Plant Gene and Traits 2024, Vol.15, No.4, 207-219 http://genbreedpublisher.com/index.php/pgt 212 6.2 Recent advancements in GS for Camelliaspecies Recent advancements in GS have significantly impacted the breeding of Camellia species. The integration of high-throughput genotyping technologies, such as next-generation sequencing (NGS), has enabled the collection of dense marker data at a reduced cost, making GS more feasible and effective. These technologies have improved the accuracy of genomic estimated breeding values (GEBVs) by providing comprehensive marker coverage and reducing ascertainment bias (Wang et al., 2018). Moreover, the development of sophisticated statistical models and algorithms, including GBLUP, Bayes, and machine learning approaches, has enhanced the prediction accuracy of GS models. These models can account for non-additive genetic effects and genotype-by-environment interactions, which are critical for the reliable selection of Camellia genotypes under varying environmental conditions (Wang et al., 2018). In practical applications, GS has been successfully implemented in breeding programs for other perennial crops, such as perennial ryegrass, demonstrating significant genetic gains and reduced breeding cycle times. These successes provide a promising outlook for the application of GS in Camellia breeding. 6.3 Incorporating genomic selection into traditional breeding programs for more efficient selection Incorporating GS into traditional Camellia breeding programs requires a strategic approach to optimize the use of genomic and phenotypic data. One effective strategy is to reorganize field designs and training populations to maximize the accuracy of GEBVs (Merrick et al., 2022). By increasing the number of lines evaluated and leveraging data collected across different growing seasons and environments, breeders can improve heritability estimates and selection accuracy (Cappetta et al., 2020; Merrick et al., 2022). Additionally, integrating GS with high-throughput phenotyping and deep learning approaches can further enhance the efficiency of selection. These technologies enable the rapid and precise measurement of phenotypic traits, which can be used to update prediction models and refine selection decisions (Cappetta et al., 2020; Merrick et al., 2022). To fully realize the benefits of GS, it is essential to develop breeding schemes that combine GS with traditional methods. For instance, GS can be used to pre-select superior genotypes, which can then be subjected to further evaluation and selection through conventional breeding techniques (Xu et al., 2019). This integrated approach can accelerate the development of new Camellia varieties with improved traits, such as disease resistance, yield, and quality. 7 Case Study: Genomic Tools for Tea Quality Improvement inCamellia sinensis 7.1 Overview of tea quality traits and their significance in the tea industry Tea quality traits are critical for the tea industry as they directly influence consumer preference and market value. Key quality traits include flavor, aroma, and biochemical composition, such as catechins, theanine, and caffeine. These compounds not only contribute to the sensory attributes of tea but also to its health benefits. For instance, catechins are known for their antioxidant properties, while theanine is associated with relaxation effects. The complexity of these traits, often influenced by both genetic and environmental factors, makes their improvement a challenging task (Wei et al., 2018; Yu et al., 2020; Lubanga et al., 2021). 7.2 Application of genomic resources for improving flavor and quality The advent of genomic resources such as genome sequencing, transcriptomics, and metabolomics has revolutionized the breeding strategies for tea quality improvement. High-quality genome assemblies of Camellia sinensis have facilitated the identification of gene families involved in the biosynthesis of key metabolites. For example, the draft genome sequence of Camellia sinensis var. sinensis has highlighted the role of specific gene duplications in the production of catechins and theanine (Wei et al., 2018). Additionally, genomic selection (GS) models have been employed to predict complex quality traits using genome-wide markers, showing promising results in improving traits like theogallin and epicatechin gallate (Lubanga et al., 2021). Transcriptomic analyses have further elucidated the expression patterns of genes associated with flavor and aroma compounds. For instance, genes involved in terpene biosynthesis, which contribute to tea aroma, have been found to be significantly amplified in the tea plant genome (Xia et al., 2020). Metabolomic studies have also identified
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