Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
23 May 2024
23 May 2024
Historique:
received:
24
07
2023
accepted:
18
04
2024
medline:
24
5
2024
pubmed:
24
5
2024
entrez:
23
5
2024
Statut:
aheadofprint
Résumé
Chickpea (Cicer arietinum L.)-an important legume crop cultivated in arid and semiarid regions-has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicer super-pangenome based on the de novo genome assemblies of eight annual Cicer wild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicer species into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea.
Identifiants
pubmed: 38783120
doi: 10.1038/s41588-024-01760-4
pii: 10.1038/s41588-024-01760-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Food and Agriculture Organization of the United Nations. FAO https://www.fao.org/faostat/en/#home (2020).
Varshney, R. K. et al. Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits. Nat. Genet. 51, 857–864 (2019).
pubmed: 31036963
doi: 10.1038/s41588-019-0401-3
Varshney, R. K. et al. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat. Biotechnol. 31, 240–246 (2013).
pubmed: 23354103
doi: 10.1038/nbt.2491
Thudi, M. et al. Whole genome re-sequencing reveals genome-wide variations among parental lines of 16 mapping populations in chickpea (Cicer arietinum L.). BMC Plant Biol. 16, 10 (2016).
pubmed: 26822060
pmcid: 4895712
doi: 10.1186/s12870-015-0690-3
Thudi, M. et al. Recent breeding programs enhanced genetic diversity in both desi and kabuli varieties of chickpea (Cicer arietinum L.). Sci. Rep. 6, 38636 (2016).
pubmed: 27982107
pmcid: 5159902
doi: 10.1038/srep38636
Varshney, R. K. et al. A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature 599, 622–627 (2021).
pubmed: 34759320
pmcid: 8612933
doi: 10.1038/s41586-021-04066-1
Bayer, P. E., Golicz, A. A., Scheben, A., Batley, J. & Edwards, D. Plant pan-genomes are the new reference. Nat. Plants 6, 914–920 (2020).
pubmed: 32690893
doi: 10.1038/s41477-020-0733-0
Edwards, D. & Batley, J. Graph pangenomes find missing heritability. Nat. Genet. 54, 919–920 (2022).
pubmed: 35739387
doi: 10.1038/s41588-022-01099-8
Liu, Y. et al. Pan-genome of wild and cultivated soybeans. Cell 182, 162–176 (2020).
pubmed: 32553274
doi: 10.1016/j.cell.2020.05.023
Bayer, P. E. et al. Sequencing the USDA core soybean collection reveals gene loss during domestication and breeding. Plant Genome 15, e20109 (2022).
pubmed: 34169673
doi: 10.1002/tpg2.20109
Zhao, Q. et al. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat. Genet. 50, 278–284 (2018).
pubmed: 29335547
doi: 10.1038/s41588-018-0041-z
Golicz, A. A. et al. The pangenome of an agronomically important crop plant Brassica oleracea. Nat. Commun. 7, 13390 (2016).
pubmed: 27834372
pmcid: 5114598
doi: 10.1038/ncomms13390
Montenegro, J. D. et al. The pangenome of hexaploid bread wheat. Plant J. 90, 1007–1013 (2017).
pubmed: 28231383
doi: 10.1111/tpj.13515
Walkowiak, S. et al. Multiple wheat genomes reveal global variation in modern breeding. Nature 588, 277–283 (2020).
pubmed: 33239791
pmcid: 7759465
doi: 10.1038/s41586-020-2961-x
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 (2020).
pubmed: 31787539
pmcid: 6988109
doi: 10.1016/j.tplants.2019.10.012
Shang, L. et al. A super pan-genomic landscape of rice. Cell Res. 32, 878–896 (2022).
pubmed: 35821092
pmcid: 9525306
doi: 10.1038/s41422-022-00685-z
Gui, S. et al. A pan-Zea genome map for enhancing maize improvement. Genome Biol. 23, 22 (2022).
doi: 10.1186/s13059-022-02742-7
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).
pubmed: 37024581
pmcid: 10181942
doi: 10.1038/s41588-023-01340-y
Abberton, M. et al. Global agricultural intensification during climate change: a role for genomics. Plant Biotechnol. J. 14, 1095–1098 (2016).
pubmed: 26360509
doi: 10.1111/pbi.12467
Bohra, A. et al. Reap the crop wild relatives for breeding future crops. Trends Biotechnol. 4, 412–431 (2021).
Zhang, H., Mittal, N., Leamy, L. J., Barazani, O. & Song, B. H. Back into the wild—apply untapped genetic diversity of wild relatives for crop improvement. Evolut. Appl. 10, 5–24 (2017).
doi: 10.1111/eva.12434
van der Maesen, L. J. G. in The Chickpea (eds Saxena, M. C. & Singh, K. B.) 11–34 (C.A.B. International, 1987).
Ladizinsky, G. & Abbo, S. in The Search for Wild Relatives of Cool Season Legumes (eds Ladizinsky, G. & Abbo, S.) 29–53 (Springer, 2015).
Chattopadhyay, D. & Francis, A. Structural annotation of the genome assembly of Cicer arietinum accession ICC4958 v.3.0. figshare https://doi.org/10.6084/m9.figshare.14579274 (2021).
Harlan, J. R. & de Wet, J. M. J. Towards a rational classification of cultivated plants. Taxon 20, 509–517 (1971).
doi: 10.2307/1218252
Nam, J., DePamphilis, C. W., Ma, H. & Nei, M. Antiquity and evolution of the MADS-box gene family controlling flower development in plants. Mol. Biol. Evol. 20, 1435–1447 (2003).
pubmed: 12777513
doi: 10.1093/molbev/msg152
Richter, S. et al. Polarized cell growth in Arabidopsis requires endosomal recycling mediated by GBF1-related ARF exchange factors. Nat. Cell Biol. 14, 80–86 (2012).
doi: 10.1038/ncb2389
Garg, V. et al. Chromosome-length genome assemblies of six legume species provide insights into genome organization, evolution, and agronomic traits for crop improvement. J. Adv. Res. 42, 315–329 (2022).
pubmed: 36513421
doi: 10.1016/j.jare.2021.10.009
Lu, F., Cui, X., Zhang, S., Liu, C. & Cao, X. JMJ14 is an H3K4 demethylase regulating flowering time in Arabidopsis. Cell Res. 20, 387–390 (2010).
pubmed: 20177424
doi: 10.1038/cr.2010.27
Ferrero-Serrano, Á. & Assmann, S. M. The α-subunit of the rice heterotrimeric G protein, RGA1, regulates drought tolerance during the vegetative phase in the dwarf rice mutant d1. J. Exp. Bot. 67, 3433–3443 (2016).
pubmed: 27194741
pmcid: 4892740
doi: 10.1093/jxb/erw183
Avni, R. et al. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science 357, 93–97 (2017).
pubmed: 28684525
doi: 10.1126/science.aan0032
Maccaferri, M. et al. Durum wheat genome highlights past domestication signatures and future improvement targets. Nat. Genet. 51, 885–895 (2019).
pubmed: 30962619
doi: 10.1038/s41588-019-0381-3
Hu, Y. et al. Gossypium barbadense and Gossypium hirsutum genomes provide insights into the origin and evolution of allotetraploid cotton. Nat. Genet. 51, 739–748 (2019).
pubmed: 30886425
doi: 10.1038/s41588-019-0371-5
Edger, P. P. et al. Origin and evolution of the octoploid strawberry genome. Nat. Genet. 51, 541–547 (2019).
pubmed: 30804557
pmcid: 6882729
doi: 10.1038/s41588-019-0356-4
Jiao, W. B. et al. Improving and correcting the contiguity of long-read genome assemblies of three plant species using optical mapping and chromosome conformation capture data. Genome Res. 27, 778–786 (2017).
pubmed: 28159771
pmcid: 5411772
doi: 10.1101/gr.213652.116
Raymond, O. et al. The Rosa genome provides new insights into the domestication of modern roses. Nat. Genet. 50, 772–777 (2018).
pubmed: 29713014
pmcid: 5984618
doi: 10.1038/s41588-018-0110-3
VanBuren, R. et al. A near complete, chromosome-scale assembly of the black raspberry (Rubus occidentalis) genome. GigaScience 7, giy094 (2018).
pubmed: 30107523
pmcid: 6131213
doi: 10.1093/gigascience/giy094
Wang, M. et al. Reference genome sequences of two cultivated allotetraploid cottons, Gossypium hirsutum and Gossypium barbadense. Nat. Genet. 51, 224–229 (2019).
pubmed: 30510239
doi: 10.1038/s41588-018-0282-x
Garg, V. et al. Near-gapless genome assemblies of Williams 82 and Lee cultivars for accelerating global soybean research. Plant Genome 16, e20382 (2023).
pubmed: 37749941
doi: 10.1002/tpg2.20382
Martin, G. B. et al. Map-based cloning of a protein kinase gene conferring disease resistance in tomato. Science 262, 1432–1436 (1993).
pubmed: 7902614
doi: 10.1126/science.7902614
Brozynska, M., Furtado, A. & Henry, R. J. Genomics of crop wild relatives: expanding the gene pool for crop improvement. Plant Biotechnol. J. 14, 1070–1085 (2016).
pubmed: 26311018
doi: 10.1111/pbi.12454
Gupta, S. et al. Draft genome sequence of Cicer reticulatum L., the wild progenitor of chickpea provides a resource for agronomic trait improvement. DNA Res. 24, 1–10 (2017).
pubmed: 27567261
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).
pubmed: 29358651
doi: 10.1038/s41588-018-0040-0
Kang, M. et al. The pan-genome and local adaptation of Arabidopsis thaliana. Nat. Commun. 14, 6259 (2023).
pubmed: 37802986
pmcid: 10558531
doi: 10.1038/s41467-023-42029-4
Qin, P. et al. Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations. Cell 184, 3542–3558 (2021).
pubmed: 34051138
doi: 10.1016/j.cell.2021.04.046
Hufford, M. B. et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science 373, 655–662 (2021).
pubmed: 34353948
pmcid: 8733867
doi: 10.1126/science.abg5289
Gao, L. et al. The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. Nat. Genet. 51, 1044–1051 (2019).
pubmed: 31086351
doi: 10.1038/s41588-019-0410-2
Zhou, Y. et al. Graph pangenome captures missing heritability and empowers tomato breeding. Nature 606, 527–534 (2022).
pubmed: 35676474
pmcid: 9200638
doi: 10.1038/s41586-022-04808-9
Moenga, S. M., Gai, Y., Carrasquilla‐Garcia, N., Perilla‐Henao, L. M. & Cook, D. R. Gene co‐expression analysis reveals transcriptome divergence between wild and cultivated chickpea under drought stress. Plant J. 104, 1195–1214 (2020).
pubmed: 32920943
doi: 10.1111/tpj.14988
Kang, W., Zhu, X., Wang, Y., Chen, L. & Duan, Y. Transcriptomic and metabolomic analyses reveal that bacteria promote plant defense during infection of soybean cyst nematode in soybean. BMC Plant Biol. 18, 1–4 (2018).
doi: 10.1186/s12870-018-1302-9
Moncalvillo, B., Méndez, M. & Iriondo, J. M. Ecotypic differentiation reveals seed colour‐related alkaloid content in a crop wild relative. Plant Biol. 21, 942–950 (2019).
pubmed: 30980687
doi: 10.1111/plb.12996
Li, M. et al. Comparison of salt tolerance in soja based on metabolomics of seedling roots. Front. Plant Sci. 8, 1101 (2017).
pubmed: 28690628
pmcid: 5481370
doi: 10.3389/fpls.2017.01101
Zhang, J., Yang, D., Li, M. & Shi, L. Metabolic profiles reveal changes in wild and cultivated soybean seedling leaves under salt stress. PLoS ONE 11, e0159622 (2016).
pubmed: 27442489
pmcid: 4956222
doi: 10.1371/journal.pone.0159622
Zhang, H. & Van Nocker, S. The VERNALIZATION INDEPENDENCE 4 gene encodes a novel regulator of FLOWERING LOCUS C. Plant J. 31, 663–673 (2002).
pubmed: 12207655
doi: 10.1046/j.1365-313X.2002.01380.x
Zheng, S. et al. The Arabidopsis H3K27me3 demethylase JUMONJI 13 is a temperature and photoperiod dependent flowering repressor. Nat. Commun. 10, 1303 (2019).
pubmed: 30899015
pmcid: 6428840
doi: 10.1038/s41467-019-09310-x
Hwang, J. U. et al. Plant ABC transporters enable many unique aspects of a terrestrial plant’s lifestyle. Mol. Plant 9, 338–355 (2016).
pubmed: 26902186
doi: 10.1016/j.molp.2016.02.003
Singh, S., Gumber, R. K., Joshi, N. & Singh, K. Introgression from wild Cicer reticulatum to cultivated chickpea for productivity and disease resistance. Plant Breed. 124, 477–480 (2005).
doi: 10.1111/j.1439-0523.2005.01146.x
Singh, K. B. & Weigand, S. Identification of resistant sources in Cicer species to Liriomyza cicerina. Genet. Resour. Crop Evol. 41, 75–79 (1994).
doi: 10.1007/BF00053051
Singh, K. B., Ocampo, B. & Robertson, L. D. Diversity for abiotic and biotic stress resistance in the wild annual Cicer species. Genet. Resour. Crop Evol. 45, 9–17 (1998).
doi: 10.1023/A:1008620002136
Katta, M. A., Khan, A. W., Doddamani, D., Thudi, M. & Varshney, R. K. NGS-QCbox and raspberry for parallel, automated and rapid quality control analysis of large-scale next generation sequencing (Illumina) data. PLoS ONE 10, e0139868 (2015).
pubmed: 26460497
pmcid: 4604202
doi: 10.1371/journal.pone.0139868
Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).
pubmed: 28336562
pmcid: 5635820
doi: 10.1126/science.aal3327
Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).
pubmed: 27467250
pmcid: 5596920
doi: 10.1016/j.cels.2015.07.012
Smit, A. F. & Hubley, R. RepeatModeler Open-1.0 www.repeatmasker.org (2008).
Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).
pubmed: 17485477
pmcid: 1933203
doi: 10.1093/nar/gkm286
Ou, S. & Jiang, N. LTR_retriever: a highly accurate and sensitive program for identification of long terminal repeat retrotransposons. Plant Physiol. 176, 1410–1422 (2018).
pubmed: 29233850
doi: 10.1104/pp.17.01310
Marçais, G. et al. MUMmer4: a fast and versatile genome alignment system. PLoS Comput. Biol. 14, e1005944 (2018).
pubmed: 29373581
pmcid: 5802927
doi: 10.1371/journal.pcbi.1005944
Lukashin, A. V. & Borodovsky, M. GeneMark. hmm: new solutions for gene finding. Nucleic Acids Res. 26, 1107–1115 (1998).
pubmed: 9461475
pmcid: 147337
doi: 10.1093/nar/26.4.1107
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–W439 (2006).
pubmed: 16845043
pmcid: 1538822
doi: 10.1093/nar/gkl200
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).
pubmed: 15123596
pmcid: 479130
doi: 10.1101/gr.1865504
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).
pubmed: 31375807
pmcid: 7605509
doi: 10.1038/s41587-019-0201-4
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
pubmed: 25690850
pmcid: 4643835
doi: 10.1038/nbt.3122
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).
pubmed: 18190707
pmcid: 2395244
doi: 10.1186/gb-2008-9-1-r7
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
pubmed: 24451626
pmcid: 3998142
doi: 10.1093/bioinformatics/btu031
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
pubmed: 26059717
doi: 10.1093/bioinformatics/btv351
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
pubmed: 24008419
pmcid: 3810854
doi: 10.1093/bioinformatics/btt509
Zhang, Z. et al. PseudoPipe: an automated pseudogene identification pipeline. Bioinformatics 22, 1437–1439 (2006).
pubmed: 16574694
doi: 10.1093/bioinformatics/btl116
Zou, C. et al. Evolutionary and expression signatures of pseudogenes in Arabidopsis and rice. Plant Physiol. 151, 3–15 (2009).
pubmed: 19641029
pmcid: 2736005
doi: 10.1104/pp.109.140632
Li, P. et al. RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants. BMC Genomics 17, 852 (2016).
pubmed: 27806688
pmcid: 5093994
doi: 10.1186/s12864-016-3197-x
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
pubmed: 19541911
pmcid: 2752132
doi: 10.1101/gr.092759.109
Lovell, J. T. et al. GENESPACE tracks regions of interest and gene copy number variation across multiple genomes. eLife 11, e78526 (2022).
pubmed: 36083267
pmcid: 9462846
doi: 10.7554/eLife.78526
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
pubmed: 31727128
pmcid: 6857279
doi: 10.1186/s13059-019-1832-y
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
pubmed: 17483113
doi: 10.1093/molbev/msm088
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).
pubmed: 22728672
pmcid: 3679285
doi: 10.4161/fly.19695
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
pubmed: 29750242
pmcid: 6137996
doi: 10.1093/bioinformatics/bty191
Goel, M. & Schneeberger, K. plotsr: visualizing structural similarities and rearrangements between multiple genomes. Bioinformatics 38, 2922–2926 (2022).
pubmed: 35561173
pmcid: 9113368
doi: 10.1093/bioinformatics/btac196
Jeffares, D. C. et al. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat. Commun. 8, 14061 (2017).
pubmed: 28117401
pmcid: 5286201
doi: 10.1038/ncomms14061
Giordano, F., Stammnitz, M. R., Murchison, E. P. & Ning, Z. scanPAV: a pipeline for extracting presence–absence variations in genome pairs. Bioinformatics 34, 3022–3024 (2018).
pubmed: 29608694
pmcid: 6129304
doi: 10.1093/bioinformatics/bty189
Garrison, E. et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat. Biotechnol. 36, 875–879 (2018).
pubmed: 30125266
pmcid: 6126949
doi: 10.1038/nbt.4227
Khan, A. W. & Varshney, R. Cicer wild genome assemblies and super-pangenome associated files. figshare https://doi.org/10.6084/m9.figshare.23599143 (2023).