Skip to main content

Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Test statistics that have been discussed in previous chapters can be used in the analysis of genome-wide association studies (GWAS). However, in addition to association analysis, GWAS contain other aspects. We give a brief introduction to GWAS in this chapter, including some aspects of quality control, genome-wide ranking, testing gene-environment and gene-gene interactions in GWAS, and replication studies. A short introduction to GWAS is first given. Some details of quality control, including testing HWE, are discussed next. For the analysis of GWAS, we consider genome-wide ranking with the trend test, Pearson’s test and two robust tests. Strategies for testing gene-environment and gene-gene interactions in GWAS are discussed. Finally, we review replication studies to confirm significant findings in GWAS.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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. Bourgain, C., Abney, M., Schneider, D., Ober, C., McPeek, M.S.: Testing for Hardy-Weinberg equilibrium in samples with related individuals. Am. J. Hum. Genet. 168, 2349–2361 (2004)

    Google Scholar 

  2. Douglas, J.A., Boehnke, M., Lange, K.: A multipoint method for detecting genotyping errors and mutation in sibling-pair linkage data. Am. J. Hum. Genet. 66, 1287–1297 (2000)

    Article  Google Scholar 

  3. Ghosh, A., Zou, F., Wright, F.A.: Estimating odds ratios in genome scans: An approximate conditional likelihood approach. Am. J. Hum. Genet. 82, 1064–1074 (2008)

    Article  Google Scholar 

  4. Gordon, D., Finch, S.J., Nothnagel, M., Ott, J.: Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms. Hum. Hered. 54, 22–33 (2002)

    Article  Google Scholar 

  5. Jeffries, N.O.: Ranking bias in association studies. Hum. Hered. 67, 267–275 (2009)

    Article  Google Scholar 

  6. Kooperberg, C., LeBlanc, M.: Increasing the power of identifying gene × gene interactions in genome-wide association studies. Genet. Epidemiol. 32, 255–263 (2008)

    Article  Google Scholar 

  7. Kooperberg, C., LeBlanc, M., Dai, J.Y., Rajapakse, I.: Structures and assumptions: Strategies to harness gene × gene and gene × environment interactions in GWAS. Stat. Sci. 24, 472–488 (2009)

    Article  MathSciNet  Google Scholar 

  8. Li, Y., Graubard, B.I.: Testing Hardy-Weinberg equilibrium and homogeneity of Hardy-Weinberg disequilibrium using complex survey data. Biometrics 65, 1096–1104 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  9. Li, Y., Willer, C., Sanna, S., Abecasis, G.: Genotype imputation. Ann. Rev. Genomics Hum. Genet. 10, 387–406 (2009)

    Article  Google Scholar 

  10. Marchini, J., Donnelly, P., Cardon, L.R.: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat. Genet. 37, 413–417 (2005)

    Article  Google Scholar 

  11. Murcray, C.E., Lewinger, J.P., Gauderman, W.J.: Gene-environment interaction in genome-wide association studies. Am. J. Epidemiol. 169, 219–226 (2009)

    Article  Google Scholar 

  12. Nature Genetics: Editorial: freely associating. Nat. Genet. 22, 1–2 (1999)

    Article  Google Scholar 

  13. NCI-NHGRI: Replicating genotype-phenotype associations. Nature 447, 655–660 (2007)

    Article  Google Scholar 

  14. Skol, A.D., Scott, L.J., Abecasis, G.R., Boehnke, M.: Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38, 209–213 (2006)

    Article  Google Scholar 

  15. Sun, L., Bull, S.B.: Reduction of selection bias in genomewide studies by resampling. Genet. Epidemiol. 28, 352–367 (2005)

    Article  Google Scholar 

  16. Thomas, D.C.: Gene-environment-wide association studies: emerging approaches. Nat. Rev. Genet. 11, 259–272 (2010)

    Article  Google Scholar 

  17. Wang, K., Li, M., Hadley, D., Liu, R., Glessner, J., Grant, S.F.A., Hakonarson, H., Bucan, M.: PennCNV: An integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007)

    Article  Google Scholar 

  18. The Wellcome Trust Case Control Consortium (WTCCC): Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–683 (2007)

    Article  Google Scholar 

  19. Xiao, R., Boehnke, M.: Quantifying and correcting for the winner’s curse in genetic association studies. Genet. Epidemiol. 33, 453–462 (2009)

    Article  Google Scholar 

  20. Zaykin, D.V., Zhivotovsky, L.A.: Ranks of genuine association in while-genome scans. Genetics 171, 813–823 (2005)

    Article  Google Scholar 

  21. Zheng, G., Marchini, J., Geller, N.L.: Introduction of the special issue: Genome-wide association studies. Stat. Sci. 24, 387 (2009)

    Article  MathSciNet  Google Scholar 

  22. Ziegler, A., König, I., Thompson, J.R.: Biostatistics aspects of genome-wide association studies. Biom. J. 50, 8–28 (2008)

    Article  MathSciNet  Google Scholar 

  23. Zöllner, S., Pritchard, J.K.: Overcoming the winner’s curse: Estimating penetrance parameters from case-control data. Am. J. Hum. Genet. 80, 605–615 (2007)

    Article  Google Scholar 

  24. Zöllner, S., Teslovich, T.M.: Using GWAS data to identify copy number variants contributing to common complex diseases. Stat. Sci. 24, 530–546 (2009)

    Article  Google Scholar 

  25. Zou, G.Y., Donner, A.: The merits of testing Hardy-Weinberg equilibrium in the analysis of unmatched case-control data: a cautionary note. Ann. Hum. Genet. 70, 923–933 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gang Zheng .

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Zheng, G., Yang, Y., Zhu, X., Elston, R.C. (2012). Genome-Wide Association Studies. In: Analysis of Genetic Association Studies. Statistics for Biology and Health. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2245-7_12

Download citation

Publish with us

Policies and ethics