SNP molecular site related to chicken egg production quantity trait, detection primer and application thereof in breeding

Abstract

The application discloses a SNP molecular site related to a chicken egg laying quantity character, detection primers and application thereof in breeding. The application compares and analyzes SNP typing data of different egg laying performance chicken breeds, and carries out association analysis by combining phenotype data related to the egg laying quantity, and finally identifies a SNP marker site significantly related to the chicken egg laying quantity, wherein the SNP marker site corresponds to sequence information of a chicken reference genome GRC7b version published in an Ensembl website, 139,927,758 bp of a No. 2 chromosome, a base is A or T, and a SNP number is rs794322365; the SNP marker site and the detection primers provided by the application can be used for accurately predicting the high and low of the chicken egg laying quantity, and can provide technical support for molecular marker assisted selection breeding of the chicken egg laying quantity, shorten a breeding period and improve breeding efficiency.

Description

Technical Field

[0001] This invention relates to SNP molecular loci related to chicken egg production traits and their detection primers, particularly to SNP molecular loci related to chicken egg production number traits, detection primers, and their application in predicting egg production number, belonging to the field of SNP molecular loci related to chicken egg production number traits and their applications. Background Technology

[0002] Egg production is a key trait for breeding laying hens and broiler breeders, and it is also a major focus of genetic research on laying hen numbers. However, existing local or specialty laying hen breeds generally suffer from low egg production rates, making it urgent to breed high-producing breeds.

[0003] SNP marker-assisted selection breeding utilizes molecular markers and SNP molecular markers that are closely linked to target trait genes through genome-wide association analysis. At the DNA molecular level, it can rapidly and accurately screen for dominant genotypes and apply them to marker-assisted breeding. Therefore, by using chicken SNP genotyping data and phenotypic data related to egg production traits, genome-wide association analysis can be performed to identify SNP marker loci that are significantly associated with egg production in chickens. This can provide technical support for marker-assisted selection breeding of chicken egg production traits, shorten breeding cycles, and improve breeding efficiency. Summary of the Invention

[0004] One objective of this invention is to provide SNP molecular loci associated with the chicken egg production trait;

[0005] The second objective of this invention is to provide PCR detection primers for detecting SNP molecular loci associated with egg production traits in chickens;

[0006] The third objective of this invention is to apply the SNP molecular sites or detection primers related to the trait of egg production in chickens to predict the level of egg production.

[0007] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0008] This invention discloses SNP molecular loci associated with the trait of egg production in chickens. The SNP molecular loci associated with the trait of egg production in chickens correspond to the sequence information of the chicken reference genome GRC7b version published on the Ensembl website (https: / / www.ensembl.org), chromosome 2, 139,927,758 bp, where the base is A or T, and the SNP number is rs794322365.

[0009] Another aspect of the present invention discloses PCR detection primers for detecting SNP molecular sites related to the egg production trait in chickens. Preferably, the PCR primers consist of the upstream primer shown in SEQ ID No. 1 and the downstream primer shown in SEQ ID No. 2.

[0010] Another aspect of the present invention provides the application of SNP molecular sites related to the trait of egg production in predicting the level of egg production in chickens, including:

[0011] (1) Extract genomic DNA from the laying hens to be tested;

[0012] (2) Detect the genotype of the SNP loci associated with the egg production trait in chickens;

[0013] (3) If the genotype of the SNP locus related to the trait of egg production is TT, the egg production of the chicken to be tested is high; if the genotype of the SNP locus related to the trait of egg production is AT or AA, the egg production of the chicken to be tested is low.

[0014] In a preferred embodiment of the present invention, the method for extracting genomic DNA from the laying hen to be tested in step (1) includes: collecting blood from the laying hen to be tested, anticoagulating it with an anticoagulant, and extracting genomic DNA.

[0015] In a preferred embodiment of the present invention, the method for detecting the genotype of SNP loci related to the egg production trait in laying hens in step (2) includes whole genome resequencing, targeted sequencing, or Sanger sequencing.

[0016] In a preferred embodiment of the present invention, the egg production trait is the egg production number at 500 days of age; the test chicken is a young chicken or an early-laying chicken; preferably, the test chicken is a chicken associated with the egg production trait at 500 days of age.

[0017] Another aspect of the present invention is to apply the PCR primers to predict the level of egg production in chickens, including:

[0018] (1) Extract genomic DNA from the laying hens to be tested;

[0019] (2) Using the extracted genomic DNA of the chicken to be tested as an amplification template, a PCR amplification system was established using the PCR primers described above for PCR amplification; the PCR amplification products were sequenced to determine the genotype of the SNP sites related to the chicken egg production trait.

[0020] (3) If the genotype of the SNP locus related to the trait of egg production is TT, the egg production of the chicken to be tested is high; if the genotype of the SNP locus related to the trait of egg production is AT or AA, the egg production of the chicken to be tested is low.

[0021] In a preferred embodiment of the present invention, the method for extracting genomic DNA from the laying hen to be tested in step (1) includes: collecting blood from the laying hen to be tested, anticoagulating it with an anticoagulant, and extracting genomic DNA.

[0022] In a preferred embodiment of the present invention, the chicken to be tested is a young chicken or an early-laying chicken; preferably, the chicken to be tested is a chicken associated with the egg production trait at 500 days of age.

[0023] Another aspect of the present invention is to provide a PCR kit for predicting the level of egg production in chickens, comprising PCR detection primers, wherein the PCR detection primers are composed of an upstream primer shown in SEQ ID No. 1 and a downstream primer shown in SEQ ID No. 2.

[0024] This invention utilizes chicken SNP genotyping data and related phenotypic data on egg production traits for association analysis to identify SNP marker loci significantly associated with chicken egg production. These SNP marker loci correspond to chromosome 2, 139,927,758 bp, of the chicken reference genome GRC7b version sequence information published on the Ensembl website, where the bases are A or T, and the SNP number is rs794322365. This invention designs specific detection primers for this SNP marker locus. These SNP marker loci and their detection primers can be used to screen and predict chicken egg production traits, providing technical support for marker-assisted selection breeding of chicken egg production traits, shortening breeding cycles, and improving breeding efficiency.

[0025] Definitions of terms involved in this invention

[0026] Single nucleotide polymorphism (SNP) refers to the polymorphism of DNA sequences at the genomic level caused by variations such as insertion, deletion, transversion, and transformation of a single nucleotide. Attached Figure Description

[0027] Figure 1 Based on the combination of high, medium and low egg production performance varieties, the common genes associated with egg production were identified by Fst, XPEHH, and XPCLR selection signals; Figure A shows the comparison results between the high and low groups; Figure B shows the comparison results between the high and medium groups; Figure C shows the comparison results between the medium and low groups.

[0028] Figure 2 The results of enrichment analysis of common genes identified by selection signals in different egg production performance groups; HL, HM and ML represent high-low group, high-medium group and medium-low group, respectively.

[0029] Figure 3Results for θπ, TajimaD, and Fst within candidate gene regions; shaded areas represent genomic regions that have undergone strong selection.

[0030] Figure 4 Box plot of egg production at 500 days of age for different genotypes of candidate SNPs in the Beijing You chicken population.

[0031] Figure 5 Image of Sanger sequencing results associated with SNP rs794322365. Detailed Implementation

[0032] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer with the description. However, it should be understood that the embodiments described are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but such modifications or substitutions all fall within the protection scope of the present invention.

[0033] Example 1: Identification of molecular networks and key genes related to egg production performance

[0034] This experiment selected six breeds—White Leghorn, Loch Ness Red, Silkie, Wenshang Lufeng, Beijing You, and Pudong—as the research population. Based on the egg production performance of each breed (data from poultry breed records and actual observation data), the six breeds were divided into three groups: high (Group H: White Leghorn, Loch Ness Red), medium (Group M: Silkie, Wenshang Lufeng), and low (Group L: Beijing You, Pudong)—for comparative genomic analysis. Genomic data included 150 hen samples, with 25 randomly selected hens from each breed.

[0035] 1. Acquisition of high-quality whole-genome SNP data

[0036] 1.1 DNA extraction and sequencing

[0037] Genomic DNA was extracted from 150 chicken whole blood samples obtained through venous blood collection using the Tiangen reagent kit. After quality control, the genomic DNA was sequenced using the BGI Genomics DNBSEQ-T7 platform at a sequencing depth of 10×, yielding resequencing data.

[0038] 1.2 SNP Genotyping

[0039] After the sequencing data underwent quality control using FastP software, it was aligned to the chicken 7.0 reference genome (GRCg7b) using BWA software. SNP genotyping was performed according to the optimal GATK workflow, generating a whole genome dataset of 150 DNA samples. The whole genome dataset was then converted into ped and map files using Plink 1.9 software.

[0040] 1.3 Quality Control and Genotyping

[0041] The genotype files were quality controlled using Plink 1.9 software, including: 1) using `--geno 0.05` to delete SNPs with excessively high deletion rates; 2) using `--maf 0.05` to delete SNPs with excessively low minimum allele frequencies; 3) using `--hwe 0.00001` to delete SNPs that did not conform to Hardy-Weinberg equilibrium; 4) deleting Z and W chromosome loci; 5) using Plink 1.9 software to convert the genotype files (.bim, .bed, and .fam) into chromosome-specific VCF format files; and 6) using Beagle 5.4 software to fill in missing genotypes. Based on these operations, 3,172,126 SNPs and 150 samples were obtained, named Dataset 1.

[0042] 1.4 Chain Disequilibrium (LD) Reduction

[0043] Using the `--indep pairwise 25 5 0.2` function in PLINK 1.9 software, linkage disequilibrium (LD) pruning was performed on the SNP sites in Dataset 1. This function calculates the LD value between pairs of SNPs in a 25-SNP window, moving at a rate of 5 SNPs. When the LD value is greater than 0.2, one of the SNPs in the pair is removed, resulting in a dataset of 426,566 SNPs and 150 samples, named Dataset 2.

[0044] 2. Population Genome Selection Signal Analysis

[0045] 2.1 Population Fixity Coefficient (Fst) Analysis

[0046] The genotype files (.bim, .bed, and .fam) of Dataset 2 were converted to VCF format using PLINK 1.9 software. Pairwise Fst analysis was performed on the high, medium, and low egg production performance groups using the `--fst-window-size 100000--fst-window-step 10000` command in VCFtools. This command calculated the average Fst value and the weighted Fst (wFst) value within a 100kb genomic interval with a step size of 10kb. The wFst value, adjusted for population structure and sample size, was used as the selection criterion for candidate intervals. Based on the wFst values, the top 1% of intervals were selected as candidate intervals. Ultimately, 929 candidate intervals were identified in the high-low (HL), high-medium (HM), and medium-low (ML) groups for subsequent annotation analysis.

[0047] 2.2 Cross-population extended haplotype homozygosity (XP-EHH) analysis

[0048] Genome VCF files for different egg production performance groups in Dataset 2 were extracted using Plink 1.9 software. Genomic data from different groups were phased using Beagle 5.4 software. After phasing, XP-EHH analysis was performed on whole-genome loci using the `-xpehh` command in Selscan 2.0.3 software. The absolute values ​​of the XP-EHH values ​​were taken, and the top 1% of loci were selected as candidate loci. Ultimately, 4244, 4239, and 4246 candidate loci were identified in the high-low (HL), high-medium (HM), and medium-low (ML) groups, respectively, for subsequent annotation analysis.

[0049] 2.3 Cross-population conformity likelihood ratio (XP-CLR) analysis

[0050] The genotype files (.bim, .bed, and .fam) of Dataset 2 were converted into chromosome-specific VCF format files using PLINK 1.9 software. Genetic distance files for each chromosome were also output. XPCLR analysis was performed pairwise between chromosomes in the high, medium, and low egg production performance groups using the command `--phased--size100000--step 10000`. This command calculated the average XP-CLR and standardized XP-CLR (nXP-CLR) values ​​within 100kb genomic intervals with a step size of 10kb. The nXP-CLR value, standardized based on population structure and sample size, was used as the selection criterion for candidate intervals. Genomic intervals without an nXP-CLR value were replaced with 0. The top 1% of intervals based on the nXP-CLR values ​​were selected as candidate intervals. Ultimately, 944 candidate intervals were identified in the high-low (HL), high-medium (HM), and medium-low (ML) groups for subsequent annotation analysis.

[0051] 3. Gene annotation and functional enrichment analysis of selected signal regions

[0052] In the above analysis, the top 1% results of three selection signals (Fst, XP-EHH, and XP-CLR) for the HL, HM, and ML groups were obtained. This experiment further performed gene annotation analysis on the selection signal results, using the annotation file of the GRCg7b reference genome (https: / / ftp.ensembl.org / pub / release-109 / gtf / gallus_gallus / Gallus_gallus.bGa lGal1.mat.broiler.GRCg7b.109.gtf.gz): 1) For Fst, the top 1% candidate regions (100kb intervals) were annotated using bedtools software. Ultimately, 2837, 2690, and 2370 ensembl genes were annotated in the HL, HM, and ML groups, respectively; 2) For XP-EHH, 50kb intervals upstream and downstream of the top 1% candidate sites were used as candidate intervals for annotation. Finally, 14,578, 13,876, and 15,479 ensembl genes were annotated in the HL, HM, and ML groups, respectively; 3) For XP-CLR, the top 1% candidate regions (100kb regions) were annotated using bedtools software. Finally, 3,472, 3,715, and 3,662 ensembl genes were annotated in the HL, HM, and ML groups, respectively. Finally, based on the annotated ensembl gene information, the intersection of genes annotated with the three selection signals in HL, HM, and ML was taken, yielding 128, 76, and 92 common genes, respectively. Figure 1 ).

[0053] Functional annotation of the intersection genes was performed using the 'clusterProfiler' package in R software. It was found that genes in the HL, HM, and ML groups were enriched in 12, 5, and 1 KEGG signaling pathways, respectively. Among these, the GnRH signaling pathway, vascular smooth muscle contraction, and adrenergic signaling in cardiomyocytes were significantly enriched in the HL and HM groups. Figure 2 The GnRH signaling pathway plays an important regulatory role in the development and function of the reproductive system. It promotes the synthesis and release of gonadotropins by the anterior pituitary gland, which in turn promotes the development and maturation of follicles. Finally, by focusing on the GnRH signaling pathway, the common gene ADCY8 in the HL and HM groups was identified as a candidate gene for molecular marker mining.

[0054] 4. Gene region analysis of candidate gene ADCY8

[0055] The ADCY8 gene region, located at 139868327–139987072 bp on chromosome 2, was extracted from the annotation file of the GRCg7b reference genome. The HL group was selected, and this gene region was extracted from dataset 1 using Plink 1.9 software. Fst, nucleotide diversity (θπ), and Tajima'D analyses were performed: 1) In the Fst analysis, the 5kb sliding window wFst was calculated using the `--fst-window-size 5000--fst-window-step 5000` command in VCFTools; 2) In the θπ analysis, the 5kb sliding window θπ was calculated using the `--window-pi 5000--window-pi-step 5000` command in VCFTools; 3) In the Tajima'D analysis, the average Tajima'D value of the 5kb window was calculated using the `--TajimaD 5000` command in VCFTools. The results are shown below. Figure 3 As shown, strong selection signals were identified by all three analysis methods in gene intervals 139900001–139905000 and 139925001–139930000. Based on dataset 1, 25 SNP sites were ultimately detected in these two selected gene intervals.

[0056] Experiment Example 2: Association Analysis of Selected Gene Region SNPs in ADCY8 with Egg Production Performance

[0057] Two breeds, White Leghorn and Beijing Oil Chicken, were selected as the research population for the experiment. The egg production of 364 hens (196 White Leghorn and 168 Beijing Oil Chicken; note: the 364 hens in Experiment 2 are completely different from the 150 laying hens of the 6 breeds in Experiment 1) at 500 days of age was measured, and whole-genome resequencing was performed on all individuals whose egg production was measured.

[0058] 1. Acquisition of high-quality whole-genome SNP data

[0059] 1.1 DNA extraction and sequencing

[0060] Genomic DNA was extracted from 364 chicken whole blood samples obtained through venous blood collection using the Tiangen reagent kit. After passing quality control, the genomic DNA was sequenced using the BGI Genomics DNBSEQ-T7 platform, with an average sequencing depth of >12×, yielding resequencing data.

[0061] 1.2 SNP Genotyping

[0062] After quality control using FASTP software, the sequencing data were aligned to the chicken 7.0 reference genome (GRCg7b) using BWA software. SNP genotyping was performed according to the GATK optimal workflow, generating a whole-genome dataset of 364 DNA samples. The whole-genome dataset was then further converted into ped and map files using PLINK 1.9 software.

[0063] 1.3 Quality Control and Genotyping

[0064] The genotype files were quality controlled using Plink 1.9 software, including: 1) using `--geno 0.05` to delete SNPs with excessively high deletion rates; 2) using `--maf 0.05` to delete SNPs with excessively low minimum allele frequencies; 3) using `--hwe 0.00001` to delete SNPs that did not conform to Hardy-Weinberg equilibrium; 4) deleting Z and W chromosome loci; 5) using Plink 1.9 software to convert the genotype files (.bim, .bed, and .fam) into chromosomal VCF format files; and 6) using Beagle 5.4 software to fill in missing genotypes. Based on these operations, 6,270,216 SNPs and 364 samples were obtained, named Dataset 3, for subsequent analysis.

[0065] 2. Association analysis of SNPs in the selected gene region of ADCY8 with egg production performance

[0066] Based on dataset 3, SNPs were extracted from the ADCY8 selected gene intervals (139900001–139905000 and 139925001–139930000) using plink 1.9 software, detecting a total of 25 SNPs. These SNPs were intersected with the 25 SNPs detected in dataset 1, yielding 6 shared SNPs, which were considered reliable SNPs. These 6 SNPs were then extracted separately using plink 1.9 software and converted to compound genotypes using the `--recode compound-genotypes` command. In the Leghorn chicken population, 99% of individuals showed dominant homozygous SNP genotypes; in the Beijing Oil chicken population, all 6 SNP genotypes showed polymorphism, but minor genotypes were less common. This demonstrates the results of the selection signal analysis, indicating that the ADCY8 gene is strongly selected in high-laying hens.

[0067] Furthermore, this experiment conducted an association analysis between four SNPs and the egg production trait at 500 days of age in a Beijing You chicken population. The pairwise.t.test function in R software was used to perform association analysis between SNP genotypes and the egg production trait at 500 days of age; P < 0.05 indicated a significant difference. Through significance testing, SNP rs794322365 was found to be significantly associated with egg production. This SNP rs794322365 is located at 139927758 bp on chromosome 2 and includes three genotypes: AA, AT, and TT.

[0068] The upstream and downstream sequences (5' to 3', +) of the SNP rs794322365 are shown below:

[0069] 2dna:primary_assemblyprimary_assembly:bGalGal1.mat.broiler.GRCg7b:2:139927358:139928158:1CAAGAATCATCAAGTCCAACTCCTGGCTCTACACAGGACTGCCAAATACTAAAAACGTATTTGTGAGACAGTTGTCCAAACACTCCTAAAACTCCTGCAGTTCAGGGCCATGCCCACTGCCCTGGGGAACGTGCTCCATGCCCATCACCCTCTGGTGAAGAACTTCTGCCTAACCCCAAGCCTCCCCTCCCCTGATGCAGCTCCATGCCGTTCCCTCGGGCCCTGTCGCTGTCACAGACAGCAGAGCTCAGTGCTGCCCTTCCTCCTGTGAGGAGCTGTGGCTGCCATGAGGCCTCCCCTCAGCTCCTCCACTCTGTACTGAGTACACTCAGGAACCTCAGCAGCTCCCCTCCAAACCCTTCACCATCTTTGTAGTCCTCCTTTGAACACTCTGATAGTTTTATGTCCTTCATATGTTGTAGCCCCAAAACTTCATGCAGTGCTTGAGCTGAGACTGCACCAATGTGTTTTTGCTGAAGTCCTTTGAAGTTTTACACGTGCCAAGGAGGGTAAAATCTGCTCTGAGCACTGAGACAAAGTTAGCTGATTGACCCCGAGATCTCCTTAAAGCACCAATAGGAGCTCACGGAAGCCTTGACCATACTGTATACTACAATATAGATATATATATTATATATATATCATTTATCTATAATGTAGCTGTATACACTGTATAGTATATATATTTACTATATGTATACTATATAGCATATATGCTACTATACAGTATGTATACTCTATACTCTATCTGGCTTTAAGGGTGCAATGCCTAGATTTCAATATTTCACAATTAAAATCAAA。

[0070] The sequences of the PCR primers for amplifying this SNP are as follows:

[0071] F:GTAGTCCCTTTTGAACACTCTGA (SEQ ID No. 1);

[0072] R: GGGGTCAATCAGCTAACTTTGT (SEQ ID No. 2).

[0073] according to Figure 4 The correlation analysis between the genotype of SNP rs794322365 and the number of eggs laid shows that the number of eggs laid by chickens with the TT genotype at SNP rs794322365 is significantly higher than that of chickens with the AT and AA genotypes.

[0074] Experiment 3: Verification of the effectiveness of the association between the SNP rs794322365 molecular marker and egg production.

[0075] To verify the reliability of the association between the molecular marker of SNP rs794322365 and egg production, this experiment used 157 hens from a Beijing You chicken flock whose egg production had been measured at 500 days of age (Note: the 157 hens used in this experiment are from a different source than the 150 hens in Experiment 1 and the 364 hens in Experiment 2). Restriction fragment length polymorphism analysis (PCR-RFLP) was performed on the significantly associated SNP rs794322365 screened in Experiments 1 and 2 to compare the egg production of hens with different genotypes.

[0076] The PCR amplification system is shown in Table 1, and the PCR amplification parameters are shown in Table 2.

[0077] Table 1 PCR amplification system

[0078]

[0079] Table 2 PCR amplification parameters

[0080] step Temperature and time 1. Pre-variation 94℃, 3min 2. Cyclic Phase 30 cycles transsexual 94℃, 30sec annealing 55℃, 30sec extend 72℃, 1min 3. Final extension 72℃, 5min

[0081] The amplified products were sequenced, and the SNP site sequencing results are as follows: Figure 5 As shown in Table 3, statistical tests revealed that the polymorphism of SNP rs794322365 was significantly associated with high and low egg production at 500 days of age (P<0.01).

[0082] Table 3. Comparison of individual phenotypes of laying hens with different SNP rs794322365 genotypes

[0083]

Claims

1. The application of reagents for detecting SNP loci genotypes associated with egg production in predicting egg production levels in chickens, among which, The SNP locus associated with the egg production trait corresponds to chromosome 2, sequence number 139,927,758 bp in the chicken reference genome GRC7b version, where the base is A or T, and the SNP number is rs794322365. The chicken is the Beijing Oil Chicken, and the egg production trait refers to the number of eggs laid at 500 days of age. If the genotype of the SNP locus associated with the egg production trait is TT, the tested chicken will have a high egg production. If the genotype of the SNP locus associated with the egg production trait is AT or AA, the tested chicken will have a low egg production.

2. The application according to claim 1, characterized in that, include: (1) Extract genomic DNA from the laying hens to be tested; (2) Detect the genotypes of the SNP loci associated with the egg production trait in chickens; (3) If the genotype of the SNP locus related to the trait of egg production is TT, the egg production of the chicken to be tested is high; if the genotype of the SNP locus related to the trait of egg production is AT or AA, the egg production of the chicken to be tested is low.

3. The application according to claim 2, characterized in that, The method for extracting genomic DNA from the laying hen to be tested in step (1) includes: collecting blood from the laying hen to be tested, anticoagulating it with an anticoagulant, and extracting genomic DNA.

4. The application according to claim 2, characterized in that, The method for detecting the genotype of SNP loci associated with egg production in laying hens in step (2) is whole genome resequencing.

5. The application according to claim 2, characterized in that, The method for detecting the genotype of SNP loci associated with egg production in laying hens in step (2) is targeted sequencing.

6. The application according to claim 2, characterized in that, In step (2), the method for detecting the genotype of SNP loci associated with egg production in laying hens is Sanger sequencing.

7. Applications of PCR primers in predicting egg production in chickens, including: (1) Extract genomic DNA from the laying hens to be tested; (2) Using the extracted genomic DNA of the chicken to be tested as an amplification template, a PCR amplification system was established using PCR primers for PCR amplification; the amplification products were sequenced to obtain the genotypes of the SNP sites related to the chicken egg production trait as described in claim 1; (3) If the genotype of the SNP locus related to the egg production trait in claim 1 is TT, then the egg production of the chicken to be tested is high; if the genotype of the SNP locus related to the egg production trait in claim 1 is AT or AA, then the egg production of the chicken to be tested is low; the chicken is Beijing Oil Chicken, and the egg production trait is the egg production at 500 days of age; the PCR primer consists of the upstream primer shown in SEQ ID No. 1 and the downstream primer shown in SEQ ID No. 2.

Images