A method for constructing a fingerprint based on goose SNP molecular markers

Abstract

The application discloses a kind of based on the construction method of fingerprint of goose SNP molecular marker, it is related to molecular marker field, the technical scheme of the application includes following steps: S1, the acquisition of multiple goose varieties resequencing data, the screening of core SNP site;S2, the genetic polymorphism and genetic similarity analysis of the screened core SNP;S3, the genotype of the screened core SNP site is combined in sequence according to site number, and forms specific sequence as the DNA fingerprint of goose germplasm resource.By genetic polymorphism and genetic similarity analysis to 88 core SNP sites of goose, the construction of fingerprint, can significantly improve the efficiency of accurate identification and evaluation of goose germplasm resource and important variety, also provide data support for subsequent goose breeding improvement, genetic resource protection.

Description

Technical Field

[0001] This invention relates to the field of molecular markers, and in particular to a method for constructing fingerprint patterns based on goose SNP molecular markers. Background Technology

[0002] Geese, belonging to the order Anseriformes and family Anatidae, possess relatively strong environmental adaptability and disease resistance. Different breeds of geese are widely distributed from the coastal areas of South China to Heilongjiang in Northeast China, and from the inland southwest to the Xinjiang Uygur Autonomous Region in Northwest China. According to Ebang Biotechnology's 2024 H1 Waterfowl Disease Epidemiological Survey Report, compared to ducks, geese are less susceptible to bacterial and viral diseases. Geese grow rapidly and lay more eggs. White geese produce high-quality down that is light, soft, fluffy, and warm, making it a valuable raw material for clothing, bedding, and other products. However, existing conservation measures for wild geese are limited, and the mixed genetic makeup of domesticated geese makes it difficult to distinguish them based solely on epimorphological differences. The convergence of genetic backgrounds and increasing market demand mean that existing germplasm resource identification and breed intellectual property protection are insufficient to effectively support breeding practices. Therefore, there is an urgent need for an efficient and precise molecular marker method to promote the accurate identification and evaluation of germplasm resources and important breeds. Traditional methods for identifying varietal germplasm resources primarily rely on observation and statistics of biological (e.g., body shape, plumage color), genetic (e.g., genetic relationships, origin and evolution), and productive traits (e.g., egg production, feed conversion ratio). While these methods are low-cost, simple to operate, and easy to master, they have long experimental cycles, are easily affected by individual subjectivity and environmental conditions, and are not suitable for large-scale germplasm identification. In particular, they struggle to distinguish closely related varieties with similar genetic backgrounds. Summary of the Invention

[0003] To address the aforementioned technical problems, this 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 accurate identification and evaluation of goose germplasm resources and important breeds, and also provide data support for subsequent goose breeding improvement and genetic resource protection.

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

[0005] S1, acquisition of resequencing data from multiple goose breeds and screening of core SNP sites;

[0006] S2. Perform population genetic polymorphism and genetic similarity analysis on the screened core SNPs;

[0007] S3. The genotypes of the selected core SNP sites are combined and arranged in the order of site numbering 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 of S3 is converted into binary format and a recognizable QR code barcode is generated.

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

[0011]

[0012]

[0013]

[0014] The beneficial effects of this invention are:

[0015] This method analyzes the genetic polymorphism and genetic similarity of 88 core SNP loci in geese and constructs fingerprint maps, which can significantly improve the efficiency of accurate identification and evaluation of goose germplasm resources and important breeds, and also provide data support for subsequent goose breeding improvement and genetic resource protection. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only 10 of the drawings in the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 Images of 18 different types of geese are displayed.

[0018] Figure 2 This shows the top 10 chromosomes containing the core SNP sites.

[0019] Figure 3 Statistics for core SNP types;

[0020] Figure 4 Construction of phylogenetic trees for 18 goose breeds;

[0021] Figure 5 Principal component analysis diagrams for 18 goose breeds;

[0022] Figure 6 This is a diagram illustrating the group structure analysis.

[0023] Figure 7 Error values ​​for cross-validation under different K values;

[0024] Figure 8 A heatmap of genetic similarity;

[0025] Figure 9 Clustering heatmap;

[0026] Figure 10 QR codes for fingerprint profiles of 18 goose breeds; Detailed Implementation

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

[0028] Example

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

[0030] S1: Acquisition of resequencing data from 18 goose breeds (366 individuals) and screening of core SNP loci;

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

[0032] S3. The genotypes of the selected core SNP sites are combined and arranged in the order of site numbering to form a specific sequence as the DNA fingerprint of goose germplasm resources.

[0033] The specific sequence of S3 is converted into binary format and a recognizable QR code / barcode is generated.

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

[0035]

[0036]

[0037]

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

[0039] The core SNP loci in this scheme have the following applications in the identification of goose germplasm resources:

[0040] 1) Population genetic polymorphism analysis

[0041] 2) Genetic similarity analysis

[0042] 3) Construction of goose DNA fingerprinting

[0043] 4) Geese germplasm identification and breeding

[0044] 5) Molecular-assisted breeding of geese

[0045] The specific implementation is as follows:

[0046] (1) Data from 18 goose breeds (366 individuals) were downloaded in this study. All goose resequencing data were obtained from the National Center for Biotechnology Information (NCBI). Specific source information for these goose breeds can be found in (see...). Figure 1 (and Table 2). Figure 1 The data are cited from the Database of goose genetic resources in China (http: / / www.yzcom.com / webdemo / goose / ) established by Zhang and Yangzhou University, and the Goose multiomics database (https: / / goosedb.com / ) established by Chongqing Academy of Animal Sciences.

[0047] Table 1. Information on goose breeds

[0048]

[0049]

[0050] (2) FastQC and Cutadapter software were used for data quality control (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 database. Then, high-quality VCF files were generated based on population merging.

[0051] (3) Based on the SNP sites obtained above, SNP sites suitable for constructing DNA fingerprinting were screened. Sites with a Miss value < 0.1, minimum allele frequency (MAF) < 0.1, polymorphism information content (PIC) > 0.35, and heterozygosity (Het) > 0.1 were filtered out, ultimately retaining 190,740 SNP sites. SNP type analysis of these 190,740 SNP sites is described in [link to analysis]. Figure 2 Of the 190,740 SNPs, 64,436 were transition types (A / G and C / T), and 30,997 were transversion types (A / C, A / T, C / G, and G / T). Transition was the predominant variation type, with a transition-to-transversion ratio (Ts / Tv) of 2.08. All SNPs were evenly distributed across the 37 chromosomes. Chromosomal distribution analysis of the 190,740 SNPs is available in [link to chromosomal analysis]. Figure 3 The results are sorted by the number of SNPs (from highest to lowest), showing only the top 10. Chromosomes 1 and Z contain 117,546 and 16,665 SNPs respectively. Furthermore, chromosome Z has the highest SNP density.

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

[0053]

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

[0055]

[0056] The distance matrix was calculated using TreeBest software. Based on this, a phylogenetic tree was constructed using the neighbor-joining (NJ) method. Figure 4 The bootstrap values ​​(BV) are obtained by performing more than 1000 calculations.

[0057] (5) Principal component analysis (PCA) was performed using R software. In this embodiment, individuals were clustered into different subgroups based on the degree of SNP differences in their genomes, according to different trait characteristics. The SNP at position i, k of individual is represented by , where = 0 if individual i is homozygous with the reference allele; = 1 if it is heterozygous; and = 2 if individual i is homozygous with a non-reference allele. M is an n×S matrix containing the standard genotype, calculated using the following formula:

[0058]

[0059] In the formula, E(d) k ) is d k The mean value and the individual sample covariance n×n matrix were calculated using X = MMT / S. In this study, eigenvectors and eigenvalues ​​were calculated using GCTA software (version 1.13), and the PCA distribution was plotted using R software. Figure 5 .

[0060] (6) Population structure analysis was performed using Admixture. First, the input file for PLINK—the Ped file—was created. Then, the population genetic structure was constructed using Admixture software. Figure 6 The line graph of cross-validation error values ​​under different K values ​​is shown below. Figure 7 .

[0061] (7) Genetic similarity analysis. For core SNPs, the genetic similarity coefficient (GS) between each pair of populations was calculated to obtain a genetic similarity 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 was calculated. The results of the genetic similarity heatmap drawn using the pheatmap package in R software are shown in [link to results]. Figure 8 Finally, a distance matrix based on the genetic similarity matrix was calculated, and a clustering heatmap was plotted. Figure 9 .

[0062] (8) Two-dimensional barcode generation. Due to the large number of core SNP loci screened in this study, we selected 88 core SNP loci from Table 1 above and used comprehensive locus genotypes to create QR codes. The SNP data was digitally encoded according to binary format, and two-dimensional barcodes for the goose core germplasm were 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 QR code clearly and intuitively displays the core SNP genetic characteristics of geese, providing a rapid and efficient tool for genetic identification, molecular marker application, and subsequent molecular breeding practices. Combined with genetic diversity analysis and population genetic structure information, the genetic research results on geese lay a solid foundation for the protection and utilization of breed resources. In summary, this study, through high-quality core SNP screening, comprehensive genetic analysis, and intuitive visualization, has deeply revealed the genetic diversity, population structure, and evolutionary history of domestic geese. These research results not only enrich our understanding of the genetic resources of domestic geese but also provide a solid data foundation for future research and practice in genome selection and molecular breeding.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0065] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for constructing a fingerprint spectrum based on goose SNP molecular markers, characterized in that: Includes the following steps: S1, acquisition of resequencing data from multiple goose breeds and screening of core SNP sites; There are 88 core SNP loci, which are as follows: S2. Perform population genetic polymorphism and genetic similarity analysis on the screened core SNPs; S3. The genotypes of the selected core SNP sites are combined and arranged in the order of site numbering to form a specific sequence as the DNA fingerprint of goose germplasm resources. The reference genome version number for geese is ASM1303099.

2. The method for constructing a fingerprint spectrum based on goose SNP molecular markers according to claim 1, characterized in that: Step S2, population genetic polymorphism, includes phylogenetic tree construction and principal component and population structure analysis.

3. The method for constructing a fingerprint spectrum based on goose SNP molecular markers according to claim 1, characterized in that: The specific sequence of S3 is converted into binary format and a recognizable QR code / barcode is generated.

Images