Multiple-effect antagonistic SNP molecular marker capable of simultaneously influencing chicken abdominal fat and skeletal muscle traits and application of multiple-effect antagonistic SNP molecular marker

Abstract

The invention discloses a multiple-effect antagonistic SNP molecular marker capable of simultaneously influencing chicken abdominal fat and skeletal muscle traits and application thereof, and belongs to the technical field of animal molecular genetics. In order to more accurately and conveniently select broilers with low abdominal fat content in breeding and give consideration to meat production and growth performance, the method comprises the following steps: estimating genetic parameters of an F2-generation resource population of 12-week-old chickens in Northeast Agricultural University and six growth traits, and then performing GWAS analysis to screen remarkable SNP in a genome, so as to select the broilers with low abdominal fat content in breeding and give consideration to meat production and growth performance. And finally, carrying out multiple comparison on the least square average of different genotypes of the screened multi-effect SNP. The result shows that the abdominal fat percentage is in negative correlation with 6 growth traits, 14 SNPs show antagonistic multiple effects in total, and 4 SNPs show synergistic multiple effects on 6 growth traits. The SNPs provided by the invention can be used for molecular marker-assisted breeding of broiler chickens, individuals can be directly bred at the genome level in the early stage, and the breeding process is accelerated.

Description

[0001] This application is a divisional application of a patent application with an application date of April 30, 2024, application number 202410537802.X, and invention name "Multi-effect SNP molecular markers related to abdominal fat deposition and growth traits in chickens, and screening methods and applications thereof." Technical Field

[0002] The invention belongs to the technical field of animal molecular genetics, and in particular relates to a pleiotropic antagonistic SNP molecular marker that simultaneously affects chicken abdominal fat and skeletal muscle traits and an application thereof. Background Art

[0003] The fat and growth traits of broilers (such as muscle production traits, bone traits and weight traits, etc.) have important economic value. However, along with the selection of broiler growth rate, excessive fat deposition in broilers, especially excessive abdominal fat deposition, has become a prominent problem in the poultry industry. Excessive fat deposition has many disadvantages, such as significantly reducing feed conversion efficiency and carcass lean meat rate; affecting the egg production and hatching rate of breeder chickens, and inducing fatty liver syndrome, bone diseases, etc., thereby increasing the mortality rate during the egg-laying period; too much useless fat is discarded, increasing the labor burden and polluting the environment, causing waste of resources. The economic losses caused by these problems to broiler producers are obvious. The abdominal fat of chickens plays a decisive role in the carcass quality of chickens. Preventing excessive fat deposition in broilers and improving the feed conversion efficiency, carcass quality and bone quality of chickens are urgent tasks in the broiler breeding industry. Genetic breeding is the most fundamental way to cultivate broilers with low abdominal fat, high meat yield and high growth rate. Therefore, it is of great significance to identify the pleiotropic SNP loci that affect abdominal fat and growth traits in chickens.

[0004] With the deepening of chicken genomics research, a series of important research results have been achieved in the field of genomics research on chicken body fat and growth traits, especially in the identification of key genes and functional variation positioning based on genome-wide association analysis (GWAS), which has greatly promoted the research process of the molecular genetic basis of chicken fat tissue and growth traits. In recent years, GWAS based on multivariate linear mixed models has been applied to the positioning of pleiotropic single nucleotide polymorphisms (SNPs) that affect important economic traits because it can improve the ability to detect genetic variation, and has made positive progress. Summary of the invention

[0005] In order to more accurately and conveniently select broilers with low abdominal fat content in breeding while taking into account meat production and growth performance, the present invention screened SNPs that affect both abdominal fat and growth traits of chickens in the whole genome, providing help for improving breeding strategies to obtain ideal production traits.

[0006] In order to solve the above technical problems and achieve corresponding technical effects, the present invention specifically provides the following technical solutions:

[0007] The first object of the present invention is to provide a screening method for pleiotropic SNP molecular markers associated with abdominal fat deposition and growth traits in chickens, the screening method comprising the following steps:

[0008] 1) Selection of experimental animals, blood collection and phenotypic trait determination: The experimental animals were the F2 generation resource group of chickens, which were constructed by hybridizing high-fat roosters of the Northeast Agricultural University broiler abdominal fat bidirectional selection strain and white-eared yellow chicken hens to construct the F1 generation. The F1 generation avoided the mating of full-siblings and half-siblings to produce the F2 generation; blood samples were collected from the wing vein at 12 weeks of age; phenotypic trait determination included the measurement of the chickens' body weight, metatarsal length, metatarsal circumference, keel length, abdominal fat rate, breast muscle rate and leg muscle rate at 12 weeks of age;

[0009] 2) Whole-genome resequencing, alignment analysis and quality control: Whole-genome resequencing was performed by extracting chicken blood genomic DNA. The genomic data obtained by sequencing were aligned and analyzed using the bioinformatics software BWA and SAMtools. The alignment results were removed by using the rmdup parameter of the SAMtools software. The SNP genotype data were detected using the GATK software. According to the F0 generation sequencing results, BEAGLE 4.0 with default parameters was used to fill in the missing genotypes in the F2 population. The quality of the SNP data obtained was controlled using the Plink software.

[0010] 3) Estimation of genetic parameters: Use the software GCTA to estimate the heritability, genetic correlation and phenotypic correlation of the traits, and establish a genetic correlation estimation model for the two traits: Y i =X i b i +Z i u i +e i , Y i is the individual phenotype, X i and Z i They are b i and u i The correlation matrix, b i is a fixed effect including gender, u i is the vector of polygenic effects, e i is a random residual. The phenotypic correlation coefficient of the two traits is calculated using the following formula based on the results of the bivariate genome restricted maximum likelihood estimation: σ uxy and σ exy represent the genetic covariance and residual covariance between traits x and y, respectively, and are the genetic variances of x and y, respectively, and are the residual variances of x and y, respectively, using Calculate the standard error of the phenotypic correlation, n is the number of sample pairs, r pxy is the phenotypic correlation coefficient;

[0011] 4) Genome-wide association analysis: GWAS analysis was performed between the chicken genome-wide SNPs genotypes and the abdominal fat and growth trait phenotypes, and a GWAS analysis model was constructed: y = Sβ + Xα + Kμ + e, where y represents the matrix of n individuals and d phenotypes, S is the fixed effect matrix including sex, β is the corresponding coefficient vector including the intercept, X is the SNP genotype, α is the vector of marker gene effects, K is the association matrix of μ, μ is the random effect, and e is the random residual;

[0012] 5) Identification of significant SNPs: The difference significance level P value in the GWAS analysis results was corrected by Bonferroni, and the Manhattan plot and QQ plot were drawn using CMplot in R software. The SNPs above the threshold line on the Manhattan plot were the SNPs significantly associated with abdominal fat and growth traits of F2 chickens;

[0013] 6) According to the positive or negative SNP marker effect value, determine whether the effect of SNP on the traits is synergistic or antagonistic, then use the Tukey-HSD method to calculate the least squares mean of different genotypes of each SNP, and analyze the differences between different genotypes to further determine the effect of multiple-effect SNP sites on each trait.

[0014] In one embodiment of the present invention, the reference genome for comparison and analysis in step 2) is the sixth edition reference genome of chicken Gallus gallus GRCg6a GCF000002315.6.

[0015] In one embodiment of the present invention, in step 4), a multivariate mixed linear model in GEMMA software is used to perform GWAS analysis between chicken abdominal fat and growth phenotypic traits and genome-wide SNPs.

[0016] The second object of the present invention is to provide a whole-genome SNP that simultaneously affects the abdominal fat and body weight traits of chickens and is a significant association with antagonistic pleiotropy, wherein the SNP is rs15494840, rs735688344, rs317815582, rs312617399, rs316160377, rs312882764, rs732864571, rs318153866, rs316369694 or rs314695997.

[0017] In one embodiment of the present invention, when the molecular marker rs15494840 allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.1, corresponding to the high abdominal fat and small body weight traits; when the allele is C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.2, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs735688344 allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.3, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.4, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs317815582 allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.5, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.6, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs312617399 allele is C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.7, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.8, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs316160377 allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.9, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.10, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs312882764 allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.11, corresponding to the high abdominal fat and small body weight traits; when the allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.12, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs732864571 allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.13, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.14, corresponding to the low abdominal fat and large body weight traits. When the molecular marker rs318153866 allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.15, corresponding to the high abdominal fat, small body weight, and unfavorable bone growth traits; when the allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.16, corresponding to the low abdominal fat, large body weight, and favorable bone growth traits. When the molecular marker rs316369694 allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.17, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.18, corresponding to the low abdominal fat and large body weight traits.When the allele of the molecular marker rs314695997 is C, the nucleotide sequence of the molecular marker is shown as SEQ ID NO.19, corresponding to high abdominal fat, small body weight, and unfavorable bone growth traits; when the allele is T, the nucleotide sequence of the molecular marker is shown as SEQ ID NO.20, corresponding to low abdominal fat, large body weight, and favorable bone growth traits.

[0018] The third object of the present invention is to provide the application of the above-mentioned genome-wide SNPs that simultaneously affect chicken abdominal fat and body weight traits and are significantly associated with antagonistic pleiotropy in assisted breeding of broiler abdominal fat and body weight traits.

[0019] The fourth object of the present invention is to provide a genome-wide SNP that simultaneously affects chicken abdominal fat and bone traits and is a significant association of antagonistic pleiotropy, wherein the SNPs are rs318153866, rs317098725 or rs314695997.

[0020] In one embodiment of the present invention, when the molecular marker rs317098725 allele is G, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.21, corresponding to high abdominal fat and unfavorable bone growth traits; when the allele is A, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.22, corresponding to low abdominal fat and favorable bone growth traits.

[0021] The fifth object of the present invention is to provide the application of the above-mentioned genome-wide SNPs that simultaneously affect chicken abdominal fat and bone traits and are significantly associated with antagonistic pleiotropy in assisted breeding of broiler abdominal fat and bone traits.

[0022] The sixth object of the present invention is to provide a genome-wide SNP that simultaneously affects chicken abdominal fat and skeletal muscle traits and is a significant antagonistic pleiotropic association, wherein the SNPs is rs14919121.

[0023] In one embodiment of the present invention, when the molecular marker rs14919121 allele is T, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.23, corresponding to high abdominal fat, which is not conducive to skeletal muscle growth traits; when the allele is C, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.24, corresponding to low abdominal fat, which is conducive to skeletal muscle growth traits.

[0024] The seventh object of the present invention is to provide the application of the above-mentioned genome-wide SNPs that simultaneously affect chicken abdominal fat and skeletal muscle traits and are significantly associated with antagonistic pleiotropy in assisted breeding of broiler abdominal fat and skeletal muscle traits.

[0025] The eighth object of the present invention is to provide a whole-genome SNP that simultaneously affects six growth traits of chicken weight, metatarsal length, metatarsal circumference, keel length, breast muscle rate and leg muscle rate and has a significant association with synergistic effects, and the SNPs are rs315982032, rs736271942, rs794230628 or rs316443018.

[0026] In one embodiment of the present invention, when the molecular marker rs315982032 allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.25, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate; when the allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.26, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate. When the molecular marker rs736271942 allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.27, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.28, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate. When the molecular marker rs794230628 allele is C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.29, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate; when the allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.30, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate. When the molecular marker rs314695997 allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.31, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.32, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate.

[0027] The ninth objective of the present invention is to provide the application of the above-mentioned whole-genome SNPs that simultaneously affect the six growth traits of chicken weight, metatarsal length, metatarsal circumference, keel length, breast muscle rate and leg muscle rate and have synergistic and significant associations in assisted breeding of broiler growth traits.

[0028] Beneficial effects of the present invention:

[0029] The present invention uses the Illumina HiSeq PE150 platform to detect the genome of 519 chickens of the F2 generation group of Northeast Agricultural University, and conducts association analysis with the weight, metatarsal length, metatarsal circumference, keel length, abdominal fat rate, breast muscle rate and leg muscle rate of the chickens in the group, estimates the genetic parameters of the chicken abdominal fat trait and growth trait based on the whole genome SNP markers, and then uses the multivariate-based GWAS to identify the multi-effect SNP markers that simultaneously affect the above traits, and finally multiple comparisons are made on the least squares means of different genotypes of the screened multi-effect SNPs. The results show that the abdominal fat rate is negatively correlated with the other 6 growth traits, and a total of 14 SNPs show antagonistic pleiotropy (i.e., inhibiting the abdominal fat rate and promoting the other 6 traits); 4 SNPs show synergistic pleiotropy on the 6 growth traits, among which the wild genotype of 1 SNP and the mutant genotypes of 3 SNPs can significantly increase the yield of the 6 growth traits. The results of the present invention provide new insights into the genetic basis between abdominal fat and growth in broiler chickens, and the SNP markers provided by the present invention can be used for molecular marker-assisted breeding of broiler chickens. Individuals can be directly selected at the genome level in the early stages without relying on phenotypic information, which speeds up the breeding process and makes it possible to obtain ideal production traits. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a graph showing the results of multi-trait GWAS of the F2 generation chicken population in the present invention; wherein Figure 1 A in is the Manhattan graph, Figure 1 B in the figure is the QQ-Plot diagram. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical scheme and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with specific implementation methods and the accompanying drawings of the specification. The experimental methods used in the following examples are conventional methods unless otherwise specified, and the materials, reagents and instruments used are conventional materials, reagents and instruments in the art unless otherwise specified, and can be obtained by those skilled in the art through commercial channels.

[0032] Example 1: Screening method for pleiotropic SNP molecular markers associated with abdominal fat deposition and growth traits in chickens

[0033] (1) Selection of experimental animals and determination of phenotypic traits

[0034] In the present invention, the experimental animals are the F2 generation resource group of Northeast Agricultural University chickens, which are hybridized by 4 high-fat roosters of the Northeast Agricultural University broiler abdominal fat bidirectional selection strain (disclosed in the patent with application number CN201610896131.1, the invention name is a method for breeding high and low abdominal fat broiler strains) and 24 white-eared yellow chickens (a Chinese egg-laying variety with low abdominal fat rate and a large genetic distance from high-fat chickens) hens, that is, the F0 generation is hybridized to produce the F1 generation, and the F1 generation avoids the mating of full-siblings and half-siblings to produce the F2 generation, and the male-female mating ratio is 1:6. A total of 519 F2 generation individuals (263 roosters and 256 hens) from 12 half-sibling families were used in this study. All chickens were free to eat and drink water during the feeding process and were kept under the same environmental conditions. Blood samples were collected from the wing vein of chickens using vacuum tubes containing the anticoagulant EDTA at 12 weeks of age, and these samples were subsequently stored at -20°C for future experimental use. Body weight, metatarsal length, metatarsal circumference, and keel length of F2 individuals were measured before slaughter at 12 weeks of age, and abdominal fat percentage, breast muscle percentage, and leg muscle percentage of F2 individuals were measured and calculated after slaughter.

[0035] (2) Whole genome resequencing, alignment analysis, and quality control

[0036] Blood genomic DNA was extracted using a blood genome extraction kit, and DNA purity was tested by ultraviolet spectrophotometer. After the test was qualified, Beijing Novogene Technology Co., Ltd. was commissioned to perform whole genome resequencing. The genome of a single individual was sequenced using the IlluminaHiSeq PE150 sequencing platform (the sequencing depth of 26 F0 individuals was 10×; the sequencing depth of 519 F2 individuals was 3×). The genomic data obtained by sequencing were aligned and analyzed using the bioinformatics software Burrows-Wheeler Aligner (BWA) and SAMtools (the alignment reference genome was the sixth version of the chicken reference genome (Gallus gallus GRCg6a GCF000002315.6)); the alignment parameters were "BWA mem-t 4-k 32-M"; the alignment results were removed by the rmdup parameter of the SAMtools software. The SNP genotype data were detected using the GATK software. The "mpileup" module of SAMtools was used to detect population SNPs. In order to obtain high-quality SNPs during detection, only SNPs with coverage depth ≥ 2, mean square value of coverage sequence quality ≥ 20, and missing rate ≤ 0.3 were retained for subsequent analysis. Finally, BEAGLE 4.0 with default parameters was used to fill in the missing genotypes of the F2 generation population based on the F0 generation sequencing results, and 10-fold cross-validation was used to detect the accuracy of genotype filling. After screening and filtering of the 15,868,916 SNPs obtained (minimum allele frequency ≥ 0.05 and missing rate ≤ 0.2), a total of 7,890,258 SNPs were obtained for subsequent GWAS analysis.

[0037] (3) Estimation of genetic parameters

[0038] The heritability estimation based on SNPs was performed using the genetic statistical model Y of the software GCTA (v1.93.2) i =X i b i +Z i u i +e i Carry on, Y i is the individual phenotype, X i and Z i They are b i and u i The correlation matrix, b i is a fixed effect including gender, u i is the vector of polygenic effects, e i is a random residual. The phenotypic correlation coefficient was calculated using the following formula based on the results of the bivariate genome restricted maximum likelihood estimation (REML): σuxy and σ exy represent the genetic covariance and residual covariance between traits x and y, respectively, and are the genetic variances of x and y, respectively, and are the residual variances of x and y respectively; using Calculate the standard error of the phenotypic correlation, n is the number of sample pairs, r pxy is the phenotypic correlation coefficient. The results are shown in Table 1. The seven traits have large heritability (0.26-0.61), and they can be effectively genetically improved. The abdominal fat rate is strongly negatively correlated with the other six growth traits (the genetic correlation is between -0.55 and -0.25, and the phenotypic correlation is between -0.26 and -0.51. There is a moderate positive genetic correlation (0.27-0.91) and phenotypic correlation (0.14-0.88) among the six growth traits. This shows that there is a strong correlation between the traits, and they may have a common genetic basis, that is, one cause with multiple effects, so they can be used in multi-trait GWAS analysis to identify pleiotropic markers.

[0039] (4) Genome-wide association analysis

[0040] GEMMA software was used to perform genome-wide association analysis (GWAS) to identify SNPs associated with abdominal fat and growth traits in chickens. The GWAS analysis used a multivariate mixed linear model, expressed as the equation y=Sβ+Xα+Kμ+e, where y represents the matrix of n individuals and d phenotypes, S is the fixed effect matrix including sex, β is the corresponding coefficient vector including the intercept, X is the genotype of the SNP, α is the vector of marker gene effects, K is the association matrix of μ, μ is a random effect, e is a random residual, and the genome-wide significance threshold is set to 0.05 / N (P value=6.337E-9), and N is all the SNPs finally used for the genome-wide association analysis. Manhattan and QQ plots were drawn using the CMplot package in R software, and the results are shown in the figure. Figure 1 shown.

[0041] Table 1 Heritability of seven traits of the chicken F2 population and the results of genetic and phenotypic correlation analysis among the traits

[0042]

[0043]

[0044] Note: The values ​​in brackets on the diagonal are the standard errors of heritability, the values ​​in brackets on the diagonal are the standard errors of genetic correlation, and the values ​​in brackets below the diagonal are the standard errors of phenotypic correlation

[0045] (5) Screening and identification of pleiotropic markers and genes

[0046] The detected SNP markers that affect multiple traits at the same time were extracted, and the positive or negative SNP marker effect value (β value) was used to determine whether the SNP had a synergistic or antagonistic effect on the traits. The Tukey-HSD method was then used to calculate the least squares mean of different genotypes of each SNP, and the differences between the effect values ​​of different genotypes were analyzed to further determine the effects of the multiple-effect SNP loci on each trait. The results are shown in Tables 2, 3, 4 and 5.

[0047] Table 2 The least square mean differences of SNPs with significant effects on abdominal fat rate and body weight of chickens and their different genotypes

[0048]

[0049]

[0050] Note: β is the effect value of the allele; when the distance between multiple SNPs of the same trait is less than 0.25Mb, the most significant SNP is retained; * indicates that the SNP genotype has a significant effect on abdominal fat rate and body weight (P<0.05); ** indicates that the SNP genotype has a very significant effect on abdominal fat rate and body weight (P<0.01)

[0051] Table 3 The least square mean differences of SNPs with significant effects on abdominal fat rate and bone in chickens and their different genotypes

[0052]

[0053] Note: β is the effect value of the allele; when the distance between multiple SNPs of the same trait is less than 0.25Mb, the most significant SNP is retained; * indicates that the SNP genotype has a significant effect on abdominal fat rate and bone (P<0.05); ** indicates that the SNP genotype has a very significant effect on abdominal fat rate and bone (P<0.01).

[0054] Table 4 The least square mean differences of SNPs with significant effects on abdominal fat rate and skeletal muscle in chickens and their different genotypes

[0055]

[0056] Note: β is the effect value of the allele; when the distance between multiple SNPs of the same trait is less than 0.25Mb, the most significant SNP is retained; * indicates that the SNP genotype has a significant effect on abdominal fat rate and skeletal muscle (P<0.05); ** indicates that the SNP genotype has a very significant effect on abdominal fat rate and skeletal muscle (P<0.01).

[0057] Table 5 The least square mean differences of SNPs with significant effects on six growth traits of chickens and their different genotypes

[0058]

[0059] Note: β is the effect value of the allele; when the distance between multiple SNPs of the same trait is less than 0.25Mb, the most significant SNP is retained; * indicates that the SNP genotype has a significant effect on the six growth traits (P<0.05); ** indicates that the SNP genotype has a very significant effect on the six growth traits (P<0.01)

[0060] Tables 2, 3 and 4 show that abdominal fat is negatively correlated with the other six growth traits, and a total of 14 SNPs show antagonistic pleiotropy (i.e., inhibiting abdominal fat and promoting other growth traits). Compared with other genotypes, the mutant genotypes CC of rs15494840, GG of rs735688344, GG of rs317815582, GG of rs312617399, GG of rs316160377, AA of rs312882764, GG of rs732864571, AA of rs318153866, and GG of rs31636969 showed negative correlations with the other genotypes. The mutant genotype GG of rs4 and the mutant genotype TT of rs314695997 are favorable genotypes, which can be used to breed broilers with high weight and low abdominal fat; the mutant genotype AA of rs318153866, the mutant genotype AA of rs317098725 and the mutant genotype TT of rs314695997 are favorable genotypes, which can promote the bone growth and development of broilers and reduce abdominal fat; the mutant genotype CC of rs14919121 is a favorable genotype, which can be used to breed broilers with high skeletal muscle yield and low abdominal fat rate.

[0061] Table 5 shows that the four SNPs exhibit synergistic pleiotropy on the six growth traits (i.e., promoting or inhibiting the six growth traits at the same time). The wild genotype GG of rs315982032, the mutant genotype GG of rs736271942, the mutant genotype TT of rs794230628 and the mutant genotype GG of rs316443018 are favorable genotypes, which can significantly improve the six growth traits. We can select these genotypes to improve the growth traits of broiler chickens.

[0062] The information of the pleiotropic SNP molecular markers obtained above is shown in Table 6.

[0063] Table 6 Information of pleiotropic SNP molecular markers

[0064]

[0065] The sequence of the base fragments before and after the molecular marker rs15494840 is as follows:

[0066] CTGCTTCGGTAGGGGGGTTGGAGCTTGATGATCTTTGAGGTCCCTTCCAACCCA AGCCATTCTATGATTCTATGATATGATTCTATGATTCAGTTTCTTC[T / C]TCATTGTGAG GAATGGCAATTACACATGGAAATTAGCTCTGAGGGTCCAGGCACCATCTTCTGGGCT ACTTGGACCTGTGTGTGCTCAGGCTTATTTTAG

[0067] When the allele of the molecular marker rs15494840 is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.1, corresponding to the high abdominal fat and small body weight traits; when the allele is C, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.2, corresponding to the low abdominal fat and large body weight traits.

[0068] The sequence of the base fragments before and after the molecular marker rs735688344 is as follows:

[0069] GTCATACAGTGGGGTTGACATCCACTCTTGTATCCTTTAAAGTCATAGAATGCCTT GGGTTGGAAAAGACCCTAAAGATCATTGAGCTCCAACCCCCTGCC[A / G]TAGGCAG GATTGCCATCCACGAGATCAGGCTGCCAGGGGCCCCAGCCAACCTGGTCCTGAACA CTTCCAGGGATGGGGCACCCACAGCCTTGCTGGGCAA

[0070] When the allele of the molecular marker rs735688344 is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.3, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.4, corresponding to the low abdominal fat and large body weight traits.

[0071] The sequence of the base fragments before and after the molecular marker rs317815582 is as follows:

[0072] ACAGCAATATAACTCAAGCGATTTAGATGGAGTTAATTCCAATTAATGTCCTGCA AAGACTGAGTAACTTGATGCATGTAAAATGATGCGCTGAATTTGA[A / G]TGAAACCT TTGAGTTTGACAGATTTTCTCCATCCTAGAATTTGGACTGCAATTCAACTGAAAAAA ATGATCAATGTGTGTGGTTAAATAATGCTCAATGG

[0073] When the allele of the molecular marker rs317815582 is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.5, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.6, corresponding to the low abdominal fat and large body weight traits.

[0074] The sequence of the base fragments before and after the molecular marker rs312617399 is as follows:

[0075] GCTTCCAGACAGTTCTACCCACACAAACATATATGTGTGTGTGTGTATCCATATAT GTATGTGTACGTATTTATATAAAATATGTATTAGGTGTTACTCT[C / G]AGATGTATAATTA TGGTCTGTGTATTAAGCTACAGACGTTTCTAGTAATATTTATAATACACAGTAATACTC AGCATCTGGTTTCTCTGTGTCAGAGACT

[0076] When the allele of the molecular marker rs312617399 is C, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.7, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.8, corresponding to the low abdominal fat and large body weight traits.

[0077] The sequence of the base fragments before and after the molecular marker rs316160377 is as follows:

[0078] CATACTTGGCATGTAAGTGCTACAGATCTCGGCCCTTTATAAGTAGCCATCGC CTGTAGGTAGTCCAGCTACTTCTGCCACTTGCCCTAATTTCTCAA[A / G]CTCCCTTCTA TAACCTTCATACAGTACCTTTGTACACAAAATGGTAGCACAAGCGTGGCACATACAT CCCGGCTGCTATCACATGGAATCAGCTCAGGCT

[0079] When the allele of the molecular marker rs316160377 is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.9, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.10, corresponding to the low abdominal fat and large body weight traits.

[0080] The sequence of the base fragments before and after the molecular marker rs312882764 is as follows:

[0081] CAAATGCAAGATTTTCATTTCTGTTTTCAGTGAGGGTGATGTGAAGCCGGAGTG CAAAATAGCAAATGGGAATGAAGTTAAAGGAAAGGAAATTATCACA[T / A]AGAACCA TAGAATAATCTGGCTGGAAAAGGCTTTCTGGGAGTGGGATGAAGTAAAAGAAGTTT CATATGAGCAAAATAGGGGAAGAGCAGGGGCTCTTGT

[0082] When the allele of the molecular marker rs312882764 is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.11, corresponding to the high abdominal fat and small body weight traits; when the allele is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.12, corresponding to the low abdominal fat and large body weight traits.

[0083] The sequence of the base fragments before and after the molecular marker rs732864571 is as follows:

[0084] TCTATGTGCCCTTTCCAGCTCTATTTCTAGTCTGTAATAAAAGCATACAAGCATTAG AAAATACAGTCATATTTTGTCTGAATCCTGTCTGCATACCTCTT[T / G]TCTAAAAGGCC TCTTGCTTTCGCTGCCCTCTCAGTGTTCTTTCTTGCAAGTGAGCATATTAGGTTCATTT GCCTAACTCTCAGGTGTCACAATTACAAAT

[0085] When the allele of the molecular marker rs732864571 is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.13, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.14, corresponding to the low abdominal fat and large body weight traits.

[0086] The sequence of the base fragments before and after the molecular marker rs318153866 is as follows:

[0087] TTACACTCCTGTTCAGGACCCGTGCTTTCTTTAGCGCTGTTTTCCTGCGCCTGGGG GAGGACGAGTAGATGTTCTGCTAGTAAATAAGTTGGCCCGATTAC[G / A]TTGTGTTGC TCCGTATGCACAAAAAGCAGACTGCCAGTAAATGAGTATTTCTTACTGAGAAATCAC TCATTCACAACCCAGACTTTCCTTATTTTTTTCCA

[0088] When the allele of the molecular marker rs318153866 is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.15, corresponding to high abdominal fat, small body weight, and unfavorable bone growth traits; when the allele is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.16, corresponding to low abdominal fat, large body weight, and favorable bone growth traits.

[0089] The sequence of the base fragments before and after the molecular marker rs316369694 is as follows:

[0090] CACATGAAAACTCATCAGAACTACATGAGCAATAGAAAAAAAAACGAGTGATT AGAGGAAATGTAGAGAGGAAAGCTCAGTACTGGGAATCACTTGCTCA[A / G]TACTTC CTTTTGTAACTGAAATTCCTATCTTTTATTTGCAAACAAGAAGCATCTTTCAGGAAAGC TGTTTGTGAGAGGACACAGTCCTGTGTACCTGCG

[0091] When the allele of the molecular marker rs316369694 is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.17, corresponding to the high abdominal fat and small body weight traits; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.18, corresponding to the low abdominal fat and large body weight traits.

[0092] The sequence of the base fragments before and after the molecular marker rs314695997 is as follows:

[0093] AGTAATAAAAGACATACTAGCTAGGATAGGCATCATTAATTAATTACAGTATGCAT AGATAAATAATGTAATATCACATTTGCTAAAATGAGGAATTAAA[C / T]TCTTATGGATA ATAAATGTGTTGTTAGATGATGTTAAACTGATGTTAGGAGAGAATTCTACACTTACTA ATCCTGAACAAATTGATTCGTTTATAATGAT

[0094] When the molecular marker rs314695997 allele is C, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.19, corresponding to high abdominal fat, small body weight, and unfavorable bone growth traits; when the allele is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.20, corresponding to low abdominal fat, large body weight, and favorable bone growth traits.

[0095] The sequence of the base fragments before and after the molecular marker rs317098725 is as follows:

[0096] TGCTGGCACCATTGTTATTATTCTTTATTAATTTGTACAGCATCCAAACAGGCAAT TTGCAGACAGACGCAGTAAGGTTTTTGCCCTGAGGAGCATACGA[G / A]TATAATTAAT CCATAGAAGTGATTTTAATAGGACATTTCAAGCACTTAATTTAAATACGTGTTGTTTCT AGAATCAGATCTAGAGGAAGATAGAGGCCGC

[0097] When the molecular marker rs317098725 allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.21, corresponding to high abdominal fat and unfavorable bone growth traits; when the allele is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.22, corresponding to low abdominal fat and favorable bone growth traits.

[0098] The sequence of the base fragments before and after the molecular marker rs14919121 is as follows:

[0099] TTCTGGTCTCTGATCTCAAATGGAGTGTATAGTATAGTATTATATGTTGTTAATTCC TGATTAAATGTTTAACAAACTCGTAAACTGGGAGGACAAATTT[T / C]CTGAGCAGAA GGATGTTACTCCTATTGAATTTTTCCAAATTATTGAAGCAAATTTGATTTAGTAGTTCA GCATTTAAACTCTATAACCGGTCTGTGGGCA

[0100] When the molecular marker rs14919121 allele is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.23, corresponding to high abdominal fat and unfavorable skeletal muscle growth traits; when the allele is C, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.24, corresponding to low abdominal fat and favorable skeletal muscle growth traits.

[0101] The sequence of the base fragments before and after the molecular marker rs315982032 is as follows:

[0102] AAATACCAAGATGCTACTTGATGCCTTGTTCCATTGGCTTGCATTTTCACTTTCTT GGCTCATGCTGGTGGAGATCATGAGTTAAAATATTATTTATAGA[G / T]GACAGAGAAG CAAGTTCTGTGTAAAGTATTGAGTATTTATTTTGAAACACAACCATTAACTTTAAAGT AAGTTGGGGGAAAAAATCAGAACAGAGAATAC

[0103] When the molecular marker rs315982032 allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.25, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate; when the allele is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.26, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate.

[0104] The sequence of the base fragments before and after the molecular marker rs736271942 is as follows:

[0105] GCCTTGAATGCTTCCAGGGATGGGGCATCCACAGTCTCCTGGGGCAACCTGTTC CAGTGTGTCACCACCCTCTGGGTGAAAAACTTCCTCCTAATATCTA[A / G]CCTAAACC TCCCTTGTCTCAGTTTAAGACCATTCCCCCTTGTCCTATCACTTTCCACCCTCATAAA CAGACATACCCCCTCCTGTTTATATGCTCCCTTC

[0106] When the molecular marker rs736271942 allele is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.27, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.28, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate.

[0107] The sequence of the base fragments before and after the molecular marker rs794230628 is as follows:

[0108] AATTCTCACCAGCAGCATCATTATCACTGAAAAACTTAGCAAGAAGTGACCTAG AAAAGCTGTAGTGCCTCCATCCTTGGAACTACTAACTCAGGTAAGG[C / T]GACTTTAT CTAGTAACAGCTGTCTTCTAAAACATTTGAGTAATCTTCTCAAGCTCCTAAATCCTAA AATCCATGCCCATCATCACTTAAATACACAAAAG

[0109] When the molecular marker rs794230628 allele is C, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.29, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate; when the allele is T, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.30, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate.

[0110] The sequence of the base fragments before and after the molecular marker rs316443018 is as follows:

[0111] GGGTAGATAAAGGGAGAGCAGCAGATATTGTCTACCTGGACTTCAGCAAGGTTT TCAACACTGTCTCCCAAAACACAGTCAGAAAATGTGCGTTAGATGA[A / G]TAGATAG TGAGGTAGAAATAAGAACTGGCTGAATGGCAGGGCTCAGAGTGTTGTGATCAGTGA CACTGAGCCTAGTTGGAAGCCTGTATCTAGTGGTATC

[0112] When the allele of the molecular marker rs314695997 is A, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.31, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle rate and low leg muscle rate; when the allele is G, the nucleotide sequence of the molecular marker is as shown above, recorded as SEQ ID NO.32, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle rate and high leg muscle rate.

[0113] When breeding broiler chickens, the above-mentioned SNP molecular markers (Table 6) can be used to design primers on the nucleotide sequences on both sides of the flanks. Blood is collected and genomic DNA is extracted when the chickens are 3-4 weeks old. The primers are used to perform genotyping on the broiler materials to be tested, and individuals with the genotype of the target trait are retained, which can speed up the breeding process.

[0114] Although the present invention has been disclosed as above in the form of preferred embodiments, 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 genome-wide significant SNP that simultaneously affects chicken abdominal fat and skeletal muscle traits and is antagonistic pleiotropic, characterized in that: The SNPs is rs14919121.

2. Application of the whole genome SNP described in claim 1 in assisted breeding of abdominal fat and skeletal muscle traits in broilers.

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