A set of SNP molecular markers for broiler abdominal fat trait selection and its application

Abstract

The present invention discloses a group of SNP molecular markers and applications for the selection of abdominal fat traits in broilers, belonging to the technical field of animal molecular markers. The SNP molecular marker combination for the selection of abdominal fat traits in broilers consists of 44,283 SNP molecular markers, and the staining positions and numbers of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1 of this specification. At the same time, the present invention utilizes the relevant SNP sites to construct a liquid phase chip for the selection of abdominal fat traits in broilers. In addition, the present invention also proposes a group selection method for the selection of abdominal fat traits in broilers. By using the liquid phase chip for genotyping and using the group selection method to select individuals, a low-abdominal-fat broiler population can be efficiently selected. The operation is simple and the results are reliable, which can promote the selection of low-fat broilers.

Description

Technical Field

[0001] The invention belongs to the technical field of animal molecular markers, and in particular relates to a group of SNP molecular markers for broiler abdominal fat trait selection and application. Background Art

[0002] White-feathered fast-growing large broilers are the mainstay of chicken production in the world and in my country. In 2023, the number of white-feathered fast-growing large broilers in my country will reach 7.195 billion, with a meat production of 14.2937 million tons, making chicken the second most consumed meat in my country after pork. The growth rate of broilers is positively correlated with abdominal fat deposition. How to reduce abdominal fat deposition while increasing growth rate is a major problem in broiler breeding. The purpose of reducing abdominal fat rate can be achieved by using traditional phenotypic selection methods. This method requires slaughtering a large number of chickens, assessing the abdominal fat rate of their compatriots based on the phenotypic information of the slaughtered chickens, and then selecting individuals for breeding. This method requires raising and slaughtering a large number of chickens, which consumes a lot of manpower, material resources and financial resources, and is time-consuming. Therefore, it is necessary to screen out a SNP molecular marker that affects the abdominal fat trait of broilers at the whole genome level, and use the SNP molecular marker for the abdominal fat trait of broilers for breeding and selection to achieve early selection of the abdominal fat trait of broilers. Summary of the invention

[0003] To solve the above problems, the present invention provides a group of SNP molecular markers for the selection of broiler abdominal fat traits, which can efficiently divide broiler groups into high abdominal fat broiler groups and low abdominal fat broiler groups.

[0004] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows: a SNP molecular marker combination for the selection of abdominal fat traits in broilers, the SNP molecular marker combination consisting of 44,283 SNP molecular markers, and the staining positions and numbers of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1 in this specification.

[0005] The present invention also provides a SNP liquid phase chip for broiler abdominal fat trait selection, wherein the SNP liquid phase chip comprises a probe or primer for detecting the above-mentioned SNP molecular marker combination.

[0006] Furthermore, the present invention also provides the use of the above-mentioned SNP molecular marker combination for broiler abdominal fat trait selection or the above-mentioned SNP liquid phase chip for broiler abdominal fat trait selection in broiler abdominal fat trait analysis.

[0007] Furthermore, the present invention also provides the use of the SNP molecular marker combination for broiler abdominal fat trait selection as described above or the SNP liquid phase chip for broiler abdominal fat trait selection as described above in broiler molecular marker-assisted selection breeding.

[0008] The present invention also provides a method for preparing a liquid phase chip for broiler abdominal fat trait selection, comprising the following steps:

[0009] Step 1: Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) and white-feathered large broiler breeder A and B lines were selected for whole genome resequencing, and the high-quality sequences obtained by sequencing were compared with the chicken reference genome GRCg6a. SNP variation detection was performed using GATK software, and a high-quality SNP data set was obtained after quality control.

[0010] Step 2: In the SNP set of high-fat and low-fat broiler chickens, the GPSM method was used to screen the SNP sites that were selected in the multi-generation breeding process. In addition, the nucleotide polymorphism ratio, genetic differentiation index and continuous homozygous fragments were used to further determine the selected areas. At the same time, combined with the chicken abdominal fat rate related QTL data in the Animal QTL database, the SNP sites located in the selected areas and QTL areas were retained as the SNP sites that can distinguish the abdominal fat levels of high-fat and low-fat broiler chickens;

[0011] Step 3: In the SNP set of white-feathered fast-growing broiler breeders, the GWAS method was used to screen SNP loci related to abdominal fat traits as SNP loci that can distinguish high and low abdominal fat levels from the A and B line populations of white-feathered fast-growing broiler breeders;

[0012] Step 4: The SNP loci derived from high- and low-fat broiler chickens and the SNP loci derived from the white-feathered fast-growing large broiler breeder group are merged and de-redundant to obtain the SNP markers that can ultimately distinguish between high and low abdominal fat levels. Based on these markers, a SNP liquid phase breeding chip is created. The staining positions and numbers of the SNP markers on the chicken reference genome GRCg6a are shown in Table 1 in this specification.

[0013] Furthermore, the threshold of significant sites screened by the GPSM method in step 2 as described above is set to a p value < 0.05 after correction based on the Bonferroni method.

[0014] Furthermore, the threshold of significant loci screened by the GWAS method in step 3 was set to p < 1 × 10 -3 .

[0015] The present invention also provides a method for selecting and breeding broiler abdominal fat traits, which is a group selection method for discriminating the abdominal fat traits of white-feathered fast-growing large broiler chickens using a machine learning method, and specifically comprises the following steps:

[0016] Step 1: Based on the AFP phenotype data of the reference group, calculate the average value of the group AFP, label the individuals with AFP higher than the average as "fat", and label the individuals with AFP lower than the average as "thin";

[0017] Step 2: Acquire genotyping data of the reference population and the candidate population based on a liquid phase chip, wherein the liquid phase chip is made of probes for detecting a combination of 44,283 SNP molecular markers in the whole chicken genome, and the staining positions and numbers of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1 of the specification;

[0018] Step 3: Use the t-distribution domain embedding algorithm to reduce the dimension of the sample's genotype information and determine the similarity of its genotype based on its plane position;

[0019] Step 4: Use the Euclidean distance between the reference population and the candidate population after dimensionality reduction as a measure of the genotype similarity between samples; based on the dimensionality reduction data in step 3, first calculate the center point positions of the two labels "fat" and "thin" in the reference population, and then divide the population according to the Euclidean distance between the samples and these two center points in the candidate population. Individuals in the candidate population that are closer to the center point of the "fat" group are divided into the "fat" group, while individuals that are closer to the center point of the "thin" group are divided into the "thin" group, thereby achieving effective selection of broiler abdominal fat traits.

[0020] The beneficial effects and advantages of the present invention are as follows: The present invention can realize accurate genotyping of SNPs related to the abdominal fat trait of broiler chickens, greatly improving the accuracy and efficiency of seed selection, thereby realizing efficient breeding of broiler chickens with low abdominal fat content and accelerating the breeding process of low-fat high-quality broiler chickens. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is the distribution of the selected regions on chromosomes of high- and low-fat broiler lines.

[0022] Figure 2 This is the distribution of SNP sites on the liquid phase chip. DETAILED DESCRIPTION

[0023] In order to facilitate the understanding of the present invention, the present invention will be described more comprehensively and meticulously in conjunction with the accompanying drawings and preferred embodiments of the specification, but the protection scope of the present invention is not limited to the following specific embodiments. Unless otherwise defined, all professional terms used hereinafter have the same meanings as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the protection scope of the present invention.

[0024] Example 1

[0025] This embodiment provides a SNP molecular marker combination for broiler abdominal fat trait selection, the SNP molecular marker combination consists of 44283 SNP molecular markers, the staining positions and numbers of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1, and the screening process is as follows:

[0026] Table 1 Location information of SNP sites

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[0209] 1. Experimental Materials

[0210] This experiment used high- and low-fat broilers of the 4th to 18th, 19th, 23rd, and 27th generations, and white-feathered fast-growing large broiler breeder A and B lines as the experimental population. There were 4,861 high- and low-fat broilers, including 1,737 roosters and 3,124 hens; 978 white-feathered fast-growing large broiler breeder groups, including 206 A-line roosters and 206 A-line hens; 281 B-line roosters and 285 B-line hens (Table 2).

[0211] Table 2 Statistics of the number of experimental chickens

[0212]

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[0214] 2. SNP site screening

[0215] In this embodiment, a professional company was commissioned to perform whole genome resequencing on the NEAUHLF broiler group and the pure line group of white-feathered fast-growing broiler breeders, and subsequent site screening was performed based on this. The sites of the liquid phase chip can be divided into sites derived from high- and low-fat lines and sites derived from white-feathered fast-growing broiler breeder groups according to their sources.

[0216] (I) Sites from high- and low-fat systems

[0217] The screening of loci from high and low fat lines is mainly divided into two steps. First, the SNP loci related to the abdominal fat traits of broilers are preliminarily screened according to the GPSM method, and then the SNP loci are finely screened according to the results of known QTL regions and selection signal analysis.

[0218] (1) Preliminary screening of SNP sites

[0219] 1) Site screening based on allele frequency

[0220] This example is based on the data of 18 generations of high- and low-fat broiler chickens from the 4th to the 18th, 19th, 23rd, and 27th generations, and screens for SNP sites whose allele frequencies change regularly during the selection process for abdominal fat traits. The main screening steps are as follows:

[0221] a. Filter the genotype data of high- and low-fat broiler chickens to retain only the autosomal SNP loci that can be detected in at least 15 generations;

[0222] b. Calculate the mutation site allele frequency of the SNP site in each strain in each generation, where the calculation formula for the allele frequency of the 4th to 18th generations is as follows:

[0223]

[0224] SNP freq : Allele frequency of SNP mutation site; n alt : Number of mutant reads; n ref : Number of reference reads.

[0225] The allele frequencies of SNP loci in the 19th, 23rd, and 27th generations were calculated using the “--freq” command of VCFtools software.

[0226] c. A linear regression equation of generation and mutation site allele frequency was constructed in high-fat and low-fat lines for preliminary site screening. The linear regression model is as follows:

[0227] y=Xα+e

[0228] Where y is the generation number, X is the mutation site allele frequency of the SNP site, α is the SNP site effect size, and e is the intercept. The p value was then corrected based on the Bonferroni method, and the significance threshold was set to 0.05.

[0229] In the high-fat line, 3895 SNP loci with gradually increasing allele frequencies and 6296 SNP loci with gradually decreasing allele frequencies were screened out; in the low-fat line, 5039 SNP loci with gradually increasing allele frequencies and 7411 SNP loci with gradually decreasing allele frequencies were screened out. After merging the above loci to remove redundancy, a total of 22597 SNP loci were obtained.

[0230] (2) Detailed screening of SNP sites

[0231] 1) Select signal analysis

[0232] Based on the data of the 19th, 23rd and 27th generations, this example uses three methods, namely, nucleotide polymorphism ratio (Pi ratio), genetic differentiation index (Fst) and continuous homozygous fragment (ROH), to detect the selected regions of high- and low-fat broiler chickens.

[0233] a. Nucleotide polymorphism ratio (Pi ratio)

[0234] This example uses a sliding window strategy (window size 40kb, step size 20kb) to calculate the nucleotide diversity (Pi) of each window of high- and low-fat broiler chickens respectively by VCFtools software. Subsequently, the selected genomic regions are detected by the nucleotide polymorphism ratio (Pi ratio) between high- and low-fat broiler chickens. According to the Pi ratio ranking, the top 1% (Piratio thresholds are 11.571 and 25.491, respectively) windows are selected as the selected regions.

[0235] Based on the nucleotide polymorphism ratio (Pi ratio) method, a total of 193 candidate regions were screened in high- and low-fat broiler chickens, totaling 14.28MB, including 9.62MB of selected regions in the high-fat line and 4.66MB of selected regions in the low-fat line.

[0236] b. Genetic differentiation index (Fst)

[0237] This example uses a sliding window strategy (window size 40 kb, step length 20 kb) to calculate the genetic differentiation index (Fst) of each window between high-fat and low-fat broiler chickens using VCFtools software. According to the Fst value sorting, the top 1% (Fst>0.683) windows are selected as the selected area.

[0238] Based on the genetic differentiation index (Fst) method, 51 candidate regions totaling 3.68MB were screened out in high- and low-fat broiler lines.

[0239] c. Continuous homozygosity fragment (ROH)

[0240] PLINK software was used to statistically analyze the continuous homozygous segments (ROH) of high-fat and low-fat broiler chickens. The ratio of each SNP site in each population within the ROH (ROH ratio) was calculated and sorted according to the ROH ratio, and the top 1% of SNPs were selected as candidate SNPs (the threshold value for high-fat lines was 0.66 and that for low-fat lines was 0.64).

[0241] Based on the continuous homozygous fragment (ROH) method, a total of 40 candidate regions were screened out in high- and low-fat broiler chickens, totaling 25.79MB, including 10.9MB of selected regions in the high-fat line and 15.16MB of selected regions in the low-fat line.

[0242] After combining the above three signal selection methods to remove redundancy, the results are as follows: Figure 1 As shown, a total of 223 selected regions, totaling 37.51MB, were detected in high- and low-fat broiler chickens.

[0243] 2) QTL

[0244] The QTL data related to abdominal fat percentage were derived from the Animal QTL database.

[0245] The 22,597 SNP sites initially screened in step (1) were further filtered to retain only SNP sites located in the selected regions of high- and low-fat broiler chickens or in the abdominal fat rate QTL region, for a total of 12,963 SNP sites.

[0246] (II) SNP loci from white-feathered fast-growing large broiler breeder population

[0247] Based on the genotype data of the white-feathered fast-growing large broiler breeder population, the GEMMA software was used to construct mixed linear models in the A and B lines to complete the GWAS analysis. The mixed linear models are as follows:

[0248] y=Xα+Zβ+Wμ+e

[0249] Where y is the observed value of the individual trait (abdominal fat percentage); α is the fixed effect, including strain, sex, pen number and population structure (PC); β is the random effect of SNPs; μ is the individual random genetic effect; e is the random residual; X, Z and W are the association matrices of α, β and μ respectively; μ is assumed to obey a normal distribution with a mean of 0 and a variance of 2.5, G matrix is ​​the molecular kinship matrix calculated based on genotype information, and the significance threshold is set to p < 1 × 10 -3 .

[0250] The sites from high- and low-fat lines and the sites from the white-feathered fast-growing large broiler breeder population were merged to remove redundancy. Then, in this example, Beijing Novogene Technology Co., Ltd. used targeted capture sequencing technology to design primers and synthesize probes to obtain a total of 44,283 SNP sites (such as Figure 2 As shown), a liquid chip was obtained for the selection of abdominal fat traits in broiler chickens.

[0251] Example 2

[0252] Application of liquid-phase breeding chip for selection of abdominal fat traits in broiler chickens

[0253] In order to verify the effectiveness of the liquid phase chip obtained in Example 1, the liquid phase chip of Example 1 was used to perform genotyping on a pure line group of 978 white-feathered fast-growing large broiler breeders, and the abdominal fat rate (AFP) of broilers was selected by a group selection method. According to the results of the embodiment in Table 3, in roosters, the average AFP of the lean group was at least reduced by 0.24% compared with the original group, and the decrease rate exceeded 21%; in hens, the average AFP of the lean group was at least reduced by 0.33% compared with the original group, and the decrease rate exceeded 22%. These data strongly confirm the significant effect and practical application value of the chip involved in the present invention in the selection and breeding of low-fat broilers.

[0254] Table 3 The effect of liquid phase chip on the grouping of various strains of white-feathered fast-growing large broiler breeders

[0255]

[0256] Note: AFP reduction range = AFP mean value of lean group - AFP mean value of original group; reduction ratio = ratio of AFP reduction range / original group mean value.

[0257] Although the present invention has been disclosed as above in the preferred embodiment, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the claims.

Claims

1. A SNP molecular marker combination for broiler abdominal fat trait selection, characterized in that: The SNP molecular marker combination consists of 44,283 SNP molecular markers, and the staining positions and numbers of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1 of the specification.

2. A SNP liquid phase chip for broiler abdominal fat trait selection, characterized in that: The SNP liquid phase chip comprises probes or primers for detecting the SNP molecular marker combination as claimed in claim 1.

3. Use of the SNP molecular marker combination for broiler abdominal fat trait selection as claimed in claim 1 or the SNP liquid phase chip for broiler abdominal fat trait selection as claimed in claim 2 in the analysis of abdominal fat traits of white-feathered fast-growing large broiler breeders.

4. Use of the SNP molecular marker combination for broiler abdominal fat trait selection as claimed in claim 1 or the SNP liquid phase chip for broiler abdominal fat trait selection as claimed in claim 2 in molecular marker-assisted selection breeding of white-feathered fast-growing large broiler breeders.

5. A method for preparing a liquid phase chip for broiler abdominal fat trait selection, characterized in that: The following steps are involved: Step 1: The Northeast Agricultural University broiler abdominal fat bidirectional selection strain and white-feathered fast-growing large broiler breeder A and B lines were selected for whole genome resequencing. The high-quality sequences obtained by sequencing were compared with the chicken reference genome GRCg6a, and SNP variation detection was performed using GATK software. After quality control, a high-quality SNP data set was obtained; Step 2: In the SNP set of high-fat and low-fat broiler chickens, the GPSM method was used to screen the SNP sites that were selected in the multi-generation breeding process, and the nucleotide polymorphism ratio, genetic differentiation index and continuous homozygous fragments were used to further determine the selected area. At the same time, combined with the chicken abdominal fat rate related QTL data in the Animal QTL database, the SNP sites located in the selected area and the QTL area were retained as the SNP sites that can distinguish the abdominal fat levels of high-fat and low-fat broiler chickens; Step 3: In the SNP set of white-feathered fast-growing broiler breeders, the GWAS method was used to screen SNP loci related to abdominal fat traits as SNP loci that can distinguish high and low abdominal fat levels from the A and B line populations of white-feathered fast-growing broiler breeders; Step 4: The SNP loci derived from high- and low-fat broiler chickens and the SNP loci derived from the white-feathered fast-growing large broiler breeder group are merged and de-redundant to obtain the SNP markers that can ultimately distinguish between high and low abdominal fat levels. Based on these markers, a SNP liquid phase breeding chip is created. The staining positions and numbers of the SNP markers on the chicken reference genome GRCg6a are shown in Table 1 of the instruction manual.

6. The method for manufacturing a liquid phase chip according to claim 5, characterized in that: In step 2, the threshold for significant sites screened by the GPSM method was set to a p-value < 0.05 after correction based on the Bonferroni method.

7. The method for manufacturing a liquid phase chip according to claim 6, characterized in that: The threshold for significant loci screened by the GWAS method in step 3 was set to p < 1 × 10 -3 .

8. A method for selecting and breeding abdominal fat traits of white-feathered fast-growing large broiler breeders, characterized in that: The following steps are involved: Step 1: Based on the AFP phenotype data of the reference group, calculate the average value of the group AFP, label the individuals with AFP higher than the average as "fat", and label the individuals with AFP lower than the average as "thin"; Step 2: Acquire genotyping data of the reference population and the candidate population based on a liquid phase chip, wherein the liquid phase chip is made of probes for detecting a combination of 44,283 SNP molecular markers in the whole chicken genome, and the staining positions and numbers of the SNP molecular markers on the chicken reference genome GRCg6a are shown in Table 1 of the specification; Step 3: Use the t-distribution domain embedding algorithm to reduce the dimension of the sample's genotype information and determine the similarity of its genotype based on its plane position; Step 4: The Euclidean distance between the reference population and the candidate population after dimensionality reduction is used as a standard to measure the genotype similarity between samples; based on the dimensionality reduction data in step 3, the center point positions of the two labels "fat" and "thin" are first calculated in the reference population, and then the groups are divided according to the Euclidean distance between the samples in the candidate population and these two center points. The individuals in the candidate population that are closer to the center point of the "fat" group are divided into the "fat" group, and the individuals that are closer to the center point of the "thin" group are divided into the "thin" group, thereby achieving effective selection of broiler abdominal fat traits.

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