Skip to main content
SpringerLink
Log in

Allowing for Population Stratification in Association Analysis

  • Protocol
  • First Online:
Statistical Human Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 850))

Abstract

In genetic association studies, it is necessary to correct for population structure to avoid inference bias. During the past decade, prevailing corrections often only involved adjustments of global ancestry differences between sampled individuals. Nevertheless, population structure may vary across local genomic regions due to the variability of local ancestries associated with natural selection, migration, or random genetic drift. Adjusting for global ancestry alone may be inadequate when local population structure is an important confounding factor. In contrast, adjusting for local ancestry can more effectively prevent false-positives due to local population structure. To more accurately locate disease genes, we recommend adjusting for local ancestries by interrogating local structure. In practice, locus-specific ancestries are usually unknown and cannot be accurately inferred when ancestral population information is not available. For such scenarios, we propose employing local principal components (PC) to represent local ancestries and adjusting for local PCs when testing for genotype–phenotype association. With an acceptable computation burden, the proposed algorithm successfully eliminates the known spurious association between SNPs in the LCT gene and height due to the population structure in European Americans.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Knowler WC, Williams RC, Pettitt DJ, Steinberg AG (1988) Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture. Am J Hum Genet 43: 520–526

    PubMed  CAS  Google Scholar 

  2. Lander ES and Schork NJ (1994) Genetic dissection of complex traits. Science 265: 2037–2048

    Article  PubMed  CAS  Google Scholar 

  3. Devlin B and Roeder K (1999) Genomic control for association studies. Biometrics 55: 997–1004

    Article  PubMed  CAS  Google Scholar 

  4. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000) Association mapping in structured populations. Am J Hum Genet 67: 170–181

    Article  PubMed  CAS  Google Scholar 

  5. Satten GA, Flanders WD, Yang Q (2001) Accounting for unmeasured population substructure in case–control studies of genetic association using a novel latent-class model. Am J Hum Genet 68: 466–477

    Article  PubMed  CAS  Google Scholar 

  6. Zhu X, Zhang S, Zhao H, Cooper RS (2002) Association mapping, using a mixture model for complex traits. Genet Epidemiol 23: 181–196

    Article  PubMed  Google Scholar 

  7. Zhang S, Zhu X, Zhao H (2003) On a semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals. Genet Epidemiol 24: 44–56

    Article  PubMed  Google Scholar 

  8. Marchini J, Cardon LR, Phillips MS, Donnelly P (2004) The effects of human population structure on large genetic association studies. Nat Genet 36: 512–517

    Article  PubMed  CAS  Google Scholar 

  9. Campbell CD et al. (2005) Demonstrating stratification in a European American population. Nat Genet 37: 868–872

    Article  PubMed  CAS  Google Scholar 

  10. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38: 904–909

    Article  PubMed  CAS  Google Scholar 

  11. Zhu X et al. (2008a) A unified association analysis approach for family and unrelated samples correcting for stratification. Am J Hum Genet 82: 352–365

    Article  PubMed  CAS  Google Scholar 

  12. Zhu X et al. (2008b) Admixture mapping and the role of population structure for localizing disease genes. Adv Genet 60: 547–569

    Article  PubMed  Google Scholar 

  13. Qin et al. (2010) Interrogating local population structure for fine mapping in genome-wide association studies. Bioinformatics 26 (23): 2961–2968

    Google Scholar 

  14. Cavalli-Sforza LL and Bodmer WF (1999) The genetics of human populations. Dover Publications, Mineola, New York

    Google Scholar 

  15. Epstein MP et al. (2007) A simple and improved correction for population stratification in case–control studies. Am J Hum Genet 80: 921–930

    Article  PubMed  CAS  Google Scholar 

  16. Novembre J and Stephens M (2008) Interpreting principal component analyses of spatial population genetic variation. Nat Genet 40: 646–649

    Article  PubMed  CAS  Google Scholar 

  17. Tang H et al. (2007) Recent genetic selection in the ancestral admixture of Puerto Ricans. Am J Hum Genet 81(3): 626–633

    Article  PubMed  CAS  Google Scholar 

  18. Wang X et al. (2011) Adjustment for local ancestry in genetic association analysis of admixed populations. Bioinformatics 27(5): 670–677

    Google Scholar 

  19. Voight BF et al. (2006) A map of recent positive selection in the human genome. PLoS Biol 4, e72

    Article  PubMed  Google Scholar 

  20. Sabeti PC et al. (2007) Genome-wide detection and characterization of positive selection in human populations. Nature 449: 913–918

    Article  PubMed  CAS  Google Scholar 

  21. Crow JF and Kimura M (1970) An introduction to population genetics theory. Harper & Row, New York, 469–478

    Google Scholar 

  22. Patterson N et al. (2004) Methods for high-density admixture mapping of disease genes. Am J Hum Genet 74: 979–1000

    Article  PubMed  CAS  Google Scholar 

  23. Tang H et al. (2006) Reconstructing genetic ancestry blocks in admixed individuals. Am J Hum Genet 79: 1–12

    Article  PubMed  CAS  Google Scholar 

  24. Zhu X et al. (2006) A classical likelihood based approach for admixture mapping using EM algorithm. Hum Genet 120: 431–445

    Article  PubMed  Google Scholar 

  25. Sankararaman, S. et al. (2008) Estimating local ancestry in admixed populations. Am J Hum Genet 82: 290–303

    Article  PubMed  CAS  Google Scholar 

  26. Price AL et al. (2009) Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet 5, e1000519

    Article  PubMed  Google Scholar 

  27. Kang, SJ et al. (2010) Genome wide association of anthropometric traits in African and African derived populations. Human Molecular Genetics 19 (13): 2725–2738

    Article  PubMed  CAS  Google Scholar 

  28. Levy D. et al. (2009) Genome-wide association study of blood pressure and hypertension. Nat Genet 41: 677–687

    Article  PubMed  CAS  Google Scholar 

  29. Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet 2(12): 2074–2093, e190. doi:10.1371/journal.pgen.0020190

    Google Scholar 

  30. Johnstone I (2001) On the distribution of the largest eigenvalue in principal components analysis. Ann Stat 29: 295–327

    Article  Google Scholar 

  31. Zou F et al. (2010) Quantification of population structure using correlated SNPs by shrinkage principal components. Human Heredity 70: 9–22

    Article  PubMed  CAS  Google Scholar 

  32. Price AL et al. (2010) New approaches to population stratification in genome-wide association studies. Nature Reviews 11: 459–463

    PubMed  CAS  Google Scholar 

Download references

Acknowledgments

We thank the members in Dr. Zhu’s lab for their helpful comments. This work was supported by NIH grants HL074166, HL086718, and HG003054 to XZ.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Huaizhen Qin & Xiaofeng Zhu

Authors
  1. Huaizhen Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huaizhen Qin .

Editor information

Editors and Affiliations

  1. School of Medicine, Dept. Epidemiology & Biostatistics, Case Western Reserve University, Cornell Road 2103, Cleveland, 44106, Ohio, USA

    Robert C. Elston

  2. Dept. Epidemiology & Biostatistics, Memorial Sloan-Kettering Cancer Center, East 63rd Street 307, New York, 10021, New York, USA

    Jaya M. Satagopan

  3. School of Medicine, Dept. Epidemiology & Biostatistics, Case Western Reserve University, Cornell Road 2103, Cleveland, 44106, Ohio, USA

    Shuying Sun

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Qin, H., Zhu, X. (2012). Allowing for Population Stratification in Association Analysis. In: Elston, R., Satagopan, J., Sun, S. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 850. Humana Press. https://doi.org/10.1007/978-1-61779-555-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-555-8_21

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-554-1

  • Online ISBN: 978-1-61779-555-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation