Method for detecting chicken back hair follicle density trait and PCR detection primer pair and detection kit

Abstract

The application discloses a chicken back hair follicle density character detection method and a PCR detection primer pair and detection kit, relates to the field of poultry genetic breeding and biotechnology, and comprises the following steps: (1) detecting the genotype of a to-be-detected chicken SNP-FAT1; the SNP-FAT1 is located at the 61238343th site of a chicken chromosome 4, the base of the site is A or C, and the corresponding genotype comprises three types of CC, CA and AA; (2) judging the back hair follicle character of the to-be-detected chicken according to the detected genotype. The application finds the correlation between a specific gene SNP-FAT1 of a chicken and the back hair follicle density character of the chicken, and according to the detected genotype, the back hair follicle density character of the chicken can be selected in vivo, the accuracy of selection can be obviously improved compared with traditional sibling selection, the hair follicle density of a breed can be stably improved by continuously applying the method, and the breeding benefit of the breed is increased.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of poultry genetics and biotechnology, and particularly relates to a detection method for chicken back hair follicle density traits, a PCR detection primer pair and a detection kit. BACKGROUND

[0002] The improvement of broiler carcass appearance is also increasingly concerned by breeding practitioners. Hair follicle density can affect the appearance of yellow broiler carcass, and thus affect the purchasing desire of consumers. Therefore, in-depth study of the regulation mechanism of chicken hair follicle density traits, excavation of key genes and molecular markers affecting hair follicle density traits, and application thereof to breeding practice, have important significance for accelerating the genetic improvement of broiler carcass appearance traits. The determination of chicken hair follicle density traits is currently mainly through counting after slaughtering and plucking, which can only be indirectly selected through sibling relationship, and the relatively high determination cost increases the breeding difficulty of the trait.

[0003] With the development of modern breeding technology, Single Nucleotide Polymorphism (SNP) and other genetic markers have been widely used in gene mapping, cloning, breeding and genetic diversity research fields. Therefore, it is of great significance to identify molecular markers related to chicken hair follicle density traits.

[0004] FAT1 gene (Dystrophin) encodes FAT atypical calcium adhesion protein 1, which is a member of the atypical calcium adhesion protein superfamily, and is widely expressed in epithelial tissues, including skin and glomerular epithelial cells. It is involved in the regulation of cell morphology and the formation and maintenance of various tissues and organs, such as the polarization of epithelial cells and cochlear hair cells. FAT1 is involved in the planar cell polarity (PCP) signaling pathway, and members of this pathway, such as Fuz, Rac1 and Frizzled6, have been shown to play an important role in mammalian hair follicle morphogenesis, hair follicle cycle and hair follicle orientation. Studies in mice have found that knockout of the Fuz gene can significantly reduce the number of back hair follicles and delay hair follicle morphogenesis; knockout of the Rac1 gene can cause hair loss in mice; knockout of Frizzled6 can change the direction of back hair follicles; and studies have found that FAT1 gene expression and site polymorphism are significantly related to traits such as wool length and diameter. The above evidence suggests that FAT1 gene is closely related to mammalian hair follicle traits, but it is not yet known whether FAT1 molecular markers can be applied to the genetic improvement of chicken carcass appearance traits.

[0005] Therefore, although it is feasible to identify molecular markers related to chicken hair follicle density traits, further research is needed to identify chicken hair follicle density traits through molecular markers. SUMMARY

[0006] The application discloses a detection method for chicken back hair follicle density traits and a PCR detection primer pair and a detection kit, so as to perfect a molecular selection method for chicken hair follicle density traits, improve selection accuracy of chicken hair follicle density traits and accelerate progress of yellow-feather broiler new variety selection.

[0007] The technical scheme adopted by the application is as follows:

[0008] Firstly, the application provides a detection method for chicken back hair follicle traits, comprising the following steps:

[0009] (1) detecting a genotype of SNP-FAT1 of a to-be-tested chicken, wherein the SNP-FAT1 is located at a 61238343th site of a chicken chromosome 4, the base of the site is A or C, and corresponding genotypes include CC, CA and AA;

[0010] (2) judging a back hair follicle trait of the to-be-tested chicken according to the detected genotype, and density relationships of the three genotypes and the chicken hair follicle density trait are as follows:

[0011]

[0012] Further, the detection method of the genotype of the SNP-FAT1 is a PCR detection method, wherein the PCR detection primer pair is composed of Primer-FAT1-F and Primer-FAT1-R, and nucleotide sequences of the Primer-FAT1-F and the Primer-FAT1-R are respectively shown in SEQ ID No: 1 and SEQ ID No: 2.

[0013] The Primer-FAT1-F (SEQ ID No: 1) is CTCCTCCATCTGTGGCTTTC.

[0014] The Primer-FAT1-R (SEQ ID No: 2) is ATGTCACCCATGTGCTGTGT.

[0015] Further, the detection process of the genotype of the SNP-FAT1 comprises the following steps:

[0016] (1) using the PCR detection primer pair to perform PCR amplification on genomic DNA of the to-be-tested chicken;

[0017] (2) performing sequencing on the obtained PCR amplification product, so that the genotype of the 61238343th site of the chromosome 4 of the to-be-tested chicken is obtained.

[0018] Secondly, the application limits application of the detection method for the chicken back hair follicle traits in broiler genetic breeding.

[0019] Further, the broiler chicken genetic breeding is selected in the direction of the chicken back hair follicle density character selection.

[0020] Further, the present application designs a PCR detection primer pair for the detection of the related gene of the chicken back hair follicle density character, and the PCR detection primer pair is composed of Primer-FAT1-F and Primer-FAT1-R, and the nucleotide sequences of the Primer-FAT1-F and the Primer-FAT1-R are shown in SEQ ID No: 1 and SEQ ID No: 2 respectively.

[0021] Further, the present application designs a PCR detection primer pair for the detection of the related gene of the chicken back hair follicle density character, and the PCR detection primer pair is composed of Primer-FAT1-F and Primer-FAT1-R, and the nucleotide sequences of the Primer-FAT1-F and the Primer-FAT1-R are shown in SEQ ID No: 1 and SEQ ID No: 2 respectively.

[0022] The present application further discloses a PCR detection kit for detecting the chicken back hair follicle density character, and the kit comprises the PCR detection primer pair Primer-FAT1-F and Primer-FAT1-R.

[0023] Further, the present application designs a PCR detection primer pair for the detection of the related gene of the chicken back hair follicle density character, and the PCR detection primer pair is composed of Primer-FAT1-F and Primer-FAT1-R, and the nucleotide sequences of the Primer-FAT1-F and the Primer-FAT1-R are shown in SEQ ID No: 1 and SEQ ID No: 2 respectively.

[0024] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0025] (1) The present application finds the connection between the specific gene SNP-FAT1 of the chicken and the chicken back hair follicle density character, and according to the detected genotype, the chicken back hair follicle density character can be selected in vivo, and compared with the traditional sibling selection, the accuracy of the selection can be obviously improved, and the hair follicle density of the breed can be stably improved by continuously applying the method, and the breeding benefit of the breed is increased.

[0026] (2) The detection scheme provided by the present application can realize early selection of the chicken hair follicle density character, reduce the number of groups that need to be bred, reduce the breeding cost investment, reduce the generation interval of breeding, and speed up the breeding progress. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 It is a Manhattan plot for whole genome association analysis of the back hair follicle density character of the Liuhua yellow-footed chicken population in Example 1 of the present application.

[0028] Figure 2 It is a QQ plot for whole genome association analysis of the back hair follicle density character of the Liuhua yellow-footed chicken population in Example 1 of the present application. DETAILED DESCRIPTION

[0029] The present invention will be described in detail below with reference to specific embodiments and examples, thereby making the advantages and various effects of the present invention more clearly apparent. Those skilled in the art should understand that these specific embodiments and examples are for illustrative purposes only and are not intended to limit the present invention.

[0030] Throughout this specification, unless otherwise specified, the terminology used herein should be understood as having the meaning commonly used in the art. Therefore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the event of any conflict, this specification shall prevail.

[0031] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.

[0032] Example 1

[0033] This embodiment uses genome-wide association analysis to identify SNP loci associated with the chicken feather follicle density trait. The specific steps are as follows:

[0034] 1. Determination of phenotypic traits of experimental animal groups and hair follicle density

[0035] The experimental subjects selected in this study were the terminal paternal line of Lihua Yellow-legged Ma chickens. The main selected traits of this strain were body weight, feed conversion ratio, and dressing percentage. It has undergone nine generations of closed-loop breeding. The experimental flocks were raised at Lihua Company's Jintan breeding base. All experimental chickens were hatched in the same batch and raised in the same chicken house. The feeding methods and immunization programs followed the standard protocols established by Lihua Company.

[0036] 1.5 ml of blood was collected from the right wing vein of all experimental animals. After blood collection, the blood was transferred into a blood collection tube containing EDTA and gently shaken up and down to mix the EDTA with the blood. The blood was then stored at -20°C for later use.

[0037] After slaughter at 60 days of age, the back skin was uniformly counted at 2 x 2 cm. 2 The number of hair follicles in the body was measured in 400 chickens. All hair follicle density phenotypic values ​​were then washed to remove outliers and duplicates, resulting in 378 hair follicle density phenotypic data points for genome-wide association analysis.

[0038] 2. Genome-wide association analysis

[0039] A total of 378 individuals with effective hair follicle density trait phenotype data were selected, genomic DNA was extracted by phenol-chloroform method, and the quality of DNA extraction was evaluated using ND-1000 spectrophotometer. DNA library construction and whole genome sequencing were performed according to the standard BGISEQ procedure. Qualified DNA samples were prepared for genomic library, and whole genome resequencing was performed using DNBSEQ T7 platform with a sequencing depth of 10x.

[0040] After obtaining the original sequencing data, the FASTP software was used for filtering to remove low-quality reads, and the quality control of the obtained Clean Reads was performed, and then the reads obtained after quality control were aligned to the reference genome GRcg7b using BWA software. The Picard software was used to remove the repeatedly aligned reads. High-quality SNP sites were obtained by GATK4 software analysis, and finally 10,266,594 SNPs were obtained for subsequent analysis.

[0041] Before carrying out whole genome association analysis, principal component analysis (PCA) of the population was carried out by PLINK software to eliminate false positives caused by population structure, and the first three principal components were added as covariate parameters to the genetic statistical model. The mixed linear model of GEMMA program was used for whole genome association analysis of hair follicle density traits, and the statistical model used was as follows:

[0042] y = Wα + xβ + u + ∈

[0043] Where y represents the back hair follicle density trait phenotype value of 378 individuals, W is the fixed effect matrix, which contains the first three principal components from PCA, α is the fixed effect vector, x represents the genotype vector, β is the SNP effect vector, u is the random effect vector, and ∈ is the residual.

[0044] Through linkage disequilibrium analysis, a total of 432,529 independent SNP markers were obtained. Using Bonferroni correction, the genome-wide significance threshold of this test was set to 1.16e-07, and the genome potential level significance threshold was set to 2.31e-06.

[0045] The results of whole genome association analysis are shown in Figure 1 , Figure 2 and Table 1. Figure 1 It can be seen from Table 1 that there is one genome-wide significantly associated site with back hair follicle density trait on chromosome 4, which is located at position 61238343 of chromosome 4, and gene annotation finds that the site is located at the 17th intron of FAT1 gene, the base is A or C, and the registration number in dbSNP database is rs15598338. The QQ plot Figure 2 ) confirms that the test results are reliable.

[0046] Table 1 Molecular markers of genome significant level for back feather follicle density trait in chicken

[0047] Marker name Genomic position Gene in which located SNP position Base mutation P value SNP-FAT1 4:61238343 FAT1 Intron A / C 1.38E-06

[0048] Example 2

[0049] This example detects and verifies the gene locus screened in Example 1, and applies the locus SNP-FAT1 to perform association analysis of the back feather follicle density trait in the breeding population of Lianghuahuangjia chicken. The specific steps are as follows:

[0050] 1. PCR primer design and synthesis: According to the DNA sequence information of FAT1 gene collected by NCBI website, PCR amplification primers were designed by primer3 online tool. The primer sequence information is shown in Table 2. The PCR primer synthesis was completed by Shanghai Sunbio Engineering Co., Ltd.

[0051] Table 2 Primer sequence

[0052]

[0053] 2. Genomic DNA extraction: Genomic DNA was extracted by phenol-chloroform method, and the quality of DNA extraction was evaluated by ND-1000 spectrophotometer.

[0054] 3. PCR amplification and sequencing: 10 μL reaction system includes 50 ng of DNA to be amplified, 10 ng of forward and reverse primers, 5 μL of 2×power Taq MasterMix, and the remaining volume is supplemented with deionized water. The reaction program includes 5 cycles of 94℃ denaturation for 30 s, annealing temperature of 54℃, annealing for 30 s, 72℃ extension for 30 s; followed by 30 cycles of 94℃ denaturation for 30 s, annealing temperature of 54℃, annealing for 30 s, 72℃ extension for 30 s; 72℃ extension for 5 min, 4℃ storage. The amplification product was sent to Shanghai Sunbio Engineering Co., Ltd. for sequence polymorphism detection.

[0055] 4. Back feather follicle density trait determination: The back skin 2×2 cm area of each individual was counted for the number of feather follicles after 60-day-old slaughter and plucking. 2

[0056] 5. Association analysis: The GLM process of SAS statistical analysis software package was used for statistical analysis. According to the general linear model, the genotypes of the test chicken population and the 60-day-old back feather follicle density trait were subjected to variance statistical analysis. P-value<0.05 indicates significant difference.

[0057] Statistical analysis model:

[0058] y = μ + G + e​

[0059] where y represents the individual phenotypic value; μ represents the population mean; G represents the genotypic effect; and e represents the residual error effect.

[0060] The results are shown in Table 3.

[0061] Table 3 Back skin follicle density of different genotypes at 60 days of age

[0062]

[0063] As shown in Table 3, the back skin follicle density of the three genotypes is significantly different. The back skin follicle density of the AA genotype is higher than that of the CA and CC genotypes, and the back skin follicle density of the CA genotype is significantly higher than that of the CC genotype. Therefore, AA is the dominant genotype of the genetic marker SNP-FAT1, and can be used as a molecular marker for the selection of the back skin follicle density and carcass appearance traits.

[0064] Finally, it should be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or inherent to such processes, methods, articles or devices. Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to the embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0065] The above-described embodiments only express the specific implementation of the present application, which is described in a more specific and detailed manner, but should not be understood as limiting the scope of protection of the present application. It should be noted that for those skilled in the art, without departing from the concept of the technical scheme of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application.

Claims

1. A method for detecting back comb characteristics in yellow-feet chickens, characterized in that, It comprises the following steps: (1) detecting the genotype of the SNP-FAT1 of the chicken to be tested, the SNP-FAT1 being located at the 61238343th site on the 4th chromosome of the chicken, the base of the site being A or C, the corresponding genotypes including CC, CA and AA, the reference genome of the chicken being GRcg7b; the detection method of the genotype of the SNP-FAT1 being PCR detection, wherein the PCR detection primer pair consists of Primer-FAT1-F and Primer-FAT1-R, the nucleotide sequences of the Primer-FAT1-F and Primer-FAT1-R being shown in SEQ ID No: 1 and SEQ ID No: 2 respectively; (2) judging the back follicle trait of the chicken to be tested according to the detection result of the genotype, the density relationship between the three genotypes and the chicken follicle density trait being as follows:

2. The method of detecting back feather follicle traits in yellow-shanked chickens of claim 1, wherein, The detection process of the genotype of the SNP-FAT1 comprises the following steps: (1) using the PCR detection primer pair to perform PCR amplification on the genomic DNA of the chicken to be tested; (2) performing sequencing on the obtained PCR amplification product, so that the genotype of the 61238343th site on the 4th chromosome of the chicken to be tested can be obtained.

3. Use of the detection method according to claim 1 or 2 in the genetic breeding of yellow-feet chicken, characterized in that, The genetic breeding selection direction of the yellow-footed chicken is the selection of the back follicle density trait of the chicken.

4. Use of the primer pair SEQ ID No: 1 and SEQ ID No: 2 for PCR detection in genetic breeding of yellow-feet chicken, characterized in that, The PCR detection primer pair SEQ ID No: 1 and SEQ ID No: 2 are used for detecting the genotype of the SNP-FAT1 of the chicken to be tested as claimed in claim 1, the genetic breeding selection direction of the yellow-footed chicken is the selection of the back follicle density trait of the chicken, and the density relationship between the genotype detected by PCR and the chicken follicle density trait is as follows:

5. Use of a PCR detection kit containing the primer pair SEQ ID No: 1 and SEQ ID No: 2 in the genetic breeding of yellow-feet chicken, characterized in that, The PCR detection primer pair SEQ ID No: 1 and SEQ ID No: 2 are used for detecting the genotype of the SNP-FAT1 of the chicken to be tested as claimed in claim 1, the genetic breeding selection direction of the yellow-footed chicken is the selection of the back follicle density trait of the chicken, and the density relationship between the genotype detected by PCR and the chicken follicle density trait is as follows:

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