A pleiotropic antagonistic snp molecular marker affecting both abdominal fat and skeletal muscle traits in chicken and application thereof

Abstract

The application discloses a multi-effect antagonistic SNP molecular marker affecting chicken abdominal fat and skeletal muscle traits simultaneously and application thereof, and belongs to the technical field of animal molecular genetics. In order to more accurately and conveniently select low abdominal fat content broilers in breeding, meanwhile, meat production and growth performance are taken into account, the application estimates genetic parameters of abdominal fat rate and six growth traits of a 12-week-old F2 generation resource population of Northeast Agricultural University chickens, then performs GWAS analysis to screen significant SNPs in the genome, and finally performs multiple comparisons on least square means of different genotypes of the screened SNPs. The results show that the abdominal fat rate is negatively correlated with the six growth traits, and 14 SNPs exhibit antagonistic multi-effectiveness, and four SNPs exhibit synergistic multi-effectiveness on the six growth traits. The SNPs provided by the application can be used for molecular marker assisted breeding of broilers, and individuals can be directly selected at the genome level in early stage, so that the breeding process is accelerated.

Description

[0001] This application is a divisional application of the patent application filed on April 30, 2024, with application number 202410537802.X, entitled "Multifunctional SNP molecular markers related to fat deposition and growth traits in chicken abdomen and their screening methods and applications". Technical Field

[0002] This invention belongs to the field of animal molecular genetics technology, specifically relating to a pleiotropic antagonistic SNP molecular marker that simultaneously affects the traits of chicken abdominal fat and skeletal muscle and its application. Background Technology

[0003] The fat and growth traits (such as muscle production, skeletal traits, and weight) of broilers have significant economic value. However, with the selective breeding for faster growth rates, excessive fat deposition in broilers, especially in the abdomen, 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 percentage; affecting egg production and hatchability in breeder hens, and inducing fatty liver syndrome, bone diseases, etc., thus increasing mortality during the laying period; and the excessive disposal of useless fat increases labor burden and pollutes the environment, resulting in resource waste. The economic losses to broiler producers caused by these problems are obvious. Abdominal fat plays a decisive role in carcass quality; preventing excessive fat deposition in broilers and improving feed conversion efficiency, carcass quality, and skeletal quality are urgent priorities for the broiler farming industry. Genetic breeding is the most fundamental way to cultivate broilers with low abdominal fat, high meat yield, and high growth rate. Therefore, identifying pleiotropic SNP sites that influence abdominal fat and growth traits in chickens is of great significance.

[0004] With the deepening of chicken genomics research, significant progress has been made in the field of genomics research on chicken body fat and growth traits, particularly in the identification of key genes and the localization of functional variations based on genome-wide association studies (GWAS). This has greatly promoted the research progress on the molecular genetic basis of chicken adipose tissue and growth traits. In recent years, GWAS based on multivariate linear mixture models has been applied to the localization of polymorphic single nucleotide polymorphisms (SNPs) affecting important economic traits due to its ability to improve the detection of genetic variations, and has achieved positive progress. Summary of the Invention

[0005] To more accurately and conveniently select broilers with low abdominal fat content in breeding while taking into account both meat production and growth performance, this invention screens SNPs across the entire genome that simultaneously affect abdominal fat and growth traits in chickens, providing assistance in improving breeding strategies to obtain ideal production traits.

[0006] To solve the above-mentioned technical problems and achieve the corresponding technical effects, the present invention specifically provides the following technical solution:

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

[0008] 1) Selection, blood collection, and phenotypic determination of experimental animals: The experimental animals were a chicken F2 generation resource population. This population was constructed by crossing high-fat male broiler chickens of the Northeast Agricultural University's double-selection broiler abdominal fat strain with white-eared yellow female chickens to create the F1 generation. The F1 generation avoided mating full-sib and half-sib chickens to produce the F2 generation. Blood samples were collected from the wing veins at 12 weeks of age. Phenotypic determination included measuring the chickens' body weight, metatarsal length, metatarsal circumference, keel length, abdominal fat percentage, breast muscle percentage, and leg muscle percentage at 12 weeks of age.

[0009] 2) Whole genome resequencing, alignment analysis and quality control: Chicken blood genomic DNA was extracted for whole genome resequencing. The genome data obtained from the sequencing was aligned and analyzed using bioinformatics software BWA and SAMtools. The alignment results were used to remove duplicates using the rmdup parameter of SAMtools software. SNP genotype data were detected using GATK software. Based on the F0 generation sequencing results, missing genotypes in the F2 population were filled using BEAGLE 4.0 with default parameter settings. The quality control of the obtained SNP data was performed using Plink software.

[0010] 3) Estimation of genetic parameters: Using the software GCTA, heritability, genetic correlation, and phenotypic correlation of traits were estimated, and a genetic correlation estimation model for two traits was established: Y i =X i b i +Z i u i +e i Y i It is an individual phenotype, X i and Z i They are b i and u i The correlation matrix, b i It is a fixed effect that includes gender, u i It is a vector of polygenic effects, e i The residuals are random. Based on the bivariate genomic restricted maximum likelihood estimation results, the phenotypic correlation coefficient between the two traits is calculated using the following formula. σ uxy and σ exy Represent the genetic covariance and residual covariance between traits x and y, respectively. and These are the genetic variances of x and y, respectively. and The residual variances of x and y are respectively, using Calculate the standard error of phenotypic correlation, where n is the number of sample pairs and r is the standard error of phenotypic correlation. pxy The phenotypic correlation coefficient;

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

[0012] 5) Identification of significant SNPs: Bonferroni correction was used for the significance level P-values ​​in the GWAS analysis results, and Manhattan plots and QQ plots were drawn using CMplot in R software. SNPs above the threshold line on the Manhattan plot are SNPs that are significantly associated with abdominal fat and growth traits in F2 generation chickens.

[0013] 6) Based on the sign of the SNP marker effect value, determine whether the effect of SNP on traits is synergistic or antagonistic. Then, use the Tukey-HSD method to calculate the least squares mean of different genotypes for each SNP and analyze the differences between different genotypes to further determine the effect of pleiotropic 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 of the chicken reference genome 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 whole genome SNPs.

[0016] The second objective of this invention is to provide a genome-wide SNP that simultaneously affects both abdominal fat and body weight traits in chickens and exhibits antagonistic pleiotropic effects, 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 traits of high abdominal fat and low body weight; when the allele is C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.2, corresponding to the traits of low abdominal fat and high body weight. 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 traits of high abdominal fat and low body weight; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.4, corresponding to the traits of low abdominal fat and high body weight. 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 traits of high abdominal fat and low body weight; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.6, corresponding to the traits of low abdominal fat and high body weight. When the molecular marker rs312617399 has an allele of C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.7, corresponding to the traits of high abdominal fat and low body weight; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.8, corresponding to the traits of low abdominal fat and high body weight. When the molecular marker rs316160377 has an allele of A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.9, corresponding to the traits of high abdominal fat and low body weight; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.10, corresponding to the traits of low abdominal fat and high body weight. When the molecular marker rs312882764 has an allele of T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.11, corresponding to the traits of high abdominal fat and low body weight; when the allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.12, corresponding to the traits of low abdominal fat and high body weight. When the molecular marker rs732864571 has an allele of T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.13, corresponding to high abdominal fat and low body weight; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.14, corresponding to low abdominal fat and high body weight. When the molecular marker rs318153866 has an allele of G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.15, corresponding to high abdominal fat, low body weight, and a trait unfavorable to bone growth; when the allele is A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.16, corresponding to low abdominal fat, high body weight, and a trait favorable to bone growth. When the molecular marker rs316369694 has an allele of A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.17, corresponding to high abdominal fat and low body weight; when the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.18, corresponding to low abdominal fat and high body weight.When the molecular marker rs314695997 has an allele of C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.19, which corresponds to high abdominal fat, low body weight, and traits unfavorable to bone growth; when the allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.20, which corresponds to low abdominal fat, high body weight, and traits favorable to bone growth.

[0018] The third objective of this invention is to provide the application of the aforementioned genome-wide SNPs that simultaneously affect both abdominal fat and weight traits and exhibit antagonistic pleiotropic effects in broiler abdominal fat and weight trait-assisted breeding.

[0019] The fourth objective of this invention is to provide a genome-wide SNP that simultaneously affects chicken abdominal fat and skeletal traits and exhibits significant antagonistic pleiotropic effects, wherein the SNPs are rs318153866, rs317098725, or rs314695997.

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

[0021] The fifth objective of this invention is to provide the application of the aforementioned genome-wide SNPs that simultaneously affect both abdominal fat and skeletal traits and exhibit antagonistic pleiotropic effects in broiler abdominal fat and skeletal trait-assisted breeding.

[0022] The sixth objective of this invention is to provide a genome-wide SNP that simultaneously affects chicken abdominal fat and skeletal muscle traits and is a significant association of antagonistic pleiotropic effects, wherein the SNP 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, which corresponds to high abdominal fat and is unfavorable to skeletal muscle growth; when the allele is C, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.24, which corresponds to low abdominal fat and is favorable to skeletal muscle growth.

[0024] The seventh objective of this invention is to provide the application of the aforementioned genome-wide SNPs that simultaneously affect the abdominal fat and skeletal muscle traits of chickens and exhibit antagonistic pleiotropic effects in broiler-assisted breeding of abdominal fat and skeletal muscle traits.

[0025] The eighth objective of this invention is to provide a genome-wide SNP that simultaneously and significantly influences six growth traits in chickens: body weight, metatarsal length, metatarsal girth, keel length, pectoral muscle rate, and leg muscle rate, and that exhibits synergistic pleiotropic effects. 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 as shown in SEQ ID NO.25, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle percentage, and high leg muscle percentage; when the allele is T, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.26, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle percentage, and low leg muscle percentage. When the molecular marker rs736271942 allele is A, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.27, corresponding to small body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle percentage, and low leg muscle percentage; when the allele is G, the nucleotide sequence of the molecular marker is as shown in SEQ ID NO.28, corresponding to large body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle percentage, and high leg muscle percentage. When the molecular marker rs794230628 has an allele of C, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.29, corresponding to low body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle percentage, and low leg muscle percentage. When the allele is T, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.30, corresponding to high body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle percentage, and high leg muscle percentage. When the molecular marker rs314695997 has an allele of A, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.31, corresponding to low body weight, short metatarsals, small metatarsal circumference, short keel, low pectoral muscle percentage, and low leg muscle percentage. When the allele is G, the nucleotide sequence of the molecular marker is shown in SEQ ID NO.32, corresponding to high body weight, long metatarsals, large metatarsal circumference, long keel, high pectoral muscle percentage, and high leg muscle percentage.

[0027] The ninth objective of this invention is to provide the application of the above-mentioned genome-wide SNPs that simultaneously affect six growth traits of chicken body weight, metatarsal length, metatarsal girth, keel length, pectoral muscle rate, and leg muscle rate and are significantly associated in a synergistic and pleiotropic manner in broiler growth trait-assisted breeding.

[0028] The beneficial effects of this invention are:

[0029] This invention utilized the Illumina HiSeq PE150 platform to analyze the genomes of 519 F2 generation chickens from Northeast Agricultural University and performed association analysis with traits such as body weight, metatarsal length, metatarsal girth, keel length, abdominal fat percentage, breast muscle percentage, and leg muscle percentage. Genetic parameters of abdominal fat percentage and growth traits were estimated based on genome-wide SNP markers. Multivariate GWAS was then used to identify pleiotropic SNP markers that simultaneously affect these traits. Finally, multiple least-squares means of different genotypes of the selected pleiotropic SNPs were compared. The results showed a negative correlation between abdominal fat percentage and the other six growth traits. A total of 14 SNPs exhibited antagonistic pleiotropic effects (i.e., inhibiting abdominal fat percentage while promoting the other six traits); 4 SNPs showed synergistic pleiotropic effects across the six growth traits, with the wild-type of one SNP and the mutant genotypes of three SNPs significantly increasing yield in all six growth traits. The results of this invention provide new insights into the genetic basis of abdominal fat and growth in broilers. Furthermore, the SNP markers provided by this invention can be used for marker-assisted breeding of broilers, allowing for early selection of individuals at the genomic level without relying on phenotypic information, thus accelerating the breeding process and making it possible to obtain ideal production traits. Attached Figure Description

[0030] Figure 1 This is a graph showing the GWAS results of the F2 generation chicken population in this invention; wherein... Figure 1 In the diagram, A represents the Manhattan diagram. Figure 1 B in the figure is the QQ-Plot plot. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods, and the materials, reagents, and instruments used are all conventional materials, reagents, and instruments in the art, which can be obtained commercially by those skilled in the art.

[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 this invention, the experimental animals were the F2 generation resource population of chickens from Northeast Agricultural University. This population was formed by crossing four high-fat male broiler chickens of a two-way selection strain of abdominal fat from Northeast Agricultural University (disclosed in patent application number CN201610896131.1, invention titled "A Breeding Method for High and Low Abdominal Fat Broiler Strains") and 24 female White-eared Yellow Chickens (a Chinese egg-laying breed with low abdominal fat percentage and a large genetic distance from high-fat chickens). Specifically, the F0 generation was crossbred to produce the F1 generation, and the F1 generation was crossbred to avoid full-sib and half-sib mating to produce the F2 generation. The male-to-female mating ratio was 1:6. This study used a total of 519 F2 generation individuals (263 roosters and 256 hens) from 12 half-sib families. All chickens had free access to feed and water during the rearing process and were kept under the same environmental conditions. Blood samples were collected from the wing veins of chickens at 12 weeks of age using vacuum tubes containing the anticoagulant EDTA. These samples were then stored at -20°C for future experimental use. Before slaughter at 12 weeks of age, the body weight, metatarsal length, metatarsal girth, and keel length of F2 generation individuals were measured. After slaughter, the abdominal fat percentage, pectoral muscle percentage, and leg muscle percentage of F2 generation individuals were measured and calculated.

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

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

[0037] (3) Estimation of genetic parameters

[0038] Heritability estimation based on SNPs was performed using the genetic statistical model Y from the software GCTA (v1.93.2). i =X i b i +Z i u i +e i Proceed, Y i It is an individual phenotype, X i and Z i They are b i and u i The correlation matrix, b i It is a fixed effect that includes gender, u i It is a vector of polygenic effects, e i These are random residuals. The phenotypic correlation coefficient is calculated using the following formula based on the results of bivariate genomic restricted maximum likelihood estimation (REML). σuxy and σ exy Represent the genetic covariance and residual covariance between traits x and y, respectively. and These are the genetic variances of x and y, respectively. and These are the residual variances of x and y, respectively; using Calculate the standard error of phenotypic correlation, where n is the number of sample pairs and r is the standard error of phenotypic correlation. pxy The correlation coefficients are phenotypic. The results are shown in Table 1. Seven traits showed high heritability (0.26–0.61), indicating potential for effective genetic modification. Abdominal fat percentage showed strong negative correlations with the other six growth traits (genetic correlations ranged from -0.55 to -0.25, phenotypic correlations ranged from -0.26 to -0.51). Moderately positive genetic correlations (0.27–0.91) and phenotypic correlations (0.14–0.88) were also observed among the six growth traits. This indicates a strong association between the traits, suggesting a possible common genetic basis, i.e., pleiotropic effects. Therefore, these traits can be used in multi-trait GWAS analysis to identify pleiotropic markers.

[0039] (4) Genome-wide association analysis

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

[0041] Table 1. Heritability of seven traits and results of genetic and phenotypic correlation analysis among traits in the F2 generation of chickens.

[0042]

[0043]

[0044] Note: The values ​​in parentheses on the diagonal are the standard errors of heritability; the values ​​in parentheses above the diagonal are the standard errors of heritability-related factors; and the values ​​in parentheses below the diagonal are the standard errors of phenotypic-related factors.

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

[0046] SNP markers that simultaneously affect multiple traits were extracted. Based on the sign of their SNP marker effect value (β value), it was determined whether the influence of SNPs on traits was synergistic or antagonistic. Then, the least squares mean of different genotypes for each SNP was calculated using the Tukey-HSD method, and the differences between the effect values ​​of different genotypes were analyzed to further determine the influence of pleiotropic SNP loci on each trait. The results are shown in Tables 2, 3, 4 and 5.

[0047] Table 2. SNPs that significantly affect abdominal fat percentage and body weight in chickens and the least squares mean differences between 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 0.25 Mb, the most significant SNP is retained; * indicates that the SNP genotype has a significant effect on abdominal fat percentage and body weight (P<0.05); ** indicates that the SNP genotype has a highly significant effect on abdominal fat percentage and body weight (P<0.01).

[0051] Table 3. SNPs that significantly affect abdominal fat percentage and bone structure in chickens and the least squares mean differences between their different genotypes.

[0052]

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

[0054] Table 4. SNPs that significantly affect abdominal fat percentage and skeletal muscle mass in chickens and the least squares mean differences between their different genotypes.

[0055]

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

[0057] Table 5 shows the SNPs that significantly affected six growth traits in chickens and the least squares mean differences between their different genotypes.

[0058]

[0059] Note: β is the effect value of alleles; when the distance between multiple SNPs of the same trait is less than 0.25 Mb, 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 highly significant effect on the six growth traits (P<0.01).

[0060] Tables 2, 3, and 4 show a negative correlation between abdominal fat and the other six growth traits, with 14 SNPs exhibiting antagonistic pleiotropic effects (i.e., inhibiting abdominal fat and promoting other growth traits). Compared to other genotypes, the mutant genotypes CC (rs15494840), GG (rs735688344), GG (rs317815582), GG (rs312617399), GG (rs316160377), AA (rs312882764), GG (rs732864571), AA (rs318153866), and AA (rs31636969) showed significant differences. The mutant genotypes GG and TT of rs314695997 are favorable genotypes and can be used to breed high-weight, low-abdominal-fat broilers; the mutant genotypes AA of rs318153866, AA of rs317098725, and TT of rs314695997 are favorable genotypes and can promote skeletal growth and development and reduce abdominal fat in broilers; the mutant genotype CC of rs14919121 is a favorable genotype and can be used to breed broilers with high skeletal muscle production and low abdominal fat percentage.

[0061] Table 5 shows that the four SNPs exhibit synergistic pleiotropic effects (i.e., simultaneously promoting or inhibiting all six growth traits) across six growth traits. The wild-type GG of rs315982032, the mutant GG of rs736271942, the mutant TT of rs794230628, and the mutant GG of rs316443018 are favorable genotypes that can significantly improve all six growth traits. We can select these genotypes to improve the growth traits of broilers.

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

[0063] Table 6 Information on pleiotropic SNP molecular markers

[0064]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0097] When the molecular marker rs317098725 has an allele of G, the nucleotide sequence of the molecular marker is shown above and is denoted as SEQ ID NO.21, which corresponds to high abdominal fat and is unfavorable to bone growth. When the allele is A, the nucleotide sequence of the molecular marker is shown above and is denoted as SEQ ID NO.22, which corresponds to low abdominal fat and is favorable to bone growth.

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

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

[0100] When the molecular marker rs14919121 has an allele of T, the nucleotide sequence of the molecular marker is shown above and is denoted as SEQ ID NO.23, which corresponds to high abdominal fat and is unfavorable to skeletal muscle growth. When the allele is C, the nucleotide sequence of the molecular marker is shown above and is denoted as SEQ ID NO.24, which corresponds to low abdominal fat and is favorable to skeletal muscle growth.

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

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

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

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

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

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

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

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

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

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

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

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

[0113] In broiler breeding, primers can be designed on the nucleotide sequences of the above-mentioned SNP molecular markers (Table 6) on both sides of the wing. Blood is collected from chickens at 3-4 weeks of age and genomic DNA is extracted. The primers are used to genotype the broiler materials to be tested. 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 above with reference to preferred embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be defined by the claims.

Claims

1. The application of a reagent for detecting SNPs that simultaneously affect abdominal fat and skeletal muscle traits in broiler assisted breeding, characterized in that, The SNP is rs14919121.

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