Genomics and Applied Biology 2024, Vol.15, No.6, 307-319 http://bioscipublisher.com/index.php/gab 308 make selection decisions early in the breeding cycle, thus reducing the time required for field evaluation and increasing genetic gain. In yam breeding, where generation times are long, GS presents a promising solution to enhance breeding efficiency and respond to the growing demand for resilient, high-yield varieties (An et al., 2024). 2.1 Basic concepts and technical background of genomic selection Genomic Selection (GS) utilizes high-density genetic marker data distributed across the entire genome to predict the breeding value of individuals, which differs significantly from traditional marker-assisted selection (MAS). MAS focuses primarily on identifying and using a limited number of key quantitative trait loci (QTLs), which only explain part of the genetic variation for complex traits. In contrast, GS captures the effects of thousands of small-effect loci using genome-wide markers, providing a more comprehensive reflection of the genetic basis of complex traits. This approach greatly enhances the accuracy and effectiveness of predictions, especially for polygenic traits controlled by multiple small-effect genes, which are common in crops such as yams. By leveraging more extensive genetic information, GS accelerates the selection process, improves the efficiency and success rate of breeding programs, and facilitates the rapid development of new varieties with superior characteristics (Crossa et al., 2017). The technical foundation of GS involves the use of genomic estimated breeding values (GEBVs), calculated by fitting statistical models that incorporate marker data. Training populations are genotyped and phenotyped to establish prediction models, which can then be applied to predict GEBVs of untested individuals based on their genotypes alone. Common statistical approaches include best linear unbiased prediction (BLUP) and various machine learning methods like Bayesian regression and random forests, which help account for complex genetic architectures and genotype-by-environment interactions (Heslot et al., 2012). 2.2 Potential of genomic selection in yam breeding The application of GS in yam breeding has the potential to address several challenges that have traditionally hindered progress in this crop. Yam, particularly the Dioscorea species, is vital for food security in regions like West Africa but suffers from a long breeding cycle due to its reproductive characteristics and the need for extensive field trials. By allowing breeders to select superior genotypes early, GS can drastically shorten breeding cycles, leading to faster varietal release and more efficient breeding programs (Asfaw et al., 2020). In addition to cycle acceleration, GS can enhance genetic gain by improving the accuracy of selection for complex traits like yield, disease resistance, and quality, which are crucial for yam improvement. Since these traits are influenced by multiple genetic loci, GS provides a more holistic approach than traditional phenotypic selection methods. Furthermore, GS allows for simultaneous improvement of multiple traits, reducing the trade-offs often encountered in conventional breeding (Lin et al., 2016). 2.3 Current status of genomic selection breeding worldwide and in domestic programs Globally, the adoption of GS has increased significantly across crops like maize, wheat, and perennial ryegrass, where it has demonstrated substantial genetic gains and reduced breeding cycle times. For instance, in maize, GS has enabled faster accumulation of favorable alleles for yield and disease resistance, establishing GS as an integral part of modern breeding programs (Xu et al., 2019; Wang, 2024). Similarly, GS has shown promise in high-value crops like tomato, where rapid selection for complex traits has accelerated the breeding process (Cappetta et al., 2020). In yam, GS is still an emerging tool but has garnered attention in regions where yam is a staple crop, particularly in West African countries. Collaborative breeding programs led by institutions such as the International Institute of Tropical Agriculture (IITA) are pioneering the integration of GS with conventional methods to enhance the genetic diversity and resilience of yam varieties. These programs focus on training populations representative of local varieties and environmental conditions, ensuring that genomic predictions are robust and applicable to target regions. As the cost of genotyping continues to decrease, it is expected that GS will become a core component of yam breeding programs worldwide, contributing to improved food security and agricultural sustainability in yam-growing regions (Saski et al., 2015).
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