Genomics and Applied Biology 2024, Vol.15, No.6, 307-319 http://bioscipublisher.com/index.php/gab 317 Another example is seen in macadamia breeding, where GS was shown to double genetic gain in nut yield by reducing the generation time from eight to four years (O'Connor et al., 2021). Applying these principles to yam, which has a similarly long breeding cycle, could expedite the development of high-yield, stress-resilient varieties. In each of these cases, the application of GS led to improved breeding efficiency, reduced time to market for new varieties, and enhanced genetic gain. These studies illustrate the promising impact of GS on breeding efficiency and underscore the potential for similar gains in yam breeding, where the combination of GS with advanced breeding techniques could revolutionize the development of improved varieties. 9 Concluding Remarks The implementation of genomic selection (GS) in yam breeding has shown immense potential for accelerating breeding cycles, enhancing genetic gain, and making the breeding process more efficient. This concluding section summarizes the key findings of this research, insights for practical application in yam breeding, and recommendations for future research. This research has highlighted the transformative impact of GS on yam breeding efficiency. By using genome-wide markers to predict the breeding values of individual genotypes, GS allows breeders to make informed selection decisions earlier in the breeding cycle. This shift from phenotypic selection to genomic prediction has been shown to reduce the breeding cycle duration, increasing the pace at which new, high-yielding, and resilient yam varieties can be developed. Additionally, integrating GS with other technologies, such as speed breeding, further enhances genetic gain by allowing for rapid generation turnover and continuous selection of favorable alleles. These combined approaches hold the potential to significantly improve trait expression in yam, particularly for complex traits such as disease resistance and tuber quality. For practical application in yam breeding, several strategies should be considered. First, optimizing marker density and sample size is crucial for maximizing the accuracy of GS models. Sufficiently dense markers ensure that the genetic variation across the genome is well-captured, while a large sample size provides a robust training population, increasing the reliability of genomic predictions. Additionally, integrating environmental data into GS models can help manage genotype-by-environment interactions, improving prediction accuracy in diverse cultivation conditions. This is particularly relevant for yam, which is grown across various ecological zones. Finally, the establishment of collaborative breeding programs that pool resources and data could help mitigate challenges such as limited marker availability and genotyping costs, making GS accessible to more yam breeding programs globally. Future research in GS for yam breeding should focus on several key areas. First, the development of more sophisticated models that account for non-additive genetic effects and complex GxE interactions is necessary. Machine learning and deep learning models, though computationally intensive, could provide significant accuracy improvements for predicting complex traits in yam. Additionally, expanding genomic resources for yam, such as creating more comprehensive SNP arrays and reference genomes, would support more accurate GS models. Lastly, exploring the integration of new phenotyping technologies, such as hyperspectral imaging and remote sensing, could enhance trait measurement accuracy and improve the overall efficiency of yam breeding programs. By addressing these areas, future research can ensure that GS becomes a staple method in yam breeding, fostering the development of resilient, high-yield yam varieties suited to diverse environments and enhancing global food security. Acknowledgments The authors would like to thank the research group for their suggestions on my manuscript. 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.
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