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

Ahmed Ismaïl, Hartikainen Anna-Liisa, Järvelin Marjo-Riitta and Richardson Sylvia

Statistical Applications in Genetics and Molecular Biology, 2011, vol. 10, issue 1, 1-20

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.

Keywords: Stability Selection; GWAS; false discovery rate (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1663 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:55

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1663

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().