Matching method for integrating genome selection and application
By integrating genomic selection matching methods, combined with simulation software and the ssGBLUP model, an objective function optimization matching strategy was established, which solved the problem of imbalance between local livestock and poultry genetic progress and diversity, and achieved a balanced development of genetic improvement and breed preservation.
Patent Information
- Application Number
- CN202511249436.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-03
- Publication Date
- 2025-12-19
AI Technical Summary
In the work of local livestock and poultry genetic improvement and conservation, it is difficult to balance genetic progress with the imbalance of genetic diversity. Existing genome selection methods have failed to effectively combine matching, resulting in an inability to balance the protection and development of genetic resources.
The mating method using integrated genomic selection was adopted. Breeding populations were constructed using simulation software, and the ssGBLUP model was used to calculate the estimated genomic breeding values of candidate individuals. An objective function was established to control changes in inbreeding, heterozygosity, and genetic background frequency. Genetic progress, heterozygosity, and inbreeding coefficient were comprehensively evaluated to optimize the mating strategy.
This approach achieves an organic combination of local livestock and poultry genetic diversity protection and genetic improvement, accelerating genetic progress while preserving population genetic diversity, breaking through the dilemma of genetic improvement and breed preservation, and providing continuous support for the development of the local livestock and poultry industry.
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Abstract
Description
TECHNICAL FIELD
[0001] The application provides a matching method and application, and particularly relates to a matching method and application integrating genomic selection. BACKGROUND
[0002] With the development of society and the improvement of living standards, the demand for high-quality meat products is also increasing, and the local livestock and poultry industry is ushering in a good development opportunity period. Therefore, it is imperative to genetically improve local livestock and poultry breeds and improve their market value.
[0003] At present, the genomic selection (GS) technology based on SNP chips and sequencing has brought great changes to animal breeding. Researchers have used SNP chip data of different densities to conduct whole genome prediction, parentage identification and breed composition research. Whole genome selection has become the mainstream technology for genetic evaluation of current livestock and poultry.
[0004] Akdemir and Sanchez (2016) applied the concept of risk (utility) in economics to genomic mating (GM), and considered and weighted the genetic relationship between GEBV and parents to select the best parent combination. Genomic mating can effectively avoid homozygosity of recessive harmful genes and make full use of dominant effects, and can effectively control the inbreeding rate during genomic selection breeding.
[0005] The genetic improvement and conservation of local livestock and poultry cannot be limited to selection and control of inbreeding, but should also be combined with matching to control from the source and process. The genetic variance, genetic progress and inbreeding of the population can be included in the GM objective function to consider the potential advantages of couples with higher estimated genetic variance, and it is also feasible for genetic selection of complex traits. Therefore, the balance between genetic improvement and genetic diversity protection of local livestock and poultry breeds requires the organic combination of genetic evaluation and scientific mating. Under the current background of the national promotion and application of genomic selection, it is of great significance to develop a livestock and poultry genomic selection method (GM-GS) integrating genomic selection. SUMMARY
[0006] The application provides a matching method and application integrating genomic selection to solve the above problems, which solves the problem of imbalance between genetic progress and genetic diversity in the selection and breeding of local livestock and poultry, and the difficulty in balancing conservation and improvement, and provides effective technical support for resource protection and development and utilization.
[0007] To solve the above technical problems, the application provides the following technical scheme: a matching method integrating genomic selection, comprising the following steps:
[0008] (1) determining a local livestock and poultry breeding population, constructing the breeding population by simulation software, wherein 5 generations are taken as reference groups, each generation contains 1000-2000 individuals, and selection is carried out for 10-20 generations from the 6th generation;
[0009] (2) constructing a local livestock and poultry reference population, determining pedigree data, phenotype data and genotype data of the reference population, wherein the genotype data is obtained by SNP chip detection, and the phenotype data covers growth traits, reproduction traits and / or carcass traits;
[0010] (3) calculating the genomic estimated breeding value (GEBV) of a candidate individual based on the ssGBLUP model and the pedigree data, phenotype data and genotype data of the reference population in step (2), and screening individuals meeting the selection requirements according to the GEBV;
[0011] (4) establishing a target function for integrating genomic mating, and implementing genomic mating on the individuals screened in step (3), wherein the target function is:
[0012]
[0013] The constraint condition is c'1=1 and c≥0;
[0014] wherein w2c'Ac+w3ΔH+w4Δf is a penalty term, w1 is a weighted vector of selection progress among each mating family, the numerical value of the element is equal to a corresponding value of the inverse of the genetic value variance among the offspring of each family. w2, w3 and w4 are adjustment vectors for the size of the three groups of the penalty term. The purpose of adding this penalty term in the target function is to control the inbreeding, heterozygosity and genetic background gene frequency change between generations; c is the mating decision vector, b is the GEBV vector, c'Ac is the binomial increase related to the coefficient of co-parents, ΔH is the heterozygosity reduction, and Δf is the genetic background gene frequency change;
[0015] (5) evaluating the mating effect from four dimensions of genetic progress, heterozygosity, inbreeding coefficient and SNP frequency change: the genetic progress is calculated by weighting the inverse of the genetic standard deviation of the offspring of each family; the inbreeding coefficient is calculated as SNP-based F and ROH-based F based on SNP sites and ROH respectively; the Δf is the average SNP frequency change between generations of the population weighted by the effect of SNP on the target trait; the heterozygosity includes observed heterozygosity (OH) and expected heterozygosity (EH).
[0016] Preferably, the local livestock and poultry is Ningxiang pig.
[0017] Preferably, the reference population in step (2) has a size of about 2000, and is composed of the boars in use in the core breeding area and the breeding sows in the breeding farm.
[0018] Preferably, the SNP chip in step (2) is Illumina Pig 60K SNP chip or GGP 50K SNP chip.
[0019] Preferably, the growth traits in step (2) include body weight and live backfat thickness; the reproduction traits include total litter size, live-born litter size and birth weight; and the carcass traits include carcass weight, actual backfat thickness and lean meat percentage.
[0020] Preferably, the selection rate of the individuals after screening in step (3) is set as 5% for boars and 30% for sows, and only the individuals from the first litter are selected in each generation.
[0021] Preferably, the calculation of Δf in step (4) is as follows:
[0022]
[0023] wherein Δf is the change of the frequency of the jth SNP between generations, b is the effect of the jth SNP on the target trait, and max(b) is the maximum absolute value of the effects of all SNPs. In the above formula, the greater the SNP effect, the smaller the weight. When b j = max(b), the weight is 0, meaning that the change of the SNP between generations is not considered; and when b j = 0, the weight is 1, meaning that the change of the SNP between generations is 100% limited.
[0024] Preferably, when the genomic selection in step (4) is performed, a random mating group and a conventional selection group are set as controls at the same time, and the conventional selection is to set a threshold of inbreeding and maximize the genetic progress of the offspring.
[0025] Preferably, the method in any one of claims 1-8 is used in the breeding and genetic improvement of local livestock and poultry.
[0026] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages compared with the prior art:
[0027] ①A set of genomic selection methods integrating the idea of genomic selection is proposed
[0028] Based on the simulation data and the Ningxiang pig population with good research foundation, a set of new GS methods integrating GM is established, and the genomic information is used to expand the existing secondary optimization method, establish a target function, and develop a theoretical method suitable for the breeding and genetic improvement of local livestock and poultry.
[0029] ②By the new method, the protection of local livestock genetic diversity and genetic improvement is realized
[0030] Different from the general genomic selection method focusing on genetic progress and the genomic mating technique focusing on maximizing the use of dominant effects in hybrid production, the new method established by the present application mainly aims at the dual needs of local livestock genetic diversity protection and production performance genetic improvement, innovatively integrates selection and mating, establishes a set of selection and mating method suitable for local livestock, and systematically answers key questions such as the changing relationship between local livestock genetic diversity and genetic progress.
[0031] ③The new method has extensive reference significance for local livestock breeds
[0032] The GM-GS method developed in the present research is expected to accelerate the genetic progress of local livestock while preserving the genetic diversity of the population as much as possible, breaking the dilemma of local livestock conservation and genetic improvement, and providing sustainable support for the development of local livestock industry. At the same time, the achievements of the present project are expected to have great significance for the sustainable development and utilization of local livestock genetic resources in China, especially for the protection and utilization of local livestock genetic resources, which will have a profound impact.
[0033] Other advantages, objects and features of the present application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the present application. BRIEF DESCRIPTION OF DRAWINGS
[0034] Figure 1 Figure 1 is a genetic progress change graph of the integrated genomic selection and mating method and application of the present application after 5 generations of selection breeding of 6 different mating schemes;
[0035] Figure 2 Figure 2 is a pedigree inbreeding coefficient and genomic inbreeding coefficient broken line graph of the integrated genomic selection and mating method and application of the present application after 5 generations of selection breeding of 6 different mating schemes;
[0036] Figure 3 Figure 3 is a genetic variance change graph of the integrated genomic selection and mating method and application of the present application after 5 generations of selection breeding of 6 different mating schemes;
[0037] Figure 4 Figure 4 is a left half part of the GM-GS method overall technical process time sequence diagram of the integrated genomic selection and mating method and application of the present application;
[0038] Figure 5The right half of the GM-GS method integrated genomic selection selection method and application whole technical flow timing diagram;
[0039] Figure 6 The Ningxiang pig reference group construction and data collection flowchart of the integrated genomic selection selection method and application of the application. DETAILED DESCRIPTION
[0040] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the application.
[0041] It should be noted that the terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are for illustrative purposes only and do not represent the only implementation.
[0042] 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 the application belongs; the terms used in the specification of the application herein are only for the purpose of describing the specific embodiments and are not intended to limit the application; the term "and / or" used herein includes any and all combinations of one or more related listed items.
[0043] As Figure 1 , 2 , 3, an integrated genomic selection selection method, a local livestock breeding population is constructed by simulation software, GEBV of a candidate individual is calculated based on a ssGBLUP model using reference population data, and a breeding individual is screened, a breeding rate of 5% of a male and 30% of a female is set, and only the first litter is bred in each generation, a target function taking balancing genetic progress and genetic diversity as the core is established to implement genomic selection, a random mating group is set at the same time, a conventional selection group with a maximum genetic progress of offspring is set as a control, genetic progress calculated by weighting according to the reciprocal of the genetic standard deviation of the offspring, inbreeding coefficient calculated based on SNP sites and ROH, heterozygosity including real heterozygosity and expected heterozygosity, and SNP frequency change dimension are used to evaluate the selection effect.
[0044] In this embodiment, in order to achieve the technical objectives of the present application, the applicant uses simulation data to study the effects of different genomic mating schemes under genomic selection. The animal breeding scheme is simulated under the assumption that there is no overlap between generations, and genomic selection is used for selection in each generation. Four different methods are used for mating of breeding individuals, including genomic mating, homogeneous mating, heterogeneous mating, and random mating. Among them, three different schemes are given in genomic mating, which almost cover the range that can be achieved by genomic mating, including the maximum genetic progress value, the minimum inbreeding, and the maximum variance between families. Each scheme is selected for 5 generations, and the genetic progress, inbreeding coefficient, and genetic variance of the offspring population are compared, and the average value is taken after repeating 5 times. The following is a specific description of the three schemes:
[0045] Figure 1 The changes of genetic progress of the six schemes in five generations are compared. As can be seen from the figure, the three schemes in genomic mating all achieve high genetic progress, among which the scheme with the maximum genetic progress value in genomic mating almost achieves the maximum genetic progress, only slightly lower than homogeneous mating in the third generation. The genetic progress of the other two schemes with minimum inbreeding and maximum variance between families is between homogeneous mating and random mating.
[0046] Based on the pedigree inbreeding coefficient, the inbreeding coefficient of homogeneous mating is significantly higher than that of other mating schemes. Heterogeneous mating has the lowest inbreeding coefficient in the first two generations, but the inbreeding coefficient rises after the third generation and is higher than that of random mating. The inbreeding coefficients of the three schemes in genomic mating are between homogeneous mating and random mating. The inbreeding coefficient of the scheme with the maximum genetic progress value in genomic mating is higher than that of the other two schemes in genomic mating, but is significantly lower than that of homogeneous mating. The inbreeding coefficient of the scheme with minimum inbreeding in genomic mating is close to that of random mating in the first two generations, and is higher than that of random mating and lower than that of heterogeneous mating after the third generation. The inbreeding coefficient of the scheme with maximum variance between families is between the scheme with the maximum genetic progress value and the scheme with minimum inbreeding.
[0047] Figure 3 The average genetic variance trend of different mating schemes is shown. With the breeding, the genetic variance of homogeneous mating decreases the fastest and the most. The genetic variance of heterogeneous mating decreases the slowest and the least. The genetic variance of the scheme with maximum variance in genomic mating is significantly higher than that of homogeneous mating after the second generation and close to that of heterogeneous mating. The genetic variance of the scheme with the maximum genetic progress value is lower than that of homogeneous mating in the first three generations, but the decline trend slows down in the fourth and fifth generations, and is higher than that of homogeneous mating. The genetic variance of the scheme with minimum inbreeding is between the other two genomic mating schemes.
[0048] As Figure 4 and Figure 5As shown, the method, the local livestock and poultry can be Ningxiang pig, the reference group size is about 2000, and the core breeding area is used to breed sows by boars and breeding farms, genotype data is obtained by pig Illumina 60K or GGP 50K SNP chip, phenotype data covers growth, reproduction and carcass traits, the objective function is obtained by weighting consideration of selection progress, binomial increase of co-parent coefficient related parameters and setting constraint conditions, the genetic background gene frequency change is calculated by weighting the effect of SNP on target traits, and the method can be applied to local livestock and poultry breeding and genetic improvement.
[0049] In the embodiment, first, a local livestock and poultry breeding population including 5 reference generations (1000-2000 individuals per generation) is constructed by simulation software such as GPOPSIM, and simulation breeding of 10-20 generations is carried out from the 6th generation; for the Ningxiang pig reference population, 360 individuals of subsequent tests are supplemented based on about 1000 individuals that have been constructed, and genotype and phenotype data of about 600 individuals are newly measured, growth and development, reproduction and other trait information is continuously collected in breeding farms, and carcass trait data is collected in slaughterhouses. After calculating the genomic estimated breeding value of the candidate individual based on the ssGBLUP model, the weighting parameters related to selection progress, co-parent coefficient, heterozygosity and genetic background gene frequency in the objective function are adjusted to optimize the genomic mating strategy; at the same time, the blood relationship composition, genomic inbreeding degree and genetic diversity status of the Ningxiang pig population are found out, and the pedigree information is perfected in combination with field records and SNP data. 50 sows using conventional mating and 50 sows using integrated method are selected to carry out breeding at the same time, the breeding rate of boars is controlled to be 5%, the breeding rate of sows is controlled to be 30%, and only the first litter is bred, and the genetic progress and genetic diversity key parameters of each generation under the two strategies are compared comprehensively to verify the effectiveness of the method.
[0050] It should be particularly pointed out that, in the program debugging aspect of the present application, all calculation processes are realized by using computer programming languages such as R based on simulation data, wherein the calculation of genomic breeding value is simulated by using BLUPF90 software, and accordingly a selection and mating program mainly based on R platform and integrated with calculation programs such as BLUPF90 is developed, after the program development is completed, debugging is carried out on different platforms such as Windows and servers to ensure the robustness and efficiency of the program, parallel calculation of large population data is realized, and the calculation efficiency of the program is comprehensively debugged and improved based on simulation data.
[0051] In the implementation of the present application, animal ear tissue sampling forceps are also needed for collecting Ningxiang pig ear tissue samples, 2ml centrifuge tubes are matched with RNA protection solution to store the collected samples to prevent degradation, animal genomic DNA extraction kit and
[0052] Thermo Scientific Sorvall ST16R high-speed refrigerated centrifuge was used for sample DNA isolation and purification, Applied Biosystems Veriti 96-well PCR instrument was used for SNP site amplification, Bio-Rad PowerPac Basic agarose gel electrophoresis instrument was used for amplification product quality detection, Illumina iScan chip scanner was used for processing Illumina 60K or GGP 50K SNP chip to obtain genotype raw data; when field phenotypic determination was needed, electronic scale was used for measuring Ningxiang pig body weight, Lean-Meater back fat thickness detector was used for detecting live back fat thickness, carcass measuring ruler was used for measuring carcass length and measured back fat thickness after slaughter, lean meat rate detector was used for analyzing carcass lean meat rate; in the data processing stage, Dell PowerEdge R750 high-performance server was needed to run R language environment and BLUPF90 software for genomic estimated breeding value calculation, Western Digital MyCloud EX2 Ultra network storage device was needed to backup pedigree, phenotype and genotype and other types of data, to ensure that the sample processing, detection analysis and data management links of the whole selection and matching process were complete and feasible.
[0053] Specifically, in the implementation process of the present scheme, first, ear tag forceps are used to wear Ningxiang pigs with ear tags with unique codes to realize individual identification, then about 0.5 grams of ear tissue samples are collected by using sampling forceps and placed into centrifuge tubes containing tissue preservation solution for sealing preservation, after being sent to the laboratory, animal genomic DNA extraction kit is used for DNA extraction, lysis buffer and protease K are sequentially added and digested in a constant temperature incubator at 56°C overnight, after centrifugation, isopropanol precipitation and 75% ethanol washing, DNA is dissolved with TE buffer, and the DNA concentration and purity are detected by using an ultraviolet spectrophotometer to ensure that the OD260 / 280 value is between 1.8-2.0. The qualified DNA samples are diluted to the required concentration for SNP chip detection with ultrapure water, and are added to the reaction holes of pig Illumina 60K or GGP 50K SNP chips through a multi-channel pipettor, the chip is placed in a gene hybridization oven and hybridized according to the parameters of 45°C and 16 hours, after hybridization, the chip is placed in a film washing instrument to remove unbound probes according to the standard washing program, then the chip is scanned by a chip scanner to obtain raw genotype data and convert it into an analyzable format. When measuring the phenotype data, the body weight of Ningxiang pigs is measured by using an electronic platform scale every month, and the live backfat thickness is measured at the position of 5 cm from the back midline between the third and fourth ribs on the left side of the pig body by using a backfat thickness detector at 6 months of age; the total number of piglets and the number of live piglets are counted within 24 hours after the sow gives birth, the body weight of each newborn piglet is weighed by using an electronic balance and recorded; after slaughter, the carcass weight is weighed by using an electronic scale, the actual backfat thickness is measured at the same rib position by using a vernier caliper, the lean meat rate data is obtained by scanning the carcass by using a lean meat rate detector, and all the phenotype data is input into the livestock and poultry breeding management system in real time through a data acquisition terminal. In the data processing stage, the missing values in the genotype data and the abnormal values in the phenotype data are removed by using data cleaning software, the pedigree data is arranged in PED format and then imported into a high-performance server together with the genotype and phenotype data, the R language environment is started under the Linux system to call related data analysis packages for data standardization processing, then the ssGBLUP module of the BLUPF90 software is run to calculate the GEBV of the candidate individuals, after the individuals meeting the requirements are screened out according to the GEBV, the optimal mating combination is generated by using a mating analysis software to load the target function parameters, and at the same time, all the original data and analysis results are regularly synchronized to a network storage device by using a data backup software.
[0054] Although the present application has been disclosed in the preferred embodiments as above, it is not intended to limit the present application, and any person skilled in the art can make various modifications and modifications without departing from the spirit and scope of the present application, therefore the protection scope of the present application should be defined by the claims.
Claims
1. A mating method that integrates genomic selection, characterized in that, The method comprises the following steps: (1) determining a local livestock and poultry breeding population, constructing the breeding population by simulation software, wherein 5 generations are used as reference groups, each generation comprises 1000-2000 individuals, and selection breeding is carried out for 10-20 generations from the 6th generation; (2) constructing a local livestock and poultry reference population, determining pedigree data, phenotype data and genotype data of the reference population, wherein the genotype data is obtained by SNP chip detection, and the phenotype data covers growth traits, reproduction traits and / or carcass traits; (3) calculating genomic estimated breeding values (GEBV) of candidate individuals based on the ssGBLUP model and the pedigree data, phenotype data and genotype data of the reference population in step (2), and screening individuals meeting the selection requirements according to the GEBV; (4) establishing a target function for integrated genomic selection, and implementing genomic selection on the individuals screened in step (3), wherein the target function takes balancing genetic progress and genetic diversity as the core, comprehensively considers binomial increases related to selection progress and coefficient of co-parents, reduction of heterozygosity and change of genetic background gene frequency by weighting, and sets the constraint condition as the sum of selection decision vectors being 1 and the elements of the decision vectors being non-negative; (5) evaluating the selection effect from four dimensions of genetic progress, heterozygosity, inbreeding coefficient and SNP frequency change: the genetic progress is calculated by weighting the reciprocal of the standard deviation of the offspring genetic line; the inbreeding coefficient is calculated based on SNP sites and ROH; the SNP frequency change is the change of the average SNP frequency of the population between generations weighted by the effect of the SNP on the target trait; and the heterozygosity includes observed heterozygosity (OH) and expected heterozygosity (EH).
2. The method of claim 1, wherein, The size of the reference population in step (2) is about 2000, which is composed of service boars in the core breeding area and breeding sows in the breeding farm.
3. The method of claim 1, wherein, The SNP chip in step (2) is Illumina Pig 60K SNP Chip or GGP 50K SNP Chip.
4. The method of claim 1, wherein, The growth traits in step (2) include body weight and live backfat thickness; the reproduction traits include total litter size, live-born litter size and birth weight; and the carcass traits include carcass weight, actual backfat thickness and lean meat rate.
5. The method of claim 1, wherein, The selection rate of the individuals screened in step (3) is set as: the selection rate of boars is 5%, the selection rate of sows is 30%, and only the first-born individuals are selected in each generation.
6. The method of claim 1, wherein, The calculation logic of the change of the genetic background gene frequency in step (4) is: the greater the effect of a SNP on the target trait, the smaller the weight of the SNP in the calculation of the frequency change; when the effect of a SNP on the target trait is the largest, the frequency change of the SNP is not considered; and when a SNP has no effect on the target trait, the frequency change of the SNP is completely limited.
7. The method of claim 1, wherein, When genomic selection is implemented in step (4), a random mating group and a conventional selection group are set as controls at the same time, and the conventional selection is to set an inbreeding threshold and maximize the genetic progress of the offspring.
8. The method according to any one of claims 1-7 is applied in local livestock and poultry breeding and genetic improvement.