1 Introduction

Gossypium, commonly known as cotton, is a genus of flowering plants in the mallow family, Malvaceae. It is of significant economic importance as it provides the primary natural fiber used in the textile industry. The two main cultivated species, Gossypium hirsutum (Upland cotton) and Gossypium barbadense (Pima or Egyptian cotton), account for the majority of global cotton production.

Upland cotton alone contributes to about 95% of the world’s cotton production, making it a critical crop for the agricultural economies of many countries (Mei et al., 2004; Fang et al., 2014). The economic value of cotton extends beyond fiber production, as it also includes by-products such as cottonseed oil and meal, which are used in food and feed industries (Ijaz et al., 2019).

Fiber quality is a crucial determinant of the market value and utility of cotton. Key fiber quality traits include fiber length, strength, fineness (micronaire), elongation, and uniformity. These traits significantly influence the spinning efficiency and the quality of the final textile products (Jamshed et al., 2016; Fan et al., 2018).

However, improving fiber quality is challenging due to its complex genetic nature and the negative correlation with yield potential (Fang et al., 2014). Traditional breeding methods have shown limited success in simultaneously enhancing both yield and fiber quality. Therefore, advanced genetic tools such as Quantitative Trait Loci (QTL) mapping and Marker-Assisted Selection (MAS) have become essential in modern cotton breeding programs to dissect the genetic basis of fiber quality traits and facilitate the development of superior cotton cultivars (Ijaz et al., 2019; Zhang et al., 2019).

This study combines the main findings of recent QTL studies related to fiber quality traits in Gossypium hirsutum and Gossypium barbadense, analyzes the progress of QTL localization technology and its application in cotton breeding, discusses the integration of multiple omics methods (such as RNA sequencing) to identify candidate genes and improve fiber quality through MAS, and identifies the challenges and future directions faced by cotton QTL localization and fiber quality improvement. By synthesizing the results of multiple studies, this study aims to provide valuable insights for researchers and breeders dedicated to genetic improvement of cotton fiber quality.

2 Overview of Quantitative Trait Loci (QTL) Mapping

2.1 Definition and concept of QTL mapping

Quantitative Trait Loci (QTL) mapping is a statistical technique used to identify regions of the genome that are associated with specific quantitative traits. These traits, such as fiber quality in cotton, are typically influenced by multiple genes and environmental factors. QTL mapping involves the use of molecular markers to link phenotypic data (observable traits) with genotypic data (genetic information), allowing researchers to locate the specific regions of the genome that contribute to the variation in these traits (Figure 1) (Zhang et al., 2015; Keerio et al., 2018; Chen et al., 2018).

Keerio et al. (2018) showcased a breeding strategy using interspecific hybrid introgression lines (ILs) for quantitative trait loci (QTL) mapping studies in cotton. This breeding strategy is designed to introduce the genetic material of Gossypium barbadense into the background of Gossypium hirsutum, thereby importing certain characteristics of G. barbadense while retaining the desirable traits of G. hirsutum. QTL mapping involves correlating the phenotypic data (such as fiber quality) of these introgressed G. hirsutum lines with their genotypic data (genetic information) to identify specific genomic regions controlling these traits. This method allows scientists to identify and locate genes associated with quantitative traits in a complex genetic background, thus providing valuable insights for the genetic improvement of cotton.

2.2 Historical development of QTL mapping techniques

The concept of QTL mapping has evolved significantly since its inception. Early QTL studies relied on low-resolution mapping techniques using a limited number of molecular markers, which often resulted in broad and imprecise QTL regions. Over time, advancements in molecular biology and genomics have led to the development of high-throughput sequencing technologies and more sophisticated statistical methods. These advancements have enabled the generation of high-density genetic maps and the identification of QTLs with greater accuracy and resolution (Jamshed et al., 2016; Keerio et al., 2018). For instance, the use of specific locus amplified fragment sequencing (SLAF-seq) has allowed for the identification of thousands of high-quality single nucleotide polymorphism (SNP) markers, significantly enhancing the precision of QTL mapping (Keerio et al., 2018).

2.3 Modern approaches in QTL mapping

Modern QTL mapping approaches leverage advanced genomic tools and computational methods to achieve high-resolution mapping and better understand the genetic architecture of complex traits. Techniques such as SLAF-seq and the use of recombinant inbred lines (RILs) have been instrumental in identifying stable QTLs across multiple environments. For example, a study using a population of 196 RILs derived from a cross between two upland cotton strains identified 165 QTLs for fiber quality traits, with 47 of these being stable across various environments (Jamshed et al., 2016). Additionally, meta-analysis techniques have been employed to integrate QTL data from multiple studies, further refining the identification of key genomic regions associated with important traits. These modern approaches not only improve the accuracy of QTL mapping but also facilitate the application of marker-assisted selection (MAS) in breeding programs, ultimately contributing to the development of superior cotton varieties with enhanced fiber quality (Figure 1) (Zhang et al., 2015; Jamshed et al., 2016; Chen et al., 2018).

3 Genetics of Fiber Quality in Gossypium

3.1 Key fiber quality traits in cotton

Fiber quality in cotton is determined by several key traits, including fiber length, strength, micronaire (a measure of fineness and maturity), elongation, and uniformity. These traits are critical for the textile industry as they influence the spinning efficiency and the quality of the final fabric. For instance, fiber length and strength are essential for producing high-quality yarns, while micronaire affects the dye uptake and overall fabric appearance (Fang et al., 2014; Jamshed et al., 2016; Fan et al., 2018).

3.2 Genetic basis of fiber quality traits

The genetic basis of fiber quality traits in cotton is complex, involving multiple quantitative trait loci (QTLs) distributed across various chromosomes. Studies have identified numerous QTLs associated with fiber quality traits. For example, a high-density genetic map constructed using genotyping-by-sequencing (GBS) identified 42 QTLs related to fiber quality in Gossypium barbadense (Fan et al., 2018). Similarly, in Gossypium hirsutum, 165 QTLs for fiber quality traits were identified, with 47 being stable across multiple environments. These QTLs often cluster together, indicating regions of the genome that have a significant impact on multiple fiber quality traits (Fang et al., 2014; Jamshed et al., 2016; Zhang et al., 2019).

3.3 Heritability and genetic variation in fiber quality

Heritability estimates for fiber quality traits in cotton are generally high, indicating a strong genetic control. For instance, broad-sense heritability estimates for fiber length, strength, micronaire, and uniformity were reported to be 0.93, 0.92, 0.85, and 0.80, respectively (Jamshed et al., 2016). This high heritability suggests that these traits can be effectively improved through selective breeding. Additionally, significant genetic variation exists within cotton populations, providing a rich resource for breeding programs. For example, a study involving a recombinant inbred line (RIL) population derived from diverse Upland cotton cultivars identified 131 fiber QTLs, with 54 being novel. This genetic diversity is crucial for the ongoing improvement of fiber quality traits through marker-assisted selection (MAS) and other breeding strategies (Fang et al., 2014; Ijaz et al., 2019; Liu et al., 2022).

4 Methodologies in QTL Mapping for Fiber Quality

4.1 Traditional QTL mapping techniques

Linkage mapping is a traditional method used to identify the relationship between genetic markers and phenotypic traits. This technique involves the construction of a genetic linkage map using a population derived from a cross between two parent lines. For instance, in a study on Gossypium barbadense, a linkage map was constructed using 15 971 markers, including gSSRs, EST-SSRs, SRAPs, and SSCP-SNPs, to analyze QTLs for yield and fiber quality traits. The map spanned 2 108.34 cM with an average distance of 6.26 cM between adjacent loci, identifying 33 QTLs for yield and fiber quality traits (Wang et al., 2013).

Interval mapping is another traditional QTL mapping technique that involves the use of statistical methods to estimate the position and effect of QTLs within a defined interval on the genetic map. For example, in a study using a 63K Illumina Infinium SNP bead chip, 29 QTLs were identified for fiber quality and yield traits in Gossypium barbadense. The linkage map covered 1 219.4 cM with an average marker interval of 2.7 cM, and the QTLs explained 0.4 to 28.0% of the phenotypic variation (Kumar et al., 2018).

4.2 Advanced QTL mapping techniques

Genome-Wide Association Studies (GWAS) are advanced techniques that involve scanning the entire genome to find genetic variations associated with specific traits. This method is particularly useful for identifying QTLs in diverse populations. For instance, a study on Gossypium hirsutum used a specific locus amplified fragment sequencing (SLAF-seq) strategy to obtain high-throughput SNP markers, identifying 74 QTLs associated with fiber quality and yield traits across multiple environments (Keerio et al., 2018).

Marker-Assisted Selection (MAS) is a technique that uses molecular markers linked to desirable traits to select plants with those traits in breeding programs. In a study involving Gossypium barbadense, MAS was used to develop improved lines with superior fiber properties. The study identified QTLs for fiber length, strength, and fineness, which were then used to enhance fiber quality through MAS (Cao et al., 2015).

High-density genetic mapping involves the use of a large number of markers to create a detailed genetic map, which can improve the accuracy of QTL identification. For example, a study on upland cotton (Gossypium hirsutum) used genotyping by sequencing (GBS) to construct a high-density genetic map with 5 571 SNP markers. This map spanned 3 828.551 cM and identified 17 QTLs for key morphological traits, providing valuable information for improving cotton fiber quality through breeding and molecular marker-assisted selection (Qi et al., 2017).

5 QTL Mapping Studies in Gossypium

5.1 Early QTL mapping studies

Early QTL mapping studies in Gossypium primarily focused on identifying genetic loci associated with fiber quality traits using various populations and marker systems. One significant study utilized a recombinant inbred line (RIL) population derived from a cross between two upland cotton strains, '0-153' and 'sGK9708', to construct a linkage map covering 4 110 cM of the upland cotton genome. This study identified 165 QTLs for fiber quality traits, with 47 QTLs being stable across multiple environments (Jamshed et al., 2016). Another early study developed a high-density genetic linkage map using a population derived from Gossypium hirsutum cultivar 'CCRI35' and G. hirsutum race 'palmeri accession TX-832', identifying 210 fiber quality QTLs and 73 yield-related QTLs, with 62 fiber quality QTLs being stable across multiple environments (Liu et al., 2022).

5.2 Recent advances in QTL mapping

Recent advances in QTL mapping have leveraged high-throughput sequencing technologies and more sophisticated statistical methods to enhance the resolution and accuracy of QTL identification. For instance, a study using specific locus amplified fragment sequencing (SLAF-seq) developed a high-density genetic map for upland cotton, identifying 18 stable QTLs for boll weight across 11 environments (Zhang et al., 2016). Another study employed genotyping by sequencing (GBS) to construct a high-density genetic map and identified 17 QTLs for plant morphological traits, which are crucial for mechanized harvesting (Qi et al., 2017). Additionally, a study focusing on chromosome 25 of upland cotton identified 37 QTLs for fiber quality traits, with 17 being stably expressed in multiple environments (Zhang et al., 2015).

5.3 Comparative Analysis of QTL Mapping Studies

Comparative analysis of QTL mapping studies reveals several key insights into the genetic architecture of fiber quality traits in Gossypium. Across different studies, a significant number of QTLs have been identified on both the At and Dt sub-genomes, with some chromosomes, such as chromosome 25, being particularly rich in QTL clusters. The stability of QTLs across multiple environments is a common theme, with several studies reporting stable QTLs that are valuable for marker-assisted selection (MAS) in breeding programs (Zhang et al., 2015; Jamshed et al., 2016; Liu et al., 2022). Moreover, the integration of multi-omics approaches, such as RNA sequencing datasets, has further refined QTL mapping and facilitated the identification of candidate genes for fiber quality improvement (Ijaz et al., 2019).

In summary, the evolution of QTL mapping studies in Gossypium from early efforts to recent advances highlights the increasing precision and utility of these genetic tools in cotton breeding. The identification of stable QTLs across diverse environments and the use of high-density genetic maps have significantly contributed to our understanding of the genetic basis of fiber quality traits, paving the way for more effective MAS strategies in cotton breeding programs.

6 Identification and Characterization of Fiber Quality QTLs

6.1 Major QTLs associated with fiber quality

Quantitative Trait Loci (QTL) mapping has been instrumental in identifying key genetic regions associated with fiber quality in Gossypium species. In a study involving introgression lines derived from Gossypium hirsutum and G. tomentosum, a total of 30 QTLs associated with fiber quality traits were identified. These QTLs were distributed across 11 chromosomes in the A sub-genome and 9 chromosomes in the D sub-genome, with phenotypic variance explained (PVE) ranging from 2.02% to 30.15% (Keerio et al., 2018). Another significant study using a backcross inbred line (BIL) population from Gossypium hirsutum and G. barbadense identified 28 QTLs for fiber quality traits across 23 chromosomes, with each QTL explaining 6.65% to 25.27% of the phenotypic variation (Yu et al., 2012). Additionally, high-resolution consensus mapping on chromosome 25 of Upland cotton identified 37 QTLs for fiber quality traits, with 17 QTLs being stably expressed in multiple environments (Zhang et al., 2015).

6.2 Functional characterization of fiber quality QTLs

The functional characterization of fiber quality QTLs involves understanding the genetic and molecular mechanisms underlying these traits. The use of high-throughput single nucleotide polymorphism (SNP) markers has facilitated the identification of QTLs with significant additive effects on fiber quality traits. For instance, in the study involving G. hirsutum and G. tomentosum, more than half of the identified QTLs showed positive additive effects for fiber traits, indicating their potential utility in breeding programs aimed at improving fiber quality (Keerio et al., 2018). Furthermore, the stable expression of certain QTLs across multiple environments, such as the fiber strength QTL qFS-chr25-4 identified on chromosome 25, underscores their functional importance and potential for marker-assisted selection (MAS) (Zhang et al., 2015).

6.3 Interaction of QTLs with environmental factors

The interaction between QTL and environmental factors is a critical aspect of QTL mapping as it determines the stability and reliability of QTL under different growth conditions. This research involving Gossypium hirsutum and Gossypium barbadense emphasizes the importance of identifying stable QTL in various environments. QTL marked on multiple chromosomes exhibit variability under different environmental conditions. For example, several fiber quality-related QTL identified on chromosome Chr.1D (D1) demonstrate varying effects across different environments (E1, E2, E3) as shown in (Figure 2) (Chen et al., 2018).

Chen et al. (2018) demonstrated that in Pop. B, quantitative trait loci (QTL) for fiber length (FL) were located on Chr.3A (A3) and Chr.5D (D5) under environmental condition E1, whereas in environments E2 and E3, the position and number of these QTLs changed, reflecting the significant impact of the environment on fiber length. In Pop. C, QTL for fiber strength (FS) also showed different distributions and effects under various environmental conditions, particularly on Chr.2D (D2) and Chr.7D (D7). These differences further illustrate the role of environmental factors. The interaction between QTL and environmental factors indicates that different environmental conditions significantly affect the expression of cotton fiber quality traits. Studying these interactions helps to understand gene expression under different environmental conditions and its impact on phenotypes. By identifying these interactions, breeders can select the optimal gene combinations in specific environments, thus improving the fiber quality of cotton varieties.

In summary, the identification and characterization of fiber quality QTLs in Gossypium species have provided valuable insights into the genetic basis of these traits. The functional characterization of these QTLs and their interaction with environmental factors are essential for the successful application of MAS in cotton breeding programs aimed at improving fiber quality.

7 Application of QTL Mapping in Cotton Breeding

7.1 Integrating QTL mapping into breeding programs

Quantitative Trait Loci (QTL) mapping has become an indispensable tool in cotton breeding, particularly for improving fiber quality traits such as fiber strength, length, and micronaire. By identifying specific genetic regions associated with these traits, breeders can make more informed decisions and accelerate the development of superior cotton varieties. For instance, in a study involving the elite cultivar Upland cotton '0-153', a population of 196 recombinant inbred lines (RILs) was developed to map QTLs for fiber quality traits across 11 environments. This study identified 37 QTLs on chromosome 25, with 17 being stably expressed in at least two environments, highlighting the robustness of QTL mapping in identifying valuable genetic markers for breeding programs (Zhang et al., 2015).

Integrating QTL mapping into breeding programs involves several steps, including the development of high-density genetic maps, identification of stable QTLs, and the use of marker-assisted selection (MAS). For example, a study on fiber length (FL) in upland cotton utilized two backcrossed inbred lines (BILs) to construct a high-density genetic map containing 9 182 single-nucleotide polymorphisms (SNPs). This map facilitated the identification of two stable QTLs for FL on chromosomes A08 and D03. The integration of these QTLs into breeding programs can significantly enhance the selection process for desirable fiber traits (Liu et al., 2019).

7.2 Case studies of successful breeding programs

Several breeding programs have successfully utilized QTL mapping to improve fiber quality in cotton. One notable case is the mapping of fiber strength QTLs in Upland cotton '0-153', where a stable QTL, qFS-chr25-4, was detected in seven different environments. This QTL explained 6.53% to 11.83% of the observed phenotypic variations, demonstrating its potential for improving fiber strength through marker-assisted selection (Zhang et al., 2015). Another successful case involved the identification of two candidate genes, cytochrome b5 (CB5) and microtubule end-binding 1C (EB1C), which may regulate fiber length during the elongation stage. These findings provide a strong foundation for further genetic and molecular studies aimed at enhancing fiber elongation (Liu et al., 2019).

7.3 Challenges and future directions in QTL-based breeding

Despite the successes, several challenges remain in QTL-based breeding. One major challenge is the environmental variability that can affect the expression of QTLs, making it difficult to identify stable markers. Additionally, the complex genetic architecture of fiber quality traits often involves multiple QTLs with small effects, requiring large populations and extensive phenotyping to detect. Future directions in QTL-based breeding should focus on integrating genomic selection and advanced biotechnological tools to overcome these challenges. For instance, combining QTL mapping with RNA-seq data and qRT-PCR analysis, as demonstrated in the study on fiber length, can help identify candidate genes and elucidate their regulatory mechanisms, thereby enhancing the precision of breeding programs (Liu et al., 2019).

8 Technological Advances and Future Prospects

8.1 Role of genomics and bioinformatics in QTL mapping

The integration of genomics and bioinformatics has revolutionized QTL mapping in Gossypium, particularly for fiber quality traits. High-throughput sequencing technologies, such as Specific Locus Amplified Fragment Sequencing (SLAF-seq), have enabled the identification of a large number of single nucleotide polymorphism (SNP) markers. For instance, a study utilizing SLAF-seq identified 3 157 high-quality SNP markers, which were subsequently used to detect 74 QTLs associated with fiber quality and yield traits in Gossypium hirsutum × G. tomentosum introgression lines (Keerio et al., 2018). These advancements facilitate the precise identification of QTLs, thereby enhancing the molecular breeding efforts aimed at improving fiber quality.

8.2 Emerging technologies in QTL mapping

Emerging technologies such as high-resolution consensus mapping and composite interval mapping have significantly improved the accuracy and reliability of QTL identification. For example, a high-resolution consensus map of chromosome 25 in Upland cotton identified 37 QTLs for fiber quality traits, with 17 QTLs being stably expressed across multiple environments (Zhang et al., 2015). These technologies allow for the detection of stable QTLs, which are crucial for marker-assisted selection and the development of elite cotton cultivars with superior fiber quality.

8.3 Future trends and research directions

Future research in QTL mapping for fiber quality in Gossypium is likely to focus on the integration of multi-omics data, including genomics, transcriptomics, and proteomics, to provide a comprehensive understanding of the genetic basis of fiber quality traits. Additionally, the development of more sophisticated bioinformatics tools and machine learning algorithms will further enhance the accuracy of QTL mapping. The use of introgression lines and recombinant inbred lines, as demonstrated in recent studies (Zhang et al., 2015; Keerio et al., 2018), will continue to be pivotal in identifying and validating QTLs. Ultimately, these advancements will contribute to the development of cotton varieties with improved fiber quality, meeting the increasing demands of the global textile industry.

9 Concluding Remarks

Quantitativ Loci (QTL) mapping in Gossypium, particularly focusing on fiber quality, has yielded significant insights into the genetic basis of fiber traits. Several studies have constructed high-density genetic maps and identified numerous QTLs associated with fiber quality traits such as fiber length, strength, and micronaire. For instance, a high-density genetic map using specific locus amplified fragment sequencing (SLAF-seq) identified 18 stable QTLs for boll weight in upland cotton. Another study identified 983 QTLs for fiber yield and quality traits, with 198 being stable across multiple environments. Additionally, 165 QTLs for fiber quality traits were identified, with 47 being stable across multiple environments. These findings highlight the complex genetic architecture of fiber quality traits and the importance of stable QTLs for marker-assisted selection (MAS).

The identification of stable QTLs across multiple environments is crucial for the application of MAS in cotton breeding programs. The stable QTLs identified in these studies can be used to improve fiber quality traits in upland cotton, which is essential for meeting the demands of the textile industry. For example, the identification of QTL clusters that include both fiber quality and yield traits can help breeders develop cotton cultivars with improved fiber quality without compromising yield. The use of high-density genetic maps and advanced molecular markers, such as single nucleotide polymorphisms (SNPs), facilitates the fine mapping of QTLs and the identification of candidate genes, which can be targeted in breeding programs to enhance fiber quality. Moreover, the integration of multi-omics approaches, such as RNA sequencing, with QTL mapping can provide deeper insights into the genetic mechanisms underlying fiber development and quality.

The advancements in QTL mapping and the identification of stable QTLs for fiber quality traits represent significant progress in cotton genetics and breeding. Future research should focus on fine mapping and functional analysis of the identified QTLs to validate candidate genes and understand their roles in fiber development. Additionally, the development of more advanced molecular markers and high-throughput genotyping techniques will further enhance the precision and efficiency of MAS in cotton breeding. Collaborative efforts between researchers and breeders are essential to translate these genetic findings into practical breeding strategies that can produce high-quality cotton cultivars to meet the evolving demands of the textile industry. The continuous improvement of fiber quality through genetic research and breeding will ensure the sustainability and competitiveness of the cotton industry in the global market.

Acknowledgments

The author thank the anonymous peer reviewers for their comments and suggestions, which have contributed to the improvement of this study.

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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