Genomics and Applied Biology 2024, Vol.15, No.6, 307-319 http://bioscipublisher.com/index.php/gab 316 Lastly, advanced machine learning models, such as random forests or deep learning approaches, offer potential for capturing complex genetic architectures, including epistatic interactions that are often missed in linear models. These models, although computationally intensive, can provide significant accuracy improvements for traits with complex inheritance patterns. Developing hybrid models that combine the simplicity of linear models with the adaptability of machine learning techniques may represent an optimal strategy for improving prediction accuracy in yam breeding (Norman et al., 2018). 8 Impact of Genomic Selection on Yam Breeding Efficiency Genomic selection (GS) has introduced transformative changes in yam breeding efficiency by accelerating breeding cycles, enhancing genetic gain, and streamlining the breeding process. By utilizing genome-wide markers to predict the breeding value of individuals, GS enables earlier selection of superior genotypes, reducing the time and resources needed for field evaluations. 8.1 Expected effects of genomic selection on accelerating breeding One of the primary benefits of GS is its potential to significantly reduce breeding cycles by allowing early selection based on genetic potential rather than waiting for full phenotypic evaluations. This is particularly valuable in yam, where conventional breeding cycles can span several years due to the plant's long growth period. GS enables rapid generation turnover by selecting genotypes with high breeding values in early stages, thus expediting the breeding process. Studies in other crops have demonstrated the effectiveness of GS in reducing breeding cycle time by as much as 50%, a benefit that can directly translate to yam breeding programs (Jighly et al., 2019). By minimizing cycle duration, GS makes it possible to develop yam varieties more quickly, which is essential for responding to changing agricultural demands and environmental challenges. Furthermore, when GS is combined with other accelerated breeding techniques, such as speed breeding, the effect on breeding cycle reduction becomes even more pronounced. This combined approach has shown success in other crops, demonstrating that both GS and accelerated growth conditions (e.g., controlled photoperiods) can synergistically reduce the breeding cycle duration and speed up the introduction of new varieties (Watson, 2019). 8.2 Enhancement of genetic gain and acceleration of breeding speed Genomic selection significantly enhances genetic gain by increasing the selection intensity and accuracy of breeding decisions. In traditional breeding, selection relies on observable traits, which are influenced by environmental variability. GS, however, uses genomic data to predict the genetic potential of individuals with high accuracy, thus enabling breeders to focus on candidates with the highest potential. This approach has proven successful in various crops, showing increased genetic gain rates compared to phenotypic selection. For example, studies in rice and wheat have reported up to a seven-fold increase in genetic gain when GS was incorporated alongside traditional breeding methods (Xu et al., 2019). In yam breeding, GS can increase genetic gain by allowing the continuous selection of favorable alleles across multiple generations. This continuous selection helps accumulate advantageous traits, such as disease resistance and yield, at a faster rate than conventional methods. By integrating GS into yam breeding, it becomes feasible to achieve higher genetic gain per generation, which is critical for improving complex traits and adapting to environmental stresses. The increased genetic gain ultimately contributes to higher productivity and resilience in yam varieties, benefiting both breeders and farmers. 8.3 Case studies of successful yam breeding applications Several case studies highlight the successful application of GS in plant breeding, underscoring its potential in yam. While specific yam-focused studies remain limited, research in other crops provides valuable insights into the potential benefits of GS in yam breeding. For instance, a study at the Bangladesh Rice Research Institute demonstrated that GS coupled with trait-specific marker-assisted selection reduced the breeding cycle by approximately 1.5 years and increased yield improvement by 117 kg per hectare per year—a seven-fold gain over baseline rates (Biswas et al., 2023). Similar approaches in yam breeding could lead to comparable advances, particularly in regions where yam is a staple food.
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