Method for identifying rice substantive derived varieties based on whole genome SNP site set

Abstract

The invention relates to the technical field of agricultural biology, and discloses a method for identifying rice substantive derived varieties based on a whole genome SNP site set. The method comprises the following steps: carrying out whole genome sequencing and genotype analysis on a rice variety to be detected and a control variety, calculating the genetic similarity between the varieties by utilizing a set of pre-screened 15,772 core SNP site sets, and setting a unified judgment threshold value for indica rice and japonica rice subgroups based on the distribution of the site sets in a large-scale group. Therefore, the defects that in the prior art, site coverage is limited, steps are tedious, and subgroup genetic structure interference is caused are overcome, and efficient, stable and cross-subgroup applicable accurate judgment on rice substantive derived varieties is achieved.

Description

Technical Field

[0001] This invention belongs to the field of agricultural biotechnology, specifically relating to a method for identifying substantial rice derivatives based on a whole-genome SNP locus set. Background Technology

[0002] Plant variety protection is a core system for incentivizing breeding innovation and ensuring the healthy development of the seed industry. To balance the interests of original innovators and subsequent improvers, there is an urgent need to determine the substantial derivation relationships between varieties using precise molecular detection methods.

[0003] Currently, methods used for genetic similarity analysis of varieties, such as SSR markers and SNP chips, have significant limitations. SSR markers have low throughput and a limited number of loci; SNP chips have fixed loci, cannot be updated, and the data cannot be retrospectively utilized. Furthermore, their detection accuracy is easily affected by flanking sequence variations.

[0004] The closest existing technology to this invention is the MNP marker method (e.g., CN104805193A), but it has significant drawbacks. First, it does not fully consider the huge genetic background differences between indica and japonica rice subpopulations, attempting to use a single threshold for judgment, resulting in poor universality. Second, this method relies on complex multiplex PCR amplification, which involves cumbersome experimental steps. Furthermore, the PCR process has stringent requirements for primer matching and is susceptible to variations in primer binding region sequences, leading to significant amplification bias and affecting the accuracy of the results.

[0005] In summary, existing technologies struggle to provide an EDV determination method that simultaneously overcomes subpopulation genetic structure interference, avoids technological bias, and possesses high versatility and stability. Therefore, developing a novel solution has become an urgent need for the protection of intellectual property rights in the seed industry. Summary of the Invention

[0006] This invention provides a set of 15,772 core SNP loci based on whole-genome sequencing and specific screening, and for the first time establishes a unified method for identifying substantial derivative varieties (EDV) applicable to both indica and japonica rice subgroups, thereby achieving accurate and stable identification of substantial derivative varieties of rice.

[0007] This invention provides a method for identifying substantial rice derivatives based on a genome-wide SNP locus set, employing the following technical solution: 1. A method for identifying substantial rice derivatives based on a genome-wide SNP locus set, comprising the following steps: S1. Whole-genome sequencing was performed on the rice variety to be tested and the control variety to obtain sequencing data; S2. The sequencing data is compared with the rice reference genome, and genotypic analysis is performed to obtain genome-wide variation information; S3. From the variation information, extract the genotypes of 15,772 core SNP loci that were predetermined; the 15,772 core SNP loci are evenly distributed throughout the rice genome; S4. Based on the genotypes of 15,772 core SNP loci, calculate the genetic similarity (GS) between the rice variety to be tested and the control variety. S5. Based on the subgroup to which the rice variety to be tested belongs, compare its GS value with the pre-set EDV judgment threshold of the corresponding subgroup: if the rice variety to be tested is indica rice, compare its GS value with the EDV judgment threshold of indica rice; if the rice variety to be tested is japonica rice, compare its GS value with the EDV judgment threshold of japonica rice. When the GS value is greater than or equal to the corresponding subgroup EDV determination threshold, the rice variety to be tested is determined to be a substantial derivative of the control variety.

[0008] Preferably, the sequencing depth of whole-genome sequencing in step S1 is not less than 10×, and the genomic region coverage of the sequencing data is greater than 70%.

[0009] Preferably, the rice reference genome in step S2 is the genome sequence of the Nipponbare IRGSP-1.0 variety.

[0010] Preferably, the formula for calculating the genetic similarity GS in step S4 is:

[0011] n ij This refers to the number of loci in which the tested rice variety and the control variety have completely identical genotypes among the 15,772 core SNP loci. N ij This represents the number of loci in both the rice variety to be tested and the control variety where the genotype was successfully detected.

[0012] Preferably, the EDV determination threshold for indica rice is set based on the 99th percentile of the GS values ​​between pairs of varieties in a large-scale indica rice population; The EDV threshold for japonica rice is set based on the 99th percentile of the GS values ​​between pairs of varieties in a large-scale japonica rice population.

[0013] Preferably, the EDV determination threshold for indica rice is 90.3%, and the EDV determination threshold for japonica rice is 91.1%.

[0014] Preferably, the 15,772 core SNP loci were obtained by resequencing and population genetic analysis of a large-scale rice germplasm resource population containing indica and japonica rice.

[0015] In summary, the beneficial effects of the present invention are as follows: This invention, for the first time, successfully established a unified identification method for substantial derivative varieties (EDVs) applicable to both indica and japonica rice subgroups using a carefully selected set of 15,772 core SNP loci. This method fundamentally overcomes the shortcomings of existing technologies (such as the MNP method), which cannot set universal thresholds due to neglecting subgroup genetic structural differences. Furthermore, it utilizes whole-genome sequencing technology to avoid biased interference from PCR amplification, achieving accurate and universal identification across subgroups.

[0016] Furthermore, the core SNP locus set selected in this invention exhibits excellent robustness and stability. Large-scale data validation shows that even under suboptimal conditions with a high proportion (e.g., 40%) of loci being randomly deleted, the calculated genetic similarity results among varieties remain highly consistent. This greatly enhances the anti-interference ability and reliability of the method in practical applications, providing stable and efficient technical support for the effective implementation of EDV. Attached Figure Description

[0017] Figure 1 This is a logarithmic distribution diagram of genetic similarity among the 93 samples in Example 2; Figure 2 In Example 3, A represents the distribution of GS values ​​within different recurrent parents of near-isogenic lines. Figure 2 In Example 3, sample B is a comparison of samples from different recurrent parents in near-isogenic lines. Figure 3 In Example 3, A represents the differential SNP distribution between the offspring of the YN108 recurrent parent and the recurrent parent. Figure 3 B represents the differential SNP distribution between the offspring of the YN20 recurrent parent and the recurrent parent in Example 3; Figure 3 C represents the differential SNP distribution between the offspring of the YN301 recurrent parent and the recurrent parent in Example 3; Figure 4 This is the distribution of genetic similarity among irradiated samples in Example 4. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the embodiments.

[0019] Example Example 1 We used resequencing data from 6,044 representative varieties from China's five major rice-producing areas as the background population to screen for SNP sites.

[0020] S1. Whole genome sequencing was performed using the NGS platform, with a sequencing depth of 30x rice genome reference sequence coverage (Oryza sativassp. japonicacv. 0.37G whole genome size), sequencing volume ≥11.1G, and read length of PE150.

[0021] S2, Data Quality Control The genomic region coverage of whole-genome sequencing data should be greater than 90%.

[0022] S3, Whole Genome Analysis Workflow The raw sequencing data (Fastq files) were evaluated for quality using software with data quality statistics capabilities. Low-quality reads and adapter contamination sequences were removed, and a quality-controlled dataset was generated.

[0023] After quality control, the data is aligned to the reference genome (Oryza sativa cv. Nipponbare IRGSP-1.0 version) using sequence alignment software (such as BWA) to generate preliminary alignment result files.

[0024] The alignment results are sorted by chromosome position using a sorting tool (such as samtools) and output as a compressed binary alignment file (BAM format) in a standard format. Sequencing coverage and depth information of the alignment data are also statistically analyzed.

[0025] Based on the sorted BAM files, genotype determination (SNP / InDel calling) is performed using software tools that conform to standard specifications, and a variant detection result file in standard format (VCF format) is output.

[0026] S4. Based on the population resequencing data, filter out non-homozygous loci in individuals. Then, select representative and polymorphic loci based on population diversity analysis. During the screening process, the following criteria were set to ensure the polymorphism and representativeness of the markers: minimum allele frequency (MAF) ≥ 0.05 and population deletion rate ≤ 0.02.

[0027] Loci were screened for indica and japonica rice subpopulations using different PIC values ​​(0.15–0.3, step size 0.01), and the selected loci were merged using linkage disequilibrium coefficient (r²) ≤ 0.1.

[0028] Calculate the GS value distribution for pairwise comparisons within different subgroups of the population under each combination, and determine the parameter selection that minimizes the difference in median GS distributions between the two subgroups. The locus obtained under this parameter condition is the final selection locus. This locus ensures the independent representativeness of the SNP and its ability to distinguish subgroups at the same scale.

[0029] Example 2 Ninety-three specific pedigrees and derived approved varieties were selected, and a total of 4,278 pairs of combinations were compared between pairs of samples.

[0030] Table 1. Names of the 93 samples measured in Example 2

[0031] S1. Whole genome sequencing was performed using the NGS platform, with a sequencing depth of 30x rice genome reference sequence coverage (Oryza sativassp. japonicacv. 0.37G whole genome size), sequencing volume ≥11.1G, and read length of PE150.

[0032] S2, Data Quality Control The raw sequencing data (Fastq files) were evaluated for quality using software with data quality statistics capabilities. Low-quality reads and adapter contamination sequences were removed, and a quality-controlled dataset was generated.

[0033] Sequencing data were evaluated and filtered in compressed or raw FASTQ format: Remove sequencing adapter sequences from reads; Reads are removed if they contain any of the following: more than 10 consecutive G bases, non-paired-end, any single end length less than 75 bp, average base mass less than 20, or reads containing more than 5 N bases.

[0034] Using fastp software, the command is --trim_poly_g --poly_g_min_len 10 --length_required 75 --cut_window_size 4 --thread 4 --cut_mean_quality 20 --cut_front--cut_right --cut_tail_window_size 8 --cut_tail_mean_quality 28 --cut_tail --n_base_limit 5 --qualified_quality_phred 20 --unqualified_percent_limit 20 –correction S3. After quality control, the data is aligned to the reference genome (Oryza sativacv. Nipponbare IRGSP-1.0 version) using sequence alignment software (such as BWA) to generate a preliminary alignment result file.

[0035] The alignment results are sorted by chromosome position using a sorting tool (such as samtools) and output as a compressed binary alignment file (BAM format) in a standard format. Sequencing coverage and depth information of the alignment data are also statistically analyzed.

[0036] Based on the sorted BAM files, genotype determination (SNP / InDel calling) is performed using software tools that conform to standard specifications, and a variant detection result file in standard format (VCF format) is output.

[0037] For mixed samples, when the minor allele frequency of a locus is less than 0.2, the locus is changed to a homozygous locus of the major allele genotype, thus removing the interference of hybrid mutations on genotype determination.

[0038] VCF filtering, recommended thresholds: DP≥10, MQ≥40, QUAL≥30, missing rate≤40%.

[0039] The genotypes of the submitted samples are compared with those of the standard samples, and the locus similarity is calculated based on the common detected and filtered loci.

[0040] S4. Site Similarity Calculation The locus genotypes of the submitted samples and the standard samples were compared in pairs. After removing heterozygous loci, the locus similarity was calculated according to formula (1): Site similarity is calculated using formula (1):

[0041] In the formula: —Genetic similarity between the tested variety and the control variety; —The number of marker loci detected in both the tested and control varieties but with no difference in genotype; —The number of marker sites detected in both the tested and control varieties.

[0042] As shown in Table 1 and Figure 1 As shown, there are 458 sample pairs with a GS greater than 0.92, accounting for approximately 10.7% of the total. There are 8 sample pairs with a GS greater than 0.97, accounting for approximately 0.18% of the total. Among them, the samples with a GS greater than 0.97 are extremely similar in their parent-child relationship.

[0043] Table 2. Gene similarity analysis among different rice varieties in Example 2

[0044] Table 3 shows the eight varietal combinations with a genetic similarity (GS) greater than 97% selected from 93 rice samples in Example 1 after pairwise genetic similarity (GS) analysis, along with their specific GS values. Among them, the GS of Goyama Richan and Goyama Michan is as high as 99.64%, which in breeding practice makes them highly likely to be derived varieties of the same original variety or highly similar sister lines.

[0045] Table 3. Eight pairs of varieties with genetic similarity greater than 97% in Example 2

[0046] Example 3 Fifty-three near-isogenic lines of japonica rice were tested. All 53 lines were constructed using the same cultivar, YN818, as the donor parent, through backcrossing into three different recurrent parental backgrounds. The three recurrent parental backgrounds were YN20, YN108, and YN301, with YN20 and YN301 being breeding sister lines. All backcrosses were performed for more than six generations.

[0047] S1. Whole genome sequencing was performed using the NGS platform, with a sequencing depth of 30x rice genome reference sequence coverage (Oryza sativassp. japonicacv. 0.37G whole genome size), sequencing volume ≥11.1G, and read length of PE150.

[0048] S2, Data Quality Control The raw sequencing data (Fastq files) were evaluated for quality using software with data quality statistics capabilities. Low-quality reads and adapter contamination sequences were removed, and a quality-controlled dataset was generated.

[0049] Sequencing data were evaluated and filtered in compressed or raw FASTQ format: Remove sequencing adapter sequences from reads; Reads are removed if they contain any of the following: more than 10 consecutive G bases, non-paired-end, any single end length less than 75 bp, average base mass less than 20, or reads containing more than 5 N bases.

[0050] Using fastp software, the command is --trim_poly_g --poly_g_min_len 10 --length_required 75 --cut_window_size 4 --thread 4 --cut_mean_quality 20 --cut_front--cut_right --cut_tail_window_size 8 --cut_tail_mean_quality 28 --cut_tail --n_base_limit 5 --qualified_quality_phred 20 --unqualified_percent_limit 20 –correction S3. After quality control, the data is aligned to the reference genome (Oryza sativacv. Nipponbare IRGSP-1.0 version) using sequence alignment software (such as BWA) to generate a preliminary alignment result file.

[0051] The alignment results are sorted by chromosome position using a sorting tool (such as samtools) and output as a compressed binary alignment file (BAM format) in a standard format. Sequencing coverage and depth information of the alignment data are also statistically analyzed.

[0052] Based on the sorted BAM files, genotype determination (SNP / InDel calling) is performed using software tools that conform to standard specifications, and a variant detection result file in standard format (VCF format) is output.

[0053] For mixed samples, when the minor allele frequency of a locus is less than 0.2, the locus is changed to a homozygous locus of the major allele genotype, thus removing the interference of hybrid mutations on genotype determination.

[0054] VCF filtering, recommended thresholds: DP≥10, MQ≥40, QUAL≥30, missing rate≤40%.

[0055] The genotypes of the submitted samples are compared with those of the standard samples, and the locus similarity is calculated based on the common detected and filtered loci.

[0056] S5. Site Similarity Calculation The locus genotypes of the submitted samples and the standard samples were compared in pairs. After removing heterozygous loci, the locus similarity was calculated according to formula (1): Site similarity is calculated using formula (1):

[0057] In the formula: —Genetic similarity between the tested variety and the control variety; —The number of marker loci detected in both the tested and control varieties but with no difference in genotype; —The number of marker sites detected in both the tested and control varieties.

[0058] like Figure 2 As shown in Figure A, in the genetic similarity analysis, the genetic similarity (GS) of sample pairs from the same recurrent parental background is greater than 98%, supporting its use as a basis for determining EDV relationships. Figure 2 As shown in Figure B, when the YN108 background sample is compared with the YN20 and YN301 background samples, their GS values ​​are all below 92%, indicating that the currently set EDV judgment threshold can effectively distinguish materials with different genetic backgrounds and has good discriminative ability. However, since YN20 and YN301 are sister lines, their genetic relationship is highly similar, making it difficult to further distinguish between the two groups of samples by GS value, reflecting the discrimination boundary characteristics of this threshold between samples with extremely high similarity.

[0059] like Figure 3 As shown, there are three subgraphs, A, B, and C, which respectively illustrate the SNP difference distribution between near-isogenic offspring and corresponding recurrent parents under three different recurrent parental backgrounds in Example 2. Figure 3 A shows the SNP difference distribution between the offspring material with YN108 as the recurrent parent and its recurrent parent YN108; Figure 3 B shows the SNP difference distribution between the offspring materials with YN20 as the recurrent parent and their recurrent parent YN20; Figure 3 Section C shows the SNP differences between offspring materials with YN301 as the recurrent parent and its recurrent parent YN301. This visualization visually presents the differences in genomic SNP loci between near-isogenic lines constructed through backcrossing breeding and their recurrent parents under different genetic backgrounds, helping to understand the genetic consistency and variation distribution characteristics among materials from different backgrounds.

[0060] Example 4 177 Kitaake japonica rice varieties were selected for testing.

[0061] S1. Dry rice seeds (YN108, YN20, and YN301) used for mutagenesis treatment, with a moisture content adjusted to 12%. Samples were irradiated with a carbon ion beam (12C6+, 967 MeV) generated by the Lanzhou Heavy Ion Research Facility (HIRFL). The average energy deposition (LET) of the carbon ion beam was 34 keV / μm. During carbon ion beam irradiation, seeds were placed in 35 mm culture dishes, approximately 100 seeds per dish, with a seed sample thickness of approximately 0.8 cm. Irradiation doses were set at 25, 50, 75, 100, 125, 150, 175, 200, 250, and 300 Gy, with a dose rate of approximately 60 Gy / min. Each dose treatment was performed in triplicate.

[0062] After S2 and CIB irradiation, 40 seeds were randomly selected from each culture dish, with each dose repeated three times. The selected seeds were placed in 10 ml centrifuge tubes, and approximately 8 mL of 1% H2O2 was added for surface sterilization for 30 minutes. The seeds were then washed three times with distilled water. The seeds were then germinated in 90 mm culture dishes covered with three layers of moist filter paper containing deionized water. The culture dishes were placed in a 28°C thermostat for germination.

[0063] S3. After germination and 14 days of seedling cultivation in the growing chamber, the seedlings are transplanted to outdoor containers (size: 23×21×18cm), with three rice plants in each container. At maturity, 12 plants are selected for each CIB dose to measure plant height, number of panicles per plant, number of tillers per plant, and seed setting rate. Each panicle of a single M1 plant is harvested individually to construct a seed bank.

[0064] S4. Perform whole-genome standard sequencing using the NGS platform, with a sequencing depth of 30x rice genome reference sequence coverage (Oryza sativassp. japonicacv. 0.37G whole genome size), sequencing volume ≥11.1G, and read length of PE150.

[0065] S5, Data Quality Control The raw sequencing data (Fastq files) were evaluated for quality using software with data quality statistics capabilities. Low-quality reads and adapter contamination sequences were removed, and a quality-controlled dataset was generated.

[0066] Sequencing data were evaluated and filtered in compressed or raw FASTQ format: Remove sequencing adapter sequences from reads; Reads are removed if they contain any of the following: more than 10 consecutive G bases, non-paired-end, any single end length less than 75 bp, average base mass less than 20, or reads containing more than 5 N bases.

[0067] Using fastp software, the command is --trim_poly_g --poly_g_min_len 10 --length_required 75 --cut_window_size 4 --thread 4 --cut_mean_quality 20 --cut_front--cut_right --cut_tail_window_size 8 --cut_tail_mean_quality 28 --cut_tail --n_base_limit 5 --qualified_quality_phred 20 --unqualified_percent_limit 20 –correction S6. After quality control, the data is aligned to the reference genome (Oryza sativacv. Nipponbare IRGSP-1.0 version) using sequence alignment software (such as BWA) to generate a preliminary alignment result file.

[0068] The alignment results are sorted by chromosome position using a sorting tool (such as samtools) and output as a compressed binary alignment file (BAM format) in a standard format. Sequencing coverage and depth information of the alignment data are also statistically analyzed.

[0069] Based on the sorted BAM files, genotype determination (SNP / InDel calling) is performed using software tools that conform to standard specifications, and a variant detection result file in standard format (VCF format) is output.

[0070] For mixed samples, when the minor allele frequency of a locus is less than 0.2, the locus is changed to a homozygous locus of the major allele genotype, thus removing the interference of hybrid mutations on genotype determination.

[0071] VCF filtering, recommended thresholds: DP≥10, MQ≥40, QUAL≥30, missing rate≤40%.

[0072] The genotypes of the submitted samples are compared with those of the standard samples, and the locus similarity is calculated based on the common detected and filtered loci.

[0073] S7. Site Similarity Calculation The locus genotypes of the submitted samples and the standard samples were compared in pairs. After removing heterozygous loci, the locus similarity was calculated according to formula (1): Site similarity is calculated using formula (1):

[0074] In the formula: —Genetic similarity between the tested variety and the control variety; —The number of marker loci detected in both the tested and control varieties but with no difference in genotype; —The number of marker sites detected in both the tested and control varieties.

[0075] like Figure 4 As shown, the SNP loci in this sample set are highly similar, and the GS values ​​generally exceed 99%, indicating that its genetic structure is highly homogeneous, which meets the criteria for EDV and is suitable for use as a typical EDV population sample.

[0076] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying a substantial derivative of rice based on a set of whole genome SNP loci, characterized in that, Comprising the following steps: S1. Whole genome sequencing is performed on the test rice variety and the control variety respectively to obtain sequencing data; S2. The sequencing data is aligned with the rice reference genome, and genotype analysis is performed to obtain whole genome variation information; S3. From the variation information, the genotypes of 15,772 core SNP sites are extracted; the 15,772 core SNP sites are uniformly distributed throughout the rice genome; S4. Based on the genotypes of the 15,772 core SNP sites, the genetic similarity between the test rice variety and the control variety is calculated; S5. According to the subpopulation to which the test rice variety belongs, its GS value is compared with the pre-set EDV determination threshold of the corresponding subpopulation: if the test rice variety is indica rice, its GS value is compared with the indica rice EDV determination threshold; if the test rice variety is japonica rice, its GS value is compared with the japonica rice EDV determination threshold; Wherein, when the GS value is greater than or equal to the EDV determination threshold of the corresponding subpopulation, the test rice variety is determined to be a substantial derivative variety of the control variety.

2. The method for identifying substantial derivative varieties of rice based on whole genome SNP site set according to claim 1, characterized in that, In step S1, the sequencing depth of whole genome sequencing is not less than 10x, and the genome region coverage of sequencing data is greater than 70%.

3. The method for identifying substantial derivative varieties of rice based on whole genome SNP site set according to claim 1, characterized in that, The rice reference genome in step S2 is the genome sequence of IRGSP-1.0 version of Nipponbare variety.

4. The method for identifying substantial derivative varieties of rice based on whole genome SNP site set according to claim 1, characterized in that, The calculation formula of genetic similarity GS in step S4 is: n ij the number of loci at which the genotype of the rice variety to be tested and the genotype of the control variety are identical at the 15,772 core SNP loci; N ij The number of loci for which the genotype of the rice variety to be tested and the control variety are successfully detected.

5. The method according to claim 1, wherein the method is characterized by, The indica rice EDV determination threshold is set based on the 99th percentile of the GS value between each pair of varieties in a large-scale indica rice population; The japonica rice EDV determination threshold is set based on the 99th percentile of the GS value between each pair of varieties in a large-scale japonica rice population.

6. The method for identifying substantial derivative varieties of rice based on a set of whole genome SNP loci according to claim 5, characterized in that, The indica rice EDV determination threshold is 90.3%, and the japonica rice EDV determination threshold is 91.1%.

7. The method according to claim 1, wherein the method is characterized by, The 15,772 core SNP sites are obtained by resequencing and population genetics analysis of a large-scale rice germplasm resource population containing indica rice and japonica rice.

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