Prediction of wheat SPAD using integrated multispectral and support vector machines.

Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69-0.79, RMSE was 2.30-12.94, and RE was 0.10%-1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.

Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

Co-localization and analysis of miR477b with fiber length quantitative trait loci in cotton.

Cotton is an important cash crop for the textile industry. However, the understanding of natural genetic variation of fiber elongation in relation to miRNA is lacking. A miRNA gene (miR477b) was found to co-localize with a previously mapped fiber length (FL) quantitative trait locus (QTL). The miR477b was differentially expressed during fiber elongation between two backcross inbred lines (BILs) differing in FL and its precursor sequences. Bioinformatics and qRT-PCR analysis were further used to analyse the miRNA genes, which could produce mature miR477b. Cotton plants with virus-induced gene silencing (VIGS) constructs to over-express the allele of miR477b from the BIL with longer fibers had significantly longer fibers as compared with negative control plants, while the VIGS plants with suppressed miRNA expression had significantly shorter fibers. The expression level of the target gene (DELLA) and related genes (RDL1 and EXPA1 for DELLA through HOX3 protein) in the two BILs and/or the VIGS plants were generally congruent, as expected. This report represents one of the first comprehensive studies to integrate QTL linkage mapping and physical mapping of small RNAs with both small and mRNA transcriptome analysis, followed by VIGS, to identify candidate small RNA genes affecting the natural variation of fiber elongation in cotton.

Genomic and co-expression network analyses reveal candidate genes for oil accumulation based on an introgression population in Upland cotton (Gossypium hirsutum).

KEY MESSAGE: Integrated QTL mapping and WGCNA condense the potential gene regulatory network involved in oil accumulation. A glycosyl hydrolases gene (GhHSD1) for oil biosynthesis was confirmed in Arabidopsis, which will provide useful knowledge to understand the functional mechanism of oil biosynthesis in cotton. Cotton is an economical source of edible oil for the food industry. The genetic mechanism that regulates oil biosynthesis in cottonseeds is essential for the genetic enhancement of oil content (OC). To explore the functional genomics of OC, this study utilized an interspecific backcross inbred line population to dissect the quantitative trait locus (QTL) interlinked with OC. In total, nine OC QTLs were identified, four of which were novel, and each QTL explained 3.62-34.73% of the phenotypic variation of OC. The comprehensive transcript profiling of developing cottonseeds revealed 3,646 core genes differentially expressed in both inbred parents. Functional enrichment analysis determined 43 genes were annotated with oil biosynthesis processes. Implementation of weighted gene co-expression network analysis showed that 803 differential genes had a significant correlation with the OC phenotype. Further integrated analysis identified seven important genes located in OC QTLs. Of which, the GhHSD1 gene located in stable QTL qOC-Dt3-1 exhibited the highest functional linkages with the other network genes. Phylogenetic analysis showed significant evolutionary differences in the HSD1 sequences between oilseed- and starch- crops. Furthermore, the overexpression of GhHSD1 in Arabidopsis yielded almost 6.78% higher seed oil. This study not only uncovers important genetic loci for oil accumulation in cottonseed, but also provides a set of new candidate genes that potentially influence the oil biosynthesis pathway in cottonseed.

EB1C forms dimer and interacts with protein phosphatase 2A (PP2A) to regulate fiber elongation in upland cotton (Gossypium hirsutum).

Cotton is the most economically important natural fiber crop grown in more than sixty-five countries of the world. Fiber length is the main factor affecting fiber quality, but the existing main varieties are short in length and cannot suit the higher demands of the textile industry. It is necessary to discover functional genes that enable fiber length improvement in cotton through molecular breeding. In this study, overexpression of GhEB1C in Arabidopsis thaliana significantly promotes trichomes, tap roots, and root hairs elongation. The molecular regulation of GhEB1C involves its interactions with itself and GhB'ETA, and the function of GhEB1C regulation mainly depends on the two cysteine residues located at the C-terminal. In particular, the function activity of GhEB1C protein triggered with the regulation of protein phosphatase 2A, while silencing of GhEB1C in cotton significantly influenced the fiber protrusions and elongation mechanisms., Further, influenced the expression of MYB-bHLH-WD40 complex, brassinosteroids, and jasmonic acid-related genes, which showed that transcriptional regulation of GhEB1C is indispensable for cotton fiber formation and elongation processes. Our study analyzed the brief molecular mechanism of GhEB1C regulation. Further elucidated that GhEB1C can be a potential target gene to improve cotton fiber length through transgenic breeding.

Genome-wide association analysis of time to heading and maturity in bread wheat using 55K microarrays.

To investigate the genetic mechanisms underlying the reproductive traits (time to flowering and maturity) in wheat and identify candidate genes associated, a phenotypic analysis was conducted on 239 wheat accessions (lines) from around the world. A genome-wide association study (GWAS) of wheat heading and maturity phases was performed using the MLM (Q+K) model in the TASSLE software, combined with the Wheat 55K SNP array. The results revealed significant phenotypic variation in heading and maturity among the wheat accessions across different years, with coefficients of variation ranging from 0.96% to 1.97%. The phenotypic data from different years exhibited excellent correlation, with a genome-wide linkage disequilibrium (LD) attenuation distance of 3 Mb. Population structure analysis, evolutionary tree analysis, and principal component analysis indicated that the 239 wheat accessions formed a relatively homogeneous natural population, which could be divided into three subgroups. The GWAS results identified a total of 293 SNP marker loci that were significantly associated with wheat heading and maturity stages (P ≤ 0.001) in different environments. Among them, nine stable SNP marker loci were consistently detected in multiple environments. These marker loci were distributed on wheat chromosomes 1A、1B、2D、3A、5B、6D and 7A. Each individual locus explained 4.03%-16.06% of the phenotypic variation. Furthermore, through careful analysis of the associated loci with large phenotypic effect values and stable inheritance, a total of nine candidate genes related to wheat heading and maturity stages were identified. These findings have implications for molecular marker-assisted selection breeding programs targeting specific wheat traits at the heading and maturity stages. In summary, this study conducted a comprehensive GWAS of wheat heading and maturity phases, revealing significant associations between genetic markers and key developmental stages in wheat. The identification of candidate genes and marker loci provides valuable information for further studies on wheat breeding and genetic improvement targeted at enhancing heading and maturity traits.

Oil candidate genes in seeds of cotton (Gossypium hirsutum L.) and functional validation of GhPXN1.

BACKGROUND: Cottonseed oil is a promising edible plant oil with abundant unsaturated fatty acids. However, few studies have been conducted to explore the characteristics of cottonseed oil. The molecular mechanism of cottonseed oil accumulation remains unclear. RESULTS: In the present study, we conducted comparative transcriptome and weighted gene co-expression network (WGCNA) analysis for two G. hirsutum materials with significant difference in cottonseed oil content. Results showed that, between the high oil genotype 6053 (H6053) and the low oil genotype 2052 (L2052), a total of 412, 507, 1,121, 1,953, and 2,019 differentially expressed genes (DEGs) were detected at 10, 15, 20, 25, and 30 DPA, respectively. Remarkably, a large number of the down-regulated DEGs were enriched in the phenylalanine metabolic processes. Investigation into the dynamic changes of expression profiling of genes associated with both phenylalanine metabolism and oil biosynthesis has shed light on a significant competitive relationship in substrate allocation during cottonseed development. Additionally, the WGCNA analysis of all DEGs identified eight distinct modules, one of which includes GhPXN1, a gene closely associated with oil accumulation. Through phylogenetic analysis, we hypothesized that GhPXN1 in G. hirsutum might have been introgressed from G. arboreum. Overexpression of the GhPXN1 gene in tobacco leaf suggested a significant reduction in oil content compared to the empty-vector transformants. Furthermore, ten other crucial oil candidate genes identified in this study were also validated using quantitative real-time PCR (qRT-PCR). CONCLUSIONS: Overall, this study enhances our comprehension of the molecular mechanisms underlying cottonseed oil accumulation.

Genome-wide characterization of trichome birefringence-like genes provides insights into fiber yield improvement.

Cotton is an important fiber crop. The cotton fiber is an extremely long trichome that develops from the epidermis of an ovule. The trichome is a general and multi-function plant organ, and trichome birefringence-like (TBL) genes are related to trichome development. At the genome-wide scale, we identified TBLs in four cotton species, comprising two cultivated tetraploids (Gossypium hirsutum and G. barbadense) and two ancestral diploids (G. arboreum and G. raimondii). Phylogenetic analysis showed that the TBL genes clustered into six groups. We focused on GH_D02G1759 in group IV because it was located in a lint percentage-related quantitative trait locus. In addition, we used transcriptome profiling to characterize the role of TBLs in group IV in fiber development. The overexpression of GH_D02G1759 in Arabidopsis thaliana resulted in more trichomes on the stems, thereby confirming its function in fiber development. Moreover, the potential interaction network was constructed based on the co-expression network, and it was found that GH_D02G1759 may interact with several genes to regulate fiber development. These findings expand our knowledge of TBL family members and provide new insights for cotton molecular breeding.

Overexpression of cotton Trihelix transcription factor GhGT-3b_A04 enhances resistance to Verticillium dahliae and affects plant growth in Arabidopsis thaliana.

Verticillium wilt is a soil-borne fungal disease that severely affects cotton fiber yield and quality. Herein, a cotton Trihelix family gene, GhGT-3b_A04, was strongly induced by the fungal pathogen Verticillium dahliae. Overexpression of the gene in Arabidopsis thaliana enhanced the plant's resistance to Verticillium wilt but inhibited the growth of rosette leaves. In addition, the primary root length, root hair number, and root hair length increased in GhGT-3b_A04-overexpressing plants. The density and length of trichomes on the rosette leaves also increased. GhGT-3b_A04 localized to the nucleus, and transcriptome analysis revealed that it induced gene expression for salicylic acid synthesis and signal transduction and activated gene expression for disease resistance. The gene expression for auxin signal transduction and trichome development was reduced in GhGT-3b_A04-overexpressing plants. Our results highlight important regulatory genes for Verticillium wilt resistance and cotton fiber quality improvement. The identification of GhGT-3b_A04 and other important regulatory genes can provide crucial reference information for future research on transgenic cotton breeding.

Analysis of the MIR396 gene family and the role of MIR396b in regulating fiber length in cotton.

Cotton fiber is one of the most important natural raw materials in the world textile industry. Improving fiber yield and quality has always been the main goal. MicroRNAs, as typical small noncoding RNAs, could affect fiber length during different stages of fiber development. Based on differentially expressed microRNA in the two interspecific backcross inbred lines (BILs) with a significant difference in fiber length, we identified the miR396 gene family in the two tetraploid cotton genomes and found MIR396b_D13 as the functional precursor to produce mature miR396 during the fiber elongation stage. Among 46 target genes regulated by miR396b, the GROWTH-REGULATING FACTOR 5 gene (GRF5, Gh_A10G0492) had a differential expression level in the two BILs during fiber elongation stage. The expression patterns indicated that the miR396b-GRF5 regulatory module has a critical role in fiber development. Furthermore, virus-induced gene silencing (VIGS) of miR396b significantly produced longer fiber than the wild type, and the expression level of GRF5 showed the reverse trends of the miR396b expression level. The analysis of co-expression network for the GRF5 gene suggested that a cytochrome P450 gene functions as an allene oxide synthase (Gh_D06G0089, AOS), which plays a critical role in jasmonate biosynthetic pathway. In conclusion, our results revealed that the miR396b-GRF5 module has a critical role in fiber development. These findings provide a molecular foundation for fiber quality improvement in the future.

Expressed genes and their new alleles identification during fibre elongation reveal the genetic factors underlying improvements of fibre length in cotton.

Interspecific breeding in cotton takes advantage of genetic recombination among desirable genes from different parental lines. However, the expression new alleles (ENAs) from crossovers within genic regions and their significance in fibre length (FL) improvement are currently not understood. Here, we generated resequencing genomes of 191 interspecific backcross inbred lines derived from CRI36 (Gossypium hirsutum) × Hai7124 (Gossypium barbadense) and 277 dynamic fibre transcriptomes to identify the ENAs and extremely expressed genes (eGenes) potentially influencing FL, and uncovered the dynamic regulatory network of fibre elongation. Of 35 420 eGenes in developing fibres, 10 366 ENAs were identified and preferentially distributed in chromosomes subtelomeric regions. In total, 1056-1255 ENAs showed transgressive expression in fibres at 5-15 dpa (days post-anthesis) of some BILs, 520 of which were located in FL-quantitative trait locus (QTLs) and GhFLA9 (recombination allele) was identified with a larger effect for FL than GhFLA9 of CRI36 allele. Using ENAs as a type of markers, we identified three novel FL-QTLs. Additionally, 456 extremely eGenes were identified that were preferentially distributed in recombination hotspots. Importantly, 34 of them were significantly associated with FL. Gene expression quantitative trait locus analysis identified 1286, 1089 and 1059 eGenes that were colocalized with the FL trait at 5, 10 and 15 dpa, respectively. Finally, we verified the Ghir_D10G011050 gene linked to fibre elongation by the CRISPR-cas9 system. This study provides the first glimpse into the occurrence, distribution and expression of the developing fibres genes (especially ENAs) in an introgression population, and their possible biological significance in FL.

Transcriptome analysis and identification of genes associated with oil accumulation in upland cotton.

Cotton is not only the most important fiber crop but also the fifth most important oilseed crop in the world because of its oil-rich seeds as a byproduct of fiber production. By comparative transcriptome analysis between two germplasms with diverse oil accumulation, we reveal pieces of the gene expression network involved in the process of oil synthesis in cottonseeds. Approximately, 197.16 Gb of raw data from 30 RNA sequencing samples with 3 biological replicates were generated. Comparison of the high-oil and low-oil transcriptomes enabled the identification of 7682 differentially expressed genes (DEGs). Based on gene expression profiles relevant to triacylglycerol (TAG) biosynthesis, we proposed that the Kennedy pathway (diacylglycerol acyltransferase-catalyzed diacylglycerol to TAG) is the main pathway for oil production, rather than the phospholipid diacylglycerol acyltransferase-mediated pathway. Using weighted gene co-expression network analysis, 5312 DEGs were obtained and classified into 14 co-expression modules, including the MEblack module containing 10 genes involved in lipid metabolism. Among the DEGs in the MEblack module, GhCYSD1 was identified as a potential key player in oil biosynthesis. The overexpression of GhCYSD1 in yeast resulted in increased oil content and altered fatty acid composition. This study may not only shed more light on the underlying molecular mechanism of oil accumulation in cottonseed oil, but also provide a set of new gene for potential enhancement of oil content in cottonseeds.

Quantitative Trait Locus Analysis and Identification of Candidate Genes Affecting Seed Size and Shape in an Interspecific Backcross Inbred Line Population of Gossypium hirsutum × Gossypium barbadense.

Seed size and shape are key agronomic traits affecting seedcotton yield and seed quality in cotton (Gossypium spp.). However, the genetic mechanisms that regulate the seed physical traits in cotton are largely unknown. In this study, an interspecific backcross inbred line (BIL) population of 250 BC1F7 lines, derived from the recurrent parent Upland CRI36 (Gossypium hirsutum) and Hai7124 (Gossypium barbadense), was used to investigate the genetic basis of cotton seed physical traits via quantitative trait locus (QTL) mapping and candidate gene identification. The BILs were tested in five environments, measuring eight seed size and shape-related traits, including 100-kernel weight, kernel length width and their ratio, kernel area, kernel girth, kernel diameter, and kernel roundness. Based on 7,709 single nucleotide polymorphic (SNP) markers, a total of 49 QTLs were detected and each explained 2.91-35.01% of the phenotypic variation, including nine stable QTLs mapped in at least three environments. Based on pathway enrichment, gene annotation, genome sequence, and expression analysis, five genes encoding starch synthase 4, transcription factor PIF7 and MYC4, ubiquitin-conjugating enzyme E27, and THO complex subunit 4A were identified as candidate genes that might be associated with seed size and shape. Our research provides valuable information to improve seed physical traits in cotton breeding.

Genome-Wide Analysis of the GW2-Like Genes in Gossypium and Functional Characterization of the Seed Size Effect of GhGW2-2D.

Cotton is one of the most economically important crops worldwide. Seed size is a vital trait for plants connected with yield and germination. GW2 encodes a RING_Ubox E3 ubiquitin ligase that controls seed development by affecting cell growth. Here, are few reports on GW2-like genes in cotton, and the function of GW2 in cotton is poorly understood. In the present study, a genome-wide analysis identified 6 and 3 GW2-like genes in each of the two cultivated tetraploids (Gossypium hirsutum and G. barbadense) and each of their diploid ancestral species (G. arboreum, G. raimondii), respectively. GhGW2-2D has the same functional domain and high sequence similarity with AtDA2 in Arabidopsis. Overexpression of GhGW2-2D in Arabidopsis significantly reduced seed and seedling size, suggesting GhGW2-2D is a potential target for regulating cotton seed size. These results provided information on the genetic and molecular basis of GW2-like genes in cotton, thus establishing a foundation for functional studies of cotton seeds.

Quantitative Trait Locus Analysis and Identification of Candidate Genes for Micronaire in an Interspecific Backcross Inbred Line Population of Gossypium hirsutum × Gossypium barbadense.

Cotton is the most important fiber crop and provides indispensable natural fibers for the textile industry. Micronaire (MIC) is determined by fiber fineness and maturity and is an important component of fiber quality. Gossypium barbadense L. possesses long, strong and fine fibers, while upland cotton (Gossypium hirsutum L.) is high yielding with high MIC and widely cultivated worldwide. To identify quantitative trait loci (QTLs) and candidate genes for MIC in G. barbadense, a population of 250 backcross inbred lines (BILs), developed from an interspecific cross of upland cotton CRI36 × Egyptian cotton (G. barbadense) Hai7124, was evaluated in 9 replicated field tests. Based on a high-density genetic map with 7709 genotyping-by-sequencing (GBS)-based single-nucleotide polymorphism (SNP) markers, 25 MIC QTLs were identified, including 12 previously described QTLs and 13 new QTLs. Importantly, two stable MIC QTLs (qMIC-D03-2 on D03 and qMIC-D08-1 on D08) were identified. Of a total of 338 genes identified within the two QTL regions, eight candidate genes with differential expression between TM-1 and Hai7124 were identified. Our research provides valuable information for improving MIC in cotton breeding.

Genome-Wide Analysis of DA1-Like Genes in Gossypium and Functional Characterization of GhDA1-1A Controlling Seed Size.

Cotton (Gossypium spp.) is an economically important crop grown for natural fiber and seed oil production. DA1 is a ubiquitin receptor that determines final seed and organ size by restricting the period of cell proliferation. In the present study, we identified 7 DA1-like genes each in cultivated tetraploid (AADD) G. hirsutum and G. barbadense, and 4 and 3 DA1-like genes in their ancestral diploid G. arboreum (A2A2) and G. raimondii (D5D5), respectively. The 7 GhDA1 genes were confirmed to be distributed on four At and three Dt subgenome chromosomes in G. hirsutum. GhDA1-1A showed a high sequence similarity to AtDA1 in Arabidopsis, and they possessed the same functional domains, suggesting conserved functions. The overexpression of GhDA1-1A R301K in Arabidopsis significantly increased seed size and seed weight, indicating that GhDA1-1A is a promising target for cotton improvement. This study provides information on the molecular evolutionary properties of DA1-like genes in cotton, which will be useful for the genetic improvement of cotton.

Improving brain tumor segmentation on MRI based on the deep U-net and residual units.

BACKGROUNDAccurate segmentation of brain tumor depicting on magnetic resonance imaging (MRI) is an important step for doctors to determine optimal treatment plan of Gliomas, which are the common malignant brain tumors that seriously damage patients' health and life.OBJECTThis study aims to improve accuracy and efficiency of brain tumor segmentation on MRI using the advanced deep learning model.METHODIn this study, an improved model based on the U-net for accurate segmentation of brain tumor MRI images, called Deeper ResU-net, is proposed. First, a deep Deeper U-net is built, which has deeper network depth compared with U-net, uses Squeeze Operator to control network parameters and attempts to enhance the feature extraction ability. Then, Deeper ResU-net is formed to eliminate degradation phenomenon of the deep network, in which residual unit is designed and integrated into the Deeper U-net to keep the number of parameters unchanged.RESULTDeeper ResU-net makes the deep network conduct stable training without degrading. Evaluation result shows that the Deeper ResU-net has achieved competitive result with average DSC metrics of 0.9, 0.82, 0.88 for Complete tumor region, Core tumor region and Enhanced tumor region, respectively.CONCLUSIONBy extending the U-net model to a deeper layer and adding the residual structure to ensure effective and stable training of the model, the experiment results demonstrate that applying the improved Deeper ResU-net can effectively eliminate the degradation phenomenon of deep network and improve segmentation performance.

Genome-wide association study of the oil content in upland cotton (Gossypium hirsutum L.) and identification of GhPRXR1, a candidate gene for a stable QTLqOC-Dt5-1.

Cottonseed oil is one of the most important renewable resources for edible oil and biodiesel. To detect QTLs associated with cottonseed oil content (OC) and identify candidate genes that regulate oil biosynthesis, a panel of upland cotton germplasm lines was selected among those previously used to perform GWASs in China. In the present study, 13 QTLs associated with 53 common SNPs on 13 chromosomes were identified in multiple environments based on 15,369 polymorphic SNPs using the Cotton63 KSNP array. Of these, the OC QTL qOC-Dt5-1 delineated by nine SNPs occurred in a confidence interval of 4 SSRs with previously reported OC QTLs. A combined transcriptome and qRT-PCR analysis revealed that a peroxidase gene (GhPRXR1) was predominantly expressed during the middle-late stage (20-35 days post anthesis) of ovule development. The overexpression of GhPRXR1 in yeast significantly increased the OC by 20.01-37.25 %. Suppression of GhPRXR1 gene expression in the virus-induced gene-silenced cotton reduced the OC by 18.11%. Our results contribute to identifying more OC QTLs and verifying a candidate gene that influences cottonseed oil biosynthesis.

QTL analysis and candidate gene identification for plant height in cotton based on an interspecific backcross inbred line population of Gossypium hirsutum × Gossypium barbadense.

KEY MESSAGE: We constructed the first high-quality and high-density genetic linkage map for an interspecific BIL population in cotton by specific-locus amplified fragment sequencing for QTL mapping. A novel gene GhPIN3 for plant height was identified in cotton. Ideal plant height (PH) is important for improving lint yield and mechanized harvesting in cotton. Most published genetic studies on cotton have focused on fibre yield and quality traits rather than PH. To facilitate the understanding of the genetic basis in PH, an interspecific backcross inbred line (BIL) population of 250 lines derived from upland cotton (Gossypium hirsutum L.) CRI36 and Egyptian cotton (G. barbadense L.) Hai7124 was used to construct a high-density genetic linkage map for quantitative trait locus (QTL) mapping. The high-density genetic map harboured 7,709 genotyping-by-sequencing (GBS)-based single nucleotide polymorphism (SNP) markers that covered 3,433.24 cM with a mean marker interval of 0.67 cM. In total, ten PH QTLs were identified and each explained 4.27-14.92% of the phenotypic variation, four of which were stable as they were mapped in at least two tests or based on best linear unbiased prediction in seven field tests. Based on functional annotation of orthologues in Arabidopsis and transcriptome data for the genes within the stable QTL regions, GhPIN3 encoding for the hormone auxin efflux carrier protein was identified as a candidate gene located in the stable QTL qPH-Dt1-1 region. A qRT-PCR analysis showed that the expression level of GhPIN3 in apical tissues was significantly higher in four short-statured cotton genotypes than that in four tall-statured cotton genotypes. Virus-induced gene silencing cotton has significantly increased PH when the expression of the GhPIN3 gene was suppressed.