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Resequencing of 429 chickpea accessions from 45 countries provides insights into genome diversity, domestication and agronomic traits

Abstract

We report a map of 4.97 million single-nucleotide polymorphisms of the chickpea from whole-genome resequencing of 429 lines sampled from 45 countries. We identified 122 candidate regions with 204 genes under selection during chickpea breeding. Our data suggest the Eastern Mediterranean as the primary center of origin and migration route of chickpea from the Mediterranean/Fertile Crescent to Central Asia, and probably in parallel from Central Asia to East Africa (Ethiopia) and South Asia (India). Genome-wide association studies identified 262 markers and several candidate genes for 13 traits. Our study establishes a foundation for large-scale characterization of germplasm and population genomics, and a resource for trait dissection, accelerating genetic gains in future chickpea breeding.

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Fig. 1: A circos diagram illustrating the genome-wide variations among 429 chickpea lines.
Fig. 2: Genome-wide variations, population structure and genetic diversity in 429 chickpea genotypes.
Fig. 3: Population diversity in 429 chickpea genotypes. Phylogenetic tree constructed using SNPs identified.
Fig. 4: Selection sweeps and reduction of diversity

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Data availability

The data that support the findings of this study have been deposited in the NCBI under accession code SRA: SRP096939; BioProject: PRJNA362278, and these data are also available in the CNSA (https://db.cngb.org/cnsa/) of CNGBdb with accession code CNP0000370.

References

  1. Mba, C., Guimaraes, E. P. & Ghosh, K. Re-orienting crop improvement for the changing climatic conditions of the 21st century. Agri. & Food Sec. 1, 7 (2012).

    Article  Google Scholar 

  2. Ritchie, H. & Roser, M. Micronutrient deficiency. Our World in Data https://ourworldindata.org/micronutrient-deficiency (2017).

  3. Atlin, G. N., Cairns, J. E. & Das, B. Rapid breeding and varietal replacement are critical to adaptation of cropping systems in the developing world to climate change. Glob. Food Sec. 12, 31–37 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Abbo, S., Berger, J. & Turner, N. C. Viewpoint: evolution of cultivated chickpea: four bottlenecks limit diversity and constrain adaptation. Funct. Plant Biol. 30, 1081–1087 (2003).

    Article  PubMed  Google Scholar 

  5. Varshney, R. K. et al. Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.). Theor. Appl. Genet. 127, 445–462 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Thudi, M. et al. Understanding the genetic architecture of drought and heat tolerance in chickpea through genome-wide and candidate gene-based association mapping. PLoS ONE 9, e96758 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Upadhyaya, H. D. et al. Genomic tools and germplasm diversity for chickpea improvement. Plant Genet. Resour. 9, 45–58 (2011).

    Article  CAS  Google Scholar 

  8. Zhou, Z. et al. Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat. Biotechnol. 33, 408–414 (2015).

    Article  CAS  PubMed  Google Scholar 

  9. 3K RGP. The 3,000 rice genomes project. GigaScience 3, 7 (2014).

    Article  Google Scholar 

  10. Varshney, R. K. et al. Whole-genome resequencing of 292 pigeonpea accessions identifies genomic regions associated with domestication and agronomic traits. Nat. Genet. 49, 1082–1088 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Varshney, R. K. et al. Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments. Nat. Biotechnol. 35, 969–976 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fang, L. et al. Genomic analyses in cotton identify signatures of selection and loci associated with fiber quality and yield traits. Nat. Genet. 49, 1089–1098 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Romero Navarro, J. A. et al. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat. Genet. 49, 476–480 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Xu, X. et al. Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat. Biotechnol. 30, 105–111 (2012).

    Article  CAS  Google Scholar 

  15. Lam, H. M. et al. Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat. Genet. 42, 1053–1059 (2010).

    Article  CAS  PubMed  Google Scholar 

  16. Varshney, R. K., Nayak, S. N., May, G. D. & Jackson, S. A. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol. 27, 522–530 (2009).

    Article  CAS  PubMed  Google Scholar 

  17. Upadhyaya, H. D. et al. Genetic structure, diversity, and allelic richness in composite collection and reference set in chickpea (Cicer arietinum L.). BMC Plant Biol. 8, 106 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Varshney, R. K. et al. Draft genome sequence of chickpea (Cicer arietinum) provides a resource for trait improvement. Nat. Biotechnol. 31, 240–246 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Mace, E. S. et al. Whole-genome sequencing reveals untapped genetic potential in Africa’s indigenous cereal crop sorghum. Nat. Commun. 4, 2320 (2013).

    Article  PubMed  Google Scholar 

  21. Pritchard, J. K., Stephens, M. & Donnelly, P. J. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Penmetsa, R. V. et al. Multiple post-domestication origins of kabuli chickpea through allelic variation in a diversification-associated transcription factor. New Phytol. 211, 1440–1451 (2016).

    Article  Google Scholar 

  23. Roorkiwal, M. et al. Exploring germplasm diversity to understand the domestication process in Cicer spp. using SNP and DArT markers. PLoS ONE 9, e102016 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Barrett, J. C. et al. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Gore, M. A. et al. A first-generation haplotype map of maize. Science 326, 1115–1117 (2009).

    Article  CAS  PubMed  Google Scholar 

  26. Branca, A. et al. Whole-genome nucleotide diversity, recombination, and linkage disequilibrium in the model legume Medicago truncatula. Proc. Natl Acad. Sci. USA 108, E864–E870 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. von Wettberg, E. J. et al. Ecology and community genomics of an important crop wild relative as a prelude to agricultural innovation. Nat. Commun. 9, 649 (2018).

    Article  Google Scholar 

  28. Eldon, B., Birkner, M., Blath, J. & Freund, F. Can the site-frequency spectrum distinguish exponential population growth from multiple-merger coalescents? Genetics 199, 841–856 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ferretti, L., Ledda, A., Wiehe, T., Achaz, G. & Ramos-Onsins, S. E. Decomposing the site frequency spectrum: the impact of tree topology on neutrality tests. Genetics 207, 229–240 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hudson, R. R. et al. Estimation of levels of gene flow from DNA sequence data. Genetics 132, 583–589 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Vavilov, N. I. Centres of origin of cultivated plants. Bull. Appl. Bot. Genet. Plant Breed. 16, 1–248 (1926).

    Google Scholar 

  32. Whitlock, M. C. & McCauley, D. E. Indirect measures of gene flow and migration: FST does not=1/(4Nm + 1). Heredity 82, 117–125 (1999).

    Article  PubMed  Google Scholar 

  33. Redden, R. J. & Berger, J. D. History and origin of chickpea. in Chickpea Breeding and Management (eds Yadav, S. S. et al.) 1–13 (C.A.B. International, 2007).

  34. McCouch, S. et al. Agriculture: feeding the future. Nature 499, 23–24 (2014).

    Article  Google Scholar 

  35. Kamran, A. et al. The effect of VRN1 genes on important agronomic traits in high-yielding Canadian soft white spring wheat. Plant Breed. 133, 321–326 (2014).

    Article  CAS  Google Scholar 

  36. Samineni, S. et al. Vernalization response in chickpea is controlled by a major QTL. Euphytica 207, 453–461 (2016).

    Article  CAS  Google Scholar 

  37. van der Maesen, L. J. G. in The Chickpea (eds Saxena, M. C. & Singh, R. B.) 11–34 (C.A.B. International, 1987).

  38. Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Kashiwagi, J. et al. Traits of relevance to improve yield under terminal drought stress in chickpea (C. arietinum L.). Field Crops Res. 145, 88–95 (2013).

    Article  Google Scholar 

  41. Lynch, J. P. Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann. Bot. 112, 347–357 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li, R. et al. SOAP2: An improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Li, S. et al. SOAPindel: Efficient identification of Indels from short paired reads. Genome Res. 23, 195–200 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Patterson, N., Price, A. L. & Reich, D. Population structure and eigen analysis. PLoS Genet. 2, 2074–2093 (2006).

    Article  CAS  Google Scholar 

  46. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Felsenstein, J. Phylip: phylogeny inference package (version 3.2). Cladistics 5, 164–166 (1989).

    Google Scholar 

  48. Tamura, K. et al. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24, 1596–1599 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lipka, A. E. et al. GAPIT: genome association and prediction integrated tool. Bioinformatics 28, 2397–2399 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Tang, Y. et al. GAPIT Version 2: an enhanced integrated tool for genomic association and prediction. Plant Genome 9, 2 (2016).

    Article  Google Scholar 

  53. Liu, X., Huang, M., Fan, B., Buckler, E. S. & Zhang, Z. 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 

Download references

Acknowledgements

R.K.V. acknowledges the funding support from CGIAR Generation Challenge Programme, Department of Science and Technology Government of India under the Australia-India Strategic Research Fund, Ministry of Agriculture and Farmers Welfare, Government of India and Bill & Melinda Gates Foundation, USA. Shenzhen Municipal Government of China (grant no. JCYJ20150831201643396 and no. JCYJ20170817145512476 under the Basic Research Program) and the Guangdong Provincial Key Laboratory of Genome Read and Write (grant no. 2017B030301011) are acknowledged to provide support to X.X. and X.L. This work has been undertaken as part of the CGIAR Research Program on Grain Legumes and Dryland Cereals. ICRISAT is a member of the CGIAR Consortium.

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Contributions

R.K.V. conceived and designed the experiments. R.K.V., X.X. and X.L. coordinated sequencing and genome analysis. W.H., W.Y., J.J., H.D.U., N.P.S., S.K.C., G.V.P.R.N., L.K., A.F., K.K.B.P., P.M.G. and S.M.S. performed the experiments. W.H., P.B., A.R., D.D., V.G., A.W.K., H.D.U., J.C. and Y.V. performed statistical analysis. R.K.V., M.T., M.R., A.C., P.C., C.B., S.T., J.W., S.-H.L., D.E., K.K.B.P., R.V.P., J.C., H.T.N., K.H.M.S., T.D.C., T.S., E.v.W., Y.V., X.X. and X.L. analyzed the data. R.K.V., H.D.U., A.C., P.M.G., N.P.S., S.K.C., G.P.D., D.X., J.W., X.X. and X.L. contributed to the reagents, materials and analysis tools. R.K.V., M.T., M.R., W.H. and X.L. wrote the manuscript. All authors read and approved the manuscript.

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Correspondence to Rajeev K. Varshney, Xun Xu or Xin Liu.

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Varshney, R.K., Thudi, M., Roorkiwal, M. 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). https://doi.org/10.1038/s41588-019-0401-3

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