Fine positioning excavation of flowering gene of short-growth-period brassica napus and development of molecular marker

Abstract

The invention relates to the field of genes, in particular to molecular markers for judging flowering time of brassica napus L. The molecular markers are Indel-282, Indel-326 and Indel-353 respectively and are located on an A02 chromosome of the brassica napus L.. The molecular marker has high specificity and stability, and is significantly related to the flowering time. The markers show high specificity and stability in brassica napus plants under different genetic backgrounds. By detecting the molecular markers through PCR amplification and electrophoresis, the flowering time of different plants can be quickly and accurately identified in the early stage of breeding. Therefore, the flowering time of the plant can be determined in the early stage, so that the breeding efficiency is improved. The molecular markers can be used for molecular marker-assisted selective breeding, and plants with early flowering or late flowering characteristics can be quickly screened by detecting the markers, so that the breeding process of excellent varieties is accelerated, and the functions and regulation mechanisms of related genes are further disclosed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of genetics, specifically to a breeding technique for determining flowering time in Brassica napus using molecular markers. BACKGROUND

[0002] Brassica napus L. is an important oil crop, widely cultivated in various parts of the world. Its flowering time directly affects the yield and quality of the crop, so accurate control of flowering time is crucial for improving the production efficiency of oilseed rape. Flowering time is regulated by multiple genes, and variations in some of these genes can lead to significant changes in flowering time. Therefore, identifying molecular markers related to flowering time in Brassica napus is of great significance for breeding work.

[0003] Currently, some genes and molecular markers related to flowering time in Brassica napus have been reported in previous studies. For example, some studies have identified candidate genes related to flowering time through genome-wide association analysis (GWAS) and quantitative trait locus (QTL) mapping. However, these markers are not highly specific, or are not stable in different genetic backgrounds, limiting their application in actual breeding. In addition, existing molecular markers often require complex experimental operations and high costs, which further limits their widespread application in breeding.

[0004] Therefore, developing new, highly specific and stable molecular markers has important application value for molecular breeding of Brassica napus. These markers not only improve breeding efficiency, but also significantly reduce breeding costs, thereby accelerating the breeding process of excellent varieties. In addition, these markers can be used for early screening, so as to determine the flowering time of plants at an early stage of breeding, further improving the accuracy and efficiency of breeding.

[0005] Although some progress has been made in the molecular markers of flowering time in Brassica napus, there are still problems such as low specificity and poor stability. Therefore, developing new, highly specific and stable molecular markers has important application prospects for molecular breeding of Brassica napus. SUMMARY

[0006] One of the purposes of the present application is to provide a molecular marker for determining flowering time, which provides a good solution for breeding selection.

[0007] To achieve the above-mentioned purposes, the technical solutions of the present application are as follows:

[0008] 1. Molecular markers for determining the flowering time of Brassica napus, wherein the molecular markers are three in number: Indel-282, Indel-326, and Indel-353, and the markers are located on chromosome A02 of Brassica napus;

[0009] The Indel-282 marker polymorphism is 7 bp in length, and the specific inserted or deleted sequences are shown in SEQ ID NO. 1;

[0010] The Indel-326 marker polymorphism is 20 bp in length, and the specific inserted / deleted sequences are shown in SEQ ID NO. 2.

[0011] The Indel-353 marker polymorphism is 29 bp in length, and the specific inserted or deleted sequences are shown in SEQ ID NO. 3.

[0012] The molecular markers described herein are used as target points for determining the flowering time of Brassica napus.

[0013] Preferably, the application of the molecular markers in determining the flowering time of Brassica napus specifically refers to their practical application in Brassica napus breeding and genetic research. These molecular markers (Indel-282, Indel-326, Indel-353) are located on chromosome A02 of Brassica napus and are significantly correlated with the flowering time of the plant. Through these molecular markers, the flowering time of different plants can be quickly and accurately identified, enabling early screening and selection during the breeding process.

[0014] For example, in early screening, during the early stages of breeding, these molecular markers can be used to quickly distinguish plants with different genotypes through PCR amplification and electrophoresis. This helps determine the flowering time of plants at an early stage, thereby improving breeding efficiency. Similarly, in marker-assisted selection (MAS), these molecular markers can be used to quickly screen for plants with early or late flowering characteristics, accelerating the selection process for superior varieties. Furthermore, in genetic research, these molecular markers can help researchers understand the genetic basis of flowering time and further reveal the functions and regulatory mechanisms of related genes. Additionally, in variety improvement, these molecular markers can quickly identify plants with superior flowering time characteristics, thereby accelerating the variety improvement process and improving the yield and quality of rapeseed. From the perspective of breeding strategy optimization, the application of these molecular markers can optimize breeding strategies, shorten breeding cycles, reduce breeding costs, and improve the accuracy and efficiency of breeding.

[0015] A second objective of this invention is to provide primers for the aforementioned molecular markers. These primers are designed based on the sequence information of the aforementioned Indel markers and can specifically amplify DNA fragments containing these Indel markers, thereby distinguishing plants of different genotypes through PCR amplification and electrophoresis detection.

[0016] (i) the positive primer of Indel-282, the sequence of which is shown in SEQ ID NO:4, and the reverse primer, the sequence of which is shown in SEQ ID NO:5; (iii) the positive primer of Indel-326, the sequence of which is shown in SEQ ID NO:6, and the reverse primer, the sequence of which is shown in SEQ ID NO:7; (iv) the positive primer of Indel-353, the sequence of which is shown in SEQ ID NO:8, and the reverse primer, the sequence of which is shown in SEQ ID NO:9.

[0017] These primers may have some single nucleotide variations in their sequence, but they can still specifically amplify DNA fragments containing the aforementioned Indel markers. These primers may include, but are not limited to: primers with 1-3 nucleotide variations; primers with degenerate nucleotides (such as N, R, Y, etc.); primers with modifying groups (such as fluorescent labels, biotin labels, etc.).

[0018] Primers with different annealing temperatures and amplification efficiencies.

[0019] The design of these primers is not limited to the specific sequences mentioned above, but also includes any primers capable of specifically amplifying DNA fragments containing the aforementioned Indel markers. These primers can be used in a variety of PCR amplification methods, including but not limited to conventional PCR, real-time quantitative PCR (qPCR), and digital PCR (dPCR).

[0020] This invention also provides a kit containing the aforementioned primers, and its application in preparing a detection reagent for determining the flowering time of Brassica napus. These kits can be used in various applications such as early screening, marker-assisted selection (MAS), genetic research, and variety improvement.

[0021] This invention also provides a method for determining the flowering time of Brassica napus, specifically including distinguishing between two genotypes through PCR amplification and electrophoresis detection: genotype A (carrying alleles of the early-flowering parent) and genotype B (carrying alleles of the late-flowering parent). Based on the genotype, the flowering time of Brassica napus can be predicted, with genotype A lines having the shortest flowering time and genotype B lines having the longest flowering time.

[0022] A method for determining the flowering time of Brassica napus involves grouping the F2 population into genotypes using the aforementioned molecular markers. Specifically, the following two genotypes are distinguished by PCR amplification and electrophoresis: Genotype A, which carries alleles of the early-flowering parent at all three Indel marker positions; and Genotype B, which carries alleles of the late-flowering parent at all three Indel marker positions. The rule for determining the length of flowering time is that the A genotype lines have the shortest flowering time, and the B genotype lines have the longest flowering time (TU).

[0023] Furthermore, for the marker Indel-282, the average flowering period of lines carrying genotype A was 120 days, while the average flowering period of lines carrying genotype B was 131 days.

[0024] The method for obtaining the molecular marker specifically includes the following steps:

[0025] S1: High-throughput sequencing was performed on the early-flowering and late-flowering parents to obtain raw sequencing data; S2: The sequencing data was aligned to the reference genome of Brassica napus to identify Indel differential sites; S3: Primers were designed based on the identified Indel differential sites; S4: PCR amplification and electrophoresis were performed on samples from the F2 population to verify the effectiveness of these Indel markers.

[0026] Furthermore, the three Indel markers showed significant phenotypic differences in the F2 population, with P values ​​less than 0.001.

[0027] The application of the molecular markers and / or the primers and / or the kits described herein in the breeding of Brassica napus.

[0028] Additionally, some descriptions of the samples are as follows.

[0029] The molecular markers and primers provided in this invention are suitable for identifying the flowering time of Brassica napus L. Specifically, these molecular markers and primers can be used to detect the genomic DNA of Brassica napus to distinguish plants of different genotypes and predict their flowering time. The following is a detailed description and requirements for the test samples:

[0030] Sample type. Genomic DNA: Genomic DNA extracted from rapeseed plants is the basis for PCR amplification and electrophoretic detection. The extracted DNA should be of high purity and integrity to ensure the specificity and accuracy of PCR amplification.

[0031] Sample source: Brassica napus plants: These can be any Brassica napus plant, including but not limited to laboratory-constructed genetic populations, field-grown plants, or breeding materials. These plants should have a clear genetic background for genotypic analysis.

[0032] DNA Extraction Methods: Genomic DNA extraction should employ standard plant DNA extraction methods, such as the CTAB method or other efficient extraction methods. The extracted DNA should be detected by agarose gel electrophoresis to ensure the main DNA band is intact, clear, and free from degradation and RNA contamination. Quality Assurance: The extracted DNA should meet the following quality standards: OD260 / 280 ratio: should be between 1.8 and 2.2, ensuring the absence of protein and visible impurities. Concentration: should be greater than 20 ng / μL, with a total concentration greater than 2 μg to ensure sufficient DNA for subsequent experiments. These molecular markers and primers are applicable to all Brassica napus plants, regardless of their genetic background. PCR amplification and electrophoresis detection can distinguish between different genotypes, thus predicting their flowering time. Genetic Diversity: These molecular markers and primers exhibit good specificity and stability in Brassica napus plants with different genetic backgrounds, making them suitable for a wide range of breeding and genetic research.

[0033] The following is an expanded understanding of genotyping.

[0034] Genotype A: Carries alleles of the early-flowering parent (such as GY041) at all three Indel marker positions.

[0035] For example, at the Indel-282 marker position, individuals with genotype A only show band 1.

[0036] At the Indel-326 marker position, individuals with genotype A only showed band 3.

[0037] At the Indel-353 marker position, individuals with genotype A only showed a band of 5.

[0038] Genotype B: Carries alleles of the late-flowering parent (such as GY023) at all three Indel marker positions.

[0039] For example, at the Indel-282 marker position, individuals with genotype B only show band 2.

[0040] At the Indel-326 marker position, only band 4 appeared in individuals with genotype B.

[0041] At the Indel-353 marker position, only band 6 appeared in individuals with genotype B.

[0042] Beneficial effects

[0043] The molecular markers and primers provided in this invention for identifying the flowering time of Brassica napus have the following significant advantages: 1. High specificity and stability, significantly correlated with flowering time. These markers exhibit high specificity and stability in Brassica napus plants with different genetic backgrounds, accurately distinguishing plants of different genotypes. 2. Rapid and accurate early screening. By amplifying these molecular markers through PCR and detecting them by electrophoresis, the flowering time of different plants can be rapidly and accurately identified in the early stages of breeding. This helps to determine the flowering time of plants at an early stage, thereby improving breeding efficiency. 3. Marker-assisted selection. These molecular markers can be used for marker-assisted selection breeding. By detecting these markers, plants with early or late flowering characteristics can be quickly screened, thereby accelerating the breeding process of superior varieties. 4. These molecular markers can also be used in genetic research, helping researchers understand the genetic basis of flowering time and further reveal the function and regulatory mechanisms of related genes. Through these molecular markers, plants with excellent flowering time characteristics can be quickly identified, thereby accelerating the variety improvement process and improving the yield and quality of rapeseed. Attached Figure Description

[0044] Figure 1 The parent plants are early-flowering female parent GY041 and late-flowering male parent GY023.

[0045] Figure 2 In the diagram, A represents the flowering period distribution of the F2 population, and B represents the flowering period distribution of the F2 population. 2:3 Flowering period distribution of the population. (Red arrows indicate the flowering period of the early-flowering female parent GY041, and black arrows indicate the flowering period of the late-flowering male parent GY023.)

[0046] Figure 3 The difference in flowering time between the two extreme groups of F2 offspring, EFM and LFM, was analyzed (t test, p<0.001).

[0047] Figure 4 This is the initial localization result of the BSA analysis.

[0048] Figure 5 This is a quality map of the extracted DNA from the population.

[0049] Figure 6 In the diagram, A represents the polymorphism test of some Indel markers between parents, and B represents the genotype of a certain marker in the F2 random population.

[0050] Figure 7 Genetic mapping of QTLs in the F2 population.

[0051] Figure 8 For F 2:3 Fine-grained localization map of the population.

[0052] Figure 9The differentially expressed gene up- and down-regulation between the leaves and shoot tips of the two parents.

[0053] Figure 10 GO and KEGG functional annotation analysis of DEGs from leaf tissues of both parents was performed. Figure 10 There are some unclear parts in the analysis chart, which are explained here.

[0054] Figure 10 -A: From top to bottom on the left side: 1.photosynthesis; 2.positive regulation ofsuperoxide dismutase activity; 3.photorespiration; 4.protein refolding; 5.response to UV-A; 6.cellular response to high light intensity; 7.cellularresponse to UV-A; 8.carbon utilization; 9.chlorophyll biosynthetic process; 10.glycolytic process; 11.translation; 12.cellular response to light intensity; 13. cellular response to far red light; 14. cellular response to red light; 15. inositol biosynthetic process; 16. chaperone cofactor-dependent protein refolding; 17. uracil salvage; 18. ER to chloroplast lipid transport; 19. cellular response to UV; 20. chloroplast rRNA processing

[0055] Figure 10On the left side of -B, from top to bottom are: 1. multivesicular body organization; 2. phragmoplast microtubule organization; 3. auxin-activated signaling pathway; 4. endosome organization; 5. formate catabolic process; 6. purine nucleoside diphosphate metabolic process; 7. purine ribonucleoside diphosphate metabolic process; 8. nucleotide phosphorylation; 9. regulation of response to water deprivation; 10. DNA topological change; 11. late endosome to vacuole transport via multivesicular body sorting pathway; 12. negative regulation of steroid biosynthetic process; 13. response to stilbenoid; 14. negative regulation of cholesterol biosynthetic process; 15. negative regulation of lipid metabolic process; 16. negative regulation of steroid metabolic process; 17. negative regulation of lipid biosynthetic process; 18. negative regulation of cholesterol metabolic process; 19. negative regulation of steroid biosynthetic process; 20. negative regulation of alcohol biosynthetic process

[0056] Figure 10- From top to bottom on the left side of C are: 1. Biosynthesis of amino acids; 2. Carbon metabolism; 3. Carbon fixation in photosynthetic organisms; 4. Photosynthesis; 5. Glycine, serine and threonine metabolism; 6. Pentose phosphate pathway; 7. Glycolysis / Gluconeogenesis; 8. Nitrogen metabolism; 9. Porphyrin and chlorophyll metabolism; 10. Phenylalanine, tyrosine and tryptophan biosynthesis; 11. 2–Oxocarboxylic acid metabolism; 12. Glyoxylate and dicarboxylate metabolism; 13. Biotin metabolism; 14. Starch and sucrose metabolism; 15. Arginine biosynthesis; 16. Sulfur metabolism; 17. Ribosome; 18. DNA replication; 19. Cysteine and methionine metabolism; 20. Purine metabolism

[0057] Figure 10-D: The left side from top to bottom is: 1.Sphingolipid metabolism; 2.Fatty aciddegradation; 3.Valine, leucine and isoleucine degradation; 4.Sulfur relaysystem; 5.Galactose metabolism; 6.Glycosphingolipid biosynthesis – ganglioseries; 7.Other glycan degradation; 8.Plant hormone signal transduction; 9.Lysine degradation; 10.Glycosaminoglycan degradation;11.Glycerolipidmetabolism;12.Autophagy – other;13.Limonene and pinene degradation;14.alpha–Linolenic acid metabolism;15.Arginine and proline metabolism;16.Tryptophanmetabolism;17.Sulfur metabolism;18.Peroxisome;19.Fatty acid metabolism;20.Glycerophospholipid metabolism

[0058] Figure 11 GO and KEGG functional annotation analysis of DEGs from the shoot apex tissues of both parents. Figure 11 There are some unclear parts in the analysis chart, which are hereby clarified.

[0059] Figure 11-A: From top to bottom on the left side are: 1. photosynthesis; 2. positive regulation of superoxide dismutase activity; 3. photorespiration; 4. protein refolding; 5. response to UV-A; 6. cellular response to high light intensity; 7. cellular response to UV-A; 8. carbon utilization; 9. chlorophyll biosynthetic process; 10. glycolytic process; 11. translation; 12. cellular response to light intensity; 13. cellular response to far red light; 14. cellular response to red light; 15. inositol biosynthetic process; 16. chaperone cofactor-dependent protein refolding; 17. uracil salvage; 18. ER to chloroplast lipid transport; 19. cellular response to UV; 20. chloroplast rRNA processing

[0060] Figure 11- On the left side of -B, from top to bottom are: 1. multivesicular body organization; 2. phragmoplast microtubule organization; 3. auxin-activated signaling pathway; 4. endosome organization; 5. formate catabolic process; 6. purine nucleoside diphosphate metabolic process; 7. purine ribonucleoside diphosphate metabolic process; 8. nucleotide phosphorylation; 9. regulation of response to water deprivation; 10. DNA topological change; 11. late endosome to vacuole transport via multivesicular body sorting pathway; 12. negative regulation of steroid biosynthetic process; 13. response to stilbenoid; 14. negative regulation of cholesterol biosynthetic process; 15. negative regulation of lipid metabolic process; 16. negative regulation of steroid metabolic process; 17. negative regulation of lipid biosynthetic process; 18. negative regulation of cholesterol metabolic process; 19. negative regulation of steroid biosynthetic process; 20. negative regulation of alcohol biosynthetic process

[0061] Figure 11- From top to bottom on the left side of C are: 1. Biosynthesis of amino acids; 2. Carbon metabolism; 3. Carbon fixation in photosynthetic organisms; 4. Photosynthesis; 5. Glycine, serine and threonine metabolism; 6. Pentose phosphate pathway; 7. Glycolysis / Gluconeogenesis; 8. Nitrogen metabolism; 9. Porphyrin and chlorophyll metabolism; 10. Phenylalanine, tyrosine and tryptophan biosynthesis; 11. 2–Oxocarboxylic acid metabolism; 12. Glyoxylate and dicarboxylate metabolism; 13. Biotin metabolism; 14. Starch and sucrose metabolism; 15. Arginine biosynthesis; 16. Sulfur metabolism; 17. Ribosome; 18. DNA replication; 19. Cysteine and methionine metabolism; 20. Purine metabolism

[0062] Figure 11-D: The left side from top to bottom is: 1.Sphingolipid metabolism; 2.Fatty aciddegradation; 3.Valine, leucine and isoleucine degradation; 4.Sulfur relaysystem; 5.Galactose metabolism; 6.Glycosphingolipid biosynthesis – ganglioseries; 7.Other glycan degradation; 8.Plant hormone signal transduction; 9.Lysine degradation; 10.Glycosaminoglycan degradation;11.Glycerolipidmetabolism;12.Autophagy – other;13.Limonene and pinene degradation;14.alpha–Linolenic acid metabolism;15.Arginine and proline metabolism;16.Tryptophanmetabolism;17.Sulfur metabolism;18.Peroxisome;19.Fatty acid metabolism;20.Glycerophospholipid metabolism

[0063] Figure 12 The effects of three tightly linked Indel markers on the F2 population.

[0064] Figure 13 The results of PCR amplification and electrophoresis were obtained from early and late flowering mixed populations. Detailed Implementation

[0065] The technical solution of the present invention will be described more clearly and completely below with reference to specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0066] To enhance understanding of the present invention, certain key technical and scientific terms will be clearly defined below. Unless specifically defined herein, all other technical and scientific terms shall follow their generally accepted and understood meanings within the art to which this invention pertains. It should be emphasized that the scope of the present invention is not limited to the specific methods, compositions, or applications described, but allows for reasonable variations and adjustments in these aspects. Furthermore, please understand that the terminology used herein is intended for elaboration.

[0067] Genotype definition

[0068] Genotype A (homozygous early-flowering parent): Individuals with this genotype carry the allele of the early-flowering parent (such as GY041) at a specific Indel marker location. For example, at the Indel-282 marker location, an individual with genotype A may carry the specific band of the early-flowering parent.

[0069] Genotype B (homozygous late-flowering parent): Individuals with this genotype carry the allele of the late-flowering parent (such as GY023) at a specific Indel marker location. For example, at the Indel-282 marker location, genotype B individuals may carry the specific band of the late-flowering parent.

[0070]

[0071] Note: If the sequence number or sequence itself differs from this table elsewhere, please refer to this table.

[0072] Materials and Methods

[0073] 1.1 Research Materials

[0074] The population material used in this study was the Brassica napus genetic population previously constructed in the laboratory. The parents were the semi-winter, early-flowering, early-maturing variety GY041 (female) and the semi-winter, late-flowering, late-maturing variety GY023 (male). Through hybridization and self-pollination, a segregating F2 population (704 individual plants) was successfully obtained. Based on this, key exchange plants in the F2 population were further screened to form the F2 generation. 2:3 The family population (1628 individual plants, 10-15 plants per family) was obtained from Xingyi City, Guizhou Province. All materials were grown under standardized field conditions and managed according to regulations throughout the growth period. Key agronomic traits, such as flowering time (number of days from sowing to first flower opening), were systematically investigated for each plant to ensure the accuracy and reliability of phenotypic data. The F2 population and the F... 2:3 The field planting and management of the group were all completed by the Guizhou Academy of Agricultural Sciences.

[0075] 1.2 Experimental Reagents

[0076] The main experimental reagents used in this study include: bromophenol blue, xylenecyanine, sucrose, 30% acrylamide, 10% ammonium persulfate (AP), tetramethylethylenediamine (TEMED), EDTA-Na2·2H2O, trihydroxyaminomethane (Tris), EasyTaq DNA polymerase, sodium chloride, ethanol (95%, 75%), dNTPs, AgNO3, formaldehyde, sodium hydroxide, boric acid, etc.

[0077] 1.3 Genomic DNA was extracted from the leaves of the population using conventional methods.

[0078] 1.4 Population phenotyping and phenotypic analysis

[0079] 1.4.1 Phenotypic statistics during flowering period

[0080] This study investigated the parental lines (GY041 and GY023) and their constructed F2 and F2 strains. 2:3 A systematic investigation of flowering period traits was conducted on the genetic population. Flowering period was defined as the cumulative number of days from sowing to the initial flowering stage of the plot.

[0081] 1.4.2 Phenotypic Analysis

[0082] The R software package was used for statistical analysis and visualization of phenotypic data. The differences in flowering time among extreme mixed F2 populations were calculated, and the parents and populations (including 704 F2 plants and 1628 F2 plants) were compared. 2:3 Descriptive statistics were performed on the flowering phenotype of the population, calculating the mean, standard deviation, extreme values ​​(minimum and maximum), and coefficient of variation to assess phenotypic variation. Simultaneously, F2 and F3 plots were generated. 2:3 Histogram of frequency distribution of flowering period in the population and analysis of differences in flowering period in the F2 extreme mixed pool.

[0083] 1.5 BSA Sequencing Analysis

[0084] An F2 population was constructed with two parents (early-flowering maternal parent GY041 and late-flowering paternal parent GY023). Thirty accessions each from the F2 population with the earliest and latest flowering times were used as pooled samples. High-throughput sequencing analysis was performed on the pooled samples of the parents and the extreme F2 offspring. The SNP-index algorithm was used, and the SNP index was calculated using QTLseqr software. The ΔSNP index was calculated using the formula Δ(SNP-index) = SNP index(D-pool) − SNP index(T-pool) to obtain the single nucleotide polymorphism index, with a sliding window of 2 Mb. The SNPNUM method was used to fit the ΔSNP-index. Then, based on the association threshold, regions above the threshold were selected as regions associated with the trait. The red line represents the threshold line with a confidence level of 0.99, and the blue line represents the threshold line with a confidence level of 0.95. Preliminary localization intervals were determined based on the distribution of Δ(SNP-index).

[0085] 1.6 Fine-grained group positioning

[0086] 1.6.1 Indel tag development

[0087] The genomes of early-flowering (GY041) and late-flowering (GY023) Brassica napus obtained from high-throughput sequencing were compared using software to identify Indel differentially expressed sites, which were then used to design primers. The specific steps for obtaining Indel differentially expressed sites are as follows:

[0088] (1) The raw image data files of the GY041 and GY023 genomes obtained by high-throughput sequencing are converted into raw sequencing reads by Base Calling analysis, which are called Raw Data or Raw Reads.

[0089] (2) The raw sequencing reads obtained from sequencing contain low-quality reads with adapters. To ensure the quality of information analysis, the raw reads are filtered to obtain clean reads for subsequent information analysis. The main steps of data filtering are as follows: ① Remove reads with adapters; ② Filter reads with an N content exceeding 10%; ③ Remove reads with more than 50% of bases having a quality value below 10.

[0090] (3) The clean reads obtained from sequencing need to be repositioned onto the ZS11 reference genome before subsequent variant analysis can be performed. The clean reads and reference genome were compared using "bwa-mem2 mem -t 4 -M". The comparison results were sorted using samtools (v1.9), and based on the sorted results, information such as sequencing depth and genome coverage of each sample was statistically analyzed.

[0091] (4) Based on the location results of Clean Reads in the reference genome, redundant Reads were filtered using samtools (v1.9) to ensure the accuracy of the detection results. The HaplotypeCaller algorithm of GATK (v3.8) was used to detect InDel variants. Each sample first generated its own gVCF, and then a population joint-genotype was performed. Finally, after filtering, the final set of variant sites was obtained. The variant results underwent strict filtering to ensure the reliability of the results. The main filtering parameters are as follows: ① SNPs within 5 bp of InDel and adjacent InDels within 10 bp were filtered out using the subroutine vcfutils.pl (varFilter -w 5 -W 10) in bcftools; ② clusterSize 2 and clusterWindowSize 5, indicating that the number of variants within a 5 bp window should not exceed 2; ③ QUAL < 30, a Phred quality value, indicating the possibility of variant variation at that site. Reads with a quality value below 30 are filtered out; ④ QD < 2.0: The ratio of the variant quality value to the coverage depth, which is the sum of the coverage depths of all samples containing variant bases at this site. Reads with a QD below 2.0 are filtered out; ⑤ MQ < 40: The root mean square of the alignment quality values ​​of all reads aligned to this site. Reads with an MQ below 40 are filtered out; ⑥ FS > 60.0: The value converted from the p-value of the Fisher test, describing whether there is significant positive or negative strand specificity for reads containing only variant bases and reads containing only reference sequence bases during sequencing or alignment. In other words, there should be no strand-specific alignment results, and the FS should be close to zero. Reads with an FS above 60 are filtered out; ⑦ Other variant filtering parameters are handled using the default values ​​specified by GATK.

[0092] (5) InDel Primer Design: Based on the InDel differential sites obtained in the previous step, 200 bp sequences above and below the InDel physical location in the corresponding chromosome sequence of the reference genome ZS11 were extracted for InDel primer design. Primer design was performed using the online software Primer3 (https: / / primer3.ut.ee / ), with the following parameters set: ① Primer sequence length 18-25 bp, 22 bp optimal; ② Tm value 50-60℃, 55℃ optimal; ③ GC content in the primer sequence 40-60%, 55% optimal; ④ PCR amplification length range 100-400 bp. Primer names were numbered in ascending order of InDel physical location within the interval.

[0093] 1.6.2 SNP marker development and targeted sequencing

[0094] (1) Based on the SNP loci obtained by resequencing the parent genomes, Primer3 software (version 2.5.0) was used to design amplification primers for each SNP target locus within the candidate region. The parameters set included: ① primer sequence length between 17-32 bp; ② Tm value between 60-64℃, with 62℃ being optimal; ③ product size not exceeding 500 bp; ④ sequencing reads must be able to cover the target locus. For each target locus, multiple primer pairs were designed, and then e-PCR software (version 2.3.12) was used to detect the amplification specificity of each primer pair. Finally, a pair of primers that could specifically amplify the target locus was selected.

[0095] (2) The DNA of the population samples was tested, and library construction was carried out after the samples were qualified. The sample testing standards are as follows: ① Agarose gel electrophoresis showed that the main band of genomic DNA was intact and clear, and there was no degradation or RNA contamination. ② Nanodrop detection showed that the OD260 / 280 ratio was between 1.8 and 2.2, and there was no protein or visible impurities. ③ Qubit 3.0 detection concentration was greater than 20 ng / μL, and the total amount was greater than 2 μg.

[0096] (3) After the sample genomic DNA test is qualified, the library is constructed according to the targeted sequencing experimental procedure developed by Biomarker. The main experimental steps are as follows: ① Mix primers according to the experimental purpose and site information; ② Use genomic DNA as a template to amplify the target site with KAPA2GFastMultiplexMix; ③ Add sequencing adapters through secondary PCR; ④ Pool all products and purify them with AMPureXPBeads.

[0097] (4) Library quality control: After the library is constructed, its quality is tested. Sequencing can only be performed after the test results meet the requirements. The testing methods are as follows: ① Use Qubit 3.0 for preliminary quantification; ② Use Agilent 2100 to test the insert size of the library. The next step of the experiment can only be performed after the insert size meets the expectations and there is no adapter contamination; ③ Use the German ANALYTIKJENA QTOWER real-time fluorescence quantitative PCR instrument to accurately quantify the effective concentration of the library. That is, the effective concentration > 2nM is a qualified library.

[0098] (5) Sequencing: The library was pooled according to the target amount of data to be sequenced, and Paired-end 150bp (PE150) sequencing was performed using the Illumina HiSeq platform.

[0099] 1.6.3 Polyacrylamide gel electrophoresis

[0100] The EasyTaq® DNA Polymerase amplification system is as follows:

[0101]

[0102] PCR reaction system:

[0103]

[0104] The PCR products need to be mixed with bromophenol blue buffer (1.5 μL is sufficient). The bromophenol blue buffer formula is as follows:

[0105]

[0106] (1) Wash the glass plate with clean water, and after drying, assemble the two types of glass plates and the insulating strip (the concave side of the concave glass plate faces the long glass plate).

[0107] (2) Prepare an adhesive agarose solution (1g agarose: 70mL ddH2O in a conical flask and heat for 2min) at the bottom of the sealed glass plate. Seal the bottom of the reverse side of the glass plate until solidification. Assemble the glass plates in the electrophoresis tank and seal the bottom between the two glass plates with agarose.

[0108] (3) Prepare polyacrylamide gel (in a beaker):

[0109]

[0110] (4) After preparing the gel, use a syringe to draw it out and pour it into the gap between the two glass plates. Gently tap the glass to avoid generating air bubbles. When the PAGE gel is almost filling the entire glass, insert a clean comb into the spotting well and lay the electrophoresis tank down to solidify for 45 minutes.

[0111] (5) Pour TBE buffer into the electrophoresis tank, filling the middle and adding appropriate amounts to the sides. Then remove the comb and spot the sample.

[0112] (6) Connect the electrophoresis apparatus and the electrophoresis tank. Set the electrophoresis apparatus to 330V, 400mA, and 45-58min.

[0113] (7) Prepare AgNO3 staining solution (1g AgNO3 : 1L ddH2O), pour it into a light-proof disc, put the gel in, and stain on a shaker for 7 minutes. The shaking speed should not be too fast. Silver nitrate should be protected from light.

[0114] (8) Prepare colorimetric solution

[0115]

[0116] Discard the AgNO3 staining solution, add ddH2O to wash the gel, wash twice, 30 seconds each time on a shaker. Add developing solution and develop on a shaker until bands appear, about 7-10 minutes.

[0117] 1.7 Parental Transcriptome Analysis

[0118] Transcriptomic sequencing analysis was performed on the leaves and shoot tips of both parents' EF and LF. Pearson correlation coefficient analysis and principal component analysis (PCA) revealed the correlations and differences between EF and LF at the same developmental stage. Differentially expressed genes were identified based on differential expression analysis between the parents.

[0119] 1.8 Integration of BSA-seq, RNA-seq, and fine mapping to identify key candidate genes

[0120] By combining the results of BSA-seq, RNA-seq, and population fine mapping, overlapping key candidate genes were identified, and GO and KEGG enrichment analyses were performed on these genes to characterize their potential biological functions and the molecular regulatory pathways they participate in.

[0121] II. Results and Analysis

[0122] 2.1 Parental and Descendant Type Observation

[0123] In the early stages of the laboratory, two semi-winter Brassica napus varieties with different flowering periods were obtained: the early-flowering and early-maturing female parent GY041 and the late-flowering and late-maturing male parent GY023. F2 and F3 strains of these two parents were constructed. 2:3 Group. Among them, F 2:3 The population consisted of exchange lines selected from the F2 population after fine mapping, with 10 lines per line. The yield traits of the parents were examined (Table 1) and compared with those of 704 F2 and 1628 F2 lines. 2:3 Flowering phenotype of the population ( Figure 2(Table 2) It was found that the flowering period phenotype of the F2 population showed a continuous distribution with a variation range of 95-154 days and a coefficient of variation of 9.94%. The large variation range and the presence of over-parental phenomena indicate that the flowering period is a quantitative trait controlled by multiple genes, which can be used for QTL mapping analysis.

[0124] Table 1. Statistics on flowering period and yield traits of both parents

[0125]

[0126] Table 2 F2, F 2:3 Phenotypic analysis of flowering period in populations

[0127]

[0128] 2.2 BSA-seq

[0129] 2.2.1 Construction of BSA Mixed Pool

[0130] Considering the complexity of quantitative traits in rapeseed and the difficulty of mapping related genes, we used BSA-seq to perform preliminary QTL mapping in 704 F2 segregating populations. The flowering time of the F2 population was ranked, and the 30 materials with the earliest and latest flowering times were selected as mixed-bucket materials. Significant differences in flowering time were found between the two extreme mixed-bucket pairs (EFM: extremely early flowering time in the F2 population, LFM: extremely late flowering time in the F2 population). Figure 3 ).

[0131] 2.2.2 Statistical Analysis of Whole Genome Resequencing Data

[0132] Four DNA samples, including two parental materials (EF and LF) and two extreme progeny pools (EFM and LFM), yielded 407 Mb clean reads and 121.36 Gbp clean data after filtering, with Q30 exceeding 94% (Table 3). The average sequencing depth for each sample was 30X. The average ratio of the sample to the reference genome was 99.7%, the average coverage depth was 20X, and the genome coverage was 95.67%. The maternal genome contained 106,263,817 reads, the paternal genome contained 103,718,226 reads, the EFM genome contained 105,533,502 reads, and the LFM genome contained 92,634,316 reads.

[0133] Table 3. Statistics of parental and F2 extreme mixed-pool whole-genome resequencing data.

[0134]

[0135] 2.2.3 BSA Analysis

[0136] A total of 5,107,362 SNPs and 1,195,258 Indels were obtained between the two extreme pooled EFM and LFM progeny, of which 49,698 SNPs caused non-synonymous mutations. Using the ΔSNP-index algorithm, one QTL candidate region was obtained on chromosome A02. Figure 4 The total length is 4.10Mb, and there are 775 candidate genes in the interval, including flowering regulatory genes with verified functions such as FLC, TFL, and EMF1.

[0137] 2.3 Fine positioning (F2, F) 2:3 )

[0138] 2.3.1 F2, F 2:3 DNA extraction from the population

[0139] Total DNA was extracted from all population materials, and the quality was assessed by 1% agarose gel electrophoresis. Figure 5 As shown, the DNA electrophoresis bands are clear and without degradation bands, proving that the DNA can be used for subsequent experimental research.

[0140] 2.3.2 QTL localization based on the F2 population

[0141] For QTL candidate regions identified by BSA-seq, combined with Indel marker information from the interparental variation analysis results, 200bp sequences before and after the mutation site were extracted, and primers were designed to develop molecular markers. We designed 63 pairs of Indel molecular markers for polymorphism screening in both parents, and randomly selected materials from the F2 population to verify population polymorphism. Specific amplification products of five molecular markers, including Indel-282 and Indel-326, were separated by polyacrylamide gel electrophoresis, revealing significant differences between the products amplified from the two parents and the F2 population. These molecular markers can be used for subsequent fine mapping. Figure 6 The amplification products of the remaining Indel markers showed no significant differences between the two parents or the F2 population, indicating that these primers could not be used for subsequent fine mapping experiments. Since this candidate region showed high homology with other genomes, we also designed 20 SNP markers for target-seq sequencing. Only 13 SNP markers, including P1380A03, P1380A04, and P1380A05, showed differences between the parents. We used these 13 polymorphic markers to detect the genotype of the F2 population.

[0142] Genotypes detected in 704 F2 populations using two markers, combined with flowering phenotypes, narrowed the initial BSA-mapped QTL interval to between P1380A07 and Indel-317, with a physical interval of approximately 0.64M. Figure 7The physical distance was 1,723,509-2,368,070 bp, and there were 137 candidate genes within the interval, including FLC, EMF1, etc., and 137 exchange strains were screened.

[0143] 2.3.3 Based on F 2:3 fine positioning of groups

[0144] We preserved the Indel markers within the fine-grained localization intervals of the F2 population and encrypted two new pairs of Indel markers in 1628 F2 samples. 2:3 The candidate interval was further narrowed down within the population. The interval was reduced to between Indel-353 and Indel-282. Figure 8 The physical interval is approximately 0.38M, with a physical distance of 1,760,958-2,104,639 bp. This interval contains 74 candidate genes, including FLC and MYB92. Variation analysis of these candidate genes revealed that 32 genes have homozygous missense mutations in their exons, and 20 genes have homozygous indel mutations in their exons.

[0145] 2.4 RNA-seq

[0146] 2.4.1 DEGs analysis of leaves and shoot apex tissues of both parents

[0147] To further identify candidate genes related to flowering time within the QTL interval, RNA-seq analysis was performed on leaves and shoot tips from both parents during bolting. Based on differential expression analysis between the parents, 7714 (3077 upregulated, 4637 downregulated) and 7031 (2605 upregulated, 4426 downregulated) differentially expressed genes (DEGs) were identified in leaves and shoot tips, respectively. 3505 DEGs were shared between the two tissue sites; the specific upregulation and downregulation details are as follows... Figure 9 As shown.

[0148] 2.4.2 GO and KEGG annotation analysis of DEGs from parental leaf tissues

[0149] GO and KEGG enrichment analysis was performed on DEGs from the leaf tissues of both parents. Figure 10 We found that the biological processes upregulating DEGs were significantly enriched in photosynthesis, light response, and chloroplast-related functions, while the biological processes downregulating DEGs were significantly enriched in the transition from biosynthesis and growth activities to metabolic maintenance and stress preparation. GO enrichment analysis indicated that upregulating DEGs ( Figure 10 A) is mainly enriched in pathways such as photosynthesis itself, glycolysis, chlorophyll biosynthesis, photorespiration, and responses to different wavelengths of light (e.g., UV-A, red light, strong light); while downregulation of DEGs ( Figure 10C) is mainly enriched in the auxin-activated signaling pathway and in the negative regulation of some biosynthesis and metabolism. KEGG analysis showed that upregulation of DEGs ( Figure 10 B) is mainly enriched in pathways such as carbon metabolism, amino acid biosynthesis and metabolism, glycolysis / gluconeogenesis, carbon fixation in photosynthetic organisms, carbon metabolism, and photosynthesis; while downregulation of DEGs ( Figure 10 D) is mainly enriched in pathways such as plant hormone signal transduction, galactose metabolism, glycerol ester metabolism, and glycerophospholipid metabolism.

[0150] 2.4.3 GO and KEGG functional annotation analysis of DEGs from parental shoot apex tissues

[0151] GO and KEGG enrichment analysis was performed on DEGs from the shoot apex tissues of both parents. Figure 11 We found that the biological processes upregulating DEGs were significantly enriched in important biological processes such as photosynthesis, light response, energy metabolism, and amino acid synthesis, while the biological processes downregulating DEGs were significantly enriched in the transition from active vegetative growth to reproductive development or dormancy, and in processes where epigenetic regulation was activated. GO enrichment analysis showed that upregulating DEGs ( Figure 11 A) is mainly enriched in pathways such as translation, photosynthesis itself, glycolysis, photorespiration, intracellular lipid transport, and endoplasmic reticulum to chloroplast transport. Downregulation of DEGs ( Figure 11 C) is mainly enriched in pathways such as translation, histone deacetylation, negative regulation of responses to extracellular stimuli, negative regulation of responses to nutrient levels, and cold stress response. KEGG analysis showed that upregulation of DEGs ( Figure 11 B) is mainly enriched in pathways such as carbon metabolism, amino acid biosynthesis, ribosomes, starch / sucrose metabolism, glycolysis / gluconeogenesis, DNA replication, mismatch repair, nucleotide excision repair, and homologous recombination; while downregulation of DEGs ( Figure 11 D) It is mainly enriched in pathways such as translation and ribosomes, photosynthesis-antenna proteins, porphyrin and chlorophyll metabolism, cold response, and superoxide radical scavenging.

[0152] 2.5 Identification of key candidate genes for rapeseed flowering stage by integrating fine mapping and RNA-seq

[0153] To further investigate key regulatory genes during rapeseed flowering, we analyzed the expression differences of 39 genes with SNP or Indel mutations identified through fine mapping. The results (Tables 4 and 5) showed 6 and 12 DEGs (defective genes) in leaves and shoot tips, respectively. Three genes shared by both tissues were identified: BnaA02.UVR8, BnaA02G0030800ZS, and BnaA02.VNI1. Corresponding functional annotations indicated that these genes mainly encode transcriptional regulators, hormone signal transduction components, energy metabolism components, and protein synthesis and degradation components. Expression analysis revealed significant expression differences in 15 candidate genes across different tissues.

[0154] Table 4 Functional annotations of 15 candidate genes

[0155]

[0156] Table 5. GO and KEGG annotations for 15 candidate genes.

[0157]

[0158] 2.6 Application of Indel tightly linked markers at three early flowering sites

[0159] To verify the effectiveness of the tightly linked Indel markers at the three early-flowering loci, we performed genotyping on an F2 segregating population of 526 individuals constructed from crosses of early-flowering and late-flowering parents. Based on the genotyping results, we grouped the population lines according to the two genotypes for each marker and recorded the flowering dates of each group. The results showed that ( Figure 12 All three Indel markers effectively distinguished between early- and late-flowering materials in the F2 population, exhibiting highly significant phenotypic differences (p < 0.001). Specifically, the average flowering period of lines with the early-flowering genotype A was approximately 120 days, significantly earlier than that of lines with the late-flowering genotype B (approximately 131 days on average). Furthermore, we performed PCR amplification and electrophoresis in a mixed early- and late-flowering population to verify the effectiveness of marker genotyping. Figure 13 The study successfully distinguished the genotypes of each marker clearly as A (homozygous early-flowering parent) and B (homozygous late-flowering parent). Therefore, the three Indel markers developed in this study are closely linked to the early-flowering locus, laying a solid foundation for the precise implementation of subsequent marker-assisted selection breeding.

Claims

1. A molecular marker for determining the flowering time of Brassica napus, characterized in that: The molecular markers are three, namely Indel-282, Indel-326, and Indel-353, and the markers are located on chromosome A02 of Brassica napus; The Indel-282 marker polymorphism is 7 bp in length, and the specific inserted / deleted sequences are shown in SEQ ID NO.1; The Indel-326 marker polymorphism is 20 bp in length, and the specific inserted / deleted sequences are shown in SEQ ID NO.

2. The Indel-353 marker polymorphism is 29 bp in length, and the specific inserted or deleted sequences are shown in SEQ ID NO.

3.

2. The application of the molecular marker described in claim 1 as a target for determining the flowering time of Brassica napus.

3. The primer for the molecular marker according to claim 1, characterized in that: (i) The positive primer of Indel-282, the sequence of which is shown in SEQ ID NO:4, and the reverse primer, the sequence of which is shown in SEQ ID NO:5; (ii) The positive primer of Indel-326, the sequence of which is shown in SEQ ID NO:6, and the reverse primer, the sequence of which is shown in SEQ ID NO:7; (iii) The positive primer of Indel-353, the sequence of which is shown in SEQ ID NO:8, and the reverse primer, the sequence of which is shown in SEQ ID NO:

9.

4. A kit comprising the primers of claim 3.

5. The application of the kit according to claim 3 in the preparation of a detection reagent for determining the flowering time of Brassica napus.

6. A method for determining the flowering time of Brassica napus, characterized in that, Genotypic grouping of the F2 population was performed using the molecular markers described in claim 1. Specifically, the following two genotypes were distinguished by PCR amplification and electrophoresis: Genotype A, which carries the alleles of the early-flowering parent at all three Indel marker positions; and Genotype B, which carries the alleles of the late-flowering parent at all three Indel marker positions. The rule for determining the length of flowering time was that the A genotype line had the shortest flowering time, while the B genotype line had the longest flowering time.

7. The method according to claim 6, characterized in that, For the marker Indel-282, the average flowering period of lines carrying genotype A was 120 days, while the average flowering period of lines carrying genotype B was 131 days.

8. The method for obtaining the molecular marker according to claim 1, characterized in that, Specifically, the following steps are included: S1: High-throughput sequencing was performed on the early-flowering and late-flowering parents to obtain raw sequencing data; S2: Align the sequencing data to the reference genome of Brassica napus to identify Indel differential sites; S3: Design primers based on the identified Indel differential sites; S4: PCR amplification and electrophoresis were performed on samples from the F2 population to verify the effectiveness of these Indel markers.

9. The method according to claim 7, characterized in that, The three Indel markers showed significant phenotypic differences in the F2 population, with P values ​​less than 0.

001.

10. The application of the molecular marker of claim 1 and / or the primer of claim 3 and / or the kit of claim 4 in the breeding of Brassica napus.

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