Cotton Genomics and Genetics 2025, Vol.16, No.6, 300-309 http://cropscipublisher.com/index.php/cgg 301 This study aims to sort out the current progress of haplotype research and genome editing in precise cotton breeding, discuss the bottlenecks of traditional methods, the role of haplotype selection, and the latest breakthroughs in editing technology. At the same time, we will also pay attention to the challenges that arise during the combination of the two, whether in terms of technology, regulation, or future development direction. The answers to these questions may be determining the sustainability of the cotton industry in the future. 2 Haplotype-Based Approaches in Cotton Breeding 2.1 Definition and significance of haplotypes In genetic research, the term "haplotype" may sound a bit academic, but its meaning is actually quite straightforward. It refers to a set of gene variations on chromosomes that are inherited from the same parent due to their close proximity. For breeding, it does not "point points" like individual SNPS, but regards a string of variations connected together as a whole (Bhat et al., 2021). This approach can capture the synergistic effects among multiple genes and is closer to the true genetic picture behind traits than analyzing individual markers alone. This is especially crucial on cotton. The complex traits of fibers, such as length, strength, and even plant type, are often not determined by a single gene but are the result of the combined efforts of multiple genes. Traditional methods find it difficult to clearly understand this combination relationship, while haplotype analysis can precisely fill this gap. Breeders can thereby more easily identify those combinations that truly have an impact on traits, thereby accelerating the breeding of superior varieties (Sivabharathi et al., 2024). 2.2 Tools and methods for haplotype identification In the past, identifying haplotypes was a troublesome thing. Now it's different. The popularization of next-generation sequencing (NGS) and high-throughput typing technologies has made all this relatively easy. Different research purposes correspond to different means. For instance, genome-wide association studies (GWAS) are often employed to identify haplotype blocks associated with target traits, relying on a large amount of high-density SNP data (Wang et al., 2022). Another method is called linkage disequilibrium (LD) mapping. It first classifies the relevant SNPS into blocks and then examines the relationships between these blocks and traits (Weber et al., 2023). Furthermore, the emergence of bioinformatics tools has made work more efficient. Software like Haploview and HaploBlocker can classify haplotypes based on the degree of LD, physical distance, or the proximity of markers. Site-specific amplification fragment sequencing (SLAF-seq) is also often used to discover and type SNPS in order to construct more accurate haplotype structures. Of course, traditional marks have not been completely phased out either. Simple sequence repeat (SSR) markers are still useful in some breeding populations for tracking the genetic flow of specific haplotypes (Wu et al., 2020). 2.3 Applications in cotton trait dissection When it comes to the usefulness of haplotype analysis, the most direct aspect lies in the analysis of traits. Take fiber quality as an example. Through genome-wide association studies (GWAS) of haplotype blocks, researchers have identified many stable loci and candidate genes related to length, strength and uniformity. This means that when breeding, those "advantageous" haplotypes can be directly selected to improve the quality (Su et al., 2020). The plant type traits also benefited a lot. Haplotype analysis helped reveal the key loci that control plant height, branching Angle and branching length, which is of great significance for mechanized harvesting. On a more macroscopic level, haplotype mapping enables people to see more clearly the domestication process of cultivated cotton. Some haplotypes in local varieties have shown outstanding adaptability or enhanced traits, and this information provides new clues for variety improvement (He et al., 2021). In addition, with the introduction of genomic prediction models, incorporating haplotype information has also been proven to improve the prediction accuracy of complex traits (Lin et al., 2024). This means that in future breeding decisions, haplotypes will no longer be merely auxiliary information but may become the core basis.
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