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 studies of 14 agronomic traits in rice landraces

Abstract

Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified 3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained 36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.

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: Divergence and geographic origins of 517 rice landraces.
Figure 2: Population structures of Chinese landraces of both subspecies.
Figure 3: Influence of populational and experimental factors on the performance of the KNN-based imputation method.
Figure 4: Genome-wide association studies of grain width and heading date.
Figure 5: Regions of the genome showing strong association signals near previously identified genes.
Figure 6: Contributions of identified loci to phenotypic variance of each of 14 agronomic traits.

Accession codes

Accessions

GenBank/EMBL/DDBJ

References

  1. Zong, Y. et al. Fire and flood management of coastal swamp enabled first rice paddy cultivation in east China. Nature 449, 459–462 (2007).

    Google Scholar 

  2. Zhang, D. et al. Genetic structure and differentiation of Oryza sativa L. in China revealed by microsatellites. Theor. Appl. Genet. 119, 1105–1117 (2009).

    Google Scholar 

  3. The International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

  4. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

  5. The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  6. Altshuler, D., Daly, M.J. & Lander, E.S. Genetic mapping in human disease. Science 322, 881–888 (2008).

    Google Scholar 

  7. Nordborg, M. & Weigel, D. Next-generation genetics in plants. Nature 456, 720–723 (2008).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

  11. Weigel, D. & Mott, R. The 1001 genomes project for Arabidopsis thaliana. Genome Biol. 10, 107 (2009).

    Google Scholar 

  12. Clark, R.M. et al. Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317, 338–342 (2007).

    Google Scholar 

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

    Google Scholar 

  14. Huang, X. et al. High-throughput genotyping by whole-genome resequencing. Genome Res. 19, 1068–1076 (2009).

    Google Scholar 

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

    Google Scholar 

  16. Zhu, Q. et al. Multilocus analysis of nucleotide variation of Oryza sativa and its wild relatives: severe bottleneck during domestication of rice. Mol. Biol. Evol. 24, 875–888 (2007).

    Google Scholar 

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

    Google Scholar 

  18. Mather, K.A. et al. The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177, 2223–2232 (2007).

    Google Scholar 

  19. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001).

    Google Scholar 

  20. Roberts, A. et al. Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows. Bioinformatics 23, i401–i407 (2007).

    Google Scholar 

  21. 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).

    Google Scholar 

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

    Google Scholar 

  23. Saitoh, K. et al. Allelic diversification at the C (OsC1) locus of wild and cultivated rice: nucleotide changes associated with phenotypes. Genetics 168, 997–1007 (2004).

    Google Scholar 

  24. Sweeney, M.T., Thomson, M.J., Pfeil, B.E. & McCouch, S. Caught red-handed: Rc encodes a basic helix-loop-helix protein conditioning red pericarp in rice. Plant Cell 18, 283–294 (2006).

    Google Scholar 

  25. Cui, J. et al. Characterization and fine mapping of the ibf mutant in rice. J. Integr. Plant Biol. 49, 678–685 (2007).

    Google Scholar 

  26. Gao, Z. et al. Map-based cloning of the ALK gene, which controls the gelatinization temperature of rice. Sci. China C Life Sci. 46, 661–668 (2003).

    Google Scholar 

  27. Wang, Z.Y. et al. The amylose content in rice endosperm is related to the post-transcriptional regulation of the waxy gene. Plant J. 7, 613–622 (1995).

    Google Scholar 

  28. Tian, Z. et al. Allelic diversities in rice starch biosynthesis lead to a diverse array of rice eating and cooking qualities. Proc. Natl. Acad. Sci. USA 106, 21760–21765 (2009).

    Google Scholar 

  29. Shomura, A. et al. Deletion in a gene associated with grain size increased yields during rice domestication. Nat. Genet. 40, 1023–1028 (2008).

    Google Scholar 

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

    Google Scholar 

  31. Buckler, E.S. et al. The genetic architecture of maize flowering time. Science 325, 714–718 (2009).

    Google Scholar 

  32. Kim, S.L. et al. OsMADS51 is a short-day flowering promoter that functions upstream of Ehd1, OsMADS14, and Hd3a. Plant Physiol. 145, 1484–1494 (2007).

    Google Scholar 

  33. Zhang, Q., Li, J.Y., Xue, Y.B., Han, B. & Deng, X.W. Rice 2020: a call for an international coordinated effort in rice functional genomics. Mol. Plant 1, 715–719 (2008).

    Google Scholar 

  34. Myles, S. et al. Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21, 2194–2202 (2009).

    Google Scholar 

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

    Google Scholar 

  36. Huang, X. et al. Genome-wide analysis of transposon insertion polymorphisms reveals intraspecific variation in cultivated rice. Plant Physiol. 148, 25–40 (2008).

    Google Scholar 

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

    Google Scholar 

  38. Barrett, J.C., Fry, B., Maller, J. & Daly, M.J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    Google Scholar 

  39. Tajima, F. Evolutionary relationship of DNA sequences in finite populations. Genetics 105, 437–460 (1983).

    Google Scholar 

  40. Nordborg, M. et al. The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol. 3, e196 (2005).

    Google Scholar 

  41. Juliano, B. Rice Chemistry and Technology 443–513 (American Association of Cereal Chemists, Saint Paul, Minnesota, USA, 1985).

  42. Little, R.R., Hilder, G.B. & Dawson, E.H. Differential effect of dilute alkali on 25 varieties of milled white rice. Cereal Chem. 35, 111–126 (1958).

    Google Scholar 

Download references

Acknowledgements

We thank the China National Rice Research Institute for providing the landrace samples, R.A. Wing for critical reading of the manuscript, P. Hu for helping assay rice grain quality and Z. Ning for assistance with sequence alignment. This work was supported by the Chinese Academy of Sciences (KSCX2-YW-N-024), China's Ministry of Science and Technology (2006AA10A102) and Ministry of Agriculture (2008ZX08009-002) and the National Natural Science Foundation of China (30821004) to B.H.

Author information

Authors and Affiliations

Authors

Contributions

B.H. conceived the project and its components. J.L., Q.-F.Z., T.S. and B.H. contributed to the original concept of the project. Q.F., D.F., Y.G., L.D., Wenjun Li, Y.L. and Q.W. performed the genome sequencing. X.H., Q.Z., Y.Z., C.Z., T.L., K.L. and T.H. performed GWAS and data analysis. Y.Z., Q.Z., C.Z. and X.H. developed the imputation program for data analyses. X.H., Y.Z. and T.S. performed statistical simulations. Z.Z., M.L., Y.Z. and E.S.B. performed GWAS using the compressed mixed linear model. X.W., C.L., A.W., L.W., T.Z., Y.J., Wei Li, Z.L. and Q.Q. collected samples and performed the phenotyping. Q.Z., T.L., Y.Z. and X.H. prepared figures and tables. X.H., T.S. and B.H. analyzed all the data and wrote the paper.

Corresponding author

Correspondence to Bin Han.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Note; Supplementary Tables 2–4, 7 and 8; Supplementary Figs. 1–25 (PDF 6688 kb)

Supplementary Table 1

The list of 517 landrace accessions sampled in this study. (XLS 71 kb)

Supplementary Table 5

The list of genes over-represented for large-effect changes. (XLS 31 kb)

Supplementary Table 6

The list of genes that contained large-effect complete-differentiation SNPs. (XLS 31 kb)

Supplementary Table 9

The genotype dataset of indica landraces on the causal polymorphic sites of three known genes. (XLS 79 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Huang, X., Wei, X., Sang, T. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42, 961–967 (2010). https://doi.org/10.1038/ng.695

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.695

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