[go: up one dir, main page]
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.

  • Letter
  • Published:

A unified mixed-model method for association mapping that accounts for multiple levels of relatedness

Nature Genetics volume 38, pages 203–208 (2006)Cite this article

Abstract

As population structure can result in spurious associations, it has constrained the use of association studies in human and plant genetics. Association mapping, however, holds great promise if true signals of functional association can be separated from the vast number of false signals generated by population structure1,2. We have developed a unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers. We applied this new approach to two samples: a family-based sample of 14 human families, for quantitative gene expression dissection, and a sample of 277 diverse maize inbred lines with complex familial relationships and population structure, for quantitative trait dissection. Our method demonstrates improved control of both type I and type II error rates over other methods. As this new method crosses the boundary between family-based and structured association samples, it provides a powerful complement to currently available methods for association mapping.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Different types of samples used for association mapping.
Figure 2: Distribution of pairwise relative kinship estimates in the CEPH sample and maize sample.
Figure 3: Model comparison with human gene expression phenotypes.
Figure 4: Model comparison with maize quantitative traits.

Similar content being viewed by others

References

  1. Lander, E.S. & Schork, N.J. Genetic dissection of complex traits. Science 265, 2037–2048 (1994).

    Article  CAS  PubMed  Google Scholar 

  2. Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

    Article  CAS  PubMed  Google Scholar 

  3. Hartl, D.L. & Clark, A.G. Principles of Population Genetics (Sinauer, Sunderland, Massachusetts, 1997).

    Google Scholar 

  4. Hey, J. & Machado, C.A. The study of structured populations-new hope for a difficult and divided science. Nat. Rev. Genet. 4, 535–543 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Allison, D.B. Transmission-disequilibrium tests for quantitative traits. Am. J. Hum. Genet. 60, 676–690 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Fulker, D.W., Cherny, S.S., Sham, P.C. & Hewitt, J.K. Combined linkage and association analysis for quantitative traits. Am. J. Hum. Genet. 64, 259–267 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Abecasis, G.R., Cardon, L.R. & Cookson, W.O.C. A general test for association for quantitative traits in nuclear families. Am. J. Hum. Genet. 66, 279–292 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Cardon, L.R. A sib-pair regression model of linkage disequilibrium for quantitative traits. Hum. Hered. 50, 350–358 (2000).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Marchini, J., Cardon, L.R., Phillips, M.S. & Donnelly, P. The effects of human population structure on large genetic association studies. Nat. Genet. 36, 512–517 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Pritchard, J.K. & Donnelly, P. Case-control studies of association in structured or admixed populations. Theor. Popul. Biol. 60, 227–237 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Thornsberry, J.M. et al. Dwarf8 polymorphisms associate with variation in flowering time. Nat. Genet. 28, 286–289 (2001).

    Article  CAS  PubMed  Google Scholar 

  14. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Flint-Garcia, S.A. et al. Maize association population: a high resolution platform for QTL dissection. Plant J. 44, 1054–1064 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Henderson, C.R. Application of Linear Models in Animal Breeding (Univ. of Guelph, Ontario, 1984).

    Google Scholar 

  17. Kennedy, B.W., Quinton, M. & van Arendonk, J.A.M. Estimation of effects of single genes on quantitative trait. J. Anim. Sci. 70, 2000–2012 (1992).

    Article  CAS  PubMed  Google Scholar 

  18. George, A.W., Visscher, P.M. & Haley, C.S. Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach. Genetics 156, 2081–2092 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Loiselle, B.A. et al. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).

    Article  Google Scholar 

  20. Ritland, K. Estimators for pairwise relatedness and individual inbreeding coefficients. Genet. Res. 67, 175–186 (1996).

    Article  Google Scholar 

  21. Hirschhorn, J.N. & Daly, M.J. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005).

    Article  CAS  PubMed  Google Scholar 

  22. Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wright, S.I. et al. The effects of artificial selection on the maize genome. Science 308, 1310–1314 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Gardiner, J. et al. Anchoring 9,371 maize expressed sequence tagged unigenes to the bacterial artificial chromosome contig map by two-dimensional overgo hybridization. Plant Physiol. 134, 1317–1326 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jurinke, C., van den Boom, D., Cantor, C.R. & Koster, H. The use of MassARRAY technology for high throughput genotyping. Adv. Biochem. Eng. Biotechnol. 77, 57–74 (2002).

    CAS  PubMed  Google Scholar 

  26. Long, A.D. & Langley, C.H. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Res. 9, 720–731 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Abecasis, G.R. et al. Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Zhang, W. et al. Impact of population structure, effective bottleneck time, and allele frequency on linkage disequilibrium maps. Proc. Natl. Acad. Sci. USA 101, 18075–18080 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hardy, O.J. & Vekemans, X. SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes 2, 618–620 (2002).

    Article  Google Scholar 

  30. SAS Institute. SAS/STAT User's Guide Version 8 (SAS Institute, Cary, North Carolina, 1999).

Download references

Acknowledgements

We thank past and present members of the Buckler lab for their assistance in phenotypic data collection. We thank P.J. Bradbury, Z. Zhang, R.L. Quass and E.J. Pollak for their insights and discussion regarding the mixed model. We thank N. Stevens for technical editing of the manuscript. This work was supported by the US National Science Foundation and the USDA-ARS. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture.

Author information

Author notes

  1. Jianming Yu and Gael Pressoir: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Genomic Diversity, Cornell University, Ithaca, 14853, New York, USA

    Jianming Yu, Gael Pressoir, Irie Vroh Bi, Stephen Kresovich & Edward S Buckler

  2. Department of Genetics, University of Wisconsin, Madison, 53706, Wisconsin, USA

    William H Briggs & John F Doebley

  3. Division of Plant Sciences, University of Missouri, Columbia, 65211, Missouri, USA

    Masanori Yamasaki & Michael D McMullen

  4. United States Department of Agriculture-Agricultural Research Service (USDA-ARS),

    Michael D McMullen, James B Holland & Edward S Buckler

  5. Department of Ecology and Evolutionary Biology, University of California, Irvine, 92697, California, USA

    Brandon S Gaut

  6. Department of Statistics, North Carolina State University, Raleigh, 27695, North Carolina, USA

    Dahlia M Nielsen

  7. Department of Crop Science, North Carolina State University, Raleigh, 27695, North Carolina, USA

    James B Holland

  8. Department of Plant Breeding and Genetics, Cornell University, Ithaca, 14853, New York, USA

    Stephen Kresovich & Edward S Buckler

Authors
  1. Jianming Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gael Pressoir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William H Briggs
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Irie Vroh Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Masanori Yamasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John F Doebley
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael D McMullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Brandon S Gaut
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dahlia M Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. James B Holland
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Stephen Kresovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Edward S Buckler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Edward S Buckler.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Model comparison with three additional human gene expression phenotypes. (PDF 85 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yu, J., Pressoir, G., Briggs, W. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38, 203–208 (2006). https://doi.org/10.1038/ng1702

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng1702

This article is cited by

Search

Advanced 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