Cold region corn SNP (Single Nucleotide Polymorphism) liquid phase chip and application thereof

Abstract

The invention provides a cold region corn SNP (Single Nucleotide Polymorphism) liquid phase chip and application thereof, and belongs to the technical field of whole genome gene chips. The invention develops a high-throughput 10K SNP liquid phase chip for corn genotyping on the basis of a targeted capture sequencing technology. The liquid-phase chip is determined through multi-point multi-sample experiments for many years, is high in pertinence, high in reliability and outstanding in practicability, can be used for corn variety identification, hybridization breeding, material heredity basic analysis and fingerprint spectrum construction, and compared with a solid-phase chip, the genetic typing cost is reduced, and a foundation is laid for rapid fusion of corn molecular breeding and conventional breeding. And an important foundation is laid for promoting localization and practicability of a molecular breeding technology.

Description

Technical Field

[0001] The present invention relates to the technical field of whole genome gene chips, in particular to a cold-region corn SNP liquid phase chip and applications thereof. Background Art

[0002] Global corn breeding has entered the Breeding 4.0 phase. Conventional breeding has made significant contributions to my country's crop production, but its shortcomings of poor predictability and long production cycles are becoming increasingly prominent. In the genetic improvement of plants and animals, molecular breeding technologies, represented by marker-assisted selection (including whole-genome selection), have long been a key breeding tool closely integrated with conventional breeding techniques by international seed companies. SNP (Single Nucleotide Polymorphism) genotyping arrays are essential tools in molecular breeding and have been successfully applied in a variety of research areas, including corn germplasm analysis, seed fingerprinting, marker-assisted selection, and whole-genome selection. The selection of SNP markers and the technology used to detect them are crucial considerations for breeders before initiating molecular breeding, and they are crucial factors in determining breeding costs and efficiency. Existing high-density SNP arrays often suffer from marker redundancy, high costs, and poor targeting, limiting their application in corn breeding. A successful corn SNP breeding array requires SNP markers with rich genetic background, high polymorphism, and low bias. Ideally, they should also include markers that are associated with the breeding goal or functionally represent the target trait. In 2021, the Seed Industry Administration Department / National Technical Committee for Standardization of Crop Seeds issued the "NY / T 4022-2021 SNP Marker Method for Authenticity Identification of Corn Varieties," which utilizes a solid-phase array and covers 61,214 designated SNP markers.

[0003] Although Heilongjiang Province is my country's largest corn-growing province, its unique geographical conditions result in significant differences in its corn germplasm base and breeding objectives compared to other regions in China. Existing breeding microarrays have redundant markers, lack specificity, and have yielded suboptimal results. Developing an economical, high-density, high-throughput corn genotyping array for genetic background analysis of cold-region corn germplasm resources in Northeast China will help better serve the industrialization of corn. Summary of the Invention

[0004] In view of this, the present invention provides a cold-region corn SNP liquid phase chip and its application to solve the above problems. The present invention selects 2000 main corn inbred lines in Heilongjiang Province for solid phase chip genotyping and simplified genome sequencing to screen SNP marker sites. GBS simplified genome sequencing technology is used to genotype some inbred lines to obtain SNP molecular markers, and finally 10852 high-quality SNP markers are selected after marker evaluation to develop a liquid phase chip. The liquid phase chip has strong pertinence, high reliability and outstanding practicality. The developed Heilongjiang Province corn breeding special chip 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 corn molecular breeding and conventional breeding, and promoting the localization and practical application of molecular breeding technology.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions: The present invention provides a cold-region corn SNP liquid phase chip, which comprises independently packaged corn probe mixture and hybridization capture reagent. The cold-region corn probe mixture contains 10,852 liquid phase probes, each probe targeting one SNP site; The information of the SNP sites is shown in Table 1; the length of the liquid phase probe is 110 bp, the GC content is between 30% and 70%, and the number of homology regions is ≤5.

[0006] The present invention also provides the application of the cold-region corn SNP liquid phase chip in the diversity analysis and dominant population division of cold-region corn germplasm resources.

[0007] The present invention also provides the application of the cold-region corn SNP liquid phase chip in cold-region corn population structure analysis.

[0008] The present invention also provides the application of the cold-region corn SNP liquid phase chip in the construction of a genetic linkage map of a cold-region corn population.

[0009] The present invention also provides the application of the cold-region corn SNP liquid phase chip in genome-wide association analysis of cold-region corn populations.

[0010] The present invention also provides the application of the cold-region corn SNP liquid phase chip in corn variety identification, kinship identification, seed purity detection and / or genomic selection breeding.

[0011] The present invention also provides a method for corn genotyping, comprising the following steps: (1) Extract genomic DNA from the sample to be tested; (2) constructing a sequencing library using the genomic DNA; (3) performing probe hybridization reaction between the sequencing library and the cold-region corn SNP liquid phase chip; (4) After cleaning the hybridization product, perform another round of PCR to complete the construction of the hybridization capture library; (5) After the library is tested and qualified, it is sequenced. After sequencing is completed, data quality control and analysis are performed. The sequencing data after quality control are compared with the reference genome sequence to obtain the genotyping results.

[0012] By adopting the above technical solution, the present invention has the following beneficial effects: 1. The present invention screened the genotypes of more than 2,000 cold-region main corn inbred lines in Heilongjiang Province, and screened out 10,852 SNP site markers. These 10,852 SNP site markers have high polymorphism and an average minimum allele frequency of 0.4, which are particularly suitable for genetic background analysis of materials.

[0013] 2. The marker also integrates 96 standard SNP markers, greatly improving the versatility of the marker.

[0014] 3. The 10,852 SNP markers selected by the maize 10K liquid phase chip of the present invention are evenly distributed on the maize genome chromosomes, with the maximum distance between markers being 7 Mb, the minimum distance being 205 bp, and the average spacing being 43 kb. When it is applied to QTL mapping, GWAS analysis, etc., no sites will be lost.

[0015] 4. The gene chip of the present 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The distribution of liquid phase array sites on the 10 maize chromosomes.

[0017] Figure 2 is the number of molecular markers in different minimum allele frequency ranges of the mixed population.

[0018] Figure 3 is the number of molecular markers in different minimum allele frequency ranges of the parental populations.

[0019] Figure 4 is the number of molecular markers in different minimum allele frequency ranges in natural populations.

[0020] Figure 5 This is the population structure diagram of natural population (K=3 and K=4).

[0021] Figure 6 Linkage genetic map of the parental population.

[0022] Figure 7 This is the quantile-quantile plot (QQ plot) of the genome-wide association analysis of plant height in natural populations.

[0023] Figure 8 Manhattan plot of genome-wide association analysis of plant height in natural populations.

[0024] Figure 9 This is the result of genome-wide selection prediction accuracy analysis of plant height in natural populations. DETAILED DESCRIPTION

[0025] The technical solutions provided by the present invention are 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.

[0026] Example 1 The cold-region corn SNP liquid-phase array described in this invention consists of a separately packaged corn probe mixture containing 10,852 liquid-phase probes, each targeting a single SNP locus, and a hybridization capture reagent. The hybridization capture reagent is the GenoBaits® hybridization kit from Shijiazhuang Boredi Biotechnology Co., Ltd. The SNP loci of the corn 10K liquid-phase array are shown in Table 1: Table 1 Information on SNP sites in maize 10K liquid phase array Note: The number after M is the chromosome number, the number after the chromosome number is the physical position of the chromosome, and the following letters are the SNP base allele variation.

[0027] Probe design was performed for the 10,852 SNPs listed in Table 1. The design principles were as follows: Based on the maize B73 V4 reference genome sequence, all 110-bp probes were selected to cover all 10,852 SNPs. The GC content of the probes covering the target region was calculated, and the number of homology regions within the probes was calculated. A region of homology was considered to be a single region if: a) the probes were identical for more than 40 bp; b) the probes had an 85% similarity and a length of 80 bp; and c) the probes had an 85% similarity and a length of 70 bp. Probe selection principles included selecting probes with a GC content between 30% and 70%; selecting probes with ≤5 homology regions; and selecting probe regions that did not contain SSR or N regions. Based on these design and selection principles, a total of 10,852 probes were designed and selected to detect all 10,852 SNPs.

[0028] Example 2. Liquid-phase chip genotyping study of parental populations and natural populations Biparental populations are the offspring of two parental lines, resulting from repeated self-pollination. They represent a type of inbred line, particularly the diadipiric lines commonly used in corn breeding. These lines have a relatively simple genetic basis, originating from only two parents. Natural populations are composed of distantly related inbred lines derived from different parents and exhibit relatively rich genetic variation. These two types of populations are the most important and representative in corn genetics and breeding.

[0029] In this example, the liquid phase array was used to perform genotyping on 491 samples, including 283 samples from the maize inbred lines B73 and Mo17 recombinant inbred lines and 208 samples from natural populations. The steps were as follows: (1) DNA extraction and quality inspection High-throughput DNA extraction from corn leaf samples was performed using a fully automated DNA extraction workstation (Ometek ME-480) combined with the GenPrep Conventional Plant DNA Rapid Extraction Kit (magnetic bead adsorption method). The extracted DNA samples were subjected to two types of tests: 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 the Qubit assay (instrument: Hanchen Guangyi MatrixAnalyzer 3110 fully automated nucleic acid concentration analysis and normalization workstation). The total DNA quality for each individual was no less than 500 ng. The double-stranded DNA fluorescence concentration was measured using the Qubit assay, and the sample concentration was ≥10 ng / μL.

[0030] (2) Library construction DNA that has passed quantitative quality control is randomly physically fragmented using an ultrasonic disruptor, with the fragment peak size controlled at 200-300bp. The fragmented DNA undergoes end-repair and ligation with A tails. The A-tailed DNA fragments are ligated to sequencing adapters using ligase. The library is then purified and fragments are selected using carboxyl-modified magnetic beads, retaining ligation products with inserts between 200-300bp. The ligation products are then amplified using barcoded sequencing primers and a high-fidelity PCR reaction system. Different barcodes are used to distinguish different samples. After purification using carboxyl magnetic beads, the amplified products are ready for probe hybridization experiments.

[0031] (3) Hybridization of corn 10K breeding chip probes 500 ng of the constructed sequencing library was lyophilized and added to 6 μL of corn 10K liquid phase chip probes and 206 μL of hybridization reagent for hybridization capture. After denaturation, the hybridization reaction was incubated at 65°C for 2 hours to complete the hybridization reaction. After the hybridization product was washed with washing solution, a round of PCR was performed to complete the construction of the hybridization capture library.

[0032] (4) Sequencing library on the machine After library construction, preliminary quantification was performed using Qubit 2.0, followed by accurate quantification of the effective concentration using qPCR to ensure library quality. Once the library passed the assay, sequencing was performed on a DNBSEQ-T7 gene sequencer (MGI, Shenzhen, China).

[0033] (5) Data quality control and analysis After sequencing is complete, data quality control (removal of adapters and low-quality data), alignment to the reference genome (B73 V4), variant detection, and annotation are performed. After sequencing is complete, raw sequence reads (called "raw data") are obtained. The results are stored in the FASTQ (fq) file format, which contains sequence information and corresponding sequencing quality information. Raw sequence reads, or raw reads, contain adapter-containing and low-quality reads.

[0034] For the second-generation sequencing technology, the sequencing error rate is caused by the following two main reasons: (1) Due to the consumption of chemical reagents during the sequencing process, the sequencing error rate is higher when the base position in the sequencing sequence is later.

[0035] (2) Incomplete binding of random primers and DNA templates during PCR may result in a high error rate in sequencing the first few bases. To ensure the quality of information analysis, raw reads must be filtered to obtain clean reads, which are then used for subsequent analysis.

[0036] Raw reads were filtered using fastp (version 0.20.0, parameters: -n 10 -q 20 -u 40). The data processing steps were as follows: adapter sequences were removed; paired reads were removed when the N content in the sequencing read exceeded 10 bases; and paired reads were removed when the number of low-quality (Q ≤ 20) bases in the sequencing read exceeded 40% of the read length.

[0037] Use BWA (mem alignment) software to align clean reads after quality control with the reference genome sequence. This alignment allows you to locate the clean reads on the reference genome. Bioinformatics analysis tools based on GATK best practices are used for variant detection. Once the reference genome annotation file is available, positional annotation can be performed based on the SNP dataset.

[0038] A custom script was used to extract genotyping information for SNPs using the target region alignment and variant results. If the coverage depth at a particular SNP was <5X, it indicated insufficient sequencing depth at that site. To ensure accurate genotyping results, the site was treated as missing and indicated as NA in the table. A heterozygous SNP was considered a heterozygous mutation if each allele was supported by at least four reads. Otherwise, the site was treated as missing and indicated as NA in the table. A genotyping matrix for 10,852 sites on the maize 10K liquid phase array was generated. The genotyping matrix was converted to hapmap format using Tassel software.

[0039] The distribution of 10852 SNPs on 10 chromosomes of maize is shown in Figure 1 Analysis revealed that these markers were distributed across the 10 chromosomes of maize: 1,698 on chromosome 1, 1,198 on chromosome 2, 1,415 on chromosome 3, 1,092 on chromosome 4, 1,190 on chromosome 5, 881 on chromosome 6, 944 on chromosome 7, 788 on chromosome 8, 853 on chromosome 9, and 793 on chromosome 10, for an average of 1,085 SNP markers per chromosome. A theoretical total of 5,328,332 markers was obtained from 491 samples, and 5,310,598 markers were actually found, for a marker recovery rate of 99.67%.

[0040] The ratios of the number of markers on each chromosome before and after filtering in the mixed population, parental population, and natural population are shown in Table 2 .

[0041] Table 2 Proportions of chromosome markers before and after filtering in each population

[0042] The number of SNP markers in different minimum allele frequency ranges of each population was plotted into a bar graph using Excel software. When the parental population and the natural population were analyzed together, the bar graph of the number of molecular markers with different minimum allele frequencies in the mixed population was as follows: Figure 2 There are only 107 molecular markers with a minimum allele frequency below 0.05, while the number of molecular markers with a minimum allele frequency of 0.4-0.5 is 7455, accounting for 68.70% of all markers, indicating that the site polymorphism screened by this chip is relatively good.

[0043] When the two populations are analyzed separately, the bar graph of the number of molecular markers with different minimum allele frequencies in the parental populations is as follows Figure 3 In the parental population, there were 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.

[0044] When natural populations are analyzed separately, the bar graph of the number of molecular markers with different minimum allele frequencies in natural populations is as follows: Figure 4 There are only 83 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.

[0045] Example 3. Population structure and heterosis analysis of natural populations The genotype vcf files of 208 natural populations were read into the tassel software, converted into Plink format using the Tassel software, and the population structure analysis was performed using the Admixture software. First, the .map and .ped files exported by the tassel software were converted into file.bed files using the plink --file file --make-bed --out test (file is the file name) command code. Then the for K in 1 2 3 4 5 6 7 8 9 10;do admixture --cv file.bed $K|tee log${K}.out;done command code was used to generate population structure data when K=1-10. Use grep -h CV log*.out to view the cross entropy values under different K. The K value with the smallest cross entropy value is the theoretical optimal number of clusters. In addition, the population structure under different K values can be analyzed as needed. The population structure data when K=3 and K=4 are plotted as shown below. Figure 5 When the K value is different, the material groups are fully divided, and the proportion of each material in different ancestral lines is successfully reflected. This shows that the SNP liquid phase chip of the present invention can be used for genetic diversity analysis and dominant group division of corn germplasm resources.

[0046] Example 4. Construction of linkage genetic map of parental populations 1. Data Preparation a. Genotype Data: Tassel software was used to convert the genotyping matrix from Example 2 into genotype files for the parental population in Hapmap format. Bases were named according to the base symbols in the Hapmap file. Bases identical to one parent were designated A, those identical to the other were designated B, and H indicated heterozygous, meaning bases from both parents were present.

[0047] b. Data format: Usually in a format compatible with R / qtl or R / AmpMap (such as .csv or .rds).

[0048] c. Marker quality filtering: remove markers with high missingness rates (e.g., missingness rate > 10%); remove redundant or collinear markers; check and correct obvious genotyping errors (e.g., using the checkGeno function in R / qtl).

[0049] 2. Haplotype inference Improve map accuracy through haplotype inference: Use Hidden Markov Model (HMM) to infer individual haplotypes and identify recombination breakpoints.

[0050] 3. Marker sorting and map construction a. Initial sorting: preliminary sorting of markers based on genetic distance or physical location.

[0051] b. Optimize the order: Use ASMAP's mdsmap or seriation algorithm to optimize the marking order.

[0052] c. Calculate genetic distance: based on recombination frequency (Kosambi or Haldane function).

[0053] 4. Graph Verification and Error Correction a. Check for outliers: Visualize the recombination frequency with plotRF to identify possible sequence errors.

[0054] b. Error correction: Manually adjust the tag order or use the ripple function for local optimization.

[0055] c. Compare to physical maps: If a reference genome is available, compare the consistency of the genetic map with the physical locations.

[0056] 5. Output the final map a. Export marker name, sequence, and genetic distance (cM).

[0057] b. Visualization map (such as ggplot2 or R / qtl's plot.map), such as Figure 6 shown. Figure 6 The upper triangle represents the linkage strength between molecular markers, and the lower triangle represents the lod value (LOD) calculated from the linkage calculation. The square in the middle represents the linkage disequilibrium block. Crossover generally occurs rarely in linkage disequilibrium blocks, while crossover is frequent in different linkage disequilibrium blocks. This example demonstrates that the SNP liquid phase chip of the present invention can be used to construct genetic linkage maps for maize parental populations.

[0058] Example 5. Correlation analysis of plant height in natural corn populations Import the Hapmap format genotype files of 208 natural populations obtained after liquid chip typing in Example 2 into the Tassel software. Select Analysis--Relatedness--PCA analysis in sequence to obtain the PCA value of the genotype file. For the calculation of the kinship matrix, select Analysis--Relatedness--Kinship once, and the kinship matrix files of 208 materials can be obtained. At the same time, the phenotype files of 208 materials are also imported into the Tassel software. When performing association analysis, first hold down the Crl key, select the imported Hapmap format genotype file, PCA file and phenotype file, and complete the merge by Data--Intersect jion. Press and hold the Crl key again and click the merged file and kinship matrix file in the previous step, and then click analysis--Association--MLM in sequence to complete the whole genome association analysis of the mixed linear model. The generated file is in Result-Assoiation. Click the file starting with MLM_statistics, and then click QQplot in the result menu. The system will automatically output the QQ graph, as shown in the following figure. Figure 7 As shown in the figure, the horizontal axis represents the quantile of the theoretical distribution (such as the normal distribution), and the vertical axis represents the quantile of the actual sample data. If the sample data follows the theoretical distribution, the scattered points in the figure should be roughly distributed on the straight line y = x. The degree of deviation from the straight line reflects the difference between the actual distribution and the theoretical distribution. Figure 7 It can be seen that when the -log(p) threshold is less than 1, the scatter points are located on the straight line. When the threshold exceeds 1, the scatter points are located in the upper triangle of the straight line, indicating that there are sites that are significantly associated with plant height. Click the Manhattanplot command in the result menu to display the Manhattan plot of the genome-wide association analysis, as shown in the figure below. Figure 8 As shown. The significance of the marker can be determined according to different thresholds, which are 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, in this case -log (1 / 10852) = 4.0. Figure 8 As shown, if 4.0 is used as the threshold, three SNP sites are significantly associated with plant height. This example 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 corn.

[0059] Example 6. Whole genome selection study Genomic Selection (GS) is a method of selective breeding that uses high-density molecular markers covering the entire genome. By constructing a prediction model, 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.

[0060] Whole-genome selection was performed using the rrBlup package based on the R language. The Hapma format files of the 208 natural populations obtained in Example 2 were converted into digital genotype files in the form of "0", "1", and "0.5" using the Data--Numerical Genotype command of the Tassel software. The digital genotype files and phenotypic data were read into the R language, and the A.mat() function of the rrBLUP package was used to establish an additive matrix for each material using the genotype file. Samples of different proportions were extracted using sample() as the training population, and the remaining part was used as the validation population. 10% of the samples were randomly selected to predict the remaining 90%, 20% of the samples were used to predict the remaining 20%, 30% of the samples were used to predict the remaining 70%, and so on. The mixed.solve() function was used to establish a prediction equation between the phenotype and the genotype. The genotype of the predicted population was then substituted into this equation to obtain the predicted value of the predicted population. The prediction accuracy is equal to the correlation coefficient between the predicted value of the predicted population and the true value, which is calculated using the COR() function. Each prediction is repeated 100 times, and the average of the 100 prediction accuracies represents the prediction accuracy of the training group. The average prediction accuracies of these 9 predictions are 0.43, 0.49, 0.53, 0.55, 0.56, 0.58, 0.59, 0.59 and 0.56 respectively. The results of the 9 predictions are plotted as a box plot, as shown in the figure below. Figure 9 As shown in the figure, a prediction accuracy of 0.53 was achieved when the training population was 30%, while the highest accuracy was achieved when the training population was 70% or 80%, reaching 0.59. However, the median value was highest when the training population was 80% (the solid black line in the box in the figure), indicating good prediction results. Note: Brackets represent R language functions.

[0061] As can be seen from the above examples, 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.

[0062] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as 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 mixture and hybridization capture reagent, wherein the cold-region corn probe mixture contains 10,852 liquid phase probes, each probe targeting one SNP site; The information of corn SNP loci is as follows: The liquid phase probe is 110 bp in length, has a GC content between 30% and 70%, and has a number of homology regions ≤5.

2. Application of the cold-region maize SNP liquid phase chip according to claim 1 in genetic diversity analysis and dominant group division of cold-region maize germplasm resources.

3. Use of the cold-region maize SNP liquid phase chip according to claim 1 in cold-region maize population structure analysis.

4. Use of the cold-region maize SNP liquid phase chip according to claim 1 in constructing a genetic linkage map of a cold-region maize population.

5. Use of the cold-region maize SNP liquid phase chip according to claim 1 in genome-wide association analysis of cold-region maize populations.

6. Use of the cold-region corn SNP liquid phase chip according to claim 1 in corn variety identification, kinship identification, seed purity detection and / or genomic selection breeding.

7. A method for corn genotyping, characterized in that: The following steps are involved: S1. Extract genomic DNA from the sample to be tested; S2. constructing a sequencing library using the genomic DNA; S3. performing a probe hybridization reaction on the sequencing library and the cold-region corn SNP liquid phase chip according to claim 1; S4. After the hybridization product is cleaned, another round of PCR is performed to complete the construction of the hybridization capture library; S5. After the library passes the test, it is sequenced. After sequencing, data quality control and analysis are performed. The quality-controlled sequencing data are compared with the reference genome sequence to obtain the genotyping results.

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