Construction method and application of Liuan tea tree germplasm molecular identity card

Abstract

The invention belongs to the field of plant variety identification, and particularly relates to a construction method and application of a molecular identity card of a Lu'an tea tree germplasm. The construction method comprises the following steps: sequencing DNA of a Liuan tea tree sample to obtain sequencing data; comparing the sequencing data with a tea tree genome, and then carrying out SNP (Single Nucleotide Polymorphism) detection; carrying out population genetic diversity and genetic similarity analysis on the screened SNPs; core SNP is screened out from the SNP sites of the original sample by using core germplasm extraction software, and data integration is performed on the core SNP to form the Liuan tea tree germplasm molecular identity card. According to the method, a big data management platform is established for quickly identifying core germplasm resources of the Liuan tea trees, and a theoretical basis is provided for germplasm identity identification and traceability management.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of plant variety identification, and particularly relates to a construction method of a molecular identity card of Camellia sinensis var. assamica and application. BACKGROUND

[0002] Camellia sinensis var. assamica germplasm resources have high heterozygosity and rich genetic diversity. These rich tea germplasm resources can provide a large amount of materials for breeding work, but the expansion of genetic materials makes the evaluation of germplasm resources relatively difficult. Traditional tea germplasm resource identification relies on morphological markers, which is easily affected by the environment and has low accuracy. Existing molecular marker technologies (such as SSR and RAPD) have low throughput and high cost, and it is difficult to meet the needs of large-scale germplasm resource accurate identification. Therefore, it is necessary to establish a construction method of a molecular identity card of Camellia sinensis var. assamica and a molecular identity card library, so as to realize effective, scientific, rapid and accurate classification and management of Camellia sinensis var. assamica. SUMMARY

[0003] In order to solve the above problems, the application provides a construction method of a molecular identity card of Camellia sinensis var. assamica and application, which is used for rapid identification of core germplasm resources of Camellia sinensis var. assamica, establishment of a big data management platform, and provision of a theoretical basis for germplasm identity identification and traceability management.

[0004] In order to achieve the above purpose, the specific technical solutions of the application are as follows: The application provides a construction method of a molecular identity card of Camellia sinensis var. assamica in a first aspect, and the method comprises the following steps: sequencing DNA of Camellia sinensis var. assamica samples to obtain sequencing data; performing SNP detection after aligning the sequencing data with a tea genome; performing population genetic diversity and genetic similarity analysis on the screened SNPs; screening core germplasm samples from original samples by using a core germplasm extraction software Core Hunter 3, screening SNP sites of the core germplasm samples, verifying after population structure analysis and genetic diversity analysis, obtaining core SNPs, and integrating data of the core SNPs to form a molecular identity card of Camellia sinensis var. assamica germplasm.

[0005] Further, the screening of the core SNP sites comprises the following steps: 1) only retaining sites on chromosomes; 2) screening sites with a minimum allele frequency (MAF) > 0.2; 3) screening sites with a heterozygosity (het) < 0.05; 4) screening sites with a deletion rate (geno) < 0.2; 5) Remove intergenic loci while retaining SNPs related to functional regions; 6) Screen out linkage disequilibrium r 2 Sites with a value < 0.2.

[0006] Furthermore, the population genetic diversity evaluation indicators include heterozygosity, expected heterozygosity, minimum allele frequency, effective allele count, and polymorphic marker ratio.

[0007] Furthermore, the genetic similarity analysis includes constructing a phylogenetic tree, cluster analysis, and population genetic structure analysis.

[0008] Furthermore, the information of the core SNP is shown in the table below:

[0009] The DNA molecular identity card can be a string, a barcode, or a QR code.

[0010] Furthermore, the tea plant genome is Shuchazao2.

[0011] Furthermore, the number of core germplasm samples is 10% to 30% of the original sample number; the core germplasm samples are samples from the main genetic branches of Lu'an tea trees in the cluster analysis.

[0012] The second aspect of this invention provides a molecular identity card for Lu'an tea tree germplasm constructed by the above-described construction method.

[0013] The third aspect of this invention provides an application of the aforementioned molecular identification card for Lu'an tea tree germplasm in the identification, classification, or germplasm-assisted breeding of Lu'an tea tree germplasm resources.

[0014] The fourth aspect of this invention provides an application of the aforementioned molecular identification card for Lu'an tea germplasm in the study of genetic diversity of Lu'an tea germplasm resources.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method for constructing and applying a molecular identity card for Lu'an tea tree germplasm. The method includes the following steps: sequencing the DNA of Lu'an tea tree samples to obtain sequencing data; comparing the sequencing data with the genome and then screening for SNPs; performing population genetic diversity and genetic similarity analysis on the screened SNPs; using core germplasm extraction software to screen core SNPs from the SNP sites of the original samples, and integrating the core SNP data to form a molecular identity card for Lu'an tea tree germplasm.

[0016] First, simplified genome sequencing was used to obtain whole-genome SNP markers, and a six-step progressive screening process was employed (including minimum gene frequency > 0.2, linkage disequilibrium r...). 2 Strict standards such as a value <0.2 and priority for functional areas significantly improve the marking quality and solve the problems of limited number of traditional SSR markings and large noise interference from SNPs.

[0017] Secondly, the Core Hunter 3 algorithm software (http: / / www.corehunter.org / ) was innovatively applied to construct core germplasm, with allele richness and genetic distance as optimization targets. Compared with the traditional M strategy or random sampling method, it can more completely preserve the unique genetic diversity of Lu'an tea trees.

[0018] Finally, this invention transforms core SNPs into QR code DNA molecular ID cards, creating a unique and tamper-proof "digital identity" for each tea germplasm, thus achieving a digital transformation in resource management. This core value is specifically reflected in the following three application levels: First, it serves as a unique "ID card" for germplasm resources. In practical applications, by extracting the DNA information of the sample to be tested, obtaining its core SNP locus data, and comparing it with the core SNP locus information in the standard Lu'an tea sample provided by this invention, the authenticity of the variety and kinship analysis can be accurately completed, effectively preventing resource confusion and providing irrefutable evidence for intellectual property protection. Second, by scanning the code, the pedigree, traits, and other full life-cycle data of the germplasm can be linked and traced, greatly improving the efficiency of breeding research and resource management. Third, from tracing the authenticity of seedling transactions to brand value enhancement of high-end tea, it provides reliable technical support for the entire industry chain from germplasm protection to market circulation.

[0019] In conclusion, this technology not only provides crucial support for the management and utilization of Lu'an tea germplasm resources, but its standardized process also has strong potential for widespread application to other crop varieties, thus possessing significant industrial value. Attached Figure Description

[0020] 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 or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is the fingerprint pattern of the screened SNP sites.

[0022] Figure 2 This is a fingerprint QR code; the name above the QR code is the sample name. Detailed Implementation

[0023] The specific embodiments of the present invention are described in detail below, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise specified, the experimental methods described in the embodiments of the present invention are conventional methods, and the materials and reagents used in the following embodiments are commercially available unless otherwise specified.

[0024] Information on 202 Lu'an tea tree germplasm resources in this embodiment of the invention is shown in Table 1.

[0025] Table 1. Information on Germplasm Resources of Tea Trees in Lu'an

[0026] Example 1: Construction of molecular identity card for Lu'an tea tree germplasm 1. Sequencing data quality control and SNP detection Simplified genome sequencing was performed on 202 collected Lu'an tea germplasm resources using ddRAD technology. The raw sequencing data was then filtered to generate high-quality sequences. FastP (v0.20.0) was used with a sliding window method to filter the raw data, based on the following criteria:

[0027] (1) Remove contamination from the connector, remove contamination from the connector at all three ends; (2) Quality filtering: The sliding window method is used for quality filtering, and the window size is set to 5bp. The window is slid from the 3 end to the 5 end, and the average Q value of the bases in the window is calculated. If the Q value is <20, the bases in the window are deleted; if the Q value is ≥20, the sliding is stopped.

[0028] (3) Length filtering: If the length of any read in the double-ended terminal is less than 50bp, then remove the double-ended terminal read. (4) Blurred base N filtering: If the number of N bases in the double-ended reads is ≥5, then the double-ended reads are removed.

[0029] Alignment and SNP detection: The filtered high-quality data was aligned to the reference genome using the bwamem program, with all alignment parameters set to bwamem's default settings. The sam files were sorted and converted to bam files using picard 1.107 software, and the "FixMateInfomation" command was used to ensure consistency among all pair-end reads. Reads near InDels are most prone to mapping errors. To minimize SNPs caused by mapping errors, reads near InDels need to be re-aligned to improve SNP calling accuracy. The IndelRealigner command in the GATK program was used to re-align all reads near InDels to improve SNP prediction accuracy.

[0030] SNP annotation: SNP sites were annotated using ANNOVAR software. The location of SNPs in the genome (e.g., upstream of genes, introns, intergenic regions) and the type of variation were clarified to provide functional basis for subsequent screening.

[0031] 2. Overall assessment of genetic diversity and genetic structure Based on SNP data from all samples, population genetic diversity indicators were analyzed, including observed heterozygosity (HO), expected heterozygosity (HE), minimum allele frequency (MAF), effective allele count, and polymorphic marker ratio (PN).

[0032] By constructing a phylogenetic tree and combining it with cluster analysis to evaluate the genetic structure and kinship of the population, a foundation is laid for the screening of core germplasm.

[0033] 3. Construction and Validation of Core Germplasm Core germplasm screening: Using core germplasm extraction software (CoreHunter3), 10% to 30% of the samples were screened from the original population. During the screening, the results of family clustering (family division based on kinship coefficient ≥0.1) could be referenced to ensure that the core subset covered the major genetic branches. A total of 306,320 SNPs were screened.

[0034] For detailed information on the 306,320 SNPs, please refer to: Yan Wang; Huanyun Peng; Xiaoyan Tong; Xiaoyuan Ding; Cheng Song; Teng Ma; Haohao Wang; Wang Wei; Cunwu Chen; Junyan Zhu; Dong Liu; Genetic diversity analysis and core germplasm construction of tea plants in Lu'an, BMC Plant Biology, 2025, 25(253).

[0035] Verification of screening accuracy: Compare the genetic diversity parameters (such as MAF distribution and heterozygosity) and principal component analysis (PCA) clustering patterns of the original germplasm and the core germplasm to verify the representativeness of the genetic structure of the original population by the core germplasm.

[0036] 4. Core SNP tagging and filtering Using Plink (v1.90b6.24) software, SNP sites were deeply filtered based on the following principles to obtain core markers for fingerprinting: (1) Retain autosomal loci and remove sex chromosome markers; (2) Select sites with minimum allele frequency (MAF) > 0.2, which can eventually be increased to MAF > 0.3; (3) Select sites with heterozygosity (het) < 0.05, and finally strictly select sites with het < 0.01; (4) Screening for sites with a geno missing rate (geno) < 0.1; (5) Remove intergenic loci and retain functional region-related SNPs; (6) Screening for linkage disequilibrium (LD) r 2 For sites with a value < 0.2, site independence is ensured.

[0037] The SNP locus information obtained after screening is shown in Table 2.

[0038] Table 2 SNP information after filtering

[0039] 5. Assessment of genetic diversity of core SNP markers and verification of population genetic structure Calculate the genetic diversity indices of core SNP markers, including HO, HE, MAF, and polymorphism information content (PIC), analyze their distribution characteristics (high polymorphism is required), and verify the effectiveness of the markers in distinguishing different germplasms.

[0040] Phylogenetic tree construction: Using the selected core SNPs, a phylogenetic tree among germplasms is constructed through the neighbor-joining method to reveal kinship relationships.

[0041] Principal component analysis (PCA): Verifies whether core SNPs can accurately reflect population genetic differentiation by analyzing sample clustering patterns, ensuring consistency with the original population structure.

[0042] After genetic diversity assessment and population genetic structure verification, core SNPs were obtained, and data on the core SNPs were integrated. Specific information is shown in Table 3.

[0043] Table 3 Information on core SNPs

[0044] Note: ". / ." in the table indicates that the genotype data for the corresponding sample at that SNP locus is missing.

[0045] 6. DNA fingerprinting construction and application Fingerprint generation: Based on the core SNP markers, the matplotlib graphics library of Python 3 is used to draw the corresponding fingerprints of the selected SNPs. The fingerprints are drawn and different colors are used to represent the genotypic differences of the samples.

[0046] like Figure 1 As shown, each row represents a sample, and each column represents a SNP locus, sorted by physical location, with different genotypes represented by different colors.

[0047] Based on the sequential sorting of different SNP marker reading results, a string-based DNA molecular ID card is constructed. An online barcode generator is used to generate scannable barcode DNA molecular ID cards from the corresponding strings. Finally, online QR code technology is used to convert the basic information of the material into QR codes, resulting in a QR code DNA molecular ID card. Figure 2 ).

[0048] It should be noted that when numerical ranges are involved in this invention, it should be understood that both endpoints of each numerical range and any value between the two endpoints can be selected. Since the steps and methods used are the same as in the embodiments, preferred embodiments are described here to avoid redundancy. Although preferred embodiments of the invention have been described, those skilled in the art, once they understand the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this invention.

[0049] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing a molecular identity card of a germplasm of a Lu'an tea tree, characterized in that, The method comprises the following steps: Sequencing the DNA of the Lu'an tea tree samples to obtain sequencing data; SNP detection after aligning the sequencing data with the tea tree genome; Population genetic diversity and genetic similarity analysis of the screened SNPs; Using the core germplasm extraction software Core Hunter 3 to screen the core germplasm samples from the original samples, screening the SNP sites of the core germplasm samples, verifying through population structure analysis and genetic diversity analysis to obtain core SNPs, and integrating the data of the core SNPs to form the Lu'an tea tree germplasm molecular identity card.

2. The method of claim 1, wherein, The screening of the core SNP sites comprises the following steps: 1) retaining autosomal sites and eliminating sex chromosome markers; 2) screening sites with a minimum gene frequency > 0.2; 3) screening sites with a heterozygosity rate < 0.05; 4) screening sites with a deletion rate < 0.1; 5) eliminating intergenic region sites and retaining functional region-related SNPs; 6) Sites with linkage disequilibrium r 2 values < 0.2 were excluded from the analysis.

3. The method of claim 1, wherein The population genetic diversity evaluation indicators include heterozygosity, expected heterozygosity, minimum allele frequency, effective allele number, and polymorphic marker proportion; the genetic similarity analysis includes constructing a phylogenetic tree, cluster analysis, and population genetic structure analysis.

4. The method of claim 1, wherein, The information of the core SNPs is shown in the following table:

5. The method of claim 1, wherein, The type of the DNA molecular identity card is a string, a barcode, or a two-dimensional code.

6. The method of constructing as defined in claim 1, wherein, The number of the core germplasm samples is 10% to 30% of the number of the original samples.

7. A Lu'an tea tree germplasm molecular identity card constructed by the construction method according to any one of claims 1 to 6.

8. Use of the Lu'an tea tree germplasm molecular identity card of claim 7 in the identification, classification, or assisted breeding of Lu'an tea tree germplasm resources.

9. Use of the Lu'an tea tree germplasm molecular identity card of claim 7 in the study of the genetic diversity of Lu'an tea tree germplasm resources.

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