Rice Genomics and Genetics 2024, Vol.15, No.4, 153-163 http://cropscipublisher.com/index.php/rgg 156 2 Molecular Breeding Techniques 2.1 Marker-assisted selection (MAS) Marker-Assisted Selection (MAS) is a molecular breeding technique that utilizes DNA markers linked to specific genes or quantitative trait loci (QTLs) to select desirable traits in breeding populations. This method allows for the precise identification and incorporation of genes associated with resistance to biotic and abiotic stresses, as well as improved agronomic traits. MAS can be combined with conventional breeding approaches to enhance the efficiency and accuracy of developing high-yielding, stress-resistant rice cultivars (Jena and Mackill, 2008; Ludwików et al., 2015; Das et al., 2017). MAS has been successfully employed in rice breeding to integrate multiple resistance genes into elite cultivars. For instance, the pyramidization of genes conferring resistance to blast, bacterial blight, and submergence has led to the development of rice varieties with broad-spectrum resistance and improved yield stability (Jena and Mackill, 2008; Ludwików et al., 2015; Das et al., 2017). Additionally, MAS has facilitated the stacking of multiple QTLs for stress tolerance, resulting in rice lines that exhibit enhanced resistance to salinity, drought, and other environmental stresses (Ludwików et al., 2015; Das et al., 2017). 2.2 Genomic selection (GS) Genomic Selection (GS) is a breeding method that uses genome-wide markers to predict the breeding value of individuals in a population. Unlike MAS, which targets specific genes, GS incorporates all marker information into the prediction model, capturing the effects of numerous small-effect QTLs. This approach improves selection accuracy, reduces phenotyping costs, and accelerates the breeding cycle, making it particularly effective for improving complex quantitative traits such as yield (Heffner et al., 2009; Grenier et al., 2015; Spindel et al., 2015; Budhlakoti et al., 2022). GS has shown promising results in rice breeding programs. For example, studies have demonstrated that GS models can achieve high prediction accuracies for traits such as grain yield, plant height, and flowering time. The integration of GS with genome-wide association studies (GWAS) has further enhanced the understanding of the genetic architecture of these traits, leading to more effective breeding strategies (Grenier et al., 2015; Spindel et al., 2015; Budhlakoti et al., 2022). Additionally, GS has been successfully applied in recurrent selection programs, resulting in significant genetic gains in rice populations (Grenier et al., 2015). 2.3 Genetic engineering and CRISPR/Cas9 Genetic engineering and CRISPR/Cas9 are powerful tools for precise gene editing in rice. CRISPR/Cas9, in particular, allows for targeted modifications of specific genes, enabling the introduction of desirable traits or the removal of deleterious alleles. This technique offers a high degree of precision and efficiency, making it a valuable tool for enhancing rice yield and stress tolerance (Jena and Mackill, 2008; Das et al., 2017). CRISPR/Cas9 has been employed to edit key yield-related genes in rice, resulting in significant improvements in grain yield and quality. For instance, targeted modifications of genes involved in plant architecture, grain size, and stress response have led to the development of rice varieties with enhanced yield potential and resilience to environmental stresses. These advancements highlight the potential of gene editing technologies to revolutionize rice breeding and contribute to global food security (Jena and Mackill, 2008; Das et al., 2017). Molecular breeding techniques such as MAS, GS, and CRISPR/Cas9 play a crucial role in enhancing rice yield by enabling the precise selection and modification of key yield-related genes. These approaches offer significant advantages in terms of accuracy, efficiency, and the ability to address complex traits, thereby contributing to the development of high-yielding, stress-resistant rice cultivars. 3 Functional Characterization of Yield-Related Genes 3.1 Gene expression studies RNA-Seq and transcriptomic analyses have been pivotal in identifying and characterizing yield-related genes in rice. For instance, the study by Wei et al. (2022) utilized transcriptomic data to identify the transcription factor
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