Molecular Plant Breeding 2025, Vol.16, No.1, 35-43 http://genbreedpublisher.com/index.php/mpb 40 6.2 Genetic basis of soybean cyst nematode resistance The genetic basis of SCN resistance in soybeans primarily involves two major loci: Rhg1 and Rhg4. The Rhg1 locus is associated with copy number variations, where high copy numbers confer resistance (PI 88788-type), and low copy numbers are linked to susceptibility (Williams 82-type) (Kadam et al., 2016; Shaibu et al., 2020). The Rhg4 locus, on the other hand, involves a resistant allele that disrupts folate homeostasis at SCN feeding sites. Additionally, other quantitative trait loci (QTL) such as GmSNAP11 have been identified, contributing to the complex genetic architecture of SCN resistance (Yang et al., 2020). 6.3 Marker development and validation Several studies have focused on developing and validating genetic markers for SCN resistance. For instance, SNP markers for Rhg1 and Rhg4 have been developed and validated using diverse soybean germplasm lines and recombinant inbred line (RIL) populations (Li et al., 2009; Kadam et al., 2016). These markers have shown high efficiency in differentiating resistant and susceptible genotypes, with selection efficiencies exceeding 90% in some cases (Santana et al., 2014). Additionally, novel markers such as KASPar assays and InDel markers have been introduced to enhance the precision of MAS (Yang et al., 2020). 6.4 Breeding program design and field implementation Breeding programs utilizing MAS for SCN resistance typically involve several stages, including marker development, validation, and field implementation. For example, microsatellite markers near QTL for SCN resistance have been used to select resistant genotypes in populations derived from crosses between resistant and susceptible cultivars (Santana et al., 2014; Espindola et al., 2016). Field trials are then conducted to evaluate the performance of selected lines under SCN-infested conditions. The integration of MAS into breeding programs has significantly reduced the reliance on phenotypic evaluations, thereby accelerating the development of SCN-resistant cultivars. 6.5 Outcomes and impact on soybean production The implementation of MAS for SCN resistance has led to the successful development of several resistant soybean cultivars. These cultivars have shown improved resistance to multiple SCN races, resulting in enhanced yield stability and reduced economic losses for soybean producers (Santana et al., 2014; Kadam et al., 2016). The use of MAS has also facilitated the pyramiding of multiple resistance genes, further strengthening the durability and effectiveness of SCN resistance in soybean breeding programs (Maroof et al., 2008; Yang et al., 2020). Overall, the adoption of MAS has had a positive impact on soybean production, contributing to sustainable agricultural practices and food security. 7 Future Directions and Perspectives 7.1 Enhancing MAS efficiency through genomic innovations The efficiency of Marker-Assisted Selection (MAS) in soybean breeding can be significantly enhanced through the integration of advanced genomic technologies. Genotyping-by-sequencing (GBS) has emerged as a powerful tool, providing high-throughput sequencing capabilities that facilitate the discovery and genotyping of single nucleotide polymorphisms (SNPs) across large crop genomes. This approach not only accelerates the breeding process but also reduces costs, making it an ultimate MAS tool for large-scale plant breeding programs. Additionally, the development of next-generation marker genotyping platforms, such as AmpSeq, offers high accuracy, flexibility, and speed, further bridging the gap between marker development and MAS implementation. These innovations are crucial for improving the precision and efficiency of MAS, enabling the selection of superior genotypes with desirable traits. 7.2 Combining MAS with emerging breeding technologies Combining MAS with emerging breeding technologies holds great promise for advancing soybean breeding programs. Integrated Genomic Selection (IGS), which combines MAS with genomic selection (GS), leverages molecular genetic markers to enhance the selection of complex traits such as yield and stress tolerance. This approach reduces the breeding cycle time and resources required, while increasing genetic gain. Moreover, the integration of MAS with technologies like speed breeding, machine learning, and environmental data can further
RkJQdWJsaXNoZXIy MjQ4ODYzNA==