Bioscience Methods 2024, Vol.15, No.6, 264-274 http://bioscipublisher.com/index.php/bm 267 identified several markers linked to these traits, which can be used to select high-yielding and high-starch varieties (Babu et al., 2004; Francia et al, 2005; Collard and Mackill, 2008; Li, 2024). For instance, markers linked to genes controlling starch biosynthesis pathways can be used to enhance starch content in sweet potatoes (Babu et al., 2004; Collard and Mackill, 2008). The application of these markers involves genotyping breeding populations to identify individuals carrying the desirable alleles. This process can be done using various molecular techniques, such as single nucleotide polymorphisms (SNPs) and sequence-characterized amplified regions (SCAR) markers (Babu et al., 2004; Beketova et al., 2021). By selecting individuals with favorable marker profiles, breeders can efficiently develop new sweet potato varieties with enhanced yield and starch content (Babu et al., 2004; Collard and Mackill, 2008). 4.3 Case studies: performance of high-yield, high-starch sweet potato varieties developed through mas Several case studies have demonstrated the effectiveness of MAS in developing high-yield and high-starch sweet potato varieties. For example, the use of MAS in maize has shown significant improvements in grain yield under drought conditions, which can be analogous to improving yield in sweet potatoes under various stress conditions (Ribaut and Ragot, 2006). Similarly, MAS has been successfully used to develop potato cultivars with resistance to late blight, showcasing the potential of this technique in improving complex traits (Beketova et al., 2021). In sweet potatoes, the application of MAS has led to the development of varieties with enhanced starch content and yield. These varieties have been evaluated under different environmental conditions, showing consistent performance and higher productivity compared to traditional varieties (Babu et al., 2004; Collard and Mackill, 2008). The integration of MAS with conventional breeding methods has thus proven to be a powerful approach in sweet potato improvement, enabling the rapid development of superior cultivars (Babu et al., 2004; Collard and Mackill, 2008; Singh and Singh, 2015). 5 Genomic Selection (GS) for Enhanced Yield and Starch Content in Sweet Potato 5.1 Application of genomic selection in sweet potato breeding Genomic selection (GS) leverages genome-wide molecular markers to predict the performance of individuals, allowing for selection without direct phenotyping. This approach accelerates genetic gain in breeding programs by enabling early and more accurate selection of desirable traits (Sverrisdóttir et al., 2017; Stich and Inghelandt, 2018). The principles of GS involve using statistical models to estimate the genetic value of individuals based on their genotypic data, which can significantly reduce the breeding cycle time and increase the efficiency of selecting high-yield and high-starch content sweet potato varieties (Slater et al., 2016; Haque et al., 2023). The feasibility of GS in crops like tetraploid potato has shown promising results, suggesting its potential application in sweet potato breeding as well (Sverrisdóttir et al., 2017; Stich and Inghelandt, 2018). 5.2 Development of multi-trait selection models Multi-trait genomic selection models are essential for optimizing the selection process in sweet potato breeding, as they allow breeders to consider multiple traits simultaneously, such as yield, starch content, disease resistance, and plant architecture (Moeinizade et al., 2020; Rosero et al., 2023). Strategies for multi-trait selection include index selection, which assigns weights to different traits based on their economic importance, and multi-objective optimization, which identifies trade-offs across traits to achieve a balanced selection. Advanced methods like the look-ahead selection (LAS) algorithm have been proposed to maximize certain traits while keeping others within desirable ranges, demonstrating superior performance compared to conventional index selection (Moeinizade et al., 2020). These models help in identifying genotypes that exhibit a better overall profile and stability across different environments (Moeinizade et al., 2020; Rosero et al., 2023). 5.3 Success stories: achievements in improving sweet potato yield and starch content using genomic selection Several studies have reported successful applications of genomic selection in improving sweet potato yield and starch content. For instance, the use of a multi-trait index in the CropInd tool facilitated the selection of superior sweet potato genotypes based on their agronomic performance across multiple environments, leading to the
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