Cotton Genomics and Genetics 2025, Vol.16, No.3, 117-125 http://cropscipublisher.com/index.php/cgg 121 6 Technological Advances Supporting QTL and Genomic Approaches 6.1 High-throughput phenotyping platforms for precise trait evaluation In recent years, technological developments have driven the emergence of high-throughput phenotyping platforms. This platform can quickly and accurately measure many agronomic traits on a large scale. It usually combines advanced imaging technology and various sensors to more clearly evaluate complex traits such as yield and fiber quality. By providing stable and reliable phenotypic data, these platforms also make genetic analysis more efficient, helping breeders better perform QTL positioning and genomic selection (Singh et al., 2022). 6.2 Next-generation sequencing and high-density SNP arrays Not all genes are easy to find. The genetic markers behind some traits are very deep, especially in the QTL region or even in the details of a gene - it was difficult to figure out in the past. Until the emergence of next-generation sequencing technology (NGS), the situation changed. This technology can do a lot of things, such as constructing a very detailed genetic map, or directly locating very close molecular markers (Jaganathan et al., 2020). Of course, NGS alone is not enough. High-density SNP chips and whole-genome sequencing methods have also been added to improve the resolution of QTL positioning and speed up gene discovery (Kumar et al., 2017). With these tools, researchers can perform genotyping in large populations, which is very helpful for QTL analysis and genomic selection of cotton and even other crops (Nguyen et al., 2019). 6.3 Bioinformatics tools and machine learning in data integration Nowadays, we have more and more genomic and phenotypic data. In order to integrate and analyze these data clearly, advanced bioinformatics tools and machine learning methods must be used. The improvement of computer technology has also brought better statistical models and algorithms, which can process large amounts of data, make gene positioning more accurate, and also improve the effect of genomic prediction (Altaf et al., 2024). Machine learning is widely used to integrate multiple omics data, narrow the scope of QTL, and predict the performance of complex traits, which is very helpful for QTL positioning and genomic selection in crop improvement. 7 Case Study: Enhancing Yield and Fiber Quality in Upland Cotton 7.1 Overview of a breeding program combining QTL mapping and genomic selection Recently, some upland cotton breeding programs have combined QTL mapping and genomic selection (GS) to successfully improve lint yield and fiber quality. These programs usually first construct genetic maps using high-density SNPs and perform genome-wide association studies (GWAS) to identify QTLs associated with major traits. Then, molecular markers and GS models are used to screen for superior genotypes (Figure 2). For example, multi-parent hybrid populations and recombinant inbred lines are often genotyped with markers linked to QTLs associated with fiber length, fiber strength, boll weight, and lint percentage. This can bring favorable alleles together and reduce the negative impact between yield and quality (Zhang et al., 2019a). 7.2 Key results In the years of cotton improvement, QTLs that can stably control yield and fiber traits are rare. But some of the ones that have been verified do play a key role. For example, one study combined several outstanding loci - FL5, FL3, FL2 (these affect fiber quality) and BW1, LP1 (related to yield). The performance after the merger is very impressive: fiber length increased by more than 10%, strength increased by 17%, and lint percentage also improved (Zhao et al., 2024). However, not every study has such neat data. Some work has also discovered a larger number of QTLs. For example, a study identified 106 QTLs related to yield and quality, of which 46 can explain more than 10% of trait differences alone. After picking out those stable ones as the priority targets in the model, the screening efficiency of GS has been significantly improved. Moreover, more than once, researchers have noticed that some QTLs have "pleiotropy" - that is, they affect multiple traits at the same time. If we can find this gene and combine it with high-quality germplasm materials, improving both yield and quality will no longer be an "impossible triangle". 7.3 Impact It used to be said that yield and fiber quality are in a tug-of-war, and it is difficult to balance them. But this is not unsolvable, it just depends on whether there are means. Through the combination of QTL positioning and genomic
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