Method for detecting plant mutant chromosome rearrangement

Abstract

The invention discloses a method for detecting plant mutant chromosome rearrangement, and belongs to the technical field of plant gene detection. The invention develops a detection means based on BSA re-sequencing and PCR amplification for plant mutant chromosome structure variation. The method comprises the steps of construction and next-generation sequencing of a mixed pool, high-quality variation site mining, interval positioning, target gene positioning, chromosome rearrangement detection and verification and the like. The method has the advantages that the mutant gene can be quickly and accurately positioned and the chromosome rearrangement event associated with the mutant gene can be detected by using a low-cost second-generation sequencing technology, and the defect that large fragment insertion and more complex chromosome rearrangement events cannot be identified by an existing algorithm for detecting chromosome structure variation based on second-generation genome sequencing data is effectively overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of plant gene detection, and in particular to a method for detecting chromosome rearrangement of plant mutants based on BSA resequencing and PCR amplification. Background Art

[0002] The breeding and production of new crop varieties are the cornerstones of ensuring food security. The completion of whole genome sequencing of various crops has laid the foundation for molecular breeding and genomic selection breeding. However, the functions of most genes are still unclear, which limits the progress of crop molecular breeding. The creation of mutant libraries can create new germplasm resources and provide genetic materials for the genetic improvement of crops. The methods for constructing plant mutant libraries mainly include: physical and chemical mutagenesis, transposon random insertion mutagenesis, CRISPR / Cas9 gene editing system construction, etc. In addition to common gene mutations (such as base substitutions, insertions, deletions, etc.), the plant mutant materials created by the above methods also occasionally have chromosomal rearrangements.

[0003] Chromosome rearrangement refers to the phenomenon that broken chromosomes undergo abnormal connection during the reconnection process, resulting in chromosome structural variation. Chromosome rearrangement is usually divided into four types: duplication, deletion, inversion and translocation. Chromosome rearrangement can cause changes in large fragments of chromosomes. Among them, inversion refers to a fragment breaking within a chromosome and reconnecting in the opposite direction. DNA may or may not be lost in this process; translocation refers to a genetic translocation that occurs when a chromosome breaks and the chromosome fragments reattach to other different chromosomes. Translocation can occur between chromosomes or within chromosomes.

[0004] In addition, spontaneous chromosome rearrangements also occur in plants, which are generally believed to be caused by genetic drift, environmental pressure or other unknown factors, promoting biological evolution and speciation.

[0005] The detection methods of plant chromosome rearrangement mainly include cell biology detection methods, molecular genetics detection methods, high-throughput sequencing and bioinformatics analysis methods. Fluorescence in situ hybridization (FISH) is often used for visual identification of plant chromosome rearrangement. Its principle is to hybridize the probe directly or indirectly labeled with fluorescein (such as biotin, digoxin, etc.) with the nucleic acid sequence in the sample to be tested according to the principle of base complementary pairing, and observe directly under a fluorescence microscope after washing. It can be used to detect events such as deletion, rearrangement or insertion on chromosomes, but because it is a cellular level detection, it cannot detect sequence variations at the genome base level with higher resolution. Molecular markers refer to characteristic DNA fragments that can reflect the genomic differences between biological individuals or species populations, including SSR (Simplesequence repeat) marker analysis, AFLP (Amplified fragment length polymorphism) marker analysis, etc. However, molecular marker technology usually requires specific amplification and detection of the target sequence, so specific sites need to be designed in advance, and may not provide effective detection for unknown chromosome rearrangement events or unstable genetic variations.

[0006] With the development of sequencing technology in the past decade, people hope to use high-throughput sequencing as an integrated genetic detection method to simultaneously detect chromosome structural variations to supplement or replace traditional cell biology detection methods. The short nucleotide sequences generated by the second-generation sequencing technology are often tens to hundreds of bases. Currently, four types of algorithms have been developed based on the second-generation genome sequencing data to detect chromosome structural variations: paired-end sequencing read matching (PEM), split read mapping (SRM), read coverage (DOC), and assembly-based approach (ASA). For variations within a small length range and longer deletion variations, the current detection software can basically identify them, but it cannot solve most insertions and more complex structural variations.

[0007] Compared with the traditional second-generation sequencing technology, the third-generation sequencing technology has an irreplaceable advantage in the detection of chromosome structural variation (SV) due to the long length of the measured sequence, but the high error rate of the third-generation sequencing and the introduction of Indel errors will bring a relatively large error correction cost, and its sequencing cost is also high. Therefore, the field still needs a method for identifying mutant genes and related chromosome rearrangement events of plant mutants based on second-generation sequencing data.

[0008] BSA (Bulked Segregant Analysis), also known as cluster separation analysis or mixed group analysis, is a classic method for locating genes in forward genetics. BSA targets the target trait, selects parents with extreme phenotypic differences to construct a family, and simultaneously performs whole-genome resequencing on two mixed pools obtained by mixing the two parents and the offspring with extreme phenotypic characteristics of the target trait of the family. The DNA difference fragments detected between the two mixed pools are candidate regions, and the genes in the region are annotated, which can further locate the genes or markers related to the target trait. Using this method, a single trait can be quickly located on the genome. However, the presence of chromosome rearrangement makes it difficult to locate mutant genes. Summary of the invention

[0009] The purpose of the present invention is to provide a method for detecting chromosome rearrangement events of plant mutants and gene mutations caused by chromosome rearrangement at low cost, quickly and accurately, so as to solve the defect that the current algorithm for detecting chromosome structural variation based on second-generation genome sequencing data cannot identify large fragment insertions and more complex chromosome rearrangement events.

[0010] To achieve the above object, the present invention adopts the following technical solution:

[0011] The present invention provides a method for detecting chromosome rearrangement of a plant mutant, comprising the following steps:

[0012] (1) Construction of mixed pools and next-generation sequencing: hybridization is performed with the wild type as the female parent and the stable inherited mutant as the male parent, followed by self-pollination to obtain the F2 segregation population; if the segregation ratio of the target trait in the F2 generation meets the segregation ratio of a single gene, gene localization can be performed; genomic DNA is extracted from two groups of individuals with extreme phenotypic differences, and wild-type phenotypic mixed pools and mutant phenotypic mixed pools are constructed for Illumina sequencing;

[0013] (2) Sequencing data processing: The raw sequencing data of each mixed pool was filtered and then aligned with the reference genome using the BWA software. The UnifiedGenotyper module of the GATK software was then used to detect SNPs and InDel sites.

[0014] (3) Interval positioning: Calculate the frequency of occurrence of each SNP and InDel site in each mixed pool, that is, the All-index value, and calculate the difference ΔAll-index between the two mixed pools after filtering; then select a sequence length of 0.1-1Mb as a window on the genome, and calculate the SNPs and InDels contained in the window at the whole genome level using the sliding window method with a step size of 1kb. The average value of ΔAll-index in each window is used to reflect the ΔAll-index distribution; then perform a permutation test and select windows with a confidence level of more than 95% as candidate intervals;

[0015] (4) Target gene localization and chromosomal rearrangement site detection: functional analysis of all annotated genes in the candidate interval was performed with reference to the plant genome database, and known genes contained in or adjacent to the candidate interval that were related to the target trait were used as candidate genes; the SAMTOOLS tool was used to perform depth statistics on the comparison of each base in the full-length region of the corresponding candidate gene in each mixed pool, and the sites covered in the wild-type phenotype mixed pool but not in the mutant phenotype mixed pool were found as candidate rearrangement site A; then, short sequences 1 and 2 were selected along the upstream and downstream of site A in the reference genome, and read1 and read2 containing two short sequences were retrieved in the sequencing data of the mutant phenotype mixed pool, and read1 and read2 were compared with the reference genome.

[0016] If an interval inversion occurs between site A and site B on the chromosome, read1 will be able to align to the upstream of point A and upstream of point B, respectively, and read2 will be able to align to the downstream of point A and downstream of point B, respectively;

[0017] If the interval fragment between sites B and C and the interval fragment between sites C and A are translocated on the chromosome, or the interval fragment between sites B and C is repeatedly inserted into point A on the chromosome, then read1 will be able to be aligned to the upstream of point A and the downstream of point B, respectively, and read2 will be able to be aligned to the downstream of point A and the upstream of point C, respectively; then, in the reference genome, short sequence 3 is selected along the upstream of site B, and short sequence 4 is selected along the downstream of site C, and read3 and read4 containing these two end segment short sequences are retrieved from the sequencing data of the mutant mixed pool, and compared with the reference genome. If read3 and read4 can be aligned to the upstream of point B and the downstream of point C, respectively, it indicates that the BC interval fragment and the CA interval fragment are translocated on the chromosome; if read3 can only be aligned to point B, and read4 can only be aligned to point C, it indicates that the BC interval fragment is repeatedly inserted into point A on the chromosome;

[0018] If the interval fragment between site A and site B is missing on the chromosome, read1 and read2 can be aligned to the upstream of point A and the downstream of point B respectively;

[0019] (5) Verification of chromosome rearrangement: Primers were designed based on the rearranged chromosomes, and PCR amplification and sequencing were performed on the rearranged junctions.

[0020] Specifically, in step (1), offspring with extreme phenotypic differences in the target trait are selected and mixed to construct two mixed pools, and whole-genome resequencing is performed using second-generation sequencing technology.

[0021] The plant mutants in the present invention may be, but are not limited to, those created by methods such as physical and chemical mutagenesis, transposon random insertion mutagenesis, CRISPR / Cas9 gene editing system construction, etc. The plants may be, but are not limited to, crops such as rice, corn, and sorghum.

[0022] The wild type is used as the female parent and the stably inherited mutant is used as the male parent to obtain the first generation. The F1 generation is self-pollinated to obtain the F2 generation. The target trait phenotype of the F2 is investigated and the trait segregation ratio is statistically calculated. When the target trait segregation ratio is close to 3:1, gene positioning can be carried out.

[0023] The F2 generation with extreme phenotypic differences is selected, genomic DNA is extracted from plant tissues, and a mixed pool is constructed. Preferably, 15-30 individuals are selected from each group, and after genomic DNA is extracted, equal amounts are mixed to construct a mixed pool. The plant tissue can be, but is not limited to, leaves, stems, roots, etc.

[0024] The integrity, purity, and concentration of the mixed pool DNA are tested. After passing the test, it is used for Illumina sequencing to obtain raw sequencing data. Raw sequencing data contains the sequence information of the sequencing sequence (reads) and its corresponding sequencing quality information. Reads (read length) refers to the base sequence obtained by a single sequencing of the sequencer.

[0025] In step (2), the original sequencing data is filtered to obtain high-quality data, and then compared with the reference genome sequence to screen out the variant sites.

[0026] Preferably, the criteria for filtering the raw sequencing data using fastp software are: removing reads with adapters; removing reads in which the N base accounts for more than 10% of the sequencing read length; removing reads in which the base with Q value ≤ 5 accounts for more than 50% of the sequencing read length.

[0027] The present invention uses the BWA-MEM algorithm of the BWA software to align the sequencing data with the reference genome. The MEM algorithm supports longer read analysis and is suitable for Illumina sequencing data. Preferably, the BWA software alignment parameters are set to: mem, -t4, -k 32, -M. mem represents the algorithm; -t 4 means using 4 threads to speed up the alignment process; -k 32 means setting the minimum seed length to 32bp. In sequence alignment, seed refers to a subsequence of a fixed length used to start or accelerate the alignment algorithm; -M means making BWA generate an alignment file for use by Picard and GATK. The above alignment parameter settings can be adjusted accordingly according to actual needs.

[0028] Preferably, the comparison results after the BWA software comparison are first processed by the SAMTOOLS tool to remove PCR duplications before SNP and InDel site detection. Specifically, the parameter for removing PCR duplications in the SAMTOOLS tool is rmdup.

[0029] The filtered comparison results are used to detect variant sites through the UnifiedGenotyper module of the GATK software. The present invention can compare the differences between the two extreme groups at the genome level by detecting SNP and InDel polymorphic sites, thereby locking in molecular markers linked to mutant genes for gene positioning.

[0030] After SNP and INDEL site detection is performed on the alignment results, the VariantFiltration module is used for filtering to improve the positioning efficiency. As a preference, the filtering parameters for SNP are set to: --clusterWindowSize 4, --filterExpression "QD<4.0||FS>60.0||MQ<40.0", -G_filter "GQ<5". --clusterWindowSize 4 is used to specify the cluster window size to detect dense SNPs. If multiple SNPs are found within the specified window size, these SNPs will be considered as a cluster, which helps to filter out dense SNPs caused by sequencing or alignment errors; --filterExpression is used to define the filter expression; -G_filter "GQ<5" means that if the genotype quality is less than 5, the genotype is filtered.

[0031] Preferably, the filtering parameter for InDel is set to: --filterExpression "QD<4.0||FS>200.0".

[0032] The above-mentioned filtering parameter settings in the present invention can be adjusted accordingly according to actual needs.

[0033] In step (3), the All-index value of the combined SNP and InDel in each mixed pool is calculated (indicating the frequency of each detected variant site in the mixed pool), and then the All-index values ​​of the two progeny mixed pools are subtracted. The resulting ΔAll-index can more intuitively show the differences in the genome between the two extreme progeny pools.

[0034] In the present invention, SNP and InDel are combined to detect the frequency of SNP and InDel in the mixed pool, and more polymorphic sites are used to increase the detection efficiency of gene positioning.

[0035] An example of calculating the All-index value is: if a SNP (G / A) is detected, it is G in the reference genome and the variant is A. In progeny pool 1, 27 Gs and 10 As are detected, then All-index = 10 / 37 = 0.27.

[0036] After calculating the All-index value, All-index is filtered to remove background noise and improve the efficiency of positioning. Preferably, the criteria for filtering SNP and InDel sites after calculating the All-index value are as follows: remove sites with All-index less than 0.3 in both mixed pools; remove sites with SNP or InDel depth less than 7 in a single mixed pool; remove sites with missing All-index in a single mixed pool. For example: If a SNP (A / C) is detected, it is A in the reference genome and the variant is C. A is detected 3 times and C is detected 7 times in progeny pool 1, then the depth is 10, and All-index = 7 / 10 = 0.7.

[0037] The calculation formula of ΔAll-index is: ΔAll-index = All-index (mutant phenotype mixed pool) - All-index (wild-type phenotype mixed pool).

[0038] Theoretically, for sites that are not related to the mutant trait, the ΔAll-index should be around 0, indicating that there is no difference between the pools; while the QTL and its linked positions should present a higher value (around 0.75).

[0039] The present invention selects a window of a certain size (sequence length 0.1-1Mb) on the genome, calculates the SNPs and InDels contained in the window at the whole genome level by the sliding window method with a step size of 1kb, calculates the average value of ΔAll-index in each window to reflect the ΔAll-index distribution, and selects the window with significant differences between the two progeny mixed pools as the candidate interval.

[0040] The present invention performs a confidence test on each window and selects a confidence level of more than 95% as the threshold line. Preferably, 1000 permutation tests are performed on each window and a confidence level of 99% is selected as the threshold line.

[0041] In step (4), refer to the N3 plant genome database (http: / / ibi.zju.edu.cn / N3database / search.php), perform functional analysis on all annotated genes in the candidate interval, and use known genes related to the target trait as candidate genes. If there are no related genes in the candidate interval, the search range can be expanded to find candidate genes upstream or downstream of the candidate interval. Then use the SAMTOOLS-depth command to count the comparison depth of each site in the candidate gene promoter and full-length region of the two pools, and find the sites without coverage in the mutant pool as candidate rearrangement sites A.

[0042] The uncovered site in the mutant pool may be a single base or several consecutive bases. If the mutant undergoes chromosome rearrangement at point A, the sequencing data upstream and downstream of point A will appear in the next-generation sequencing results of the mutant phenotype pool, and can be aligned to the reference genome, but since point A itself is lost during the rearrangement process, the alignment result will show no coverage.

[0043] The present invention selects two short sequences extending upstream and downstream of site A in the reference genome for reference. Read1 (including the upstream sequence of site A) and read2 (including the downstream sequence of site A) containing these two short sequences are retrieved in the sequencing data of the mutant mixed pool, and compared with the reference genome to determine the type of chromosome rearrangement. The chromosome rearrangement includes four types: chromosome segment duplication, deletion, inversion and translocation. The determination method is as follows:

[0044] If an inversion occurs in the AB interval on the chromosome, read1 will be able to align to the upstream of point A and upstream of point B, and read2 will be able to align to the downstream of point A and downstream of point B.

[0045] If the BC interval segment and the CA interval segment translocation occurs on the chromosome, or the BC interval segment is repeatedly inserted into point A on the chromosome, then read1 will be able to be aligned to the upstream of point A and the downstream of point B, respectively, and read2 will be able to be aligned to the downstream of point A and the upstream of point C, respectively. At this time, for the two newly found rearrangement connection points B and C, the selected sequence is extended to the other side, and read3 (including the upstream sequence of point B) and read4 (including the downstream sequence of point C) containing these two short sequences are retrieved from the sequencing data of the mutant mixed pool, and compared with the reference genome. If read3 and read4 can be aligned to the upstream of point B and the downstream of point C, respectively, it means that the BC interval segment and the CA interval segment translocation occurs on the chromosome; if read3 can only be aligned to point B, and read4 can only be aligned to point C, it means that the BC interval segment is repeatedly inserted into point A on the chromosome;

[0046] If a deletion of the AB interval occurs on the chromosome, read1 and read2 will be able to be aligned to the upstream of point A and the downstream of point B respectively.

[0047] The above points A, B, and C correspond to the reference genome.

[0048] Preferably, the length of the short sequence is 50 bp. The read length of the second generation sequencing platform is between 50-300 bp. Considering the sequencing read length and the subsequent comparison efficiency, 50 bp is selected.

[0049] In step (5), the inferred chromosome rearrangement form is verified by PCR amplification technology. Primers are designed for the junctions before and after the chromosome rearrangement, respectively, and PCR amplification is performed using wild-type genomic DNA and mutant genomic DNA as templates. Primers designed for the junction before chromosome rearrangement should be able to amplify in the wild type but not in the mutant; primers designed for the junction after chromosome rearrangement should not be able to amplify in the wild type but should be able to amplify in the mutant, indicating that it is consistent with expectations.

[0050] As a specific embodiment of the present invention, the plant mutant is rice 9311 60 The endosperm powdery mutant created by Co-γ ray irradiation mutagenesis has a wild-type rice phenotype of transparent endosperm. The method provided by the invention can be used to detect the chromosome rearrangement event occurring in the endosperm powdery mutant.

[0051] The present invention has the following beneficial effects:

[0052] The present invention develops a detection method based on BSA resequencing and PCR amplification for chromosome structural variation of plant mutants, including the construction of mixed pools and second-generation sequencing, high-quality variant site mining, interval positioning, target gene positioning, chromosome rearrangement detection and verification, etc. The advantage is that the mutant gene can be quickly and accurately located and the chromosome rearrangement events associated with it can be detected with the second-generation sequencing technology at a low cost, which effectively makes up for the defect that the current algorithm for detecting chromosome structural variation based on second-generation genome sequencing data cannot identify large fragment insertions and more complex chromosome rearrangement events. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 ΔAll-index distribution of the two progeny mixed pools on chromosomes in Example 1.

[0054] Figure 2 The figure shows the gene structure of OsPPDKB and the positions of F1 / R1 primers in Example 1.

[0055] Figure 3 This is the alignment result of the sequencing data of the two progeny mixed pools in Example 1 at the 6th exon of OsPPDKB.

[0056] Figure 4 This is the original sequencing data of the selected sequence upstream of the 20478381 site on chromosome 5 in the clean reads of the floury endosperm mixed pool in Example 1.

[0057] Figure 5 This is the original sequencing data of the selected sequence downstream of the 20478381 site on chromosome 5 in the clean reads of the floury endosperm mixed pool in Example 1.

[0058] Figure 6 This is the original sequencing data of the selected sequence downstream of the 7,833,327 site on chromosome 5 in the clean reads of the floury endosperm mixed pool in Example 1.

[0059] Figure 7 This is the original sequencing data of the selected sequence downstream of the 8,956,022 site on chromosome 5 in the clean reads of the floury endosperm mixed pool in Example 1.

[0060] Figure 8 The figure is a schematic diagram of the rearrangement of chromosome 5 of the M31 mutant and the positions of primers in Example 1. R3 / F1, F2 / R2, and F4 / R1 have correct binding sites only on the M31 rearranged chromosome; while F1 / R1 has correct binding sites only on chromosome 9311.

[0061] Fig. 9 The agarose gel electrophoresis diagram of the PCR amplification product in Example 1.

[0062] Fig.10 This is the DNA sequencing peak diagram of the chromosome rearrangement junction of the M31 mutant in Example 1, and the blue arrow indicates the rearrangement junction. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with specific embodiments. The following embodiments are only used to illustrate the present invention and are not intended to limit the scope of application of the present invention. Without departing from the spirit and essence of the present invention, modifications or replacements made to the inventive method, steps or conditions all belong to the scope of the present invention.

[0064] Unless otherwise specified, the experimental methods used in the following examples are all conventional methods; the materials and reagents used are reagents and materials that can be obtained from commercial channels unless otherwise specified.

[0065] The software functions used in the following examples are as follows: BWA is a tool for aligning DNA sequences to large reference genomes. SAMTOOLS is a software for processing the alignment data in SAM, BAM and CRAM formats generated by the BWA alignment software. GATK3.8 is a software for analyzing variation information from high-throughput sequencing data. Fastp software can perform preprocessing and quality control on FASTQ data.

[0066] Example 1

[0067] In this embodiment, rice 9311 was 60 Mutant library was created by mutagenesis using Co-γ-ray irradiation, and the endosperm floury mutant M31 with stable inheritance was screened out through phenotypic identification. M31 was hybridized with japonica rice JD2021, and the endosperm phenotype of F2 seeds was statistically analyzed. The separation ratio of transparent endosperm to floury endosperm seeds was close to 3:1 (Table 1), indicating that the floury endosperm phenotype was controlled by a recessive nuclear gene.

[0068] Table 1. Segregation ratio of transparent and powdery endosperm plants in F2 population

[0069]

[0070] 1. Construction of mixed pool and next-generation sequencing:

[0071] Twenty-four F2 transparent endosperm seeds and powdery endosperm seeds were selected for tissue culture germination, and the DNA of young leaves 14 days after tissue culture was extracted using the DNA extraction kit FastPure Plant DNA Isolation Mini Kit-DC104 from Novezan according to the steps in the instruction manual. Equal amounts of DNA were mixed to construct transparent endosperm DNA pools and powdery endosperm DNA pools, which were sent to Novogene for BSA resequencing gene positioning.

[0072] 2. High-quality variant site mining

[0073] After the endosperm DNA pool and the powdery endosperm DNA pool are qualified, Illumina sequencing is performed. The sequencing results are stored in the FASTQ file format, and the original sequencing data includes the sequence information of the sequencing sequence (reads) and its corresponding sequencing quality information.

[0074] Fastp software was used to filter the raw sequencing data. The filtering criteria were to remove reads with adapters, remove reads in which the N base accounted for more than 10% of the sequencing read length, and remove reads in which the number of low-quality (Q≤5) bases accounted for more than 50% of the sequencing read length; valid sequencing data were obtained.

[0075] The valid sequencing data were aligned to the Nipponbare reference genome (http: / / rice.plantbiology.msu.edu / ) using BWA software (using the BWA-MEM algorithm, parameters: mem-t4-k 32-M, where -t 4 means using 4 threads to speed up the alignment process; -k 32 means setting the minimum seed length to 32 bp. In sequence alignment, seed refers to a fixed-length subsequence used to start or accelerate the alignment algorithm; -M enables BWA to generate alignment files for use by Picard and GATK). The alignment results were processed by SAMTOOLS software (parameter: rmdup) to remove PCR duplications. Then, the UnifiedGenotyper module of GATK3.8 software was used to detect SNPs and InDels of multiple samples, and the VariantFiltration module was used for filtering (the filtering parameters for SNP were --clusterWindowSize 4 (used to specify the cluster window size to detect dense SNPs), --filterExpression "QD<4.0||FS>60.0||MQ<40.0" (define the filter expression), -G_filter "GQ<5 (indicates that if the genotype quality is less than 5, the genotype is filtered)"; the filtering parameters for InDel were --filterExpression "QD<4.0||FS>200.0").

[0076] After filtering, 2,280,851 SNP sites and 338,639 InDel sites were detected.

[0077] 3. Interval positioning

[0078] After merging SNPs and InDels, the All-index value is calculated (i.e., the frequency of merging SNPs and InDels, indicating the frequency of each detected variant site in the progeny pool. For example: if a SNP (G / A) is detected in Chr1_2097, it is G in the reference genome and the variant is A. 27 Gs and 10 A were detected in progeny pool 1, then All-index = 10 / 37 = 0.27). All-index is filtered to remove background noise and improve the efficiency of positioning. The filtering criteria include removing sites with All-index less than 0.3 in both progeny pools, removing sites with SNP or InDel depth less than 7 in a single progeny pool, and removing sites with missing All-index in a single progeny pool.

[0079] Calculate the difference of All-index between the two progeny pools: Δ(All-index) = Allindex (powdery endosperm) - Allindex (transparent endosperm). Then select 1Mb as the window and 1kb as the step size, and calculate the average value of Δ(All-index) in each window to reflect the distribution of Δ(All-index); perform 1000 permutation tests, and select 95% (blue) and 99% (purple) confidence levels as threshold lines to intuitively reflect the difference between the two progeny pools, such as Figure 1 As shown, a 0.46 Mb segment chr05:20,541,001-21,004,000 on chromosome 5 was identified at a 99% confidence level.

[0080] 4. Target gene location and chromosomal rearrangement site detection

[0081] In the N3 plant genome database (http: / / ibi.zju.edu.cn / N3database / search.php), the rice genome annotation database was searched and the functional analysis of all annotated genes in the above candidate interval was performed. This segment contains a total of 72 annotated genes, but according to the functional annotation of the Rice Genome Annotation Project (RGAP), they were not found to be related to the endosperm powdery phenotype. By expanding the search range, a cloned endosperm powdery gene OsPPDKB (LOC_Os05g33570) was found 0.8Mb upstream. OsPPDKB produces two transcripts, encoding chloroplast chOsPPDKB and cytoplasmic cyOsPPDKB, respectively. chOsPPDKB is highly expressed in leaves and sheaths, and cyOsPPDKB is highly expressed in developing endosperm ( Figure 2 ).

[0082] The BSA resequencing data was aligned to the 9311 reference genome (https: / / bigd.big.ac.cn / gwh, GWH:GWHBEBR00000000), and the samtools-depth command was used to perform alignment depth statistics for each site (GWHBEBR00000005:20478381-20475086) in the two mixed pools in the cyOsPPDKB promoter and full-length region. The statistical results showed that the coverage depth of the GWHBEBR00000005:20478381 site in the powdery endosperm mixed pool was 0, while the coverage depth in the transparent endosperm mixed pool was 10; the coverage depth of GWHBEBR00000005:20478382 in the powdery endosperm mixed pool was 1, and in the transparent endosperm mixed pool was 11. The coverage depths of the remaining cyOsPPDKB sites in the two mixed pools were all above 3. The alignment was checked by IGV software as shown in the figure. Figure 3 As shown in the figure, the alignment result of the mixed pool reads of the floury endosperm was abnormal at GWHBEBR00000005:20478381-20478382 (nucleotides 88 and 89 of exon 6 of OsPPDKB), with no correct reads coverage, while the alignment result of the mixed pool reads of the transparent endosperm was normal. Therefore, GWHBEBR00000005:20478381 was selected as a candidate rearrangement site.

[0083] The selected sequences were extended 50 bp upstream and downstream from GWHBEBR00000005:20478381, which were AGTCAGGTCAGTGTCGTTGTGCAGCCCCTTGACTGCTTTCATGGCTTCGA and CTTCAACATAGACATCCTTGTACTGTGCCACTAGTTCTTTGAGGTCAGTG. The original sequencing data related to these two sequences in the clean reads of the floury endosperm mixed pool were retrieved as follows: Figure 4 and Figure 5 shown.

[0084] After comparison, it was found that the sequences on both sides of the 88th position of exon 6 of OsPPDKB (GWHBEBR00000005:20478381) in the powdery endosperm pool were connected to the upstream sequences of 7,833,327 and 8,956,022 on chromosome 5 of 9311, respectively, and it was preliminarily judged that there was a translocation event on chromosome 5 of the mutant. Further, the sequences GTCGCATGTTTTTTTGGTTGATGGAAGAAATTCATAAAAACCGGTCGAAA and TCAATACCTCAAAAGCCATTATCTTCTATATGCTGATTCATTAATGTGAA were selected by extending 50bp downstream from the sites 7,833,327 and 8,956,022 on chromosome 5 of 9311, and the related raw sequencing data of these two sequences in the clean reads of the powdery endosperm pool were retrieved as shown in the figure. Figure 6 and Figure 7 shown.

[0085] After comparison, it was found that the downstream sequence of 7,833,327 was connected to the downstream sequence of 8,956,022, and the downstream sequence of 8,956,022 was connected to the downstream sequence of 7,833,324. Based on this, it is speculated that the interval fragment of chromosome 5 of M31 from 7.8Mb to 8.9Mb and the interval fragment from 8.9Mb to 20.5Mb were translocated, and the chromosome fragment from 8.9Mb to 20.5Mb was inverted ( Figure 8 ).

[0086] 5. Verification of chromosome rearrangement

[0087] Primers were designed for the rearranged junction for PCR amplification and Sanger sequencing verification. The designed primers are shown in Table 2. The binding sites of the primers on chromosome 5 are shown in Figure 8 .

[0088] Table 2. Primers and their corresponding physical locations on chromosome 9311

[0089]

[0090]

[0091] Among them, F1 / R1 is located before and after the rearrangement junction of exon 6 of OsPPDKB. After the chromosome rearrangement, its binding site changes and should not be amplified in the M31 mutant. The other three pairs of primers F2 / R2, F1 / R3, and F4 / R1 are designed based on the chromosome structure after the rearrangement of M31. They should not be amplified in 9311 without the correct binding site, but should be amplified in M31.

[0092] The results of PCR electrophoresis and Sanger sequencing were consistent with expectations ( Fig. 9 , Fig.10 ). This indicates that the interval fragment of chromosome 5 of M31, 7.8Mb-8.9Mb, and the interval fragment of 8.9Mb-20.5Mb, are translocated, and the chromosome fragment of 8.9Mb-20.5Mb is inverted.

[0093] In summary, the method of the present invention successfully locates a gene related to rice floury endosperm and its associated chromosome rearrangement event; the present invention can quickly and accurately detect spontaneous and mutagenesis-induced chromosome rearrangements in plant mutants at a relatively low cost.

[0094] The above embodiment is only a specific application mode of the method of the present invention and is not intended to limit the present invention. The method of the present invention can be used to detect other chromosome rearrangement events. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for detecting chromosome rearrangement in a plant mutant, characterized in that: The following steps are involved: (1) Construction of mixed pools and next-generation sequencing: hybridization is performed with the wild type as the female parent and the stable inherited mutant as the male parent, followed by self-pollination to obtain the F2 segregation population; if the segregation ratio of the target trait in the F2 generation meets the segregation ratio of a single gene, gene localization can be performed; genomic DNA is extracted from two groups of individuals with extreme phenotypic differences, and wild-type phenotypic mixed pools and mutant phenotypic mixed pools are constructed for Illumina sequencing; (2) Sequencing data processing: The raw sequencing data of each mixed pool was filtered and then aligned with the reference genome using the BWA software. The UnifiedGenotyper module of the GATK software was then used to detect SNPs and InDel sites. (3) Interval positioning: Calculate the frequency of occurrence of each SNP and InDel site in each mixed pool, that is, the All-index value, and calculate the difference ΔAll-index between the two mixed pools after filtering; then select a sequence length of 0.1-1Mb as a window on the genome, and calculate the SNPs and InDels contained in the window at the whole genome level using the sliding window method with a step size of 1kb. The average value of ΔAll-index in each window is used to reflect the ΔAll-index distribution; then perform a permutation test and select windows with a confidence level of more than 95% as candidate intervals; (4) Target gene localization and chromosomal rearrangement site detection: functional analysis of all annotated genes in the candidate interval was performed with reference to the plant genome database, and known genes contained in or adjacent to the candidate interval that were related to the target trait were used as candidate genes; the SAMTOOLS tool was used to perform depth statistics on the comparison of each base in the full-length region of the corresponding candidate gene in each mixed pool, and the sites covered in the wild-type phenotype mixed pool but not in the mutant phenotype mixed pool were found as candidate rearrangement site A; then, short sequences 1 and 2 were selected along the upstream and downstream of site A in the reference genome, and read1 and read2 containing two short sequences were retrieved in the sequencing data of the mutant phenotype mixed pool, and read1 and read2 were compared with the reference genome. If an interval inversion occurs between site A and site B on the chromosome, read1 will be able to align to the upstream of point A and upstream of point B, respectively, and read2 will be able to align to the downstream of point A and downstream of point B, respectively; If the interval fragment between sites B and C and the interval fragment between sites C and A on the chromosome are translocated, or the interval fragment between sites B and C is repeatedly inserted into point A on the chromosome, read1 will be able to align to the upstream of point A and the downstream of point B, and read2 will be able to align to the downstream of point A and the upstream of point C. Then, in the reference genome, short sequence 3 is selected along the upstream of site B, and short sequence 4 is selected along the downstream of site C. Read3 and read4 containing these two short sequences are retrieved from the sequencing data of the mutant pool, and compared with the reference genome. If read3 and read4 can be compared to the upstream of point B and the downstream of point C, respectively, it indicates that the BC interval fragment and the CA interval fragment are translocated on the chromosome; if read3 can only be compared to point B and read4 can only be compared to point C, it indicates that the BC interval fragment is repeatedly inserted into point A on the chromosome; If the interval fragment between site A and site B is missing on the chromosome, read1 and read2 can be aligned to the upstream of point A and the downstream of point B respectively; (5) Verification of chromosome rearrangement: Primers were designed based on the rearranged chromosomes, and PCR amplification and sequencing were performed on the rearranged junctions.

2. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (1), 15-30 individuals are selected from each group, and genomic DNA is extracted and then mixed in equal amounts to construct a mixed pool.

3. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (2), the criteria for filtering the raw sequencing data using fastp software are as follows: remove reads with adapters; remove reads in which the N base accounts for more than 10% of the sequencing read length; Remove the reads whose bases with Q value ≤ 5 account for more than 50% of the sequencing read length.

4. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (2), the BWA software comparison parameters are set to: mem, -t 4, -k 32, -M.

5. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (2), the comparison results after alignment by BWA software were first processed by SAMTOOLS tool to remove PCR duplications before SNP and InDel site detection.

6. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (2), SNP and InDel site detection were performed on the alignment results and then filtered using the VariantFiltration module. The filtering parameters for SNP were set to: --clusterWindowSize 4, --filterExpression "QD<4.0||FS>60.0||MQ<40.0", -G_filter "GQ<5"; the filtering parameters for InDel were set to: --filterExpression "QD<4.0||FS>200.0".

7. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (3), after calculating the All-index value, the criteria for filtering SNP and InDel sites are as follows: remove sites with All-index less than 0.3 in both pools; remove sites with SNP or InDel depth less than 7 in a single pool; remove sites with missing All-index in a single pool.

8. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (3), 1000 permutation tests were performed on each window, and the 99% confidence level was selected as the threshold line.

9. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (4), the length of the short sequence is 50 bp.

10. The method for detecting chromosome rearrangement of a plant mutant according to claim 1, characterized in that: In step (5), primers are designed for the junctions before and after chromosome rearrangement, respectively.

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