Gene set analysis for GWAS: assessing the use of modified Kolmogorov-Smirnov statistics

Debrabant Birgit () and Soerensen Mette
Additional contact information
Debrabant Birgit: Institute of Public Health, Department of Epidemiology, Biostatistics and Biodemography, University of Southern Denmark, J.B. Winsloews Vej 9B, 5000 Odense C, Denmark
Soerensen Mette: Institute of Public Health, Department of Epidemiology, Biostatistics and Biodemography, University of Southern Denmark, J.B. Winsloews Vej 9B, 5000 Odense C, Denmark Department of Clinical Genetics, Odense University Hospital, Sdr. Boulevard 29, 5000 Odense C, Denmark

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 5, 553-566

Abstract: We discuss the use of modified Kolmogorov-Smirnov (KS) statistics in the context of gene set analysis and review corresponding null and alternative hypotheses. Especially, we show that, when enhancing the impact of highly significant genes in the calculation of the test statistic, the corresponding test can be considered to infer the classical self-contained null hypothesis. We use simulations to estimate the power for different kinds of alternatives, and to assess the impact of the weight parameter of the modified KS statistic on the power. Finally, we show the analogy between the weight parameter and the genesis and distribution of the gene-level statistics, and illustrate the effects of differential weighting in a real-life example.

Keywords: competitive hypothesis; gene set analysis; GWAS; modified Kolmogorov-Smirnov statistics; self-contained hypothesis (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0015 (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:13:y:2014:i:5:p:14:n:3

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

DOI: 10.1515/sagmb-2013-0015

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 ().