Genome editing technologies are being widely adopted in plant breeding1. However, a looming challenge of engineering desirable genetic variation in diverse genotypes is poor predictability of phenotypic outcomes due to unforeseen interactions with pre-existing cryptic mutations2,3,4. In tomato, breeding with a classical MADS-box gene mutation that improves harvesting by eliminating fruit stem abscission frequently results in excessive inflorescence branching, flowering and reduced fertility due to interaction with a cryptic variant that causes partial mis-splicing in a homologous gene5,6,7,8. Here, we show that a recently evolved tandem duplication carrying the second-site variant achieves a threshold of functional transcripts to suppress branching, enabling breeders to neutralize negative epistasis on yield. By dissecting the dosage mechanisms by which this structural variant restored normal flowering and fertility, we devised strategies that use CRISPR–Cas9 genome editing to predictably improve harvesting. Our findings highlight the under-appreciated impact of epistasis in targeted trait breeding and underscore the need for a deeper characterization of cryptic variation to enable the full potential of genome editing in agriculture.

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Data availability

The DNA sequencing data used to map branching QTLs in the S. lycopersicum j2 ej2W × S. lycopersicum Fla.8924 F2 population and the RNA sequencing data of transition and floral meristem stages for S. lycopersicum M82, S. lycopersicum j2 ej2W and S. lycopersicum Fla.8924 has been deposited in SRA (http://ncbi.nlm.nih.gov/sra) under the accession code PRJNA509653.

Source Data files for all main and supplementary figures are available in the online version of the paper. All additional data sets are available from the corresponding author on request.

Additional information

Journal peer review information: Nature Plants thanks Allen Van Deynze, Jianbing Yan and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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


  1. 1.

    Wallace, J. G., Rodgers-Melnick, E. & Buckler, E. S. On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu. Rev. Genet. 52, 421–444 (2018).

  2. 2.

    Gibson, G., Dworkin, I. & Hall, G. Uncovering cryptic genetic variation. Nat. Rev. Genet. 5, 1–10 (2004).

  3. 3.

    Paaby, A. B. & Rockman, M. V. Cryptic genetic variation: evolution’s hidden substrate. Nat. Rev. Genet. 15, 247–258 (2014).

  4. 4.

    Sackton, T. B. & Hartl, D. L. Genotypic context and epistasis in individuals and populations. Cell 166, 279–287 (2016).

  5. 5.

    Reynard, G. B. New source of the j2 gene governing jointless pedicel in tomato. Science 134, 4–6 (1961).

  6. 6.

    Rick, C. M. A new jointless gene from the Galapagos L. pimpinellifolium. TGC Rep. 6, 23 (1956).

  7. 7.

    Zahara, M. B. & Scheuerman, R. W. Hand-harvesting jointless vs. jointed-stem tomatoes. Calif. Agric. 42, 14–14 (1988).

  8. 8.

    Soyk, S. et al. Bypassing negative epistasis on yield in tomato imposed by a domestication gene. Cell 169, 1142–1155 (2017).

  9. 9.

    Alonso-Blanco, C. et al. 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166, 481–491 (2016).

  10. 10.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  11. 11.

    Aflitos, S. et al. Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. Plant J. 80, 136–148 (2014).

  12. 12.

    Lin, T. et al. Genomic analyses provide insights into the history of tomato breeding. Nat. Genet. 46, 1220–1226 (2014).

  13. 13.

    Le Rouzic, A. & Carlborg, Ö. Evolutionary potential of hidden genetic variation. Trends Ecol. Evol. 23, 33–37 (2008).

  14. 14.

    McGuigan, K. & Sgrò, C. M. Evolutionary consequences of cryptic genetic variation. Trends Ecol. Evol. 24, 305–311 (2009).

  15. 15.

    Lauter, N. & Doebley, J. Genetic variation for phenotypically invariant traits detected in teosinte: implications for the evolution of novel forms. Genetics 342, 333–342 (2002).

  16. 16.

    Mcguigan, K., Nishimura, N., Currey, M., Hurwit, D. & Cresko, W. A. Cryptic genetic variation and body size evolution in threespine stickleback. Evolution 65, 1203–1211 (2011).

  17. 17.

    Pires, N. D. et al. Genetic variation involved in the paternal regulation of seed development. PLoS Genet. 12, e1005806 (2016).

  18. 18.

    Monniaux, M. et al. The role of APETALA1 in petal number robustness. eLife 7, 1–22 (2018).

  19. 19.

    Reynard, G. B. New source of the j2 gene governing jointless pedicel in tomato. Science 134, 2102 (1961).

  20. 20.

    Boiteux, L. S., Giordano, L., de, B., Furumoto, O. & Aragao, F. A. S. Estimating the pleiotropic effect of the jointless-2 gene on the processing and agronomic traits of tomato by using near-isogenic lines. Plant Breed. 114, 457–459 (1995).

  21. 21.

    Lee, T. G., Shekasteband, R., Menda, N., Mueller, L. A. & Hutton, S. F. Molecular markers to select for the j-2 –mediated jointless pedicel in tomato. HortScience 53, 153–158 (2018).

  22. 22.

    Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15, 461–468 (2018).

  23. 23.

    Bemer, M. et al. The tomato FRUITFULL homologs TDR4/FUL1 and MBP7/FUL2 regulate ethylene-independent aspects of fruit ripening. Plant Cell 24, 4437–4451 (2012).

  24. 24.

    Park, S. J., Jiang, K., Schatz, M. C. & Lippman, Z. B. Rate of meristem maturation determines inflorescence architecture in tomato. Proc. Natl Acad. Sci. USA 109, 639–644 (2012).

  25. 25.

    Park, S. J., Eshed, Y. & Lippman, Z. B. Meristem maturation and inflorescence architecture - lessons from the Solanaceae. Curr. Opin. Plant Biol. 17, 70–77 (2014).

  26. 26.

    Kyozuka, J., Tokunaga, H. & Yoshida, A. Control of grass inflorescence form by the fine-tuning of meristem phase change. Curr. Opin. Plant Biol. 17, 110–115 (2014).

  27. 27.

    Lemmon, Z. H. et al. The evolution of inflorescence diversity in the nightshades and heterochrony during meristem maturation. Genome Res. 26, 1676–1686 (2016).

  28. 28.

    Zhu, G. et al. Rewiring of the fruit metabolome in tomato breeding. Cell 172, 249–261 (2018).

  29. 29.

    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).

  30. 30.

    Blanca, J. et al. Genomic variation in tomato, from wild ancestors to contemporary breeding accessions. BMC Genom. 16, 257 (2015).

  31. 31.

    Rick, C. M. The tomato. Sci. Am. 239, 76–87 (1978).

  32. 32.

    Brooks, C., Nekrasov, V., Lippman, Z. B. & Van Eck, J. Efficient gene editing in tomato in the first generation using the CRISPR/Cas9 system. Plant Physiol. 166, 1292–1297 (2014).

  33. 33.

    Scott, J. W. Fla. 7946 tomato breeding line resistant to Fusarium oxysporum f.sp. lycopersici races 1, 2, and 3. HortScience 39, 440–441 (2004).

  34. 34.

    Scott, J. W., Hutton, S. F. & Freeman, J. H. Fla. 8638B and Fla. 8624 tomato breeding lines with begomovirus resistance genes Ty-5 plus Ty-6 and Ty-6, respectively. HortScience 50, 1405–1407 (2015).

  35. 35.

    Lye, Z. N. & Purugganan, M. D. Copy number variation in domestication. Trends Plant Sci. 24, 352–365 (2019).

  36. 36.

    Maron, L. G. et al. Aluminum tolerance in maize is associated with higher MATE1 gene copy number. Proc. Natl Acad. Sci. USA 110, 5241–5246 (2013).

  37. 37.

    Wang, Y. et al. Copy number variation at the GL7 locus contributes to grain size diversity in rice. Nat. Genet. 47, 944–948 (2015).

  38. 38.

    Würschum, T., Boeven, P. H. G., Langer, S. M., Longin, C. F. H. & Leiser, W. L. Multiply to conquer: copy number variations at Ppd-B1 and Vrn-A1 facilitate global adaptation in wheat. BMC Genet. 16, 1–8 (2015).

  39. 39.

    Gresham, D. et al. The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet. 4, e1000303 (2008).

  40. 40.

    Farslow, J. C. et al. Rapid Increase in frequency of gene copy-number variants during experimental evolution in Caenorhabditis elegans. BMC Genom. 16, 1–18 (2015).

  41. 41.

    Debolt, S. Copy number variation shapes genome diversity in Arabidopsis over immediate family generational scales. Genome Biol. Evol. 2, 441–453 (2010).

  42. 42.

    Vlad, D. et al. Leaf shape evolution through duplication, regulatory diversification, and loss of a homeobox gene. Science 343, 780–783 (2014).

  43. 43.

    Vuolo, F. et al. Coupled enhancer and coding sequence evolution of a homeobox gene shaped leaf diversity. Genes Dev. 30, 2370–2375 (2016).

  44. 44.

    Hickey, J. M., Chiurugwi, T., Mackay, I. & Powell, W. Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery. Nat. Genet. 49, 1297–1303 (2017).

  45. 45.

    Hou, J., van Leeuwen, J., Andrews, B. J. & Boone, C. Genetic network complexity shapes background-dependent phenotypic expression. Trends Genet. 34, 578–586 (2018).

  46. 46.

    Van Leeuwen, J. et al. Exploring genetic suppression interactions on a global scale. Science 354, aag0839 (2016).

  47. 47.

    Bazakos, C., Hanemian, M., Trontin, C., Jiménez-Goméz, J. M. & Loudet, O. New strategies and tools in quantitative genetics: how to go from the phenotype to the genotype. Annu Rev. Plant Biol. 68, 435–455 (2017).

  48. 48.

    Takagi, H. et al. QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant J. 74, 174–183 (2013).

  49. 49.

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

  50. 50.

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

  51. 51.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  52. 52.

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

  53. 53.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  54. 54.

    Bolger, A. et al. The genome of the stress-tolerant wild tomato species Solanum pennellii. Nat. Genet. 46, 1034–1038 (2014).

  55. 55.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013); http://www.R-project.org/

  56. 56.

    Tomato Genome Consortium.The tomato genome sequence provides insights into fleshy fruit evolution. Nature 485, 635–641 (2012).

  57. 57.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

  58. 58.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  59. 59.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

  60. 60.

    Aflitos, S. A. et al. Introgression browser: high-throughput whole-genome SNP visualization. Plant J. 82,174–182 (2015).

  61. 61.

    Dennenmoser, S. et al. Genome-wide patterns of transposon proliferation in an evolutionary young hybrid fish. Mol. Ecol. 28, 1491–1505 (2018).

  62. 62.

    Schmidt, M. H. et al. De novo assembly of a new Solanum pennellii accession using nanopore sequencing. Plant Cell 29, 2336–2348 (2017).

  63. 63.

    Werner, S., Engler, C., Weber, E., Gruetzner, R. & Marillonnet, S. Fast track assembly of multigene constructs using Golden Gate cloning and the MoClo system. Bioeng. Bugs 3, 38–43 (2012).

  64. 64.

    van Eck, J., Tjahjadi, P. & Keen, M. Agrobacterium tumefaciens-mediated transformation of tomato. Methods Mol. Biol. 1864, 225–234 (2019).

  65. 65.

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

  66. 66.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

  67. 67.

    Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics 20, 289–290 (2004).

  68. 68.

    Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2016).

  69. 69.

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

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We thank all members of the Lippman laboratory for valuable discussions. We thank A. Krainer, J. Dalrymple, G. Robitaille and J. Kim for technical support. We thank K. Swartwood for assistance with tomato transformation. We thank T. Mulligan, S. Vermylen, A. Krainer, S. Qiao and K. Schlecht, from CSHL, and staff from Cornell University’s Long Island Horticultural Research and Extension Center, for assistance with plant care. We thank S. Goodwin, S. Muller, R. Wappel and E. Ghiban from the CSHL Genome Center for sequencing support. We thank D. Zamir (Hebrew University, Israel) and E. van der Knaap (University of Georgia) for providing seed. This research was supported by an EMBO Long-Term Fellowship (no. ALTF 1589-2014) to S.S., a National Science Foundation Postdoctoral Research Fellowship in Biology Grant (no. IOS-1523423) to Z.H.L., a National Institute of Health Research Project with Complex Structure Cooperative Agreement (3UM1HG008898-01S2) to F.J.S., the ANR grant tomaTE (no. ANR-17-CE20-0024-02) to J.M.J.-G., a Research Grant from BARD (no. IS-4818-15), the United States–Israel Binational Agricultural Research and Development Fund, to Z.B.L., an Agriculture and Food Research Initiative competitive grant no. 2016-67013-24452 of the USDA National Institute of Food and Agriculture to S.H and Z.B.L. and the National Science Foundation Plant Genome Research Program (no. IOS-1732253) to J.V.E., M.C.S. and Z.B.L.

Author information


  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    • Sebastian Soyk
    • , Zachary H. Lemmon
    •  & Zachary B. Lippman
  2. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    • Fritz J. Sedlazeck
  3. Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, Versailles, France

    • José M. Jiménez-Gómez
  4. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    • Michael Alonge
    •  & Michael C. Schatz
  5. Horticultural Sciences Department, University of Florida, Wimauma, FL, USA

    • Samuel F. Hutton
  6. The Boyce Thompson Institute, Ithaca, NY, USA

    • Joyce Van Eck
  7. Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    • Joyce Van Eck
  8. Department of Oncologye, Johns Hopkins Medicine, Baltimore, MD, USA

    • Michael C. Schatz
  9. Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    • Zachary B. Lippman


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S.S., Z.H.L, F.J.S., J.M.J.-G., S.H., J.V.E., M.C.S. and Z.B.L. designed and planned experiments. S.S., Z.H.L, F.J.S., J.M.J.-G., S.H. and Z.B.L. performed experiments and collected the data. S.S., Z.H.L, F.J.S., J.M.J.-G., M.A., M.C.S. and Z.B.L. analysed the data. S.S., Z.H.L. and Z.B.L. designed the research. S.S. and Z.B.L. wrote the paper with the input from all authors.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Sebastian Soyk or Zachary B. Lippman.

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