Rice Genomics and Genetics 2025, Vol.16, No.3, 150-158 http://cropscipublisher.com/index.php/rgg 156 instance, the integration of genome-wide association studies (GWAS) with QTL mapping has led to the identification of meta-QTLs and candidate genes associated with RSA traits (Daryani et al., 2021). These integrated datasets can be used to develop climate-resilient rice varieties by targeting specific genes and pathways involved in root development and stress responses. Moreover, the use of Bayesian networks and machine learning models can improve the prediction of RSA traits from genotypic data, facilitating the selection of superior genotypes for breeding programs (Sharma et al., 2021). 6.3 Breeding strategies for optimal RSA in diverse agroecosystems Breeding strategies aimed at optimizing RSA must consider the diverse agroecosystems in which rice is cultivated. Identifying and utilizing natural alleles of key genes, such as DRO1 and its homologs, can help develop rice varieties with root systems adapted to specific environmental conditions, such as saline or drought-prone soils (Kitomi et al., 2020). Marker-assisted selection and QTL cloning are essential tools for incorporating desirable RSA traits into breeding programs (Dorlodot et al., 2007). Additionally, understanding the genetic flexibility and evolutionary mechanisms of RSA in different rice strains, including weedy rice, can provide insights into developing competitive and resilient root systems (Wedger et al., 2019; Piacentini et al., 2023). By focusing on the genetic and phenotypic diversity of RSA traits, breeders can create rice varieties that optimize water and nutrient uptake, enhance stress tolerance, and ultimately improve yield in various agroecosystems (Panda et al., 2021; Abdirad et al., 2022). 8 Conclusion The study of root system architecture (RSA) in rice highlights the significant role of RSA in enhancing resource-use efficiency and grain yield, particularly under stress conditions such as drought and nutrient deficiency. Key genetic factors influencing RSA include quantitative trait loci (QTLs) and specific genes like PSTOL1, qSOR1, and DRO1, which have been identified and validated for their roles in root growth and stress adaptation. Environmental factors also play a crucial role, with RSA traits such as root length, density, and branching being critical for nutrient and water uptake under varying soil conditions. Advances in phenotyping technologies, including non-invasive imaging and high-throughput genotyping, have facilitated the detailed study of RSA, enabling the identification of genetic variations and the development of rice varieties with optimized root traits. The interplay between genetic and environmental factors in shaping RSA is complex and multifaceted. Genetic factors provide the foundational blueprint for root development, while environmental conditions modulate the expression and effectiveness of these genetic traits. For instance, genes like OsVST1 and various QTLs have been shown to influence root growth patterns, which are further modified by soil moisture, nutrient availability, and other environmental stresses. This synergy between genetics and environment underscores the importance of a holistic approach in RSA research, where both intrinsic genetic potential and extrinsic environmental influences are considered to develop resilient and high-yielding rice varieties. Future research in RSA should focus on integrating advanced genomic tools with environmental modeling to predict and enhance root traits that contribute to sustainable rice production. The use of genome-wide association studies (GWAS) and meta-QTL analysis can help identify key genetic regions associated with desirable RSA traits, which can then be targeted for breeding programs. Additionally, the development of high-throughput phenotyping platforms and non-invasive imaging techniques will be crucial in accurately assessing root traits in diverse environmental conditions. By leveraging these technological advancements, researchers can develop rice varieties with optimized RSA that are better equipped to cope with climate change and resource limitations, ultimately contributing to global food security and sustainable agriculture. Acknowledgments The authors sincerely thank Dr. Chen for carefully reviewing the initial draft of the manuscript and providing detailed revision suggestions. The author also extends deep gratitude to the two anonymous peer reviewers for their valuable comments and suggestions on the initial draft of this study.
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