A low-density SNP chip for the whole genome of laying hens and its application

Abstract

This invention relates to the field of molecular breeding technology for laying hens, and more particularly to a low-density SNP chip for the whole genome of laying hens and its application. The SNP chip data is evenly distributed at both the whole genome and chromosome levels, and the linkage disequilibrium level is not reduced compared to the 50K chip, thus lowering breeding costs while ensuring accuracy for genetic assessment of the laying hen genome. Furthermore, this low-density chip collects significantly associated loci of important economic traits in laying hens, making it more suitable for selective laying hen breeding. In addition, the loci in the chip are derived from phenotypic significantly associated loci throughout the entire laying period of laying hens, meeting the genetic assessment needs at any stage of production and also satisfying the research needs of scientists.

Description

Technical Field

[0001] This invention relates to the field of molecular breeding technology for laying hens, and in particular to a low-density SNP chip for the whole genome of laying hens and its application. Background Technology

[0002] Single nucleotide polymorphisms (SNPs) are the most common and important genetic variations in the genome, accounting for more than 90% of known polymorphisms. They have advantages such as wide distribution, high density, genetic stability, and convenient detection, and are the third generation of molecular genetic markers after first-generation restriction fragment length polymorphism markers and second-generation microsatellite markers. High-throughput molecular marker detection technologies based on SNPs mainly fall into two categories: one is based on high-throughput sequencing to obtain SNP information, which provides a wealth of SNP information, but its high detection cost prevents its widespread application in large-scale populations; the other is molecular marker detection technology based on gene chip genotyping. Gene chips can integrate a large number of molecular recognition probe targets through tiny substrates, offering high throughput, high accuracy, and low cost, and are widely used in livestock and poultry molecular breeding, germplasm resource protection, kinship identification, and functional gene mapping.

[0003] Genome selection, a technique that uses SNP chips to detect individual genotypes and evaluates individual breeding potential using mature genomic selection models, is widely used in livestock and poultry breeding. Currently, commercially available chips for chickens include Axiom 600K, Illumina 50K, and Illumina 60K, which are widely used by breeding companies and research institutions both domestically and internationally. For example, the Chicken Phoenix Core No. 1 50K SNP chip, developed based on the Illumina platform, has been widely used in egg-laying hen breeding in China, greatly accelerating the breeding process.

[0004] However, existing egg-laying hen breeding chips have a high density of SNP molecular markers. If low-density chips are used to directly estimate genomic breeding values, or if low-density chips are filled with high density before estimating genomic breeding values, and if the accuracy can be similar to that of 50K chips, then the cost of genotyping can be reduced to about 50% of that of 50K chips, significantly reducing the cost of reference population testing, expanding the scale of reference population testing, and further improving the effect of genomic selection.

[0005] There are currently no reports on the development of low-density chips for molecular breeding of laying hens. Therefore, in order to further reduce the application cost of molecular breeding of laying hens, how to select suitable SNPs from 50K SNP chips to design low-density chips, and then use the high-density SNP marker information obtained by genotype filling technology for genome selection is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] To address the aforementioned technical challenges, this invention is proposed.

[0007] First, the present invention provides a whole genome SNP molecular marker combinatorial of laying hens, which consists of 11,146 SNP molecular markers. The positions of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1, numbered 1 to 11,146.

[0008] In this name, the first number or letter indicates the chromosome, and the number after the underscore indicates the physical location. For example, number 1, 1_546750 means that the chromosome is number 1 and the physical location is 546750; for example, number 11146, Z_78570779 means that the chromosome is chromosome Z and the physical location is 78570779.

[0009] Table 1

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[0064] Furthermore, this invention provides a whole-genome SNP chip for laying hens, which is prepared using combinations of SNP molecular markers in Table 1. The whole-genome SNP chip for laying hens is a low-density SNP chip.

[0065] The SNP molecular marker combination includes 7,643 SNP sites associated with economic traits in laying hens and 3,503 SNP sites that compensate for regions of the genome not covered by the probe.

[0066] In some implementations, the SNP chip is a solid-phase chip or a liquid-phase chip.

[0067] When using a liquid-phase chip, the SNP chip can directly adjust the target SNP sites by adding or removing probes, making it more flexible in application compared to a solid-phase chip.

[0068] In some implementations, the SNP molecular marker combination is associated with an economic trait of laying hens; the economic trait of laying hens is at least one of the following: age at first laying, number of eggs laid, egg weight, albumen height, Haugh unit, eggshell strength, and eggshell color.

[0069] Furthermore, the present invention provides the application of the SNP molecular marker combination or the SNP chip in the selection of different breeds of laying hens.

[0070] Furthermore, the present invention provides the application of the SNP molecular marker combination or the SNP chip in a filled chip; preferably, the filled chip is a high-density chip.

[0071] Furthermore, the present invention provides the application of the SNP molecular marker combination or the SNP chip in laying hen breeding.

[0072] Furthermore, the present invention provides the application of the SNP molecular marker combination or the SNP chip in the identification of kinship in laying hens or the identification of chicken breeds.

[0073] Furthermore, the present invention provides the application of the SNP molecular marker combination or the SNP chip in the evaluation of genetic diversity in laying hens.

[0074] Furthermore, the present invention provides the application of the SNP molecular marker combination or the SNP chip in genome-wide association analysis of laying hens.

[0075] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0076] This invention provides a low-density SNP chip for the whole genome of laying hens. The SNP data are evenly distributed at the whole genome and chromosome levels, and the linkage disequilibrium is not reduced compared to the 50K chip, which reduces breeding costs while ensuring the accuracy of genetic assessment of the laying hen genome. Moreover, this low-density chip collects significant associated loci of important economic traits in laying hens, making it more suitable for laying hen breeding. In addition, the loci in the chip are phenotypic significant associated loci from the entire laying period of laying hens, which can meet the genetic assessment at any stage of production and also meet the research needs of scientific researchers. Attached Figure Description

[0077] Figure 1 This is a flowchart illustrating the preparation process of the low-density chip for laying hens according to the present invention.

[0078] Figure 2 This is a distribution map of SNP sites on each chromosome of a laying hen using a 10K chip.

[0079] Figure 3 This is a statistical chart of the SNP distribution of each chromosome on a 10K chip for laying hens.

[0080] Figure 4 This is a descriptive statistical analysis of the 10K chip loci in laying hens.

[0081] Figure 5 This is a comparison of the parameters of the 10K chip and the 50K chip for laying hens.

[0082] Figure 6 This is a comparison of descriptive statistics between 10K and 50K chips for laying hens.

[0083] Figure 7 This is a comparison of the one-step calculation results using 10K SNP chips and 50K SNP chips.

[0084] Figure 8 The inbreeding coefficient and kinship coefficient are calculated based on data from two different chips.

[0085] Figure 9 This is a graph showing the accuracy results of genotype filling.

[0086] Figures 10-14 It is a Manhattan plot of egg production, egg weight, eggshell color SCI value, eggshell color brightness value, eggshell strength, albumen height and Haugh units for laying hens at 36 and 72 weeks of age. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0088] Unless otherwise specified, all techniques or conditions used in the examples were performed using conventional methods or in accordance with techniques or conditions described in the literature in this field, or according to the product instructions. Reagents and instruments used without specified manufacturers were all conventional products that could be purchased from legitimate channels. The SNP nucleotide sequences were derived from the sixth version of the chicken reference genome (GRCg6a, 2018).

[0089] Example 1: Design and fabrication of a low-density SNP chip

[0090] This embodiment provides a low-density SNP chip for the whole genome of laying hens, and its fabrication method includes the following steps, the flowchart of which is shown below. Figure 1 As shown:

[0091] Genotyping was performed on 4435 individuals across four generations using the Phoenix Core No. 1 50K SNP chip for laying hens. Plink software was used to perform quality control on the genotypic data of all individuals, removing SNPs with a detection rate below 95%, a minimum allele frequency of less than 0.05, and genotypic errors. Beagle software was used to fill in missing genotypes on the quality-controlled data to obtain the final 50K genotypic data.

[0092] First-order SNP marker acquisition: Phenotypic data of the above 4435 individuals, as well as the final filtered 50K genotype data and phenotypic records obtained after 50K chip detection, were collected. EMMAX software was used to analyze and obtain a total of 7643 SNPs significantly associated with age at first laying, 36 weeks of age, and 72 weeks of age, including egg count (EN), egg weight (EW), eggshell color SCI value (ESCI), eggshell color brightness value (ESCL), eggshell strength (ESS), albumen height (AH), and Haugh units (HU). Figures 10-14 The mixed linear model used is as follows:

[0093] y = Xα + Zβ + Wμ + e

[0094] Where y is the phenotypic value; Xα is the fixed effect, such as date of birth, hatching batch, sex, etc.; Zβ represents the effect of SNP; Wμ represents the random effect, whose variance-covariance is estimated based on the genotype kinship matrix calculated from whole-genome SNPs; and e represents the random residual.

[0095] Acquisition of Type II SNP markers: Considering the significant differences in length between different chromosome segments in chickens, and the uneven distribution of Type I SNP markers across the genome, the remaining molecular marker information, excluding Type I markers, was obtained from the 50K microarray. Specifically: using 10K loci as a standard, the number of SNPs selected on each chromosome was determined proportionally based on the differences in chromosome length. Since Type I SNP markers already cover part of the genomic region, the principle of ensuring a uniform distribution of SNP markers across chromosomes was followed when selecting Type II markers. A total of 3503 SNP loci were selected to fill the genomic regions not covered by the aforementioned 7643 loci.

[0096] The first type of markers and the second type of markers are combined to generate a low-density 10K chip containing 11,146 SNP markers as shown in Table 1.

[0097] Figure 2 This is a distribution map of SNP sites on each chromosome obtained from the low-density 10K chip. Figure 3 This is a statistical distribution diagram of the obtained low-density 10K chip SNP markers on each chromosome. Figure 4 This is a descriptive statistical diagram of the obtained low-density 10K chip sites.

[0098] The mean spacing, linkage disequilibrium, and minimum allele frequency at a single locus were compared between the 10K and 50K chips (43,681 markers). The results are as follows: Figure 5 and Figure 6 As shown.

[0099] The average interval between SNPs is an important part of genotype statistics. It can be seen that as the number of markers decreases significantly, the average interval between SNPs also increases proportionally, from 30kb interval in 50K SNP chips to 118kb in 10K SNP chips.

[0100] The statistical methods for determining the degree of chain disequilibrium vary, among which r 2 This is the most commonly used and stable method. Assume there are two loci, each with two alleles, namely A, a and B, b. The following formula can be used to calculate the linkage disequilibrium between these two loci:

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[0102] like Figure 5 and Figure 6 As shown, although the number of markers decreased significantly, the degree of linkage disequilibrium at the genome and chromosome levels remained almost unchanged, with the genome-wide average linkage disequilibrium for 10K and 50K being 0.2625 and 0.2608, respectively.

[0103] The minimum allele frequency (MAF) of a SNP locus is the lower allele frequency among two allele frequencies at a given locus, and is an important indicator for assessing the quality of genotype data. For example... Figure 5 and Figure 6 As shown, the difference in mean MAF at sites on the genome and chromosome is very small compared to the 50K chip in this embodiment: the mean MAF of the whole genome for the 10K SNP chip and the 50K SNP chip are 0.30 and 0.29, respectively.

[0104] Finally, after considering the functional association, average spacing, linkage disequilibrium, and minimum allele frequency of the loci, the 11,146 SNP markers shown in Table 1 were identified as effective SNP loci for the 10K low-density chip of the whole genome of laying hens.

[0105] Example 2: Genomic selection effect of low-density SNP chip for the whole genome of laying hens

[0106] Using the SSGBLUP model combined with 10K genotype, phenotype, and pedigree records of 500 laying hens, the heritability of the phenotype and the breeding value were estimated using the one-step method of genomic selection (SSGBLUP).

[0107] The study population consisted of four generations of laying hens of a certain breed. 500 individuals were selected, and pedigree data were compiled. SNP data were obtained using the 10K and 50K chips from Example 1 for genotyping. After genotyping, the SNP data underwent quality control. The quality control criteria were: removal of SNPs with a detection rate below 95%, a minimum allele frequency less than 0.05, and incorrect genotyping. Phenotypic data, pedigree data, and the two sets of quality-controlled genotypic data were used together to estimate relevant parameters of Haugh units (HU) at 46 weeks of age and albumin height (AH) at 72 weeks of age using the SSGBLUP model. The one-step model used is as follows:

[0108] y = Xb + Zu + e

[0109] Where y represents the phenotypic observation vector, X is the fixed effects matrix, b is the fixed effects vector (BLUE value), Z is the random effects matrix, u is the random effects vector (BLUP value), and e is the residual vector.

[0110] The difference between the ssGBLUP model and traditional prediction models is that the random effects vector of this model follows the pattern u ~ N(0, Hσ). u 2 The H matrix differs from the A matrix used in traditional pedigree data construction and the G matrix used in genotype data construction. H is a matrix that integrates pedigree and genomic information. Its construction formula is as follows:

[0111]

[0112] Genotype of an individual.

[0113] Taking the Haugh unit at 46 weeks and the high dichotomy of protein at 72 weeks as examples, the genetic assessment parameters of 10K SNP chip and 50K SNP are compared as follows: Figure 7 As shown, the variance components, heritability, and individual breeding value prediction results of the 10K chip and the 50K chip are consistent, and the correlation of the predicted individual breeding value results is 0.998. This indicates that the 10K low-density chip in Example 1 has good accuracy and can be applied to actual breeding.

[0114] Example 3: Phylogenetic identification using low-density SNP microarrays of the entire chicken genome.

[0115] This embodiment uses individuals from four generations of a certain breed of laying hens in a population, and randomly selects 1000 laying hens. First, genotyping is performed using two sets of microarrays to obtain 10K SNP markers and 50K SNP markers respectively. A kinship matrix based on the microarrays is constructed, and the inbreeding coefficient and the kinship coefficient between individuals are calculated using the software Plink.

[0116] The inbreeding coefficient is the probability that two alleles at the same locus in an individual are homologous and identical; it is an indirect measure of the kinship between an individual's parents. The kinship coefficient is the correlation coefficient between the additive gene effects of two individuals; it is a direct measure of the kinship between them. The results of the inbreeding coefficient and kinship coefficient calculated for these 1000 individuals are as follows: Figure 8 As shown, the inbreeding coefficient and kinship coefficient calculated by 10K SNP are not different from those of 50K SNP and are significantly strongly correlated, which can also accurately and truly reflect the kinship between individuals.

[0117] Example 4: Genotyping using a low-density SNP chip of the whole genome of laying hens

[0118] This embodiment uses 4435 individuals from a certain laying hen breed, all of whom have 50K microarray genotyping results. 1000 individuals were selected as a reference group, while 100 and 500 individuals were selected as validation groups, respectively. In the validation groups, only the 10K marker data from the low-density microarray in Example 1 were selectively used. Using Beagle software, with the reference group as a template, the genotypes of the two validation groups were filled from 10K to 50K. Finally, the consistency results of the measured 50K SNP genotypes in the validation groups and the filled genotypes were evaluated to determine the accuracy of the genotype filling.

[0119] Depend on Figure 9 It can be seen that, under two validation population sizes of 500 and 100 individuals, after filling the 10K chip SNP data to the 50K level, the filled genotypes are very close to the true genotypes, with filling accuracies of 97.25% and 97.29%, respectively. In this embodiment, even when the validation population size is increased from 100 to 500, the filling accuracy hardly decreases, indicating that high-quality genomic data can still be obtained by using the low-density chip of Example 1 for large-scale population genotyping. Therefore, when carrying out genomic selection, the lower-cost 10K chip of this invention can be used on newly hatched chicks, and the data can be filled to the 50K marker level for breeding value estimation, enabling large-scale early breeding work.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A whole-genome SNP chip for laying hens, characterized in that, It is prepared using SNP molecular marker combinations; the SNP molecular marker combinations consist of 11,146 SNP molecular markers, and the positions of the SNP molecular markers on the chicken reference genome GRCg6a are shown as numbers 1 to 11,146 in Table 1 of the specification.

2. The SNP chip according to claim 1, characterized in that, It is either a solid-phase chip or a liquid-phase chip.

3. The SNP chip according to claim 2, characterized in that, The SNP molecular marker combinations are associated with economic traits of laying hens; The economic traits of the laying hens are at least one of the following: age at first laying, number of eggs laid, egg weight, albumen height, Haugh unit, eggshell strength, and eggshell color.

4. The application of the SNP chip according to any one of claims 1 to 3 in the genomic selection of different breeds of laying hens.

5. The application of the SNP chip according to any one of claims 1 to 3 in a filled chip.

6. The application of the SNP chip according to any one of claims 1 to 3 in egg-laying hen breeding.

7. The application of the SNP chip according to any one of claims 1 to 3 in the evaluation of genetic diversity in laying hens.

8. The application of the SNP chip according to any one of claims 1 to 3 in genome-wide association analysis of laying hens.

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