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Promises and challenges of crop translational genomics

  • McCabe, E. R. B. Translational genomics in medical genetics. Genet. Med. 4, 468–471 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Zeggini, E., Gloyn, A. L., Barton, A. C. & Wain, L. V. Translational genomics and precision medicine: moving from the lab to the clinic. Science 365, 1409–1413 (2019).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Salentijn, E. M. J. et al. Plant translational genomics: from model species to crops. Mol. Breed. 20, 1–13 (2007).

    Article 

    Google Scholar
     

  • Cannon, S. B., May, G. D. & Jackson, S. A. Three sequenced legume genomes and many crop species: rich opportunities for translational genomics. Plant Physiol. 151, 970–977 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ronald, P. C. Lab to farm: applying research on plant genetics and genomics to crop improvement. PLoS Biol. 12, e1001878 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, Y., Shang, L., Zhu, Q.-H., Fan, L. & Guo, L. Twenty years of plant genome sequencing: achievements and challenges. Trends Plant Sci. 27, 391–401 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Bennetzen, J. L. & Ma, J. The genetic colinearity of rice and other cereals on the basis of genomic sequence analysis. Curr. Opin. Plant Biol. 6, 128–133 (2003).

    Article 
    PubMed 

    Google Scholar
     

  • Carlson, E. A. H. J. Muller’s contributions to mutation research. Mutat. Res. 752, 1–5 (2013).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Simmonds, N. W. Bandwagons I Have Known. Tropical Agriculture Association Newsletter December 1991, 7–10 (Tropical Agriculture Association International, 1991).

  • Davey, J. W. et al. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 12, 499–510 (2011).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Schneeberger, K. et al. SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nat. Methods 6, 550–551 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Rhie, A. et al. Towards complete and error-free genome assemblies of all vertebrate species. Nature 592, 737–746 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alkan, C., Coe, B. P. & Eichler, E. E. Genome structural variation discovery and genotyping. Nat. Rev. Genet. 12, 363–376 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ho, S. S., Urban, A. E. & Mills, R. E. Structural variation in the sequencing era. Nat. Rev. Genet. 21, 171–189 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Lei, L. et al. Plant pan-genomics comes of age. Annu. Rev. Plant Biol. 72, 411–435 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Orlando, L. et al. Ancient DNA analysis. Nat. Rev. Methods Primers 1, 14 (2021).

    Article 

    Google Scholar
     

  • Tanksley, S. D., Young, N. D., Paterson, A. H. & Bonierbale, M. W. RFLP mapping in plant breeding: new tools for an old science. Bio/Technology 7, 257–264 (1989).


    Google Scholar
     

  • Rafalski, J. A. Association genetics in crop improvement. Curr. Opin. Plant Biol. 13, 174–180 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Bernardo, R. Bandwagons I, too, have known. Theor. Appl. Genet. 129, 2323–2332 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Holland, J. B. Genetic architecture of complex traits in plants. Curr. Opin. Plant Biol. 10, 156–161 (2007).

    Article 
    PubMed 

    Google Scholar
     

  • Korte, A. & Farlow, A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9, 29 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Distelfeld, A., Li, C. & Dubcovsky, J. Regulation of flowering in temperate cereals. Curr. Opin. Plant Biol. 12, 178–184 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Comadran, J. et al. Natural variation in a homolog of Antirrhinum CENTRORADIALIS contributed to spring growth habit and environmental adaptation in cultivated barley. Nat. Genet. 44, 1388–1392 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • Cheng, S. et al. Harnessing landrace diversity empowers wheat breeding. Nature 632, 823–831 (2024).

  • Wulff, B. B. & Krattinger, S. G. The long road to engineering durable disease resistance in wheat. Curr. Opin. Biotechnol. 73, 270–275 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Athiyannan, N. et al. Long-read genome sequencing of bread wheat facilitates disease resistance gene cloning. Nat. Genet. 54, 227–231 (2022). A good example of how the recent progress in genome sequencing has made gene isolation easier.

    Article 
    PubMed 

    Google Scholar
     

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lin, Z., Hayes, B. J. & Daetwyler, H. D. Genomic selection in crops, trees and forages: a review. Crop Pasture Sci. 65, 1177–1191 (2014).

    Article 

    Google Scholar
     

  • Rembe, M., Zhao, Y., Jiang, Y. & Reif, J. C. Reciprocal recurrent genomic selection: an attractive tool to leverage hybrid wheat breeding. Theor. Appl. Genet. 132, 687–698 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Poland, J. & Rutkoski, J. Advances and challenges in genomic selection for disease resistance. Annu. Rev. Phytopathol. 54, 79–98 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Zhou, Y. et al. Graph pangenome captures missing heritability and empowers tomato breeding. Nature 606, 527–534 (2022). Structural variants derived from pangenomes improve the accuracy of quantitative genetic analyses.

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jensen, S. E. et al. A sorghum practical haplotype graph facilitates genome-wide imputation and cost-effective genomic prediction. Plant Genome 13, e20009 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Seyum, E. G. et al. Genomic selection in tropical perennial crops and plantation trees: a review. Mol. Breed. 42, 58 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wolfe, M. D. et al. Prospects for genomic selection in cassava breeding. Plant Genome 10, https://doi.org/10.3835/plantgenome2017.03.0015 (2017).

  • Flor, H. H. Current status of the gene-for-gene concept. Annu. Rev. Phytopathol. 9, 275–296 (1971).

    Article 

    Google Scholar
     

  • Tamborski, J. & Krasileva, K. V. Evolution of plant NLRs: from natural history to precise modifications. Annu. Rev. Plant Biol. 71, 355–378 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Barragan, A. C. & Weigel, D. Plant NLR diversity: the known unknowns of pan-NLRomes. Plant Cell 33, 814–831 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Moore, J. W. et al. A recently evolved hexose transporter variant confers resistance to multiple pathogens in wheat. Nat. Genet. 47, 1494–1498 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Krattinger, S. G. et al. A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science 323, 1360–1363 (2009).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Ercoli, M. F. et al. Plant immunity: rice XA21-mediated resistance to bacterial infection. Proc. Natl Acad. Sci. USA 119, e2121568119 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jupe, F. et al. Resistance gene enrichment sequencing (RenSeq) enables reannotation of the NB-LRR gene family from sequenced plant genomes and rapid mapping of resistance loci in segregating populations. Plant J. 76, 530–544 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hafeez, A. N. et al. Creation and judicious application of a wheat resistance gene atlas. Mol. Plant 14, 1053–1070 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Guo, Y. et al. Population genomics of Puccinia graminis f.sp. tritici highlights the role of admixture in the origin of virulent wheat rust races. Nat. Commun. 13, 6287 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Seong, K. & Krasileva, K. V. Prediction of effector protein structures from fungal phytopathogens enables evolutionary analyses. Nat. Microbiol. 8, 174–187 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Förderer, A. et al. A wheat resistosome defines common principles of immune receptor channels. Nature 610, 532–539 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao, Y.-B. et al. Pathogen effector AvrSr35 triggers Sr35 resistosome assembly via a direct recognition mechanism. Sci. Adv. 8, eabq5108 (2022).

    Article 
    ADS 
    MathSciNet 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ma, S. et al. Direct pathogen-induced assembly of an NLR immune receptor complex to form a holoenzyme. Science 370, eabe3069 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Frankel, O. H. Genetic conservation: our evolutionary responsibility. Genetics 78, 53–65 (1974).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Altieri, M. A. & Merrick, L. In situ conservation of crop genetic resources through maintenance of traditional farming systems. Econ. Bot. 41, 86–96 (1987).

    Article 

    Google Scholar
     

  • Meilleur, B. A. & Hodgkin, T. In situ conservation of crop wild relatives: status and trends. Biodivers. Conserv. 13, 663–684 (2004).

    Article 

    Google Scholar
     

  • Marden, E., Sackville Hamilton, R., Halewood, M. & McCouch, S. International agreements and the plant genetics research community: a guide to practice. Proc. Natl Acad. Sci. USA 120, e2205773119 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schulthess, A. W. et al. Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat. Genet. 54, 1544–1552 (2022). Genomics helps to bridge the gap between the conservation of plant genetic resources and practical breeding.

    Article 
    PubMed 

    Google Scholar
     

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

    Article 
    PubMed 

    Google Scholar
     

  • Romay, M. C. et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 14, R55 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McCouch, S. R., McNally, K. L., Wang, W. & Sackville Hamilton, R. Genomics of gene banks: a case study in rice. Am. J. Bot. 99, 407–423 (2012).

    Article 
    PubMed 

    Google Scholar
     

  • De Beukelaer, H., Davenport, G. F. & Fack, V. Core Hunter 3: flexible core subset selection. BMC Bioinformatics 19, 203 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, X. et al. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat. Plants 2, 16150 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Bhullar, N. K., Street, K., Mackay, M., Yahiaoui, N. & Keller, B. Unlocking wheat genetic resources for the molecular identification of previously undescribed functional alleles at the Pm3 resistance locus. Proc. Natl Acad. Sci. USA 106, 9519–9524 (2009).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Milne, R. J. et al. The wheat Lr67 gene from the Sugar Transport Protein 13 family confers multipathogen resistance in barley. Plant Physiol. 179, 1285–1297 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Risk, J. M. et al. The wheat Lr34 gene provides resistance against multiple fungal pathogens in barley. Plant Biotechnol. J. 11, 847–854 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Luo, M. et al. A five-transgene cassette confers broad-spectrum resistance to a fungal rust pathogen in wheat. Nat. Biotechnol. 39, 561–566 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Wulff, B. B. & Moscou, M. J. Strategies for transferring resistance into wheat: from wide crosses to GM cassettes. Frontiers Plant Sci. 5, 692 (2014).

    Article 

    Google Scholar
     

  • Athiyannan, N. et al. Long-read genome sequencing of bread wheat facilitates disease resistance gene cloning. Nat. Genet. 54, 227–231 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, Y. et al. An unusual tandem kinase fusion protein confers leaf rust resistance in wheat. Nat. Genet. 55, 914–920 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cavalet-Giorsa, E. et al. Origin and evolution of the bread wheat D genome. Nature https://doi.org/10.1038/s41586-024-07808-z (2024).

  • Cardi, T. et al. CRISPR/Cas-mediated plant genome editing: outstanding challenges a decade after implementation. Trends Plant Sci. 28, 1144–1165 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Watson, A. et al. Speed breeding is a powerful tool to accelerate crop research and breeding. Nat. Plants 4, 23–29 (2018).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Cha, J.-K. et al. Speed vernalization to accelerate generation advance in winter cereal crops. Mol. Plant 15, 1300–1309 (2022).

    Article 
    PubMed 

    Google Scholar
     

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

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • The International Wheat Genome Sequencing Consortium. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361, eaar7191 (2018). In the past, large international consortia were needed to assemble reference sequences of large crop genomes.

    Article 

    Google Scholar
     

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

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Przewieslik-Allen, A. M. et al. The role of gene flow and chromosomal instability in shaping the bread wheat genome. Nat. Plants 7, 172–183 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • van Rengs, W. M. J. et al. A chromosome scale tomato genome built from complementary PacBio and Nanopore sequences alone reveals extensive linkage drag during breeding. Plant J. 110, 572–588 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Wendler, N. et al. Bulbosum to go: a toolbox to utilize Hordeum vulgare/bulbosum introgressions for breeding and beyond. Mol. Plant 8, 1507–1519 (2015).

    Article 
    PubMed 

    Google Scholar
     

  • Mieulet, D. et al. Unleashing meiotic crossovers in crops. Nat. Plants 4, 1010–1016 (2018). Single genes can have large effects on the recombination landscape.

    Article 
    PubMed 

    Google Scholar
     

  • Rönspies, M., Dorn, A., Schindele, P. & Puchta, H. CRISPR–Cas-mediated chromosome engineering for crop improvement and synthetic biology. Nat. Plants 7, 566–573 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Schmidt, C. et al. Changing local recombination patterns in Arabidopsis by CRISPR/Cas mediated chromosome engineering. Nat. Commun. 11, 4418 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schwartz, C. et al. CRISPR–Cas9-mediated 75.5-Mb inversion in maize. Nat. Plants 6, 1427–1431 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Bartlett, M. E., Moyers, B. T., Man, J., Subramaniam, B. & Makunga, N. P. The power and perils of de novo domestication using genome editing. Annu. Rev. Plant Biol. 74, 727–750 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Yu, H. & Li, J. Breeding future crops to feed the world through de novo domestication. Nat. Commun. 13, 1171 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hanak, T., Madsen, C. K. & Brinch-Pedersen, H. Genome editing-accelerated re-domestication (GEaReD)—a new major direction in plant breeding. Biotechnol. J. 17, 2100545 (2022).

    Article 

    Google Scholar
     

  • Zhang, S. et al. Sustained productivity and agronomic potential of perennial rice. Nat. Sust. 6, 28–38 (2023).

    Article 

    Google Scholar
     

  • Singh, D., Buhmann, A. K., Flowers, T. J., Seal, C. E. & Papenbrock, J. Salicornia as a crop plant in temperate regions: selection of genetically characterized ecotypes and optimization of their cultivation conditions. AoB Plants 6, plu071 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lenser, T. & Theißen, G. Molecular mechanisms involved in convergent crop domestication. Trends Plant Sci. 18, 704–714 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Larson, S. et al. Genome mapping of quantitative trait loci (QTL) controlling domestication traits of intermediate wheatgrass (Thinopyrum intermedium). Theor. Appl. Genet. 132, 2325–2351 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Stetter, M. G., Gates, D. J., Mei, W. & Ross-Ibarra, J. How to make a domesticate. Curr. Biol. 27, R896–R900 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Abbo, S. et al. Plant domestication versus crop evolution: a conceptual framework for cereals and grain legumes. Trends Plant Sci. 19, 351–360 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Fuller, D. Q. Contrasting patterns in crop domestication and domestication rates: recent archaeobotanical insights from the Old World. Ann. Bot. 100, 903–924 (2007).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lemmon, Z. H. et al. Rapid improvement of domestication traits in an orphan crop by genome editing. Nat. Plants 4, 766–770 (2018). Agronomically relevant traits in a minor crop were improved by targeted mutagenesis.

    Article 
    PubMed 

    Google Scholar
     

  • Li, T. et al. Domestication of wild tomato is accelerated by genome editing. Nat. Biotechnol. 36, 1160–1163 (2018).

    Article 

    Google Scholar
     

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

    Article 
    PubMed 

    Google Scholar
     

  • Bevan, M. W. et al. Genomic innovation for crop improvement. Nature 543, 346–354 (2017).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Khoury, C. K. et al. Crop genetic erosion: understanding and responding to loss of crop diversity. New Phytol. 233, 84–118 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Brown, W. L. Genetic diversity and genetic vulnerability—an appraisal. Econ. Bot. 37, 4–12 (1983).

    Article 

    Google Scholar
     

  • Mayer, M. et al. Discovery of beneficial haplotypes for complex traits in maize landraces. Nat. Commun. 11, 4954 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stephan, W. Genetic hitchhiking versus background selection: the controversy and its implications. Philos. Trans. R. Soc. B 365, 1245–1253 (2010).

    Article 

    Google Scholar
     

  • 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
     

  • Wang, L. et al. The interplay of demography and selection during maize domestication and expansion. Genome Biol. 18, 215 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lozano, R. et al. Comparative evolutionary genetics of deleterious load in sorghum and maize. Nat. Plants 7, 17–24 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Liu, Q., Zhou, Y., Morrell, P. L. & Gaut, B. S. Deleterious variants in Asian rice and the potential cost of domestication. Mol. Biol. Evol. 34, 908–924 (2017).

    PubMed 

    Google Scholar
     

  • Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ng, P. C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Khan, A. W. et al. Super-pangenome by Integrating the wild side of a species for accelerated crop improvement. Trends Plant Sci. 25, 148–158 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Gao, H. et al. The landscape of tolerated genetic variation in humans and primates. Science 380, eabn8153 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ramstein, G. P. & Buckler, E. S. Prediction of evolutionary constraint by genomic annotations improves functional prioritization of genomic variants in maize. Genome Biol. 23, 183 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    PubMed 

    Google Scholar
     

  • Roze, D. A simple expression for the strength of selection on recombination generated by interference among mutations. Proc. Natl Acad. Sci. USA 118, e2022805118 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gabriel, W., Lynch, M. & Bürger, R. Muller’s ratchet and mutational meltdowns. Evolution 47, 1744–1757 (1993).

    Article 
    PubMed 

    Google Scholar
     

  • Naeem, M., Demirel, U., Yousaf, M. F., Caliskan, S. & Caliskan, M. E. Overview on domestication, breeding, genetic gain and improvement of tuber quality traits of potato using fast forwarding technique (GWAS): a review. Plant Breed. 140, 519–542 (2021).

    Article 

    Google Scholar
     

  • Jansky, S. H. et al. Reinventing potato as a diploid inbred line–based crop. Crop Sci. 56, 1412–1422 (2016).

    Article 

    Google Scholar
     

  • ter Steeg, E. M. S., Struik, P. C., Visser, R. G. F. & Lindhout, P. Crucial factors for the feasibility of commercial hybrid breeding in food crops. Nat. Plants 8, 463–473 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Zhou, Q. et al. Haplotype-resolved genome analyses of a heterozygous diploid potato. Nat. Genet. 52, 1018–1023 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun, H. et al. Chromosome-scale and haplotype-resolved genome assembly of a tetraploid potato cultivar. Nat. Genet. 54, 342–348 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tang, D. et al. Genome evolution and diversity of wild and cultivated potatoes. Nature 606, 535–541 (2022). Initial analysis of a genus-wide pangenome of potato and its wild relatives.

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, C. et al. The genetic basis of inbreeding depression in potato. Nat. Genet. 51, 374–378 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Wu, Y. et al. Phylogenomic discovery of deleterious mutations facilitates hybrid potato breeding. Cell 186, 2313–2328.e2315 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Ye, M. et al. Generation of self-compatible diploid potato by knockout of S-RNase. Nat. Plants 4, 651–654 (2018).

    Article 
    PubMed 

    Google Scholar
     

  • Mascher, M., Jayakodi, M. & Stein, N. The reinvention of potato. Cell Res. 31, 1144–1145 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Servin, B., Martin, O. C., Mézard, M. & Hospital, F. Toward a theory of marker-assisted gene pyramiding. Genetics 168, 513–523 (2004).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hurni, S. et al. The powdery mildew resistance gene Pm8 derived from rye is suppressed by its wheat ortholog Pm3. Plant J. 79, 904–913 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Cordell, H. J. Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum. Mol. Genet. 11, 2463–2468 (2002).

    Article 
    PubMed 

    Google Scholar
     

  • Soyk, S. et al. Bypassing negative epistasis on yield in tomato imposed by a domestication gene. Cell 169, 1142–1155.e1112 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Soyk, S., Benoit, M. & Lippman, Z. B. New horizons for dissecting epistasis in crop quantitative trait variation. Annu. Rev. Genet. 54, 287–307 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Jiang, Y., Schmidt, R. H., Zhao, Y. & Reif, J. C. A quantitative genetic framework highlights the role of epistatic effects for grain-yield heterosis in bread wheat. Nat. Genet. 49, 1741–1746 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Bouché, F., Lobet, G., Tocquin, P. & Périlleux, C. FLOR-ID: an interactive database of flowering-time gene networks in Arabidopsis thaliana. Nucleic Acids Res. 44, D1167–D1171 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Chen, D., Yan, W., Fu, L.-Y. & Kaufmann, K. Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana. Nat. Commun. 9, 4534 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ahsan, A. et al. Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect. DNA Res. 26, 119–130 (2018).

    Article 
    PubMed Central 

    Google Scholar
     

  • Mathew, B., Léon, J., Sannemann, W. & Sillanpää, M. J. Detection of epistasis for flowering time using Bayesian multilocus estimation in a barley MAGIC population. Genetics 208, 525–536 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Durand, E. et al. Flowering time in maize: linkage and epistasis at a major effect locus. Genetics 190, 1547–1562 (2012).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Padmarasu, S., Himmelbach, A., Mascher, M. & Stein, N. In situ Hi-C for plants: an improved method to detect long-range chromatin interactions. Methods Mol. Biol. 1933, 441–472 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Liu, L. et al. Enhancing grain-yield-related traits by CRISPR–Cas9 promoter editing of maize CLE genes. Nat. Plants 7, 287–294 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Aguirre, L., Hendelman, A., Hutton, S. F., McCandlish, D. M. & Lippman, Z. B. Idiosyncratic and dose-dependent epistasis drives variation in tomato fruit size. Science 382, 315–320 (2023). On the molecular genetics of regulatory variation in tomato.

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao, L. et al. Integrative analysis of reference epigenomes in 20 rice varieties. Nat. Commun. 11, 2658 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Han, T. et al. An epigenetic basis of inbreeding depression in maize. Sci. Adv. 7, eabg5442 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thiel, J. et al. Transcriptional landscapes of floral meristems in barley. Sci. Adv. 7, eabf0832 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, T.-Q., Chen, Y., Liu, Y., Lin, W.-H. & Wang, J.-W. Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root. Nat. Commun. 12, 2053 (2021).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Watt, M. et al. Phenotyping: new windows into the plant for breeders. Annu. Rev. Plant Biol. 71, 689–712 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Araus, J. L. et al. Crop phenotyping in a context of global change: what to measure and how to do it. J. Integr. Plant Biol. 64, 592–618 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Sweet, D. D., Tirado, S. B., Springer, N. M., Hirsch, C. N. & Hirsch, C. D. Opportunities and challenges in phenotyping row crops using drone-based RGB imaging. Plant Phenome J. 5, e20044 (2022).

    Article 

    Google Scholar
     

  • Barker, J. et al. Development of a field-based high-throughput mobile phenotyping platform. Comput. Electron. Agric. 122, 74–85 (2016).

    Article 

    Google Scholar
     

  • Araus, J. L. & Cairns, J. E. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52–61 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Heuermann, M. C., Knoch, D., Junker, A. & Altmann, T. Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation. Nat. Commun. 14, 5783 (2023).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Perez de Souza, L., Alseekh, S., Scossa, F. & Fernie, A. R. Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research. Nat. Methods 18, 733–746 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Dubin, M. J. et al. DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation. eLife 4, e05255 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nica, A. C. & Dermitzakis, E. T. Expression quantitative trait loci: present and future. Philos. Trans. R. Soc. B 368, 20120362 (2013).

    Article 

    Google Scholar
     

  • Monroe, J. G. et al. Mutation bias reflects natural selection in Arabidopsis thaliana. Nature 602, 101–105 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S. & Cairns, J. E. Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. 23, 451–466 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hu, Y. & Schmidhalter, U. Opportunity and challenges of phenotyping plant salt tolerance. Trends Plant Sci. 28, 552–566 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Reynolds, M. et al. Breeder friendly phenotyping. Plant Sci. 295, 110396 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Awada, L., Phillips, P. W. B. & Smyth, S. J. The adoption of automated phenotyping by plant breeders. Euphytica 214, 148 (2018).

    Article 

    Google Scholar
     

  • Papoutsoglou, E. A., Athanasiadis, I. N., Visser, R. G. F. & Finkers, R. The benefits and struggles of FAIR data: the case of reusing plant phenotyping data. Sci. Data 10, 457 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Papoutsoglou, E. A. et al. Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytol. 227, 260–273 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Selby, P. et al. BrAPI—an application programming interface for plant breeding applications. Bioinformatics 35, 4147–4155 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bell, G., Hey, T. & Szalay, A. Beyond the data deluge. Science 323, 1297–1298 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Jones, J. W. et al. Brief history of agricultural systems modeling. Agric. Syst. 155, 240–254 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chenu, K. et al. Contribution of crop models to adaptation in wheat. Trends Plant Sci. 22, 472–490 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • De Souza, A. P. et al. Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection. Science 377, 851–854 (2022).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Habier, D., Fernando, R. L. & Garrick, D. J. Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194, 597–607 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hammer, G., Messina, C., Wu, A. & Cooper, M. Biological reality and parsimony in crop models—why we need both in crop improvement! in silico Plants 1, diz010 (2019).

    Article 

    Google Scholar
     

  • Roeder, A. H. K. et al. Fifteen compelling open questions in plant cell biology. Plant Cell 34, 72–102 (2021). A collection of thought-provoking perspectives on future directions in basic plant science.

    Article 
    PubMed Central 

    Google Scholar
     

  • Alexandratos, N. & Bruinsma, J. World agriculture towards 2030/2050: the 2012 revision. ESA Working Paper 12-03 (FAO, 2012).

  • Roser, M. Breaking out of the Malthusian trap: How pandemics allow us to understand why our ancestors were stuck in poverty. Our World in Data https://ourworldindata.org/breaking-the-malthusian-trap (2020).

  • Ritchie, H., Rosado P. & Roser, M. Hunger and Undernourishment. Our World in Data https://ourworldindata.org/hunger-and-undernourishment (2023).

  • Ghazal, H. et al. Plant genomics in Africa: present and prospects. Plant J. 107, 21–36 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Jamnadass, R. et al. Enhancing African orphan crops with genomics. Nat. Genet. 52, 356–360 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • VanBuren, R. et al. Exceptional subgenome stability and functional divergence in the allotetraploid Ethiopian cereal teff. Nat. Commun. 11, 884 (2020).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, M. et al. Improved assembly and annotation of the sesame genome. DNA Res. 29, dsac041 (2022).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Qi, W. et al. The haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar reveal novel pan-genome and allele-specific transcriptome features. GigaScience 11, giac028 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kuon, J.-E. et al. Haplotype-resolved genomes of geminivirus-resistant and geminivirus-susceptible African cassava cultivars. BMC Biol. 17, 75 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Varshney, R. K. et al. Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnol. Adv. 31, 1120–1134 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Mboowa, G., Sserwadda, I. & Aruhomukama, D. Genomics and bioinformatics capacity in Africa: no continent is left behind. Genome 64, 503–513 (2021).

    Article 
    PubMed 

    Google Scholar
     

  • Santantonio, N. et al. Strategies for effective use of genomic information in crop breeding programs serving Africa and South Asia. Frontiers Plant Sci 11, 353 (2020).

    Article 

    Google Scholar
     

  • Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018). This paper presents strong arguments for why environmental concerns matter to everyone, including plant breeders.

    Article 
    ADS 
    PubMed 

    Google Scholar
     

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