Detecting Two-Locus Gene-Gene Effects Using Monotonisation of the Penetrance Matrix
Henningsson Susanne and
Nilsson Staffan I
Additional contact information
Henningsson Susanne: Göteborg University
Nilsson Staffan I: Chalmers University of Technology
Statistical Applications in Genetics and Molecular Biology, 2008, vol. 7, issue 1, 16
Abstract:
As more genetic loci are genotyped simultaneously and as the interest in effects of combinations of loci increases, the need for more powerful analysis methods is increased. In the present paper we present a method aimed at increasing the power of likelihood ratio tests for case-control studies investigating possible two-locus effects. The method is based on the notion that the expected effect pattern of one locus, as well as the expected pattern of a penetrance matrix representing the effect of two loci, is a monotone one. By using an algorithm for making the estimated penetrance matrix monotone, the alternative hypothesis is restricted to monotone penetrance matrices only. The evaluation of the likelihood ratio tests for several underlying monotone models shows that the power is substantially increased by using a monotone alternative as compared to when an unrestricted alternative is used.
Keywords: genetics; case-control; power; two-locus; monotone (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.2202/1544-6115.1343 (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:7:y:2008:i:1:n:17
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html
DOI: 10.2202/1544-6115.1343
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 ().