Molecular marker related to chicken hair follicle density character and application thereof

Abstract

The invention discloses a molecular marker related to chicken hair follicle density character and application thereof, and belongs to the technical field of poultry breeding. The method comprises the following steps: selecting 505 spotted-brown chickens as experimental subjects for whole genome association analysis, collecting back hair follicle density and leg hair follicle density characters, collecting subwing blood when the spotted-brown chickens are 77 days old, extracting genome DNA, detecting the integrity, purity and concentration of the extracted genome DNA, re-sequencing qualified samples, and determining whether the spotted-brown chickens are in the whole genome association analysis. And then performing whole genome association analysis on the chicken hair follicle density character to detect 105 SNPs significantly associated with the back hair follicle density and 175 SNPs significantly associated with the leg follicle density. And verification analysis is carried out on part of SNP sites, the back hair follicle density and the leg hair follicle density, so that theoretical support is provided for breeding chicken varieties related to chicken hair follicle characters.

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 invention provides a molecular marker related to a chicken hair follicle density trait. The molecular marker comprises a molecular marker I and a molecular marker II. The SNP site of the molecular marker I is located at the 18248647th base of chromosome 6 of a chicken genome, and a G / T mutation exists in the site; the SNP site of the molecular marker II is located at the 7479108th base of chromosome Z of the chicken genome, and a G / A mutation exists in the site; and the accession number of the chicken genome in the Ensemble database is GRCg6a_release95.

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

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

[0009] Preferably, the molecular marker I is associated with the density of hair follicles on the chicken back, and the molecular marker II is associated with the density of hair follicles on the chicken legs.

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

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

[0012] (1) Application in identification of chicken hair follicle density traits;

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

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

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

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

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

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

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

[0020] The present invention selects Jiangfeng Ephedra chicken hair follicle phenotype for genome-wide association analysis, and through the selection of these traits, identifies single nucleotide polymorphisms significantly associated with hair follicle traits. As a result, 105 SNPs significantly associated with back hair follicle density and 175 SNPs significantly associated with leg follicle density were detected.

[0021] The present invention also takes the Chr6:18248647 site and the ChrZ:7479108 site as examples to analyze their association with the chicken hair follicle density trait. The results show that the Chr6:18248647 site is significantly correlated with the chicken back hair follicle density, and the back hair follicle density of the TT genotype group is higher than that of the GG and GT groups. The phenotypic difference between TT and other genotypes is significant (P<0.05), and the phenotypic difference between GG and GT genotypes is not significant (P>0.05); the ChrZ:7479108 site is significantly correlated with the chicken leg hair follicle density, and the leg hair follicle density of the AA genotype is significantly greater than that of other genotypes (P<0.05), and the phenotypic difference between GG and GA genotypes is not significant (P>0.05).

[0022] The present invention explores the key genes and signal pathways that affect the phenotypic traits of poultry hair follicles, reveals the genetic mechanisms behind them, fills the gap in the research of hair follicle traits of Ephedra chicken in genome-wide association analysis, and provides theoretical support for the breeding of new high-quality chicken varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 1% agarose gel electrophoresis of genomic DNA stock solution (partial display); wherein 456-472 represents the sample number, and M represents the standard DNA molecule;

[0025] Figure 2 Manhattan plot of the results of the whole gene association analysis of back hair follicle density;

[0026] Figure 3 Manhattan plot of the results of the whole gene association analysis of leg hair follicle density;

[0027] Figure 4 This is the QQ plot result of the back hair follicle density;

[0028] Figure 5 This is the QQ plot result of the hair follicle density on the legs. DETAILED DESCRIPTION

[0029] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0030] It should be understood that the terms described in the present invention are only for describing a particular embodiment and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. The intermediate value in any stated value or stated range, and each smaller range between any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0031] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only 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 associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0032] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present invention description and examples are exemplary only.

[0033] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0034] Example 1

[0035] 1. Experimental Animals

[0036] The experimental animals used were purebred 17th generation G-line Ephedra chickens from Guangdong Jiangfeng Industrial Co., Ltd. The experimental chickens were caged, 77 days old, and managed according to the factory's breeding procedures. The chicken house was well ventilated, with appropriate ambient temperature, humidity, and light, and routine immunization and epidemic prevention were performed.

[0037] 2. Sample collection and processing

[0038] When the feeding period was 77 days, 505 Jiangfeng Ephedra chickens (including 170 roosters and 335 hens) were randomly selected from the caged group for subwing blood sampling. 1 mL of blood was drawn with a syringe, injected into a blood collection tube containing sodium heparin and mixed to prevent blood coagulation. The individual wing number was marked on the blood collection tube and then stored in a -80°C refrigerator.

[0039] 3. Experimental methods

[0040] 3.1 Phenotypic data determination

[0041] The experimental chickens were provided with drinking water 12 hours before slaughter. When slaughtering, the chickens were bled through the carotid artery and then placed in water at a temperature of about 60°C for 3 minutes to remove the hair. The surface moisture of the carcasses was wiped dry with kitchen paper and then the appearance of the carcasses was measured. The back hair follicle density and leg hair follicle density were measured using 2×2cm 2 The wire mesh is used to define the range, and then the number of hair follicles within the range is recorded.

[0042] 3.2 Extraction and detection of genomic DNA

[0043] The genomic DNA was extracted using the cetyltrimethylammonium ammonium bromide (CTAB) method. The genomic DNA was extracted according to the conventional extraction steps. The extracted genomic DNA was tested 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 tested.

[0044] 3.3 Sequencing data quality control and comparison with reference genome

[0045] The experiment was performed according to the standard protocol provided by the sequencing company. For qualified genomic DNA samples, appropriate size fragments were selected by gel electrophoresis, and then amplified by PCR to build a library. The quality of the built library was tested, and the qualified library was sequenced by the sequencer DNBSEQ-T7, which was performed by Biomarker Biotechnology Co., Ltd. To ensure the quality of information analysis, base sequencing quality distribution analysis, base type distribution check, insert fragment distribution statistics, depth distribution statistics, and filtering of the raw image data (RawReads) files obtained by high-throughput sequencing were performed during sequencing on the DNBSEQ-T7 sequencing system.

[0046] The final sequence obtained by sequencing is relocated to the reference genome for subsequent analysis. The ratio of Clean-Reads that can be mapped 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 hair follicle-related traits

[0049] The collected hair follicle density data were preliminarily sorted out using Excel, and outliers were removed from the data of each trait according to the μ±3σ principle. Descriptive statistical analysis of the data was performed using R software. The results of the analysis included sample number, mean, standard deviation, and coefficient of variation.

[0050] 4.2 Genome-wide association analysis

[0051] The samples were sent to Beijing Biomarker Biotechnology Co., Ltd. for genome-wide association analysis. In order to screen genes or molecular markers related to hair follicle traits in the whole genome, the present invention performed low coverage sequencing (6X) on 505 samples that passed the quality inspection.

[0052] Using the mixed linear model (LMM) in the GEMMA software, combined with phenotypic and genotypic data, a genome-wide association analysis was performed to mine SNPs associated with the chicken hair follicle density trait. Considering fixed factors (SNP effects) and random effects (kinship between individuals), the statistical model is as follows:

[0053] The present invention is based on the developed high-density molecular marker data and uses GEMMA to perform 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 phenotype vector; W is the indicator matrix of fixed effects, α is the coefficient vector of fixed effects; x is the genotype vector, β is the SNP effect; μ is the random effect vector; and e is the residual.

[0056] 4.3 Group Stratification

[0057] Population stratification refers to the difference in allele frequencies due to 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 hair follicle density of Jiangfeng Ephedra chicken, a QQ plot (Quantile-Quantile Plot) was drawn for the hair follicle density of Jiangfeng Ephedra chicken to determine whether there is deviation in the association analysis and stratification of the sample population.

[0058] 4.4 Gene annotation of significant SNPs

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

[0060] 5. Results and Analysis

[0061] 5.1 Genomic DNA test results

[0062] The genomic DNA extracted from 505 samples needs to be tested for quality by agarose gel electrophoresis. The test results of some genomic DNA are as follows: Figure 1 As shown, the electrophoresis sample wells must be clean and free of contamination, the main band must be clear, and there must be no tailing. In addition, the DNA purity test must be 1.6 <OD 260 / OD 280 <2.0, 1.8 <OD 260 / OD 230 <2.1. The test results of genomic DNA must meet the above requirements at the same time before the library can be constructed, and unqualified samples must be eliminated or DNA extracted again. All samples of the present invention were tested and a total of 505 samples met the qualified requirements.

[0063] 5.2 Sequencing data quality control and comparison with reference genome

[0064] The results of sequencing data quality control are shown in Table 1. The base type distribution test is mainly used to check whether there is AT and CG separation, which may come from sequencing or library construction. If there is obvious separation, it will affect the subsequent analysis. The average percentage of G and C bases in the sample 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 comparison efficiency of the sample genomic DNA and the reference genomic DNA is above 97.04%, with an average of 99.19%, indicating that the library construction and sequencing of the sample are normal.

[0065] Table 1 Sample sequencing data evaluation statistics

[0066]

[0067] Note: Clean-Reads: number of reads after filtering; Clean-Base: number of bases after filtering, the number of Clean-Reads multiplied by the sequence length.

[0068] 5.3 Statistics of SNP detection results between samples and reference genome

[0069] There are two main 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 speaking, the probability of transition is higher than that of transversion, that is, Ti / Tv is greater than 1. As shown in Table 2, the total number of SNPs detected in this experiment is 1996687423, the Ti / Tv value is about 2.46, and the heterozygous ratio (Het-ratio) is 48.27%.

[0070] Table 2 Statistics of SNP detection results between the reference genome

[0071]

[0072] Note: Heterozygosity Number is the number of heterozygotes, Homozygosity Number is the number of homozygotes, and Het-ratio is the ratio 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 are shown in Table 3. From the results of descriptive statistical analysis, it can be seen that the total number of chickens measured was 505 (170 roosters and 335 hens). The hair follicle density of the legs of male and female Ephedra chickens reached an extremely significant level (P<0.01); the hair follicle density of the back was greater than that of the legs regardless of gender; the hair follicle density of the same part of different sexes was similar; the coefficient of variation of the hair follicle density of the back and legs of male and female was greater than 10%; and the hair follicle density of the legs of male and female Ephedra 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 letters in the shoulders indicate extremely significant differences (P<0.01), and the same letters or no letters indicate no significant differences (P>0.05).

[0079] 5.5 Results of genome-wide association analysis

[0080] This experiment used Fastlmm, Emmax, and Gemma software for association analysis, and conducted a genome-wide association analysis of the hair follicle density of 505 Ephedra chickens (505 Ephedra chickens that met quality requirements after genomic DNA extraction). SNPs significantly associated with back hair follicle density and leg hair follicle density were found in the whole genome. The SNP sites of the SNP molecular markers were the international chicken reference genome GRCg6a_release95, and the genome source was Ensemble.

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

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

[0083] Table 4 Results of genome-wide association analysis of back hair follicle density (first 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 hair follicle density

[0087] The results of the whole genome association analysis of leg hair follicle density are shown in Table 5, which shows the information of the top 10 SNPs significantly associated with leg hair follicle density screened under the condition of -log10(p)>5. The corresponding Manhattan plot is shown in Figure 3 There are 175 SNPs that are significantly associated with leg hair follicle density, which are 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 sex chromosomes W and Z.

[0088] Table 5 Results of genome-wide association analysis of leg hair follicle density (first 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 Group stratification assessment results

[0092] There are significant SNPs sites in the back hair follicle density and the leg hair follicle density, and their QQ plots are as follows: Figure 4 and Figure 5 As shown, the horizontal axis represents the expected value and the vertical axis 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 scattered points on the figure. The farther the SNP is from the solid line, the better the association strength. Figure 4-Figure 5 It can be seen that most of the sites in the lower left corner of the figure are on the diagonal line, indicating that the model selection is reasonable, and the sites in the upper right corner that exceed the diagonal line and the confidence interval have a high significance with the target trait. There is no population stratification in the experimental population.

[0093] 5.7 Gene annotation of genome-wide significantly associated SNPs

[0094] Through GWAS, there are 105 SNPs that are significantly associated with the density of back hair follicles, which are located on chromosomes Chr 1, Chr 3, Chr 4, Chr 5, Chr 6, Chr 13 and sex chromosome Z. Preliminary gene annotation of these sites was performed through NCBI and Ensembl, and some annotation results are shown in Table 6.

[0095] Table 6 Annotation results of genome-wide association analysis of back hair follicle density (partial)

[0096]

[0097]

[0098] Through GWAS, there are 175 SNPs that are significantly associated with leg hair follicle density, which are 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 sex chromosomes W and Z. Preliminary gene annotations were performed on these sites through NCBI and Ensembl, and some annotation results are shown in Table 7.

[0099] Table 7 Annotation results of whole genome association analysis of leg hair follicle density (partial)

[0100]

[0101] 5.8 Association analysis between significant loci and hair follicle traits

[0102] SPSS software was used to perform association analysis between genotypes and the phenotypic traits of back and leg hair follicle density. The results showed that there were three genotypes (GG, GT and TT) at the Chr6:18248647 locus in the Ephedra chicken population and it showed an upward trend. The back hair follicle density of the TT genotype population was higher than that of the GG and GT populations. The phenotypic differences between TT and other genotypes were significant (P<0.05), and the phenotypic differences between the GG and GT genotypes were not significant (P>0.05), as shown in Table 8.

[0103] There are three genotypes (GG, GA and AA) at the ChrZ:7479108 locus in the Ephedra chicken population. The leg hair follicle density of the AA genotype is significantly greater than that of the other genotypes (P<0.05), and there is no significant phenotypic difference between the GG and GA genotypes (P>0.05), as shown in Table 9.

[0104] Table 8 SNPs loci significantly associated with back hair follicle density traits

[0105]

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

[0107] Table 9 Information on SNPs significantly associated with leg hair follicle density traits

[0108]

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

[0110] It can also be seen from the above results that the present invention provides a SNP marker at the Chr6:18248647 site, and the Ephedra chicken corresponding to the TT genotype has a higher back hair follicle density, the Ephedra chicken corresponding to the GT genotype has a lower back hair follicle density, and the Ephedra chicken corresponding to the GG genotype has an even lower back hair follicle density; the SNP marker at the ChrZ:7479108 site, the Ephedra chicken corresponding to the AA genotype has a higher leg hair follicle density, the Ephedra chicken corresponding to the GA genotype has a lower leg hair follicle density, and the Ephedra chicken corresponding to the GG genotype has an even lower leg hair follicle density. The hair follicle density of the chicken back and legs is an important indicator of the beauty of the chicken carcass. The higher the hair follicle density, the better the appearance of the carcass. Therefore, in the breeding of chicken hair follicle traits, selecting the back hair follicle density as the TT genotype and / or the leg hair follicle density as the AA genotype can improve the breeding efficiency and quickly select chicken varieties with excellent hair follicle density traits.

[0111] The back hair follicle density and leg hair follicle density were genotyped using the SNP sites Chr6:18248647 and ChrZ:7479108 of the molecular markers provided above, and chicken breeds with back hair follicle density of TT genotype and / or leg hair follicle density of AA genotype were retained, 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 are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A molecular marker associated with chicken hair follicle density, characterized in that: The molecular markers include molecular marker I and molecular marker II, the SNP site of the molecular marker I is located at the 18248647th base of chromosome 6 of the chicken genome, and a G / T mutation exists at the site; the SNP site of the molecular marker II is located at the 7479108th base of chromosome Z of the chicken genome, and a G / A mutation exists at the site; the accession number of the chicken genome in the Ensemble database is GRCg6a_release95.

2. The molecular marker according to claim 1, characterized in that The genotypes of the molecular marker I are GG, GT and TT, and the genotypes of the molecular marker II are GG, GA and AA.

3. The molecular marker according to claim 1, characterized in that The chicken hair follicle density trait includes the chicken's back hair follicle density and leg hair follicle density.

4. The molecular marker according to claim 3, characterized in that The molecular marker I is associated with the density of hair follicles on the chicken back, and the molecular marker II is associated with the density of hair follicles on the chicken leg.

5. The molecular marker according to claim 1, characterized in that The chicken includes Jiangfeng Ephedra chicken.

6. Use of the molecular marker according to any one of claims 1 to 5 in any of the following: (1) Application in identification of chicken hair follicle density traits; (2) Application in improving chicken hair follicle density traits; (3) Application in screening new breeds related to chicken hair follicle density traits.

7. A breeding method related to the chicken hair follicle density trait, characterized in that: The following steps are involved: Extract the genomic DNA of the chicken to be tested, perform whole genome sequencing or obtain the 100 kb sequence upstream and downstream of the SNP site of the molecular marker described in any one of claims 1 to 5, judge the hair follicle density trait of the chicken to be tested according to the genotype of the SNP site, and select chicken breeds with excellent hair follicle density according to the judgment result.

8. The method according to claim 7, characterized in that The determination method is as follows: using the genotype of the SNP site of the molecular marker I to determine the density of hair follicles on the back of the chicken, the genotype with the best density of hair follicles on the back is TT; using the genotype of the SNP site of the molecular marker II to determine the density of hair follicles on the legs of the chicken, the genotype with the best density of hair follicles on the legs is AA.

9. The method according to claim 8, characterized in that In the breeding of chicken hair follicle density traits, chicken breeds with the TT genotype for back hair follicle density and / or the AA genotype for leg hair follicle density are retained.

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