A frozen semen-living body linkage breeding effect prediction and evaluation method for poultry

Abstract

The application provides a poultry frozen semen-living body linkage breeding effect prediction and evaluation method, and belongs to the technical field of poultry breeding. The poultry frozen semen-living body linkage breeding effect prediction and evaluation method simulates frozen semen-living body linkage breeding prediction by using computer information technology, the method is not interfered by time, environment, physiology and other factors in simulation analysis, solves the bottleneck of few actual breeding populations and lack of repetition, understands the dynamic change of population genes, and can specify the number of simulation generations and the number of simulation times, improves the reliability of simulation results, and solves the problem that the traditional breeding scheme has no repetition or few repetitions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of poultry conservation, in particular to a poultry frozen semen-living body linkage conservation effect prediction and evaluation method. BACKGROUND

[0002] China has the most abundant poultry genetic resources in the world. The "National Livestock and Poultry Genetic Resources Variety Directory" (2024 edition) includes 289 chicken genetic resources, of which 140 are local varieties; 66 duck genetic resources, of which 42 are local varieties; and 43 goose genetic resources, of which 32 are local varieties. Poultry genetic resources are a valuable asset of the livestock industry. In response to the "Office of the State Council on Strengthening the Protection and Utilization of Agricultural Germplasm", we must "carry out systematic collection and protection and achieve the goal of preserving as much as possible". Livestock and poultry germplasm conservation includes living body conservation and low-temperature frozen conservation. Living body conservation is carried out in its original habitat or conservation area. Living body conservation not only preserves the adaptability of livestock and poultry to the external environment, but also checks and eliminates genetic deficiencies while conserving, and improves certain undesirable traits. It is currently the most commonly used method for protecting poultry genetic resources. At present, there are 3 national and local chicken gene banks and 27 national and local chicken resource conservation farms in the country. In recent years, poultry semen cryopreservation technology has made breakthroughs. Semen cryopreserved at ultra-low temperature can restore its fertilization ability after thawing and warming. Semen cryopreservation technology is of great significance in livestock and poultry conservation and has become another important means of poultry germplasm conservation.

[0003] However, how to collect and utilize ultra-low temperature semen and achieve effective linkage with living body conservation has not yet been established.

[0004] Therefore, a poultry frozen semen-living body linkage conservation effect prediction and evaluation method is provided, which can guide the establishment and optimization of poultry conservation programs and promote the further development of poultry conservation technology. SUMMARY

[0005] The purpose of the present application is to provide a poultry frozen semen-living body linkage conservation effect prediction and evaluation method, which aims to solve the technical problem that the collection and utilization of ultra-low temperature semen and the living body conservation program cannot be linked in the prior art, and the conservation program is affected by many realistic factors.

[0006] In order to achieve the above-mentioned purpose of the application, the present application provides the following technical solutions:

[0007] The present application provides a poultry frozen semen-living body linkage conservation effect prediction and evaluation method, comprising the following steps:

[0008] (1) Determine the genetic diversity of the starting population;

[0009] (2) randomly generating a starting population according to the population genetic diversity; or using an actual population with known individual genetic variation site information;

[0010] (3) establishing an in vivo conservation scheme, a frozen semen collection and recovery scheme based on the starting population;

[0011] (4) generating a next-generation conservation population by computer simulation analysis based on the in vivo conservation scheme, the frozen semen collection and recovery scheme;

[0012] (5) repeating step (4) to generate a conservation population of a specified generation;

[0013] (6) calculating the genetic diversity of the population;

[0014] (7) analyzing the population distribution and genetic diversity parameter values of the final generation of 1 simulation according to the results of step (6);

[0015] (8) repeating steps (4) to (7) for a specified number of simulations to generate population genetic diversity analysis results for the specified number of simulations, and predicting the effect of the conservation scheme based on the population genetic diversity analysis results.

[0016] Further, the genetic diversity of the starting population is specifically specified for a plurality of genetic variation sites, and then the number of variations on each genetic variation site is specified; there is no genetic linkage between the genetic variation sites.

[0017] Further, the method for randomly generating the starting population is to use an R language program R1 to establish a functional function to randomly generate the starting population.

[0018] Further, the method for establishing the in vivo conservation scheme is specifically to specify the number of conservation seeds, the method of conservation seeds, and the method of mating.

[0019] Further, the method for establishing the frozen semen collection and recovery scheme is to specify the frozen semen collection and recovery method, which includes random collection and recovery, family collection and recovery, and the cycle of frozen semen collection and recovery.

[0020] Further, the specific method for generating the next-generation conservation population by computer simulation analysis is to use an R language program R2 to establish a functional function to simulate frozen-in vivo linked conservation and generate the next-generation conservation population.

[0021] Further, the generation of a conservation population of a specified generation is specifically to specify the number of generations and use an R language program for function to realize the random generation of a conservation population of a specified generation.

[0022] Further, the calculation of the genetic diversity of the population is specifically: using the R language program to write a function R3 to calculate the genetic diversity evaluation index.

[0023] Further, the analysis of the population distribution and the genetic diversity parameter values of the 1-time simulation final generation is specifically: calculating the PIC, Ho, He, Na, Ne, I of the 1-time simulation starting generation and final generation, and the differences δPIC, δHo, δHe, δNa, δNe, δI of the starting generation and the final generation.

[0024] Further, the step (8) is specifically: calculating the PIC, Ho, He, Na, Ne, I of the starting generation and the final generation of the multiple simulation results, obtaining the average value and the standard deviation, and evaluating the effect of the frozen semen-living body linkage breeding scheme.

[0025] Compared with the prior art, the technical scheme of the present application has the following beneficial effects:

[0026] The present application uses computer information technology to simulate the frozen semen-living body linkage breeding prediction, and the method simulates the analysis without being disturbed by time, environment, physiology and other factors, solves the bottleneck of few actual breeding populations and lack of repetition, understands the dynamic changes of population genes, and can specify the number of simulation generations and the number of simulations, improves the reliability of the simulation results, and solves the problem that the traditional breeding scheme has no repetition or few repetitions.

[0027] In addition, the method can analyze and explore the influence of different breeding strategies on the genetic diversity of the population and the application of frozen semen recovery in the breeding scheme, which is helpful for in-depth understanding of the management and protection of poultry genetic resources, and has high popularization and application value in poultry breeding. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flow chart of the poultry frozen semen-living body linkage breeding effect prediction and evaluation method of the present application;

[0029] Figure 2 The population clustering diagram of 100 generations randomly selected from the 500 simulation analyses of Example 1 is drawn;

[0030] Figure 3 The population clustering diagram of 100 generations randomly selected from the 500 simulation analyses of Example 2 is drawn;

[0031] Figure 4 The population clustering diagram of 100 generations randomly selected from the 500 simulation analyses of Example 3 is drawn;

[0032] Figure 5A population cluster plot of 100 generations is plotted for a randomly selected simulation of 1 of the 500 simulated analyses described in Example 4;

[0033] Figure 6 A population cluster plot of 100 generations is plotted for a randomly selected simulation of 1 of the 500 simulated analyses described in Example 5;

[0034] Figure 7 A population cluster plot of 100 generations is plotted for a randomly selected simulation of 1 of the 500 simulated analyses described in Example 6. DETAILED DESCRIPTION

[0035] Various illustrative embodiments of the present application are described in detail below. The present application should not be considered limited to only these embodiments, but rather these embodiments are provided as examples of the present application. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application, exemplary methods and materials are described herein.

[0037] Many modifications and variations of this application can be made in the light of the above teachings without departing from the spirit and scope thereof, and it is to be understood that all such modifications and variations warrant the patentable subject matter under the patent laws. Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The specification and examples given are exemplary only.

[0038] The present application provides a poultry frozen semen-living body linkage breeding effect prediction and evaluation method, comprising the following steps:

[0039] (1) determining the genetic diversity of the starting population;

[0040] (2) randomly generating a starting population according to the genetic diversity of the population;

[0041] (3) establishing a living body breeding scheme, a frozen semen collection and recovery scheme based on the starting population;

[0042] (4) based on the living body breeding scheme, the frozen semen collection and recovery scheme, using computer simulation analysis to generate the next generation breeding population;

[0043] (5) repeating step (4) to generate a breeding population of a specified generation;

[0044] (6) calculating the genetic diversity of the population;

[0045] (7) according to the result of the step (6), analyzing the population distribution and the genetic diversity parameter value of the 1st simulation end generation;

[0046] (8) specifying the simulation times, repeating the steps (4)-(7), generating the population genetic diversity analysis results of the specified simulation times, and predicting the effect of the conservation scheme based on the population genetic diversity analysis results.

[0047] Specifically:

[0048] (1) determining the genetic diversity of the starting population;

[0049] In the present application, the genetic diversity of the starting population is specifically specified as a plurality of genetic variation sites, and then the number of variations on each genetic variation site is specified; there is no genetic linkage between the genetic variation sites.

[0050] (2) randomly generating a starting population according to the population genetic diversity;

[0051] In the present application, the method for randomly generating a starting population is to use R language program R1 to establish a functional function to randomly generate a starting population, or to use an actual population with known individual genetic variation site information as a starting population.

[0052] The R1 is specifically:

[0053]

[0054] (3) establishing an in vivo conservation scheme, a frozen semen collection and recovery scheme based on the starting population;

[0055] In the present application, the method for establishing the in vivo conservation scheme is specifically to specify the number of seed stocks, the method of seed stock and the method of mating of in vivo conservation.

[0056] In the present application, the method for establishing the frozen semen collection and recovery scheme is to specify the mode of frozen semen collection and recovery, and the mode of frozen semen collection and recovery includes random collection and recovery, family collection and recovery, and the period of frozen semen collection and recovery.

[0057] (4) generating the next generation of conservation population based on the in vivo conservation scheme, the frozen semen collection and recovery scheme, and the computer simulation analysis;

[0058] In the present application, the specific method for generating the next generation of conservation population by computer simulation analysis is to use R language program R2 to establish a functional function to simulate frozen-in vivo linkage conservation, and generate the next generation of conservation population.

[0059] The R2 in the present application is specifically:

[0060]

[0061]

[0062] (5) repeating step (4) to generate the conservation population of the designated generation;

[0063] In the present application, the generating the conservation population of the designated generation specifically refers to: using the R language program for function to realize the random generation of the conservation population of the designated generation.

[0064] (6) calculating the genetic diversity of the population;

[0065] In the present application, the calculating the genetic diversity of the population specifically refers to: using the R language program to write the function function R3 to calculate the genetic diversity evaluation index.

[0066] The R3 in the present application specifically refers to:

[0067]

[0068] The genetic diversity evaluation index in the present application includes polymorphism information content (Polymorphism Information Content, PIC), observed heterozygosity (Observed Heterozygosity, Ho), expected heterozygosity (Expected Heterozygosity, He), mean number of alleles (Mean Number of Alleles, Na), effective number of alleles (Effective Number of Alleles, Ne), Shannon index (Shannon Index, I) and the like. The skilled in the art can adaptively extend the function function according to the actual needs, and add the number of genetic index calculation.

[0069] (7) analyzing the population distribution and genetic diversity parameter values of the terminal generation of the simulation according to the result of step (6);

[0070] In the present application, the analyzing the population distribution and genetic diversity parameter values of the terminal generation of the simulation specifically refers to: calculating the PIC, Ho, He, Na, Ne, I of the starting generation and the terminal generation of the simulation, and the difference δPIC, δHo, δHe, δNa, δNe, δI between the starting generation and the terminal generation.

[0071] (8) repeating steps (4) to (7) to generate the population genetic diversity analysis results of the designated simulation times, and predicting the effect of the conservation scheme based on the population genetic diversity analysis results.

[0072] In the present application, the step (8) is specifically calculating the PIC, Ho, He, Na, Ne and I of the initial generation and the terminal generation of the multiple simulation results, obtaining the average value and the standard deviation, and evaluating the effect of the frozen semen-living body linkage breeding scheme.

[0073] The technical solutions provided by the present application will be described in detail below in combination with the embodiments, but they should not be understood as limiting the scope of protection of the present application.

[0074] Embodiment 1

[0075] (1) Determine the genetic diversity of the initial population; the population size of the initial population is 330, of which 30 are male birds and 30 are female birds, 40 genetic variation sites are designated, and the number of variations on each genetic variation site is shown in the following table; there is no genetic linkage between each genetic variation site.

[0076] Table 1 Number of variations on each genetic variation site

[0077]

[0078] (2) Randomly generate the initial population according to the population genetic diversity, specifically:

[0079]

[0080]

[0081] (3) Establish a living body breeding scheme, and designate the number of breeding birds as 30 male birds and 300 female birds, and the breeding scheme is random breeding, and the mating scheme is random mating.

[0082] (4) Establish a frozen semen collection and recovery scheme; designate the frozen semen collection scheme as random preservation, designate the collection cycle as 5 years, and designate the frozen semen recovery cycle as 5 years.

[0083] (5) According to the frozen-living body linkage breeding scheme established in steps three and four, use R language program simulation analysis to generate the next generation breeding population, define Freq as 10, and generate the next generation breeding population, specifically:

[0084]

[0085]

[0086]

[0087] Step six: repeat step five to generate the breeding population of the designated generation; specifically, use the R language program for function to realize the random generation of the breeding population of 100 generations.

[0088] Step seven: Calculate the genetic diversity of the population; the indicators include Polymorphism Information Content (PIC), Observed Heterozygosity (Ho), Expected Heterozygosity (He), Mean Number of Alleles (Na), Effective Number of Alleles (Ne), Shannon Index (I), etc. The R language program is used to establish a function function, specifically:

[0089]

[0090] Step eight: According to the results of step seven, analyze the population distribution in 100 generations and the genetic diversity parameter values of the 100th generation in one simulation, and the results are as shown in Figure 2 Table 2;

[0091] Table 2 Genetic diversity parameter table of the 1st generation and the 100th generation in one simulation

[0092]

[0093]

[0094] Step nine: Specify the number of simulations, repeat steps five to eight, and generate the population genetic diversity analysis results of the specified number of simulations to evaluate the frozen semen-living body linkage breeding program. The specified number of simulations is 500, and the average of the 500 simulated population genetic diversity parameters of the 100th generation is shown in Table 3;

[0095] Table 3 Genetic diversity parameter table of the 1st generation and the 100th generation in 500 simulations

[0096]

[0097] Example 2

[0098] Step one: Same as step one of the example.

[0099] Step two: Same as step two of the example.

[0100] Step three: Same as step three of the example.

[0101] Step four: Establish a frozen semen collection and recovery program; specify the frozen semen collection scheme as random preservation, specify the collection period as 10 years, and specify the frozen semen recovery period as 10 years.

[0102] Step five: According to the frozen-vivo linkage breeding scheme established in step three and step four, the R language program is used to establish a functional function to simulate frozen-vivo linkage breeding, define Freq as 10, and generate the next generation of breeding population.

[0103] Step six: Same as step six of the example.

[0104] Step seven: Same as step seven of the example.

[0105] Step eight: According to the results of step seven, analyze the population distribution in 100 generations and the genetic diversity parameter values of the 100th generation in 1 simulation. The results are shown in Figure 3 and Table 4:

[0106] Table 4 Genetic diversity parameter table of 500 simulations of the 1st and 100th generations

[0107]

[0108] Step nine: Same as step nine of the example. The number of simulations is specified as 500 times, and the average of the 500 simulation population genetic diversity parameters of the 100th generation is shown in Table 5:

[0109] Table 5 Genetic diversity parameter table of 500 simulations of the 1st and 100th generations

[0110]

[0111] Example 3

[0112] Step one: Same as step one of the example.

[0113] Step two: Same as step two of the example.

[0114] Step three: Same as step three of the example.

[0115] Step four: Establish a frozen semen collection and recovery scheme; specify the frozen semen collection scheme as random preservation, specify the collection period as 20 years, and specify the frozen semen recovery period as 20 years.

[0116] Step five: According to the frozen-vivo linkage breeding scheme established in step three and step four, the R language program is used to establish a functional function to simulate frozen-vivo linkage breeding, define Freq as 10, and generate the next generation of breeding population.

[0117] Step six: Same as step six of the example.

[0118] Step seven: Same as step seven of the example.

[0119] Step eight: According to the results of step seven, analyze the population distribution within 100 generations and the genetic diversity parameter values of the 100th generation of 1 simulation. The results are shown in Table 5 and Table 6: Figure 4 and Table 6:

[0120] Table 6 Genetic diversity parameter table of the 1st generation and the 100th generation of 1 simulation

[0121]

[0122] Step nine: The same as step nine of Example one. The number of simulations is specified as 500, and the average of the population genetic diversity parameters of the 100th generation of 500 simulations is shown in Table 7:

[0123] Table 7 Genetic diversity parameter table of the 1st generation and the 100th generation of 500 simulations

[0124]

[0125] Example 4

[0126] Step one: The same as step one of Example one.

[0127] Step two: The same as step two of Example one.

[0128] Step three: The same as step three of Example one.

[0129] Step four: Establish a frozen semen collection and recovery program; specify the frozen semen collection program as random storage, specify the collection period as 25 years, and specify the frozen semen recovery period as 25 years.

[0130] Step five: According to the frozen-vivo linkage breeding program established in steps three and four, use R language program to establish a function function to simulate frozen-vivo linkage breeding, define Freq as 25, and generate the next generation of breeding population.

[0131] Step six: The same as step six of Example one.

[0132] Step seven: The same as step seven of Example one.

[0133] Step eight: According to the results of step seven, analyze the population distribution within 100 generations and the genetic diversity parameter values of the 100th generation of 1 simulation. The results are shown in Table 5 and Table 6: Figure 5 and Table 8:

[0134] Table 8 Genetic diversity parameter table of the 1st generation and the 100th generation of 1 simulation

[0135]

[0136] Step nine: same as example step nine. The number of simulations is specified as 500, and the average of the genetic diversity parameters of the 500 simulated populations in the 100th generation is shown in Table 9:

[0137] Table 9 Genetic diversity parameters of the 1st and 100th generations of 500 simulations

[0138]

[0139]

[0140] Example 5

[0141] Step one: same as example step one.

[0142] Step two: same as example step two.

[0143] Step three: same as example step three.

[0144] Step four: establish a frozen semen collection and recovery program; specify the frozen semen collection program as random preservation, specify the collection period as 50 years, and specify the frozen semen recovery period as 50 years.

[0145] Step five: according to the frozen-vivo linkage breeding program established in steps three and four, use R language program to establish a functional function to simulate frozen-vivo linkage breeding, define Freq as 50, and generate the next generation of breeding populations.

[0146] Step six: same as example step six.

[0147] Step seven: same as example step seven.

[0148] Step eight: according to the results of step seven, analyze the population distribution within 100 generations and the genetic diversity parameter values of the 100th generation of 1 simulation. The results are shown in Figure 6 and Table 10:

[0149] Table 10 Genetic diversity parameters of the 1st and 100th generations of 1 simulation

[0150]

[0151] Step nine: same as example step nine. The number of simulations is specified as 500, and the average of the genetic diversity parameters of the 500 simulated populations in the 100th generation is shown in Table 11:

[0152] Table 11 Genetic diversity parameters of the 1st and 100th generations of 500 simulations

[0153]

[0154] Example 6

[0155] Step one: same as example step one.

[0156] Step two: same as example step two.

[0157] Step three: same as example step three.

[0158] Step four: establish frozen semen collection and recovery program; specify frozen semen collection program as random preservation, specify collection period as 150 years, and specify frozen semen recovery period as 150 years. That is, there is no frozen semen collection and recovery within 100 years.

[0159] Step five: according to the frozen-vivo linkage breeding program established in steps three and four, use R language program to establish functional function to simulate frozen-vivo linkage breeding, define Freq as 150, and generate next generation breeding population.

[0160] Step six: same as example step six.

[0161] Step seven: same as example step seven.

[0162] Step eight: according to the results of step seven, analyze the population distribution in 100 generations and the genetic diversity parameter values of the 100th generation in 1 simulation. The results are shown in Figure 7 and Table 12:

[0163] Table 12 Genetic diversity parameter table of the 1st generation and the 100th generation in 1 simulation

[0164]

[0165] Step nine: same as example step nine. The number of simulations is specified as 500, and the average of the genetic diversity parameters of the 500 simulated populations of the 100th generation is shown in Table 13:

[0166] Table 13 Genetic diversity parameter table of the 1st generation and the 100th generation in 500 simulations

[0167]

[0168] The above only describes the preferred embodiments of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for predicting and evaluating the effect of a frozen-thawed semen- live-embryo combined insemination method for poultry, characterized by, The method comprises the following steps: (1) determining the genetic diversity of the starting population; The genetic diversity of the starting population is determined by specifying a plurality of genetic variation sites and then specifying the number of variations at each genetic variation site; there is no genetic linkage between the genetic variation sites; (2) randomly generating a starting population according to the genetic diversity of the population; the method for randomly generating the starting population is to use an R language program R1 to establish a functional function to randomly generate the starting population; or an actual population with known individual genetic variation site information is used as the starting population; (3) establishing an in vivo conservation scheme, a frozen semen collection and recovery scheme based on the starting population; the method for establishing the in vivo conservation scheme is to specify the number of seed stocks, the seed stock method and the mating method; the method for establishing the frozen semen collection and recovery scheme is to specify the frozen semen collection and recovery method, which includes random collection and recovery, family collection and recovery, and the period of frozen semen collection and recovery; (4) generating a next-generation conservation population by computer simulation analysis based on the in vivo conservation scheme and the frozen semen collection and recovery scheme; (5) repeating step (4) to generate a conservation population of a specified generation; (6) calculating the genetic diversity of the population; the method for calculating the genetic diversity of the population is to use an R language program to write a functional function R3 to calculate genetic diversity evaluation indexes, including polymorphic information content PIC, observed heterozygosity Ho, expected heterozygosity He, average allele number Na, effective allele number Ne, and Shannon index I; (7) analyzing the population distribution and genetic diversity parameter values of the terminal generation of 1 simulation according to the results of step (6); the method for analyzing the population distribution and genetic diversity parameter values of the terminal generation of 1 simulation is to calculate the PIC, Ho, He, Na, Ne, I of the starting generation and the terminal generation of 1 simulation, and the difference δPIC, δHo, δHe, δNa, δNe, δI between the starting generation and the terminal generation; (8) specifying the number of simulations, repeating steps (4) to (7) to generate a population genetic diversity analysis result of a specified number of simulations, and predicting the effect of the conservation scheme based on the population genetic diversity analysis result; specifically, the PIC, Ho, He, Na, Ne, I of the starting generation and the terminal generation of multiple simulations are calculated to obtain the average value and the standard deviation, and the effect of the frozen semen-in vivo combined conservation scheme is evaluated and predicted.

2. The method for predicting and evaluating the effect of the insemination- live body linkage of poultry frozen semen according to claim 1, characterized in that, The method for generating the next-generation conservation population by computer simulation analysis is to use an R language program R2 to establish a functional function to simulate the frozen-in vivo combined conservation and generate the next-generation conservation population.

3. The method of claim 1, wherein the method is characterized by: The method for generating a conservation population of a specified generation is to specify the number of generations and use an R language program for function to realize the random generation of the conservation population of the specified generation.

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