Open Access
Open access
volume 11 issue 1 publication number 4447

Population sequencing enhances understanding of tea plant evolution

Xinchao Wang 1
Hu Feng 2
YUXIAO CHANG 2
Chunlei Ma 1
Liyuan Wang 1
Xinyuan Hao 1
Alun Li 2
Hao Cheng 1
Lu Wang 1
Peng Cui 2
JIQIANG JIN 1
Wang Xiaobo 2
Kang Wei 1
Cheng Ai 2
Sheng Zhao 2
Zhichao Wu 2
Youyong Li 3
Benying Liu 3
Guo Dong Wang 4, 5
Liang Chen 1
Jue Ruan 2
Yajun Yang 1
Publication typeJournal Article
Publication date2020-09-07
scimago Q1
wos Q1
SJR4.761
CiteScore23.4
Impact factor15.7
ISSN20411723
General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy
Abstract
Tea is an economically important plant characterized by a large genome, high heterozygosity, and high species diversity. In this study, we assemble a 3.26-Gb high-quality chromosome-scale genome for the ‘Longjing 43’ cultivar of Camellia sinensis var. sinensis. Genomic resequencing of 139 tea accessions from around the world is used to investigate the evolution and phylogenetic relationships of tea accessions. We find that hybridization has increased the heterozygosity and wide-ranging gene flow among tea populations with the spread of tea cultivation. Population genetic and transcriptomic analyses reveal that during domestication, selection for disease resistance and flavor in C. sinensis var. sinensis populations has been stronger than that in C. sinensis var. assamica populations. This study provides resources for marker-assisted breeding of tea and sets the foundation for further research on tea genetics and evolution. Tea is an important beverage crop with a large and heterozygous genome. Here, the authors assemble the genome of the cultivar Longjing 43 and conduct a population genetics study to reveal divergent selection for disease resistance and flavor between the two variety groups.
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GOST |
Cite this
GOST Copy
Wang X. et al. Population sequencing enhances understanding of tea plant evolution // Nature Communications. 2020. Vol. 11. No. 1. 4447
GOST all authors (up to 50) Copy
Wang X., Feng H., CHANG Y., Ma C., Wang L., Hao X., Li A., Cheng H., Wang L., Cui P., JIN J., Xiaobo W., Wei K., Ai C., Zhao S., Wu Z., Li Y., Liu B., Wang G. D., Chen L., Ruan J., Yang Y. Population sequencing enhances understanding of tea plant evolution // Nature Communications. 2020. Vol. 11. No. 1. 4447
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1038/s41467-020-18228-8
UR - https://doi.org/10.1038/s41467-020-18228-8
TI - Population sequencing enhances understanding of tea plant evolution
T2 - Nature Communications
AU - Wang, Xinchao
AU - Feng, Hu
AU - CHANG, YUXIAO
AU - Ma, Chunlei
AU - Wang, Liyuan
AU - Hao, Xinyuan
AU - Li, Alun
AU - Cheng, Hao
AU - Wang, Lu
AU - Cui, Peng
AU - JIN, JIQIANG
AU - Xiaobo, Wang
AU - Wei, Kang
AU - Ai, Cheng
AU - Zhao, Sheng
AU - Wu, Zhichao
AU - Li, Youyong
AU - Liu, Benying
AU - Wang, Guo Dong
AU - Chen, Liang
AU - Ruan, Jue
AU - Yang, Yajun
PY - 2020
DA - 2020/09/07
PB - Springer Nature
IS - 1
VL - 11
PMID - 32895382
SN - 2041-1723
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2020_Wang,
author = {Xinchao Wang and Hu Feng and YUXIAO CHANG and Chunlei Ma and Liyuan Wang and Xinyuan Hao and Alun Li and Hao Cheng and Lu Wang and Peng Cui and JIQIANG JIN and Wang Xiaobo and Kang Wei and Cheng Ai and Sheng Zhao and Zhichao Wu and Youyong Li and Benying Liu and Guo Dong Wang and Liang Chen and Jue Ruan and Yajun Yang},
title = {Population sequencing enhances understanding of tea plant evolution},
journal = {Nature Communications},
year = {2020},
volume = {11},
publisher = {Springer Nature},
month = {sep},
url = {https://doi.org/10.1038/s41467-020-18228-8},
number = {1},
pages = {4447},
doi = {10.1038/s41467-020-18228-8}
}