Abstract

Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases1,2, genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly self-fertilizing model plant known to harbour considerable genetic variation for many adaptively important traits3. Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of them excellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    & Genome-wide association studies for common diseases and complex traits. Nature Rev. Genet. 6, 95–108 (2005)

  2. 2.

    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)

  3. 3.

    , & Naturally occurring genetic variation in Arabidopsis thaliana. Annu. Rev. Plant Biol. 55, 141–172 (2004)

  4. 4.

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

  5. 5.

    et al. Role of FRIGIDA and FLC in determining variation in flowering time of Arabidopsis thaliana. Plant Physiol. 138, 1163–1173 (2005)

  6. 6.

    et al. Recombination and linkage disequilibrium in Arabidopsis thaliana. Nature Genet. 39, 1151–1155 (2007)

  7. 7.

    et al. The extent of linkage disequilibrium in Arabidopsis thaliana. Nature Genet. 30, 190–193 (2002)

  8. 8.

    et al. Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genet. 1, e60 (2005)

  9. 9.

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

  10. 10.

    , , & Association mapping in structured populations. Am. J. Hum. Genet. 67, 170–181 (2000)

  11. 11.

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

  12. 12.

    et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genet. 38, 203–208 (2005)

  13. 13.

    et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008)

  14. 14.

    et al. Structure of the Arabidopsis RPM1 gene enabling dual-specificity disease resistance. Science 269, 843–846 (1995)

  15. 15.

    et al. Molecular analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis flowering time. Science 290, 344–347 (2000)

  16. 16.

    et al. A non-parametric test reveals selection for rapid flowering in the Arabidopsis genome. PLoS Biol. 4, e137 (2006)

  17. 17.

    & FLOWERING LOCUS C encodes a novel MADS domain protein that acts as a repressor of flowering. Plant Cell 11, 949–956 (1999)

  18. 18.

    , , & Cloning of DOG1, a quantitative trait locus controlling seed dormancy in Arabidopsis. Proc. Natl Acad. Sci. USA 103, 17042–17047 (2006)

  19. 19.

    et al. Natural variants of AtHKT1 enhance Na+ accumulation in two wild populations of Arabidopsis. PLoS Genet. 2, e210 (2006)

  20. 20.

    et al. Variation in molybdenum content across broadly distributed populations of Arabidopsis thaliana is controlled by a mitochondrial molybdenum transporter (MOT1). PLoS Genet. 4, e1000004 (2008)

  21. 21.

    , & A single amino acid replacement in ETC2 acts as major modifier of trichome patterning in natural Arabidopsis populations. Curr. Biol. 19, 1747–1751 (2009)

  22. 22.

    , , & ACD6, a novel ankyrin protein, is a regulator and an effector of salicylic acid signaling in the Arabidopsis defense response. Plant Cell 15, 2408–2420 (2003)

  23. 23.

    et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009)

  24. 24.

    et al. A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS Genet. 4, e1000074 (2008)

  25. 25.

    et al. A genomewide association study of skin pigmentation in a South Asian population. Am. J. Hum. Genet. 81, 1119–1132 (2007)

  26. 26.

    & Genomic control for association studies. Biometrics 55, 997–1004 (1999)

  27. 27.

    & A general population-genetic model for the production by population structure of spurious genotype-phenotype associations in discrete, admixed, or spatially distributed populations. Genetics 173, 1665–1678 (2006)

  28. 28.

    & Next-generation genetics in plants. Nature 456, 720–723 (2008)

Download references

Acknowledgements

We thank B. Carvalho for his advice on how to modify the OLIGO package. This work was primarily supported by US National Science Foundation (NSF) grant DEB-0519961 (J.B., M.N.), US National Institutes of Health (NIH) grant GM073822 (J.O.B.), and NSF grant DEB-0723935 (M.N.). Additional support was provided by the Dropkin Foundation, NIH grant GM057994 and NSF grant MCB-0603515 (J.B.), the Max Planck Society (D.W., M.T.), the Austrian Academy of Sciences (M.N.), the University of Lille 1 (F.R.), NIH grant GM078536 and NIH grant P42ES007373 (D.E.S.), NIH grant GM62932 (J.C., D.W.), the Howard Hughes Medical Institute (J.C.), the Deutsche Forschungsgemeinschaft (DFG) SFB 680 (J.d.M.), a Marie Curie International Outgoing Fellowship ‘ANAVACO’ (project number 220833; G.W.), and a Gottfried Wilhelm Leibniz Award of the DFG (D.W.). The project would not have been possible without the existence of The Arabidopsis Information Resource (http://arabidopsis.org).

Author information

Author notes

    • Susanna Atwell
    • , Yu S. Huang
    • , Bjarni J. Vilhjálmsson
    •  & Glenda Willems

    These authors contributed equally to this work.

Affiliations

  1. Molecular and Computational Biology,

    • Susanna Atwell
    • , Yu S. Huang
    • , Bjarni J. Vilhjálmsson
    • , Glenda Willems
    • , Dazhe Meng
    • , Alexander Platt
    • , Aaron M. Tarone
    • , Tina T. Hu
    • , Rong Jiang
    • , Muhammad Ali Amer
    • , Chunlao Tang
    •  & Magnus Nordborg
  2. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California 90089, USA

    • Paul Marjoram
  3. Department of Ecology & Evolution, University of Chicago, Chicago, Illinois 60637, USA

    • Matthew Horton
    • , Yan Li
    • , N. Wayan Muliyati
    • , Xu Zhang
    • , Joel M. Kniskern
    • , Fabrice Roux
    • , M. Brian Traw
    • , Justin O. Borevitz
    •  & Joy Bergelson
  4. Bindley Bioscience Center,

    • Ivan Baxter
  5. Purdue University, West Lafayette, Indiana 47907, USA

    • David E. Salt
  6. Laboratoire de Génétique et Evolution des Populations Végétales, UMR CNRS 8016, Université des Sciences et Technologies de Lille 1, F-59655 Villeneuve d’Ascq Cedex, France

    • Benjamin Brachi
    • , Nathalie Faure
    •  & Fabrice Roux
  7. Howard Hughes Medical Institute, La Jolla, California 92037, USA

    • Joanne Chory
  8. Plant Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA

    • Joanne Chory
    • , Joseph R. Ecker
    •  & Todd Michael
  9. Department of Cell and Development Biology, John Innes Centre, Norwich NR4 7UH, UK

    • Caroline Dean
  10. Max Planck Institute for Plant Breeding Research, D-50829 Cologne, Germany

    • Marilyne Debieu
    •  & Juliette de Meaux
  11. Sainsbury Laboratory, Norwich NR4 7UH, UK

    • Jonathan D. G. Jones
    •  & Adnane Nemri
  12. Department of Molecular Biology, Max Planck Institute for Developmental Biology, D-72076 Tübingen, Germany

    • Marco Todesco
    •  & Detlef Weigel
  13. Gregor Mendel Institute, A-1030 Vienna, Austria

    • Magnus Nordborg

Authors

  1. Search for Susanna Atwell in:

  2. Search for Yu S. Huang in:

  3. Search for Bjarni J. Vilhjálmsson in:

  4. Search for Glenda Willems in:

  5. Search for Matthew Horton in:

  6. Search for Yan Li in:

  7. Search for Dazhe Meng in:

  8. Search for Alexander Platt in:

  9. Search for Aaron M. Tarone in:

  10. Search for Tina T. Hu in:

  11. Search for Rong Jiang in:

  12. Search for N. Wayan Muliyati in:

  13. Search for Xu Zhang in:

  14. Search for Muhammad Ali Amer in:

  15. Search for Ivan Baxter in:

  16. Search for Benjamin Brachi in:

  17. Search for Joanne Chory in:

  18. Search for Caroline Dean in:

  19. Search for Marilyne Debieu in:

  20. Search for Juliette de Meaux in:

  21. Search for Joseph R. Ecker in:

  22. Search for Nathalie Faure in:

  23. Search for Joel M. Kniskern in:

  24. Search for Jonathan D. G. Jones in:

  25. Search for Todd Michael in:

  26. Search for Adnane Nemri in:

  27. Search for Fabrice Roux in:

  28. Search for David E. Salt in:

  29. Search for Chunlao Tang in:

  30. Search for Marco Todesco in:

  31. Search for M. Brian Traw in:

  32. Search for Detlef Weigel in:

  33. Search for Paul Marjoram in:

  34. Search for Justin O. Borevitz in:

  35. Search for Joy Bergelson in:

  36. Search for Magnus Nordborg in:

Contributions

J.O.B., J.B. and M.N. are equal senior authors. J.R.E. and D.W. generated the SNPs used in this project. S.A., M.H., Y.L., N.W.M., X.Z., J.O.B. and J.B. were responsible for the experimental aspects of genotyping. Y.S.H., B.J.V., M.H., T.T.H., R.J., X.Z., M.A.A., P.M., J.O.B., J.B. and M.N. were responsible for data management and the bioinformatics pipeline. S.A., I.B., B.B., J.C., C.D., M.D., J.d.M., N.F., J.M.K., J.D.G.J., T.M., A.N., F.R., D.E.S., C.T., M.T., M.B.T., D.W., J.B. and M.N. were responsible for phenotyping. S.A., Y.S.H., B.J.V., G.W., D.M., A.P., A.M.T., P.M. and M.N carried out the GWA analyses. Y.S.H. and D.M. developed the project website. M.N. wrote the paper with significant contributions from S.A., Y.S.H., B.J.V., G.W., A.P. and J.B. J.O.B., J.B. and M.N. designed and supervised the project.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Magnus Nordborg.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Information which comprises: 1 Genotyping; 2 Association Mapping Methods; 3 Enrichment for a priori candidates, Supplementary Figures 1-152 with legends, Supplementary References and Supplementary Tables 1-7.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature08800

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.