Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Nature Communications
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. nature communications
  3. articles
  4. article
Sub-pangenome analysis reveals structural variants associated with fruit color and bacterial wilt resistance in eggplant
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 23 February 2026

Sub-pangenome analysis reveals structural variants associated with fruit color and bacterial wilt resistance in eggplant

  • Qian You  ORCID: orcid.org/0000-0003-1034-036X1 na1,
  • Ze Peng  ORCID: orcid.org/0000-0003-1665-35192 na1,
  • Zhiliang Li1 na1,
  • Yaolan Jiang1 na1,
  • Penglong Wan2 na1,
  • Yahui Zhao2,
  • Wei Zhao1,
  • Songyuan Zhang1,2,
  • Hefen Cheng1,
  • Chengjie Chen2,
  • Zhou Heng1,
  • Ming Hu2,
  • Yongfeng Zhou  ORCID: orcid.org/0000-0003-0780-29733,
  • Brandon S. Gaut  ORCID: orcid.org/0000-0002-1334-55564,
  • Baojuan Sun  ORCID: orcid.org/0000-0001-9698-48731,
  • Tao Li  ORCID: orcid.org/0000-0002-8589-75601 &
  • …
  • Yi Liao  ORCID: orcid.org/0000-0002-7724-17992 

Nature Communications , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Agricultural genetics
  • Natural variation in plants
  • Plant breeding
  • Structural variation

Abstract

Eggplant (Solanum melongena L.) is a globally important Solanaceae crop, yet trait-relevant genomic variants remain poorly characterized. Here, we perform population genomic analyses of 226 eggplant accessions sampled mainly from a major domestication center spanning Southeast Asia and South China, and find that genetic relationships closely track geographic origin. We generate chromosome-scale assemblies for 11 representative accessions using long-read sequencing and integrate six published genomes to build a pangenome resource. Using this resource, association scans identify a 12.4 Mb inversion on chromosome 10 segregating at 50.44% frequency that is strongly associated with fruit color, likely through hitchhiking with SmMYB1. We also detect variants associated with bacterial wilt resistance, including a premature stop codon in SmCYP82D47 and copy number variations in SmEPS1 and SmRoq1 homologs. Together, our results illuminate the evolution and phenotypic impact of large structural variants and provide genomic resources for eggplant genetics and breeding.

Data availability

All the raw sequencing data, genome assemblies and annotations have been submitted to China National GeneBank (CNGB) database under Project accession number CNP0006177. The variant (VCF) and pangenome graph files have been deposited in the Zenodo database [https://doi.org/10.5281/zenodo.18425195]. Source data are provided with this paper.

Code availability

The scripts associated with the pangenome analysis are available at Github (https://github.com/yiliao1022/eggplantpangenome) and Zendo (https://doi.org/10.5281/zenodo.18467477).

References

  1. Page, A., Gibson, J., Meyer, R. S. & Chapman, M. A. Eggplant domestication: pervasive gene flow, feralization, and transcriptomic divergence. Mol. Biol. Evol. 36, 1359–1372 (2019).

    Google Scholar 

  2. Meyer, R. S., Karol, K. G., Little, D. P., Nee, M. H. & Litt, A. A. Phylogeographic relationships among Asian eggplants and new perspectives on eggplant domestication. Mol. Phylogenet. Evol. 63, 685–701 (2012).

    Google Scholar 

  3. Meyer, R. S. et al. Parallel reductions in phenolic constituents resulting from the domestication of eggplant. Phytochemistry 115, 194–206 (2015).

    Google Scholar 

  4. Benoit, M. et al. Solanum pan-genetics reveals paralogues as contingencies in crop engineering. Nature 640, 135–145 (2025).

    Google Scholar 

  5. Taher, D. et al. World vegetable center eggplant collection: origin, composition, seed dissemination and utilization in breeding. Front. Plant Sci. 8, 1484 (2017).

    Google Scholar 

  6. Arnoux, S., Fraïsse, C. & Sauvage, C. Genomic inference of complex domestication histories in three Solanaceae species. J. Evol. Biol. 34, 270–283 (2021).

    Google Scholar 

  7. Barchi, L. et al. Analysis of >3400 worldwide eggplant accessions reveals two independent domestication events and multiple migration-diversification routes. Plant J. 116, 1667–1680 (2023).

    Google Scholar 

  8. Wang, J.-X., Gao, T.-G. & Knapp, S. Ancient Chinese literature reveals pathways of eggplant domestication. Ann. Bot. 102, 891–897 (2008).

    Google Scholar 

  9. Hirakawa, H. et al. Draft genome sequence of eggplant (Solanum melongena L.): the representative Solanum species indigenous to the old world. DNA Res. 21, 649–660 (2014).

    Google Scholar 

  10. Barchi, L. et al. A chromosome-anchored eggplant genome sequence reveals key events in Solanaceae evolution. Sci. Rep. 9, 11769 (2019).

    Google Scholar 

  11. Barchi, L. et al. Improved genome assembly and pan-genome provide key insights into eggplant domestication and breeding. Plant J. 107, 579–596 (2021).

    Google Scholar 

  12. Wei, Q. et al. A high-quality chromosome-level genome assembly reveals genetics for important traits in eggplant. Hortic. Res. 7, 153 (2020).

    Google Scholar 

  13. Li, D. et al. A high-quality genome assembly of the eggplant provides insights into the molecular basis of disease resistance and chlorogenic acid synthesis. Mol. Ecol. Resour. 21, 1274–1286 (2021).

    Google Scholar 

  14. Fang, H. et al. Telomere-to-telomere genome assembly of eggplant (Solanum melongena L.) promotes gene fine localization of the green stripe (GS) in pericarp. Int. J. Biol. Macromol. 284, 138094 (2025).

    Google Scholar 

  15. Wu, Y. et al. Phylogenomic discovery of deleterious mutations facilitates hybrid potato breeding. Cell 186, 2313–2328.e15 (2023).

    Google Scholar 

  16. Shi, J., Tian, Z., Lai, J. & Huang, X. Plant pan-genomics and its applications. Mol. Plant 16, 168–186 (2023).

    Google Scholar 

  17. Gaccione, L. et al. Graph-based pangenomes and pan-phenome provide a cornerstone for eggplant biology and breeding. Nat. Commun. 16, 9919 (2025).

    Google Scholar 

  18. Yu, C. et al. Graph pangenome advances genetic discoveries and the improvement of eggplant. Hortic. Res. 13, uhaf248 (2025).

    Google Scholar 

  19. You, Q. et al. Mapping and validation of the epistatic and genes controlling anthocyanin biosynthesis in the peel of eggplant (L.) fruit. Hortic. Res. 10, uhac268 (2023).

    Google Scholar 

  20. Arrones, A. et al. Mutations in the transcription factor suppressing chlorophyll pigmentation in the eggplant fruit peel are key drivers of a diversified colour palette. Front. Plant Sci. 13, 1025951 (2022).

    Google Scholar 

  21. AFLP and SCAR Markers Associated with Peel Color in Eggplant (Solanum melongena). Agri. Sci. China 8, 1466–1474 (2009).

  22. Salgon, S. et al. Eggplant resistance to the species complex involves both broad-spectrum and strain-specific quantitative trait loci. Front. Plant Sci. 8, 828 (2017).

    Google Scholar 

  23. Lebeau, A. et al. Genetic mapping of a major dominant gene for resistance to Ralstonia solanacearum in eggplant. Theor. Appl Genet 126, 143–158 (2013).

    Google Scholar 

  24. Salgon, S. et al. Genotyping by sequencing highlights a polygenic resistance to ralstonia pseudosolanacearum in eggplant (Solanum melongena L.). Int J. Mol. Sci. 19, 357 (2018).

    Google Scholar 

  25. Gong, C. et al. A QTL of eggplant shapes the rhizosphere bacterial community, co-responsible for resistance to bacterial wilt. Hortic. Res. 11, uhad272 (2024).

    Google Scholar 

  26. Ro, N. et al. Genome-wide association study for agro-morphological traits in eggplant core collection. Plants 11, 2627 (2022).

    Google Scholar 

  27. Jin, S. et al. Structural variation (SV)-based pan-genome and GWAS reveal the impacts of SVs on the speciation and diversification of allotetraploid cottons. Mol. Plant 16, 678–693 (2023).

    Google Scholar 

  28. Li, N. et al. Super-pangenome analyses highlight genomic diversity and structural variation across wild and cultivated tomato species. Nat. Genet. 55, 852–860 (2023).

    Google Scholar 

  29. He, Q. et al. A graph-based genome and pan-genome variation of the model plant Setaria. Nat. Genet. 55, 1232–1242 (2023).

    Google Scholar 

  30. Li, X. et al. Large-scale gene expression alterations introduced by structural variation drive morphotype diversification in Brassica oleracea. Nat. Genet. 56, 517–529 (2024).

    Google Scholar 

  31. Omondi, E. O. et al. Landscape genomics reveals genetic signals of environmental adaptation of African wild eggplants. Ecol. Evol. 14, e11662 (2024).

    Google Scholar 

  32. Liao, Y., Zhang, X., Chakraborty, M. & Emerson, J. J. Topologically associating domains and their role in the evolution of genome structure and function in Drosophila. Genome Res. 31, 397–410 (2021).

    Google Scholar 

  33. Liao, Y. et al. The 3D architecture of the pepper genome and its relationship to function and evolution. Nat. Commun. 13, 3479 (2022).

    Google Scholar 

  34. Hu, M. et al. Accurate, scalable structural variant genotyping in complex genomes at population scales. Mol. Biol. Evol. 42, msaf180 (2025).

  35. Alseekh, S., Scossa, F. & Fernie, A. R. Mobile Transposable Elements Shape Plant Genome Diversity. Trends Plant Sci. 25, 1062–1064 (2020).

    Google Scholar 

  36. Kou, Y. et al. Evolutionary genomics of structural variation in asian rice (Oryza sativa) domestication. Mol. Biol. Evol. 37, 3507–3524 (2020).

  37. Hämälä, T. et al. Genomic structural variants constrain and facilitate adaptation in natural populations of Theobroma cacao, the chocolate tree. Proc. Natl. Acad. Sci. USA 118, e2102914118 (2021).

    Google Scholar 

  38. Zhou, Y. et al. The population genetics of structural variants in grapevine domestication. Nat. Plants 5, 965–979 (2019).

    Google Scholar 

  39. Hickey, G. et al. Genotyping structural variants in pangenome graphs using the vg toolkit. Genome Biol. 21, 35 (2020).

    Google Scholar 

  40. Garrison, E. et al. Building pangenome graphs. Nat. Methods 21, 2008–2012 (2024).

    Google Scholar 

  41. Xiao, K. et al. Fine mapping of candidate gene controlling anthocyanin biosynthesis for purple peel in L. Int J. Mol. Sci. 25, 5241 (2024).

    Google Scholar 

  42. Florio, F. E. et al. A acyltransferase variant causes a major difference in eggplant (L.) peel anthocyanin composition. Int J. Mol. Sci. 22, 9174 (2021).

    Google Scholar 

  43. Zhang, Y. et al. Anthocyanin accumulation and molecular analysis of anthocyanin biosynthesis-associated genes in eggplant (Solanum melongena L.). J. Agric. Food Chem. 62, 2906–2912 (2014).

    Google Scholar 

  44. Li, J. et al. RNA-sequencing analysis reveals novel genes involved in the different peel color formation in eggplant. Hortic. Res. 10, uhad181 (2023).

    Google Scholar 

  45. Jiang, M., Ren, L., Lian, H., Liu, Y. & Chen, H. Novel insight into the mechanism underlying light-controlled anthocyanin accumulation in eggplant (Solanum melongena L.). Plant Sci. 249, 46–58 (2016).

    Google Scholar 

  46. Mangino, G. et al. Newly developed MAGIC population allows identification of strong associations and candidate genes for anthocyanin pigmentation in eggplant. Front Plant Sci. 13, 847789 (2022).

    Google Scholar 

  47. Zhou, X., Liu, S., Yang, Y., Liu, J. & Zhuang, Y. Integrated metabolome and transcriptome analysis reveals a regulatory network of fruit peel pigmentation in eggplant (L.). Int. J. Mol. Sci. 23, 13475 (2022).

    Google Scholar 

  48. Wu, X. et al. Chalcone synthase (CHS) family members analysis from eggplant (Solanum melongena L.) in the flavonoid biosynthetic pathway and expression patterns in response to heat stress. PLoS One 15, e0226537 (2020).

    Google Scholar 

  49. Wang, H.-Y. et al. Overexpression of cucumber CYP82D47 enhances resistance to powdery mildew and Fusarium oxysporum f. sp. cucumerinum. Funct. Integr. Genom. 24, 1–16 (2024).

    Google Scholar 

  50. Yan, Q. et al. GmCYP82A3, a soybean cytochrome P450 family gene involved in the jasmonic acid and ethylene signaling pathway, enhances plant resistance to biotic and abiotic stresses. PLoS One 11, e0162253 (2016).

    Google Scholar 

  51. Torrens-Spence, M. P. et al. PBS3 and EPS1 complete salicylic acid biosynthesis from isochorismate in arabidopsis. Mol. Plant 12, 1577–1586 (2019).

    Google Scholar 

  52. Jia, X. et al. The origin and evolution of salicylic acid signaling and biosynthesis in plants. Mol. Plant 16, 245–259 (2023).

    Google Scholar 

  53. Thomas, N. C. et al. The immune receptor Roq1 confers resistance to the bacterial pathogens, and in tomato. Front. Plant Sci. 11, 463 (2020).

    Google Scholar 

  54. Zhou, Y. et al. Graph pangenome captures missing heritability and empowers tomato breeding. Nature 606, 527–534 (2022).

    Google Scholar 

  55. Chen, J. et al. Pangenome analysis reveals genomic variations associated with domestication traits in broomcorn millet. Nat. Genet. 55, 2243–2254 (2023).

    Google Scholar 

  56. Tang, D. et al. Genome evolution and diversity of wild and cultivated potatoes. Nature 606, 535–541 (2022).

    Google Scholar 

  57. Jayakodi, M. et al. The barley pan-genome reveals the hidden legacy of mutation breeding. Nature 588, 284–289 (2020).

    Google Scholar 

  58. Gao, L. et al. The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nat. Genet. 51, 1044–1051 (2019).

    Google Scholar 

  59. Bozan, I. et al. Pangenome analyses reveal impact of transposable elements and ploidy on the evolution of potato species. Proc. Natl. Acad. Sci. USA 120, e2211117120 (2023).

    Google Scholar 

  60. Kim, S. et al. Genome sequence of the hot pepper provides insights into the evolution of pungency in Capsicum species. Nat. Genet. 46, 270–278 (2014).

    Google Scholar 

  61. Liu, F. et al. Genomes of cultivated and wild Capsicum species provide insights into pepper domestication and population differentiation. Nat. Commun. 14, 5487 (2023).

    Google Scholar 

  62. Chen, W. et al. Two telomere-to-telomere gapless genomes reveal insights into Capsicum evolution and capsaicinoid biosynthesis. Nat. Commun. 15, 4295 (2024).

    Google Scholar 

  63. Cao, Y. et al. Pepper variome reveals the history and key loci associated with fruit domestication and diversification. Mol. Plant 15, 1744–1758 (2022).

    Google Scholar 

  64. Ge, H. Y. et al. Simple sequence repeat-based association analysis of fruit traits in eggplant (Solanum melongena). Genet. Mol. Res. 12, 5651–5663 (2013).

    Google Scholar 

  65. Lee, J.-H. et al. High-quality chromosome-scale genomes facilitate effective identification of large structural variations in hot and sweet peppers. Hortic. Res 9, uhac210 (2022).

    Google Scholar 

  66. Gaut, B. S., Seymour, D. K., Liu, Q. & Zhou, Y. Demography and its effects on genomic variation in crop domestication. Nat. Plants 4, 512–520 (2018).

    Google Scholar 

  67. Yuan, Y., Bayer, P. E., Batley, J. & Edwards, D. Current status of structural variation studies in plants. Plant Biotechnol. J. 19, 2153–2163 (2021).

    Google Scholar 

  68. Zhou, Y. et al. Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice. Nat. Commun. 14, 1567 (2023).

    Google Scholar 

  69. Hu, H. et al. Unravelling inversions: Technological advances, challenges, and potential impact on crop breeding. Plant Biotechnol. J. 22, 544–554 (2024).

    Google Scholar 

  70. Kirkpatrick, M. How and why chromosome inversions evolve. PLoS Biol. 8, e1000501 (2010).

    Google Scholar 

  71. Twyford, A. D. & Friedman, J. Adaptive divergence in the monkey flower Mimulus guttatus is maintained by a chromosomal inversion. Evolution 69, 1476–1486 (2015).

    Google Scholar 

  72. Lowry, D. B. & Willis, J. H. A widespread chromosomal inversion polymorphism contributes to a major life-history transition, local adaptation, and reproductive isolation. PLoS Biol. 8, e1000500 (2010).

    Google Scholar 

  73. Soudi, S. et al. Repeatability of adaptation in sunflowers reveals that genomic regions harbouring inversions also drive adaptation in species lacking an inversion. Elife 12, RP88604 (2023).

    Google Scholar 

  74. Roesti, M., Gilbert, K. J. & Samuk, K. Chromosomal inversions can limit adaptation to new environments. Mol. Ecol. 31, 4435–4439 (2022).

    Google Scholar 

  75. Lv, Z. et al. Fine mapping and candidate gene analysis of the gv1 locus controlling green-peel color in eggplant (Solanum melongena L.). Horticulturae 9, 888 (2023).

    Google Scholar 

  76. Fang, H. et al. Fine mapping and identification of regulating rind color in eggplant (L.). Int. J. Mol. Sci. 24, 3059 (2023).

    Google Scholar 

  77. Tigchelaar, E. C., Janick, J. & Erickson, H. T. The genetics of anthocyanin coloration in eggplant (SOLANUM MELONGENA L.). Genetics 60, 475–491 (1968).

    Google Scholar 

  78. Babak, O. et al. Identification of DNA Markers of Anthocyanin Biosynthesis Disorders Based on the Polymorphism of Anthocyanin 1 Tomato Ortholog Genes in Pepper and Eggplant. Crop Breeding, Genet. Genomics 2, e200011 (2020).

  79. Salanoubat, M. et al. Genome sequence of the plant pathogen Ralstonia solanacearum. Nature 415, 497–502 (2002).

    Google Scholar 

  80. Qiu, Z. et al. The eggplant transcription factor MYB44 enhances resistance to bacterial wilt by activating the expression of spermidine synthase. J. Exp. Bot. 70, 5343–5354 (2019).

    Google Scholar 

  81. Barik, S. et al. Breeding for bacterial wilt resistance in eggplant (Solanum melongena L.): progress and prospects. Crop Prot. 137, 105270 (2020).

    Google Scholar 

  82. Yan, S. et al. A putative E3 ubiquitin ligase substrate receptor degrades transcription factor SmNAC to enhance bacterial wilt resistance in eggplant. Hortic. Res. 11, uhad246 (2024).

    Google Scholar 

  83. Nachman, M. W. Variation in recombination rate across the genome: evidence and implications. Curr. Opin. Genet. Dev. 12, 657–663 (2002).

    Google Scholar 

  84. Fernandez-Pozo, N., Rosli, H. G., Martin, G. B. & Mueller, L. A. The SGN VIGS tool: user-friendly software to design virus-induced gene silencing (VIGS) constructs for functional genomics. Mol. Plant 8, 486–488 (2015).

    Google Scholar 

  85. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).

    Google Scholar 

  86. Zhang, X., Zhang, S., Zhao, Q., Ming, R. & Tang, H. Assembly of allele-aware, chromosomal-scale autopolyploid genomes based on Hi-C data. Nat. Plants 5, 833–845 (2019).

    Google Scholar 

  87. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Google Scholar 

  88. Marçais, G. et al. MUMmer4: a fast and versatile genome alignment system. PLoS Comput. Biol. 14, e1005944 (2018).

    Google Scholar 

  89. Manni, M., Berkeley, M. R., Seppey, M. & Zdobnov, E. M. BUSCO: assessing genomic data quality and beyond. Curr. Protoc. 1, e323 (2021).

    Google Scholar 

  90. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Google Scholar 

  91. Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res 27, 573–580 (1999).

    Google Scholar 

  92. Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinform. 4, 4.10.1–4.10.14 (2009).

  93. Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).

    Google Scholar 

  94. Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res 35, W265–W268 (2007).

    Google Scholar 

  95. Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).

    Google Scholar 

  96. Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl. Acad. Sci. USA 117, 9451–9457 (2020).

    Google Scholar 

  97. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Google Scholar 

  98. Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res 14, 988–995 (2004).

    Google Scholar 

  99. Keller, O., Kollmar, M., Stanke, M. & Waack, S. A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics 27, 757–763 (2011).

    Google Scholar 

  100. Alioto, T., Picardi, E., Guigó, R. & Pesole, G. ASPic-GeneID: a lightweight pipeline for gene prediction and alternative isoforms detection. Biomed. Res. Int. 2013, 502827 (2013).

    Google Scholar 

  101. Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).

    Google Scholar 

  102. Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).

    Google Scholar 

  103. Korf, I. Gene finding in novel genomes. BMC Bioinforma. 5, 59 (2004).

    Google Scholar 

  104. Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).

    Google Scholar 

  105. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Google Scholar 

  106. Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 20, 278 (2019).

    Google Scholar 

  107. Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).

    Google Scholar 

  108. Haas, B. J. et al. Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies. Nucleic Acids Res 31, 5654–5666 (2003).

    Google Scholar 

  109. Chan, P. P., Lin, B. Y., Mak, A. J. & Lowe, T. M. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res 49, 9077–9096 (2021).

    Google Scholar 

  110. Griffiths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res 33, D121–D124 (2005).

    Google Scholar 

  111. Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).

    Google Scholar 

  112. Mulder, N. & Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol. Biol. 396, 59–70 (2007).

    Google Scholar 

  113. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Google Scholar 

  114. McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20, 1297–1303 (2010).

    Google Scholar 

  115. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Google Scholar 

  116. Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).

    Google Scholar 

  117. Alexander, D. H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinforma. 12, 1–6 (2011).

    Google Scholar 

  118. Chen, C. et al. TBtools-II: A ‘one for all, all for one’ bioinformatics platform for biological big-data mining. Mol. Plant 16, 1733–1742 (2023).

    Google Scholar 

  119. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Google Scholar 

  120. Zhang, C., Dong, S.-S., Xu, J.-Y., He, W.-M. & Yang, T.-L. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics 35, 1786–1788 (2019).

    Google Scholar 

  121. Kent, W. J., Baertsch, R., Hinrichs, A., Miller, W. & Haussler, D. Evolution’s cauldron: duplication, deletion, and rearrangement in the mouse and human genomes. Proc. Natl. Acad. Sci. USA 100, 11484–11489 (2003).

    Google Scholar 

  122. Wala, J. A. et al. SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res. 28, 581–591 (2018).

    Google Scholar 

  123. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    Google Scholar 

  124. Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

    Google Scholar 

  125. Jeffares, D. C. et al. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat. Commun. 8, 1–11 (2017).

    Google Scholar 

  126. Zheng, Z. et al. A sequence-aware merger of genomic structural variations at population scale. Nat. Commun. 15, 960 (2024).

    Google Scholar 

  127. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    Google Scholar 

  128. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Google Scholar 

  129. Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).

    Google Scholar 

  130. Tang, H. et al. JCVI: a versatile toolkit for comparative genomics analysis. Imeta 3, e211 (2024).

    Google Scholar 

  131. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).

    Google Scholar 

  132. Li, M.-X., Yeung, J. M. Y., Cherny, S. S. & Sham, P. C. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum. Genet. 131, 747–756 (2011).

    Google Scholar 

  133. R: A Language and Environment for Statistical Computing: Reference Index. (2010).

  134. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Google Scholar 

  135. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Google Scholar 

Download references

Acknowledgements

We thank Y. Wang and C. Yu for help with the germplasm collection. This work was founded by grants from Guangdong S&T Program (Grant No. 2025B0202070003 to T.L.), the Guangdong Provincial Natural Science Foundation (Grant No. 2023A1515012563 and 2025A1515012414 to Q.Y.), the Guangdong Provincial Rural Revitalization Strategy Special Fund Seed Industry Revitalization Project (Grant No. 2022-NJS-00-005 and 2023-NJS-00-003 to Q.Y.), the Special fund for scientific innovation strategy-construction of high level Academy of Agriculture Science (Grant No. R2021YJ-YB3019 and R2023PY-QY004 to Q.Y.), Modern Seed Industry Innovation Capability Enhancement Project of Guangdong Academy of Agricultural Sciences (Grant No. 2025ZYTS0505 to T.L.), the Department of agriculture and rural areas of Guangdong province of China (Grant No. 2025-NBH-00-001 to B.S.), the Basic Research Project of Guangdong Vegetable Research Institute (Grant No. 202110 to Q.Y.), and Research Start-up Funding from South China Agricultural University to Y.L.

Author information

Author notes
  1. These authors contributed equally: Qian You, Ze Peng, Zhiliang Li, Yaolan Jiang, Penglong Wan.

Authors and Affiliations

  1. Guangdong Key Laboratory for New Technology Research of Vegetables, Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, Guangdong, China

    Qian You, Zhiliang Li, Yaolan Jiang, Wei Zhao, Songyuan Zhang, Hefen Cheng, Zhou Heng, Baojuan Sun & Tao Li

  2. Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (South China), Ministry of Agriculture and Rural Affairs, College of Horticulture, South China Agricultural University, Guangdong, China

    Ze Peng, Penglong Wan, Yahui Zhao, Songyuan Zhang, Chengjie Chen, Ming Hu & Yi Liao

  3. National Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

    Yongfeng Zhou

  4. Department of Ecology and Evolutionary Biology, University of California, Irvine, CA, USA

    Brandon S. Gaut

Authors
  1. Qian You
    View author publications

    Search author on:PubMed Google Scholar

  2. Ze Peng
    View author publications

    Search author on:PubMed Google Scholar

  3. Zhiliang Li
    View author publications

    Search author on:PubMed Google Scholar

  4. Yaolan Jiang
    View author publications

    Search author on:PubMed Google Scholar

  5. Penglong Wan
    View author publications

    Search author on:PubMed Google Scholar

  6. Yahui Zhao
    View author publications

    Search author on:PubMed Google Scholar

  7. Wei Zhao
    View author publications

    Search author on:PubMed Google Scholar

  8. Songyuan Zhang
    View author publications

    Search author on:PubMed Google Scholar

  9. Hefen Cheng
    View author publications

    Search author on:PubMed Google Scholar

  10. Chengjie Chen
    View author publications

    Search author on:PubMed Google Scholar

  11. Zhou Heng
    View author publications

    Search author on:PubMed Google Scholar

  12. Ming Hu
    View author publications

    Search author on:PubMed Google Scholar

  13. Yongfeng Zhou
    View author publications

    Search author on:PubMed Google Scholar

  14. Brandon S. Gaut
    View author publications

    Search author on:PubMed Google Scholar

  15. Baojuan Sun
    View author publications

    Search author on:PubMed Google Scholar

  16. Tao Li
    View author publications

    Search author on:PubMed Google Scholar

  17. Yi Liao
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Q.Y., Y.L., T.L., and B.S. conceived and designed the study. Z.L., T.L., and B.S. prepared the materials. Z.P., Y.L. and M.H. performed the pangenome and structural variation analyses. Q.Y., Y.J., P.W. and Y.Z. performed the GWAS analyses. W.Z., S.Z., H.C. and H.Z. contributed to the field phenotyping. Q.Y., W.Z., and S.Z. performed the gene silencing experiments. Y.L., Q.Y., Z.P., Z.L., Y.J., P.W., T.L., Y.Z., B.S., C.C., and B.S.G. wrote and revised the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Qian You, Baojuan Sun, Tao Li or Yi Liao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Rachel Meyer, Junpeng Shi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Peer Review file

Description of Additional Supplementary Files

Supplementary Data 1

Supplementary Data 2

Supplementary Data 3

Supplementary Data 4

Supplementary Data 5

Supplementary Data 6

Supplementary Data 7

Supplementary Data 8

Supplementary Data 9

Supplementary Data 10

Supplementary Data 11

Supplementary Data 12

Supplementary Data 13

Supplementary Data 14

Supplementary Data 15

Supplementary Data 16

Supplementary Data 17

Supplementary Data 18

Reporting Summary

Source data

Source Data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

You, Q., Peng, Z., Li, Z. et al. Sub-pangenome analysis reveals structural variants associated with fruit color and bacterial wilt resistance in eggplant. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69764-8

Download citation

  • Received: 26 September 2024

  • Accepted: 09 February 2026

  • Published: 23 February 2026

  • DOI: https://doi.org/10.1038/s41467-026-69764-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Download PDF

Advertisement

Explore content

  • Research articles
  • Reviews & Analysis
  • News & Comment
  • Videos
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • Aims & Scope
  • Editors
  • Journal Information
  • Open Access Fees and Funding
  • Calls for Papers
  • Editorial Values Statement
  • Journal Metrics
  • Editors' Highlights
  • Contact
  • Editorial policies
  • Top Articles

Publish with us

  • For authors
  • For Reviewers
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Nature Communications (Nat Commun)

ISSN 2041-1723 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing