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Graph-based pangenome reveals structural variation dynamics during cucumber breeding

Nature Genetics volume 58, pages 643–654 (2026)Cite this article

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Abstract

Structural variants (SVs) represent an important yet underexplored component of plant genome diversity. Here we present a graph-based cucumber pangenome constructed from 39 reference-quality genomes, including 27 newly assembled and 12 previously published. The pangenome captures 171,892 high-confidence SVs, which were genotyped across 447 wild and cultivated accessions. Our analyses reveal that, during cucumber domestication, a substantial portion of mildly deleterious SNPs were retained, whereas SVs were consistently purged, highlighting their highly deleterious nature. During geographical expansion, a reduced SV burden and a younger age of SVs compared to SNPs were observed, suggesting stronger purifying selection acting on SVs. Introgressions from wild populations increased SV burden, potentially due to hitchhiking. Notably, incorporating SV burden into genomic prediction models improved prediction accuracy for several agronomically important traits. This study illuminates SV dynamics during cucumber domestication and range expansion and underscores the implications of SVs for future cucumber breeding.

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Fig. 1: Pangenome of wild and cultivated cucumbers.
Fig. 2: SV detection and genotyping in cucumber.
Fig. 3: SV dynamics during cucumber domestication and improvement.
Fig. 4: Spatiotemporal profiles of SV burden across different cucumber populations.
Fig. 5: Adaptive introgressions in cucumber.
Fig. 6: Genomic prediction incorporating SV burden information.

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Data availability

Raw genome resequencing reads have been deposited in the National Center for Biotechnology Information (NCBI) BioProject database under the accession no. PRJNA1192329. Raw HiFi reads and genome assemblies have been deposited in the NCBI Bioproject database under the accession no. PRJNA844366. Genome assemblies and annotations, SNPs, small indels and SVs in VCF format are available at CuGenDBv2 (http://cucurbitgenomics.org/v2/ftp/pan-genome/cucumber/).

Code availability

All pipelines and customized scripts used in this study are available via GitHub at https://github.com/xuebozhao16/CucurbitGenomics and via Zenodo at https://doi.org/10.5281/zenodo.17872506 (ref. 93).

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Acknowledgements

We thank S. Beyer (US Department of Agriculture, Agricultural Research Service (USDA-ARS)) for technical help in developing the core collection. This research was supported by grants from USDA National Institute of Food and Agriculture Specialty Crop Research Initiative (nos. 2015-51181-24285 and 2020-51181-32139).

Author information

Author notes

  1. Jingyin Yu

    Present address: Xianghu Laboratory, Hangzhou, China

  2. Shan Wu

    Present address: Syngenta Group China, Beijing, China

Authors and Affiliations

  1. Boyce Thompson Institute, Ithaca, NY, USA

    Xuebo Zhao, Jingyin Yu, Honghe Sun, Shan Wu, Jiantao Zhao & Zhangjun Fei

  2. State Key Laboratory of Vegetable Biobreeding, National Engineering Research Center for Vegetables, Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing Vegetable Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing, China

    Jie Zhang & Yong Xu

  3. State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yao Zhou

  4. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Yao Zhou

  5. Department of Horticulture, Graduate Program in Plant Breeding, Genetics and Biotechnology, Michigan State University, East Lansing, MI, USA

    Sue A. Hammar, Ying-Chen Lin & Rebecca Grumet

  6. Engineering Laboratory of Genetic Improvement of Horticultural Crops of Shandong Province, College of Horticulture, Qingdao Agricultural University, Qingdao, China

    Zhonghua Zhang

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

    Sanwen Huang

  8. National Key Laboratory of Tropical Crop Breeding, Chinese Academy of Tropical Agricultural Sciences, Haikou, China

    Sanwen Huang

  9. Department of Plant and Agroecosystem Sciences, University of Wisconsin, Madison, WI, USA

    Ronald T. Dymerski, Feifan Chen & Yiqun Weng

  10. USDA-ARS Vegetable Crops Research Unit, Madison, WI, USA

    Yiqun Weng

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Contributions

Z.F. and Y.X. conceived the project. Z.F. designed and supervised the study. X.Z., J.Y., H.S. and S.W. contributed to genome assembly and annotation, pangenome construction and SV genotyping. X.Z. performed population genetic analyses. X.Z., J. Zhao and Y.Z. contributed to genomic prediction analysis. R.G., S.A.H. and Y.-C.L. contributed to sample collection, DNA extraction and phenotyping. R.T.D. and F.C. helped develop the population for sequencing. J. Zhang, Y.X., Y.W. and Z.F. coordinated genome sequencing. X.Z. wrote the paper. Z.F., Z.Z, S.H., Y.W., R.G. and Y.X. revised the paper.

Corresponding authors

Correspondence to Yong Xu or Zhangjun Fei.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Total length of SVs across different cucumber populations.

For each boxplot, the lower and upper bounds indicate the first and third quartiles, respectively, the center line indicates the median, and the whiskers extend to 1.5× the interquartile range. XSBN, Xishuangbanna; AF, Africa; WA, Central/West Asia; EU, Europe; EA, East Asia; AM, America.

Extended Data Fig. 2 SVs under selection during cucumber domestication and improvement.

a, Comparison of SV occurrence frequencies between wild and landrace populations (domestication). b, Comparison of SV occurrence frequencies between landrace and cultivar populations (improvement). SVs associated with known genes regulating key agronomic traits, including Psm (Paternal sorting of mitochondria), lgp (light green peel), bt (bitter fruit), up (upward-pedicel), ten (tendril-less), and lgf (light green fruit), are shown.

Extended Data Fig. 3 Phylogeny of 447 cucumber accessions based on SVs.

African accessions are marked with red arrows.

Extended Data Fig. 4 Population structure and principal component analysis of cucumber accessions.

a, Population structure of cucumber accessions based on SNPs, with the number of clusters (K) ranging from 2 to 6. b, Cross-validation (CV) error plotted against K for inference of population structure. c, Principal component analysis (PCA) of cucumber accessions based on SVs. d, PCA of cucumber accessions based on SNPs. The right panel shows an enlarged view of the cluster indicated by the dotted box in the left panel.

Extended Data Fig. 5 Site frequency spectrum (SFS) of sSNPs, nSNPs, and SVs in introgressed and non-introgressed regions.

a, b, SFS of sSNPs, nSNPs, insertions, and deletions in regions with (a) and without (b) introgressions from wild to the European population.

Extended Data Fig. 6 Genomic prediction accuracies for four traits significantly correlated with SV burden.

a–d, Genomic prediction accuracies for young fruit shape (a), mature fruit shape (b), fruit curvature (c), and fruit hollowness (d). Values are from 50 independent cross-validation replicates. For each boxplot, the lower and upper bounds indicate the first and third quartiles, respectively, the center line indicates the median, and the whiskers extend to 1.5× the interquartile range.

Extended Data Fig. 7 Genomic prediction accuracies for traits not correlated with SV burden.

Values are from 50 independent cross-validation replicates. For each boxplot, the lower and upper bounds indicate the first and third quartiles, respectively, the center line indicates the median, and the whiskers extend to 1.5× the interquartile range.

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Zhao, X., Yu, J., Zhang, J. et al. Graph-based pangenome reveals structural variation dynamics during cucumber breeding. Nat Genet 58, 643–654 (2026). https://doi.org/10.1038/s41588-026-02506-0

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