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Article|10 Aug 2021|OPEN
Genome-level diversification of eight ancient tea populations in the Guizhou and Yunnan regions identifies candidate genes for core agronomic traits
Litang Lu1,2, Hufang Chen1,2, Xiaojing Wang1, Yichen Zhao1,2, Xinzhuan Yao1, Biao Xiong1, Yanli Deng1 & Degang Zhao2,3,
1College of Tea Science, Guizhou University, Guiyang 550025, People’s Republic of China
2College of Life Sciences and The Key Laboratory of Plant Resources Conservation and Germplasm Innovation in the Mountainous Region (Ministry of Education), Institute of Agro-Bioengineering, Guizhou University, Guiyang 550025, People’s Republic of China
3Guizhou Academy of Agricultural Sciences, Guiyang 550025, People’s Republic of China

Horticulture Research 8,
Article number: 190 (2021)
doi: 10.1038/hortres.2021.190
Views: 24

Received: 02 Sep 2020
Revised: 20 May 2021
Accepted: 24 May 2021
Published online: 10 Aug 2021


The ancient tea plant, as a precious natural resource and source of tea plant genetic diversity, is of great value for studying the evolutionary mechanism, diversification, and domestication of plants. The overall genetic diversity among ancient tea plants and the genetic changes that occurred during natural selection remain poorly understood. Here, we report the genome resequencing of eight different groups consisting of 120 ancient tea plants: six groups from Guizhou Province and two groups from Yunnan Province. Based on the 8,082,370 identified high-quality SNPs, we constructed phylogenetic relationships, assessed population structure, and performed genome-wide association studies (GWAS). Our phylogenetic analysis showed that the 120 ancient tea plants were mainly clustered into three groups and five single branches, which is consistent with the results of principal component analysis (PCA). Ancient tea plants were further divided into seven subpopulations based on genetic structure analysis. Moreover, it was found that the variation in ancient tea plants was not reduced by pressure from the external natural environment or artificial breeding (nonsynonymous/synonymous = 1.05). By integrating GWAS, selection signals, and gene function prediction, four candidate genes were significantly associated with three leaf traits, and two candidate genes were significantly associated with plant type. These candidate genes can be used for further functional characterization and genetic improvement of tea plants.