of egg-laying performance.

[0024] Compared with the prior art, the present invention has the following beneficial technical effects:

[0025] 1. The present invention is adapted to the needs of genetic evaluation of egg-laying traits of yellow-feathered broiler chickens, and can improve the genetic progress of egg-laying performance while maintaining the growth rate.

[0026] 2. Based on the genetic effect value of the genomics of the weight and egg-laying traits of yellow-feathered broilers, the present invention directly acts on the cause of the antagonistic effect, greatly improving the accuracy of individual genetic evaluation. The egg-laying trait regulation parameters can be used to regulate the egg-laying and weight weights to adapt to changes in breeding needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to enable those skilled in the art to better understand the technical solution of the present invention, the above and other features of the present invention will be more obvious by describing in detail the embodiments shown in the accompanying drawings below. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0028] Figure 1 A flow chart of a method for genetic evaluation of genomic breeding values ​​of high-yielding yolk-feather broiler chickens provided by the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. The specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0030] It should also be understood that the following examples are only used to further illustrate the present invention and cannot be understood as limiting the scope of protection of the present invention. Some non-essential improvements and adjustments made by those skilled in the art based on the above content of the present invention belong to the scope of protection of the present invention. The specific process parameters and the like in the following examples are also only examples within a suitable range, that is, those skilled in the art can make a selection within a suitable range through the description herein, and are not limited to the specific values ​​​​exemplified below.

[0031] The following is an exemplary description of a genetic evaluation method for genomic breeding values ​​of high-yielding egg yolk-feather broiler chickens provided by the present invention.

[0032] like Figure 1 The flowchart of a method for genetic evaluation of genomic breeding values ​​of high-yielding egg yolk broiler chickens is shown below. Figure 1 To illustrate a method for genetic evaluation of genomic breeding values ​​of high-yielding egg yolk broiler chickens according to an embodiment of the present invention, the method comprises the following steps:

[0033] Step 1: Determine the weight and egg production of the flock: Determine the weight of the flock at the age of listing and record the total number of eggs produced in 65 weeks; Step 2: Estimate the genetic effect value of gene markers: Based on the genomic marker information of the flock, estimate the effect value of the gene marker for body weight and egg production at the same time;

[0034] Step 3, establish the gene marker effect evaluation index: based on the weight and egg production gene marker effect values ​​estimated in step 2, calculate the individual evaluation index through the following model:

[0035]

[0036]

[0037]

[0038] In the formula, κ represents the genetic evaluation index of the individual genome, m represents the number of gene markers, and a i and b i Respectively represent the genetic effect value of the ith gene marker for egg production and body weight traits, a' i and b' i represents the genetic evaluation index of the ith gene marker for egg production and body weight traits, c i is the genotype of the i-th gene marker, with values ​​of 0, 1, and 2 (where 0 and 2 represent homozygous types, and 1 represents heterozygous types), and α is the egg-laying weight control parameter;

[0039] Step 4, select individuals for seed retention: sort the genetic evaluation index of the individual genome by gender, determine the number of individuals to be retained based on the seed retention rate, and determine the individuals to be retained for seed retention from large to small.

[0040] The α value described in step 3 can be changed according to the heritability of egg production and body weight, as well as breeding needs, and the value range is 0.5 to 1. According to the current breeding needs, that is, to maximize the genetic progress of egg production while maintaining a small increase in individual body weight, the α value is preferably 0.8.

[0041] The mechanism of the present invention is:

[0042] According to the fundamental cause of the antagonism between growth and egg-laying traits and based on the genetic effect values ​​of genomic markers of these two traits, the present invention formulates a genetic evaluation index for egg-laying performance of yellow-feathered broilers from a genomic level, thereby ensuring weight gain while improving egg-laying performance, thereby overcoming the deficiency that traditional yellow-feathered broiler breeding methods only focus on individuals and do not optimize the selection of genomic antagonistic markers, thereby resulting in low efficiency in improving the genetic progress of egg-laying performance.

[0043] Example 1

[0044] The genetic evaluation method of body weight and egg-laying genomic breeding values ​​of simulated broiler data is illustrated using simulated broiler data for purposes of illustrating the effect of long-term selection of the method of the present invention.

[0045] The simulation data was generated by QMSim Version 2.0 software (http: / / www.aps.uoguelph.ca / ~msargol / qmsim / ), which simulated the historical population for 500 generations, and gradually expanded from 400 chickens (200 roosters + 200 hens) to 600 chickens (300 roosters + 300 hens). From the historical population, 150 roosters and 300 hens were randomly selected for random mating, and a basic group was generated after 1 generation. 1100 breeders (100 roosters + 1000 hens) were selected from the basic group, and the males and females were randomly mated at a ratio of 1:10. Each hen can hatch 10 healthy chicks, which are passed on for 30 generations, and the population size of each generation is 10,000 chickens.

[0046] Assume that the phenotypic variance of body weight and total number of eggs is 100, the heritability is 0.5 and 0.2 respectively, body weight and egg production are antagonistic traits, the genetic correlation is -0.3, and all genetic variation is caused by the action of quantitative trait loci. The broiler chicken genome does not consider the chicken's microchromosomes and sex chromosomes. It is assumed that the number of chromosomes in the genome is 28 pairs, the length of each chromosome is 1 morgen, and each chromosome is evenly distributed with 50 quantitative trait loci.

[0047] The specific data information provided by the simulation data is: pedigree file, gene marker file, and weight and egg production phenotype files, where the phenotype file contains the true breeding value of each individual.

[0048] This simulation experiment is repeated 30 times, and each repetition contains 30 generations of the current group. The following will take the data of 2 generations and 10 chickens as an example to implement this method according to the following process:

[0049] (1) Organize the genomic breeding value estimation files, including the body weight and egg production phenotype data of yellow-feathered broiler chickens, as well as the genotype data;

[0050] (2) Estimate the gene marker effect value based on the gene marker effect estimation method. This example adopts the Bayesian method and uses the JWAS software (https: / / reworkhow.github.io / JWAS.jl / latest / ) for demonstration. The sorted phenotype and genotype files are used to build a double trait model to estimate the genetic effect of genomic markers. The double trait model is expressed as follows:

[0051]

[0052] where y i Represents the phenotype vector of body weight and egg number of individual i, X ij is the correlation matrix of the jth fixed effect of individual i, p brepresents the number of fixed effects, b j is the jth fixed effect vector of body weight and egg production, M ik is the genotype covariate of individual i at locus k, p u is the number of loci, u k is the site effect vector for body weight and egg production, e i is the residual effect vector of body weight and egg number of individual i. To simplify the calculation, this example directly uses QTL markers as genetic markers and uses the true effect value for demonstration. Table 1 shows the effect values ​​of body weight and egg number of 10 QTL markers.

[0053] Table 1 Genetic marker effect values ​​for body weight and egg number

[0054] Genetic marker number weight Egg production Q1 0.061 0.418 Q3 0.59 -0.124 Q5 -0.901 0.286 Q6 0.266 -0.14 Q11 0.246 -0.705 Q12 -0.218 -0.142 Q15 0.238 0.05 Q16 0.31 -0.394 Q17 0.284 0.14 Q20 -0.517 -0.264

[0055] (3) Based on the genetic marker effect values ​​of body weight and egg production, the individual genetic evaluation index was calculated using the following model.

[0056]

[0057]

[0058]

[0059] Where κ represents the individual genome genetic evaluation index, m represents the number of gene markers, and a i and b i Respectively represent the genetic effect value of the ith gene marker for egg production and body weight traits, a' i and b' i represents the genetic evaluation index of the ith gene marker for egg production and body weight traits, c i is the genotype of the i-th gene marker, taking values ​​of 0, 1, and 2 (where 0 and 2 represent homozygous types, and 1 represents heterozygous types), α is the egg-laying weight control parameter, and in this embodiment, the value is 0.8. As shown in Table 2, the genetic marker effect values ​​of body weight and egg production of 10 gene markers of broiler chickens with individual number 501 are shown.

[0060] To verify the effect of the method of the present invention, the accuracy of the genomic breeding values ​​of the method of the present invention and two other commonly used methods were compared below.

[0061] Method 1: The difference from the method of the present invention is that this method directly uses the breeding value of the target trait egg production as the evaluation index, without considering the influence of body weight.

[0062] Method 2: The difference from the method of the present invention is that although this method takes body weight and egg production into consideration at the same time and uses the weighted breeding values ​​of the two traits as evaluation indicators, it does not take into account the antagonistic effect of marker sites and does not optimize or improve the marker sites that cause the antagonistic effect.

[0063] In the broiler chicken with individual number 501 in Table 2, the individual evaluation values ​​obtained corresponding to 10 gene markers are: Method 1, directly using the cumulative value of the genotype effect of egg production, that is, -0.721;

[0064] Method 2: directly use the cumulative value of the genotype effect of body weight and egg number to obtain the weighted value.

[0065] (-1.217)*0.2+(-0.721)*0.8=-0.8202;

[0066] The method of the present invention uses the weighted cumulative value of the body weight and egg production genotype evaluation index to obtain

[0067] (-1.171)*0.2+(-0.767)*0.8=-0.8478.

[0068] Table 2. Genetic marker effect values ​​for body weight and egg production of individual No. 501

[0069]

[0070] (4) Selection of yellow-feathered broiler chickens: Sort the genetic evaluation index of the individuals by gender, determine the number of individuals to be retained based on the seed retention rate, and decide on the individuals to be retained from large to small. Table 3 shows the selection of 10 broilers with individual numbers 501 to 510 using the three methods.

[0071] Table 3. Individual genetic assessment index

[0072]

[0073] The comparison results of the three methods are shown in Table 4. After 30 generations of breeding, the population genetic means obtained by the three methods were 129.663, 52.173 and 107.007, respectively, and the average growth per generation was 4.352, 1.769 and 3.597, respectively. The method of the present invention is superior to the other two methods in broiler gene evaluation.

[0074] Table 4 Population genetic values ​​of three methods

[0075]

[0076]

[0077] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

[0078] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0079] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for genetic evaluation of genomic breeding values ​​of high-yielding egg yolk broiler chickens, characterized in that: The method comprises the following steps: Step 1, measuring the weight and egg production of the chicken group; Step 2, estimate the genetic effect value of gene markers; Step 3, establishing a gene marker effect evaluation index; Step 4, selecting individuals for seed retention; In step 1, the sub-steps for determining the weight and egg production of the chicken flock are: At the age of market, measure the weight of the chickens to be assessed and record the total number of eggs laid in 65 weeks; In step 2, the sub-steps for estimating the genetic effect values ​​of gene markers are: Estimate genetic effect values ​​of gene markers and obtain estimated genetic marker effect values ​​of body weight and egg production; In step 3, the sub-steps for establishing the gene marker effect evaluation index are: Based on the estimated weight and egg production gene marker effect values, the individual assessment index was calculated using the following model, ； ； ； In the formula, κ represents the genetic evaluation index of the individual genome, m represents the number of gene markers, a i and b i Represent the genetic effect value of the ith gene marker for egg production and body weight traits, a' i and b' i Represent the genetic evaluation index of the ith gene marker for egg production and body weight traits, c i is the genotype of the i-th gene marker, with values ​​of 0, 1, and 2, where 0 and 2 represent homozygous types, and 1 represents heterozygous types. α is the egg-laying weight control parameter; In step 4, the sub-steps for selecting individuals for seed retention are: The genetic evaluation index of the individual genome is sorted in descending order by sex, and the number of individuals to be retained is determined according to the seed retention rate, and the individuals to be retained for breeding are determined from large to small.

Images