Bioscience Methods 2024, Vol.15, No.6, 255-263 http://bioscipublisher.com/index.php/bm 257 Quantitative trait loci (QTLs) are genomic regions associated with quantitative traits, such as yield and disease resistance. Identifying QTLs linked to these traits allows breeders to select for multiple genes simultaneously, enhancing the efficiency of breeding programs. QTL mapping has been instrumental in identifying regions associated with important agronomic traits in soybeans (Miklas et al., 2006; Sebastian et al., 2010). 2.3 Technological advances in MAS for soybean High-throughput genotyping platforms, such as SNP arrays and genotyping-by-sequencing (GBS), have revolutionized MAS by enabling the rapid and cost-effective analysis of large populations. These technologies allow for the simultaneous detection of thousands of markers, facilitating the identification of beneficial alleles and accelerating the breeding process (He et al., 2014; Ludwików et al., 2015). The integration of Genomic Selection (GS) with MAS combines the strengths of both approaches, allowing for the prediction of breeding values based on genome-wide marker data. This integration enhances the accuracy of selection and accelerates the development of superior soybean cultivars with improved yield and disease resistance (Miedaner and Korzun, 2012). 2.4 Application of MAS for trait improvement in soybean MAS has been successfully applied to improve soybean yield by selecting for QTLs associated with high yield potential. Context-specific MAS (CSM) has been used to identify and select subline haplotypes with superior yield traits, resulting in significant yield gains in selected sublines (Sebastian et al., 2010). The use of high-throughput genotyping platforms has further enhanced the efficiency of yield improvement programs (He et al., 2014). MAS has been instrumental in developing soybean cultivars with enhanced disease resistance. By identifying and selecting markers linked to resistance genes, breeders have been able to develop cultivars resistant to various diseases, such as pod shattering and bacterial blight (Miklas et al., 2006; Ludwików et al., 2015; Kim et al., 2020). The use of MAS for pyramiding multiple resistance genes has also been successful, providing broad-spectrum resistance to multiple pathogens (Miklas et al., 2006; Jena and Mackill, 2008). 3 Impact of MAS on Soybean Yield Improvement 3.1 Genetic basis of yield traits in soybean The genetic basis of yield traits in soybean is complex, involving multiple quantitative trait loci (QTL) that contribute to phenotypic variation. Yield is influenced by numerous genetic factors, including genes related to plant height, seed weight, and maturity. For instance, studies have identified several QTL associated with yield and other agronomic traits, such as the E1 and E3 maturity genes and the Dt2 stem growth habit gene, which have pleiotropic effects on yield and plant height (Miedaner and Korzun, 2012; Zhu et al., 2021). The identification and understanding of these genetic components are crucial for effective marker-assisted selection (MAS) strategies aimed at yield improvement. 3.2 QTL mapping for yield-related traits QTL mapping has been instrumental in identifying loci associated with yield-related traits in soybean. For example, a study involving 875 recombinant inbred lines (RILs) from a cross between Essex and Williams 82 identified 46 yield QTLs, explaining 4.5% to 11.9% of the phenotypic variation for yield (Fallen et al., 2015). Another study mapped QTLs in a BC1 population using specific-locus amplified fragment sequencing technology, identifying 46 significant QTLs for seven yield-related traits across three environments (Mei et al., 2021). These QTLs provide valuable targets for MAS, enabling breeders to select for high-yielding genotypes more efficiently (Ludwików et al., 2015). 3.3 Case study: development of high-yielding soybean varieties using MAS A notable breeding program utilized context-specific MAS (CSM) to improve grain yield in elite soybean populations. This approach involved leveraging residual heterogeneity in elite cultivars to detect yield QTL within specific environmental contexts. The selected subline haplotypes were then compared to their mother lines in replicated yield trials across multiple environments and years (Sebastian et al., 2010). This program highlights the importance of considering both genetic and environmental contexts in MAS.
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