Accounting for Non-genetic Factors Improves the Power of eQTL Studies

  • Oliver Stegle
  • Anitha Kannan
  • Richard Durbin
  • John Winn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)


The recent availability of large scale data sets profiling single nucleotide polymorphisms (SNPs) and gene expression across different human populations, has directed much attention towards discovering patterns of genetic variation and their association with gene regulation. The influence of environmental, developmental and other factors on gene expression can obscure such associations. We present a model that explicitly accounts for non-genetic factors so as to improve significantly the power of an expression Quantitative Trait Loci (eQTL) study. Our method also exploits the inherent block structure of haplotype data to further enhance its sensitivity. On data from the HapMap project, we find more than three times as many significant associations than a standard eQTL method.


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  1. 1.
    Kendziorski, C.M., Chen, M., Yuan, M., Lan, H., Attie, A.D.: Statistical methods for expression quantitative trait loci (eQTL) mapping. Biometrics 62(1), 19–27 (2006)CrossRefMathSciNetMATHGoogle Scholar
  2. 2.
    Brem, R.B., Kruglyak, L.: The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl. Acad. Sci. 102(5), 1572–1577 (2005)CrossRefGoogle Scholar
  3. 3.
    Huang, J., Kannan, A., Winn, J.: Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations. Bioinformatics 23(13), i212–i221 (2007)CrossRefGoogle Scholar
  4. 4.
    The International HapMap Consortium: A haplotype map of the human genome. Nature 437, 1299–1320 (2005)Google Scholar
  5. 5.
    Roweis, S.T., Ghahramani, Z.: A unifying review of linear Gaussian models. Neural Computation 11(2), 305–345 (1999)CrossRefGoogle Scholar
  6. 6.
    Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 21(3), 611–622 (1999)MathSciNetGoogle Scholar
  7. 7.
    Liebermeister, W.: Linear modes of gene expression determined by independent component analysis. Bioinformatics 18(1), 51–60 (2002)CrossRefGoogle Scholar
  8. 8.
    Iosifina, P., Lorenz, W.: Factor analysis for gene regulatory networks and transcription factor activity profiles. BMC BioinformaticsGoogle Scholar
  9. 9.
    Lan, H., Stoehr, J.P., Nadler, S.T., Schueler, K., Yandell, B., Attie, A.D.: Dimension reduction for mapping mRNA abundance as quantitative traits. Genetics 121, 1607–1614 (2003)Google Scholar
  10. 10.
    Hastie, T., Tibshirani, R., Eisen, A., Levy, R., Staudt, L., Chan, D., Brown, P.: Gene shaving as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology (2000)Google Scholar
  11. 11.
    Bishop, C.M.: Bayesian PCA. Advances in Neural Information Processing Systems 11, 382–388 (1999)Google Scholar
  12. 12.
    Stranger, B., Forrest, M., Dunning, M., Ingle, C., Beazley, C., et al.: Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 315, 848–853 (2007)CrossRefGoogle Scholar
  13. 13.
    Bishop, C.M., Winn, J., Spiegelhalter, D.: VIBES: A variational inference engine for Bayesian networks. In: Advances in Neural Information Processing Systems, vol. 15, pp. 793–800 (2002)Google Scholar
  14. 14.
    Lander, E., Botstein, D.: Mapping Mendelian Factors Underlying Quantitative Traits Using RFLP Linkage Maps. Genetics 121(1), 185–199 (1989)Google Scholar
  15. 15.
    Kamisetty, H., Kannan, A., Winn, J.: A Bayesian model for population-stratified haplotype block inference,
  16. 16.
    Kummerfeld, S., Teichmann, S.: DBD: a transcription factor prediction database. Nucleic Acids Res. 34(Database issue), D74–D81 (2006)CrossRefGoogle Scholar
  17. 17.
    Sen, S., Churchill, G.A.: A statistical framework for quantitative trait mapping. Genetics 159, 371–387 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Oliver Stegle
    • 1
  • Anitha Kannan
    • 2
  • Richard Durbin
    • 3
  • John Winn
    • 2
  1. 1.University of CambridgeUK
  2. 2.Microsoft ResearchCambridgeUK
  3. 3.Wellcome Trust Sanger Institute, HinxtonCambridgeUK

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