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Cited by 253

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Population sequencing enhances understanding of tea plant evolution

Open access
Wang, Xinchao, Feng, Hu, Chang, Yuxiao, Ma, Chunlei, Wang, Liyuan, Hao, Xinyuan, Li, A’lun, Cheng, Hao, Wang, Lu, Cui, Peng, Jin, Jiqiang, Wang, Xiaobo, Wei, Kang, Ai, Cheng, Zhao, Sheng, Wu, Zhichao, Li, Youyong, Liu, Benying, Wang, Guo-Dong, Chen, Liang, Ruan, Jue, Yang, Yajun·2020·Nature Communications
DOI 10.1038/s41467-020-18228-8
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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.

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Metadata

Authors
Wang, X., Feng, H., Chang, Y., Ma, C., Wang, L., Hao, X., Li, A., Cheng, H., Wang, L., Cui, P., Jin, J., Wang, X., Wei, K., Ai, C., Zhao, S., Wu, Z., Li, Y., Liu, B., Wang, G.-D., Chen, L., Ruan, J., & Yang, Y.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-09-07
Volume
11
Issue
1
Pages
4447-4447
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-18228-8
OpenAlex
W3083607706
Cited by
253
References
55
FWCI
17.01
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Population sequencing enhances understanding of tea plant evolution. https://app.cochat.ai/papers/10.1038/s41467-020-18228-8
The paper
Wang, X., Feng, H., Chang, Y., Ma, C., Wang, L., Hao, X., Li, A., Cheng, H., Wang, L., Cui, P., Jin, J., Wang, X., Wei, K., Ai, C., Zhao, S., Wu, Z., Li, Y., Liu, B., Wang, G.-D., . . . Yang, Y. (2020). Population sequencing enhances understanding of tea plant evolution. Nature Communications. https://doi.org/10.1038/s41467-020-18228-8

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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