1 Introduction

Cotton (Gossypium spp.) is a crucial natural fiber crop, providing the primary raw material for the global textile industry. The quality of cotton fiber significantly influences the performance of textile processes and the final product quality. Key fiber quality traits such as fiber length, strength, fineness, and uniformity are essential for producing high-quality textiles (Kohel et al., 2004; Teodoro et al., 2019; Liu et al., 2020). These traits determine the efficiency of spinning, weaving, and dyeing processes, ultimately affecting the durability, appearance, and comfort of the finished fabric (Gu et al., 2020).

Despite the importance of fiber quality, cotton breeding programs face several challenges in improving these traits. The complex genetic architecture of fiber quality traits, coupled with significant genotype-environment interactions, complicates the selection process (Diouf et al., 2018; Teodoro et al., 2019). Traditional breeding methods are often time-consuming and less efficient in achieving the desired improvements. Additionally, the need to balance fiber quality with other agronomic traits such as yield further complicates breeding efforts (Jiang et al., 2021). As the textile industry evolves with new technologies and higher standards, there is an increasing demand for cotton varieties with superior fiber quality (Zhang et al., 2015).

Genetic mapping has emerged as a powerful tool to understand the genetic basis of cotton fiber quality traits. By identifying quantitative trait loci (QTLs) and associated genes, researchers can gain insights into the genetic mechanisms underlying these traits (Shang et al., 2016; Liu et al., 2020). Advances in molecular markers, such as single nucleotide polymorphisms (SNPs), and high-density genetic maps have facilitated the identification of numerous QTLs associated with fiber quality (Diouf et al., 2018; Fan et al., 2018; Gu et al., 2020; Fang, 2024). These genetic markers can be used in marker-assisted selection (MAS) to accelerate the breeding process and develop cotton varieties with enhanced fiber quality (Kohel et al., 2004; Xu et al., 2020).

This study synthesizes current knowledge on the role of genetic mapping in understanding cotton fiber quality traits. It aims to summarize recent key findings from genetic mapping studies on cotton fiber quality, highlight the major QTLs and candidate genes related to fiber quality traits, and discuss the implications of these findings for cotton breeding programs. Additionally, it identifies gaps in current research and suggests future directions for improving cotton fiber quality through genetic mapping. This study provides valuable information and guidance for future research and breeding efforts, contributing to the enhancement of cotton fiber quality for the textile industry.

2 Key Cotton Fiber Quality Traits

2.1 Fiber length

Fiber length (FL) is a critical determinant of cotton fiber quality, significantly impacting the efficiency of the spinning process and the quality of the final textile products. Longer fibers are preferred in the textile industry as they can be spun into finer and stronger yarns, which in turn produce superior fabrics. Several studies have focused on identifying the genetic basis of fiber length to aid in the breeding of cotton varieties with improved fiber quality. For instance, the development of functional markers (FMs) through kompetitive allele-specific PCR (KASP) assays has been shown to effectively differentiate cotton accessions with longer fiber lengths, providing valuable tools for marker-assisted selection (MAS) in breeding programs (Li et al., 2022). Additionally, quantitative trait loci (QTL) mapping and RNA-sequencing have identified candidate genes associated with fiber length, such as Cellulose synthase-like protein D3 (CSLD3) and expansin-A1 (EXPA1), which are crucial for fiber elongation (Lu et al., 2021).

Moreover, genome-wide association studies (GWAS) and linkage analyses have been employed to identify high-quality QTLs responsible for fiber length. These studies have revealed significant QTLs on various chromosomes, with some explaining more than 10% of the phenotypic variation in fiber length. For example, a major QTL on chromosome D03 was found to be associated with fiber elongation by regulating sucrose synthesis, highlighting the importance of metabolic pathways in fiber development (Zhang et al., 2019a). The integration of genetic and transcriptomic data has further enhanced our understanding of the molecular mechanisms underlying fiber length, providing a robust foundation for improving cotton fiber quality through genetic manipulation (Naoumkina et al., 2019).

2.2 Fiber strength

Fiber strength (FS) is another essential quality trait that influences the performance of cotton fibers during the spinning process. Stronger fibers are less likely to break during spinning, resulting in higher efficiency and better quality yarns. The genetic basis of fiber strength has been extensively studied, with several QTLs identified that contribute to this trait. For instance, a study using a high-density genetic map identified QTLs associated with fiber strength on multiple chromosomes, providing insights into the genetic architecture of this trait (Lu et al., 2021). Additionally, functional markers developed through KASP assays have been shown to effectively differentiate cotton accessions with higher fiber strength, facilitating the selection of superior varieties in breeding programs (Li et al., 2022).

Research has also highlighted the importance of specific genes in determining fiber strength. For example, the identification of candidate genes such as Polygalacturonase (At1g48100) and plasmodesmata callose-binding protein 3 (PDCB3) has provided valuable targets for genetic manipulation to enhance fiber strength (Lu et al., 2021). Furthermore, genome-wide association studies have identified pleiotropic SNPs that are associated with both fiber strength and other quality traits, suggesting that these genetic loci play a crucial role in the overall fiber quality (Figure 1) (Liu et al., 2020). The integration of genetic and transcriptomic data has thus provided a comprehensive understanding of the molecular mechanisms underlying fiber strength, paving the way for the development of cotton varieties with improved fiber quality.

Figure 1  Correlation Analysis of Five Major Cotton Fiber Quality Traits (Adapted from Liu et al., 2020) Image caption: The five traits shown are fiber elongation (FE), fiber micronaire (FM), fiber strength (FS), fiber length (FL), and fiber uniformity (FU). The color and size of the circles indicate the correlation coefficients between different traits, with green representing positive correlations and red representing negative correlations. Asterisks (**) indicate significant correlations at the P<0.01 level (Adapted from Liu et al., 2020).

Liu et al. (2020) found a strong positive correlation between fiber strength (FS) and fiber length (FL) (r = 0.84), indicating that an increase in fiber length is associated with higher fiber strength. Additionally, fiber uniformity (FU) showed significant correlations with both fiber length and fiber strength, suggesting a close relationship between these three traits in quality assessment. Fiber micronaire (FM) displayed negative correlations with other traits, particularly with fiber strength and fiber length, indicating a potential quality trade-off between fiber fineness and strength/length. These correlations provide valuable genetic insights for cotton fiber quality improvement and selection, aiding in the optimization of fiber performance in breeding programs.

2.3 Fiber fineness

Fiber fineness is a critical quality trait that affects the processing properties of cotton fibers during spinning and the comfort of the final textile product. Finer fibers can be spun into finer yarns, which are softer and more comfortable to wear. The genetic basis of fiber fineness has been explored through various studies, with several QTLs identified that contribute to this trait. For instance, a study using a polymorphic mapping population derived from an interspecific cross identified six QTLs associated with fiber fineness, providing valuable insights into the genetic architecture of this trait (Kohel et al., 2004).

Moreover, the integration of genetic and transcriptomic data has identified candidate genes that play a crucial role in determining fiber fineness. For example, the identification of genes involved in the regulation of fiber elongation and cell wall biosynthesis has provided valuable targets for genetic manipulation to improve fiber fineness (Lu et al., 2021). Additionally, genome-wide association studies have identified SNPs that are significantly associated with fiber fineness, further enhancing our understanding of the genetic basis of this trait (Liu et al., 2020). The identification of these genetic loci and candidate genes provides a robust foundation for the development of cotton varieties with improved fiber fineness through marker-assisted selection and genetic engineering.

2.4 Fiber maturity and uniformity

Fiber maturity and uniformity are important quality traits that impact the consistency of cotton fibers and the quality of the final textile product during processing. Mature and uniform fibers are less likely to break during spinning, resulting in higher efficiency and better quality yarns. The genetic basis of fiber maturity and uniformity has been explored through various studies, with several QTLs identified that contribute to these traits. For instance, a study using a high-density genetic map identified QTLs associated with fiber maturity and uniformity on multiple chromosomes, providing valuable insights into the genetic architecture of these traits (Fan et al., 2018).

Additionally, the integration of genetic and transcriptomic data has identified candidate genes that play a crucial role in determining fiber maturity and uniformity. For example, the identification of genes involved in the regulation of fiber elongation and cell wall biosynthesis has provided valuable targets for genetic manipulation to improve fiber maturity and uniformity (Lu et al., 2021). Moreover, genome-wide association studies have identified SNPs that are significantly associated with these traits, further enhancing our understanding of the genetic basis of fiber maturity and uniformity (Liu et al., 2020). The identification of these genetic loci and candidate genes provides a robust foundation for the development of cotton varieties with improved fiber maturity and uniformity through marker-assisted selection and genetic engineering.

3 Construction of Cotton Genetic Maps

3.1 Traditional genetic mapping methods

Classical genetic mapping methods have been instrumental in understanding cotton fiber quality traits. These methods primarily rely on phenotypic data and molecular markers such as Simple Sequence Repeats (SSRs) and Amplified Fragment Length Polymorphisms (AFLPs). SSR markers have been widely used due to their high polymorphism and reproducibility. For instance, a study utilized SSR markers to construct linkage maps in Upland cotton (Gossypium hirsutum L.), identifying 39 QTLs for fiber quality traits across different generations, which are crucial for marker-assisted selection (MAS) (Shen et al., 2005). Another research employed 359 SSR markers for association mapping in Upland cotton, detecting 46 markers associated with fiber quality traits across multiple environments, thus providing valuable markers for MAS (Qin et al., 2015).

AFLP markers, although less frequently used compared to SSRs, have also contributed to cotton genetic mapping. These markers help in identifying polymorphisms at a higher resolution, which is beneficial for constructing detailed genetic maps. The integration of SSR and AFLP markers has enabled the development of comprehensive genetic maps that cover a significant portion of the cotton genome, facilitating the identification of QTLs associated with fiber quality traits (Yu et al., 2019).

3.2 High-density genomic mapping

High-density genomic mapping has revolutionized cotton genetics by providing high-precision genetic maps based on Single Nucleotide Polymorphisms (SNPs) and high-throughput sequencing technologies. SNP markers, due to their abundance and distribution throughout the genome, have become the markers of choice for constructing high-density genetic maps. For example, a study on Upland cotton (Gossypium hirsutum L.) used the CottonSNP63K array to develop a high-density SNP-based linkage map, identifying 106 QTLs for fiber quality, yield, and morphological traits (Zhang et al., 2019). This high-density map allows for more accurate QTL mapping and facilitates the identification of candidate genes for fiber quality improvement.

Genotyping-by-sequencing (GBS) is another high-throughput technology that has been employed to develop high-density genetic maps. In Gossypium barbadense (Sea Island cotton), GBS was used to construct a genetic map with 3557 SNPs, spanning a total genetic distance of 3076.23 cM. This map enabled the identification of 42 QTLs for fiber quality and lint yield traits, providing a valuable resource for fine mapping and marker-assisted selection (Fan et al., 2018). Similarly, specific locus amplified fragment sequencing (SLAF-seq) has been used to construct a high-density genetic map in Upland cotton, identifying 18 stable QTLs for boll weight across multiple environments (Zhang et al., 2016).

3.3 Application of genetic maps in different cotton species

Genetic maps have been applied to various cotton species, including Upland cotton (Gossypium hirsutum) and Sea Island cotton (Gossypium barbadense), to understand and improve fiber quality traits. In Upland cotton, high-density SNP-based maps have been used to identify QTLs for fiber quality traits, which are essential for breeding programs aimed at improving fiber strength, length, and fineness (Ijaz et al., 2019; Zhang et al., 2019b). The integration of QTL mapping with multi-omics approaches, such as RNA sequencing, has further enhanced the understanding of the genetic basis of fiber quality traits in Upland cotton (Ijaz et al., 2019).

In Sea Island cotton, which is known for its superior fiber quality, genetic mapping has also been extensively utilized. A high-density genetic map constructed using GBS-SNPs in Gossypium barbadense identified multiple QTLs for fiber quality traits, providing insights into the genetic factors that contribute to its exceptional fiber properties (Fan et al., 2018). These genetic maps are not only useful for understanding the genetic architecture of fiber quality traits but also for transferring desirable traits from Sea Island cotton to Upland cotton through marker-assisted breeding (He et al., 2006).

4 Using Genetic Maps to Unravel the Genetic Basis of Cotton Fiber Quality Traits

4.1 Quantitative trait locus (QTL) mapping

QTL mapping has been instrumental in identifying the genetic basis of various fiber quality traits in cotton, such as fiber length, strength, and fineness. High-density genetic maps constructed using single nucleotide polymorphism (SNP) markers have enabled the precise localization of QTLs associated with these traits. For instance, a study on Gossypium barbadense developed a high-density genetic map using genotyping-by-sequencing (GBS) and identified 42 QTLs related to fiber quality and lint yield traits (Fan et al., 2018). Similarly, another study using a recombinant inbred line (RIL) population in upland cotton identified 106 QTLs for fiber quality and yield traits, with significant contributions from the parental accessions (Zhang et al., 2019b).

In a comprehensive study, a high-density genetic map was constructed using a RIL population derived from a cross between two upland cotton accessions. This map identified 110 QTLs for various traits, including fiber length and yield. Notably, two major QTL clusters were found on chromosomes 17 and 26, which were associated with fiber length and yield traits, respectively. These findings highlight the potential for simultaneous improvement of fiber quality and yield through marker-assisted selection (Diouf et al., 2018).

4.2 Application of association analysis in fiber quality traits

Genome-wide association studies (GWAS) have been employed to identify candidate genes associated with fiber quality traits. By analyzing the genetic variation across a wide range of cotton genotypes, GWAS can pinpoint specific genes that contribute to desirable fiber characteristics.

A study focused on fine-mapping a QTL for fiber strength (qFS-Chr. D02) identified several candidate genes involved in cell-wall synthesis. Among these, two genes, GH_D02G2269 and GH_D02G2289, were significantly downregulated in the introgression line with improved fiber strength, suggesting their potential role in enhancing fiber quality (Feng et al., 2020). This research underscores the utility of GWAS in uncovering the genetic basis of fiber strength and fineness, facilitating targeted breeding efforts.

4.3 Molecular marker-assisted fiber quality improvement

Marker-assisted selection (MAS) leverages QTLs and associated molecular markers to enhance fiber quality traits in cotton breeding programs. By integrating these markers into breeding strategies, it is possible to achieve significant improvements in fiber characteristics. A study on upland cotton utilized a comprehensive PCR-based marker linkage map to identify QTLs for fiber quality traits. Thirteen QTLs were detected, including those for fiber length, strength, and fineness. Notably, five QTLs were consistently detected across multiple environments, making them reliable targets for MAS. These stable QTLs can be used to improve fiber maturity and other quality traits in cotton breeding programs (Zhang et al., 2009).

5 Revealing Cotton Fiber Development Mechanisms through Genetic Maps

5.1 Key genes and regulatory networks in fiber development

Genetic mapping has significantly advanced our understanding of the transcription factors and regulatory networks involved in cotton fiber development. Studies have identified numerous transcription factors (TFs) that play crucial roles in fiber development stages, including initiation, elongation, and secondary cell wall synthesis. For instance, a comprehensive study remapped over 380 cotton RNAseq datasets, revealing stage-specific features and putative cis-regulatory elements critical for fiber cell commitment and development (Figure 2) (Prasad et al., 2022). Another study mapped 977 TF primers, identifying 31 polymorphic loci on 15 cotton chromosomes, which were associated with fiber quality traits such as fiber length (Chen et al., 2015). These findings underscore the importance of TFs in regulating fiber development and highlight the potential for genetic improvement through targeted breeding programs.

Figure 2 RNA Sequencing Data Processing and Mapping Statistics (adapted from Prasad et al., 2022) Image caption: A: The complete workflow for analyzing RNA sequencing data from 18 different cotton tissues, covering the steps processed on a high-performance computing (HPC) platform, from data preprocessing to quality control and differential expression gene (DEG) identification; B: Lists the RNA sequencing data volume for 18 different cotton tissue samples; C: Mapping statistics for five tissue types, including total reads, uniquely mapped reads, and unmapped reads; D: Differential expression gene (DEG) analysis results between ovules and fibers (OF) compared to leaf, root, and seed tissues (LRS), showing 302 genes upregulated and 671 genes downregulated in OF. (adapted from Prasad et al., 2022)

5.2 Genetic regulation at different stages of fiber development

The genetic regulation of cotton fiber development varies across different stages, including fiber initiation, elongation, thickening, and maturation. Genetic maps have been instrumental in elucidating these stage-specific regulatory mechanisms. For example, a genome-wide association study (GWAS) identified 28 genetic loci associated with fiber quality, and further analysis revealed a genetic regulatory network orchestrating the initiation of secondary cell wall development (Li et al., 2020). This network includes key genes that modulate the transition from rapid cell elongation to secondary cell wall synthesis, highlighting the temporal regulation of fiber development. Additionally, a study on the genetic mapping of genes specifically expressed during fiber development identified 51 genes preferentially expressed during fiber elongation and secondary wall biosynthesis, further emphasizing the complexity of fiber development regulation (Li et al., 2013).

A notable example of the role of transcription factors in fiber elongation is the identification of a KIP-related protein gene within an eQTL hotspot (Hot216). This gene was found to regulate the expression of 962 genes involved in cell wall synthesis, thereby contributing to fiber length by modulating the developmental transition from cell elongation to secondary cell wall synthesis (Li et al., 2020). This case study illustrates the critical role of specific transcription factors in the genetic regulation of fiber elongation and their potential as targets for improving fiber quality.

5.3 Epigenetic regulation in fiber development

Epigenetic factors, such as DNA methylation and histone modification, also play a significant role in controlling cotton fiber quality. Genetic maps have facilitated the investigation of these epigenetic mechanisms. For instance, a study on the transcriptional landscape of cotton fiber development identified differentially expressed genes (DEGs) enriched in pathways related to transcription regulation and DNA replication, suggesting the involvement of epigenetic modifications in fiber development (Tahmasebi et al., 2019).

Another study highlighted the accumulation of genome-specific transcripts and transcription factors during early fiber cell development, indicating that epigenetic regulation is crucial for fiber cell fate determination and development (Yang et al., 2006). These findings provide insights into the complex epigenetic landscape governing cotton fiber quality and offer potential avenues for enhancing fiber traits through epigenetic modifications.

6 Influence of Environment and Genotype Interactions on Fiber Quality

6.1 Environmental factors affecting cotton fiber quality

Environmental factors such as temperature, water availability, and soil conditions significantly influence cotton fiber traits by regulating gene expression. For instance, water management regimes, whether well-watered or water-limited, have a profound impact on the genetic control of fiber quality traits. Under water-limited conditions, 17 QTLs were detected, compared to only two under well-watered conditions, indicating that fiber quality improvement under water stress is more complex (Paterson et al., 2003).

Additionally, abiotic stresses such as drought and heat can alter the expression of numerous genes involved in fiber development, as revealed by transcriptomic analyses. These studies have identified differentially expressed genes (DEGs) that are enriched in pathways related to stress response and secondary metabolite biosynthesis, which are crucial for maintaining fiber quality under adverse conditions (Tahmasebi et al., 2019).

6.2 Genotype-by-environment interactions in genetic analysis

Genotype-by-environment (GxE) interactions play a critical role in the genetic analysis of cotton fiber quality traits. These interactions can be analyzed through genetic mapping techniques such as QTL mapping and genome-wide association studies (GWAS). For example, a study using two sets of introgression lines across multiple environments identified 76 and 120 QTLs in different genetic backgrounds, with 61 of these QTLs being stable across environments (Shi et al., 2020). This indicates that genetic background can have a more significant impact on fiber quality traits than environmental factors alone.

A case study on QTL stability analysis for fiber length and strength under various environmental conditions demonstrated that certain QTLs are consistently expressed across different environments, while others are environment-specific. For instance, a study involving a MAGIC population identified significant associations between candidate genes and fiber quality traits across multiple environments.

This study found that some loci had simple additive effects, while others were only important in specific environmental conditions, highlighting the complexity of GxE interactions (Thyssen et al., 2018). Another study identified 31 QTLs significantly associated with fiber quality traits, with some QTLs being novel and others previously reported, further emphasizing the importance of stable QTLs for marker-assisted selection in breeding programs (Liu et al., 2020).

7 Application of Genetic Maps in Cotton Breeding

7.1 Application of marker-assisted selection (MAS) in cotton fiber improvement

Marker-Assisted Selection (MAS) has revolutionized cotton breeding by enabling the rapid and efficient selection of desirable fiber quality traits. Genetic maps, which provide detailed information on the location of genes and quantitative trait loci (QTLs) associated with fiber quality, are integral to the MAS process. By using molecular markers linked to these QTLs, breeders can select plants with superior fiber traits at the seedling stage, significantly accelerating the breeding cycle.

A notable example of MAS in action is the improvement of fiber strength in Upland cotton (Gossypium hirsutum L.). Researchers have identified multiple QTLs associated with fiber strength and other quality traits through extensive genetic mapping and QTL analysis. For instance, a study involving a recombinant inbred line (RIL) population derived from diverse Upland cotton cultivars identified 131 fiber QTLs, including clusters on chromosomes 7 and 16, which were crucial for improving fiber strength (Fang et al., 2014). These QTLs were then used in MAS programs to develop new cotton cultivars with enhanced fiber strength, demonstrating the practical application of genetic maps in cotton breeding (Tan et al., 2018; Ijaz et al., 2019; Kushanov et al., 2021).

7.2 Potential of genomic selection (GS) in fiber quality breeding

Genomic Selection (GS) is an advanced breeding strategy that integrates genetic map information with genome-wide marker data to predict the performance of cotton fiber traits. Unlike MAS, which focuses on a few markers linked to specific QTLs, GS uses a large number of markers distributed across the entire genome to capture the genetic variance of complex traits.

By leveraging high-density genetic maps and comprehensive marker datasets, GS can predict fiber quality traits with high accuracy. This approach allows breeders to select the best candidates for breeding programs even before phenotypic evaluation, thus saving time and resources. Studies have shown that GS can significantly enhance the efficiency of breeding programs aimed at improving fiber quality traits such as length, strength, and fineness (Li et al., 2016; Fan et al., 2018).

7.3 Breeding strategies combining genetic maps and gene editing

The advent of gene-editing technologies, such as CRISPR-Cas, has opened new avenues for precision breeding in cotton. By combining genetic maps with gene-editing tools, breeders can precisely target and modify genes associated with fiber quality traits, leading to more efficient and targeted improvements. A case study highlighting the potential of this approach involved the precise regulation of fiber length genes in cotton. Researchers utilized CRISPR-Cas to edit specific genes identified through genetic mapping as being crucial for fiber length. This precise gene editing resulted in significant improvements in fiber length without adversely affecting other important traits (Li et al., 2016; Tan et al., 2018; Ijaz et al., 2019). The integration of genetic maps with gene-editing technologies thus represents a powerful strategy for the precision improvement of cotton fiber traits, offering a promising future for cotton breeding programs.

8 Future Research Directions

8.1 Integrated multi-omics maps

Combining genomic, transcriptomic, metabolomic, and other multi-omics data can significantly enhance our understanding of the complex regulatory networks governing cotton fiber traits. The integration of these diverse datasets allows for a comprehensive analysis of the genetic and molecular mechanisms underlying fiber development and quality. For instance, the CottonMD database integrates multi-omics datasets, including genomes, transcriptomes, epigenomes, and metabolomes, to facilitate the identification of candidate genes and their roles in phenotype formation and regulation (Yang et al., 2022).

Additionally, studies have demonstrated the power of combining meta-QTL, significant SNP, and transcriptomic data to identify candidate genes controlling fiber quality traits (Xu et al., 2020). The use of integrated multi-omics approaches can thus provide a holistic view of the regulatory networks and aid in the development of improved cotton varieties.

8.2 Epigenetic regulation of fiber quality traits

Exploring the role of epigenetics in regulating cotton fiber traits is a promising area of research. Epigenetic modifications, such as DNA methylation and histone modifications, play crucial roles in gene expression regulation and can influence fiber development and quality. For example, a study on the epigenetic basis of cotton fiber differentiation revealed that DNA methylation levels increase during fiber development, which is mediated by an active H3K9me2-dependent pathway rather than the RNA-directed DNA methylation (RdDM) pathway (Wang et al., 2016). This highlights the importance of epigenetic regulation in fiber quality traits and suggests that further research into epigenetic mechanisms could uncover new targets for improving cotton fiber quality.

8.3 Integration of genetic maps with precision breeding

The integration of genetic maps with precision breeding technologies holds great potential for accelerating the improvement of cotton fiber quality. High-density genetic maps and QTL analyses have already identified numerous QTLs associated with fiber quality and yield traits (Li et al., 2018; Zhang et al., 2019). By combining these genetic maps with precision breeding techniques, such as marker-assisted selection (MAS) and genomic selection (Wang and Li, 2024), breeders can more effectively select for desirable traits and develop superior cotton varieties. For instance, the use of high-density SNP-based genetic maps has facilitated the identification of stable QTLs and candidate genes for fiber quality traits, which can be targeted in breeding programs to enhance fiber quality (Ijaz et al., 2019). The integration of genetic maps with precision breeding approaches can thus streamline the breeding process and lead to significant improvements in cotton fiber quality.

Acknowledgments

We also thank the anonymous reviewers for their insightful comments and suggestions that greatly improved the 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.

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