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False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies

Author

Listed:
  • Ahmed Ismaïl

    (Inserm)

  • Hartikainen Anna-Liisa

    (University of Oulu)

  • Järvelin Marjo-Riitta

    (Imperial College London)

  • Richardson Sylvia

    (Imperial College London)

Abstract

Stability Selection, which combines penalized regression with subsampling, is a promising algorithm to perform variable selection in ultra high dimension. This work is motivated by its evaluation in the context of genome-wide association studies (GWAS). One critical aspect for its use lies in the choice of a decision rule that accounts for the massive number of comparisons realised. The current decision rule relies on the control of the Family Wise Error Rate (FWER) by means of an upper bound derived theoretically. Alternatively, we propose to set the detection threshold according to the more liberal false discovery rate (FDR) criterion. The procedure we propose for its estimation relies on permutations. This procedure is evaluated by simulations according to several scenarios mimicking various correlation structures of genetic data and is compared to the original FWER upper bound. The proposed procedure is shown to be less conservative, and able to pick up more true signals than the FWER upper bound. Finally, the proposed methodology is illustrated on a GWAS analysis of a lipid phenotype (high-density lipoproteins, HDL) in the Northern Finland Birth Cohort.

Suggested Citation

  • Ahmed Ismaïl & Hartikainen Anna-Liisa & Järvelin Marjo-Riitta & Richardson Sylvia, 2011. "False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-20, November.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:55
    DOI: 10.2202/1544-6115.1663
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    References listed on IDEAS

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    1. Clive J Hoggart & John C Whittaker & Maria De Iorio & David J Balding, 2008. "Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies," PLOS Genetics, Public Library of Science, vol. 4(7), pages 1-8, July.
    2. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    3. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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