1 Introduction

Cotton (Gossypium spp.) is a crucial crop for the global textile industry, providing natural fibers that are essential for fabric production. The genus Gossypium includes over 50 species, but only four are cultivated for commercial purposes, with Gossypium hirsutum (Upland cotton) and Gossypium barbadense (Pima cotton) being the most significant. The complexity of the cotton genome, particularly in polyploid species like G. hirsutum, presents unique challenges and opportunities for genomic research. The tetraploid nature of G. hirsutum, resulting from the hybridization of two diploid progenitors, complicates the genetic landscape, making it a model system for studying polyploidization and genomic organization (Yu et al., 2010).

Advances in genomic technologies have led to the sequencing of the cotton genome, providing a comprehensive understanding of its structure and function. These genomic resources include whole-genome sequences, gene expression data, single nucleotide polymorphism (SNP) genotyping, and quantitative trait loci (QTL) mapping. Databases like CottonGen facilitate access to these data, allowing researchers to integrate and analyze genetic and genomic information effectively (Yu et al., 2013).

Cytogenetic data play a crucial role in understanding the chromosomal organization and behavior of cotton. Cytogenetics involves the study of chromosome structure, number, and function, providing insights into genome stability, evolution, and the mechanisms underlying genetic diversity. Techniques such as fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH) are employed to visualize chromosome structures and identify specific genetic loci. These methods have been instrumental in characterizing the structural variations between homoeologous chromosomes in allotetraploid cotton, revealing significant differences in genome organization and size (Wang et al., 2010).

Cytogenetic studies complement genomic data by providing a physical framework for mapping and validating genetic markers. They help in identifying chromosomal rearrangements, translocations, and other structural changes that may affect gene function and expression. The integration of cytogenetic and genomic data enhances our understanding of the relationship between genotype and phenotype, facilitating the identification of candidate genes for important traits such as fiber quality, disease resistance, and stress tolerance (Xu et al., 2008).

The aim of this study is to integrate genomic and cytogenetic data to create a comprehensive understanding of cotton genetics, bridging the gap between sequence-based information and chromosomal architecture. This integrative approach seeks to improve the accuracy of genome assembly and annotation by incorporating chromosomal context, facilitating the identification and mapping of structural variations and chromosomal rearrangements. Additionally, it enhances our understanding of the relationship between genetic variation and phenotypic expression, supporting the development of molecular markers for breeding programs and genetic improvement initiatives. By combining these datasets, researchers can gain insights into the evolutionary dynamics of cotton genomes, ultimately leading to innovative strategies for crop improvement.

2 Genomic Data in Cotton Research

2.1 Advances in cotton genome sequencing

The sequencing of cotton genomes has been a significant milestone in cotton research, providing comprehensive insights into the genetic makeup of different cotton species. Early efforts in sequencing focused on model diploid species, such as Gossypium raimondii and Gossypium arboreum, which represent the progenitors of modern tetraploid cottons (Li et al., 2014). These initial sequences paved the way for more complex sequencing projects involving allotetraploid species like Gossypium hirsutum (Upland cotton) and Gossypium barbadense (Pima cotton).

Recent advancements have been driven by next-generation sequencing (NGS) technologies, which offer high throughput and lower costs, enabling the sequencing of large and complex genomes. The Cotton Genome Consortium has made significant strides, sequencing and assembling the genomes of G. hirsutum and G. barbadense with high resolution. These assemblies have facilitated the identification of key genes and regulatory elements involved in fiber quality, disease resistance, and stress tolerance (Wang et al., 2018).

2.2 High-density genetic maps

High-density genetic maps are essential tools in cotton research, providing a framework for locating genes associated with important traits. These maps are constructed using various molecular markers, including simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), and amplified fragment length polymorphisms (AFLPs). The development of high-density maps has been accelerated by the advent of sequencing technologies, which enable the identification of thousands of SNPs across the genome (Zhang et al., 2016).

Specific Locus Amplified Fragment Sequencing (SLAF-seq) is one such technology that has been used to construct a high-density genetic map of upland cotton. This map included over 5,500 SNP markers, providing a detailed and comprehensive view of the cotton genome. High-density genetic maps are invaluable for quantitative trait locus (QTL) mapping, enabling researchers to identify genomic regions associated with traits like boll weight, fiber quality, and disease resistance (Zhang et al., 2016).

2.3 Genome-wide association studies (GWAS)

Genome-Wide Association Studies (GWAS) have become a powerful approach for identifying genetic variations linked to complex traits in cotton. GWAS leverages high-throughput genotyping and phenotyping data to associate genetic markers with phenotypic traits across a diverse population. This method has been used to uncover genetic loci associated with fiber quality, yield, and stress tolerance in cotton.

One significant GWAS study involved sequencing a multi-parent advanced generation intercross (MAGIC) population of G. hirsutum. This study identified several genomic loci associated with fiber quality traits, providing candidate genes for further functional validation and breeding efforts (Thyssen et al., 2018). By integrating GWAS with genomic data, researchers can better understand the genetic architecture of complex traits and enhance the efficiency of marker-assisted selection (MAS).

2.4 Functional genomics and transcriptomics

Functional genomics aims to elucidate the roles of genes and their interactions within the genome, while transcriptomics focuses on the study of RNA transcripts to understand gene expression patterns. In cotton, functional genomics and transcriptomics have provided insights into the molecular mechanisms underlying fiber development, stress responses, and other critical traits.

High-throughput sequencing technologies, such as RNA-Seq, have been instrumental in transcriptomic studies, enabling the comprehensive profiling of gene expression in different tissues and developmental stages. For instance, RNA-Seq analysis of developing cotton fibers has revealed key genes involved in cellulose biosynthesis, cell elongation, and secondary cell wall formation (Wang et al., 2010).

Moreover, the integration of transcriptomic data with genomic information allows researchers to identify regulatory networks and pathways that control important agronomic traits. This integrative approach enhances our understanding of the functional elements within the cotton genome and provides targets for genetic manipulation and improvement (Huang et al., 2021).

3 Cytogenetic Data in Cotton Research

3.1 Chromosome mapping techniques

Chromosome mapping is a critical component of cytogenetic research, providing a detailed physical map of chromosomes, which helps in identifying the locations of specific genes and genetic markers. Traditional chromosome mapping techniques involve the study of chromosome structure and behavior during cell division, using methods such as karyotyping and fluorescence in situ hybridization (FISH). High-resolution cytogenetic maps have been developed for cotton, particularly for allotetraploid species like Gossypium hirsutum, revealing significant structural variations between homoeologous chromosomes (Wang et al., 2010).

3.2 Fluorescence in situ hybridization (FISH)

Fluorescence in situ hybridization (FISH) is a powerful technique used to visualize specific DNA sequences on chromosomes. In cotton research, FISH has been instrumental in identifying and mapping various genetic elements, including repetitive sequences, genes, and transposable elements. For example, FISH has been used to map the distribution of 45S rDNA and other repetitive sequences in the cotton genome, providing insights into chromosome organization and the evolutionary dynamics of the genome (Liu et al., 2020).

The use of bulked oligonucleotide probes in FISH has further enhanced the resolution and specificity of chromosome painting in cotton, allowing for the precise identification of chromosomal regions and structural variations. These advancements facilitate the study of complex genome structures and the detection of chromosomal abnormalities that may impact cotton breeding and genetics.

3.3 Karyotyping and chromosome banding

Karyotyping involves the examination of chromosome number, size, shape, and structure, typically during metaphase of cell division. This technique is fundamental in identifying chromosomal abnormalities such as aneuploidy, polyploidy, and structural variations. Chromosome banding techniques, such as G-banding and C-banding, enhance the visualization of chromosomal regions, enabling the differentiation of chromosomes based on their banding patterns.

In cotton, karyotyping and chromosome banding have been used to study the chromosomal basis of genetic diversity and to identify specific chromosomal rearrangements associated with important agronomic traits. These techniques have revealed significant meiotic abnormalities, such as lagging chromosomes and stickiness, which can impact fertility and crop yield (Sheidai and Dezfolian, 2008).

3.4 Polyploidy in cotton

Polyploidy, the presence of more than two sets of chromosomes, is a common phenomenon in plant evolution and has played a crucial role in the diversification of the cotton genus. Allotetraploid species like Gossypium hirsutum and Gossypium barbadense have genomes derived from the hybridization of two diploid progenitors, leading to a complex genomic architecture.

Cytogenetic studies have provided valuable insights into the mechanisms of polyploidization and genome evolution in cotton. Research has shown that polyploidy in cotton is associated with significant structural and size variations between homoeologous chromosomes. These variations are primarily due to differential expansion or contraction of genomic regions, which influence gene expression and trait development (Wang et al., 2012).

Polyploid cotton species serve as excellent models for studying the effects of genome duplication and the evolutionary dynamics of polyploid genomes. Understanding these processes is essential for optimizing cotton breeding programs and developing new varieties with improved traits such as fiber quality, disease resistance, and environmental adaptability.

4 Integration of Genomic and Cytogenetic Data

4.1 Bridging the gap between genomic and cytogenetic maps

Integrating genomic and cytogenetic maps is essential for providing a comprehensive understanding of the cotton genome. This integration involves aligning genetic markers from genomic maps with their physical locations on chromosomes as determined by cytogenetic techniques. One successful example is the construction of a tetraploid cotton genome-wide comprehensive reference map, which incorporated data from 28 public genetic maps, enhancing the density and coverage of markers and facilitating the identification and positioning of QTLs and candidate genes (Yu et al., 2010).

4.2 Use of comparative genomics

Comparative genomics involves comparing the genome sequences of different species to identify conserved and divergent elements. This approach can be particularly powerful when combined with cytogenetic data, as it allows researchers to correlate chromosomal structures with genomic features across species. In cotton, comparative genomics has been used to study the evolutionary dynamics of polyploidization and genome organization. By comparing the genomes of diploid progenitors and their polyploid descendants, researchers have gained insights into the mechanisms of genome duplication, divergence, and stability (Liu et al., 2020).

4.3 Integrative mapping approaches

Integrative mapping approaches combine genetic, physical, and cytogenetic maps to create a detailed and coherent representation of the genome. These approaches leverage various types of data, including SNP markers, BAC libraries, and FISH-based localization, to anchor genetic loci to specific chromosomal regions. An example of such an approach in cotton research is the integration of genetic and physical maps of homoeologous chromosomes 12 and 26 in Gossypium hirsutum. This integration provided a platform for positional cloning and the targeted sequencing of important genomic regions (Xu et al., 2008).

4.4 Case studies of integrated data in cotton research

The first case study involves the development and utilization of CottonMD, a comprehensive multi-omics database designed to facilitate cotton research. CottonMD integrates a vast array of datasets, including 25 genomes, transcriptomes from 76 tissue samples, epigenome data from five species, and metabolome data from 768 metabolites across four tissues. Additionally, it includes genetic variation, trait, and transcriptome datasets from 4180 cotton accessions. This extensive integration allows researchers to identify associations between genetic variations and phenotypes efficiently.

The database employs multiple statistical methods to analyze these associations and provides user-friendly tools for multi-omics data analysis. The power of CottonMD is demonstrated through two case studies that highlight its potential in identifying and analyzing candidate genes, thereby advancing cotton genetic breeding and functional genomics research.

The second case study focuses on the integration of high-throughput RNA sequencing techniques to map the transcriptional landscape of cotton. Researchers employed four complementary techniques: Pacbio long-read Iso-seq, strand-specific RNA-seq, CAGE-seq, and PolyA-seq, to explore gene expression across 16 tissues in Gossypium arboreum. This multi-strategic approach enabled the reconstruction of accurate gene structures and revealed a dynamic and diverse transcriptional map (Figure 1) (Wang et al., 2019).

The study identified tissue-specific gene expression, alternative usage of transcription start sites (TSSs) and polyadenylation sites, hotspots of alternative splicing, and transcriptional read-through events. These findings provide valuable resources for further functional genomic studies and enhance the understanding of natural SNP variations in cotton.

The third case study illustrates the application of chromosome painting using bulked oligonucleotides in cotton. Chromosome painting is a cytogenetic technique that accurately identifies chromosomes or chromosome regions. In this study, researchers developed bulked oligos for the two arms of chromosome seven in Gossypium raimondii, each containing 12 544 oligos. These chromosome-specific painting probes were successfully used to identify chromosome seven in both D genome and AD genome cotton species.

Additionally, the probes were employed to correct the chromosomal localization of 45S ribosomal DNA (rDNA) in G. raimondii. This study demonstrates the utility of bulked oligos for chromosome painting, providing a valuable tool for cytogenetic research in cotton (Liu et al., 2020) (Figure 2).

The integration of genomic and cytogenetic data in cotton research has opened new avenues for understanding the genetic basis of important traits and improving cotton breeding programs. The case studies presented here highlight the potential of multi-omics databases, high-resolution transcriptional mapping, and chromosome painting techniques in advancing cotton research. By leveraging these integrated approaches, researchers can gain deeper insights into the complex genetic and phenotypic landscape of cotton, ultimately contributing to the development of superior cotton varieties.

5 Applications of Integrated Data

5.1 Enhancing cotton breeding programs

Integrating genomic and cytogenetic data significantly enhances cotton breeding programs by providing a comprehensive understanding of the genetic architecture of key traits. By combining genetic markers with cytogenetic maps, breeders can more accurately identify and select for desirable traits, such as fiber quality, yield, and disease resistance. High-density genetic maps, combined with cytogenetic techniques like fluorescence in situ hybridization (FISH), enable precise localization of genes and quantitative trait loci (QTLs) on chromosomes, thereby improving the efficiency of breeding programs (Wang et al., 2010).

Marker-assisted selection (MAS) and genomic selection (GS) are key strategies enhanced by integrated data. MAS uses markers linked to desirable traits to select plants in early developmental stages, reducing the breeding cycle. GS predicts the performance of breeding candidates based on their genomic profiles, allowing for the selection of superior genotypes before phenotypic traits are expressed.

5.2 Understanding genetic diversity and evolution

The integration of genomic and cytogenetic data provides insights into the genetic diversity and evolutionary history of cotton. Comparative genomics and cytogenetics reveal the structural variations and chromosomal rearrangements that have occurred during the evolution of cotton species. Studies on polyploidization events in cotton, for example, have uncovered significant genomic changes that have contributed to the adaptation and diversification of tetraploid cottons (Wang et al., 2012).

Cytogenetic techniques such as FISH and karyotyping, combined with genomic sequencing, help identify conserved and divergent regions within and between cotton species. This information is crucial for understanding the mechanisms of speciation and the genetic basis of important agronomic traits. Integrative approaches also facilitate the study of gene flow and introgression between wild and cultivated cotton species, which is essential for maintaining genetic diversity and improving crop resilience (Liu et al., 2020).

5.3 Identifying and utilizing quantitative trait loci (QTL)

Quantitative Trait Loci (QTL) mapping is a critical application of integrated genomic and cytogenetic data in cotton research. QTL mapping involves identifying regions of the genome that are associated with specific quantitative traits, such as fiber length, boll size, and drought tolerance. By integrating genetic maps with cytogenetic data, researchers can accurately locate QTLs on chromosomes and identify candidate genes within these regions.

For instance, the integration of high-density SNP maps with cytogenetic maps has enabled the precise mapping of QTLs for fiber quality traits in Upland cotton. This approach has identified several key loci that contribute to fiber strength, length, and fineness, providing valuable targets for breeding programs aimed at improving these traits (Zhang et al., 2016).

5.4 Disease resistance and stress tolerance

The integration of genomic and cytogenetic data is pivotal in improving disease resistance and stress tolerance in cotton. By identifying and mapping genes associated with resistance to pests and diseases, such as the cotton bollworm and Verticillium wilt, researchers can develop varieties with enhanced resistance. Genomic data, combined with cytogenetic techniques, facilitate the discovery of resistance genes and the study of their chromosomal locations and inheritance patterns (Huang et al., 2021).

Additionally, integrated data approaches are crucial for understanding and improving stress tolerance in cotton. By mapping QTLs associated with drought, salinity, and temperature stress, and integrating this information with transcriptomic data, researchers can identify key regulatory genes and pathways involved in stress responses. This holistic understanding enables the development of cotton varieties that can withstand adverse environmental conditions, ensuring stable yield and productivity (Thyssen et al., 2018).

6 Challenges and Future Directions

6.1 Technical and methodological challenges

Integrating genomic and cytogenetic data in cotton research presents several technical and methodological challenges. One of the primary technical challenges is the complexity of the cotton genome, particularly in polyploid species like Gossypium hirsutum and Gossypium barbadense. The presence of multiple sets of homologous chromosomes complicates the assembly and annotation of the genome, making it difficult to accurately identify and map genes and genetic markers. Additionally, the large size and repetitive nature of the cotton genome pose significant hurdles for sequencing and data analysis (Yu et al., 2010).

Methodological challenges include the integration of data from different sources and platforms, such as combining high-resolution cytogenetic maps with next-generation sequencing data. Ensuring consistency and accuracy across various datasets is critical, as discrepancies can lead to erroneous conclusions. Moreover, developing robust and standardized protocols for cytogenetic techniques, such as FISH and karyotyping, is essential for producing reproducible and high-quality data (Wang et al., 2010).

6.2 Data management and bioinformatics

The vast amounts of data generated from integrative genomic and cytogenetic studies require efficient data management and bioinformatics tools. Managing, storing, and analyzing large-scale datasets pose significant challenges, particularly in terms of computational resources and infrastructure. Developing comprehensive databases and user-friendly interfaces that facilitate data access, integration, and analysis is crucial for advancing cotton research (Yu et al., 2013).

Bioinformatics tools and pipelines must be capable of handling diverse data types, including genomic sequences, genetic markers, cytogenetic images, and phenotypic data. Integrative analysis platforms that combine genomic, transcriptomic, and cytogenetic data can provide a holistic view of the cotton genome, facilitating the identification of key genetic elements and their functional roles. Machine learning and artificial intelligence (AI) approaches also hold promise for enhancing data analysis and integration, allowing for more accurate predictions and insights into complex biological processes (Liu et al., 2020).

6.3 Future prospects in integrative cotton research

The future of integrative cotton research lies in the continued development and application of advanced genomic and cytogenetic techniques. Emerging technologies such as single-cell sequencing, CRISPR-Cas9 genome editing, and long-read sequencing platforms will further enhance our ability to dissect the cotton genome at unprecedented resolution. These technologies will enable the precise modification of genetic elements, facilitating the development of improved cotton varieties with desirable traits such as enhanced fiber quality, disease resistance, and stress tolerance (Wang et al., 2018).

Integrative approaches that combine genomic, transcriptomic, proteomic, and metabolomic data will provide a comprehensive understanding of the molecular mechanisms underlying key traits. These multi-omics strategies will allow researchers to elucidate complex gene networks and regulatory pathways, identifying novel targets for genetic improvement. Furthermore, collaborative efforts and interdisciplinary research will be essential for addressing the challenges and harnessing the full potential of integrative cotton research (Huang et al., 2021).

6.4 Potential impact on cotton industry

The integration of genomic and cytogenetic data has the potential to revolutionize the cotton industry by accelerating the development of superior cotton varieties. Enhanced breeding programs that leverage integrative data will produce cotton plants with improved yield, fiber quality, and resilience to biotic and abiotic stresses. These advancements will contribute to the sustainability and profitability of cotton farming, addressing the challenges posed by climate change and increasing global demand for cotton (Zhang et al., 2016).

In addition, the adoption of precision agriculture practices, supported by integrative genomic and cytogenetic data, will optimize crop management and resource use. Precision breeding and targeted interventions based on genetic and cytogenetic insights will enable more efficient and sustainable cotton production. Ultimately, the integration of genomic and cytogenetic data will drive innovation and competitiveness in the cotton industry, ensuring its long-term viability and success (Yu et al., 2013).

7 Concluding Remarks

The integration of genomic and cytogenetic data in cotton research represents a transformative approach to understanding and improving this critical crop. Advances in sequencing technologies have allowed for the comprehensive analysis of cotton genomes, particularly the complex polyploid genomes of species like Gossypium hirsutum and Gossypium barbadense. High-density genetic maps and genome-wide association studies (GWAS) have facilitated the identification of key genetic loci associated with important traits such as fiber quality, yield, and disease resistance.

Cytogenetic techniques, including fluorescence in situ hybridization (FISH), karyotyping, and chromosome banding, have been instrumental in elucidating the structural organization of the cotton genome. These methods have revealed significant chromosomal variations and provided a physical framework for mapping genetic markers. Integrating these cytogenetic insights with genomic data enhances the precision of locating genes and quantitative trait loci (QTLs), which is critical for effective breeding and genetic studies.

Applications of integrated genomic and cytogenetic data have shown promise in enhancing cotton breeding programs, understanding genetic diversity and evolution, identifying and utilizing QTLs, and improving disease resistance and stress tolerance. These integrated approaches provide a holistic understanding of the molecular mechanisms underlying key traits, facilitating the development of superior cotton varieties. However, the integration process faces several technical and methodological challenges, such as the complexity of the cotton genome and the need for efficient data management and bioinformatics tools. Addressing these challenges through emerging technologies like single-cell sequencing and CRISPR-Cas9 genome editing will be crucial for advancing integrative cotton research.

The integration of genomic and cytogenetic data holds immense promise for the future of cotton research and breeding. By combining the strengths of both genomic and cytogenetic techniques, researchers can achieve a comprehensive and detailed understanding of the cotton genome's genetic and chromosomal landscape. This integrative approach enhances the precision and efficiency of breeding programs, enabling the development of cotton varieties with improved yield, fiber quality, and resilience to biotic and abiotic stresses.

The potential impact on the cotton industry is profound. Improved cotton varieties will contribute to sustainable agricultural practices, ensuring stable production in the face of climate change and increasing global demand. Precision agriculture practices, supported by integrative genomic and cytogenetic data, will optimize crop management and resource use, driving innovation and competitiveness in the cotton industry.

Acknowledgments

We extend our sincere thanks to two anonymous peer reviewers for their feedback on the initial draft of this study, whose conscientious evaluations and constructive suggestions have contributed to the improvement of our manuscript.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Huang G., Huang J.Q., Chen X.Y., and Zhu Y.X., 2021, Recent advances and future perspectives in cotton research, Annu Rev. Plant Biol., 72(1): 437-462.

https://doi.org/10.1146/annurev-arplant-080720-113241

Li F.G., Fan G.Y., Wang K.B., Sun F.M., Yuan Y.L., Song G.L., Li Q., Ma Z.Y., Lu C.R., Zou C.S., Chen W.B., Liang X.M., Shang H.L., Liu W.Q., Shi C.C., Xiao G.H., Gou C.Y., Ye W.W., Xu X., Zhang X.Y., Wei H.L., Li Z.F., Zhang G.Y., Wang J.Y., Liu K., Kohel R.J., Percy R.G., Yu J.Z., Zhu Y.X., Wang J., and Yu S.X., 2014, Genome sequence of the cultivated cotton Gossypium arboreum, Nature Genetics, 46(6): 567-572.

https://doi.org/10.1038/ng.2987

Liu Y.L., Wang X.J., Wei Y.Y., Liu Z., Lu Q.W., Liu F., Zhang T., and Peng R.H., 2020, Chromosome painting based on bulked oligonucleotides in cotton, Frontiers in Plant Science, 11: 802.

https://doi.org/10.3389/fpls.2020.00802

Sheidai M., and Dezfolian M., 2008, Cytogenetic and morpho-agronomic study of some gamma irradiated cotton (Gossypium hirsutum L.) cultivars, Cytologia, 73(1): 33-39.

https://doi.org/10.1508/cytologia.73.33

Wang K., Guo W.Z., Yang Z.J., Hu Y., Zhang W.P., Zhou B.L., Stelly D.M., Chen Z.J., and Zhang T.Z., 2010, Structure and size variations between 12A and 12D homoeologous chromosomes based on high-resolution cytogenetic map in allotetraploid cotton, Chromosoma, 119(3): 255-266.

https://doi.org/10.1007/s00412-009-0254-0

Wang K., Zhang W.P., Cao Y.J., Zhang Z.X., Zheng D.W., Zhou B.L., Guo W.Z., and Zhang T.Z., 2012, Localization of high level of sequence conservation and divergence regions in cotton, Theoretical and Applied Genetics, 124: 1173-1182.

https://doi.org/10.1007/s00122-011-1777-3

Wang M.J., Tu L.L., Yuan D.J., Zhu D., Shen C., Li J.Y., Liu F.Y., Pei L.L., Wang P.C., Zhao G.N., Ye Z.X., Huang H., Yan F.L., Ma Y.Z., Liu M., You J.Q., Yang Y.C., Liu Z.P., Huang F., Li B.Q., Qiu P., Zhang Q.H., Zhu L.F., Jin S.X., Yang X.Y., Min L., Li G.L., Chen L.L., Zheng H.K., Lindesy K., Lin Z.X., and Udall J.A., 2018, Reference genome sequences of two cultivated allotetraploid cottons, Gossypium hirsutum and Gossypium barbadense, Nature Genetics, 51: 224-229.

https://doi.org/10.1038/s41588-018-0282-x

Wang Q.Q., Liu F., Chen X.S., Ma X.J., Zeng H.Q., and Yang Z.M., 2010, Transcriptome profiling of early developing cotton fiber by deep-sequencing reveals significantly differential expression of genes in a fuzzless/lintless mutant, Genomics, 96(6): 369-376.

https://doi.org/10.1016/j.ygeno.2010.08.009

Wang K., Wang D.H., Zheng X.M., Qin A., Zhou J., Guo B.Y., Chen Y.J., Wen X.P., Ye W., Zhou Y., and Zhu Y.X., 2019, Multi-strategic RNA-seq analysis reveals a high-resolution transcriptional landscape in cotton, Nature Communications, 10(1): 4741.

https://doi.org/10.1038/s41467-019-12575-x

Xu Z.Y., Kohel R.J., Song G.L., Cho J., Yu J., Yu S.X., Tomkins J.Z., and Yu J.Z., 2008, An integrated genetic and physical map of homoeologous chromosomes 12 and 26 in Upland cotton (G. hirsutum L.), BMC Genomics, 9: 108.

https://doi.org/10.1186/1471-2164-9-108

Yu J., Jung S., Cheng C.H., Ficklin S.P., Lee T., Zheng P., Jones D., Percy R.G., and Main D., 2013, CottonGen: a genomics, genetics and breeding database for cotton research, Nucleic Acids Research, 42(D1): D1229-D1236.

https://doi.org/10.1093/nar/gkt1064

Yu J., Kohel R.J., and Smith C.W., 2010, The construction of a tetraploid cotton genome wide comprehensive reference map, Genomics, 95(4): 230-240.

https://doi.org/10.1016/j.ygeno.2010.02.001

Zhang Z., Shang H.H., Shi Y.Z., Huang L., Li J.W., Ge Q., Gong J.W., Liu A.Y., Chen T.T., Wang D., Wang Y., Palanga K.K., Muhammad J., Li W.J., Lu Q.W., Deng X.Y., Tan Y.N., Song W.W., Cai J., Li P.T., Rashid H., Gong W.K., and Yuan Y.L., 2016, Construction of a high-density genetic map by specific locus amplified fragment sequencing (SLAF-seq) and its application to Quantitative Trait Loci (QTL) analysis for boll weight in upland cotton (Gossypium hirsutum.), BMC Plant Biol., 16: 79.

https://doi.org/10.1186/s12870-016-0741-4