Journal of Tea Science Research, 2024, Vol.14, No.5, 293-303 http://hortherbpublisher.com/index.php/jtsr 297 4.2 SV-mediated gene family evolution Structural variations, especially gene duplications and presence/absence variations (PAVs), have promoted the expansion of several secondary metabolism-related gene families in tea plants. For example, genes involved in the biosynthesis of tea polyphenols—such as catechins—as well as theanine and caffeine, have been shown to undergo significant amplification through SVs. These compounds not only shape tea quality but are also closely linked to its health benefits (Wei et al., 2018). In recent years, tandem duplications have further increased the number of genes related to aroma biosynthesis and stress resistance. These genes are often clustered together, forming functional modules (Xia et al., 2020). At the same time, some gene families have contracted due to SVs. For instance, disease resistance gene clusters may have shrunk under the selective pressures of domestication and environmental adaptation. This dynamic restructuring of gene families influences how tea plants respond to both biotic and abiotic stresses (Xia et al., 2020; Tong et al., 2024). 5 Structural Variations and Trait Diversity 5.1 Metabolic traits and tea quality Structural variations (SVs), including large-scale insertions, deletions, and copy number changes, are prevalent in genes involved in flavonoid and catechin biosynthesis. These SVs have contributed to the expansion and transcriptional divergence of gene families such as acyltransferases and leucoanthocyanidin reductases, which are critical for the accumulation of monomeric galloylated catechins—a key determinant of tea quality (Wei et al., 2018; Tong et al., 2024). Domestication signatures in Camellia sinensis var. assamica (CSA) are particularly enriched in flavonoid and alkaloid biosynthesis genes, highlighting the role of SVs in metabolic trait diversity (Tong et al., 2024). SVs, especially tandem duplications and presence/absence variations (PAVs), have amplified genes related to aroma compound biosynthesis and theanine production. In Camellia sinensis var. sinensis (CSS), SVs are associated with genes involved in amino acid metabolism and aroma, directly impacting the sensory qualities and health benefits of tea (Wei et al., 2018; Xia et al., 2020; Tong et al., 2024). Functional divergence of the glutamine synthetase gene family, driven by SVs, has led to the evolution of theanine synthetase, a key enzyme for theanine accumulation (Wei et al., 2018). 5.2 Abiotic and biotic stress responses SVs and PAVs are linked to stress response traits, including drought and cold tolerance. For example, specific PAV genes (CSS0049975 and CSS0006599) have been experimentally shown to drive cold tolerance differences between CSA and CSS (Tong et al., 2024). The expansion of stress resistance gene clusters through SVs further enhances the adaptability of tea plants to diverse environments (Xia et al., 2020). SVs also affect disease resistance loci, contributing to the contraction or expansion of resistance gene clusters. These changes influence the plant’s ability to respond to biotic stresses and are important for breeding disease-resistant cultivars (Xia et al., 2020; Tong et al., 2024). The pangenome approach has revealed dispensable genes related to disease resistance, underscoring the functional impact of SVs on plant health (Tariq et al., 2024). 5.3 Morphological and developmental traits Genome-wide association studies (GWAS) and selection signal analyses have shown that many structural variations (SVs) are significantly associated with morphological traits such as leaf size, shape, and hair density in both ancient and cultivated tea varieties (Lu et al., 2021; Tong et al., 2024). In a study of 415 tea accessions from Guizhou, researchers used GBS technology to obtain 30,282 high-quality SNP markers and performed GWAS analysis. They identified nine SNPs significantly associated with leaf length (MLL), leaf width (MLW), leaf area (MLA), and leaf shape index (MLSI) (P < 1.655×10⁻6). These markers were widely distributed across different genetic backgrounds (Niu et al., 2020). Similarly, Zhang et al. (2022) conducted a GWAS using SLAF-seq on 123
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