Journal of Tea Science Research, 2025, Vol.15, No.1, 1-11 http://hortherbpublisher.com/index.php/jtsr 1 Review Article Open Access Functional Genomics in Tea: Pathways and Mechanisms Underlying Key Traits Baofu Huang, Xiaocheng Wang Traditional Chinese Medicine Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: xiaocheng.wang@cuixi.org Journal of Tea Science Research, 2025, Vol.15, No.1 doi: 10.5376/jtsr.2025.15.0001 Received: 02 Dec., 2024 Accepted: 05 Jan., 2025 Published: 18 Jan., 2025 Copyright © 2025 Huang and Wang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Huang B.F., and Wang X.C., 2025, Functional genomics in tea: pathways and mechanisms underlying key traits, Journal of Tea Science Research, 15(1): 1-11 (doi: 10.5376/jtsr.2025.15.0001) Abstract Recent years, the research on functional genomics of tea trees has accelerated, with the release of high-quality genomes, pan genomes, and the maturity of multi omics technologies being key driving forces behind it. Many key traits, such as the synthesis of flavor compounds (catechins, theanine, terpenes, etc.) and stress resistance (cold resistance, drought resistance), their corresponding core genes, regulatory pathways, and even hidden structural variations, are being gradually uncovered. This study mainly focuses on several typical methods, including transcriptome analysis, gene silencing, metabolome integration, QTL mapping, GWAS to explore their practical applications in functional gene analysis. By combining representative varieties such as Longjing 43 and Zikui, the association pathways between gene expression, copy number variation, and specific phenotypes were further demonstrated. This study not only explains the mechanism of trait formation, but provides support for molecular breeding and quality improvement of tea trees. Keywords Tea tree; Functional genomics; Multi omics integration; Molecular breeding; Quality traits; Anti stress mechanism 1 Introduction Tea (Camellia sinensis), one of the most widely consumed beverages in the world, with a unique flavor, health benefits and economic value. The initial sequencing of the tea tree genome revealed its large genome size, high proportion of repetitive sequences, and complex evolutionary history, including genome-wide replication events, and large-scale expansion of gene families related to secondary metabolism (Xia et al., 2017; Wei et al., 2018; Xia et al., 2020a; Tariq et al., 2024). Later, researchers constructed pan-genome and reference genome assemblies of various tea tree varieties, identified and annotated core genes, optional genes, structural variations, and allelic diversity related to important traits (Chen et al., 2023). These resources have also facilitated the development of molecular markers, and databases, such as the tea tree information archive, providing support for comparative analysis and functional studies (Xia et al., 2019; 2020b). Functional genomics methods, including transcriptomics, gene co expression network analysis, gene silencing techniques, and multi omics integration strategies, have played an important role in studying the association between annotated genes and phenotypic traits (Tai et al., 2018; Kong et al., 2022; Li et al., 2022a). These studies identified candidate genes and regulatory modules involved in the biosynthesis, stress response, and developmental processes of key metabolites (e.g., catechins, theanine, and caffeine), providing mechanistic explanations for understanding tea plant trait variations (Qiu et al., 2020; Fang et al., 2021). Functional genomics has revealed the complex regulatory mechanisms of gene expression, and metabolic networks in tea plants, clarifying how transcription factors, epigenetic modifications and gene co-expression modules synergistically regulate the synthesis of quality-related metabolites, and responses to environmental stress (Tai et al., 2018; Wang et al., 2018; Shen et al., 2019; Krishnatreya et al., 2021). Through the analysis of integrated transcriptomic, metabolomic and epigenomic data, researchers further deepened their understanding of these networks and their roles in tea quality and adaptability (Han et al., 2024). The combination of functional genomics with genome assisted breeding strategies, such as genome-wide association analysis (GWAS), marker assisted selection, and QTL mapping, accelerates the identification process
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