A molecular marker related to the chicken hair follicle density trait and its application

Abstract

The present invention discloses a molecular marker related to the chicken hair follicle density trait and its application, belonging to the technical field of poultry breeding. The present invention selects 505 Mahuang chickens as experimental subjects for genome-wide association analysis, collects the back hair follicle density and leg hair follicle density traits, collects blood from under the wings at 77 days of age, extracts genomic DNA, and after detecting the integrity, purity and concentration of the extracted genomic DNA, performs resequencing on qualified samples, and then conducts genome-wide association analysis on the chicken hair follicle density trait, and detects 105 SNPs significantly associated with the back hair follicle density and 175 SNPs significantly associated with the leg hair follicle density. And verification analysis is carried out on some of the SNP loci and the back hair follicle density and the leg hair follicle density, providing a theoretical support for breeding chicken breeds related to the chicken hair follicle trait.

Description

Technical Field

[0001] The invention relates to the technical field of poultry breeding, and in particular to a molecular marker related to chicken hair follicle density traits and an application thereof. Background Art

[0002] Poultry development is an indispensable part of animal husbandry development, and its development level is of great significance to the development of animal husbandry and agriculture in my country. The hair follicle density trait of chickens is an important indicator reflecting the number of skin hair follicles and an important indicator for measuring the aesthetics of chicken carcasses, mainly including traits such as back hair follicle density and leg hair follicle density. In the study of hair follicle traits in poultry in my country, although some key genes and signal pathways related to hair follicle growth and development have been found, their genetic mechanisms are still not clear enough. In particular, the relationship between polygenic inheritance and phenotype is complex, and there is a lack of detailed genetic maps and genome-wide association analysis (GWAS) research results. In order to get rid of the current predicament, it is necessary to genetically improve and select existing varieties. The genetic improvement of poultry varieties is inseparable from the discovery of relevant key genes. With the development of science and technology, the application of genome-related technologies in the study of quantitative traits of animals and plants has become more and more extensive and effective, and further through means such as genome selection, their production performance has been significantly improved. As an effective method, genome-wide association study (GWAS) is increasingly used in animal and plant breeding and even human disease research. It can combine resequencing methods with statistical principles to find candidate regions and genes associated with target traits in the whole genome. At present, there are few reports on genes related to chicken hair follicle density traits at home and abroad, and there is still a large research gap in the study of chicken hair follicle density-related traits using large-scale sequencing methods.

[0003] The whole genome association analysis method can provide breeders with a deep understanding of the genetic mechanism and key genes of the chicken hair follicle density trait, provide a scientific basis for chicken breed improvement, and accelerate the breeding process. Therefore, the application of whole genome association analysis in chicken genetic breeding has an urgent need and broad prospects, and the relevant whole genome association analysis methods are also urgently needed to be developed. Summary of the invention

[0004] The purpose of the present invention is to provide a molecular marker related to the trait of chicken hair follicle density and its application, so as to solve the problems existing in the above-mentioned prior art, and screen out candidate genes and SNP sites significantly correlated with the chicken back hair follicle density and leg hair follicle density through whole genome association analysis of chicken genomic DNA, so as to provide theoretical support for the future selection and breeding of high-quality chicken varieties.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The present invention provides a molecular marker related to the chicken hair follicle density trait. The molecular marker includes molecular marker I and molecular marker II. The SNP locus of molecular marker I is located at the 18,248,647th base of chromosome 6 of the chicken genome, and there is a G / T mutation at this locus; the SNP locus of molecular marker II is located at the 7,479,108th base of chromosome Z of the chicken genome, and there is a G / A mutation at this locus; the accession number of the chicken genome in the Ensemble database is GRCg6a_release95.

[0007] Preferably, the genotypes of molecular marker I are GG, GT, and TT, and the genotypes of molecular marker II are GG, GA, and AA.

[0008] Preferably, the chicken hair follicle density trait includes the back hair follicle density and leg hair follicle density of the chicken.

[0009] Preferably, molecular marker I is related to the back hair follicle density of the chicken, and molecular marker II is related to the leg hair follicle density of the chicken.

[0010] Preferably, the chicken includes Jiangfeng Mahuang chicken.

[0011] The present invention also provides the application of the described molecular marker in any one of the following:

[0012] (1) Application in identifying the chicken hair follicle density trait;

[0013] (2) Application in improving the chicken hair follicle density trait;

[0014] (3) Application in screening new chicken breeds related to the chicken hair follicle density trait.

[0015] The present invention also provides a breeding method related to the chicken hair follicle density trait, including the following steps:

[0016] Extract the genomic DNA of the chicken to be tested, perform whole-genome sequencing or obtain the 100 kb sequences upstream and downstream of the SNP locus of the molecular marker, judge the hair follicle density trait of the chicken to be tested according to the genotype of the SNP locus, and select and breed chicken breeds with excellent hair follicle density according to the judgment result.

[0017] Preferably, the judgment method is: use the genotype of the SNP locus of molecular marker I to judge the back hair follicle density of the chicken, and the optimal genotype for the back hair follicle density is TT; use the genotype of the SNP locus of molecular marker II to judge the leg hair follicle density of the chicken, and the optimal genotype for the leg hair follicle density is AA.

[0018] Preferably, in the breeding of the chicken hair follicle density trait, retain the chicken breeds with the TT genotype for the back hair follicle density and / or the AA genotype for the leg hair follicle density.

[0019] The present invention discloses the following technical effects:

[0020] The present invention selects to conduct a genome-wide association study on the follicular phenotypes of Jiangfeng yellow chickens. By selecting these traits, single nucleotide polymorphisms significantly associated with follicular traits are identified. As a result, 105 SNPs significantly associated with the dorsal follicle density and 175 SNPs significantly associated with the leg follicle density are detected.

[0021] Taking the loci of Chr6:18248647 and ChrZ:7479108 as examples, the present invention analyzes their associations with the chicken follicle density traits. The results show that the locus of Chr6:18248647 is significantly correlated with the dorsal follicle density of chickens, and the dorsal follicle density of the TT genotype population is higher than that of the GG and GT populations. The phenotypic difference between TT and other genotypes is significant (P<0.05), while the phenotypic difference between GG and GT genotypes is not significant (P>0.05); the locus of ChrZ:7479108 is significantly correlated with the leg follicle density of chickens, and the leg follicle density of the AA genotype is significantly greater than that of other genotypes (P<0.05), while the phenotypic difference between GG and GA genotypes is not significant (P>0.05).

[0022] The present invention excavates the key genes and signaling pathways affecting the follicular phenotypic traits of poultry, reveals the underlying genetic mechanisms, fills the gap in the research of genome-wide association analysis of the follicular traits of yellow chickens, and provides a theoretical support for the breeding of new high-quality chicken varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is the 1% agarose gel electrophoresis map of genomic DNA stock solution (partially shown); among them, 456-472 represent the sample numbers, and M represents the standard DNA molecule;

[0025] Figure 2 It is the Manhattan plot of the results of the genome-wide association study on the dorsal follicle density;

[0026] Figure 3 It is the Manhattan plot of the results of the genome-wide association study on the leg follicle density;

[0027] Figure 4 It is the Q-Q plot result of the dorsal follicle density;

[0028] Figure 5 Q-Q plot results for leg hair follicle density. Detailed implementation manners

[0029] The various exemplary implementation manners of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and implementation schemes of the present invention.

[0030] It should be understood that the terms described in the present invention are only for describing specific implementation manners and are not used to limit the present invention. Additionally, for the numerical ranges in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any intermediate value within any stated value or stated range, as well as each smaller range between any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0031] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention pertains. Although the present invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In case of conflict with any incorporated document, the content of this specification shall prevail.

[0032] Without departing from the scope or spirit of the present invention, various improvements and changes can be made to the specific implementation manners of the present invention specification, which are obvious to those skilled in the art. Other implementation manners obtained from the present invention specification are also obvious to those skilled in the art. The present invention specification and examples are only exemplary.

[0033] Regarding the use of "comprising", "including", "having", "containing", etc. in this article, they are all open-ended terms, meaning including but not limited to.

[0034] Example 1

[0035] 1. Experimental animals

[0036] The experimental animals used were the 17th generation G line of the pure line of Huangma chickens from Jiangfeng Industrial Co., Ltd. in Guangdong Province. The experimental chickens were raised in cages, with a feeding age of 77 days, and were managed according to the feeding process of the factory. The chicken house maintained good ventilation, and the environmental temperature, humidity, and lighting were appropriate. Routine immunization and epidemic prevention were carried out.

[0037] 2. Sample collection and processing

[0038] When the feeding time reached 77 days, 505 Jiangfeng yellow chickens (including 170 roosters and 335 hens) were randomly selected from the group raised in cages and fed indoors. Blood samples were collected from under the wings. Using a syringe, 1 mL of blood was drawn and injected into a blood collection tube containing sodium heparin. After mixing to prevent blood coagulation, the individual wing numbers were marked on the blood collection tube, and then it was stored in a -80°C refrigerator.

[0039] 3. Experimental methods

[0040] 3.1 Determination of phenotypic data

[0041] Twelve hours before slaughter, the experimental chickens were only provided with water. At the time of slaughter, after the yellow chickens were bled through the carotid artery, they were immersed in water at about 60°C for 3 minutes to remove hair. After drying the surface moisture of the carcass with kitchen paper, the external appearance traits of the carcass were measured. For both the back hair follicle density and the leg hair follicle density, a 2×2 cm 2 wire mesh was used to frame the area, and then the number of hair follicles within this area was recorded.

[0042] 3.2 Extraction and detection of genomic DNA

[0043] The cetyltrimethylammonium bromide method (CTAB) was used to extract genomic DNA. Genomic DNA was extracted according to the conventional extraction steps. The extracted genomic DNA needed to be detected for integrity, purity, and concentration. Those that met the requirements were retained, and those that did not meet the requirements were eliminated or re-extracted and re-detected.

[0044] 3.3 Quality control of sequencing data and alignment statistics with the reference genome

[0045] The experiment was carried out according to the standard protocol provided by the sequencing company. For the qualified genomic DNA samples detected, appropriate-sized fragments were selected by gel electrophoresis, and then a library was constructed by PCR amplification. Then, the constructed library was subjected to quality detection. The qualified library was sequenced using the DNBSEQ-T7 sequencer, and this sequencing was carried out by Biomarker Technologies Corporation. To ensure the quality of information analysis, during the sequencing on the DNBSEQ-T7 sequencing system, base sequencing quality distribution analysis, base type distribution inspection, insert fragment distribution statistics, depth distribution statistics, and filtering of the raw image data (RawReads) files obtained by high-throughput sequencing were carried out.

[0046] The finally obtained sequences from sequencing are re-aligned to the reference genome, and then subsequent analyses are performed. The ratio of Clean-Reads that can be aligned to the reference genome to the total Clean-Reads is called the alignment efficiency, i.e., Mapped (%). The reference gene species is Gallus gallus, the reference genome is: GRCg6a_release95, and the genome source is the Ensemble database.

[0047] 4. Data processing and statistical analysis

[0048] 4.1 Descriptive statistical analysis of follicle-related traits

[0049] The collected follicle density data are preliminarily sorted out by Excel, and the data of each trait are removed outliers according to the μ±3σ principle; the data are subjected to descriptive statistical analysis by R software; the results of the analysis are the sample number, mean, standard deviation, and coefficient of variation.

[0050] 4.2 Genome-wide association study

[0051] The samples are sent to Beijing Biomarker Technologies Co., Ltd. for genome-wide association analysis. In order to screen for genes or molecular markers related to follicle traits within the whole genome, 505 samples with qualified quality inspection in the present invention are subjected to low-coverage sequencing (6X).

[0052] Using the mixed linear model (LMM) in the GEMMA software, combined with phenotypic and genotypic data, genome-wide association analysis is performed to identify SNPs related to chicken follicle density traits. Considering the fixed factor (SNP effect) and random effect (kinship among individuals), the statistical model is as follows:

[0053] Based on the developed high-density molecular marker data, the present invention uses GEMMA for association analysis. The LMM model formula of the GEMMA software is as follows:

[0054] y = Wα + xβ + μ + e (1)

[0055] Among them, y is the phenotypic vector; W is the indicator matrix of the fixed effect, α is the coefficient vector of the fixed effect; x is the genotypic vector, β is the SNP effect; μ is the random effect vector; e is the residual.

[0056] 4.3 Population stratification

[0057] Population stratification refers to the difference in allele frequencies caused by different ancestors, which has been proven to be a confounding factor and may lead to many false positive results. Therefore, when conducting an association analysis on the follicle density of Jiangfeng Ma chickens, a Q-Q plot (Quantile-Quantile Plot) was drawn for the follicle density of Jiangfeng Ma chickens to determine whether there are biases and population stratification phenomena in the association analysis.

[0058] 4.4 Gene annotation of significant SNPs

[0059] After obtaining the significant SNPs loci of the genome-wide association analysis, based on the reference genome, the genes within 100 kb upstream and downstream of the said loci were retrieved for gene annotation.

[0060] 5. Results and analysis

[0061] 5.1 Genomic DNA detection results

[0062] The genomic DNA extracted from 505 samples all needed to be quality-tested through agarose gel electrophoresis diagrams. The detection results of some genomic DNA are as Figure 1 shown. It is required that the electrophoresis loading wells are clean and free of contamination, the main band is clear, and there is no trailing. In addition, it is required that the purity detection of DNA satisfies 1.6 < OD 260 / OD 280 < 2.0, 1.8 < OD 260 / OD 230 < 2.1. The detection results of genomic DNA need to meet the above requirements simultaneously before constructing libraries, etc. Unqualified samples need to be eliminated or DNA extraction needs to be carried out again. After detection, all 505 samples of the present invention meet the qualified requirements.

[0063] 5.2 Sequencing data quality control and alignment results with the reference genome

[0064] The sequencing data quality control results are shown in Table 1. The detection of base type distribution is mainly used to check for AT and CG separation phenomena, which may come from sequencing or library construction. If there is an obvious separation phenomenon, it will affect subsequent analysis. The average percentage of G and C bases in the total bases of the samples (GC(%)) is 41.36, the average percentage of bases with a quality value greater than or equal to 20 in the total number of bases (Q20(%)) is 98.03%, and the average percentage of bases with a quality value greater than or equal to 30 in the total number of bases (Q30(%)) is 94.55%. The alignment efficiency of the sample genomic DNA with the reference genomic DNA is above 97.04%, with an average of 99.19%, indicating that the library construction and sequencing of this sample are normal.

[0065] Table 1 Evaluation statistics of sample sequencing data

[0066]

[0067] Note: Clean-Reads: The number of filtered reads; Clean-Base: The number of filtered bases, which is the number of Clean-Reads multiplied by the sequence length.

[0068] 5.3 Statistical Results of SNP Detection between Samples and the Reference Genome

[0069] There are mainly two types of SNP mutations, namely transition (Transition / Ti, variation of the same type of base) and transversion (Transversion / Tv, variation between different types of bases). Generally, the probability of transition is higher than that of transversion, that is, Ti / Tv is greater than 1. As can be seen from Table 2, the total number of SNPs detected in this experiment is 1,996,687,423, the Ti / Tv value is approximately 2.46, and the heterozygote ratio (Het-ratio) is 48.27%.

[0070] Table 2 Statistical Results of SNP Detection between Samples and the Reference Genome

[0071]

[0072] Note: Heterozygosity Number: The number of heterozygotes, Homozygosity Number: The number of homozygotes, Het-ratio: The proportion of heterozygotes.

[0073] 5.4 Results of Descriptive Statistical Analysis of Hair Follicle Density Traits

[0074] The results of descriptive statistical analysis of hair follicle density traits are shown in Table 3. From the results of descriptive statistical analysis, it can be seen that the total number of chickens measured is 505 (170 roosters and 335 hens). The leg hair follicle density between male and female Ma Huang chickens reached an extremely significant level (P<0.01); regardless of gender, the back hair follicle density is greater than the leg hair follicle density; the hair follicle density of the same part is similar between different genders; the coefficient of variation of the back and leg hair follicle density of male and female chickens is greater than 10%; and the leg hair follicle density between male and female Ma Huang chickens reached an extremely significant level (P<0.01).

[0075] Table 3 Descriptive Statistical Analysis of Hair Follicle Density Traits

[0076]

[0077]

[0078] Note: Different superscript letters indicate extremely significant differences (P<0.01), the same letter or no letter indicates no significant difference (P>0.05).

[0079] 5.5 Results of Genome-Wide Association Analysis

[0080] In this experiment, three software, namely Fastlmm, Emmax, and Gemma, were used for association analysis, and a genome-wide association analysis of the follicle density of 505 (after extracting genomic DNA, 505 Shendan chickens that met the quality requirements) Shendan chickens was carried out. SNPs significantly associated with the back follicle density and leg follicle density were identified across the genome. The SNP loci of the SNP molecular markers were the international chicken reference genome GRCg6a_release95, and the genomic source was Ensemble.

[0081] 5.5.1 Results of genome-wide association analysis of back follicle density

[0082] Partial results of the genome-wide association analysis of back follicle density are shown in Table 4, which presents the information of the top 10 SNPs significantly associated with the back follicle density screened under the condition of -log10(p)>5. The corresponding Manhattan plot is as Figure 2 shown. There were 105 SNPs significantly associated with the back follicle density, which were located on chromosomes Chr 1, Chr 3, Chr 4, Chr 5, Chr 6, Chr 13, and the sex chromosome Z respectively.

[0083] Table 4 Results of genome-wide association analysis of back follicle density (top 10 SNPs)

[0084] Serial number SNP Chr Allele MAF 1 T56857166C Z T / C 0.41 2 G1847078C 13 G / C 0.05 3 C34389948G 5 C / G 0.32 4 G18248647T 6 G / T 0.15 5 C18302186T 6 C / T 0.13 6 T104031833C 3 T / C 0.40 7 C136240114T 1 C / T 0.07 8 C1061509T 4 C / T 0.06 9 A56857180C Z A / C 0.40 10 A20415284T 6 A / T 0.31

[0085] Note: Chr: Chromosome; Allele: Allele; MAF: Minor allele frequency.

[0086] 5.5.2 Results of genome-wide association analysis of leg follicle density

[0087] Partial results of the genome-wide association analysis of leg follicle density are shown in Table 5, which presents the information of the top 10 SNPs significantly associated with the leg follicle density screened under the condition of -log10(p)>5. The corresponding Manhattan plot is as Figure 3 shown. There were 175 SNPs significantly associated with the leg follicle density, which were located on chromosomes Chr 1, Chr 2, Chr 3, Chr 4, Chr 5, Chr 6, Chr 7, Chr 8, Chr 11, Chr 12, Chr 17, Chr 23, Chr 24, and the sex chromosomes W and Z respectively.

[0088] Table 5 Results of genome-wide association analysis of leg follicle density (top 10 SNPs)

[0089] Serial number SNP Chr Allele MAF 1 C7468958T Z C / T 0.28 2 C7468861T Z C / T 0.28 3 T7468936C Z T / C 0.27 4 C7468948A Z C / A 0.28 5 C2475250T W C / T 0.37 6 A61237231G Z A / G 0.28 7 C52915537A 2 C / A 0.11 8 G7479108A Z G / A 0.23 9 T6505122C W T / C 0.35 10 G2475256A W G / A 0.38

[0090] Note: Chr: Chromosome; Allele: Allele; MAF: Minor Allele Frequency.

[0091] 5.6 Population Stratification Assessment Results

[0092] There are significant SNP sites in both dorsal follicle density and leg follicle density, and their Q-Q plots are respectively as Figure 4 and Figure 5 shown. The abscissa represents the expected value, and the ordinate represents the observed value. The thin line in the figure represents the 45° line, which is the predicted threshold. The gray area is the 95% confidence interval of the scatter points on the graph. The farther the distance between the SNP and the solid line, the better the association strength. As Figure 4 - Figure 5 can be seen, most of the sites in the lower left corner of the figure are on the diagonal line, indicating that the model selection is reasonable. Those exceeding the diagonal line and the confidence interval in the upper right corner represent a high significance with the target trait. There is no population stratification phenomenon in the experimental population.

[0093] 5.7 Gene Annotation of Genome-Wide Significantly Associated SNPs

[0094] Through GWAS, 105 SNPs that reached a significant correlation level with dorsal follicle density were respectively located on chromosomes Chr 1, Chr 3, Chr 4, Chr 5, Chr 6, Chr 13 and the sex chromosome Z. Preliminary gene annotation of these sites was carried out through NCBI and Ensembl, and partial annotation results are shown in Table 6.

[0095] Table 6 Partial Annotation Results of Genome-Wide Association Analysis of Dorsal Follicle Density

[0096]

[0097]

[0098] Through GWAS, 175 SNPs that reached a significant correlation level with leg follicle density were respectively located on chromosomes Chr 1, Chr 2, Chr 3, Chr 4, Chr 5, Chr 6, Chr 7, Chr 8, Chr 11, Chr 12, Chr 17, Chr 23, Chr 24 and the sex chromosomes W, Z. Preliminary gene annotation of these sites was carried out through NCBI and Ensembl, and partial annotation results are shown in Table 7.

[0099] Table 7 Partial Annotation Results of Genome-Wide Association Analysis of Leg Follicle Density

[0100]

[0101] 5.8 Association Analysis of Significant Sites with Follicle Traits

[0102] Using SPSS software, an association analysis was conducted on the genotype and phenotypic traits of dorsal and leg follicle densities. The results showed that at the Chr6:18248647 locus, there were three genotypes (GG, GT, and TT) in the yellow chicken population and showed an upward trend. The dorsal follicle density of the TT genotype population was higher than that of the GG and GT populations. The phenotypic difference between TT and other genotypes was significant (P<0.05), while the phenotypic difference between GG and GT genotypes was not significant (P>0.05), as shown in Table 8.

[0103] At the ChrZ:7479108 locus, there were three genotypes (GG, GA, and AA) in the yellow chicken population. The leg follicle density of the AA genotype was significantly greater than that of other genotypes (P<0.05), while the phenotypic difference between GG and GA genotypes was not significant (P>0.05), as shown in Table 9.

[0104] Table 8 Information on SNPs loci significantly correlated with dorsal follicle density traits

[0105]

[0106] Note: * indicates significant correlation at the 0.05 level; different superscript letters indicate significant differences.

[0107] Table 9 Information on SNPs loci significantly correlated with leg follicle density traits

[0108]

[0109] Note: * indicates significant correlation at the 0.05 level; different superscript letters indicate significant differences.

[0110] From the above results, it can also be seen that the SNP marker at the Chr6:18248647 locus provided by the present invention shows that the yellow chicken corresponding to the TT genotype has a higher dorsal follicle density, the yellow chicken corresponding to the GT genotype has a lower dorsal follicle density, and the yellow chicken corresponding to the GG genotype has an even lower dorsal follicle density; for the SNP marker at the ChrZ:7479108 locus, the yellow chicken corresponding to the AA genotype has a higher leg follicle density, the yellow chicken corresponding to the GA genotype has a lower leg follicle density, and the yellow chicken corresponding to the GG genotype has an even lower leg follicle density. As the dorsal and leg follicle densities of chickens are important indicators of the aesthetics of chicken carcasses, a higher follicle density results in a better carcass appearance. Therefore, in chicken follicle trait breeding, selecting yellow chickens with a TT genotype for dorsal follicle density and / or an AA genotype for leg follicle density can improve breeding efficiency and quickly select chicken breeds with excellent follicle density traits.

[0111] Genotype the back hair follicle density and leg hair follicle density using the SNP loci Chr6:18248647 and ChrZ:7479108 of the molecular markers provided above, and retain the chicken breeds with the TT genotype for the back hair follicle density and / or the AA genotype for the leg hair follicle density, providing new molecular markers for the screening of chicken hair follicle traits.

[0112] The embodiments described above are only descriptions of the preferred modes of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A breeding method related to the hair follicle density trait of Ephedra chicken, characterized in that: The following steps are involved: Extract the genomic DNA of the tested Ephedra chicken, and obtain the genotype of the tested Ephedra chicken at the SNP sites of molecular marker I and molecular marker II, wherein the SNP site of the molecular marker I is located at the 18248647th base of chromosome 6 of the chicken genome, and the site has a G / T mutation; the SNP site of the molecular marker II is located at the 7479108th base of chromosome Z of the chicken genome, and the site has a G / A mutation; the accession number of the chicken genome in the Ensemble database is GRCg6a_release95, and the hair follicle density trait of the tested Ephedra chicken is judged according to the genotype of the SNP site, and the Ephedra chicken variety with excellent chicken hair follicle density is bred according to the judgment result; The method for judging the result is: using the genotype of the SNP site of the molecular marker I to judge the back hair follicle density of the Ephedra chicken, and the optimal genotype for the back hair follicle density of the Ephedra chicken is TT; using the genotype of the SNP site of the molecular marker II to judge the leg hair follicle density of the Ephedra chicken, and the optimal genotype for the leg hair follicle density of the Ephedra chicken is AA.

2. The method according to claim 1, characterized in that In the breeding of chicken hair follicle density traits, the Ephedra chicken breed with the TT genotype at the SNP site of molecular marker I and / or the AA genotype at the SNP site of molecular marker II is retained.

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