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Gene mining and genomics-assisted breeding empowered by the pangenome of tea plant Camellia sinensis

Nature Plants volume 9, pages 1986–1999 (2023)Cite this article

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Abstract

Tea is one of the world’s oldest crops and is cultivated to produce beverages with various flavours. Despite advances in sequencing technologies, the genetic mechanisms underlying key agronomic traits of tea remain unclear. In this study, we present a high-quality pangenome of 22 elite cultivars, representing broad genetic diversity in the species. Our analysis reveals that a recent long terminal repeat burst contributed nearly 20% of gene copies, introducing functional genetic variants that affect phenotypes such as leaf colour. Our graphical pangenome improves the efficiency of genome-wide association studies and allows the identification of key genes controlling bud flush timing. We also identified strong correlations between allelic variants and flavour-related chemistries. These findings deepen our understanding of the genetic basis of tea quality and provide valuable genomic resources to facilitate its genomics-assisted breeding.

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Fig. 1: A total of 22 representative cultivars of tea plant were selected for the pangenome analysis.
Fig. 2: Detection of SVs and construction of the pangenome based on the 22 de novo assembled tea genomes.
Fig. 3: Functional impact of SVs associated with the regulation of tea leaf colour.
Fig. 4: Application of graphical pangenome-based GWAS in mining of key genes associated with the TBF trait in tea.
Fig. 5: The pangenome empowers genomics-assisted breeding.
Fig. 6: Pangenomic basis of the effect of CsAFS1 expression on α-farnesene content.

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Data availability

The raw data, genome assemblies and annotation have been submitted to the National Genomics Data Center (https://ngdc.cncb.ac.cn/) under accession number PRJCA013847 for the pangenome samples. All the assemblies, annotations, variant VCF files and graph-based genome are also available at the Tea Graph Pangenome Database (https://www.tea-pangenome.cn/).

Code availability

The PanMarker and SRI approach is freely available at GitHub (https://github.com/chaiyuangungun/PanMarker; https://github.com/Yujiaxin419/SRI-pipeline).

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Acknowledgements

This work was supported by the Key-Area Research and Development Program of Guangdong Province (grant no. 2020B020220004), the Shenzhen Science and Technology Program (grant no. RCYX20210706092103024) and a National Natural Science Foundation of China grant (no. 32222019).

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Author notes

  1. These authors contributed equally: Shuai Chen, Pengjie Wang, Weilong Kong, Kun Chai.

Authors and Affiliations

  1. National Key Laboratory for Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Shuai Chen, Pengjie Wang, Weilong Kong, Shengcheng Zhang, Jiaxin Yu, Yibin Wang, Xiao Chen, Yingying Gao, Shenyang Qu, Fang Wang, Qing Zhang & Xingtan Zhang

  2. Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Key Laboratory of Genetics, Fujian Agriculture and Forestry University, Fuzhou, China

    Kun Chai, Mengwei Jiang, Wenlong Lei, Wenling Wang & Yinghao Wang

  3. College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou, China

    Mengya Gu, Weijiang Sun & Naixing Ye

  4. Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Provincial Key Laboratory of Tea Plant Resources Innovation and Utilization, Guangzhou, China

    Kaixing Fang & Hualing Wu

  5. Key Laboratory of Biology, Genetics and Breeding of Special Economic Animals and Plants, Ministry of Agriculture and Rural Affairs, Tea Research Institute of the Chinese Academy of Agricultural Sciences, Hangzhou, China

    Chunlei Ma

Authors
  1. Shuai Chen
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  2. Pengjie Wang
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Contributions

X.Z. designed this project and coordinated the research activities. P.W., S.C., N.Y., H.W., K.F., Q.Z., M.G., C.M. and W.S. collected and provided the plant materials. N.Y., H.W. and K.F. participated in the genome sequencing and resequencing. X.Z., S.C., S.Z., J.Y. and Yibin Wang assembled the genomes. S.C., K.C., W.W., M.J., W.L., S.Q., F.W. and Y.G. performed the gene annotation. S.C., P.W., K.C., Yinghao Wang and W.K. analysed the RNA-seq data. S.C. constructed the sequence- and gene-based pangenome. K.C., S.C. and P.W. analysed the metabolomic data. S.C. and W.K. contributed to the population GWAS analysis. S.C., K.C. and S.Z. developed the PanMarker approach. X.C. constructed the database for the pangenome. X.Z., S.C., P.W. and W.K. interpreted the data and contributed to the manuscript writing.

Corresponding authors

Correspondence to Naixing Ye, Hualing Wu or Xingtan Zhang.

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Extended data

Extended Data Fig. 1 Geographical distribution and sample selection based on phylogeny of 736 tea accessions.

a) Geographic locations of the 736 re-sequencing Camellia samples that are used to query the phylogenetic relationship. These samples were mainly collected from eight tea-cultivated countries, namely China, India, Korea, Japan, Laos, Sri Lanka, Georgia, and Kenya. The world map was constructed using the Python script with the Natural Earth dataset (http://www.naturalearthdata.com). b) Population structure analysis of 736 re-sequencing Camellia accession. The highlighted clades on the phylogenetic tree are the pan genomic individuals. The admixture was estimated using standard error with 2000 bootstrap replicates. ‘SEKJ’ represents the tea plant cultivation areas that are distributed in the southeastern provinces of China, including Zhejiang, Anhui, Henan, Fujian, Taiwan, and countries such as South Korea and Japan. On the other hand. ‘CPC’ represents the tea cultivation areas in the central provinces of China.

Extended Data Fig. 2 Characteristics of tea genome assemblies.

a-c) The contig N50 values are influenced by different factors, including (a) sequencing coverage, (b) genome heterozygosity and (c) length of sequencing reads. The correlation was assessed by calculating the Pearson correlation coefficient. The dot represents each of assembled samples (n = 20, Due to the 20× coverage HiFi reads for ‘HD’, and lack of the raw reads for ‘SCZ’, it has not been included in this statistical analysis). The P-value was calculated from the two-sided t-test. d-f) Genome-wide analysis of chromatin interactions at 150-kbp resolution in ZJ (d), LJ43 (e), and ZYQ (f) genome. g) Syntenic plot among 22 tea assemblies. The chromosomes are represented by colorful boxes, and collinear regions among these genomes are shown as grey blocks.

Extended Data Fig. 3 Recent duplicated genes derived by TEs.

a-b) Whole genome duplication (WGD) events were detected in the tea plant, with species names abbreviated as follows: Nn, Nelumbo nucifera, Vv, Vitis vinifera L, Ach, Actinidia chinensis, Cl, C. lanceoleosa, Cs, C. sinensis. The Ks values between each gene pairs. c). Recent LTR burst detected in tea plant genome. d). A schematic diagram of transposable genes carried by LTRs (long terminal repeats). e) Classification of TE-derived genes. f). Two examples of gene duplication originated from LTR transposition events.

Extended Data Fig. 4 Genome-wide sequence variation of the 22 tea genomes.

a) Assessment of the Synteny relationship using Synteny Relationship Index (SRI) among 7 plant pan-genomes. These species include Camellia sinensis (Cs, TE ratio, 78.2%, n = 22), Solanum tuberosum L (St, 63.0%, n = 44), Solanum lycopersicum (Sly, 60.7%, n = 31), Arabidopsis thaliana (At, 17.6%, n = 8), Sorghum bicolor L (Sb, 61.0%, n = 13), Zea mays (Zm, 83.2%, n = 10), Oryza sativa L(Os, 52.9%, n = 33). Each box plot shows the distribution of data, with the median value represented by the bold line at the center of the box. The box itself represents the first (25%) and third (75%) quartiles. The minimum and maximum values are illustrated by the lower and upper whiskers respectively. b) The proportion of genetic variations between any two samples out of the 22 genomes calculated based on pairwise genomic alignments. The white dot in the center of the violin plot represents the median value, and the bounds of each black box indicate first (25%) and third (75%) quartiles. The lower and upper bounds of the whiskers are the minima and maxima, respectively. c) Distribution of SNPs/Indels identified from the 736 re-sequencing samples and SVs identified from 22 genomes along 15 pseudo-chromosomes. The density of SNPs/indels is represented by the colored bands on these pseudo-chromosomes, while the red lines alongside those pseudo-chromosomes indicate the distribution of SVs. d-e) Pearson correlation coefficients, which show the comparisons between LTR or TIR count per window (10 Mbp window size, 5Mbp step) and SV count per window. For d and e, Total of 572 and 598 windows were plotted, respectively. The P-value was calculated by two-sided t-test.

Extended Data Fig. 5 Concentration of metabolites.

a) Concentration of ChlorophyII (Chl) a, Chl b and Chl a + b in five tea plant cultivars (HJY, AJBC, FDDB, JMZ and ZJ). b) Concentration of carotenoids in five tea plant cultivars (HJY, AJBC, FDDB, JMZ and ZJ). c) Concentration of anthocyanins in five tea plant cultivars (HJY, AJBC, FDDB, JMZ and ZJ). The asterisk represents the statistical significance (two-sided Student’s t-test). Data presented in mean ± SEM, n = 3. Three independent experiments are carried out.

Extended Data Fig. 6 Identification of SVs in six key genes in chlorophyll and carotenoid biosynthesis, namely CYP97A3, CAO, ChID, ChIP, NOL and GluTR.

The rectangles illustrate the physical position of SVs. ‘Others’ represents the accessions ‘ZJ’, ‘FDDB’, and ‘JMZ’. These variations are shown in the sequence alignments.

Extended Data Fig. 7 Expression profiles of genes encoding enzymes involved in anthocyanin biosynthesis.

Enzymes abbreviations in each step are highlighted in bold. The black boxes contain the metabolites responsible for anthocyanins production. The gradient color of heatmap represents gene expression levels across different cultivars.

Extended Data Fig. 8 Read coverage along the promoter region of CsMYB114 gene.

The x-axis indicates the genomic coordinates, and y-axis represents the sequencing depth.

Extended Data Fig. 9 3D protein modeling and haplotype analysis of AFS1.

a) Diagrams illustrate 3D protein modeling of the single amino acid mutation (G- > A) in the CsAFS1 protein. the substrate binding center of the GG-AFS1 type is represented by orange spheres, while the substrate binding center of the AA-AFS1 type is depicted by red spheres. b) Distribution of allelic variants (AA, AG and GG) of CsAFS1 gene using 587 re-sequenced tea accessions. c) Distribution of allelic variants (AG and GG) of AFS1 gene in 17 wild tea relatives.

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Supplementary Figs. 1–18, Tables 1–15, Note and references.

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Supplementary Data 1

Supplementary Tables 16–23.

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Chen, S., Wang, P., Kong, W. et al. Gene mining and genomics-assisted breeding empowered by the pangenome of tea plant Camellia sinensis. Nat. Plants 9, 1986–1999 (2023). https://doi.org/10.1038/s41477-023-01565-z

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