Molecular marker related to pheasant weight character and application thereof
By applying the ASM414374v1 genome version of molecular markers in pheasant breeding, the problem of low efficiency in traditional breeding methods has been solved, enabling rapid and precise selection of pheasant weight traits and improving breeding efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI ANIMAL EPIDEMIC PREVENTION & CONTROL CENT
- Filing Date
- 2026-02-26
- Publication Date
- 2026-05-12
AI Technical Summary
Traditional family-based breeding methods are inefficient and time-consuming in improving weight traits in pheasant breeding, making it difficult to meet market demand for high-yielding and stable-yielding pheasant breeds.
It provides molecular markers based on genome version ASM414374v1, including SNP sites with polymorphisms of T or C. By detecting the polymorphism of these SNP sites, combined with primer pairs and kits, rapid and precise breeding of pheasant weight traits can be achieved.
By using molecular marker-assisted selection, the breeding cycle can be significantly shortened, the breeding efficiency of pheasants can be improved, and the early and efficient selection of high-weight pheasants can be achieved.
Smart Images
Figure CN122012734A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of animal breeding technology, and in particular to a molecular marker related to the weight trait of pheasants and its application. Background Technology
[0002] Pheasant farming is an important part of specialty poultry farming. However, for a long time, the pheasant industry has suffered from problems such as mixed breed sources, low levels of systematic breeding, and an incomplete breeding system, which have to some extent restricted the further development of the industry.
[0003] To improve the production performance of pheasants, some breeding institutions have developed superior breeds that combine meat and egg production performance through crossbreeding and systematic selection of pheasants from different sources (such as certain introduced breeds and local breeds). These new breeds typically exhibit stable genetic traits and uniform appearance. Compared to ordinary pheasants that have not undergone systematic selection, they show significant improvements in production performance, such as faster growth, better meat performance, and superior egg production. Furthermore, their high uniformity in breed performance brings considerable comprehensive benefits to poultry farming and demonstrates broad application prospects. With the improvement of living standards, consumers' demand for high-quality and distinctive poultry products is increasing, providing a vast market space for high-performance pheasant breeds.
[0004] Among the many economic traits of pheasants, body weight is a core indicator reflecting their growth performance, and genetic improvement of this trait directly affects the economic benefits of pheasant farming. In particular, body weight in the later stages of production plays a crucial role in pheasant breeding. On the one hand, pheasants reach physical maturity at this stage, and their body weight comprehensively reflects an individual's energy reserves, fat deposition, and skeletal development. On the other hand, this trait is closely related to multiple reproductive performance indicators, such as the duration of peak egg production, eggshell quality, and the final culling value of breeding hens, making it an important indicator for comprehensively evaluating later-stage production performance.
[0005] However, when selecting for traits such as body weight in the later stages of production, traditional breeding methods, such as family selection, have limitations such as low improvement efficiency and long breeding cycles, making it difficult to efficiently meet the market's urgent demand for high-yielding and stable-producing pheasant breeds. Therefore, the industry urgently needs to identify molecular markers closely related to key economic traits (such as body weight) and utilize these markers to conduct marker-assisted selection or genomic selection. Through these modern biotechnologies, the breeding cycle can be significantly shortened, and the selection of target traits can be carried out precisely and efficiently, thereby effectively improving the overall efficiency of pheasant breeding. Summary of the Invention
[0006] To address the problems existing in the prior art, this invention provides a molecular marker related to the weight trait of pheasants and its application.
[0007] In a first aspect, the present invention provides a molecular marker based on genome version ASM414374v1, wherein the molecular marker includes any one or more of the following: NW_022205466.1:1015314, NW_022205466.1:1015332, NW_022205466.1:1015344, NW_022205466.1:1018235, NW_022205466.1:1018236, NW_022205466.1:1007748, NW_022205466.1:1007756, NW_022205466.1:1007763 and NW_022205466.1:1041908; The polymorphism of NW_022205466.1:1015314 is T or C; The polymorphism of NW_022205466.1:1015332 is T or C; The polymorphism of NW_022205466.1:1015344 is C or T; The polymorphism of NW_022205466.1:1018235 is C or T; The polymorphism of NW_022205466.1:1018236 is A or G; The polymorphism of NW_022205466.1:1007748 is G or A; The polymorphism of NW_022205466.1:1007756 is C or T; The polymorphism of NW_022205466.1:1007763 is G or T; The polymorphism of NW_022205466.1:1041908 is T or C.
[0008] Furthermore, the nucleotide sequence of NW_022205466.1:1015314 is shown in SEQ ID NO.1; The nucleotide sequence of NW_022205466.1:1015332 is shown in SEQ ID NO.2; The nucleotide sequence of NW_022205466.1:1015344 is shown in SEQ ID NO.3; The nucleotide sequence of NW_022205466.1:1018235 is shown in SEQ ID NO.4; The nucleotide sequence of NW_022205466.1:1018236 is shown in SEQ ID NO.5; The nucleotide sequence of NW_022205466.1:1007748 is shown in SEQ ID NO.6; The nucleotide sequence of NW_022205466.1:1007756 is shown in SEQ ID NO.7; The nucleotide sequence of NW_022205466.1:1007763 is shown in SEQ ID NO.8; The nucleotide sequence of NW_022205466.1:1041908 is shown in SEQ ID NO.9.
[0009] The nucleotide sequence shown in SEQ ID NO.1: TGGAATCATCATAATGGTCCTGCCAGCCCTTTTCACTTGAGAAAATGAAGTTCCTACAGATTAAAAACTCTTAAATAAAGCCTAACATACACACACACAAA.
[0010] The nucleotide sequence shown in SEQ ID NO.2: CCTGCCAGCCCTTTTCACTTGAGAAAATGAAGTTCCTACAGATTAAAAACTCTTAAATAAAGCCTAACATACACACACACAAACAATCACTGCTTCTCTTC.
[0011] The nucleotide sequence shown in SEQ ID NO.3: TTTCACTTGAGAAAATGAAGTTCCTACAGATTAAAAACTCTTAAATAAAGCCTAACATACACACACACAAACAATCACTGCTTCTCTTCATTTAAGTTTAC.
[0012] The nucleotide sequence shown in SEQ ID NO.4: ATTATCCAGCACCAGAAATGCCAAATTCCAGTCGCCTGGGAATTTGTCAGCACAGACCTGCTCAGCCCTAGCAAGGATTTTAGCTTTTTCCCACTCACGGA.
[0013] The nucleotide sequence shown in SEQ ID NO.5: TTATCCAGCACCAGAAATGCCAAATTCCAGTCGCCTGGGAATTTGTCAGCACAGACCTGCTCAGCCCTAGCAAGGATTTTAGCTTTTTCCCACTCACGGAG.
[0014] The nucleotide sequence shown in SEQ ID NO.6: TACCTCCTCTTGTCCACTGGAGTTTTTGTCTTGGACTTCAAGGGAGATGGGGACTCGCCCTCAGAGGTTATTAAAATCTTCTGTAATAACAGCACTTCTCCA.
[0015] The nucleotide sequence shown in SEQ ID NO.7: CTTGTCCACTGGAGTTTTTGTCTTGGACTTCAAGGGAGATGGGGACTCGCCCTCAGAGGTTATTAAAATCTTCTGTAATAACAGCACTTCTCCAGTAACCCT.
[0016] The nucleotide sequence shown in SEQ ID NO.8: ACTGGAGTTTTGTCTTGGACTTCAAGGGAGATGGGGACTCGCCCTCAGAGGTTATTAAAATCTTCTGTAATAACAGCACTTCTCCAGTAACCCTTTAGACC.
[0017] The nucleotide sequence shown in SEQ ID NO.9: CACCCAATTGTATTGTACTCCTGCATATGGTGATGTTCAAATGCACTTTTCTTCCCTCCATAACTCTGACAAGTAACTGTGGACCAACAAACTGAGGGATGC.
[0018] Secondly, the present invention provides a primer pair for amplifying the aforementioned molecular marker.
[0019] The primer pair design method described in this invention can be a conventional method of this invention. Technicians can design primer pairs (including primer pairs or KASP primer combinations) of different lengths based on existing primer design rules and primer design software (such as Primer) to amplify the aforementioned molecular markers.
[0020] Furthermore, the primer pairs are linked to different fluorescent tags, including one or more of the following: FAM, TET, HEX, ROX, Cy3, Cy5, Alexa Fluor, SYBR Green, DAPI, FITC, or Texas Red.
[0021] Thirdly, the present invention provides a kit comprising the aforementioned molecular markers or the aforementioned primer pairs.
[0022] Fourthly, the present invention provides the application of the aforementioned molecular markers as targets in any of the following: (1) Predicting or detecting pheasant weight, or preparing reagents for predicting or detecting pheasant weight; (2) Identify or breed high-weight pheasants, or prepare reagents for identifying or breeding high-weight pheasants; (3) Molecular marker-assisted breeding of pheasants; (4) Pheasant breed improvement related to body weight; (5) Germplasm resource improvement of pheasants.
[0023] The targets described in this invention include existing conventional methods and reagents for detecting nucleotides, such as gene sequencing, primer design for amplification, and probe design for targeted detection.
[0024] Fifthly, the present invention provides the use of the aforementioned molecular markers, or the aforementioned primer pairs, or the aforementioned kits in any of the following: (1) Predicting or detecting pheasant weight, or preparing reagents for predicting or detecting pheasant weight; (2) Identify or breed high-weight pheasants, or prepare reagents for identifying or breeding high-weight pheasants; (3) Molecular marker-assisted breeding of pheasants; (4) Pheasant breed improvement related to body weight; (5) Germplasm resource improvement of pheasants.
[0025] Sixthly, the present invention provides a method for determining the weight of a pheasant, comprising: The polymorphism of the molecular markers described above was tested in the pheasants to be tested, and the weight of the pheasants was determined based on the genotype test results.
[0026] Furthermore, the detection method includes one or more of the following: gene sequencing, molecular probes, liquid phase capture, or mass spectrometry.
[0027] Furthermore, determining the weight of the pheasant to be tested based on the genotype detection results includes: According to the corresponding body weight from largest to smallest, the genotype of NW_022205466.1:1015314 is CC>CT>TT; The genotype of NW_022205466.1:1015332 is CC>CT>TT; The genotype of NW_022205466.1:1015344 is TT>TC>CC; The genotype of NW_022205466.1:1018235 is TT>TC>CC; The genotype of NW_022205466.1:1018236 is GG>GA>AA; The genotype of NW_022205466.1:1007748 is AG>GG; The genotype of NW_022205466.1:1007756 is TC>CC; The genotype of NW_022205466.1:1007763 is TG>GG; The genotype of NW_022205466.1:1041908 is CC>CT>TT.
[0028] The present invention has the following beneficial effects: This invention screens multiple SNP markers associated with chicken weight traits based on genome-wide association analysis. By detecting the polymorphism of these SNP molecular markers, the weight traits of pheasants can be predicted rapidly, cost-effectively, and efficiently. The SNP molecular markers provided by this invention can be applied to marker-assisted breeding of pheasant weight, providing an effective detection method for early selection and improving breeding efficiency, and are of great significance for the genetic improvement of pheasants. Attached Figure Description
[0029] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0030] Figure 1 This is a roadmap for screening molecular marker technology provided in Embodiment 1 of the present invention.
[0031] Figure 2 This is the SNP density map provided in Embodiment 1 of the present invention.
[0032] Figure 3 This is the SNP distribution diagram provided in Embodiment 1 of the present invention.
[0033] Figure 4 This is a weight phenotype distribution diagram provided in Embodiment 1 of the present invention.
[0034] Figure 5 This is a Manhattan plot of the genome-wide association analysis results of the weight trait provided in Embodiment 1 of the present invention; the vertical axis represents -lg of the association value, and each point represents one SNP locus.
[0035] Figure 6 This is a QQ graph of genome-wide association analysis of weight-related SNPs provided in Embodiment 1 of the present invention.
[0036] Figure 7 This is the functional annotation result of candidate genes related to significant SNPs of body weight trait provided in Example 1 of the present invention. Detailed Implementation
[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0038] Unless otherwise specified, the experimental methods involved in the following embodiments are conventional methods in the art. For example, you can refer to the experimental manual in the art or follow the conditions recommended in the manufacturer's instructions.
[0039] Unless otherwise specified, all experimental materials and reagents used in the following examples are commercially available.
[0040] Example 1 In this embodiment, a method for screening and verifying molecular markers related to pheasant body weight includes the following (screening method as follows): Figure 1 (as shown) 1. Materials and Methods 1.1 Collection of experimental animals and samples The 333 Shenhong Rainbow Pheasants used in this invention were provided by Shanghai Xinhao Rare Poultry Breeding Co., Ltd., including 28 roosters and 305 hens. Blood samples were collected from this group, and weight phenotypic data were recorded.
[0041] 1.2 Whole genome resequencing Genomic DNA was extracted using a magnetic bead method, following standard extraction procedures. The extracted DNA underwent integrity and purity testing. For samples that passed the tests, appropriate fragment sizes were selected using gel electrophoresis, followed by PCR enrichment to construct libraries. After library construction, Quantitative quality control was performed using Qubit, and qualified libraries were sequenced. Following sequencing, base sequencing quality distribution analysis, base content distribution analysis, and filtering of the raw image data (Raw reads) obtained from high-throughput sequencing were performed.
[0042] The final sequence obtained from sequencing was re-aligned onto the reference genome before further analysis. The reference genome species was the ring-necked pheasant, and the genome version was ASM414374v1.
[0043] 1.3 Quality Control of Genomic Data To obtain reliable GWAS results, PLINK 1.9 software was used to perform quality control on the genotype data. The quality control conditions were as follows: (1) Remove sites with a deletion rate greater than 10%. (2) Remove loci with a minor allele frequency of less than 0.05. (3) Remove those that do not meet the Hardy-Weinberg test and have a p-value less than 10. -6 The site.
[0044] After screening, a total of 5,141,823 SNP loci were obtained. The density map of these loci after quality control is shown in [link to map]. Figure 2 The changes in the number of contigs on chromosomes before and after quality control (the reference genome for this species was not assembled to the chromosome level, so only the first 20 contigs are shown in the figure) are shown below. Figure 3 .
[0045] 1.4 Introduction to Genome-wide Association Analysis Software Using GMAT software for genome-wide association analysis (GWAS) of body weight traits, current GWAS analysis software only considers additive gene effects, ignoring non-additive effects, especially interactions, leading to incomplete analysis of complex traits. Furthermore, GMAT focuses on cross-sectional data collected at a specific time point, neglecting longitudinal traits. GMAT software, however, features six modules, including single-trait analysis, multi-trait analysis, and longitudinal data analysis. Compared to traditional GWAS analysis software, it offers greater model richness and trait applicability, significantly improving statistical power and facilitating the discovery of significant markers associated with traits. Computationally, GMAT is written in C++, utilizes the Eigen and MKL libraries, and employs block-based reading and computation techniques to conserve memory. It also utilizes techniques such as eigenvalue decomposition relation matrices, linear transformation genome estimation, and weighted EM and AI algorithms, resulting in extremely high computational efficiency—thousands of times faster than the Fast-LMM algorithm.
[0046] 1.5 Genome-wide association analysis After quality control, the sequencing data were analyzed using GMAT software based on the LMM model to identify SNPs with significant effects. The LMM model is as follows: In the formula, Y is the phenotypic vector. For fixed effects (group structure, gender, building, generation). Let ξ be the labeling effect to be tested, and ξ ~ N(0, Kσ). a 2 ) represents a polygenic effect, e~N(0,Iσ) e 2 ) represents the residual effect. In polygenic effects, K represents the marker-inferred kinship matrix.
[0047] 1.6 Candidate Gene Identification and Functional Annotation This invention involves downloading the reference genome information of the corresponding species from the ENSEMBL website and using ANNOVAR software to annotate genes near significant SNPs.
[0048] Then, the clusterProfiler package was used to perform gene function enrichment analysis on the annotated candidate genes based on the GO and KEGG databases.
[0049] 1.7 Association analysis of significant loci with traits and multiple comparisons The association between marker genotypes and phenotypes was tested using R4.2 software. The model is as follows: y is the phenotypic vector, γ is the label effect to be tested, and Z is the correlation matrix of γ. This is to account for the residual effect. The LSD method was used to perform multiple comparisons between different genotypes.
[0050] 2. Results and Analysis 2.1 Sequencing data quality control and alignment results with the reference genome The quality control data for sample sequencing are shown in Table 1. Base type distribution detection was mainly used to check for AT and CG segregation. As shown in Table 1, the average percentage of G and C in the total bases was 41.99%, bases with a mass value greater than or equal to 20 accounted for 97.93% of the total bases, and bases with a mass value greater than 30 accounted for 94.91%. The average alignment efficiency between sample DNA and genomic DNA was 89.30%. This indicates that the library construction and sequencing of this population of samples were normal.
[0051] Table 1. Quality control statistics of sample sequencing data
[0052] 2.2 Statistical analysis of pheasant body weight traits Table 2 shows the mean, maximum, minimum, and coefficient of variation of the body weight phenotype of 324 pheasants. The phenotypic distribution is shown in Table 2. Figure 4 .
[0053] Table 2 Statistical analysis of pheasant body weight phenotype
[0054] 2.3 GWAS Analysis Results Based on resequencing, 5,141,823 SNPs were screened for further analysis. Using the commonly used GWAS model, LMM statistical analysis, 39 SNPs were found to be significantly associated with pheasant body weight. Their genome-wide association analysis Manhattan plot is shown below. Figure 5 As shown, the QQ image result is as follows: Figure 6 As shown.
[0055] 2.4 Identification of candidate genes related to pheasant body weight and GO functional annotation Gene function enrichment analysis was performed using NCBI and Ensembl sequence alignment and based on the GO and KEGG databases, annotating 27 genes. The functional annotation results are shown below. Figure 7 To further identify candidate genes related to pheasant weight traits, nine sites near the WNT11 gene were anchored through literature review and multiple comparisons. Detailed information on these sites is shown in Table 3.
[0056] The annotated WNT11 gene encodes member 11 of the Wnt family and belongs to the secreted glycoprotein ligand family; it activates the non-canonical Wnt pathway (Wnt / PCP and Wnt / Ca). 2+(Branches) are involved in cell proliferation, myofibril differentiation and skeletal development, and have been shown to be directly related to body weight and carcass traits in chickens and other mammals.
[0057] Table 3. Information on significant SNPs related to pheasant body weight
[0058] 3. Association analysis between significant loci and traits and multiple comparisons For the nine SNP markers related to pheasant weight traits obtained above, this invention analyzed 300 pheasant samples and conducted association tests between marker genotypes and phenotypes, as well as multiple comparisons between different genotypes using the LSD method. The results showed that: (1) The NW_022205466.1:1015314 locus has three genotypes (CC, CT and TT) in the pheasant population, among which the CC and CT pheasants have significantly higher body weight than the TT pheasant.
[0059] (2) The NW_022205466.1:1015332 locus has three genotypes (CC, CT and TT) in the pheasant population, among which the CC and CT pheasants have significantly higher body weight than the TT pheasant.
[0060] (3) The NW_022205466.1:1015344 locus has three genotypes (TT, TC and CC) in the pheasant population, among which the TT and TC pheasants have significantly higher body weight than the CC pheasant.
[0061] (4) The NW_022205466.1:1018235 locus has three genotypes (TT, TC and CC) in the pheasant population, among which the TT and TC pheasants have significantly higher body weight than the CC pheasant.
[0062] (5) The NW_022205466.1:1018236 locus has three genotypes (GG, GA and AA) in the pheasant population, among which the GG and GA pheasants have significantly higher body weight than the AA pheasant.
[0063] (6) The NW_022205466.1:1007748 locus has two genotypes (AG and GG) in the pheasant population, with the AG pheasant having a significantly higher body weight than the GG pheasant.
[0064] (7) The NW_022205466.1:1007756 locus has two genotypes (TC and CC) in the pheasant population, with the TC pheasant having a significantly higher body weight than the CC pheasant.
[0065] (8) The NW_022205466.1:1007763 locus has two genotypes (TG and GG) in the pheasant population, with the TG pheasant having a significantly higher body weight than the GG pheasant.
[0066] (9) The NW_022205466.1:1041908 locus has three genotypes (CC, CT and TT) in the pheasant population, among which the CC and CT pheasants have significantly higher body weight than the TT pheasant.
[0067] Table 4. Results of trait association analysis and multiple comparisons of different genotypes
[0068] Note: For the same trait, there is no significant difference between data with the same lowercase letter above the number, while there is a significant difference between data with different lowercase letters above the number.
[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A molecular marker, characterized in that, Based on genome version ASM414374v1, the molecular markers include any one or more of the following: NW_022205466.1:1015314, NW_022205466.1:1015332, NW_022205466.1:1015344, NW_022205466.1:1018235, NW_022205466.1:1018236, NW_022205466.1:1007748, NW_022205466.1:1007756, NW_022205466.1:1007763, and NW_022205466.1:1041908; The polymorphism of NW_022205466.1:1015314 is T or C; The polymorphism of NW_022205466.1:1015332 is T or C; The polymorphism of NW_022205466.1:1015344 is C or T; The polymorphism of NW_022205466.1:1018235 is C or T; The polymorphism of NW_022205466.1:1018236 is A or G; The polymorphism of NW_022205466.1:1007748 is G or A; The polymorphism of NW_022205466.1:1007756 is C or T; The polymorphism of NW_022205466.1:1007763 is G or T; The polymorphism of NW_022205466.1:1041908 is T or C.
2. The molecular marker combination according to claim 1, characterized in that, The nucleotide sequence of NW_022205466.1:1015314 is shown in SEQ ID NO.1; The nucleotide sequence of NW_022205466.1:1015332 is shown in SEQ ID NO.2; The nucleotide sequence of NW_022205466.1:1015344 is shown in SEQ ID NO.3; The nucleotide sequence of NW_022205466.1:1018235 is shown in SEQ ID NO.4; The nucleotide sequence of NW_022205466.1:1018236 is shown in SEQ ID NO.5; The nucleotide sequence of NW_022205466.1:1007748 is shown in SEQ ID NO.6; The nucleotide sequence of NW_022205466.1:1007756 is shown in SEQ ID NO.7; The nucleotide sequence of NW_022205466.1:1007763 is shown in SEQ ID NO.8; The nucleotide sequence of NW_022205466.1:1041908 is shown in SEQ ID NO.
9.
3. A primer pair, characterized in that, The primer pair is used to amplify the molecular marker described in claim 1 or 2.
4. A reagent kit, characterized in that, Includes the molecular marker as described in claim 1 or 2, or the primer pair as described in claim 3.
5. The use of the molecular marker of claim 1 or 2 as a target in any of the following: (1) Predicting or detecting pheasant weight, or preparing reagents for predicting or detecting pheasant weight; (2) Identify or breed high-weight pheasants, or prepare reagents for identifying or breeding high-weight pheasants; (3) Molecular marker-assisted breeding of pheasants; (4) Pheasant breed improvement related to body weight; (5) Germplasm resource improvement of pheasants.
6. The use of the molecular marker of claim 2, or the primer pair of claim 3, or the kit of claim 4, in any of the following: (1) Predicting or detecting pheasant weight, or preparing reagents for predicting or detecting pheasant weight; (2) Identify or breed high-weight pheasants, or prepare reagents for identifying or breeding high-weight pheasants; (3) Molecular marker-assisted breeding of pheasants; (4) Pheasant breed improvement related to body weight; (5) Germplasm resource improvement of pheasants.
7. A method for determining the weight of pheasants, characterized in that, include: The polymorphism of the molecular marker as described in claim 1 or 2 is detected in the pheasant to be tested, and the weight of the pheasant to be tested is determined based on the genotype detection results.
8. The method according to claim 7, characterized in that, The detection methods include one or more of the following: gene sequencing, molecular probes, liquid phase capture, or mass spectrometry.
9. The method according to claim 7 or 8, characterized in that, The determination of the weight of the pheasant to be tested based on the genotype test results includes: According to the corresponding body weight from largest to smallest, the genotype of NW_022205466.1:1015314 is CC>CT>TT; The genotype of NW_022205466.1:1015332 is CC>CT>TT; The genotype of NW_022205466.1:1015344 is TT>TC>CC; The genotype of NW_022205466.1:1018235 is TT>TC>CC; The genotype of NW_022205466.1:1018236 is GG>GA>AA; The genotype of NW_022205466.1:1007748 is AG>GG; The genotype of NW_022205466.1:1007756 is TC>CC; The genotype of NW_022205466.1:1007763 is TG>GG; The genotype of NW_022205466.1:1041908 is CC>CT>TT.