Rice Genomics and Genetics 2024, Vol.15, No.4, 153-163 http://cropscipublisher.com/index.php/rgg 153 Research Report Open Access Molecular Breeding for Enhanced Rice Yield: The Role of Key Yield-Related Genes Shubiao Zhang College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, 350002, Fujian, China Corresponding email: zhangsbiao@aliyun.com Rice Genomics and Genetics, 2024, Vol.15, No.4 doi: 10.5376/rgg.2024.15.0016 Received: 02 Jul., 2024 Accepted: 08 Aug., 2024 Published: 15 Aug., 2024 Copyright © 2024 Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhang S.B., 2024, Molecular breeding for enhanced rice yield: the role of key yield-related genes, Rice Genomics and Genetics, 15(4): 153-163 (doi: 10.5376/rgg.2024.15.0016) Abstract Rice (Oryza sativa) is one of the important global food crops, and the increase in its yield is of great significance for ensuring food security and alleviating the food crisis. This study analyzes the research progress and application of molecular breeding in improving rice yield. By revealing the potential role of key yield related genes in improving rice yield and quality, molecular breeding technologies such as marker assisted selection (MAS), genome selection (GS), genetic engineering, and CRISPR/Cas9 are introduced. The principles, applications, and successful cases of these technologies in rice breeding are discussed. In addition, this study also delves into the functional characteristics, gene expression research, functional genomics methods, and strategies and challenges of integrating yield related genes into breeding plans. By summarizing the successful experience, lessons learned, and best practices of molecular breeding in improving rice yield, the aim is to provide valuable reference and inspiration for future rice breeding work, and promote innovation and development in rice breeding work. Keywords Rice (Oryza sativa); Molecular breeding; Key genes; Mark assisted selection; CRISPR/Cas9 1 Introduction Rice (Oryza sativa) is a staple food for more than half of the world's population, making it a critical crop for global food security. The continuous increase in the global population necessitates the enhancement of rice yield to meet the growing food demand. Traditional breeding methods have significantly contributed to yield improvements; however, the advent of molecular breeding offers new avenues to further enhance rice productivity by targeting specific yield-related genes. The importance of improving rice yield cannot be overstated. Rice production has seen a steady increase during the Green Revolution, but the rate of yield growth has slowed in recent decades. This slowdown poses a significant challenge as the population in major rice-consuming countries continues to grow at a rate of more than 1.5% per year (Jeon et al., 2011). Enhancing rice yield is essential not only to meet the food demands of a burgeoning population but also to ensure food security in the face of global climate change and other environmental stresses (Nutan et al., 2020). High-yielding rice varieties are crucial for sustaining the world's food supply and addressing the challenges posed by limited arable land and water resources (Sakamoto and Matsuoka, 2008; Xu et al., 2016; Singh et al., 2022). Molecular breeding involves the use of genetic and genomic tools to identify and manipulate genes associated with desirable traits, such as yield, in crop plants. This approach has revolutionized rice breeding by enabling the precise selection and combination of beneficial alleles (Ikeda et al., 2013). Techniques such as marker-assisted selection (MAS), quantitative trait locus (QTL) mapping, and genome editing (e.g., CRISPR/Cas9) have been employed to enhance rice yield by targeting key genes involved in grain number, grain size, and plant architecture (Singh et al., 2022). For instance, the identification and manipulation of genes like OsSPL16/qGW8 have shown significant potential in increasing grain size and overall yield (Usman et al., 2020). Additionally, the integration of omic data, such as transcriptomics and metabolomics, has further improved the predictability and efficiency of hybrid yield prediction (Xu et al., 2016).
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