Cotton Genomics and Genetics 2025, Vol.16, No.3, 117-125 http://cropscipublisher.com/index.php/cgg 120 not only speeds up the breeding process, but also reduces the work of a large number of phenotyping tests (Li and Sillanpää, 2012). However, the effectiveness of MAS mainly depends on the accuracy of QTL positioning and whether these QTLs can be stably expressed under different environments. 4.2 Enhancing genomic prediction accuracy using known QTLs When doing genomic selection, adding known QTLs to the model as fixed effects or priority markers can make the prediction results more accurate, especially for those traits where the main effect QTL can explain a large part of the phenotypic differences (Kemper et al., 2015). Studies have found that if these QTL markers related to the target traits are used instead of randomly selected markers from the whole genome, the prediction effect will be better (Chen et al., 2022). Because these specific markers are more representative of the real genetic differences. In addition, using some multivariate statistical methods or Bayesian models, QTLs with multiple effects can also be used at the same time to improve the prediction accuracy of multiple related traits. 4.3 Strategies to incorporate both tools into cotton improvement programs To combine QTL mapping and genomic selection, some supporting strategies need to be adopted. First, QTL mapping can be used to identify key genetic loci, and then these loci can be given priority when making genomic predictions, which can improve the accuracy and efficiency of predictions (Lan et al., 2020). Second, linkage mapping and association mapping can be combined. This method can better narrow the scope of QTL and help find important candidate genes, thereby improving their application value in actual breeding (Daware et al., 2020). In addition, multivariate analysis and Bayesian methods are also very useful. These models can predict multiple traits at the same time and can also take advantage of the pleiotropy of genes and their commonalities (Kemper et al., 2018). Finally, the breeding process needs to be constantly updated. New QTL data and the latest phenotypic information can be added regularly to adjust the training population and model so that the prediction effect can continue to keep up with environmental and genetic changes and maintain good genetic gain. 5 Traits Targeted for Improvement 5.1 Yield components: boll weight, number of bolls, and lint percentage Whether a cotton variety produces a lot or not depends on how many bolls it has on a plant, how many bolls there are, how heavy each boll is, and what the lint percentage is. These are not new problems, and the older generation of breeders have been thinking about them for a long time. Traditional breeding methods are still in use, of course, but there are more tools now than before. For example, now we can use genotyping technology, combined with gene editing methods, to directly target key sites related to these traits (Singh et al., 2020). We don't have to look at the performance of each plant, we can also judge which plant has potential. To put it bluntly, this method helps breeders save time and improve efficiency, and the goal is also very direct-select varieties with higher yields. 5.2 Fiber quality traits: length, strength, micronaire, and uniformity Whether cotton can be sold in the market depends not only on the amount of production, but also on the fiber quality. Textile mills are very concerned about indicators such as length, strength, and micronaire. If the uniformity is not good, there may be a problem with the machine. These traits are actually more difficult to change than yields - because there are many details and high requirements. However, in recent years, genomics has made rapid progress, and breeders can locate the genes that control these traits more quickly. Take CRISPR/Cas for example, it can specifically modify a specific gene without touching other parts (Sedeek et al., 2019). If operated properly, the two goals of yield and quality can actually be achieved at the same time. 5.3 Agronomic resilience: drought tolerance, pest resistance, and maturity timing Whether cotton is easy to grow is not just about whether it can produce high yields. Some places are dry, some places are prone to pests and diseases, and some places take too long to mature and cannot be harvested in time. These problems are complex and simple at the same time - the key is whether there is a way to take into account multiple traits at the same time. The climate has changed, and the environment is not as stable as before, and cotton must keep up. At this time, genomics and new technologies come in handy. Scientists will use whole genome screening to pick out genes that can help plants "take on things" (Li et al., 2024). The final variety must be the type that can survive, grow, and produce fruits in all kinds of land.
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