Journal of Tea Science Research, 2024, Vol.14, No.4, 225-237 http://hortherbpublisher.com/index.php/jtsr 231 superior genotypes with greater precision and efficiency. For instance, the identification of an EST-SSR molecular marker associated with Blister blight resistance in tea (Camellia sinensis) has marked a significant milestone in tea molecular breeding. This marker, EST-SSR073, has been validated across multiple tea cultivars and can be effectively used to expedite breeding programs by selecting for Blister blight resistance (Karunarathna et al., 2020). Moreover, the application of MAS in other crops, such as coffee, has demonstrated the potential for transferring disease resistance alleles from different species, thereby enhancing the genetic diversity and resilience of cultivated varieties. For example, molecular markers linked to resistance genes in coffee have been successfully used to identify and select resistant individuals, facilitating preventive breeding against multiple diseases (Alkimim et al., 2017). These advancements underscore the potential of MAS to revolutionize tea breeding by integrating resistance traits more efficiently and accurately. 6.2 Biotechnological tools for enhancing tea plant immunity 6.2.1 Role of gene editing (e.g., CRISPR/Cas9) and transgenic approaches Gene editing technologies, such as CRISPR/Cas9, and transgenic approaches offer powerful tools for enhancing tea plant immunity. These biotechnological methods enable precise modifications of the plant genome to introduce or enhance resistance traits. CRISPR/Cas9, for instance, allows for targeted editing of specific genes associated with disease resistance, providing a means to develop tea plants with improved immunity against pathogens. This technology has been successfully applied in various crops to enhance resistance by knocking out susceptibility genes or introducing resistance genes (Michelmore, 1995; McDowell and Woffenden, 2003). Transgenic approaches involve the introduction of foreign genes into the tea plant genome to confer resistance. This method has been used to introduce resistance genes from other species, thereby broadening the spectrum of resistance in tea plants. For example, the introduction of resistance genes from other Coffea species into Arabica coffee has resulted in plants with enhanced resistance to multiple diseases (Alkimim et al., 2017). These approaches hold promise for developing tea plants with robust and durable resistance to a wide range of pathogens. 6.2.2 Current progress in biotechnological advancements for tea plant immunity Recent advancements in biotechnological tools have significantly contributed to the understanding and enhancement of tea plant immunity. The identification and characterization of resistance gene analogs (RGAs) in various crops have provided insights into the genetic basis of disease resistance. For instance, the genomic survey of RGAs in sugarcane has revealed the differential expression of these genes in response to pathogen infection, highlighting their potential as markers for breeding pathogen-resistant crops (Rody et al., 2019). Furthermore, the integration of genomic selection models in breeding programs has shown promise in predicting and selecting for disease resistance traits. These models utilize high-density marker arrays and extensive phenotyping data to predict the genomic estimated breeding values of untested genotypes, thereby accelerating the breeding process (Poland and Rutkoski, 2016; Miedaner et al., 2020). The application of these models in tea breeding could enhance the selection of disease-resistant varieties, ultimately improving crop resilience and productivity. 7 Challenges and Future Directions 7.1 Challenges in identifying and characterizing R genes in tea plants Identifying and characterizing resistance (R) genes in tea plants presents several challenges. One significant hurdle is the complex genomic organization of R genes, which often exist in clusters and exhibit high variability (Michelmore and Meyers, 1998; van Wersch and Li, 2019). This clustering can complicate the identification process, as traditional methods may not capture the full repertoire of R genes due to repeat masking during genome annotation (Andolfo et al., 2022). Additionally, the hypervariability in the leucine-rich repeat (LRR) regions of these genes, driven by divergent selection, further complicates their characterization (Michelmore and
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