Fine wool sheep genome selection method

Abstract

The invention provides a fine-wool sheep genome selection method, which relates to the field of animal genetic breeding, and comprises the following steps: establishing a sheep genome reference group, and carrying out main phenotypic character determination on the sheep genome reference group; designing a liquid phase chip based on re-sequencing data of a plurality of sheep varieties, and performing fine-wool sheep genome selection to obtain GWAS prior information; estimating heritability of the genetic variance and the error variance to different characters based on an RRBLUP model, and obtaining a character heritability statistical result; performing genome estimation breeding value prediction analysis based on a character heritability statistical result; constructing a breeding target character according to the breeding target, and constructing a genome comprehensive breeding value estimation model based on the breeding target character; comprehensive breeding values of the sheep individuals are calculated based on a genome comprehensive breeding value estimation model, comprehensive breeding value ranking is carried out, and sheep seed selection and matching are carried out according to the comprehensive breeding values; and carrying out important economic character genome breeding value prediction analysis based on the GWAS priori information.

Description

Technical Field

[0001] The invention relates to the technical field of animal genetic breeding, and in particular to a fine-wool sheep genome selection method. Background Art

[0002] The Alpine Merino sheep is a new breed that has been cultivated for 20 years using modern breeding technology, with Australian Merino sheep as the father and Gansu Alpine fine wool sheep as the mother (Yue Yaojing et al., 2014). It has gone through three main breeding stages: hybridization improvement, cross-crossing fixation, and breeding improvement. It lives in the alpine grassland area of ​​the Qilian Mountains at an altitude of 2400-4070m all year round. It is also the only high-quality fine wool sheep breed that can adapt to high mountains, cold, drought, and harsh ecological conditions at home and abroad (Yang Bohui, 2017). The Alpine Merino sheep is large in size, rich in wool, and has a soft and delicate texture with good elasticity and warmth. It is widely used in the production of wool products, such as wool sweaters and blankets. In terms of meat quality, the Alpine Merino sheep is tender and delicious, and has excellent quality and versatility and is often loved by people. Alpine Merino sheep play an important role in animal husbandry. They not only bring economic benefits to local herders, but also provide high-quality raw materials for the wool and meat products industries. They have become a livestock breed that is loved and valued by people and have received widespread attention and protection.

[0003] Since the Alpine Merino sheep was approved as a new breed in 2015, many researchers have conducted research on the whole genome association analysis and genomic breeding value prediction of important traits such as wool quality, reproductive performance and meat. In terms of wool quality, the research results of Li Hui et al. found that the heritability of wool bundle strength was significantly correlated with yearling weight, wool fiber diameter, wool fiber diameter coefficient of variation, and net wool volume, which shows that wool bundle strength should be one of the breeding goals (Li Wenhui et al., 2023). Zhu Shaohua combined high-density (Singlenucleotidepolymorphism, SNP) SNPs microarray typing information to conduct a whole genome association analysis study on the wool quality traits of Alpine Merino sheep, and identified four genes, including PBX1, TRPC3, SLITRK5 and PVRL1, as candidate genes related to wool quality traits (Zhu Shaohua, 2022). Qiao Guoyan counted the phenotypic data records of wool traits of Alpine Merino sheep from 2003 to 2018, and estimated the estimated wool production, net wool rate, net wool volume, wool fiber diameter, wool fiber diameter coefficient of variation and wool length heritability to be 0.3253, 0.4394, 0.2893, 0.4222, 0.3174 and 0.3669 respectively based on the best animal model (Qiao Guoyan, 2021). Lei Zhihui et al. used the selection signal analysis method to analyze the selection signals of 21 fine wool sheep breeds at home and abroad, including Alpine Merino sheep. The results showed that genes such as FGF1, SOX18, ALX4 and LRP4 are closely related to the development cycle of hair follicles and hair formation (Lei Zhihui et al., 2022). Zhao et al. conducted a genome-wide association analysis on the wool quality of Alpine Merino sheep at one year old based on whole genome resequencing. The results detected 16 significant SNPs and annotated 14 candidate genes, which provided theoretical support for marker-assisted selection in Alpine Merino wool breeding (ZHAO et al., 2021b). In terms of weight, Li et al. used 50k chip density combined with whole genome association analysis results to construct a G matrix for genomic selection of Alpine Merino sheep weight traits. The results showed that the genomic prediction accuracy of weight traits was improved (LI et al., 2023). Xiao et al. screened out 20 SNPs related to important traits such as birth weight, bundle strength, cleanliness rate, and fiber diameter of Alpine Merino sheep based on genome resequencing data, of which 8 were monomorphic sites and 12 were polymorphic sites (XIAO et al., 2023). Lu et al. conducted a genome-wide association analysis of birth weight, weaning weight, adult weight and one-year-old weight of Alpine Merino sheep based on resequencing data. The results showed that 113 single nucleotide polymorphisms (SNPs) reached the genome-wide significance level for four weight traits, and 30 genes were effectively annotated, including AADACL3, VGF, NPC1 and SERPINA12.It can be seen that although many researchers have conducted extensive research on the molecular genetic basis of important economic traits of Alpine Merino sheep such as weight and wool, they have not discovered genes with breeding value and established a comprehensive breeding index based on genomic breeding values. A set of Alpine Merino sheep genomic selection technology has not yet been established, which limits the application of molecular breeding technology in improving the selection of Alpine Merino sheep.

[0004] Therefore, it is necessary to design a genomic selection method for fine-wool sheep to discover genes with breeding value and establish a comprehensive breeding index based on genomic breeding value to fill the gap in the existing technology of the lack of systematic genomic selection technology for Alpine Merino sheep. Summary of the invention

[0005] In view of this, the present invention proposes a fine wool sheep genome selection method to fill the gap in the prior art that there is no systematic genome selection technology for Alpine Merino sheep.

[0006] To achieve the above object, the present invention proposes a fine wool sheep genome selection method, comprising:

[0007] Establishing a sheep genome reference population, and measuring main phenotypic traits of the sheep genome reference population;

[0008] Design of liquid phase arrays based on resequencing data of several sheep breeds;

[0009] Perform fine wool sheep genome selection based on the liquid phase chip to obtain GWAS prior information;

[0010] Based on the RRBLUP model, the heritability of genetic variance and error variance for different traits was estimated to obtain the statistical results of heritability of traits;

[0011] Based on the statistical results of the heritability of the traits, the genomic estimated breeding value prediction analysis was performed using the GBLUP, BayesA, BayesB, BayesC and BayesLasso models;

[0012] Constructing breeding target traits according to the breeding objectives, and constructing a genome comprehensive breeding value estimation model based on the breeding target traits;

[0013] Calculating the comprehensive breeding values ​​of individual sheep based on the genome comprehensive breeding value estimation model, ranking the comprehensive breeding values, and selecting and matching sheep according to the comprehensive breeding values;

[0014] Based on the GWAS prior information, prediction analysis of genomic breeding values ​​of important economic traits was performed.

[0015] Furthermore, the main phenotypic trait determination includes breeding records, lamb birth identification, weaning identification, breeding identification, and adult identification;

[0016] The phenotypic categories of lamb birth identification include birth weight, birth type, gestation time, sex, birth grade, birth date, and lamb group;

[0017] The phenotypic categories of weaning identification include weaning weight, weaning hair length, horn type, wool fineness, weight gain before weaning, weaning date, and weaning group;

[0018] The phenotypic categories of breeding identification include body weight, daily weight gain, wool length, wool fiber diameter, wool fiber diameter coefficient of variation, net wool rate, shearing amount, net wool volume, and breeding group;

[0019] The phenotypic categories for adult identification include body weight, hair length, wool fiber diameter, wool fiber diameter coefficient of variation, net wool rate, clipping amount, net wool amount, and group category.

[0020] Furthermore, the process of wool genome selection based on the liquid phase chip includes:

[0021] The samples are sequenced based on the liquid phase chip, the sample comparison rate is counted, and the genome obtained after the test is selected for individual completeness filtering, heterozygosity filtering, minimum allele frequency filtering, and multi-allelic variation site filtering;

[0022] PCA analysis is performed on the selected individuals of the genome to determine whether there is a clustering phenomenon, and whether to perform population structure correction is determined based on the impact of the population structure on the genome prediction.

[0023] Furthermore, the process of constructing a genomic integrated breeding value estimation model based on the breeding target traits includes:

[0024] The weighting coefficient of each trait is determined according to the marginal benefit and importance of the breeding target trait, and a genomic comprehensive breeding value estimation model is constructed based on the weighting coefficients of each trait.

[0025] Furthermore, group and age were taken as fixed factors in the process of genomic estimated breeding value prediction analysis.

[0026] Furthermore, the genome comprehensive breeding value estimation model is shown as follows:

[0027]

[0028]

[0029]

[0030] Among them, GEBV i is the genomic estimated breeding value for a trait; is the mean of the estimated breeding value of the trait genome; σ GEBV Estimate standard deviations of breeding values ​​for genomes; is the result of the standardization of the estimated breeding value of the trait; GPI is the comprehensive breeding selection index model of Alpine Merino sheep, GPI i , , σ GPI* They represent the comprehensive selection index of traits, the mean of the comprehensive selection index, and the standard deviation of the comprehensive selection index, respectively.

[0031] Furthermore, in the process of predicting and analyzing the genomic breeding values ​​of important economic traits based on the GWAS prior information, the number of molecular genetic markers used as prior information is set based on the genetic parameter estimation and prediction accuracy analysis results of the WT trait.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention conducts genome-wide association analysis on important economic traits of Alpine Merino sheep, such as birth weight, weaning weight, one-year-old weight, net wool volume, net wool rate, wool fiber diameter, wool fiber diameter coefficient of variation, and wool length, screens out significant SNP sites associated with target traits, achieves fine positioning of genes associated with important traits, and simultaneously conducts genome selection analysis in combination with GWAS prior marker information, improves the accuracy of genome selection prediction, and optimizes the gene selection breeding model of Alpine Merino sheep.

[0034] 2. The present invention uses genomic selection breeding technology based on GBLUP theory and Bayes theory to estimate the genomic estimated breeding value of important economic traits of Alpine Merino sheep, and obtains the individual comprehensive selection index by weighting the best model of different genomes for each trait. Individuals are selected according to the constructed comprehensive breeding index, so that the genetic value and economic value of individuals can be comprehensively evaluated in the early stage of selection, and scientific and reasonable breeding goals can be established according to the actual production needs of individuals, which meets the market demand for the use of different traits and improves the breeding efficiency and economic benefits of farmers. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0036] Figure 1 This is a schematic diagram of the process of selecting a fine wool sheep genome in an embodiment of the present invention;

[0037] Figure 2The results of the liquid phase chip GWAS study in the embodiment of the present invention; wherein (a) is the Manhattan plot of the wool fiber diameter trait of the Alpine Merino sheep, and (b) is the QQ plot of the wool fiber diameter trait of the Alpine Merino sheep;

[0038] Figure 3 The PCA analysis results of the individuals selected for the genome in the embodiment of the present invention;

[0039] Figure 4 is the comprehensive breeding value ranking result in the embodiment of the present invention;

[0040] Figure 5 These are the results of genetic parameter estimation and prediction accuracy analysis of the WT trait in the embodiments of the present invention, wherein (a) is the Manhattan plot of the yearling weight trait of Alpine Merino sheep, (b) is the yearling weight trait of Alpine Merino sheep, and (c) is a statistical plot of the genetic parameters of the yearling weight trait population. DETAILED DESCRIPTION

[0041] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] This embodiment proposes a method for genomic selection of fine wool sheep, the purpose of which is to determine the phenotypic values ​​of wool production, growth, reproduction and other traits of Alpine Merino, establish a genomic selection reference population; develop genomic breeding chips, and establish a genomic genetic evaluation technology system for Alpine Merino, such as Figure 1 As shown, the steps include:

[0043] Establish a sheep genome reference population and measure the main phenotypic traits of the sheep genome reference population;

[0044] Design of liquid phase arrays based on resequencing data of several sheep breeds;

[0045] Fine wool sheep genome selection based on liquid phase array to obtain GWAS prior information;

[0046] Based on the RRBLUP model, the heritability of genetic variance and error variance for different traits was estimated to obtain the statistical results of heritability of traits;

[0047] Based on the statistical results of trait heritability, the GBLUP, BayesA, BayesB, BayesC and BayesLasso models were used to perform genomic estimated breeding value prediction analysis;

[0048] Construct breeding target traits according to breeding objectives, and construct a genome-wide breeding value estimation model based on breeding target traits;

[0049] Calculate the comprehensive breeding value of individual sheep based on the genome comprehensive breeding value estimation model, rank the comprehensive breeding values, and select and mate sheep according to the comprehensive breeding values;

[0050] Prediction and analysis of genomic breeding values ​​of important economic traits based on GWAS prior information

[0051] As a preferred implementation, in this example, a reference population of Alpine Merino sheep genome was established, with a population size of 2,663, including 397 superfine and fine Alpine Merino sheep breeding rams and 2,266 ewes in 6 core groups. The reference population mainly carried out 4 stages of 33 major phenotypic trait measurement records:

[0052] a. Breeding records (pedigree).

[0053] b. Identification of lambs at birth: birth weight, birth type (single / twin / multiple), gestation time, sex, birth grade, date of birth, and group.

[0054] c. Weaning identification: weaning weight, weaning hair length, horn type, wool fineness (count), weight gain on the day before weaning, weaning date, and group type.

[0055] d. Breeding appraisal (14 months old): body weight, daily weight gain, wool length, wool fiber diameter, wool fiber diameter variation coefficient, net wool rate, shearing amount, net wool volume, group type, etc.

[0056] e. Adult identification (≥26 months): weight, wool length, wool fiber diameter, wool fiber diameter variation coefficient, net wool rate, shearing amount, net wool amount, group type, etc.

[0057] Among them, weight, wool production, wool length and horn type are subjective identification traits; net wool rate and wool fiber diameter are inspected by the inspection and testing center.

[0058] Based on the resequencing data of 620 Merino sheep of 12 breeds from all over the world, a 50k chip containing 45,213 loci (including 1K functional loci chip with 1,188 functional loci) was designed, with a probe design area coverage of 87.745%, a total of 84,035 probe designs, and a sample locus capture efficiency of 93.27% to 95.35% for 45,213 loci, with an average detection rate of 94.2%. The results of the GWAS study based on the 50KSNP liquid phase chip of fine wool sheep are as follows: Figure 2 shown.

[0059] In this example, 2178 samples were sequenced, and a total of 26,052,146,716 CleanReads were aligned to the reference sequence, with the highest sample alignment rate being 96.9% and the lowest being 87.23%. After detection, a total of 46178 markers were obtained, of which 44604 were filtered by integrity, 43847 by heterozygosity, 40549 by minimum allele frequency, and 40539 by multi-allelic variant sites.

[0060] like Figure 3 As shown, PCA analysis was performed on the genome-selected individuals, and the results showed that there was no grouping phenomenon in the fine-wool sheep population. In this embodiment, the population structure did not have a significant impact on the genome prediction, so the population structure was not corrected in the model.

[0061] The heritability of genetic variance and error variance for different traits was estimated by using the kin.blup function of the R software package (rrBLUP). The heritability of wool fiber diameter, wool fiber diameter coefficient of variation, net wool rate, net wool quantity, wool length, and birth, weaning and yearling weight were 0.534, 0.193, 0.400, 0.385, 0.293, 0.157, 0.205 and 0.401, respectively. Among them, wool fiber diameter, net wool rate and yearling weight were all high heritability, and other traits were all medium heritability. The statistical results are shown in Table 1.

[0062] Table 1

[0063]

[0064] Genomic selection prediction accuracy

[0065] GBLUP, BayesA, BayesB, BayesC and BayesLasso models were used to predict gEBV with flock and age as fixed factors. The statistical results are shown in Table 2.

[0066] Table 2

[0067]

[0068] Estimation of comprehensive breeding value

[0069] According to the goals of breeding and strain cultivation of Alpine Merino sheep, body weight, hair length, net hair volume, and wool fiber diameter are determined as breeding target traits. According to the marginal benefits and importance of the breeding target traits, the weighted coefficients of each trait are determined, and the estimation model of the comprehensive breeding value of the Alpine Merino sheep genome is obtained as follows:

[0070]

[0071]

[0072]

[0073] Among them, GEBV i is the genomic estimated breeding value for a trait; is the mean of the estimated breeding value of the trait genome; σ GEBV Estimate standard deviations of breeding values ​​for genomes; is the result of normalization of the estimated breeding value of the trait; GPI is the comprehensive breeding selection index model for Alpine Merino sheep. i , , σ GPI* They represent the comprehensive selection index of traits, the mean of the comprehensive selection index, and the standard deviation of the comprehensive selection index, respectively.

[0074] The comprehensive breeding value of the individual is calculated based on the comprehensive breeding value model of the target trait, and the comprehensive breeding value ranking is obtained. The sheep individuals are selected and matched according to the comprehensive breeding value, such as Figure 4 shown.

[0075] Based on the GWAS prior information obtained from the liquid phase chip, the genomic breeding value prediction analysis of important economic traits was performed. The results are as follows: Figure 5 As shown, the results of genetic parameter estimation and prediction accuracy analysis of the WT trait showed that using the selected Top 20% SNPs as prior information increased the accuracy of the breeding value of body weight by 2.59% to 7.79%.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for selecting fine wool sheep genomes, characterized in that: include: Establishing a sheep genome reference population, and measuring main phenotypic traits of the sheep genome reference population; Design of liquid phase arrays based on resequencing data of several sheep breeds; Perform fine wool sheep genome selection based on the liquid phase chip to obtain GWAS prior information; Based on the RRBLUP model, the heritability of genetic variance and error variance for different traits was estimated to obtain the statistical results of heritability of traits; Based on the statistical results of the heritability of the traits, the genomic estimated breeding value prediction analysis was performed using the GBLUP, BayesA, BayesB, BayesC and BayesLasso models; Constructing breeding target traits according to the breeding objectives, and constructing a genome comprehensive breeding value estimation model based on the breeding target traits; Calculating the comprehensive breeding values ​​of individual sheep based on the genome comprehensive breeding value estimation model, ranking the comprehensive breeding values, and selecting and matching sheep according to the comprehensive breeding values; Based on the GWAS prior information, prediction analysis of genomic breeding values ​​of important economic traits was performed.

2. The fine wool sheep genome selection method according to claim 1, characterized in that: The main phenotypic traits determination includes breeding records, lamb birth identification, weaning identification, breeding identification, and adult identification; The phenotypic categories of lamb birth identification include birth weight, birth type, gestation time, sex, birth grade, birth date, and lamb group; The phenotypic categories of weaning identification include weaning weight, weaning hair length, horn type, wool fineness, weight gain before weaning, weaning date, and weaning group; The phenotypic categories of breeding identification include body weight, daily weight gain, wool length, wool fiber diameter, wool fiber diameter coefficient of variation, net wool rate, shearing amount, net wool volume, and breeding group; The phenotypic categories for adult identification include body weight, hair length, wool fiber diameter, wool fiber diameter coefficient of variation, net wool rate, clipping amount, net wool amount, and group category.

3. The fine wool sheep genome selection method according to claim 1, characterized in that: The process of wool genome selection based on the liquid phase chip includes: The samples are sequenced based on the liquid phase chip, the sample comparison rate is counted, and the genome obtained after the test is selected for individual completeness filtering, heterozygosity filtering, minimum allele frequency filtering, and multi-allelic variation site filtering; PCA analysis is performed on the selected individuals of the genome to determine whether there is a clustering phenomenon, and whether to perform population structure correction is determined based on the impact of the population structure on the genome prediction.

4. The fine wool sheep genome selection method according to claim 1, characterized in that: The process of constructing a genomic integrated breeding value estimation model based on the breeding target traits includes: The weighting coefficient of each trait is determined according to the marginal benefit and importance of the breeding target trait, and a genomic comprehensive breeding value estimation model is constructed based on the weighting coefficients of each trait.

5. The fine wool sheep genome selection method according to claim 1, characterized in that: In the process of predictive analysis of genomic estimated breeding values, herd type and age were taken as fixed factors.

6. The fine wool sheep genome selection method according to claim 1, characterized in that: The genome comprehensive breeding value estimation model is shown in the following formula: Among them, GEBV i is the genomic estimated breeding value for a trait; is the mean of the estimated breeding value of the trait genome; σ GEBV Estimate standard deviations of breeding values ​​for genomes; is the result of the standardization of the estimated breeding value of the trait; GPI is the comprehensive breeding selection index model of Alpine Merino sheep, GPI i , σ GPI* They represent the comprehensive selection index of traits, the mean of the comprehensive selection index, and the standard deviation of the comprehensive selection index, respectively.

7. The fine wool sheep genome selection method according to claim 1, characterized in that: In the process of predicting and analyzing the genomic breeding values ​​of important economic traits based on the GWAS prior information, the number of molecular genetic markers used as prior information is set based on the genetic parameter estimation and prediction accuracy analysis results of the WT trait.

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