Journal of Tea Science Research, 2024, Vol.14, No.5, 293-303 http://hortherbpublisher.com/index.php/jtsr 301 The construction of the tea pangenome integrates multiple high-quality genome assemblies. This approach has allowed researchers to comprehensively catalog SVs across different tea resources. It covers both core and variable genes and reveals the functional impacts of structural variations on traits such as flavor, stress tolerance, and disease resistance (Tariq et al., 2024). The establishment of the tea pangenome has greatly advanced the identification of novel sequences and gene-centric variations. It provides a valuable genetic foundation for trait association studies and molecular breeding (Tong et al., 2024; Tariq et al., 2024). 7.2 Multi-omics integration with SV datasets Integrating transcriptomic data with SV datasets enables the validation of SV impacts on gene expression. For example, SVs affecting promoter regions or gene copy number have been linked to transcriptional changes in genes involved in catechin biosynthesis and stress responses, supporting their functional relevance in trait diversity (Wei et al., 2018; Tong et al., 2024). Metabolomic analyses, combined with genomic and transcriptomic data, provide insights into how SVs influence metabolic pathways. Studies have shown that SV-driven gene family expansions and transcriptional divergence are associated with the accumulation of key metabolites, such as catechins and theanine, which are central to tea quality and health benefits (Wei et al., 2018). 7.3 Application in tea breeding and improvement Structural variations (SVs) and high-impact mutations identified through advanced sequencing and pangenome analysis have become important molecular markers in tea breeding. SVs and SNPs associated with agronomic and biochemical traits have been successfully used to develop markers that support the application of marker-assisted selection (MAS) in tea improvement programs (Hazra et al., 2021). Genomic selection strategies that incorporate SV information can improve the prediction accuracy of complex traits and accelerate the breeding of superior tea cultivars. Using 1,421 DArTseq markers, researchers developed a multi-model prediction framework targeting drought resistance and quality traits. Among the models, those integrating KEGG pathway and protein annotation data performed best. Notably, the extreme learning machine (ELM) model showed the highest accuracy in predicting catechin content, astringency, and leaf color (Koech et al., 2019). Integrating SV data with genome-wide association studies (GWAS) and multi-omics approaches can help pinpoint key candidate genes. This strategy facilitates the development of new tea varieties with desirable traits such as high quality, stress tolerance, and disease resistance (Lu et al., 2021; Tong et al., 2024; Tariq et al., 2024). 8 Concluding Remarks Structural variations (SVs), including large-scale insertions, deletions, presence/absence variations (PAVs), and copy number changes, are abundant in the tea genome. Over 217,000 SVs and more than 56,000 PAVs have been identified across diverse tea accessions, significantly expanding the gene pool and contributing to the remarkable genetic and functional diversity observed in tea plants. SVs are unevenly distributed and often cluster in regions associated with key agronomic and adaptive traits. SVs play a vital role in shaping important tea traits, including metabolic pathways (flavonoid, catechin, and theanine biosynthesis), stress responses (cold and drought tolerance), and morphological features (leaf size, shape, and plant architecture). Many SVs are linked to domestication and adaptation, with subspecies-specific patterns observed betweenC. sinensis var. sinensis and var. assamica. Despite the identification of numerous SVs, only a small subset has been functionally validated for their direct impact on gene expression and phenotypic traits. Most associations remain correlative, and experimental evidence for causality is limited. Although recent studies have expanded the number of sequenced accessions, population-scale SV data remain insufficient, especially for wild and underrepresented tea populations. This limits the ability to fully capture the spectrum of SV diversity and its effects on trait variation.
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