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Plant height is a critical agronomic that affects both plant architecture and yield. To decipher the genetic mechanisms underlying winter wheat plant height and identify candidate genes associated with this trait, we conducted phenotypic analysis on 239 wheat varieties (lines) collected from around the world. This analysis was complemented by genotyping using the wheat 55K SNP chip. A Wholegenome association analysis (GWAS) of wheat plant height was conducted utilizing the MLM (Q+K) model within TASSLE software. The results revealed significant phenotypic variation in wheat plant height across different years, with coefficients of variation ranging from 0.96% to 1.97%. Additionally, there was a strong correlation in plant height measurements between different years. GWAS identified 44 SNP markers significantly associated with wheat plant height across various environments (P ≤ 0.00001), predominantly distributed on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 6B, 6D, and 7D, explaining individual phenotypic variance rates ranging from 5.00% to 11.11%. Further, by mining association loci with substantial phenotypic effects and stability across multiple environments, seven candidate genes related to wheat plant height have been identified. This study provides new genetic markers and resources for improving wheat plant height.

Cotton (Gossypium spp.) not only serves as a primary textile fiber crop but also as a vital oilseed crop. It stands as the world’s fifth-largest oil crop and is rich in essential fatty acids. At present, the mechanisms underlying the biosynthesis of cottonseed oil have been extensively studied in cotton. 3-Hydroxyacyl-CoA dehydratase (HACD) is the third rate-limiting enzyme in the elongase complex, which plays a critical role in the biosynthesis of Very Long Chain Fatty Acids (VLCFA). However, the members of the HACD family and their roles in cottonseed oil remain uncharacterized in cotton. This study identified that G. arboreum and G. raimondii have two HACD genes, while four HACD genes exists in G. hirsutum, and G. barbadense. The phylogenetic relationships of the 12 HACD genes from the four cotton species further divided them into two subfamilies. Gene structure and conserved motif analysis revealed that members of the HACD family were relatively conserved during the evolution of cotton, but members within the same subfamily exhibited more similar structures. Homology and collinearity analysis suggest whole-genome duplication/segmental duplication may be a key factor in the amplification of the cotton HACD gene family. The qRT-PCR analysis of high-oil and low-oil genotype found significant differences in the expression levels of GhHACD1-4, which indicates GhHACD1-4 is expected to participate in the lipid oil biosynthesis process. Subcellular localization experiments confirmed the presence of the GhHACD2 inendoplasmic reticulum. The KEGG pathway enrichment analysis of co-expressed genes of GhHACD1 and GhHACD2 genes were conducted to confirm their potential involvement in fatty acid elongation and oil biosynthesis. Furthermore, transgenic overexpression analysis of GhHACD2 caused a 5.02% decrease in oil content compared with the control in yeast, while the levels of C28:0, C30:0, and VLCFAs were significantly improved. This study characterizes HACD gene family members in cotton and provides rich genetic resources for increasing cottonseed oil content and improving the nutritional value of cottonseed oil.

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.

Abstract 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.

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.

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.

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.

Abstract Background Micronaire is a comprehensive index reflecting the fineness and maturity of cotton fiber. Micronaire is one of the important internal quality indicators of the cotton fiber and is closely related to the value of the cotton fiber. Understanding the genetic basis of micronaire is required for the genetic improvement of the trait. However, the genetic architecture of micronaire at the genomic level is unclear. The present genome-wide association study (GWAS) aimed to identify the genetic mechanism of the micronaire trait in 83 representative upland cotton lines grown in multiple environments. Results GWAS of micronaire used 83 upland cotton accessions assayed by a Cotton 63 K Illumina Infinium single nucleotide polymorphism (SNP) array. A total of 11 quantitative trait loci (QTLs) for micronaire were detected on 10 chromosomes. These 11 QTLs included 27 identified genes with specific expression patterns. A novel QTL, qFM-A12–1, included 12 significant SNPs, and GhFLA9 was identified as a candidate gene based on haplotype block analysis and on strong and direct linkage disequilibrium between the significantly related SNPs and gene. GhFLA9 was expressed at a high level during secondary wall thickening at 20∼25 days post-anthesis. The expression level of GhFLA9 was significantly higher in the low micronaire line (Msco-12) than that in the high micronaire line (Chuangyou-9). Conclusions This study provides a genetic reference for genetic improvement of cotton fiber micronaire and a foundation for verification of the functions of GhFLA9.

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.

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.

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.

Cotton fiber is the most important natural raw material for the textile industry, and fiber length (FL) is one of the most important traits in cotton. Quantitative trait locus (QTL) mapping based on high-density genetic maps is an efficient approach to identify genetic regions for FL. In our study, two backcrossed inbred lines (BILs) were chosen as parents to construct a high-density genetic map in F2 which was used to fine map FL QTL in F2:3 population. The genetic map had a total size of 3462.8 cM, containing 9182 single-nucleotide polymorphisms (SNPs) based on genotyping-by-sequencing. Two FL related stable QTL were identified on two chromosomes (qFL-A08–1 on A08 and qFL-D03–1 on D03), and qFL-A08–1 was confirmed by a meta-analysis. Utilizing previously obtained RNA-seq data for the two BILs and qRT-PCR analysis, two candidate genes annotated as cytochrome b5 (CB5, Gh_A08G1729) and microtubule end-binding 1C (EB1C, Gh_D03G0232) that may regulate FL during the fiber elongation stage were identified. In addition, nine recombination hotspots in this population were found. The results of this study will provide an important foundation for further studies on the molecular and genetic regulation of fiber elongation. Keywords: Gossypium, Single-nucleotide polymorphism, Fiber length, Quantitative trait locus

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 GhDA1-1AR301K 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.