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.

Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism

Abstract

Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on 6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Manhattan plots of mGWAS results with genetic association.
Figure 2: Two-locus interactions between significant loci and mGWAS genetic architecture analysis between the indica and japonica subspecies.
Figure 3: Functional identification of Os02g57760 (O-methyltransferase) and the assignment of possible causative sites.
Figure 4: Functional annotation of Os07g32060 (UDP-glucosyl transferase) and the assignment of possible causative sites.

Accession codes

Primary accessions

BioProject

Referenced accessions

European Nucleotide Archive

Sequence Read Archive

References

  1. Saito, K. & Matsuda, F. Metabolomics for functional genomics, systems biology, and biotechnology. Annu. Rev. Plant Biol. 61, 463–489 (2010).

    CAS  PubMed  Google Scholar 

  2. Schwab, W. Metabolome diversity: too few genes, too many metabolites? Phytochemistry 62, 837–849 (2003).

    CAS  PubMed  Google Scholar 

  3. Keurentjes, J.J. Genetical metabolomics: closing in on phenotypes. Curr. Opin. Plant Biol. 12, 223–230 (2009).

    CAS  PubMed  Google Scholar 

  4. De Luca, V., Salim, V., Atsumi, S.M. & Yu, F. Mining the biodiversity of plants: a revolution in the making. Science 336, 1658–1661 (2012).

    PubMed  Google Scholar 

  5. Hellmann, H. & Mooney, S. Vitamin B6: a molecule for human health? Molecules 15, 442–459 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Herrero, S., Gonzalez, E., Gillikin, J.W., Velez, H. & Daub, M.E. Identification and characterization of a pyridoxal reductase involved in the vitamin B6 salvage pathway in Arabidopsis. Plant Mol. Biol. 76, 157–169 (2011).

    CAS  PubMed  Google Scholar 

  7. Kaur, H., Heinzel, N., Schottner, M., Baldwin, I.T. & Galis, I. R2R3-NaMYB8 regulates the accumulation of phenylpropanoid-polyamine conjugates, which are essential for local and systemic defense against insect herbivores in Nicotiana attenuata. Plant Physiol. 152, 1731–1747 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Luo, J. et al. A novel polyamine acyltransferase responsible for the accumulation of spermidine conjugates in Arabidopsis seed. Plant Cell 21, 318–333 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Butelli, E. et al. Enrichment of tomato fruit with health-promoting anthocyanins by expression of select transcription factors. Nat. Biotechnol. 26, 1301–1308 (2008).

    CAS  PubMed  Google Scholar 

  10. Niggeweg, R., Michael, A.J. & Martin, C. Engineering plants with increased levels of the antioxidant chlorogenic acid. Nat. Biotechnol. 22, 746–754 (2004).

    CAS  PubMed  Google Scholar 

  11. Luo, J. et al. AtMYB12 regulates caffeoyl quinic acid and flavonol synthesis in tomato: expression in fruit results in very high levels of both types of polyphenol. Plant J. 56, 316–326 (2008).

    CAS  PubMed  Google Scholar 

  12. Morohashi, K. et al. A genome-wide regulatory framework identifies maize Pericarp Color1 controlled genes. Plant Cell 24, 2745–2764 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Keurentjes, J.J. et al. The genetics of plant metabolism. Nat. Genet. 38, 842–849 (2006).

    CAS  PubMed  Google Scholar 

  14. Huang, X. et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat. Genet. 44, 32–39 (2012).

    Google Scholar 

  15. Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42, 961–967 (2010).

    CAS  PubMed  Google Scholar 

  16. Gong, L. et al. Genetic analysis of the metabolome exemplified using a rice population. Proc. Natl. Acad. Sci. USA 110, 20320–20325 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Yamamoto, T., Yonemaru, J. & Yano, M. Towards the understanding of complex traits in rice: substantially or superficially? DNA Res. 16, 141–154 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Matsuda, F. et al. AtMetExpress development: a phytochemical atlas of Arabidopsis development. Plant Physiol. 152, 566–578 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Riedelsheimer, C. et al. Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize. Proc. Natl. Acad. Sci. USA 109, 8872–8877 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Chan, E.K., Rowe, H.C., Corwin, J.A., Joseph, B. & Kliebenstein, D.J. Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol. 9, e1001125 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Chan, E.K., Rowe, H.C., Hansen, B.G. & Kliebenstein, D.J. The complex genetic architecture of the metabolome. PLoS Genet. 6, e1001198 (2010).

    PubMed  PubMed Central  Google Scholar 

  22. International Rice Genome Sequencing Project. The map-based sequence of the rice genome. Nature 436, 793–800 (2005).

    Google Scholar 

  23. Caicedo, A.L. et al. Genome-wide patterns of nucleotide polymorphism in domesticated rice. PLoS Genet. 3, 1745–1756 (2007).

    CAS  PubMed  Google Scholar 

  24. Han, B. & Xue, Y. Genome-wide intraspecific DNA-sequence variations in rice. Curr. Opin. Plant Biol. 6, 134–138 (2003).

    CAS  PubMed  Google Scholar 

  25. Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–501 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Heuberger, A.L. et al. Metabolomic and functional genomic analyses reveal varietal differences in bioactive compounds of cooked rice. PLoS ONE 5, e12915 (2010).

    PubMed  PubMed Central  Google Scholar 

  27. Kawaura, K. et al. Assessment of adaptive evolution between wheat and rice as deduced from full-length common wheat cDNA sequence data and expression patterns. BMC Genomics 10, 271 (2009).

    PubMed  PubMed Central  Google Scholar 

  28. Kusano, M. et al. Deciphering starch quality of rice kernels using metabolite profiling and pedigree network analysis. Mol. Plant 5, 442–451 (2012).

    CAS  PubMed  Google Scholar 

  29. Redestig, H. et al. Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics. BMC Syst. Biol. 5, 176 (2011).

    PubMed  PubMed Central  Google Scholar 

  30. Weng, J.K., Li, Y., Mo, H. & Chapple, C. Assembly of an evolutionarily new pathway for α-pyrone biosynthesis in Arabidopsis. Science 337, 960–964 (2012).

    CAS  PubMed  Google Scholar 

  31. Lippert, C. et al. FaST linear mixed models for genome-wide association studies. Nat. Methods 8, 833–835 (2011).

    CAS  PubMed  Google Scholar 

  32. Atwell, S. et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature 465, 627–631 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Chan, E.K., Rowe, H.C. & Kliebenstein, D.J. Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome-wide association mapping. Genetics 185, 991–1007 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Shimizu, T. et al. Purification and identification of naringenin 7-O-methyltransferase, a key enzyme in biosynthesis of flavonoid phytoalexin sakuranetin in rice. J. Biol. Chem. 287, 19315–19325 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Swaminathan, S., Morrone, D., Wang, Q., Fulton, D.B. & Peters, R.J. CYP76M7 is an ent-cassadiene C11α-hydroxylase defining a second multifunctional diterpenoid biosynthetic gene cluster in rice. Plant Cell 21, 3315–3325 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Ko, J.H. et al. Four glucosyltransferases from rice: cDNA cloning, expression, and characterization. J. Plant Physiol. 165, 435–444 (2008).

    CAS  PubMed  Google Scholar 

  37. Saitoh, K., Onishi, K., Mikami, I., Thidar, K. & Sano, Y. Allelic diversification at the C (OsC1) locus of wild and cultivated rice: nucleotide changes associated with phenotypes. Genetics 168, 997–1007 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Cheng, A.X. et al. The rice (E)-β-caryophyllene synthase (OsTPS3) accounts for the major inducible volatile sesquiterpenes. Phytochemistry 68, 1632–1641 (2007).

    CAS  PubMed  Google Scholar 

  39. Evers, D. et al. Identification of drought-responsive compounds in potato through a combined transcriptomic and targeted metabolite approach. J. Exp. Bot. 61, 2327–2343 (2010).

    CAS  PubMed  Google Scholar 

  40. Minorsky, P.V. The hot and the classic. Plant Physiol. 128, 1167–1168 (2002).

    CAS  PubMed  Google Scholar 

  41. Lehmann, T. & Pollmann, S. Gene expression and characterization of a stress-induced tyrosine decarboxylase from Arabidopsis thaliana. FEBS Lett. 583, 1895–1900 (2009).

    CAS  PubMed  Google Scholar 

  42. Marinova, K. et al. The Arabidopsis MATE transporter TT12 acts as a vacuolar flavonoid/H+-antiporter active in proanthocyanidin-accumulating cells of the seed coat. Plant Cell 19, 2023–2038 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Johns, M.A. & Mao, L. Differentiation of the two rice subspecies indica and japonica: a Gene Ontology perspective. Funct. Integr. Genomics 7, 135–151 (2007).

    CAS  PubMed  Google Scholar 

  44. Jung, K.H. et al. Genome-wide identification and analysis of japonica and indica cultivar-preferred transcripts in rice using 983 Affymetrix array data. Rice 6, 19 (2013).

    PubMed  PubMed Central  Google Scholar 

  45. Brachi, B., Morris, G.P. & Borevitz, J.O. Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol. 12, 232 (2011).

    PubMed  PubMed Central  Google Scholar 

  46. Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).

    CAS  PubMed  Google Scholar 

  47. Zhao, K. et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat. Commun. 2, 467 (2011).

    PubMed  Google Scholar 

  48. Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Rowe, H.C., Hansen, B.G., Halkier, B.A. & Kliebenstein, D.J. Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20, 1199–1216 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Joseph, B., Corwin, J.A., Li, B., Atwell, S. & Kliebenstein, D.J. Cytoplasmic genetic variation and extensive cytonuclear interactions influence natural variation in the metabolome. eLife 2, e00776 (2013).

    PubMed  PubMed Central  Google Scholar 

  51. Fernie, A.R. & Schauer, N. Metabolomics-assisted breeding: a viable option for crop improvement? Trends Genet. 25, 39–48 (2009).

    CAS  PubMed  Google Scholar 

  52. Traka, M.H. & Mithen, R.F. Plant science and human nutrition: challenges in assessing health-promoting properties of phytochemicals. Plant Cell 23, 2483–2497 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Kliebenstein, D.J., Gershenzon, J. & Mitchell-Olds, T. Comparative quantitative trait loci mapping of aliphatic, indolic and benzylic glucosinolate production in Arabidopsis thaliana leaves and seeds. Genetics 159, 359–370 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang, H. et al. A core collection and mini core collection of Oryza sativa L. in China. Theor. Appl. Genet. 122, 49–61 (2011).

    PubMed  Google Scholar 

  55. Yu, S.B. et al. Molecular diversity and multilocus organization of the parental lines used in the International Rice Molecular Breeding Program. Theor. Appl. Genet. 108, 131–140 (2003).

    CAS  PubMed  Google Scholar 

  56. Yan, W.G. et al. Association mapping of stigma and spikelet characteristics in rice (Oryza sativa L.). Mol. Breed. 24, 277–292 (2009).

    PubMed  PubMed Central  Google Scholar 

  57. McNally, K.L. et al. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proc. Natl. Acad. Sci. USA 106, 12273–12278 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Murray, M.G. & Thompson, W.F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 8, 4321–4325 (1980).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang, J. et al. The diploid genome sequence of an Asian individual. Nature 456, 60–65 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Xie, W. et al. Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing. Proc. Natl. Acad. Sci. USA 107, 10578–10583 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 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 (Austin) 6, 80–92 (2012).

    CAS  Google Scholar 

  63. Chen, W. et al. A novel integrated method for large-scale detection, identification, and quantification of widely targeted metabolites: application in the study of rice metabolomics. Mol. Plant 6, 1769–1780 (2013).

    CAS  PubMed  Google Scholar 

  64. Dresen, S., Ferreiros, N., Gnann, H., Zimmermann, R. & Weinmann, W. Detection and identification of 700 drugs by multi-target screening with a 3200 Q TRAP LC-MS/MS system and library searching. Anal. Bioanal. Chem. 396, 2425–2434 (2010).

    CAS  PubMed  Google Scholar 

  65. Retief, J.D. Phylogenetic analysis using PHYLIP. Methods Mol. Biol. 132, 243–258 (2000).

    CAS  PubMed  Google Scholar 

  66. Zhao, K. et al. An Arabidopsis example of association mapping in structured samples. PLoS Genet. 3, e4 (2007).

    PubMed  PubMed Central  Google Scholar 

  67. Li, M.X., Yeung, J.M., Cherny, S.S. & Sham, P.C. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum. Genet. 131, 747–756 (2012).

    CAS  PubMed  Google Scholar 

  68. Barrett, J.C. Haploview: visualization and analysis of SNP genotype data. Cold Spring Harb. Protoc. 2009, pdb.ip71 (2009).

    PubMed  Google Scholar 

  69. Zhou, G. et al. Genetic composition of yield heterosis in an elite rice hybrid. Proc. Natl. Acad. Sci. USA 109, 15847–15852 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Yu, H. et al. Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers. PLoS ONE 6, e17595 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Zeng, Z.B. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc. Natl. Acad. Sci. USA 90, 10972–10976 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Broman, K.W., Wu, H., Sen, S. & Churchill, G.A. R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889–890 (2003).

    CAS  PubMed  Google Scholar 

  73. Wang, J. et al. A global analysis of QTLs for expression variations in rice shoots at the early seedling stage. Plant J. 63, 1063–1074 (2010).

    CAS  PubMed  Google Scholar 

  74. Chu, Z. et al. Promoter mutations of an essential gene for pollen development result in disease resistance in rice. Genes Dev. 20, 1250–1255 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Hiei, Y., Ohta, S., Komari, T. & Kumashiro, T. Efficient transformation of rice (Oryza sativa L.) mediated by Agrobacterium and sequence analysis of the boundaries of the T-DNA. Plant J. 6, 271–282 (1994).

    CAS  PubMed  Google Scholar 

  76. Livak, K.J. & Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We appreciate the critical reading and helpful comments on the manuscript made by C. Martin from John Innes Centre, UK and Q. Zhang from Huazhong Agricultural University, China. We thank W. Yan for kindly providing 148 varieties from a mini-core subset of the US Department of Agriculture rice gene bank. This work was supported by the Major State Basic Research Development Program of China (973 Program) (number 2013CB127001), the National High Technology R&D Program of China (863 Program) (numbers 2012AA10A303 and 2012AA10A304), the National Natural Science Foundation of China (numbers 31070267 and 31100962) and the Program for New Century Excellent Talents in University of Ministry of Education in China (NCET-09-0401). We are also thankful for support from the Ministry of Science and Technology (numbers 2010CB125901 and 2011CB100304).

Author information

Authors and Affiliations

Authors

Contributions

J.L. conceived the project and supervised the study. X. Lian and K.L. prepared the material for genotyping. W.C., Y.G., L.G. and Y.L. performed most of the experiments. W.C., X. Liu, H.Z. and J.L. carried out the metabolite analyses. X. Lian, S.Y., H.D., W.Z., L.Z. and G.W. participated in the material preparation. W.C., W.X., W.W. and J.L. analyzed the data. J.L. wrote the paper. All of the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Xingming Lian or Jie Luo.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1-15 and Supplementary Tables 6, 7 and 23. (PDF 16370 kb)

Supplementary Table 1

Metabolite reporting checklist and recommendations for LC-MS (XLSX 11 kb)

Supplementary Table 2

The (almost) non-redundant MS2T library of rice leaf (XLSX 124 kb)

Supplementary Table 3

Scheduled MRM (multiple reaction monitoring) transitions for widely targeted metabolite analysis in rice leaf (XLSX 147 kb)

Supplementary Table 4

The list of collected 529 rice accessions (XLSX 84 kb)

Supplementary Table 5

Data matrix of 840 metabolites in 524 accessions of rice germplasms (including repeat1 and repeat2) (XLSX 10486 kb)

Supplementary Table 8

The list of total 2,947 significant SNPs detected in at least one of the populations (XLSX 267 kb)

Supplementary Table 9

The list of 634 loci detected in at least one of the populations (XLSX 56 kb)

Supplementary Table 10

The list of 551 lead SNPs that were repeatedly detected (XLSX 110 kb)

Supplementary Table 11

The list of 356 loci that were repeatedly detected (XLSX 77 kb)

Supplementary Table 12

The full lists of significant associations of metabolic GWAS (mGWAS) (XLSX 4949 kb)

Supplementary Table 13

The full lists of significant associations of metabolic GWAS (mGWAS) (XLSX 19291 kb)

Supplementary Table 14

Manhattan plots of 356 loci that were repeatedly detected (XLSX 39 kb)

Supplementary Table 15

Results of analysis of two-locus interactions (XLSX 205 kb)

Supplementary Table 16

Statistics of significant loci on the chromosomes (XLSX 16 kb)

Supplementary Table 17

The list of significant loci detected in indica and japonica subspecies (XLSX 58 kb)

Supplementary Table 18

The list of significant loci detected for subspecies differentiation metabolites in indica and japonica subspecies (XLSX 21 kb)

Supplementary Table 19

The full list of identified or annotated metabolites that were supported by GWAS (XLSX 40 kb)

Supplementary Table 20

The full list of candidate genes (XLSX 20 kb)

Supplementary Table 21

Metabolic profiling of transgenic lines overexpressing the candidate genes (XLSX 47 kb)

Supplementary Table 22

The results of overlap between GWAS loci and mQTL (XLSX 24 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Gao, Y., Xie, W. et al. Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46, 714–721 (2014). https://doi.org/10.1038/ng.3007

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3007

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing