A cold-region maize SNP liquid phase chip and its application

Abstract

The application provides a cold region corn SNP liquid phase chip and application thereof, and belongs to the technical field of whole genome gene chip. The application develops a high-throughput 10K SNP liquid phase chip for corn genotyping based on a targeted capture sequencing technology. The liquid phase chip is determined through multi-year, multi-point and multi-sample experiments, is high in pertinence, reliability and practicability, can be used for corn variety identification, hybrid breeding, material genetic basis analysis and fingerprint construction, and reduces genotyping cost compared with a solid phase chip, so as to lay an important foundation for accelerating the rapid fusion of corn molecular breeding and conventional breeding and promoting the localization and practicality of molecular breeding technology.

Description

Technical Field

[0001] This invention relates to the field of whole genome gene chip technology, and in particular to a cold-region maize SNP liquid phase chip and its application. Background Technology

[0002] Maize breeding worldwide has entered the Breeding 4.0 stage. Conventional breeding has made significant contributions to crop production in my country, but its shortcomings, such as poor predictability and long cycles, are becoming increasingly apparent. In the genetic improvement of plants and animals, molecular breeding technologies, represented by marker-assisted selection (including genome-wide selection), have long been an important breeding method closely integrated with conventional breeding techniques in international multinational seed companies. SNP (Single Nucleotide Polymorphism) genotyping chips are important tools in molecular breeding and have been successfully applied in various fields such as maize germplasm analysis, seed fingerprinting, marker-assisted selection, and genome-wide selection. Choosing which SNP markers to use and what technologies to employ for their detection are crucial questions that breeders must address and carefully consider before conducting molecular breeding, and they are also key factors determining breeding costs and efficiency. Existing high-density SNP chips often suffer from marker redundancy, high costs, and poor specificity, which limits their application in maize breeding. Successful maize SNP breeding microarrays require SNP markers to have a rich genetic background, high polymorphism, and low bias. Ideally, they should also include markers associated with the breeding objective or functional markers of the target trait. In 2021, the Seed Industry Management Department / National Crop Seed Standardization Technical Committee issued "NY / T 4022-2021 Maize Variety Authenticity Identification SNP Marker Method," which uses a solid-phase microarray covering 61,214 specified SNP markers.

[0003] Although Heilongjiang Province is the largest maize-producing province in my country, its unique geographical conditions result in significant differences in its maize germplasm foundation and breeding objectives compared to other regions. Existing breeding chip markers are redundant, lack specificity, and their application effects are not ideal. Developing an economical, high-density, and high-throughput maize genotyping chip for genetic background analysis of cold-region maize germplasm resources in Northeast China will better serve the industrial application of maize. Summary of the Invention

[0004] In view of this, the present invention provides a cold-region maize SNP liquid-phase chip and its application to solve the above problems. The present invention selects 2000 maize inbred lines from Heilongjiang Province for solid-phase chip genotyping and simplified genome sequencing, and then screens for SNP marker loci. Using GBS simplified genome sequencing technology, genotyping of some inbred lines is performed to obtain SNP molecular markers. Finally, after marker evaluation, 10852 high-quality SNP markers are selected to develop a liquid-phase chip. This liquid-phase chip is highly targeted, reliable, and practical. The developed maize breeding-specific chip for Heilongjiang Province can be used for material genetic basis analysis. Compared with solid-phase chips, it reduces the cost of genotyping and lays an important foundation for accelerating the rapid integration of maize molecular breeding and conventional breeding, and promoting the localization and practical application of molecular breeding technology.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] This invention provides a cold-region maize SNP liquid phase chip, which comprises an individually packaged maize probe mixture and a hybridization capture reagent. The cold-region maize probe mixture contains 10,852 liquid phase probes, each targeting one SNP site.

[0007] The SNP site information is shown in Table 1; the length of the liquid probe is 110 bp, the GC content is between 30% and 70%, and the number of homology regions is ≤5.

[0008] The present invention also provides the application of the aforementioned cold-region maize SNP liquid phase chip in the diversity analysis and dominant population classification of cold-region maize germplasm resources.

[0009] This invention also provides the application of the aforementioned cold-region maize SNP liquid phase chip in the analysis of cold-region maize population structure.

[0010] This invention also provides the application of the aforementioned cold-region maize SNP liquid phase chip in the construction of genetic linkage maps of cold-region maize populations.

[0011] The present invention also provides the application of the aforementioned cold-region maize SNP liquid phase chip in genome-wide association analysis of cold-region maize populations.

[0012] This invention also provides the application of the aforementioned cold-region maize SNP liquid phase chip in maize variety identification, kinship identification, seed purity detection and / or genomic selection breeding.

[0013] This invention also provides a method for maize genotyping, comprising the following steps:

[0014] (1) Extract genomic DNA from the sample to be tested;

[0015] (2) Construct a sequencing library using the genomic DNA;

[0016] (3) Perform probe hybridization reaction between the sequencing library and the cold-region maize SNP liquid phase chip;

[0017] (4) After washing the hybridization product, perform another round of PCR to complete the construction of the hybridization capture library;

[0018] (5) After the library passes the test, sequencing is performed. After sequencing, data quality control and analysis are performed. The quality-controlled sequencing data is compared with the reference genome sequence to obtain the genotyping results.

[0019] By adopting the above technical solution, the present invention has the following beneficial effects:

[0020] 1. This invention screened the genotypes of more than 2,000 major cold-region maize inbred lines in Heilongjiang Province and identified 10,852 SNP markers. These 10,852 SNP markers have high polymorphism and an average minimum allele frequency of 0.4, making them particularly suitable for genetic background analysis of materials.

[0021] 2. This tag also integrates 96 standard SNP tags, greatly improving the tag's universality.

[0022] 3. The 10,852 SNP markers selected by the maize 10K liquid phase chip of this invention are evenly distributed on the maize genome chromosome, with a maximum distance of 7 Mb, a minimum distance of 205 bp, and an average distance of 43 kb between markers. When applied to QTL mapping, GWAS analysis, etc., no sites will be lost.

[0023] 4. The gene chip of this invention adopts the latest GBTS targeted sequencing genotype detection technology for marker genotype detection, which has the advantages of low cost, high accuracy and high detection sensitivity. Attached Figure Description

[0024] Figure 1 The distribution of liquid-phase chip sites on the 10 chromosomes of maize.

[0025] Figure 2 This represents the number of molecular markers in the mixed population across different minimum allelic frequency ranges.

[0026] Figure 3 This represents the number of molecular markers in different minimum allelic frequency ranges of the parent populations.

[0027] Figure 4 This represents the number of molecular markers in different minimum allelic frequency ranges within a natural population.

[0028] Figure 5This is a population structure diagram of a natural population (K=3 and K=4).

[0029] Figure 6 This is a linkage genotype diagram of the parent population.

[0030] Figure 7 Quantile-quantile plot (QQ plot) for genome-wide association analysis of plant height in natural populations.

[0031] Figure 8 Manhattan plot for genome-wide association analysis of plant height in natural populations.

[0032] Figure 9 The figure shows the results of the genome-wide selection prediction accuracy analysis for plant height in natural populations. Detailed Implementation

[0033] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0034] Example 1

[0035] The cold-region maize SNP liquid phase chip of this invention comprises individually packaged maize probe mixtures and hybridization capture reagents. The cold-region maize probe mixture contains 10,852 liquid phase probes, each targeting one SNP site; the hybridization capture reagent is the GenoBaits® hybridization kit from Shijiazhuang Borui Biotechnology Co., Ltd. Information on the SNP sites of the maize 10K liquid phase chip is shown in Table 1.

[0036] Table 1. Information on SNP sites in a 10K liquid-phase microarray of corn.

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[0092] Note: The number after M is the chromosome number, the number after the chromosome number is the physical location of the chromosome, and the following letters are SNP allelic variations.

[0093] Probe design was performed on the 10852 SNP sites listed in Table 1. The design principles were as follows: based on the maize B73 V4 version reference genome sequence, all probes with a length of 110 bp that could cover the 10852 SNP sites were selected; the GC content of the probes covering the target region was calculated; and the number of homologous regions of the probes was calculated. Specifically, a. probes with more than 40 bp of identical length; b. 85% similarity with a length of 80 bp; and c. 95% similarity with a length of 70 bp were all considered as a single homologous region. Probe selection principles were: probes with a GC content between 30% and 70% were selected; probes with ≤5 homologous regions were selected; and probe regions did not contain SSR or N regions. Based on these design and selection principles, a total of 10852 probes were designed and selected, capable of detecting 10852 SNP sites.

[0094] Example 2. Genotyping of parental and natural populations using liquid-phase microarrays

[0095] Biparental lines are offspring produced through crossbreeding between two parents and subsequent self-pollination. They represent a type of inbred line, particularly the two-ring lines commonly bred from hybrids in maize breeding. These materials have a relatively simple genetic basis, originating from only two parents. Natural lines, on the other hand, refer to a group of inbred lines from different parents with distant kinship. They possess relatively rich genetic variation. These two types of lines are the most important and representative in maize genetic breeding.

[0096] In this embodiment, the liquid phase chip was used to perform genotyping analysis on 491 samples from a population of 283 recombinant inbred lines B73 and Mo17 and 208 natural populations. The steps are as follows:

[0097] (1) DNA extraction and quality inspection

[0098] High-throughput DNA extraction from maize leaf samples was performed using a fully automated DNA extraction workstation (Aomeitek ME-480) combined with the GenPrep standard plant DNA rapid extraction kit (magnetic bead adsorption method). The extracted DNA samples underwent two assays:

[0099] ① DNA purity and integrity were analyzed using 1% agarose gel electrophoresis (instrument: Hanchen Guangyi GeneAuto 96 automated dispensing platform); ② DNA concentration was accurately quantified using Qubit (instrument: Hanchen Guangyi MatrixAnalyzer 3110 fully automated nucleic acid concentration analysis and homogenization workstation), ensuring that the total DNA amount for each individual was not less than 500 ng, and that the fluorescence concentration of double-stranded DNA was measured using Qubit, with a sample concentration ≥10 ng / μL.

[0100] (2) Library construction

[0101] Quantitatively qualified DNA was randomly physically disrupted using an ultrasonic disruptor, with the peak fragment size controlled at 200-300 bp. After end repair, the disrupted DNA was ligated with A-tails. The A-tailed DNA fragments were then ligated to sequencing adapters using ligase. The library was then purified and fragments selected using carboxyl-modified magnetic beads, retaining ligation products with insert fragments in the 200-300 bp range. The ligation products were then added to barcoded sequencing primers and a high-fidelity PCR reaction system for PCR amplification. Different barcodes were used to distinguish different samples. After purification with carboxyl-modified magnetic beads, the amplified products were ready for probe hybridization experiments.

[0102] (3) Maize 10K breeding chip probe hybridization

[0103] Take 500 ng of the constructed sequencing library, freeze-dry it, and add 6 μL of corn 10K liquid phase chip probe and 206 μL of hybridization reagent for hybridization capture. After denaturation, incubate at 65°C for 2 hours to complete the hybridization reaction. After washing the hybridization product with washing buffer, perform one round of PCR to complete the construction of the hybridization capture library.

[0104] (4) Sequencing of sequencing libraries

[0105] After library construction, preliminary quantification was performed using Qubit 2.0, followed by accurate quantification of the effective concentration of the library using qPCR to ensure library quality. Once the library passed the initial testing, it was then sequenced using a DNBSEQ-T7 gene sequencer (Shenzhen BGI Genomics Co., Ltd.).

[0106] (5) Data quality control and analysis

[0107] After sequencing, data quality control (removal of adapters and low-quality data), alignment with the reference genome (B73 V4), variant detection, and annotation are performed. After sequencing, raw sequencing reads, also known as raw data or raw reads, are obtained and stored in FASTQ (fq) file format. These reads contain sequence information and corresponding sequencing quality information. Raw sequencing reads contain adapter-containing, low-quality reads.

[0108] For next-generation sequencing technology, there are two main reasons for the sequencing error rate:

[0109] (1) Due to the consumption of chemical reagents during the sequencing process, the sequencing error rate is higher the later the base position in the sequencing sequence.

[0110] (2) Incomplete binding of random primers and DNA templates during PCR may lead to a high error rate in the first few bases of sequencing. To ensure the quality of information analysis, raw reads must be filtered to obtain clean reads, which are then used for subsequent analysis.

[0111] The raw reads were filtered using the software fastp (version 0.20.0, parameters: -n 10 -q 20 -u 40). The data processing steps were as follows: adapter sequences were removed; when the number of N bases in a sequencing read exceeded 10, the paired reads needed to be removed; when the number of low-quality (Q≤20) bases in a sequencing read exceeded 40% of the read length, the paired reads needed to be removed.

[0112] The BWA (mem alignment method) software is used to align the quality-controlled clean reads with the reference genome sequence, allowing the clean reads to be located on the reference genome. Variation detection is then performed using GATK best practices for bioinformatics analysis. Once the reference genome annotation file is available, positional annotation can be performed on the SNP dataset.

[0113] Genotyping information for SNP loci was extracted using a self-developed script based on the alignment results and variant results of the target region. If the coverage depth of a sample at a certain SNP locus is <5X, it indicates insufficient sequencing depth at that locus. To ensure accurate genotyping results, this locus is treated as a deletion and represented as NA in the table. For a heterozygous genotype, each allele must have at least 4 supported reads; otherwise, the SNP locus is treated as a deletion and represented as NA in the table. A genotyping matrix of 10852 loci from the maize 10K liquid phase microarray was obtained. The genotyping matrix was converted into hapmap format using Tassel software.

[0114] The distribution of 10,852 SNPs on the 10 chromosomes of maize is shown in the figure. Figure 1Analysis revealed that the distribution of these markers across the 10 chromosomes of maize was as follows: 1698 on chromosome 1, 1198 on chromosome 2, 1415 on chromosome 3, 1092 on chromosome 4, 1190 on chromosome 5, 881 on chromosome 6, 944 on chromosome 7, 788 on chromosome 8, 853 on chromosome 9, and 793 on chromosome 10, averaging 1085 SNP markers per chromosome. Theoretically, 5,328,332 markers were obtained from 491 studies, while 5,310,598 markers were actually obtained, resulting in a marker success rate of 99.67%.

[0115] The ratios of chromosome markers before and after filtering in mixed populations, parental populations, and natural populations are shown in Table 2.

[0116] Table 2. Proportions of each chromosome marker before and after filtering in each population

[0117]

[0118] Excel software was used to plot the number of SNP markers under different minimum allele frequency ranges in each population into a bar chart. When the parental population and the natural population were analyzed together, the bar chart of the number of molecular markers with different minimum allele frequencies in the mixed population was as follows: Figure 2 As shown, there were only 107 molecular markers with a minimum allele frequency below 0.05, while there were 7455 molecular markers with a minimum allele frequency of 0.4-0.5, accounting for 68.70% of all markers, indicating that the site polymorphism screened by this chip is relatively good.

[0119] When the two populations are analyzed separately, the histogram of the number of molecular markers with different minimum allelic frequencies in the parental populations is shown below. Figure 3 As shown, the parental population had 2037 SNPs with a minimum allele frequency below 0.05, accounting for 18.77% of all markers. Even so, there were still 4874 (44.91%) SNPs with a minimum allele frequency between 0.4 and 0.5.

[0120] When natural populations are analyzed individually, the histograms of the number of molecular markers with different minimum allelic frequencies in the natural population are as follows: Figure 4 As shown, there are only 83 molecular markers with a minimum allele frequency below 0.05, accounting for 0.76% of all markers, while there are 6180 molecular markers with a minimum allele frequency of 0.4-0.5, accounting for 56.95% of all markers.

[0121] Example 3. Population structure and heterosis group analysis of natural populations

[0122] Genotype VCF files from 208 natural populations were read into Tassel software and converted to Plink format. Admixture software was then used for population structure analysis. First, the .map and .ped files exported from Tassel were converted to a file named file.bed using the command `plink --file file --make-bed --out test` (where file is the filename). Then, the command `for K in 1 2 3 4 5 6 7 8 9 10;do admixture --cv file.bed $K|tee log${K}.out;done` generated population structure data for K=1-10. The cross-entropy values ​​at different K values ​​were viewed using `grep -h CV log*.out`. The K value with the lowest cross-entropy value is the theoretically optimal number of clusters. Furthermore, population structure analysis at different K values ​​can be performed as needed. The population structure data for K=3 and K=4 were plotted as follows: Figure 5 As shown, when the K value is different, the material groups are fully classified, and the proportion of each material to different ancestral lineages is successfully reflected. This demonstrates that the SNP liquid-phase chip of the present invention can be used for genetic diversity analysis and dominant group classification of maize germplasm resources.

[0123] Example 4. Construction of linkage maps of parental populations

[0124] 1. Data Preparation

[0125] a. Genotype data: The genotyping matrix from Example 2 was converted into genotype files of the parent populations in hapmap format using Tassel software. Genotypes were named according to the base symbols in the hapmap file: if a genotype matches one parent, it was named A; if it matches the other, it was named B; and H indicated heterozygosity, meaning the genotype contained bases from both parents.

[0126] b. Data format: Usually an R / QTL or R / AmpMap compatible format (such as .csv or .rds).

[0127] c. Marker quality filtering: Remove markers with high deletion rates (e.g., deletion rate > 10%); remove redundant or collinear markers; check and correct obvious genotype errors (e.g., using the checkGeno function in R / qtl).

[0128] 2. Haplotype inference

[0129] Improving map accuracy through haplotype inference: Using Hidden Markov Model (HMM) to infer individual haplotypes and identify recombination breakpoints.

[0130] 3. Label sorting and graph construction

[0131] a. Initial sorting: Markers are initially sorted based on genetic distance or physical location.

[0132] b. Optimize the order: Use ASMAP's mdsmap or seriation algorithm to optimize the marking order.

[0133] c. Calculate genetic distance: based on recombination frequency (Kosambi or Haldane function).

[0134] 4. Graph Verification and Error Correction

[0135] a. Check for outliers: Visualize recombination frequencies using plotRF to identify possible order errors.

[0136] b. Error correction: Manually adjust the marking order or use the ripple function for local optimization.

[0137] c. Compare physical maps: If a reference genome is available, compare the genetic map with the physical location.

[0138] 5. Output the final graph

[0139] a. Export marker name, order, and genetic distance (cM).

[0140] b. Visualize the graph (e.g., ggplot2 or R / qtl's plot.map), such as Figure 6 As shown. Figure 6 The upper triangle represents the linkage strength between molecular markers, and the lower triangle represents the LOD value calculated for linkage. The central square represents the linkage non-equilibrium region (LD block), where exchanges generally occur very rarely, while exchanges between different linkage non-equilibrium regions are frequent. This embodiment illustrates that the SNP liquid-phase chip of the present invention can be used to construct genetic linkage maps of maize parent populations.

[0141] Example 5. Association analysis of plant height in natural maize populations

[0142] Import the 208 Hapmap format genotype files of natural populations obtained after liquid phase chip typing in Example 2 into Tassel software. Select Analysis--Relatedness--PCA analysis to obtain the PCA values ​​of the genotype files. Calculate the kinship matrix by selecting Analysis--Relatedness--Kinship, which will generate kinship matrix files for all 208 materials. Simultaneously import the phenotypic files of the 208 materials into Tassel software. When performing association analysis, first hold down the Ctrl key, select the imported Hapmap format genotype file, PCA file, and phenotypic file, and then select Data--Intersect join to merge them. Then, hold down the Ctrl key and click on the merged file and the kinship matrix file, and then click Analysis--Association--MLM to complete the genome-wide association analysis of the mixed linear model. The generated file is in Result-Association. Click on the file starting with MLM_statistics, and then click QQplot in the result menu. The system will automatically output a QQ plot, as shown below. Figure 7 As shown, the horizontal axis represents the quantiles of the theoretical distribution (such as the normal distribution), and the vertical axis represents the quantiles of the actual sample data. If the sample data follows a theoretical distribution, the scatter points in the graph should be roughly distributed along the line y = x. The degree of deviation from the straight line reflects the difference between the actual and theoretical distributions. Figure 7 As can be seen, when the -log(p) threshold is less than 1, the scatter plot lies on a straight line; when the threshold exceeds 1, the scatter plot lies in the upper triangular part of the straight line, indicating the existence of loci significantly associated with plant height. Clicking the Manhattanplot command in the Result menu displays the Manhattan plot of the genome-wide association analysis, as shown below. Figure 8 As shown in the figure, the significance of the markers can be determined based on different thresholds, i.e., the horizontal lines in the figure. The threshold is determined by -log(1 / N), where N is the number of molecular markers in the association analysis, which in this example is -log(1 / 10852) = 4.0. Figure 8 As shown, if a threshold of 4.0 is used, three SNP loci are significantly associated with plant height. This embodiment illustrates that the SNP liquid-phase chip of the present invention can be used for genome-wide association analysis of traits such as plant height in maize.

[0143] Example 6. Genome-wide selection study

[0144] Genomic selection (GS) is a method of selective breeding that uses high-density molecular markers covering the entire genome. By constructing predictive models, early individuals can be predicted and selected based on the Genomic Estimated Breeding Value (GEBV), thereby shortening the generation interval, accelerating the breeding process, and saving a lot of costs.

[0145] Genome-wide selection was performed using the rrBlup package in R. The Hapma format files of the 208 natural populations obtained in Example 2 were converted into numeric genotype files in the form of "0", "1", and "0.5" using the Data-Numerical Genotype command in Tassel software. The numeric genotype files and phenotypic data were then imported into R. The A.mat() function of the rrBLUP package was used to construct an additive matrix for each material using the genotype files. The sample() function was used to extract samples of different proportions as the training population, with the remaining portion serving as the validation population. 10% of the samples were randomly selected to predict the remaining 90%, 20% to predict the remaining 20%, 30% to predict the remaining 70%, and so on. The mixed.solve() function was used to establish a prediction equation between phenotype and genotype. The genotypes of the predicted populations were then substituted into this equation to obtain the predicted values ​​for the predicted populations. The prediction accuracy is equal to the correlation coefficient between the predicted values ​​and the actual values ​​of the predicted populations, calculated using the COR() function. Each prediction was repeated 100 times, and the mean of the 100 prediction accuracies was used to represent the prediction accuracy for that training group. The mean prediction accuracies of these 9 predictions were 0.43, 0.49, 0.53, 0.55, 0.56, 0.58, 0.59, 0.59, and 0.56, respectively. The results of these 9 predictions were plotted as a box plot, as shown below. Figure 9 As shown in the figure, a prediction accuracy of 0.53 can be obtained when the training population is 30%, while the highest prediction accuracy (0.59) is achieved when the training population is 70% and 80%. However, the median is highest when the training population is 80% (the solid black line within the box in the figure), indicating that a good prediction effect is achieved. Note: Parentheses represent R language functions.

[0146] As can be seen from the above embodiments, the present invention provides a cold-region maize SNP liquid phase chip and its application. The cold-region maize SNP liquid phase chip of the present invention has the advantages of low cost, high accuracy, and high detection sensitivity.

[0147] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A cold region corn SNP liquid phase chip, characterized in that, The cold region corn SNP liquid phase chip comprises independently packaged corn probe mixed liquid and hybridization capture reagent, and the corn probe mixed liquid comprises 10852 liquid phase probes, each probe being directed to one SNP site. The information of the corn SNP site is as follows: The length of the liquid phase probe is 110 bp, the GC content is between 30% and 70%, and the number of homologous regions is less than or equal to 5. The number after M is the chromosome number, the number after the chromosome number is the physical position of the chromosome, and the letter after the number is the SNP base allelic variation; the corn reference genome is the corn B73 V4 version.

2. The application of the cold region corn SNP liquid phase chip in claim 1 in genetic diversity analysis and advantage group division of cold region corn germplasm resources.

3. The application of the cold region corn SNP liquid phase chip in claim 1 in cold region corn population structure analysis.

4. The application of the cold region corn SNP liquid phase chip in claim 1 in construction of a genetic linkage map of a cold region corn population.

5. The application of the cold region corn SNP liquid phase chip in claim 1 in whole genome association analysis of a cold region corn population.

6. The application of the cold region corn SNP liquid phase chip in claim 1 in cold region corn genomic selection breeding.

7. A method of genotyping corn, characterized by, The method comprises the following steps: S1. Extracting genomic DNA of a sample to be detected; S2. Constructing a sequencing library by using the genomic DNA; S3. Performing probe hybridization reaction of the sequencing library and the cold region corn SNP liquid phase chip in claim 1; S4. Washing the hybridization product and then performing a round of PCR to complete construction of a hybridization capture library; S5. After the library is detected to be qualified, performing sequencing, performing data quality control and analysis after the sequencing is completed, comparing the sequencing data after quality control with a reference genome sequence, and obtaining a genotyping result.

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