International Journal of Aquaculture, 2025, Vol.15, No.5, 229-239 http://www.aquapublisher.com/index.php/ija 231 al., 2023). Water quality factors such as ammonia nitrogen and nitrite have a recessive effect on growth: chronic low-concentration ammonia nitrogen stress can reduce the feeding rate and digestive enzyme activity of shrimps, and the growth rate is significantly reduced (Li et al., 2024). A study of vannabinoid shrimp found that under the dual stress of sub-chronic ammonia nitrogen and salinity, the specific growth rate and intake of shrimp were inhibited and induced changes in stress-related gene expression. In addition, stocking density and nutritional level can also affect growth traits. High-density farming often leads to slow growth and small individuals, which is due to crowded stress and intra-species competition. In terms of bait nutrition, insufficient protein content will limit growth, and adding an appropriate amount of immune enhancer (such as astaxanthin) can improve digestive enzyme activity and growth-promoting gene expression, thereby improving growth performance. 3 Application of QTL Study in Shrimp 3.1 Basic principles and methods of QTL research Quantitative trait loci (QTL) localization is a classic method to discover quantitative trait genes through genetic linkage analysis. The basic principle is to detect the linkage association between phenotypic traits and molecular markers on the constructed genetic linkage map. If the marker typing of a region is significantly correlated with the trait value, it is inferred that the region contains genolo points that affect the trait. Traditional QTL mapping usually uses two-parent hybrid families, such as crossing parents with different growth traits, obtaining F2 or backcrossing populations, and then phenotyping and genotyping of population individuals. According to different mapping models, statistical methods such as interval mapping and composite interval mapping can be used to scan the map to locate the significance QTL. The accuracy of QTL positioning depends on the spectrum density and population size. The QTL map of early shrimps used microsatellite markers (SSRs), etc., and the number of markers was limited, resulting in a wide QTL interval. In recent years, second-generation sequencing technology has promoted the construction of high-density maps, each map can contain thousands to tens of thousands of marks, improving mapping accuracy (Huang et al., 2019). 3.2 Research progress on localization of QTL related to shrimp growth traits In shrimp, the study of growth-related QTL has made some progress in recent years. The earliest attempt can be traced back to the QTL positioning study of the Chinese shrimp "Huanghai No. 1". Although those studies were published more than a decade ago, they demonstrated the possibility of genetic localization of shrimp growth traits for the first time. In the past five years, with the emergence of vannerbine shrimp genome sequencing and high-density maps, new achievements have been made in QTL localization for growth traits. Experts used a 268-individual shrimp family to detect 11 QTLs with significantly associated growth rates (Ma et al., 2024). These QTLs are distributed in multiple linkage groups of shrimp genomes. Each QTL can explain about phenotypic variations of about 5%~10%, with the sum of contribution rates exceeding 50%, indicating that growth traits are regulated by multiple genes and there are several main-effect sites. Some of these QTLs show colocalization among different traits, suggesting that there may be core genes that affect overall growth ability. 3.3 Comparison of QTLs in different population and genetic background It should be emphasized that the QTL effect is often population-specific, and the same trait may be controlled by different sites in different genetic backgrounds. This is particularly evident among shrimps. The sites found in early single-line-based QTL studies were not necessarily significant in other strains or wild populations. This is due to the differences in allelic frequencies, linkage imbalance structures of different groups, and the growth traits themselves are affected by environmental and gene interactions. For example, Wang et al. (2019) found a candidate gene in different groups is the scavenger receptor Class C on the 13th linkage group. These results suggest that growth regulation in different breeding populations may be mediated by different gene pathways (Wang et al., 2019). Therefore, when applying QTL to molecular breeding, its population applicability needs to be taken into account, and it is best to be able to re-verify the QTL effect in the target breeding population. One way to improve the universality of QTL is to use multi-population joint maps or genome-wide associations to directly analyze natural populations or breeding populations, thereby localizing "shared sites" associated with traits. In fact, GWAS makes up for the limitations of single-family QTL to some extent and can be used for cross-validation of results in different contexts (Rio et al., 2020).
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