Animal Molecular Breeding 2024, Vol.14, No.5, 297-306 http://animalscipublisher.com/index.php/amb 302 influence on egg number during early and mid-laying periods. This discovery provided insight into the genetic regulation of egg production at different life stages of the hens (Lien et al., 2020). In addition to egg number, traits such as eggshell strength and egg weight were key focuses of the study. A QTL on GGA23 was identified to be strongly associated with both eggshell strength and egg weight, particularly under tropical climate conditions. This QTL helps explain the adaptability of chickens in high-temperature environments, enabling them to maintain high productivity in challenging conditions. These findings suggest potential genetic mechanisms that control egg production under environmental stress (Haqani et al., 2021). Furthermore, by integrating whole-genome sequencing (WGS) with QTL analysis, researchers were able to validate the precision of these identified QTLs. Significant SNP variants were found on GGA1 and GGA6, pointing to candidate genes related to egg weight, eggshell quality, and egg production rate. These genes provide important targets for future functional studies and potential genetic editing to improve egg production in layer hens (Stainton et al., 2015). 4.3 Interpretation of results and application in breeding programs The findings from QTL mapping offer significant potential for future breeding programs. Marker-assisted selection (MAS) can be applied using the identified QTLs to select chickens with favorable genetic traits, such as higher egg production, increased egg weight, and stronger eggshells. In tropical climates, using these QTL markers will help breeders choose hens with better environmental adaptability, improving overall productivity and egg quality (Lien et al., 2020). Moreover, the identification of these QTLs opens the door for functional validation through gene editing technologies such as CRISPR/Cas9. By targeting key genes associated with egg production traits, researchers can validate the roles these genes play in regulating egg weight, shell strength, and production rates. This not only enhances the precision of genetic improvement in breeding programs but also provides new opportunities for functional genomics research into layer hens (Goto and Tsudzuki, 2017). In conclusion, these QTL discoveries pave the way for more targeted breeding strategies. By utilizing the identified genetic regions, breeders can achieve faster genetic progress in improving key egg production traits, reducing production costs and enhancing the overall economic efficiency of layer hen operations. These results provide a solid foundation for breeding chickens that are better adapted to environmental stresses while maintaining high productivity (Meng et al., 2015). 5 Challenges and Limitations in QTL Mapping 5.1 Genetic complexity of egg production traits Egg production traits in layer hens are highly complex, controlled by multiple genes, each with a small effect. This polygenic nature complicates the identification of significant QTLs as they may interact with each other through additive, dominant, or epistatic effects. Furthermore, QTLs can exhibit age-specific effects, meaning that their influence on egg production traits might vary at different life stages. This genetic complexity requires sophisticated methods to accurately capture these interactions and account for the dynamic nature of gene expression throughout the hen's laying period (Goto and Tsudzuki, 2017). Another significant challenge is the influence of environmental factors on the expression of egg production traits. Environmental factors such as temperature, feed quality, and housing conditions can mask the effects of QTLs, making it difficult to isolate the genetic contributions from phenotypic data. This is particularly problematic in traits with low heritability, where environmental variance plays a substantial role. Consequently, QTL mapping for egg production often requires well-controlled environments or large sample sizes to ensure statistical power (Lan et al., 2020). Moreover, genetic heterogeneity across different chicken breeds can also complicate QTL discovery. Breeds adapted to different environments may possess unique genetic architectures that contribute to egg production.
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