Computational Molecular Biology 2024, Vol.14, No.4, 145-154 http://bioscipublisher.com/index.php/cmb 152 Overall, the evolving landscape of GS is characterized by continuous innovation and integration of new technologies, making it a cornerstone of modern quantitative genetics and breeding strategies (Koning, 2016; Krishnappa et al., 2021; Xu et al., 2019). 7 Concluding Remarks Genomic selection has brought significant innovations to the field of quantitative genetics, utilizing single-nucleotide polymorphisms (SNPs) and other genomic markers to enable the early identification of genetically superior individuals. This approach has greatly improved selection accuracy, particularly in animal breeding, by calculating breeding value indexes that encompass almost all quantitative trait loci (QTLs). The application of high-throughput sequencing technologies has further enhanced our ability to identify genomic regions related to adaptation and species differentiation, and to deepen our understanding of the genomic structure of diversity. Emerging methods like deep learning and convolutional neural networks have provided critical support for uncovering the role of natural selection from large-scale genomic data. The future of quantitative genetics lies in the continued integration of genomic data with advanced computational methods. Genome-wide association studies (GWAS) and population genetics will help us understand the evolutionary mechanisms that maintain genetic variation for quantitative traits. Additionally, genomic selection holds great promise in plant breeding for improving agricultural productivity, though it must be carefully adapted to different breeding systems and environmental conditions. Furthermore, exploring the importance of balancing selection in genetic diversity will provide more insights into species evolution. To further advance genomic selection and quantitative genetics, future research should focus on several key areas. First, integrating multivariate selection will be crucial for understanding how correlational selection shapes genomic architecture. Second, more accurate prediction models are needed to account for the complex interactions between genetic and environmental factors, especially in breeding value predictions. Additionally, genomic selection methods should be expanded to include a wider range of species, particularly those with complex breeding systems or those underrepresented in current research. The potential of deep learning and artificial intelligence is vast, and future research should explore how these tools can be applied in genomics. Finally, developing new methods to identify and quantify balancing selection in genomes will help us better understand its role in maintaining genetic diversity within populations. Acknowledgments We sincerely appreciate the important resources provided by the Biotechnology Research Center of Cuixi College of Biotechnology. At the same time, I also thank the reviewers for their valuable feedback, which helped improve this article. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bradshaw J.I., 2016, Quantitative genetics and genomic selection, Journal of Dairy Science, 94(3): 1082-1090. https://doi.org/10.1007/978-3-319-23285-0_6 Budhlakoti N., Kushwaha A., Rai A.K., Chaturvedi K.K., Kumar A., Pradhan A., Kumar U., Kumar R.R., Juliana P., Mishra D.C., and Kumar S., 2022, Genomic selection: a tool for accelerating the efficiency of molecular breeding for development of climate-resilient crops, Frontiers in Genetics, 13: 832153. https://doi.org/10.3389/fgene.2022.832153 Burri R., 2017, Linked selection demography and the evolution of correlated genomic landscapes in birds and beyond, Molecular Ecology, 26: 3853-3856. https://doi.org/10.1111/mec.14167 Cappetta E., Andolfo G., Matteo A., Barone A., Frusciante L.G., and Ercolano M.R., 2020, Accelerating tomato breeding by exploiting genomic selection approaches, Plants, 9(9): 1236. https://doi.org/10.3390/plants9091236 Covarrubias-Pazaran G., 2016, Genome-assisted prediction of quantitative traits using the R package sommer, PLoS ONE, 11(6): e0156744. https://doi.org/10.1371/journal.pone.0156744
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