Animal Molecular Breeding 2024, Vol.14, No.5, 297-306 http://animalscipublisher.com/index.php/amb 303 Therefore, QTLs identified in one population may not be transferable to another. This necessitates further research across multiple populations and environments to identify broadly applicable QTLs (Teh et al., 2020). 5.2 Statistical and computational challenges The large volume of data generated from high-throughput genotyping and phenotyping technologies presents significant statistical and computational challenges in QTL mapping. For instance, analyzing thousands of molecular phenotypes while controlling for multiple testing is computationally intensive. To address this, tools like FastQTL have been developed, which apply efficient permutation-based methods to control for multiple testing. Despite improvements, the computational cost of large-scale QTL studies remains a limitation for many researchers (Ongen et al., 2015). Additionally, the statistical power of QTL mapping is often limited by the sample size. In small or narrowly bred populations, recombination events are infrequent, making it difficult to accurately detect QTLs. This issue is exacerbated in traits controlled by many small-effect loci, where large samples are needed to achieve sufficient statistical power. New statistical models, such as mixed linear models (MLMs) and Bayesian methods, attempt to overcome these limitations by better modeling the genetic architecture and population structure (Wang et al., 2015). Moreover, handling the correlations among individuals due to family structure or shared environments can introduce bias into QTL mapping. This is particularly problematic in structured populations or multiparent crosses. Tools such as lme4qtl, which extend mixed-model approaches to account for complex covariance structures, have been developed to address these issues, though these require significant computational resources (Ziyatdinov et al., 2017). 5.3 Integration with genomic selection and other molecular tools While QTL mapping has been a useful tool for identifying regions of the genome associated with complex traits, integrating these findings into genomic selection programs presents another set of challenges. Genomic selection relies on the use of dense marker maps, but the incorporation of QTL markers into these models can introduce statistical biases or overfitting. This has led to the development of new statistical frameworks that balance the contributions of both genome-wide markers and QTLs (Lan et al., 2020). Another challenge is the integration of genomic data with transcriptomics and proteomics. Emerging molecular tools such as eQTL mapping provide insights into gene expression patterns, yet combining these data with QTL mapping can be computationally demanding. Tools like TORUS are being developed to integrate genomic annotations into QTL discovery, providing a more comprehensive understanding of gene regulation in complex traits (Wen, 2016). Finally, practical challenges remain in applying QTL discoveries to breeding programs. While marker-assisted selection (MAS) can leverage QTL information, the full potential of QTLs in breeding is often limited by difficulties in functional validation. This is especially true for QTLs with small effects or those influenced by epistatic interactions, requiring further research to translate genomic insights into breeding improvements (Khalil et al., 2021). 6 Future Directions and Applications 6.1 Enhancing breeding efficiency through qtl mapping One of the main future directions for QTL mapping is its integration into breeding programs to enhance the efficiency of genetic selection. QTL mapping allows breeders to identify specific chromosomal regions associated with key traits, such as egg production in layer hens. This provides breeders with genetic markers that can accelerate the selection of individuals with superior traits, reducing the time needed for genetic improvement compared to traditional methods. Studies have shown that high-density SNP chips and advanced genotyping techniques can significantly improve the precision of QTL mapping, thus enhancing the overall breeding process (Lien et al., 2020).
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