Journal of Tea Science Research, 2024, Vol.14, No.6, 304-312 http://hortherbpublisher.com/index.php/jtsr 304 Research Insight Open Access Unraveling the Genetic Networks Controlling Tea Quality Traits Jianmin Zheng1, Jiayao Zhou2 1 Institute of Life Sciences, Jiyang Colloge of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China 2 Traditional Chinese Medicine Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China Corresponding author: jiayao.zhou@cuixi.org Journal of Tea Science Research, 2024, Vol.14, No.6 doi: 10.5376/jtsr.2024.14.0028 Received: 10 Sep., 2024 Accepted: 21 Oct., 2024 Published: 08 Nov., 2024 Copyright © 2024 Zheng and Zhou, 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: Zheng J.M., and Zhou J.Y., 2024, Unraveling the genetic networks controlling tea quality traits, Journal of Tea Science Research, 14(6): 304-312 (doi: 10.5376/jtsr.2024.14.0028) Abstract Camellia sinensis, the tea plant, is a globally significant beverage crop with great economic, cultural, and nutritional value. Tea's quality traits, including flavor, aroma, mouthfeel, and major biochemical constituents, are very complex and are tightly regulated by multilevel genetic and metabolic networks. With advances in molecular biology and multi-omics technology, researchers have found more functional genes and key metabolic pathways involved in tea polyphenol biosynthesis, amino acids, caffeine, and aroma compounds. Quality-forming regulatory factors such as transcription factors, non-coding RNAs, and epigenetics also have vital roles. This review systematically integrates advances in genomics, transcriptomics, metabolomics, proteomics, and epigenomics, and considers how systems biology approaches (e.g., WGCNA, Bayesian networks, machine learning) could be applied to construct genetic regulatory networks underlying tea quality traits, and identify central regulators and tea-specific modules. It also considers the potential of molecular breeding technologies—e.g., molecular marker development, QTL mapping, and gene editing (e.g., CRISPR)—to enhance tea quality. A deep understanding of the genetic bases and regulatory mechanisms of tea quality traits is of great importance in the quest for enhancing molecular breeding, supporting high-quality industry development, and enhancing the international competitiveness of China's tea industry. Keywords Tea quality; Genetic network; Functional genes; Transcriptional regulation; Metabolomics; Molecular breeding 1 Introduction Tea (Camellia sinensis) is one of the most widely consumed beverages globally, with a long-standing cultural heritage and immense economic importance. As a major cash crop in countries such as China, India, Sri Lanka, Kenya, and Japan, tea supports the livelihoods of millions of smallholder farmers and plays a crucial role in international trade. According to the Food and Agriculture Organization (FAO), global tea production has continued to rise steadily, driven by increasing consumer demand for high-quality and health-promoting beverages (Wang et al., 2021; Moreira et al., 2024). The quality of tea is established by a complex array of properties, which include flavor, aroma, taste, appearance, and biochemical composition. Such properties are influenced by both genetic and environmental factors and are buttressed by highly advanced metabolic activities involving polyphenols (e.g., catechins and theaflavins), amino acids (in particular, theanine), caffeine, and a wide variety of volatile organic compounds (VOCs) (Kong et al., 2025). The sensory characteristics of tea—bitterness, astringency, umami, and roasty or floral aromas—result from highly controlled interactions among these compounds, which are differentially expressed in tea cultivars, cultivation conditions, and processing. An understanding of the genetic regulation of these quality traits is valuable to both basic and applied research. With next-generation sequencing (NGS), transcriptome profiling, metabolomics, and genome editing technologies, researchers have at their command the unparalleled tools to dissect the molecular foundation of tea quality. However, as information on single biosynthetic genes and pathways is being accumulated, the genetic networks that control trait integration and coordination are still largely unresolved. This has presented a challenge to the development of molecular markers and precise methods of improving tea quality through breeding (Xia et al., 2020).
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