Amplification primers for kasp markers on chromosome d03 for cotton variety development for low seed moisture content

Abstract

The application discloses an amplification primer of a KASP marker on a D03 chromosome for cotton variety breeding with low cottonseed water content, which comprises a forward primer F1 with FAM fluorescent labeling: GAAGGTGACCAAGTTCATGCTGCTCTAATGATAAACTAGTGTGTGTGTTG, a forward primer F2 with HEX fluorescent labeling: GAAGGTCGGAGTCAACGGATTGCTCTAATGATAAACTAGTGTGTGTTG, and a reverse primer: AAAGCTCAACCAGCGAGAAAGT. The application can effectively distinguish genotypes with significant differences in cottonseed water content, and provides a genetic resource and a convenient detection tool for cotton mechanical picking suitability improvement and low-cottonseed-water-content variety breeding.

Description

Technical Field

[0001] This invention relates to the field of molecular-assisted breeding technology, and in particular to an amplification primer for the KASP marker on chromosome D03 used for breeding cotton varieties with low cottonseed moisture content. Background Technology

[0002] Cotton (Gossypium hirsutum L.) is one of the world's most important natural fiber crops, and also a significant source of plant protein and oil. Cottonseed quality directly affects seed germination rate, seedling vigor, and post-harvest processing efficiency. Seed moisture content (SMC) is a key indicator for evaluating cottonseed quality, influencing not only the physiological and biochemical metabolic state of the seed but also its stability and safety during harvesting, storage, and processing. With the increasing mechanization of cotton production, cottonseed moisture content at maturity has become an indispensable factor in the comprehensive evaluation of varietal traits.

[0003] The formation of cottonseed water content during maturity is a continuous physiological developmental process, mainly involving two stages: physiological dehydration and natural drying. During physiological dehydration, with the accumulation of storage substances such as proteins and lipids, the seed gradually acquires dehydration tolerance and completes physiological maturation, at which point the transport of substances between the embryo and the parent plant ceases. In the natural drying stage, cottonseed water is primarily lost through physical evaporation, the rate of which is jointly regulated by environmental conditions such as temperature, air humidity, and water supply, as well as seed structural characteristics such as seed coat permeability and cell wall composition. Environmental signals are sensed and transformed into molecular regulatory signals through stress response mechanisms. Cis-acting elements related to dehydration and osmotic stress are prevalent in the promoter regions of maturation-related genes, allowing environmental information to enter the genetic regulatory network and influence gene expression. This also explains, at the molecular level, why the seed water content phenotype is highly sensitive to the environment.

[0004] At the endogenous regulatory level, plant hormone signaling plays a central role in coordinating seed maturation and dehydration. Abscisic acid (ABA) is considered a key hormone promoting seed maturation and water content reduction. By activating maturation-related transcriptional programs, it induces the expression of late embryonic development-rich proteins (LEA proteins), thereby enhancing cellular tolerance to dehydration and propelling seeds into a mature, dehydrated state. In contrast to ABA, gibberellin (GA) plays a crucial role in maintaining germination physiology and inhibiting dormancy; the balance between these two factors has a decisive impact on the final seed water content. Furthermore, the developmental regulatory network composed of transcription factors such as ABI3, FUS3, LEC1, and LEC2 plays a vital role in connecting hormone signaling with the maturation program and is one of the key nodes regulating seed water content formation. In-depth research on the key genes in these regulatory pathways is of significant guiding importance for elucidating the genetic basis of cottonseed water content.

[0005] In recent years, genome-wide association studies (GWAS) have become an effective tool for elucidating the genetic basis of complex quantitative traits. In cotton, GWAS has been widely applied to the genetic analysis of traits such as fiber quality, lint percentage, seed vigor, and stress resistance, identifying numerous associated loci and candidate genes, providing crucial support for cotton molecular breeding. However, compared to fiber yield and quality traits, cottonseed-related traits have received significantly less attention in basic cotton research. High-moisture cottonseeds exhibit strong respiration and are prone to heat accumulation, making them highly susceptible to mold and spontaneous combustion. Existing research on suitability for machine harvesting primarily focuses on plant architecture and boll opening traits, while systematic genetic analysis of seed moisture content at maturity—a key trait affecting the processing quality and storage safety of machine-harvested cotton—is lacking. Therefore, breeding cotton varieties with low seed moisture content suitable for machine harvesting has become an urgent problem to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide an amplification primer for the KASP marker on chromosome D03 for the breeding of cotton varieties with low cottonseed moisture content. This primer can effectively distinguish genotypes with significant differences in cottonseed moisture content, providing genetic resources and a convenient detection tool for improving the suitability of cotton for mechanized harvesting and for the breeding of varieties with low cottonseed moisture content.

[0007] The technical solution adopted by this invention to solve its technical problem is: A primer for amplifying the KASP marker on chromosome D03 for breeding cotton varieties with low cottonseed moisture content includes: Forward primer F1 with FAM fluorescent label: GAAGGTGACCAAGTTCATGCTGCTCTAATGATAAACTAGTGTGTGTGTTG; Forward primer F2 with HEX fluorescent label: GAAGGTCGGAGTCAACGGATTGCTCTAATGATAAACTAGTGTGTGTTG; Reverse primer: AAAGCTCAACCAGCGAGAAAGT; The cotton variety in question is upland cotton.

[0008] A kit for detecting KASP markers on chromosome D03 for breeding cotton varieties with low cottonseed moisture content, the kit comprising the aforementioned amplification primers, wherein the cotton variety is upland cotton.

[0009] A PCR reagent for detecting KASP markers on chromosome D03 for breeding cotton varieties with low cottonseed moisture content, the PCR reagent comprising the aforementioned amplification primers, wherein the cotton variety is upland cotton.

[0010] The application of the aforementioned amplification primer in the breeding of cotton varieties with low cottonseed moisture content, wherein the cotton variety is upland cotton.

[0011] A molecular marker-assisted breeding method for low-seed moisture content upland cotton varieties involves using the aforementioned amplification primers to perform a KASP reaction on the cotton to be tested, and detecting the reaction products. If only HEX fluorescent labeling is detected in the reaction products, then the cotton to be tested is a low-seed moisture content variety.

[0012] The beneficial effects of this invention are: for the first time, this invention systematically identified stable QTLs associated with cottonseed moisture content at maturity and developed a KASP marker that can be used for marker-assisted selection, providing genetic resources and convenient detection tools for improving the suitability of cotton for mechanized harvesting and breeding varieties with low cottonseed moisture content. Attached Figure Description

[0013] Figure 1 This is a frequency distribution map of moisture content under six different environments (E1: Liaocheng, Shandong SD-2021, E2: Huanggang, Hubei HB-2021, E3: Sanya, Hainan HN-2021, E4: Liaocheng, Shandong SD-2022, E5: Huanggang, Hubei HB-2022, E6: Sanya, Hainan HN-2022); the red line represents the fitted normal distribution curve.

[0014] Figure 2 This is a correlation analysis of moisture content across six environmental conditions. The lower triangle displays the correlation coefficient r, the upper triangle pie chart shows the strength of the correlation, and the asterisk indicates the significance level of the correlation: ** indicates a significance level of 0.01.

[0015] Figure 3 This section presents phenotypic variations in moisture content of germplasm resources at different breeding stages and in different watersheds. The box plots inside the violins illustrate the median and interquartile range of the data. Different lowercase letters above the figures indicate significant differences (P < 0.05) obtained from one-way ANOVA and Tukey HSD tests.

[0016] Figure 4 The BLUP values ​​calculated across multiple environments are used as phenotypic data, and a GWAS Manhattan plot based on SNP markers is generated. The horizontal axis represents the physical location of the variant site on the 26 cotton chromosomes; the vertical axis represents the association significance level; the horizontal red dashed line in the figure represents the genome-wide significance threshold P=1×10⁻⁶. -6 .

[0017] Figure 5The BLUP values ​​calculated across multiple environments are used as phenotypic data, and a GWAS Manhattan plot based on InDel markers is generated. The horizontal axis represents the physical location of the variant site on the 26 cotton chromosomes; the vertical axis represents the association significance level; the horizontal red dashed line in the figure represents the genome-wide significance threshold P=1×10⁻⁶. -6 .

[0018] Figure 6 This is a heatmap of candidate gene expression patterns on chromosome D03 at various stages of growth.

[0019] Figure 7 This contains information on the structure of the Ghir_D03G012440 gene and the target InDel variant site. The reference sequence is GTT, and the variant sequence is GTT / G, which corresponds to two haplotype diagrams: HapA and HapB.

[0020] Figure 8 This is a violin box plot showing the moisture content of cotton grains from two haplotypes, HapA and HapB, under six experimental conditions. Blue represents haplotype HapA, and brownish-yellow represents haplotype HapB; *** represents P<0.001, and ** represents P<0.01, indicating a statistically significant difference in moisture content between the two haplotypes.

[0021] Figure 9 This is a genotyping result of the GhHSD1 haplotype in the first 355 materials of 492 populations based on the developed KASP marker. Detailed Implementation

[0022] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0023] In this invention, unless otherwise specified, all raw materials and equipment used are commercially available or commonly used in the field. The methods described in the following embodiments are conventional methods in the field, unless otherwise specified.

[0024] Example 1: Materials Method

[0025] 1.1 Plant materials and field trials This invention utilizes 492 upland cotton germplasm resources provided by Zhejiang A&F University (originally sourced from the National Cotton Germplasm Resource Intermediate Bank), covering Xinjiang-approved varieties, intermediate breeding materials, and local varieties from different ecological regions. All materials were field-planted in 2021 and 2022 at three representative ecological sites: the Linqing Experimental Station of the Shandong Cotton Research Center in Liaocheng, Shandong (E1, 2021; E4, 2022); the Huanggang Modern Agricultural Science and Technology Demonstration Park in Hubei (E2, 2021; E5, 2022); and the Sanya National Wild Cotton Germplasm Resource Nursery in Hainan (E3, 2021; E6, 2022), forming a total of six environments. The field trials employed a completely randomized block design. In the Shandong pilot project, each material was planted in a single row with a row length of 5 m, a row spacing of 0.60 m, a plant spacing of 0.30 m, and 17-20 plants per material. In the pilot program in Huanggang, each sample was planted in a single row, with a row length of 6 m, a row spacing of 0.80 m, a plant spacing of 0.40 m, and 17-20 plants per sample. In the pilot program in Sanya, each sample was planted in a single row, with a row length of 3 m, a row spacing of 0.50 m, a plant spacing of 0.20 m, and 15-18 plants per sample.

[0026] 1.2 Phenotypic Determination and Statistical Analysis of Cottonseed Moisture Content A single harvest was conducted at the seed physiological maturity stage. Thirty mature cotton bolls were randomly selected from each sample, and the cotton seeds were extracted, short fibers removed, and then stored for later use. Near-infrared spectroscopy (NIRS) was used for non-destructive determination of cotton seed moisture content. Each sample was measured three times, and the average value was taken as the moisture content phenotypic value for that material under that environment. The moisture content was calculated using the formula: (fresh weight - dry weight) / fresh weight × 100%.

[0027] In R 4.3.3, the best linear unbiased prediction (BLUP) for each material under six environments was calculated using the mixed linear model (MLM) of the lme4 package to eliminate genotype-environment interaction effects. The six single-environment phenotypic values ​​and BLUP values ​​were used together as phenotypic data for subsequent GWAS analysis. The Shapiro-Wilk test was used to assess the normality of the phenotypic data, and the Pearson correlation coefficient was used to assess the correlation between phenotypes across environments.

[0028] 1.3 Genotype data quality control and genome-wide association analysis Based on whole-genome resequencing data from 492 upland cotton germplasm resources, SNPs and InDels were identified and filtered using the GATK genome analysis toolkit. The upland cotton reference genome HAU V1.1 (TM-1, the publicly available upland cotton V1.1 genome from Huazhong Agricultural University) was used as a comparison reference. Basic quality control was performed using PLINK software, with filtering criteria of minor allele frequency (MAF) ≥ 0.05 and genotype deletion rate < 0.2. After filtering, a total of 2,316,343 high-quality SNP loci and 377,778 high-quality InDel loci were obtained for subsequent analysis.

[0029] Six single-environment phenotypic values ​​and BLUP values ​​were used as phenotypic data. Genome-wide association analysis was performed using a linear mixed model (LMM) in GEMMA software. The IBS kinship matrix was used as a random-effects covariate in the model to control for false positives caused by inter-individual kinship. The genome-wide significance threshold was set at P = 1 × 10⁻⁶. -6 All significant SNPs and InDel sites were visualized using Manhattan plots and QQ plots generated by the CMplot package in R 4.3.3.

[0030] 1.4 Multi-model GWAS Validation To evaluate the detection stability of association signals in the target QTL interval, six GWAS models were used for multi-model association analysis in the target interval of chromosome D03, including: Linear Mixture Model (LMM), Multi-site Mixed Linear Model (MLMM), Fixed and Random Alternating Probability Unified Model (FarmCPU), Continuously Stable Unique Relationship Mixed Linear Model (SUPER), Bayesian Information and Linkage Disequilibrium Iterative Nested Model (BLINK), and General Linear Model (GLM). Among these, FarmCPU, MLMM, and SUPER models were based on GAPIT software, and the GLM model was based on Plink2. All six models used BLUP values ​​as phenotypic input and were run with default parameters. The detection reliability of the target interval was evaluated by comparing the consistency of association peak positions among different models.

[0031] 1.5 Chaining Imbalance Analysis and QTL Fine-Track Positioning For stable association signals identified on chromosomes by GWAS, linkage disequilibrium (LD) analysis was performed using the LDheatmap, genetics, and vcfR packages in the R language environment. Genotype data for all SNPs within the target region were extracted from the VCF file, and the LD coefficients (r) between each pair of SNPs were calculated. 2A heatmap of inverted triangular LDs is drawn. By observing the continuous high-LD inverted triangular structures in the heatmap, complete LD blocks are identified, and the QTL interval is precisely located to the smallest physical range by combining the LD attenuation situation.

[0032] 1.6 Candidate gene screening and variant annotation For all genes within the finely mapped QTL regions, a systematic screening was performed based on the HAU v1.1 annotation information from the reference genome. ANNOVAR was used to annotate the functional regions of significantly associated SNPs and InDels within the regions, and the distribution of variants in upstream and downstream regulatory regions, 5′UTR, 3′UTR, exons, introns, splice sites, and intergenic regions was analyzed, with a focus on variant sites located in regulatory regions.

[0033] Based on the "TM-1" public transcriptome data, the expression levels of candidate genes in different tissues and at the 0-25 DPA ovule development stage were extracted. After logarithmic transformation and Z-score normalization, cluster heatmaps were generated using the R package pheatmap to evaluate the tissue specificity and developmental dynamic expression patterns of each gene. Simultaneously, homology functional information of candidate genes was obtained by comparing them with Arabidopsis homology gene annotation files and the local blastp database in the nr database, with a focus on screening genes related to water regulation, ABA signaling, and seed maturation dehydration.

[0034] Genome-wide significant associated loci were mapped to neighboring genes, and GO annotation was performed using the eggNOG database. Hypergeometric tests were used for enrichment analysis at the biological process, molecular function, and cellular component levels, with a P < 0.05 threshold. GO entries related to water response, water stress, and the ABA signaling pathway were prioritized. Based on functional annotation, expression characteristics, homology information, and enrichment results, candidate genes most closely related to cottonseed water content regulation within the target region were identified for subsequent haplotype analysis and marker development.

[0035] 1.7 Haplotype Analysis and Phenotypic Association Validation For key loci identified through ANNOVAR annotation, homozygous genotypes were classified into haplotypes based on allelic variation patterns at these loci. The reference type was assigned to haplotype HapA, and the variant type to haplotype HapB. Using phenotypic data on cotton seed moisture content at maturity under six different environments, one-way ANOVA was employed to compare the differences in moisture content between the two haplotypes, verifying the stability of the association between this locus and the moisture content trait and its genetic effect.

[0036] 1.8 Development and Genotyping Validation of KASP Molecular Markers Competitive allele-specific PCR (KASP) molecular markers were developed based on allele variant types at specific loci. Based on the sequence differences between HapA and HapB, two allele-specific forward primers and one universal reverse primer were designed: the forward primer HSD-F (FAM) specifically recognizes the HapA allele, with a FAM fluorescent tag attached to its 5′ end; the forward primer HSD-F (HEX) specifically recognizes the HapB allele, with a HEX fluorescent tag attached to its 5′ end; and the universal reverse primer HSD-R was used for co-amplification.

[0037] Using genomic DNA from 492 upland cotton germplasm resources as templates, KASP genotyping was performed. The reaction system and amplification procedure followed the standard KASP protocol. Genotyping was determined by reading fluorescence signals after the reaction. To evaluate the genotyping accuracy of this KASP marker, genotypes obtained from manual haplotype analysis based on VCF files were used as a reference. The KASP genotyping results were compared one-to-one with the reference genotypes, and the genotypic concordance rate was calculated.

[0038] Example 2: Results

[0039] 2.1 Phenotypic Variation Analysis of Cottonseed Moisture Content Regarding phenotypic data, a systematic determination of seed moisture content at maturity was completed for 492 upland cotton germplasm resources under 6 environments. Figure 1 The water content of each environment showed an approximately normal distribution: E1 (Liaocheng, Shandong 2021) had a water content concentrated between 6.71% and 13.11%, with an average of about 9.0%; E2 (Huanggang, Hubei 2021) had a water content concentrated between 7.27% and 13.92%, with an average of about 10.0%; E3 (Sanya, Hainan 2021) had a generally low water content, concentrated between 6.05% and 13.32%; E4 (Liaocheng, Shandong 2022) had a water content concentrated between 7.02% and 14.28%; E5 (Huanggang, Hubei 2022) had the highest average water content, about 10.5%, with a relatively wide distribution range; E6 (Sanya, Hainan 2022) also had a low water content, consistent with the trend of E3. Multi-environment correlation analysis showed that the correlation coefficients between any two of the six environments ranged from 0.57 to 0.75, and all were highly significant (P < 0.01), indicating that the cottonseed moisture content trait has high genetic stability and is suitable for multi-environment GWAS analysis. Figure 2 ).

[0040] Further population structure analysis showed that, when grouped by breeding year, the moisture content of varieties bred after the 2000s was significantly lower than that of germplasm bred before 1950 (P<0.05), reflecting a continuous improvement trend in moisture content traits during cotton breeding. Figure 3 When grouped by ecological region, the ecotype from the Northwest inland had the highest water content, while the ecotype from the Yellow River basin had the lowest water content, with significant differences between different ecotypes. Figure 3 This study reveals that the water content trait is profoundly influenced by domestication and adaptive selection, and also demonstrates that identifying functional loci has important breeding application value.

[0041] 2.2 Genome-wide association analysis identified stable major-effect QTLs In terms of genotype data processing, the raw resequencing data were strictly quality controlled with minor allele frequency (MAF) ≥ 0.05 and deletion rate < 0.2%. Ultimately, 2,316,343 high-quality SNP loci and 377,778 high-quality InDel loci were obtained, covering all 26 chromosomes of upland cotton. The marker density was sufficient to effectively support genome-wide association analysis.

[0042] Combining the processed phenotypic and whole-genome resequencing genotypic data of 492 germplasm resources, GWAS analysis was conducted using a linear mixture model (LMM) with GEMMA software, employing six single-environment phenotypic values ​​and multi-environment BLUP values ​​for phenotypes. The p-value was calculated as 1 × 10⁻⁶. -6 The threshold for genome-wide significance was set at 1. To comprehensively assess the environmental stability of associated loci, GWAS was first performed on each environment using a single-environment phenotype, and the results from the six environments were then aggregated. Based on this, a combined GWAS was conducted using multi-environment BLUP values ​​as phenotypes to identify QTLs stably expressed in multiple environments. SNP marker-based analysis showed that chromosome A08 exhibited the strongest association signal, with a peak value of -log 1. 10 (P) was approximately 8.34, far exceeding the significance threshold, making it the most prominent major-effect site in the entire genome; in addition, association signals exceeding the significance threshold were also detected on chromosomes A07, A13, D03, and D13. Figure 4 The analysis results based on InDel markers showed a high degree of agreement with SNPs, and the peak value of chromosome A07 was log-1. 10 (P) approximately 7.0, and significant signals were also detected on chromosomes D09 and D13. Figure 5The SNPs and InDel markers with significant associations across multiple environments were integrated and QTLs were segmented. Results showed that a stable QTL region qMC-A07-2 was identified on chromosome A07. This region contains both SNP and InDel markers, and significant association signals were detected in four environments (E1, E2, E6, and BLUP), encompassing a total of 45 significant markers. Another stable QTL region qMC-D03-1 was identified on chromosome D03, also containing both SNP and InDel markers. Significant association signals were detected in four environments (E1, E2, E5, and BLUP), encompassing a total of 37 significant markers. The significant signals detected by the two independent marker sets (SNPs and InDel) on chromosomes A07 and D03 were highly consistent, further confirming the existence of actual genetic loci regulating cottonseed water content in these regions, making them priority target regions for subsequent fine mapping and candidate gene mining (Table 1). Table 1

[0043] Note: E1 represents Liaocheng, Shandong in 2021, E2 represents Huanggang, Hubei in 2021, E3 represents Sanya, Hainan in 2020-2021, E4 represents Liaocheng, Shandong in 2022, E5 represents Huanggang, Hubei in 2022, E6 represents Sanya, Hainan in 2021-2022, and BLUP represents best linear unbiased prediction.

[0044] 2.3 Functional annotation and GO enrichment analysis of candidate QTL intervals To further evaluate the potential functional effects of variations within candidate QTL intervals, this invention used ANNOVAR software to systematically annotate the functional regions of all significantly associated SNPs and InDel sites within the qMC-A07-2 and qMC-D03-1 intervals. The annotation results showed significant differences in the distribution of functional variations between the two QTL intervals. All significant variations within the qMC-A07-2 interval were located in the intergenic region; no variation sites were detected in the coding or regulatory regions, suggesting that this QTL may affect the target trait through long-distance regulation. In contrast, significant variations within the qMC-D03-1 interval were distributed across multiple functional regions, including upstream, downstream, 3' UTR, and intergenic regions. The majority of variations (19) were located in the intergenic region, followed by the upstream regulatory region, then the downstream regulatory region (3 variations), and the 3' UTR region (1 variation). Of particular note is the critical InDel site rs_D03_41852097 located in the 3' UTR region of the Ghir_D03G012440 gene, which is the only significant variant located in the 3' UTR within the qMC-D03-1 interval. As a key regulatory region for mRNA stability, translation efficiency, and miRNA binding, genetic variations in the 3' UTR may participate in the fine regulation of gene expression levels by influencing post-transcriptional regulatory processes.

[0045] Based on the completed functional annotation, this invention further performed GO functional enrichment analysis on genes corresponding to all significant associated loci identified by genome-wide GWAS. After mapping significant loci to their neighboring genes, GO annotation was performed using the eggNOG database, and enrichment analysis was conducted at three levels: biological process, molecular function, and cellular component. The results showed that several GO entries related to water regulation and abscisic acid signaling pathways were significantly enriched (P < 0.05). Specifically, GO:0009415 (water response) contained 14 target genes, GO:0009414 (water deficiency response) contained 13 target genes, GO:0009787 (regulation of abscisic acid activation signaling pathway) contained 5 target genes, and GO:0071215 (cellular response to abscisic acid stimulation) contained 8 target genes. The enrichment of these GO entries indicates that the identified associated loci may be involved in plant water stress response and ABA signal transduction processes. Given the core regulatory role of ABA in seed maturation, dehydration tolerance, and stress adaptation, this enrichment result further suggests that genes within these QTL regions may be involved in water regulation during cotton seed development.

[0046] Based on the combined results of ANNOVAR annotation and GO enrichment analysis, the variations in the qMC-A07-2 region were all intergenic variations, suggesting that its regulatory mechanism may be complex or it may function through long-range regulation. Meanwhile, the variations in the qMC-D03-1 region were distributed across multiple non-coding functional regions, and a key InDel site significantly associated with water content was found in the 3'UTR region. Therefore, future research will target the qMC-D03-1 region, conducting linkage disequilibrium fine mapping, expression analysis, and candidate gene function verification to further elucidate its molecular mechanism in regulating cottonseed water content.

[0047] 2.4 Fine-grained region localization based on LD and multi-model GWAS validation The most significant SNP on the interval qMC-D03-1 is labeled rsGhir_D03_41816782 (in E1 environment -log). 10 Using (P) = 7.17 as the core, extending 100 kb to both sides as the initial analysis window, genotype data for all SNP loci within this interval were extracted, and the LD coefficient (r) between each pair of SNPs was calculated. 2 The LD heatmap was then used to create an inverted triangular LD heatmap. The LD heatmap showed that the genomic region centered on rsGhir_D03_41816782 formed a continuous and well-defined strongly linked disequilibrium block. Within this block, the SNPs exhibited significant differences in r... 2 The overall values ​​were relatively high, and the linkage relationships were stable. In the upstream and downstream regions of the core block, the LD intensity decreased significantly, forming independent LD blocks with different patterns, suggesting that there may be recombination hotspots or genomic structural variations at the boundaries of this QTL interval.

[0048] To further confirm the detection stability and reliability of this QTL interval, six GWAS models (LMM, MLMM, FarmCPU, SUPER, BLINK, and GLM) were used to perform multi-model association analysis on chromosome D03. The results showed that all six models detected significant association signals within the qMC-D03-1 interval, and the association peak positions among different models were highly consistent, further validating the reliability of the association signal in this interval. The cross-validation results of multiple models and multiple marker types were consistent, strongly confirming the existence of a true genetic locus regulating cottonseed water content within this interval.

[0049] Combining the high LD inverted triangle distribution and LD decay characteristics in the LD heatmap with the co-localization results of six GWAS models, the core physical region of qMC-D03-1 was precisely compressed to 84.1 kb. The linkage relationships between SNPs within this region formed a complete and well-defined LD block, and the detection results of the six GWAS models and the two types of markers were highly consistent, providing a reliable genomic range and genetic basis for subsequent candidate gene screening and functional verification.

[0050] 2.5 Screening and Determination of Candidate Genes Based on the precise localization of the qMC-D03-1 region to the 84.1 kb physical region of chromosome D03, this invention systematically screened and identified genes within this region. According to the annotation information of the upland cotton reference genome HAU V1.1, this 84.1 kb region contains a total of 8 candidate genes, namely Ghir_D03G012380, Ghir_D03G012390, Ghir_D03G012400, Ghir_D03G012410, Ghir_D03G012420, Ghir_D03G012430, Ghir_D03G012440, and Ghir_D03G012450.

[0051] To preliminarily assess the function of the aforementioned candidate genes, this invention first extracted the expression levels of seven genes in different tissues and developmental stages based on the "TM-1" public transcriptome data. After logarithmic transformation and Z-score normalization, cluster heatmaps were generated. The results showed that Ghir_D03G012440 exhibited extremely low expression levels in vegetative organs, with low expression during the early to mid-stages of ovule development (0-15 DPA), but significantly upregulated expression during the late stage of ovule development (20-25 DPA). Its expression dynamics were highly consistent with the physiological process of cottonseed transitioning from the material filling stage to the mature dehydration stage. In contrast, Ghir_D03G012390 and Ghir_D03G012400 maintained high expression levels from 0-20 DPA but significantly decreased at 25 DPA. Both also showed high expression in vegetative organs such as roots and stems, without exhibiting significant tissue or developmental stage specificity. The remaining genes did not show significant expression changes related to the mature dehydration stage during ovule development. Figure 6 ).

[0052] Based on expression analysis, this invention further performed homology functional annotation on seven candidate genes. By comparing the HAU_V1.1_Arabidopsis_Annotation.txt file and verifying with the local BLASTP database of the nr database, the homology of Ghir_D03G012440 in Arabidopsis was found to be AT5G50700, encoding the hydroxysteroid dehydrogenase HSD1, a member of the short-chain dehydrogenase / reductase (SDR) superfamily. SDR family proteins are commonly involved in steroid and lipid metabolism and play an important role in plant stress adaptation and energy metabolism regulation. Furthermore, the homology of Ghir_D03G012400 in Arabidopsis is AT5G50860, encoding a member of the protein kinase superfamily, which may be involved in plant signal transduction and stress response regulation. However, given its weak tissue expression specificity, its central role in this QTL region requires further evaluation.

[0053] Based on ANNOVAR annotation, tissue expression characteristics, and homologous gene function information, this invention identifies Ghir_D03G012440 (named GhHSD1) as the candidate gene most closely related to the regulation of cottonseed moisture content within the qMC-D03-1 interval, for subsequent haplotype analysis and molecular marker development.

[0054] 2.6 Haplotype analysis and phenotypic association validation of key InDel sites To verify the stability of the association between the rs_D03_41852097 locus and cottonseed moisture content and its genetic effect, this invention classified homozygous genotype materials into haplotypes based on the allelic variation type of this InDel locus. The reference genomic allele for this locus is "G", and the variation type is deletion. Among them, haplotype HapA is the reference type, and haplotype HapB carries the InDel deletion type. Using phenotypic data of cottonseed moisture content at maturity under six environments, the differences in moisture content between the two haplotype materials were compared.

[0055] The results showed that the water content of the HapB haplotype was significantly lower than that of the HapA haplotype in all six environments. This trend was highly consistent across different years (2021, 2022) and different ecological sites (Shandong, Hubei, Hainan), with no reversal of the effect direction or loss of significance. The average water content of the HapB haplotype was lower than that of the HapA haplotype in all environments, and their distribution ranges were clearly separated, indicating that this deletion allelic variation was significantly associated with lower cottonseed maturity water content. Figure 8 ).

[0056] The above results indicate that the rs_D03_41852097 site can serve as a stable molecular marker associated with cottonseed moisture content. The HapB haplotype carrying this InDel deletion is associated with lower cottonseed moisture content at maturity and has potential priority selection value in machine-harvested cotton breeding.

[0057] 2.7 Development and Validation of KASP Molecular Markers for Typing Accuracy To transform the rs_D03_41852097 locus into a readily applicable detection tool for breeding practices, this invention develops a competitive allele-specific PCR (KASP) molecular marker based on the allelic variation types of this InDel locus. Based on the sequence differences between the reference type HapA and the InDel-deleted HapB, two allele-specific forward primers and one universal reverse primer were designed (Table 2). The forward primer HSD-F (FAM) specifically recognizes the HapA allele, with a FAM fluorescent tag attached to its 5' end; the forward primer HSD-F (HEX) specifically recognizes the HapB allele, with a HEX fluorescent tag attached to its 5' end; the universal reverse primer HSD-R is used for co-amplification. The sequence difference between the two forward primers lies in the different number of "TG" repeats at the ends, corresponding to the insertion / deletion polymorphism of the two alleles at this InDel locus.

[0058] Table 2

[0059] KASP genotyping was performed using the first 355 genomic DNA accessions from 492 upland cotton germplasm resources as templates. Genotyping was determined by reading fluorescence signals after the reaction. Figure 9 To evaluate the genotyping accuracy of the KASP marker, the genotypes obtained from manual haplotype analysis based on VCF files were used as a reference. The KASP genotyping results were compared one-to-one with the reference genotypes. Out of 355 samples, 325 were complete matches, indicating that the developed KASP marker can accurately distinguish between the HapA and HapB alleles, effectively identify germplasm carrying the HapB haplotype, and provide a molecular-assisted screening tool for the breeding of low cottonseed moisture content varieties.

[0060] The above results indicate that the KASP molecular marker developed based on the rs_D03_41852097 locus can efficiently and accurately perform genotyping on cotton germplasm resources, and is suitable for large-scale molecular marker-assisted selection breeding practices, providing a convenient detection tool for the genetic improvement of cotton seed moisture content.

[0061] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A primer for amplifying the KASP marker on chromosome D03 for breeding cotton varieties with low cottonseed moisture content, characterized in that, include: Forward primer F1 with FAM fluorescent label: GAAGGTGACCAAGTTCATGCTGCTCTAATGATAAACTAGTGTGTGTGTTG; Forward primer F2 with HEX fluorescent label: GAAGGTCGGAGTCAACGGATTGCTCTAATGATAAACTAGTGTGTGTTG; Reverse primer: AAAGCTCAACCAGCGAGAAAGT; The cotton variety in question is upland cotton.

2. A kit for detecting KASP markers on chromosome D03 for cotton variety breeding with low cottonseed moisture content, characterized in that, The kit includes the amplification primers as described in claim 1, and the cotton variety is upland cotton.

3. A PCR reagent for detecting KASP markers on chromosome D03 for cotton variety breeding with low cottonseed moisture content, characterized in that, The PCR reagent includes the amplification primers as described in claim 1, and the cotton variety is upland cotton.

4. The application of the amplification primer as described in claim 1 in the breeding of cotton varieties with low cottonseed moisture content, wherein the cotton variety is upland cotton.

5. A molecular marker-assisted breeding method for upland cotton varieties with low cottonseed moisture content, characterized in that, Using the amplification primers described in claim 1, a KASP reaction was performed on the cotton to be tested. The reaction product was detected. If only HEX fluorescent labeling was detected in the reaction product, then the cotton to be tested is a cotton variety with low cottonseed moisture content.

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