Detection of epistatic effects with logic regression and a classical linear regression model

Malina Magdalena (), Ickstadt Katja, Schwender Holger, Martin Posch and Bogdan Małgorzata
Additional contact information
Malina Magdalena: Section for Medical Statistics, Center of Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
Ickstadt Katja: Faculty of Statistics, Technische Universität Dortmund, Vogelpothsweg 87, 44227 Dortmund, Germany
Schwender Holger: Heinrich Heine University Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany
Bogdan Małgorzata: Department of Mathematics and Computer Science, Wrocław University of Technology, ul. Wybrzeze Wyspiańskiego 27, 50-370 Wrocław, Poland Department of Mathematics and Computer Science, Jan Dlugosz University in Czestochowa, Poland

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 1, 83-104

Abstract: To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham’s model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham’s approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham’s approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Keywords: Cockerham’s model; epistatic effects; experimental study; high order interactions; generalized linear models; logic regression (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0028 (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:1:p:83-104:n:6

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

DOI: 10.1515/sagmb-2013-0028

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 ().