Journal of Tea Science Research, 2024, Vol.14, No.5, 293-303 http://hortherbpublisher.com/index.php/jtsr 294 metabolism are influenced by SVs and differentially expressed genes in purple tea flowers and leaves. Some structural genes, such as DAHPS and F3′5′H, are significantly upregulated in specific cultivars (Mei et al., 2021). Certain SVs are directly associated with trait differentiation. Traits like cold tolerance and tea quality show clear links to these variations, highlighting the dual role of SVs in both natural evolution and artificial domestication (Xia et al., 2020; Tong et al., 2024; Tariq et al., 2024). This study summarizes current research on structural variations in tea plants. It focuses on methods for detecting SVs, strategies for integrating multi-omics data, and the application of SVs in breeding. Special attention is given to how SVs influence phenotype expression. The goal is to provide a systematic overview for genetic studies of tea. At the same time, it aims to offer theoretical support for molecular breeding and germplasm conservation. 2 Genome Structural Complexity of Tea Plants 2.1 Genome size and polyploidy in tea The tea plant genome is large and structurally complex. It has been shaped by at least two whole-genome duplication (WGD) events. These events occurred roughly 30-40 million years ago and 90-100 million years ago. As a result of WGD and subsequent paralogous gene expansions, many gene families—especially those involved in secondary metabolite biosynthesis—have been enlarged. This expansion provides the genetic basis for the diversity of tea quality traits. Studies have also shown that WGDs contributed to the emergence and retention of lineage-specific genes. These genes play key roles in environmental stress responses and in determining the composition of tea aroma compounds (Zhao and Ma, 2021). Repetitive sequences, particularly long terminal repeat retrotransposons (LTR-RTs), make up a significant portion of the tea genome. In some assembly versions, they account for as much as 70% of the genome. These transposable elements have driven genome expansion. By inserting preferentially into promoter regions and introns, they also influence gene expression and trait variation (Wei et al., 2018; Xia et al., 2020). With the help of long-read sequencing technologies, researchers have gained much better resolution in studying transposon distribution. Recent studies have revealed shared patterns of transposon organization across different tea genomes (Tariq et al., 2024). 2.2 Heterozygosity and genome plasticity Cultivated tea resources show a high level of heterozygosity. This reflects their hybrid origin and rich genetic diversity. A study of 24 tea tree varieties showed that their heterozygosity (HS) ranged from 37.5% to 71.0%, with an average of 51.3%. Among them, the Fujian tea tree varieties had the highest heterozygosity, reaching 59.8%, which was much higher than Zhejiang (48.5%) and Yunnan (44.5%) (Tan et al., 2015). The heterozygosity is especially prominent in ancient landraces and hybrid wild types. Their genetic variation is much greater than that of pure wild species or modern cultivars (Niu et al., 2019; Hazra et al., 2021). This high genetic diversity forms the basis for the adaptability and trait variability seen in cultivated tea. Wild and ancient tea populations also display clear structural dynamics. A large number of structural variations (SVs) and presence/absence variations (PAVs) have been found across populations from different geographic regions. These variations are often closely linked to important agronomic traits such as cold tolerance and leaf shape. Interestingly, they do not seem to have been strongly reduced by natural or artificial selection (Lu et al., 2021; Tong et al., 2024). The genetic structure of wild populations remains highly complex. This complexity continues to support ongoing diversification and adaptive evolution. 2.3 Implications for structural variation discovery The tea plant genome contains a high proportion of repetitive sequences, shows clear signs of polyploidy, and has strong heterozygosity. These factors make it difficult to accurately detect structural variations. Traditional short-read sequencing technologies struggle to resolve complex regions, often resulting in incomplete or unclear identification of SVs (Samarina et al., 2022; Tariq et al., 2024). To address these challenges, long-read sequencing and pangenome strategies have become essential tools. Long-read sequencing improves the resolution of repetitive and structurally complex regions. Meanwhile,
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