Liquid phase chip detection method for Leymus chinensis variety identification

Abstract

The invention belongs to the field of biotechnology and agricultural science, and particularly relates to a liquid phase chip detection method for leymus chinensis variety identification. According to the method, leymus chinensis variety identification is carried out by using a leymus chinensis whole-genome 50K liquid phase chip, the method combines a high-throughput liquid phase chip technology and a machine learning algorithm, accurate detection of whole-genome SNP loci is realized through high-throughput SNP detection, an efficient and stable classification model is innovatively constructed, and the precision and efficiency of variety identification are remarkably improved. By screening specific SNP loci and combining population genetic analysis and a random forest algorithm, the reliability of the model is enhanced, and the problems of low efficiency and the like of a traditional method are solved.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology and agricultural science, and specifically relates to a liquid chip detection method for identifying Leymus chinensis varieties. Background Art

[0002] Leymus chinensis Leymus chinensis (Trin.) Tzvel.) is a perennial forage grass belonging to the genus Leymus in the Poaceae family. It is one of the important community-building species in the meadow steppe and arid steppe in the eastern steppe region of Eurasia. The Songnen Plain in northeastern my country and eastern Inner Mongolia are also the main distribution centers of Leymus chinensis. It is also widely distributed in Hebei, Shanxi, Henan, Shaanxi, Ningxia, Gansu, Qinghai, Xinjiang and other regions; in addition, it is also scattered in Russia, Mongolia, North Korea, Japan and other places. As an important native forage grass in northern my country, Leymus chinensis not only has high nutritional value, stable yield, and good palatability, and is a favorite food for all kinds of livestock, but also has superior stress resistance and can adapt to extreme climates and diverse soil environments. Moreover, the developed horizontal rhizomes of Leymus chinensis give it a strong ability to penetrate and invade, and by forming a strong root network, it has a significant role in consolidating soil and retaining water. Therefore, Leymus chinensis has played an irreplaceable role in the ecological construction of northern grasslands, vegetation restoration, and animal husbandry development.

[0003] In recent years, with the degradation of grassland ecology and the increasing demand for high-quality forage in animal husbandry, the breeding of new varieties and resource protection of Leymus chinensis have become research hotspots. However, due to its genetic diversity and morphological complexity, traditional classification and identification methods are difficult to meet the needs of rapid and accurate classification. At present, there is still a lack of efficient molecular markers and genotype detection technology for Leymus chinensis, which limits the progress of genetic research and molecular breeding. Therefore, the development of efficient molecular tools suitable for the identification of Leymus chinensis varieties is of great significance to promote its research and application.

[0004] With the rapid development of molecular biology and genomics technology, traditional phenotypic identification methods are gradually being replaced by molecular identification methods. Molecular marker technology (such as SSR, RAPD, AFLP, etc.) and high-throughput detection platforms have been widely used in forage breeding and resource evaluation. Among them, molecular detection technology based on SNP (single nucleotide polymorphism) has become an important tool for plant genetic research and molecular breeding due to its high efficiency, sensitivity and high-throughput characteristics. As a highly stable molecular marker that is widely present in the genome, SNP is very suitable for large-scale, high-throughput genotyping analysis. The SNP liquid phase chip is a genotyping technology platform based on high-throughput detection, which can detect thousands to tens of thousands of SNP sites at a time, with high sensitivity, low cost and strong flexibility. At present, the SNP liquid phase chip has been successfully used in variety identification and molecular breeding of crops including rice, corn, wheat and various forages, providing strong technical support for agricultural and forage improvement.

[0005] However, although SNP liquid phase chip technology has achieved remarkable results in crop and forage research, the identification of Leymus chinensis varieties still mainly relies on traditional morphological indicators. These traditional methods require long-term observation, are easily affected by environmental factors and human subjective judgment, and have problems such as long identification cycle, low accuracy and strong subjectivity of results. In addition, as an allotetraploid plant, Leymus chinensis has a complex genome structure, a large genome size and a high proportion of repetitive sequences. Compared with other major crops and forages, the genome research of Leymus chinensis is relatively lagging behind. At present, molecular research on Leymus chinensis has not yet introduced advanced technologies such as SNP liquid phase chips. The lack of high-throughput molecular detection and genotype analysis tools limits the efficiency and accuracy of Leymus chinensis variety identification. Therefore, the art expects to develop a method for molecular identification of Leymus chinensis varieties based on liquid phase chip technology, which can achieve rapid and accurate identification of Leymus chinensis varieties, and has positive significance for the protection and utilization of Leymus chinensis germplasm resources. Summary of the invention

[0006] To this end, the purpose of the present invention is to provide a liquid chip detection method for identifying Leymus chinensis varieties, which enhances model reliability by screening specific SNP sites and combining population genetic analysis with random forest algorithm to solve the problems of low efficiency of traditional methods.

[0007] In order to solve the above technical problems, the present invention provides a liquid chip detection method for identifying Leymus chinensis varieties, the method comprising the following steps: (1) SNP detection of different varieties of Leymus chinensis was performed using a 50K liquid phase chip of the whole genome of Leymus chinensis, wherein the sites of the liquid phase chip include multiple SNP sites located on the reference genome of Leymus chinensis Lc6-5 or its updated version, covering gene regions and their related non-gene regions; The core SNP site information of the liquid phase chip is specifically shown in Table 1 in the specification; wherein the position and variation information of the SNP site are represented in the form of chromosome / physical position / reference genotype / variant allele type, and the physical position of the SNP molecular marker combination is based on the positioning analysis of the reference genome of Leymus chinensis; (2) Compare the SNP sequencing results with the reference genome of Leymus chinensis, analyze and annotate the SNPs, and establish a SNP information database for different varieties.

[0008] Furthermore, the liquid chip detection method for identifying Leymus chinensis varieties uses the SNPs identified by sequencing and uses TreeBeST software to calculate the distance matrix. Based on this, a phylogenetic tree is constructed for 11 varieties using the neighbor-joining method. Principal component analysis (PCA) is performed using GCTA software. Based on the degree of SNP differences in individual genomes, individuals are clustered into different subgroups according to the principal components according to different trait characteristics. The population genetic structure of the samples was analyzed using the software admixstructure.

[0009] Furthermore, the liquid chip detection method for identifying Leymus chinensis varieties encodes the SNP data set obtained by sequencing, and then uses PCA and singular value decomposition to extract the main SNP sites. After PCA pre-selection and feature filtering, the number of SNP sites is still large. In order to evaluate the importance of the selected SNP sites, the samples are assigned to the corresponding varieties, and the varieties are classified using random forests.

[0010] Furthermore, in the liquid chip detection method for identifying sheepgrass varieties, the optimal number of decision trees has a monotonically increasing trend in the accuracy of the random forest model, but after reaching a certain value, the effect tends to be stable or even fluctuates, and the computational cost increases significantly. Therefore, the optimal number of trees was determined after balancing accuracy and computational cost. Random forest uses a self-service sampling method to generate decision trees, evaluates classification performance through unextracted OOB data, and obtains an unbiased OOB error rate or accuracy rate. In addition, in order to further verify the stability and reliability of the model, k-fold cross validation was also performed.

[0011] Furthermore, the liquid chip detection method for identifying Leymus chinensis varieties, first, uses the trained model to score the OOB data and calculates the AUC or other evaluation indicators. Then, for each feature in the OOB data, the following operations are performed in sequence: randomly disrupt the value of the feature to observe the change in model performance; re-score and calculate the evaluation index; calculate the index change rate. Through the above steps, the change rate of each feature is obtained, and the features are sorted according to the change rate, and then the feature importance (i.e., site importance) is quantified. According to the importance sorting, the feature importance is accumulated, and the sites with accumulated values ​​exceeding 90% are selected, and the random forest model is retrained, and finally 615 SNP sites are screened out. Finally, the model is used for accuracy testing and cross-validation.

[0012] Furthermore, the liquid chip detection method for the identification of Leymus chinensis varieties, in order to further verify the model performance, randomly divided each population into a training set and a test set at a ratio of 7:3, and iterated 100 times to evaluate the prediction accuracy. In addition, the screened SNP site information was encoded into a QR code, and the user could obtain the molecular identity information of the corresponding variety by scanning the QR code.

[0013] Furthermore, the liquid phase chip detection method for identifying sheep fescue varieties also includes the step of identifying the variety of the sheep fescue to be tested; including the step of obtaining the genotype data of the sheep fescue individual to be tested based on the sheep fescue whole genome 50K liquid phase chip, and the step of comparing and identifying the sheep fescue varieties based on the constructed reference sample library.

[0014] Furthermore, the liquid phase chip detection method for identifying Leymus chinensis varieties, the Leymus chinensis whole genome 50K liquid phase chip includes an independently packaged 50K probe mixture and a reagent suitable for liquid phase hybridization capture; wherein, The probe mixture contains 50K probes with high sequence specificity, which can identify and detect the core SNP molecular marker sites shown in Table 1 in the specification. The range of SNP sites that the probes can identify and detect includes but is not limited to the sites listed in Table 1.

[0015] As an exemplary embodiment, the Leymus chinensis whole genome 50K liquid phase chip includes an independently packaged 50K probe mixture and a Hyb&Wash kit suitable for liquid phase hybridization capture; wherein, The probe mixture contains 50K probes with high sequence specificity, which can effectively identify and detect the core SNP molecular marker sites shown in Table 1 in the specification.

[0016] Specifically, in the liquid phase chip detection method for identifying Leymus chinensis varieties, the 50K liquid phase targeted capture probe is designed based on the positive strand of the Leymus chinensis reference genome; Preferably, the probe is designed to have a length ranging from 55 to 120 nt, with a target average length of 100 nt.

[0017] Preferably, the 50K liquid phase targeted capture probe is designed based on the principle of thermodynamic stability to ensure 99% capture in the conventional area; Preferably, the 50K liquid phase targeted capture probe targets complex and difficult areas by adjusting the probe position and placing multiple layers of "shingled" probes to improve the effective coverage of difficult areas, and a 3X shingled structure is more preferred.

[0018] The method for identifying the varieties of Leymus chinensis described in the present invention utilizes the 50K liquid phase chip of the whole gene of Leymus chinensis. The method combines high-throughput liquid phase chip technology with machine learning algorithms, and realizes accurate detection of SNP sites in the whole genome through high-throughput SNP detection, innovatively constructs an efficient and stable classification model, and significantly improves the accuracy and efficiency of variety identification. By screening specific SNP sites, combining population genetic analysis with random forest algorithm, the reliability of the model is enhanced, and the problem of low efficiency of traditional methods is solved. Using specific SNP to generate a QR code, variety traceability and promotion are realized, providing scientific support for the protection of forage germplasm resources and the development of agriculture and animal husbandry.

[0019] The liquid chip detection method for identifying sheep fescue varieties described in the present invention, through library construction and sequencing of 228 samples of 11 sheep fescue varieties (including 9 varieties, 1 strain and 1 germplasm), compared with the sheep fescue reference genome, and performed SNP detection and annotation, achieved rapid and accurate identification of sheep fescue varieties, which not only overcomes the limitations of traditional methods, but also provides important technical support for grassland ecological restoration and forage industry development, while promoting the application and promotion of high-quality new varieties, and laying a foundation for the protection and utilization of sheep fescue germplasm resources.

[0020] The liquid phase chip detection method for identifying sheep fescue varieties described in the present invention is based on a 50K liquid phase chip of the sheep fescue whole genome for detection, and the SNP molecular marker combination of the sheep fescue is determined by using whole genome resequencing. A total of 51,696 SNP molecular marker sites are screened out based on the sheep fescue reference genome Leymus_chinensis_Lc6-5. The sheep fescue whole genome 50K liquid phase chip is developed based on targeted capture sequencing technology, and high-throughput and high-precision genotyping of the sheep fescue genome is achieved by combining high-density SNP site probes, so that the genetic characteristics of the sheep fescue can be comprehensively evaluated.

[0021] The liquid chip detection method for the identification of Leymus chinensis varieties of the present invention is based on the 50K liquid chip of the whole genome of Leymus chinensis for detection. In addition to using the reference genome of Leymus chinensis, the probe design is carried out by using the homology comparison analysis of the transcriptome without reference, optimizing the SNP site selection and genome coverage, and ensuring the uniform distribution and good polymorphism of the SNP sites in the Leymus chinensis genome. The probe site covers the key functional gene regions of Leymus chinensis, including important trait genes such as growth, development, and resistance, and can provide an important basis for the molecular marker identification of agronomic traits, quality traits, and stress resistance traits of Leymus chinensis.

[0022] The liquid chip detection method for identifying Leymus chinensis varieties described in the present invention is based on the 50K liquid chip of the whole genome of Leymus chinensis for detection, and is designed based on the principle of thermodynamic stability to ensure 99% capture of conventional areas. For complex areas with high difficulty, the effective coverage of high-difficulty areas is improved by adjusting the probe position and placing multi-layer "shingled" probes. At the same time, the uniform distribution of SNP sites in the genome and the coverage of key functional areas are ensured, the coverage of functional areas is improved, and the research on agronomic traits and stress resistance traits is supported.

[0023] The liquid phase chip detection method for identifying Leymus chinensis varieties of the present invention is based on the Leymus chinensis whole genome 50K liquid phase chip for detection, and can provide high-quality genotyping data. Compared with other typing technologies (such as GBS), the data consistency is higher and has stronger data comparison and accumulation capabilities. The high throughput and data consistency of the chip provide a strong guarantee for long-term data storage and comprehensive utilization, greatly improving the long-term availability of data and the simplicity of analysis.

[0024] The liquid chip detection method for identifying Leymus chinensis varieties of the present invention is based on the 50K liquid chip of the whole genome of Leymus chinensis for detection. The liquid chip technology has higher flexibility than the traditional solid chip, and can adjust the SNP density of the chip according to demand, providing 50K, 40K, 30K and other versions, and can capture the sequence information of 100 bp upstream and downstream of the target SNP site, providing more data for functional gene research, and adapting to research and application needs of different scales. The liquid chip can complete high-throughput detection, has high detection flexibility, can effectively shorten the sample detection cycle, and improve scientific research efficiency.

[0025] The liquid phase chip detection method for identifying the varieties of Leymus chinensis described in the present invention is based on the 50K liquid phase chip of the whole genome of Leymus chinensis for detection. Based on the targeted capture sequencing technology, the tolerance of the flanking sequence is fully considered in the probe design. When the flanking sequence variation does not exceed 10%, the target sequence can still be stably captured. This technical feature ensures the high reliability of the data. At the same time, in addition to obtaining the target SNP site information, the chip can also obtain the sequence information of 100bp upstream and downstream, providing more supporting data for the functional gene research of Leymus chinensis.

[0026] The 50K liquid phase chip of the whole genome of Leymus chinensis described in the present invention realizes the complete localization of probe synthesis, sample detection and reagent use, avoiding the high cost of imported equipment and reagents. By reducing material costs and reducing technology dependence, it realizes independent control of production and application, reduces the risk of core data leakage, and avoids the inconvenience caused by external trade frictions, which meets the domestic demand for independent control of agricultural technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 This is a flow chart of SNP site screening in Example 1 of the present invention; Figure 2 The distribution diagram of 51696 SNP site probes on chromosomes in Example 3 of the present invention; Figure 3This is a distribution statistical diagram of SNP markers in the gene structure in Example 3 of the present invention; Figure 4 The radar chart of the expected performance and actual test performance of the Aneurolepidium chinense 50K liquid phase chip in Example 4 of the present invention; Figure 5 The flowchart of library construction and sequencing of the Leymus chinensis liquid phase chip in Example 5 of the present invention is as follows; Figure 6 This is a diagram of the annotation results of the SNP sites detected in Example 6 of the present invention; Figure 7 The result diagram of the population evolution tree of the Leymus chinensis variety in Example 6 of the present invention; Figure 8 This is a diagram showing the principal component analysis results of the population of Leymus chinensis varieties in Example 6 of the present invention; Fig. 9 The population genetic structure diagram of the Leymus chinensis variety in Example 6 of the present invention; Fig.10 This is a graph of model prediction accuracy in Example 7 of the present invention, taking breed_11 as an example; Fig.11 This is the SNP fingerprint information of the Leymus chinensis variety in Example 7 of the present invention, taking breed_11 as an example. DETAILED DESCRIPTION

[0028] In the following embodiment of the present invention, the liquid chip detection method for identifying Leymus chinensis varieties, the Leymus chinensis whole genome 50K liquid chip is developed based on targeted capture technology, and its working principle is based on probe design for target region sequences, targeted capture of target fragments, and high-throughput sequencing using a sequencing platform, targeted detection of target genes and sites, and rapid detection of large quantities of samples, thereby achieving the purpose of target region gene detection and typing.

[0029] In the following embodiments of the present invention, the Leymus chinensis whole genome 50K liquid phase chip comprises a set of Leymus chinensis 50K SNP probe sites.

[0030] In the following embodiments of the present invention, the Leymus chinensis whole genome 50K liquid phase chip comprises an independently packaged 50K probe mixture and a Hyb&Wash kit suitable for liquid phase hybridization capture.

[0031] In some embodiments, the liquid phase chip of the present invention adopts the principle of nucleic acid hybridization for targeted capture. When two nucleic acid chains are complementary, a stable double-stranded structure will be formed between them. Through the denaturation and renaturation process, the target region library is first denatured to remove the repeated sequence and the adapter sequence, and then a probe complementary to the base of the target region is added for renaturation; the non-specific binding is washed away, and finally a specific probe-library conjugate is obtained, thereby completing the capture of the target region.

[0032] In some embodiments, the preparation of the liquid-phase targeted capture probe of the liquid-phase chip of the present invention includes template synthesis and RNA preparation. The probe template is composed of organically synthesized single-stranded DNA (oligo). After quality control (QC) and pooling, qualified templates are used to prepare RNA analog probes. In the synthesis of RNA probes, biotin-labeled nucleotide analogs (such as LNA, PNA, etc.) are used to improve the binding stability between the probe and the library. The biotin-labeled probe can be combined with magnetic beads coated with streptavidin to further improve the capture efficiency.

[0033] In some embodiments, the Leymus chinensis whole genome 50K liquid phase chip, the 50K liquid phase targeted capture probe is designed based on the positive strand of the Leymus chinensis reference genome; Preferably, the probe is designed to have a length range of 55-120 nt, with a target average length of 100 nt;.

[0034] Specifically, the probe design of the liquid chip is based on the principle of thermodynamic stability, including factors such as melting temperature (Tm), GC content and sequence specificity, and comprehensively considers the genomic complexity of the target species, the location of the target site and the GC content around it to ensure 99% capture of conventional areas. For complex areas with too high or too low GC content, the probe position is adjusted and a multi-layer 'shingled' probe design is used to increase the coverage of high-difficulty areas. This scheme improves the capture efficiency in GC extreme areas by increasing the complementarity and stability between probes. The probe design is based on the positive chain of the latest version of the sheep grass reference genome. The probe design length ranges from 55-120nt, and its target average length is 100nt, which is optimized to ensure efficient SNP capture.

[0035] In the following embodiments of the present invention, the liquid chip detection method for the identification of Leymus chinensis varieties, using the whole genome 50K liquid chip of Leymus chinensis, combined with high-throughput liquid chip technology and machine learning algorithm, through high-throughput SNP detection, to achieve accurate detection of whole genome SNP sites, innovatively construct an efficient and stable classification model, and significantly improve the accuracy and efficiency of variety identification. By screening specific SNP sites, combining population genetic analysis and random forest algorithm, the reliability of the model is enhanced, and the problems of low efficiency of traditional methods are solved. Generate a QR code using specific SNP to achieve variety tracing and promotion.

[0036] Example 1 This embodiment provides a 50K liquid phase chip of the whole genome of Leymus chinensis. The specific screening process is as follows: Figure 1 The specific screening process is as follows.

[0037] 1. Preliminary SNP filtration and screening In this example, 100 samples of Leymus chinensis were resequenced and SNP variation detection was performed on these sequencing data. The reference genome version used was Leymus_chinensis_Lc6-5. The preliminary SNP filtering and screening process was as follows: (1) Depth filtering: On the basis of the average sequencing depth of the sample reaching 10X, select SNP sites with a depth of no less than 3X to ensure the reliability of the data and filter out sites with low depth; (2) Completeness filtering: SNP markers whose genotypes cover at least 50% of all sample individuals were screened; specifically, for each polymorphic marker site, at least 50 samples out of 100 samples were required to have a definite genotype, thereby filtering out markers with poor genotype integrity; (3) Minimum allele frequency (MAF) filtering: sites with MAF values ​​lower than 0.05 were filtered out to ensure that the selected sites had sufficient variation information; (4) After the above preliminary filtering, 40M basic SNP sites were retained.

[0038] 2. SNP depth filtering Filtering based on linkage disequilibrium (LD): PLINK software was used for linkage disequilibrium analysis with two parameter settings: one was 50 kb window, 10 bp step, R2<0.2R2<0.2; the other was 100 kb window, 1 bp step, R2<0.2R2<0.2. These filtering parameters refer to the reported screening criteria for allopolyploid plant loci.

[0039] Hardy-Weinberg equilibrium (HWE) filtering: Calculate the p-value of Hardy-Weinberg equilibrium for all SNP loci and filter according to 1e-6.

[0040] Gene region screening: Select SNP sites located in the gene region and its upstream and downstream, and filter out other sites within 100bp upstream and downstream of the SNP site. For the exon region, if the distance between a SNP and the previous SNP is less than 100bp and it is a non-synonymous mutation, then keep the SNP.

[0041] Non-exon region filtering: In non-exon regions, A / T and G / C type variant sites are filtered out.

[0042] After deep filtering of SNPs, 95,954 background sites, namely VIP SNP sites, were finally retained.

[0043] 3. Screening of trait-associated loci This example uses the resequencing data of 100 samples of Leymus chinensis to perform a genome-wide association study (GWAS) on eight agronomic traits of Leymus chinensis using a mixed linear model (MLM) using GEMMA software. In the analysis, the default parameters were used and the significance threshold was set to 6 to identify the associated sites for each trait. The strategy for site retention is as follows: (1) For traits associated with less than 450 loci, all associated loci were retained; (2) For traits associated with more than 450 loci, we screened loci that were less than 100K away from the gene and sorted them by P-value to select the top 450 loci; (3) Among the selected sites, if the distance between two sites is less than 1K, only the site closest to the gene is retained; (4) Through these steps, 1,667 loci associated with the agronomic traits of Leymus chinensis were finally selected, namely the VVIP SNP loci.

[0044] 4. Determination of SNP sites on 50K liquid phase array The basic, VIP and VVIP SNPs obtained in the above analysis were merged, and after removing duplicates, the site data set was screened according to the sliding window method. In each window, VIP and VVIP SNPs were preferentially selected, and the remaining positions were supplemented with background sites. 51,696 SNP sites can be screened as a 50K chip site set, and the specific information of the core SNP sites included is recorded in Table 1 below. Among them, the position and variation information of the SNP site are expressed in the form of chromosome: physical position: reference genotype / variant allele type, and the physical position of the SNP molecular marker combination is based on the reference genome of sheep grass for positioning analysis.

[0045] Table 1 Core SNP locus information

[0046] Example 2 In this example, liquid phase chip probes were designed based on the SNP site information screened in Example 1.

[0047] The chip design of this embodiment is based on the principle of thermodynamic stability, including factors such as melting temperature (Tm), GC content and sequence specificity, and comprehensively considers the complexity of the target species genome, the position of the target site and the GC content around it to ensure 99% capture in the conventional area. The probe is based on the positive strand of the reference genome of Leymus chinensis, and the probe design length range is 55-120nt, and its target average length is 100nt.

[0048] For complex areas with high or low GC content, the probe position is adjusted and a multi-layer 'shingled' probe design is used to improve the coverage of difficult areas. This solution improves the capture efficiency in extreme GC areas by increasing the complementarity and stability between probes. In this embodiment, the key design parameters of the 50K probe include: (1) GC content: 30%-80%; the number of sites near a single target site is less than 10; site specificity is greater than 50%; based on these criteria, the chip will select the lowest quantile site set according to the score; (2) All 1,667 GWAS-related loci were retained regardless of their scoring values.

[0049] Based on the above standards, this embodiment designed and synthesized a liquid chip probe.

[0050] Example 3 This embodiment is based on the distribution and annotation of chromosomes of SNP sites in liquid phase microarray.

[0051] This example statistically analyzed the distribution of the liquid phase chip screened in Example 1 on 14 chromosomes of Leymus chinensis. On average, each chromosome contained about 3,689 SNPs, and the average spacing between adjacent sites was 145 kb. The 51,697 SNPs screened showed a nearly uniform distribution. The site distribution diagram is shown in Figure 2 The above SNP annotation results show that among the 51,697 sites, 34% of the sites are located in the gene region and 2% are located in the UTR region. In addition, 34% of the sites may have a significant impact on gene function, potentially leading to changes in protein-coding gene function. The annotation information of the SNP sites is shown in Figure 3 The combination of SNPs screened by this chip significantly improved the detection success rate of candidate gene loci, providing strong support for subsequent functional gene research.

[0052] Example 4 This example is based on the Leymus chinensis liquid phase chip developed in the aforementioned Example 2. Twelve Leymus chinensis samples were selected to test the performance of the chip, which specifically involves the following steps.

[0053] 1. Probe preparation The preparation of the liquid phase targeted capture probe of Leymus chinensis described in this embodiment includes template synthesis and RNA preparation. The probe template is composed of organically synthesized single-stranded DNA (oligo). After quality control (QC) and pooling, qualified templates are used to prepare RNA analog probes. In the synthesis of RNA probes, biotin-labeled nucleotide analogs (such as LNA, PNA, etc.) are used to improve the binding stability between the probe and the library. The biotin-labeled probe can be combined with magnetic beads coated with streptavidin to further improve the capture efficiency.

[0054] 2. Library construction and capture The DNA samples are sheared by ultrasound or enzyme digestion, end repair and 3' end A addition, adapter ligation and purification, and Pre-PCR library amplification to obtain the library required for hybrid capture. The capture method is carried out according to the SOP for hybrid capture, and after library and probe hybridization (12-18h), probe and magnetic bead binding, non-specific binding library rinsing, post-capture PCR amplification, library quantification and quality inspection, it is sequenced on the machine.

[0055] (III) Library quality inspection and quantification Take 1 μL of library and use Qubit dsDNA HS Assay Kit reagent to detect the library concentration on Qubit 4.0 Fluorometer. The library concentration before capture >25 ng / μL is considered a qualified library.

[0056] Take 1 μL sample and use Qsep 100 for detection. The main peak of the library should be around 270-450 bp, with no impurity peaks before and after the main peak.

[0057] 4. Sequencing Take 1 μL of library and use Qubit dsDNA HS Assay Kit reagent to detect the library concentration on Qubit 4.0 Fluorometer and record the library concentration. The concentration of the library after capture is about 1-20 ng / μL.

[0058] Take 1 μL sample and use Qsep 100 to measure the length of library fragments. The length of the library is approximately between 270-450 bp.

[0059] Sequencing was performed using a high-throughput sequencing platform.

[0060] 5. Data Analysis In this embodiment, the raw image data obtained by high-throughput sequencing (Nova platform, MGI platform, etc.) is identified by a specific program, converted into raw sequencing data in Fastq format, and then compared with the reference genome for bioinformatics analysis, including the following steps: (1) Quality control: Remove low-quality sequences, adapter sequences, ployG and other abnormal sequences in the original sequencing data to obtain clean data; (2) Alignment: Align, sort, and remove duplicates of the Clean data with the reference genome to obtain a bam file; (3) Statistics: Statistical analysis of indicators such as alignment rate, coverage rate, capture rate, and uniformity.

[0061] (VI) Test results In this embodiment, after the above test analysis, the test results include various indicators of the target area, various indicators of the probe area, coverage of different areas, etc., which comprehensively and in detail demonstrate the various performances of the chip. The product has a high site detection rate, with an average site detection rate of 98.9%; the product has good stability and high genotype accuracy, and the average consistency rate of genotypes of repeated samples is 99%.

[0062] In this example, in order to intuitively display the expected performance and actual test performance of the chip, the QC rate, comparison rate, coverage, capture rate, uniformity and other indicators of two samples were taken to calculate the average value and draw a radar chart. The results are shown in the figure. Figure 4 As shown, it can be seen that all indicators are good and in line with expectations.

[0063] Example 5 This embodiment provides a liquid phase chip detection method for identifying Leymus chinensis varieties, using the Leymus chinensis 50K liquid phase chip prepared above to construct a library for sequencing Leymus chinensis variety samples, and the specific experimental process is as follows.

[0064] 1. Extraction and detection of DNA samples In this embodiment, the samples to be tested, breed_1 to breed_11, are respectively: SZ1 Leymus chinensis strain, Nongmu No. 1 Leymus chinensis variety, Jisheng No. 4 Leymus chinensis variety, PL Leymus chinensis germplasm, Zhongke No. 3 Leymus chinensis variety, Jisheng No. 3 Leymus chinensis variety, Jisheng No. 2 Leymus chinensis variety, Zhongke No. 2 Leymus chinensis variety, Zhongke No. 1 Leymus chinensis variety, Jisheng No. 1 Leymus chinensis variety and Zhongke No. 5 Leymus chinensis variety. Among them, the sample size of Zhongke No. 5 Leymus chinensis variety (breed_11) is 30 individual plants, the sample size of Jisheng No. 3 Leymus chinensis variety (breed_6) and Jisheng No. 2 Leymus chinensis variety (breed_6) is 19 individual plants, and the sample size of the remaining varieties is 20 individual plants, for a total of 228 samples to be tested.

[0065] The leaves of the samples to be tested were taken, and the genomic DNA was extracted by CTAB method. The concentration, integrity and purity of the DNA were detected by agarose gel electrophoresis combined with Agilent5400.

[0066] 2. Library construction and sequencing The 50K liquid phase chip technology of Leymus chinensis prepared by the above-mentioned embodiment comprises independently packaged 50K probe mixture and hybrid capture reagent. The probe mixture provides 50K liquid phase targeted capture probes in total.

[0067] like Figure 5 The library construction process shown in the figure includes the following steps: treating the DNA sample with ultrasound or enzyme digestion to break the fragments, followed by end repair, 3' end A addition, adapter ligation and purification, and Pre-PCR library amplification, and finally obtaining a library that meets the hybridization capture requirements. The capture method is strictly in accordance with the SOP, and the specific process includes: hybridization of the library with the probe (lasting 12 to 18 hours), binding of the probe with magnetic beads, washing of the non-specifically bound library, post-capture PCR amplification, library quantification and quality detection. After all steps are completed, the library is sequenced on the machine.

[0068] Example 6 This example performs standard bioinformatics analysis on the data after sequencing in Example 5, specifically including data quality control and statistical analysis, SNP detection and annotation, and population genetic polymorphism analysis (population evolution tree analysis, population principal component analysis, and population structure analysis).

[0069] 1.1 Quality control analysis of sequencing data The raw image data generated by high-throughput sequencing is identified and processed by a specific algorithm and converted into raw sequencing data in Fastq format (i.e., Raw data). After obtaining the raw data, it is compared with the reference genome of Leymus chinensis and analyzed by bioinformatics. The main analysis steps are as follows: (1) Quality control: Perform quality assessment on raw sequencing data, remove low-quality sequences, adapter sequences, polyG and other abnormal sequences, and generate high-quality data (Clean data); (2) Alignment processing: Align the clean data to the reference genome, then sort and remove duplicates to generate an alignment file (bam file); (3) Statistical analysis: Statistical analysis was performed on key indicators such as matching rate, coverage, capture rate, and uniformity. The results showed that the average matching rate of the samples was 93.76%.

[0070] 2. SNP detection and annotation The samples were tested for SNPs using GATK 4.5 software, and the VariantFiltration module was used for preliminary filtering. The default filtering conditions included: QD<2.0, QUAL<30.0, SOR>3.0, FS>60.0, MQ<40.0, MQRankSum<-12.5, and ReadPosRankSum<-8.0. On this basis, the following screening criteria were further used for deep filtering of high-quality SNPs: sequencing depth (Depth) <3 filters, minor allele frequency (MAF)>0.05, and missing rate (Miss)>0.1. For SNP functional annotation, the efficient ANNOVAR tool was used to perform multi-dimensional analysis of the test results by inputting the chromosome position, start and end positions, reference bases, and variant base information of the variant site, including gene annotation (Gene-basedannotation), region annotation (Region-based annotation), filter annotation (Filter-basedannotation), etc. The SNP annotation results are shown in the figure. Figure 6 shown.

[0071] (III) Population evolution tree analysis After SNP detection, the individual SNP data were used to calculate the genetic distance between populations. The distance matrix was calculated using TreeBeST software (Tree Building guided by Species Tree, http: / / treesoft.sourceforge.net / treebest.shtml), and the phylogenetic tree was constructed using the Neighbor-Joining Method (NJ method) based on this. The bootstrap values ​​of the phylogenetic tree were obtained by repeating the calculation 1000 times to ensure the reliability and statistical support of the results. Population evolutionary tree analysis was as follows Figure 7 shown.

[0072] 4. Group principal component analysis Principal component analysis (PCA) is a commonly used data dimensionality reduction method that can be used to analyze population structure and clustering in genetics. In the present invention, single nucleotide polymorphism (SNP) sites in genomic data are analyzed by principal component analysis (PCA), the main variables are extracted and a population structure model is constructed. This method is further used to optimize the accuracy of genetic cluster analysis. The PCA analysis results are shown in Figure 2. Figure 8 shown.

[0073] 5. Population genetic structure analysis Population structure analysis plays an important role in understanding evolutionary processes and population differentiation, and can further clarify the subpopulation to which an individual belongs. The software AdmixStructure was used to perform population structure analysis. The analysis results are shown in Figure 2. Fig. 9 As shown in the figure, each column represents an individual, and the length of the different colored segments indicates the proportion of the individual's genome that comes from a specific ancestral population. The K=2 to K=12 marked on the right side of the figure indicates the range of the number of ancestral populations assumed in the analysis.

[0074] Example 7 This example continues to perform advanced information analysis on sequencing data, using principal component analysis (PCA) combined with a random forest algorithm for variety identification.

[0075] During the analysis, the variety identification results are first evaluated and key important sites are identified. These sites are further evaluated to ensure their contribution to variety identification. Finally, combined with the accuracy evaluation of the model, a unique site QR code for the specific variety is generated as a logo for variety identification.

[0076] 1. Evaluation of variety identification results The SNP data obtained by sequencing were encoded, and the main SNP sites were extracted through principal component analysis (PCA) and singular value decomposition (SVD). The score of each SNP marker was calculated and ranked within the chromosome. Despite PCA pre-selection and feature filtering, the number of SNP sites after screening was still large. In order to evaluate the importance of the selected sites, it was necessary to establish rules to assign samples to the corresponding varieties.

[0077] This embodiment uses a random forest algorithm for modeling. Random forest achieves classification by integrating multiple decision trees, and its two key parameters are the number of decision trees (n_estimators) and the number of node pre-selected variables. n_estimators has a monotonic effect on model accuracy, but when a certain threshold is reached, increasing n_estimators no longer significantly improves accuracy, and the computational cost and training time increase accordingly. The present invention balances model accuracy and computational cost, and selects n_estimators as the optimal parameter value when the accuracy tends to be stable.

[0078] The training set of each decision tree in the random forest is generated by bootstrap sampling, so that 30% to 40% of the data is not extracted as "out-of-bag data" (OOB). The OOB data is used for evaluation during the generation process to obtain the OOB error rate or accuracy, providing an unbiased estimate of the classification effect. At the same time, to ensure the stability and reliability of the results, the model is evaluated in combination with k-fold cross validation.

[0079] 2. Obtaining important sites for variety identification Use the trained model to score the OOB data and calculate AUC or other evaluation indicators. Then perform the following operations on each feature in the OOB data: (1) Randomly disrupt the value of the current feature; (2) Re-score the data and calculate the evaluation indicators; (3) Calculate the index change rate. By calculating the change rate of all features and sorting them, the importance of the features (i.e., site importance) can be quantified.

[0080] 3. Evaluation of important loci for variety identification The loci obtained during the variety identification process are sorted according to importance and their importance values ​​are accumulated. When the accumulated importance reaches or exceeds 90%, the corresponding loci set is selected to retrain the random forest model. The trained model is used to evaluate the accuracy of the test data, and the cross-validation method is used to verify the model performance.

[0081] 4. Model Accuracy Evaluation To further verify the accuracy of the above model, the data of each group was randomly divided into a training set and a test set in a ratio of 7:3. The training set was used for model training, and the test set was used for model prediction. This process was repeated 100 times to verify the accuracy of the model in each prediction. The prediction results are shown in Figure 2. Fig.10 shown.

[0082] (V) Variety-specific site QR code The identified SNP site information is converted into the corresponding QR code format. By scanning the QR code, the molecular identity information of the variety can be quickly obtained. The specific variety SNP fingerprint information is as follows: Fig.11 shown.

[0083] In summary, the liquid phase chip detection method for identifying sheep fescue varieties described in the present invention is based on the sheep fescue whole genome 50K liquid phase chip for detection, and the SNP molecular marker combination of the sheep fescue is determined by whole genome resequencing. A total of 51,696 SNP molecular marker sites were screened out based on the sheep fescue reference genome Leymus_chinensis_Lc6-5. The sheep fescue whole genome 50K liquid phase chip was developed based on targeted capture sequencing technology, and by combining high-density SNP site probes, high-throughput and high-precision genotyping of the sheep fescue genome is achieved, which can comprehensively evaluate the genetic characteristics of sheep fescue.

[0084] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A liquid phase chip detection method for identifying Leymus chinensis varieties, characterized in that: The method comprises the following steps: (1) SNP detection of different varieties of Leymus chinensis based on a 50K liquid phase chip of the whole genome of Leymus chinensis, wherein the liquid phase chip includes SNP sites located on the reference genome of Leymus chinensis, Leymus_chinensis_Lc6-5 or its updated version, covering gene regions and related non-gene regions; The core SNP site information of the liquid phase chip is specifically shown in Table 1 in the specification; (2) Compare the SNP detection results with the reference genome of Leymus chinensis, analyze and annotate the SNPs, and establish a SNP information database for different varieties.

2. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 1, characterized in that: The SNP analysis step includes: using the SNPs identified by sequencing, calculating the distance matrix, and constructing a phylogenetic tree for different varieties.

3. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 2, characterized in that: The method also includes the step of performing principal component analysis on the SNP data obtained by sequencing, clustering the individuals into different subgroups according to principal components based on the degree of SNP differences in individual genomes and different trait characteristics, and performing population genetic structure analysis on the samples.

4. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 3, characterized in that: The method also includes the step of encoding the SNP data set obtained by sequencing, and then extracting the main SNP sites by principal component analysis and singular value decomposition.

5. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 4, characterized in that: The method also includes the step of using random forest to classify the samples into species; In the random forest process, the optimal number of trees is determined after balancing accuracy and computational cost; In the random forest processing, a decision tree is generated by using a bootstrap sampling method, and the classification performance is evaluated by using unsampled OOB data to obtain an unbiased OOB error rate or accuracy rate; The random forest processing also includes using k-fold crossover to perform model validation.

6. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 5, characterized in that: The random forest processing includes: Use the trained model to score the OOB data and calculate AUC or other evaluation metrics; For each feature in the OOB data, the following operations are performed in sequence: randomly shuffle the value of the feature to observe the change in model performance; re-score and calculate the evaluation index; calculate the index change rate; According to the importance sorting, the feature importance was accumulated, and the sites with cumulative values ​​exceeding 90% were selected. The random forest model was retrained to finally screen out the required number of SNP sites.

7. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 6, characterized in that: The SNP annotation step includes encoding the screened SNP site information into a QR code to display the molecular identity information of the corresponding variety.

8. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 7, characterized in that: The method also includes the step of performing variety identification on the tested Leymus chinensis; the step of obtaining genotype data of the tested Leymus chinensis individuals based on the Leymus chinensis whole genome 50K liquid phase chip; and the step of performing variety comparison and identification on the Leymus chinensis based on the constructed reference sample library.

9. The liquid phase chip detection method for identifying Leymus chinensis varieties according to any one of claims 1 to 8, characterized in that: The 50K liquid phase chip of the whole genome of Leymus chinensis includes an independently packaged 50K probe mixture and a reagent suitable for liquid phase hybridization capture; wherein, The probe mixture contains 50K probes with high sequence specificity, which can identify and detect the core SNP molecular marker sites as shown in Table 1 in the specification.

10. The liquid phase chip detection method for identifying Leymus chinensis varieties according to claim 9, characterized in that: The 50K liquid phase targeted capture probe is designed based on the positive strand of the Leymus chinensis reference genome; The probes are designed to have a length range of 55-120 nt, with a target average length of 100 nt; The 50K liquid phase targeted capture probe uses 1X probe coverage in conventional areas and a multi-layer shingled probe structure in difficult and complex areas.

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