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.

  • Perspective
  • Published:

Advancing crop genomics from lab to field

Abstract

Crop genomics remains a key element in ensuring scientific progress to secure global food security. It has been two decades since the sequence of the first plant genome, that of Arabidopsis thaliana, was released, and soon after that the draft sequencing of the rice genome was completed. Since then, the genomes of more than 100 crops have been sequenced, plant genome research has expanded across multiple fronts and the next few years promise to bring further advances spurred by the advent of new technologies and approaches. We are likely to see continued innovations in crop genome sequencing, genetic mapping and the acquisition of multiple levels of biological data. There will be exciting opportunities to integrate genome-scale information across multiple scales of biological organization, leading to advances in our mechanistic understanding of crop biological processes, which will, in turn, provide greater impetus for translation of laboratory results to the field.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Timeline of release of genome sequences for key crop species.
Fig. 2: Maps of genome-wide functional genomic and epigenomic information.
Fig. 3: Multiple levels of functional genomic, epigenomic organismal and ecosystem information.

Similar content being viewed by others

References

  1. Food and Agriculture Organization. The State of Food Security and Nutrition in the World 2020 (FAO, 2019).

    Google Scholar 

  2. Foley, J. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. The Arabidopsis Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000).

    Article  Google Scholar 

  4. Goff, S. et al. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296, 92–100 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Yu, J. et al. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296, 79–92 (2002).

    Article  CAS  PubMed  Google Scholar 

  6. Bevan, M. et al. Genomic innovation for crop improvement. Nature 543, 347–354 (2017).

    Article  Google Scholar 

  7. Briggs, S. P. Plant genomics: more than food for thought. Proc. Natl Acad. Sci. USA 95, 1986–1988 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. International Wheat Genome Sequencing Consortium (IWGSC) et al. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361, eaar7191 (2018)

  9. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Logsdon, G. A., Vollger, M. R. & Eichler, E. E. Long-read human genome sequencing and its applications. Nat. Rev. Genet. 21, 597–614 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. EPIC Planning Committee. Reading the second code: mapping epigenomes to understand plant growth, development, and adaptation to the environment. Plant Cell 24, 2257–2261 (2012).

    Article  Google Scholar 

  12. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ricci, W. et al. Widespread long-range cis-regulatory elements in the maize genome. Nat. Plants 5, 1237–1249 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ramírez-González, R. H. et al. The transcriptional landscape of polyploid wheat. Science 361, eaar6089 (2018).

    Article  PubMed  Google Scholar 

  15. Chen, W. et al. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat. Genet. 46, 714–721 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Mergner, J. et al. Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 579, 409–414 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Furbank, R. & Tester, M. Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16, 635–644 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Araus, L. et al. Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. 23, 451–466 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zaidem, M. L., Groen, S. C. & Purugganan, M. D. Evolutionary and ecological functional genomics, from lab to the wild. Plant J. 97, 40–55 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Tattaris, M., Reynolds, M. P. & Chapman, S. C. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front. Plant Sci. 7, 1131 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Clevers, J., Kooistra, L. & van den Brande, M. Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens. 9, 405 (2017).

    Article  Google Scholar 

  22. Ma, C., Zhang, H. H. & Wang, X. Machine learning for Big Data analytics in plants. Trends Plant Sci. 19, 798–808 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Esposito, S. et al. Applications and trends of machine learning in genomics and phenomics for next-generation breeding. Plants 9, 34 (2020).

    Article  CAS  Google Scholar 

  24. Wang, H., Cimen, E., Singh, N. & Buckler, E. Deep learning for plant genomics and crop improvement. Curr. Opin. Plant Biol. 54, 34–41 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Harfouche, A. et al. Accelerating climate resilient plant breeding by applying next-generation artificial intelligence. Trends Biotech. 37, 1217–1235 (2019).

    Article  CAS  Google Scholar 

  26. Belhaj, K. et al. Editing plant genomes with CRISPR/Cas9. Curr. Opin. Biotech. 32, 76–84 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Chen, K. et al. CRISPR/Cas genome editing and precision plant breeding in agriculture. Ann. Rev. Plant Biol. 70, 667–697 (2019).

    Article  CAS  Google Scholar 

  28. Fernie, A. R. & Yan, J. De novo domestication: an alternative route toward new crops for the future. Mol. Plant 12, 615–631 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Chen, F. et al. Genome sequences of horticultural plants: past, present, and future. Hort. Res. 6, 112 (2019).

    Article  Google Scholar 

  30. Ott, A. et al. Linked read technology for assembling large complex and polyploid genomes. BMC Genomics 19, 651 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Roberts, R., Carneiro, M. & Schatz, M. The advantages of SMRT sequencing. Genome Biol. 14, 405 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Branton, D. et al. The potential and challenges of nanopore sequencing. Nat. Biotechnol. 26, 1146–1153 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Belser, C. et al. Chromosome-scale assemblies of plant genomes using nanopore long reads and optical maps. Nat. Plants 4, 879–887 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Choi, J. Y. et al. Nanopore sequencing-based genome assembly and evolutionary genomics of circum-basmati rice. Genome Biol. 21, 21 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Stein, J. C. et al. Genomes of 13 domesticated and wild rice relatives highlight genetic conservation, turnover and innovation across the genus Oryza. Nat. Genet. 50, 285–296 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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).

    Article  CAS  PubMed  Google Scholar 

  38. Levy-Sakin, M. & Ebenstein, Y. Beyond sequencing: optical mapping of DNA in the age of nanotechnology and nanoscopy. Curr. Opin. Biotechnol. 24, 690–696 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Jiao, Y. et al. Improved maize reference genome with single-molecule technologies. Nature 546, 524–527 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rabinowicz, P. D. et al. Differential methylation of genes and retrotransposons facilitates shotgun sequencing of the maize genome. Nat. Genet. 23, 305–308 (1999).

    Article  CAS  PubMed  Google Scholar 

  41. Bertioli, D. J. et al. The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat. Genet. 48, 438–446 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Kopecký, D. et al. Flow sorting and sequencing meadow fescue chromosome 4F. Plant Physiol. 163, 1323–1337 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Schnable, P. S. et al. The B73 maize genome: complexity, diversity, and dynamics. Science 326, 1112–1115 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Jiao, Y. et al. Improved maize reference genome with single-molecule technologies. Nature 546, 524–527 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mascher, M. et al. A chromosome conformation capture ordered sequence of the barley genome. Nature 544, 427–433 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Kreplak, J. et al. A reference genome for pea provides insight into legume genome evolution. Nat. Genet. 51, 1411–1422 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Wang, M. et al. Reference genome sequences of two cultivated allotetraploid cottons, Gossypium hirsutum and Gossypium barbadense. Nat. Genet. 51, 224–229 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Zhang, J. et al. Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L. Nat. Genet. 50, 1565–1573 (2018).

    Article  CAS  PubMed  Google Scholar 

  49. Schaal, B. Plants and people: our shared history and future. Plants People Planet 1, 14–19 (2019).

    Article  Google Scholar 

  50. Varshney, R. et al. Can genomics boost productivity of orphan crops? Nat. Biotech. 30, 1172–1176 (2012).

    Article  CAS  Google Scholar 

  51. Brozynska, M., Furtado, A. & Henry, R. Genomics of crop wild relatives: expanding the gene pool for crop improvement. Plant Biotech. J. 14, 1070–1085 (2016).

    Article  CAS  Google Scholar 

  52. Dempewolf, H. et al. Past and future use of wild relatives in crop breeding. Crop Sci. 57, 1070–1082 (2017).

    Article  Google Scholar 

  53. Mascher, M. et al. Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding. Nat. Genet. 51, 1076–1081 (2019).

    Article  CAS  PubMed  Google Scholar 

  54. McCouch, S. et al. Mobilizing crop biodiversity. Mol. Plant 13, 1341–1344 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Varshney, R. V. et al. Can genomics deliver climate-change ready crops? Curr. Opin. Plant Biol. 45, 205–211 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wing, R. A., Purugganan, M. D. & Zhang, Q. The rice genome revolution: from an ancient grain to Green Super Rice. Nat. Rev. Genet. 19, 505–517 (2018).

    Article  CAS  PubMed  Google Scholar 

  58. Varshney, R. Exciting journey of 10 years from genomes to fields and markets: Some success stories of genomics-assisted breeding in chickpea, pigeonpea and groundnut. Plant Sci. 242, 98–107 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. Sansaloni, C. et al. Diversity analysis of 80,000 wheat accessions reveals consequences and opportunities of selection footprints. Nat. Commun. 11, 4572 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Milner, S. G. et al. Genebank genomics highlights the diversity of a global barley collection. Nat. Genet. 51, 319–326 (2019).

    Article  CAS  PubMed  Google Scholar 

  61. Horton, M. et al. Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel. Nat. Genet. 44, 212–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ferrero-Serrano, A. & Assmann, S. M. Phenotypic and genome-wide association with the local environment of Arabidopsis. Nat. Ecol. Evol. 3, 274–285 (2019).

    Article  PubMed  Google Scholar 

  63. Lasky, J. R. et al. Characterizing genomic variation of Arabidopsis thaliana: the roles of geography and climate. Mol. Ecol. 22, 5512–5529 (2012).

    Article  Google Scholar 

  64. Gutaker, R. et al. Genomic history and ecology of the geographic spread of rice. Nat. Plants 6, 492–502 (2020).

    Article  PubMed  Google Scholar 

  65. Bilinski, P. et al. Parallel altitudinal clines reveal trends in adaptive evolution of genome size in Zea mays. PLoS Genet. 14, e1007162 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Lasky, J. R. et al. Genome-environment associations in sorghum landraces predict adaptive traits. Sci. Adv. 1, e1400218 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Rhoné, B. et al. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. Nat. Commun. 11, 5274 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Abrouk, M. et al. Fonio millet genome unlocks African orphan crop diversity for agriculture in a changing climate. Nat. Commun. 11, 4488 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Bayer, P. et al. Plant pan-genomes are the new reference. Nat. Plants 6, 914–920 (2020).

    Article  PubMed  Google Scholar 

  70. Danilevicz, M. et al. Plant pangenomics: approaches, applications and advancements. Curr. Opin. Plant Biol. 54, 18–25 (2020).

    Article  CAS  PubMed  Google Scholar 

  71. Zhao, Q. et al. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat. Genet. 50, 278–284 (2018).

    Article  CAS  PubMed  Google Scholar 

  72. Brohammer, A. B., Kono, T. J. and Hirsch, C. N. Chapter 2: The maize pan-genome. in The Maize Genome (eds Bennetzen, J. et al) (Springer, 2018).

  73. Walkowiak, S. et al. Multiple wheat genomes reveal global variation in modern breeding. Nature 588, 277–283 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Liu, Y. et al. Pan-genome of wild and cultivated soybeans. Cell 182, 1–15 (2020).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  77. Liu, H. J. & Yan, J. Crop genome-wide association study: a harvest of biological relevance. Plant J. 97, 8–18 (2019).

    Article  CAS  PubMed  Google Scholar 

  78. Yang, J., Zhu, J. & Williams, R. W. Mapping the genetic architecture of complex traits in experimental populations. Bioinformatics 23, 1527–1536 (2007).

    Article  CAS  PubMed  Google Scholar 

  79. Fan, C. et al. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor. Appl. Genet. 112, 1164–1171 (2006).

    Article  CAS  PubMed  Google Scholar 

  80. Zhang, X. et al. Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc. Natl Acad. Sci. USA 109, 21534–21539 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Xue, W. et al. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat. Genet. 40, 761–767 (2008).

    Article  CAS  PubMed  Google Scholar 

  82. Akakpo, R. et al. The impact of transposable elements on the structure, evolution and function of the rice genome. New Phytol. 226, 44–49 (2020).

    Article  PubMed  Google Scholar 

  83. Liu, X. et al. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 12, e1005767 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Huang, M. et al. BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions. GigaScience 8, giy154 (2019).

    Article  Google Scholar 

  85. Voichek, Y. & Weigel, D. Identifying genetic variants underlying phenotypic variation in plants without complete genomes. Nat. Genet. 52, 534–540 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Kover, P. X. et al. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 5, e1000551 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Zaw, H. et al. Exploring genetic architecture of grain yield and quality traits in a 16-way indica by japonica rice MAGIC global population. Sci. Rep. 9, 19605 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. McMullen, M. D. et al. Genetic properties of the maize nested association mapping population. Science 325, 737–740 (2009).

    Article  CAS  PubMed  Google Scholar 

  89. Abe, A. et al. Genome sequencing reveals agronomically important loci in rice using MutMap. Nat. Biotechnol. 30, 174–178 (2012).

    Article  CAS  PubMed  Google Scholar 

  90. Hammer, G. et al. Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci. 11, 587–593 (2006).

    Article  CAS  PubMed  Google Scholar 

  91. Civelek, M. & Lusis, A. Systems genetics approaches to understand complex traits. Nat. Rev. Genet. 15, 34–48 (2014).

    Article  CAS  PubMed  Google Scholar 

  92. Rich-Griffin, C. et al. Single-cell transcriptomics: a high-resolution avenue for plant functional genomics. Trends Plant Sci. 25, 186–197 (2020).

    Article  CAS  PubMed  Google Scholar 

  93. Libault, M. et al. Plant systems biology at the single-cell level. Trends Plant Sci. 22, 949–960 (2017).

    Article  CAS  PubMed  Google Scholar 

  94. Schneider, D. J. & Collmer, A. Studying plant-pathogen interactions in the genomics era: beyond molecular Koch’s postulates to systems biology. Annu. Rev. Phytopathol. 48, 457–479 (2010).

    Article  CAS  PubMed  Google Scholar 

  95. Whiteman, N. K. & Jander, G. Genome-enabled research on the ecology of plant-insect interactions. Plant Physiol. 154, 475–478 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Turner, T., James, E. K. & Poole, P. S. The plant microbiome. Genome Biol. 14, 209 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Joly-Lopez, Z. et al. An inferred fitness consequence map of the rice genome. Nat. Plants 6, 119–130 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Luo, C. A. R., Fernie & Yan, J. Single-cell genomics and epigenomics: technologies and applications in plants. Trends Plant Sci. 25, 1030–1040 (2020).

    Article  CAS  PubMed  Google Scholar 

  99. Efroni, I. et al. Quantification of cell identity from single-cell gene expression profiles. Genome Biol. 16, 9 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Rich-Griffin, C. et al. Single-cell transcriptomics: a high-resolution avenue for plant functional genomics. Trends Plant Sci. 25, 186–197 (2020).

    Article  CAS  PubMed  Google Scholar 

  101. Sotelo-Silveira et al. Entering the next dimension: plant genomes in 3D. Trends Plant Sci. 23, 598–612 (2018).

    Article  CAS  PubMed  Google Scholar 

  102. Plessis, A. et al. Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions. eLife 4, e08411 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Groen, S. C. et al. The strength and pattern of natural selection on rice gene expression. Nature 578, 572–576 (2020).

    Article  CAS  PubMed  Google Scholar 

  104. Kitano, H. Systems biology: a brief overview. Science 295, 1662–1664 (2002).

    Article  CAS  PubMed  Google Scholar 

  105. Dada, J. & Mendes, P. Multi-scale modelling and simulation in systems biology. Integr. Biol. 3, 86–96 (2011).

    Article  Google Scholar 

  106. Xu, K. et al. Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature 442, 705–708 (2006).

    Article  CAS  PubMed  Google Scholar 

  107. Spindel, J. et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity 116, 395–408 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Hamblin, M. T., Buckler, E. S. & Jannink, J.-L. Population genetics of genomics-based crop improvement methods. Trends Genet. 27, 98–106 (2011).

    Article  CAS  PubMed  Google Scholar 

  109. Spindel, J. et al. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet. 11, e1004982 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Meuwissen, T. H., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Mulder, H. A. Is GXE a burden or a blessing? Opportunities for genomic selection and big data. J. Anim. Breed. Genet. 134, 435–436 (2017).

    Article  CAS  PubMed  Google Scholar 

  112. Bailey-Serres, J. et al. Genetic strategies for improving crop yields. Nature 575, 109–118 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Kono, T. J. Y. et al. The role of deleterious substitutions in crop genomes. Mol. Biol. Evol. 33, 1669–1678 (2016).

    Article  Google Scholar 

  114. Yang, J. et al. Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize. PLoS Genet. 13, e1007019 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Liu, Q. et al. Deleterious variants in Asian rice and the potential cost of domestication. Mol. Biol. Evol. 34, 908–924 (2017).

    CAS  PubMed  Google Scholar 

  116. Ramu, P. et al. Cassava haplotype map highlights fixation of deleterious mutations during clonal propagation. Nat. Genet. 49, 959–963 (2017).

    Article  CAS  PubMed  Google Scholar 

  117. 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. Ann. Rev. Genet. 52, 421–444 (2018).

    Article  CAS  PubMed  Google Scholar 

  118. Dwivedi, S. et al. Landrace germplasm for improving yield and abiotic stress adaptation. Trends Plant Sci. 21, 31–42 (2016).

    Article  CAS  PubMed  Google Scholar 

  119. Carroll, D. Genome engineering with targetable nucleases. Ann. Rev. Biochem. 83, 409–439 (2014).

    Article  CAS  PubMed  Google Scholar 

  120. Urnov, F. et al. Genome editing with engineered zinc finger nucleases. Nat. Rev. Genet. 11, 636–646 (2010).

    Article  CAS  PubMed  Google Scholar 

  121. Zhang, Y. et al. Transcription activator-like effector nucleases enable efficient plant genome engineering. Plant Physiol. 161, 20–27 (2013).

    Article  CAS  PubMed  Google Scholar 

  122. Hua, K. et al. Perspectives on the application of genome-editing technologies in crop breeding. Mol. Plant 12, 1047–1059 (2019).

    Article  CAS  PubMed  Google Scholar 

  123. Oliva, R. et al. Broad-spectrum resistance to bacterial blight in rice using genome editing. Nat. Biotech. 37, 1344–1350 (2019).

    Article  CAS  Google Scholar 

  124. Kwon, C.-T. et al. Rapid customization of Solanaceae fruit crops for urban agriculture. Nat. Biotech. 38, 182–188 (2020).

    Article  CAS  Google Scholar 

  125. Mahat, D. B. et al. Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-seq). Nat. Protoc. 11, 1455–1476 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank J. Young Choi, R. Gutaker and A. Kurbidaeva for helpful discussions, and R. Rahni for graphical support. The work is funded by grants from the US National Science Foundation Plant Genome Research Program IOS (grant no. 15-46218), the Zegar Family Foundation (grant no. A168) and the New York University Abu Dhabi Research Institute (grant no. 1205H) (M.D.P.).

Author information

Authors and Affiliations

Authors

Contributions

M.D.P. conceived the paper, and wrote it with S.A.J.

Corresponding author

Correspondence to Michael D. Purugganan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Julia Bailey-Serres, Nils Stein and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Purugganan, M.D., Jackson, S.A. Advancing crop genomics from lab to field. Nat Genet 53, 595–601 (2021). https://doi.org/10.1038/s41588-021-00866-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-021-00866-3

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research