Goose SNP (Single Nucleotide Polymorphism) molecular marker-based fingerprint spectrum construction method

Abstract

The invention discloses a construction method of a fingerprint spectrum based on goose SNP (Single Nucleotide Polymorphism) molecular markers, and relates to the field of molecular markers. According to the technical scheme, the construction method comprises the following steps: S1, obtaining re-sequencing data of a plurality of goose varieties, and screening core SNP loci; s2, performing population genetic polymorphism and genetic similarity analysis on the screened core SNPs; and S3, combining and arranging the genotypes of the screened core SNP sites according to a site numbering sequence to form a specific sequence as the DNA fingerprint of the goose germplasm resource. Genetic polymorphism and genetic similarity analysis is carried out on 88 core SNP loci of the goose, and the fingerprint spectrum is constructed, so that the efficiency of accurate identification and evaluation of goose germplasm resources and important varieties can be remarkably improved, and data support is provided for subsequent goose breeding improvement and genetic resource protection.

Description

Technical Field

[0001] The present invention relates to the field of molecular markers, and particularly to a method for constructing a fingerprint map based on goose SNP molecular markers. Background Art

[0002] Geese, as anseriformes in the avian class and anatidae family, have relatively strong environmental adaptability and disease resistance. Different varieties of geese are widely distributed from the southern coastal areas of China to Heilongjiang in the northeast, and from the southwestern inland to the Xinjiang Uygur Autonomous Region in the northwest. According to the epidemiological investigation report of waterfowl diseases in the first half of 2024 by Yibang Biology, compared with ducks, geese have relatively fewer bacterial and viral diseases. Geese grow fast, have a high egg production rate, and white geese have a high down production rate. The down is light, soft, fluffy, and warm, making it a raw material for high-quality clothing, bedding, and other products. However, the existing protection methods for wild geese are limited, and coupled with the mixed germplasm of domestic geese, it is difficult to distinguish them only based on apparent morphological differences. Due to the genetic background convergence and the increasing market demand, the existing germplasm resource identification and variety property right protection are difficult to effectively support breeding practices. Therefore, there is an urgent need for an efficient and accurate molecular marker method to promote the precise identification and evaluation of germplasm resources and important varieties. The traditional methods for identifying variety germplasm resources mainly rely on the observation and statistics of biology (such as body shape, feather color), genetics (such as genetic relationship, origin and evolution), and production traits (such as egg production rate, feed conversion rate). Although the input is low, the operation is simple, and it is easy to master. However, the experimental cycle is long, it is easily affected by personal subjective and environmental conditions, and it is not suitable for the identification of large-scale germplasm. Especially, it is difficult to distinguish closely related varieties with similar genetic backgrounds. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a method for constructing a fingerprint map based on goose SNP molecular markers. By analyzing the genetic polymorphism and genetic similarity of 88 core SNP loci in geese, a fingerprint map is constructed, which can significantly improve the efficiency of precise identification and evaluation of goose germplasm resources and important varieties, and also provide data support for subsequent goose breeding improvement and genetic resource protection.

[0004] The technical solution adopted by the present invention to solve its technical problems is: a method for constructing a fingerprint map based on goose SNP molecular markers, characterized by including the following steps:

[0005] S1, obtaining the resequencing data of multiple goose varieties and screening core SNP loci;

[0006] S2, analyzing the population genetic polymorphism and genetic similarity of the screened core SNPs;

[0007] S3, arranging the genotypes of the screened core SNP loci in the order of locus numbers to form a specific sequence as the DNA fingerprint of goose germplasm resources.

[0008] Preferably, the population genetic polymorphism in step S2 includes phylogenetic tree construction and principal component and population structure analysis.

[0009] Preferably, the specific sequence in S3 is converted into a binary format, and a recognizable two-dimensional code barcode is generated.

[0010] Preferably, there are 88 SNP core sites, and the 88 core SNP sites are as follows:

[0011]

[0012]

[0013]

[0014] Advantages of the present invention:

[0015] By analyzing the genetic polymorphism and genetic similarity of 88 core SNP sites in geese, this solution constructs a fingerprint map, which can significantly improve the efficiency of accurate identification and evaluation of goose germplasm resources and important varieties, and also provides data support for subsequent goose breeding improvement and genetic resource protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 for the description of the embodiments. Obviously, the following drawings are only 10 of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 Showing pictures of 18 kinds of geese;

[0018] Figure 2 Showing the top 10 situations of the chromosomes where the core SNP sites are located;

[0019] Figure 3 Showing the statistics of the core SNP types;

[0020] Figure 4 Showing the construction of the phylogenetic tree of 18 goose breeds;

[0021] Figure 5 Showing the principal component analysis diagram of 18 goose breeds;

[0022] Figure 6 Showing the population structure analysis diagram;

[0023] Figure 7 Showing the error values of cross-validation under different K values;

[0024] Figure 8 is a heat map of genetic similarity;

[0025] Figure 9 is a clustering heat map;

[0026] Figure 10 is the QR code of the fingerprint map of 18 goose breeds; Detailed implementation mode

[0027] To deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with the drawings and embodiments. The embodiments are only used to explain the present invention and do not limit the protection scope of the present invention.

[0028] Embodiment

[0029] A method for constructing a fingerprint map based on goose SNP molecular markers includes the following steps:

[0030] S1. Obtaining the resequencing data of 18 goose breeds (366 individuals) and screening the core SNP loci;

[0031] S2. Conducting population genetic polymorphism (phylogenetic tree construction, principal component and population structure analysis) and genetic similarity analysis on the screened core SNPs;

[0032] S3. Arranging the genotypes of the screened core SNP loci in the order of locus numbers to form a specific sequence as the DNA fingerprint of goose germplasm resources.

[0033] Converting the specific sequence of S3 into a binary format and generating a recognizable QR code barcode.

[0034] There are 88 SNP core loci, and the 88 core SNP loci are as follows:

[0035]

[0036]

[0037]

[0038] This solution constructs a fingerprint map by analyzing the genetic polymorphism and genetic similarity of 88 core SNP loci in geese, which can significantly improve the efficiency of accurate identification and evaluation of goose germplasm resources and important breeds, and also provides data support for subsequent goose breeding improvement and genetic resource protection.

[0039] The following are the applications of the core SNP loci identified in goose germplasm resources in this solution:

[0040] 1) Population genetic polymorphism analysis

[0041] 2) Genetic similarity analysis

[0042] 3) Construction of goose DNA fingerprint map

[0043] 4) Identification and breeding of goose germplasm

[0044] 5) Molecular assisted breeding of geese

[0045] The specific implementation is as follows:

[0046] (1) In this scheme, data of 18 goose breeds (366 individuals) were downloaded. The re-sequencing data of all geese were all from the National Center for Biotechnology Information (NCBI). The specific source information of these goose breeds (see Figure 1 and Table 2). Figure 1 They were respectively cited from the Database of goose genetic resources in china established by Zhang and Yangzhou University (http: / / www.yzcom.com / webdemo / goose / ) and the Goose multiomics database established by Chongqing Academy of Animal Science (https: / / goosedb.com / ).

[0047] Table 1 Goose breed information

[0048]

[0049]

[0050] (2) FastQC and Cutadapter software were used to perform quality control on the data (including filtering low-quality reads and removing adapters). Then, BWA and GATK software were used to accurately align the sequencing data of each individual to the goose reference genome (ASM1303099) for variant detection, obtaining a goose-specific SNP locus database. Then, a high-quality VCF file was generated according to the population combination.

[0051] (3) Based on the obtained SNP sites above, screen the SNP sites that can be used to construct DNA fingerprint maps. Finally, filter out the sites with deletion (Miss) < 0.1, minor allele frequency (MAF) < 0.1, polymorphism information content (PIC) > 0.35, and heterozygosity (Het) > 0.1. Finally, 190,740 SNP sites are retained. The SNP type analysis of the 190,740 SNP sites is shown in Figure 2 , 64,436 are transition types (A / G and C / T), 30,997 are transversion types (A / C, A / T, C / G, and G / T). The main variant type of SNP sites is transition, and the transition / transversion ratio (Ts / Tv) is 2.08. All SNPs are evenly distributed on 37 chromosomes. The chromosomal distribution analysis of the 190,740 SNP sites is shown in Figure 3 , only the top 10 cases are shown sorted by the number of SNPs distributed (from high to low). Among them, the numbers of SNPs contained in chromosomes 1 and Z are 117,546 and 16,665 respectively. And chromosome Z has the highest density of SNPs.

[0052] (4) Construct a NJ phylogenetic tree. After SNP detection, 190,740 individual SNPs can be used to calculate the distance between populations. The calculation formula for the p-distance between two individuals i and j is as follows:

[0053]

[0054] In the formula, L is the length of the high-quality SNPs region, and the allele at position 1 is A / C. The calculation formula is as follows:

[0055]

[0056] Use the TreeBest software to calculate the distance matrix. Based on this, a phylogenetic tree is constructed by the neighbor-joining (NJ) method as shown in Figure 4 . Among them, the bootstrap values (BV) are obtained by > 1000 times of calculation.

[0057] (5) R software was used for principal component analysis (PCA). This embodiment clusters individuals into different subgroups according to principal components based on the degree of SNP differences in individual genomes and different trait characteristics. The SNP at position i, k of individual is represented by , if individual i is homozygous with the reference allele, then = 0; if heterozygous, then = 1; if individual i is homozygous with the non-reference allele, then = 2. M is an n×S matrix containing standard genotypes, and the calculation formula is as follows:

[0058]

[0059] In the formula, E(d k ) is d k The average value of the individual sample covariance n×n matrix was calculated by X=MMT / S. In this study, GCTA software (version 1.13) was used to calculate the eigenvectors and eigenvalues, and R software was used to draw the PCA distribution diagram. Figure 5 .

[0060] (6) Use admixture to analyze population structure. First, create the PLINK input file, the Ped file, and then use the admixture software to construct the population genetic structure. Figure 6 , the cross-validation error value line chart under different K values is shown in Figure 7 .

[0061] (7) Genetic similarity analysis. For core SNPs, the genetic similarity coefficient (GS) between two groups was calculated to obtain the genetic similarity coefficient matrix. The calculation formula is as follows: GS = a / (a+b) where a represents the number of identical genotypes between the two samples; b represents the number of different genotypes between the two samples; and the number of identical genotypes between the two samples. The genetic similarity heat map was drawn using the R software pheatmap package. The results are shown in Figure 8 Finally, the distance matrix is calculated based on the genetic similarity matrix, and the cluster heat map is drawn. Figure 9 .

[0062] (8) Two-dimensional barcode generation. Since there are many core SNP loci in this screening, we screened the 88 core SNP loci in Table 1 and used comprehensive locus genotypes to generate two-dimensional codes. The SNP data was digitally encoded according to the binary format, and the two-dimensional barcode of the goose core germplasm was generated using forage (http: / / cli.im / ). Figure 10 .

[0063] This embodiment provides a simple and efficient tool for the rapid application of molecular markers in actual production. This two-dimensional code can clearly and intuitively display the core SNP genetic characteristics of geese, providing a rapid and efficient tool for genetic identification, molecular marker application, and subsequent molecular breeding practice. Combining genetic diversity analysis and population genetic structure information, the genetic research results of geese lay a solid foundation for the protection and utilization of breed resources. In summary, through high-quality core SNP screening, comprehensive genetic analysis, and intuitive visualization, this study deeply reveals the genetic diversity, population structure, and evolutionary history of domestic geese. These research results not only enrich the understanding of the genetic resources of domestic geese but also provide a solid data foundation for future research and practice of genomic selection and molecular breeding.

[0064] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0065] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for constructing a fingerprint map based on goose SNP molecular markers, characterized in that: It includes the following steps: S1. Obtaining the resequencing data of multiple goose breeds and screening the core SNP loci; S2. Conducting population genetic polymorphism and genetic similarity analysis on the screened core SNPs; S3. Combining and arranging the genotypes of the screened core SNP loci in the order of locus numbers to form a specific sequence as the DNA fingerprint of goose germplasm resources.

2. The construction method of a fingerprint map based on goose SNP molecular markers according to claim 1, wherein: The population genetic polymorphism in step S2 includes phylogenetic tree construction and principal component and population structure analysis.

3. The construction method of a fingerprint map based on goose SNP molecular markers according to claim 1, characterized in that: Converting the specific sequence in S3 into binary format and generating a recognizable QR code or barcode.

4. The construction method of a fingerprint map based on goose SNP molecular markers according to claim 1, characterized in that: There are 88 SNP core loci, and the 88 core SNP loci are as follows:

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