Rice Genomics and Genetics 2024, Vol.15, No.4, 164-177 http://cropscipublisher.com/index.php/rgg 168 3.2 Transcriptomics and gene expression RNA sequencing (RNA-Seq) has become a powerful tool for transcriptome analysis in Oryza, allowing researchers to identify and quantify RNA molecules in different tissues and under various conditions. Transcriptome analyses have revealed significant insights into gene expression patterns, regulatory networks, and functional genomics of Oryza species. For example, transcriptome analysis of Oryza granulata identified high-quality transcripts that provide valuable resources for functional and evolutionary studies, including genes associated with stress responses (Yang et al., 2018). Gene regulation in rice involves intricate mechanisms, including the roles of natural antisense transcripts (NATs) and posttranscriptional events. The high-density genomic arrangement of NAT genes suggests their potential roles in multifaceted gene expression control (Chen et al., 2019). Additionally, the functional impact map of GVs provides insights into tissue-specific gene regulation, emphasizing the importance of chromatin accessibility in regulatory regions (Zhao et al., 2021). These findings underscore the complexity of gene regulation and the diverse expression patterns observed in rice. Gene regulation and expression patterns in Oryza are influenced by various genetic and environmental factors. Studies have shown that differentially expressed genes under stress conditions can provide insights into the plant's adaptive mechanisms. For instance, genome-wide analysis of salinity and submergence stress-responsive genes in Oryza coarctata has identified key regulatory genes and pathways involved in stress tolerance (Bansal et al., 2020). 3.3 Proteomics and metabolomics Proteomics, the large-scale study of proteins, is essential for understanding the functional aspects of the Oryza genome. Protein profiling has been used to identify and characterize proteins involved in various biological processes and stress responses. For instance, protein structure modeling in Magnaporthe oryzae, a fungal pathogen of rice, has identified numerous effector proteins that interact with the host plant, providing insights into pathogen-host interactions (Seong and Krasileva, 2021). The integration of functional genomics, transcriptomics, proteomics, and metabolomics has provided comprehensive insights into the gene functions, regulatory mechanisms, and metabolic pathways in rice. These advancements have significant implications for crop enhancement and the development of stress-resistant rice varieties. Metabolomics, the study of metabolites, complements proteomics by providing a comprehensive understanding of metabolic pathways and networks. Studies on metabolic pathways in Oryza have revealed critical insights into plant metabolism and stress responses. For example, the identification of metabolic pathways involved in secondary metabolite biosynthesis and hormone signal transduction in Oryza coarctata has provided valuable information for improving stress tolerance in rice (Bansal et al., 2020). 4 Insights into Key Agronomic Traits 4.1 Yield-related traits The identification and mapping of quantitative trait loci (QTLs) associated with yield-related traits have been pivotal in understanding the genetic basis of yield in rice. For instance, a study using F2:3:4 populations derived from two alien introgression lines identified several QTLs linked to yield traits such as plant height, tiller number, and grain weight. Notably, qTGW8.1 was consistently identified across generations, indicating its potential for improving grain weight in rice breeding programs (Beerelli et al., 2022). Similarly, research on interspecific backcross populations of Oryza sativa and Oryza glaberrima identified 20 QTLs associated with yield-enhancing traits, with qGY-4.1 showing significant phenotypic variance and potential for yield improvement (Bharamappanavara et al., 2023). These findings underscore the importance of wild and relative species in broadening the genetic base and enhancing yield traits in cultivated rice. Genomic selection (GS) has emerged as a powerful tool for yield improvement in rice. By using high-throughput genotyping and phenotyping data, GS allows breeders to predict the genetic potential of plants for yield traits
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