Reference : A robustness study to investigate the performance of parametric and non-parametric tests...

	Document type :	Scientific congresses and symposiums : Poster
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/108440

Title :	A robustness study to investigate the performance of parametric and non-parametric tests used in Model-Based Multifactor Dimensionality Reduction Epistasis Detection.
Language :	English
Author, co-author :	Mahachie John, Jestinah [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
	Gusareva, Elena [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
	Van Lishout, François [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
	Van Steen, Kristel [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
Publication date :	19-Sep-2011
On invitation :	No
Audience :	International
Event name :	20th Annual IGES Conference
Event date :	18-20.09.2011
Event organizer :	International Genetic Epidemiology Society
Event place (city) :	Heidelberg
Event country :	Germany
Abstract :	[en] Model-Based Multifactor Dimensionality Reduction (MB-MDR) is data mining technique to identify gene-gene interactions among 1000nds of SNPs in a fast way, without making assumptions about the mode of genetic interactions. By construction, one of the implementations of MB-MDR involves testing one multi-locus genotype cell versus the remaining cells, hereby creating two imbalanced groups for trait distribution comparison. To date, for continuous traits, we have adopted a standard F-test to compare these groups. When normality assumption or homoscedasticity no longer hold, highly inflated results are to be expected. The power and type I error control of MB-MDR under these assumptions has been thoroughly investigated in Mahachie John et al [1]. The aim of this study is to assess, through simulations, the effects of ANOVA model violations on the performance of Model-Based Multifactor Dimensionality Reduction (MB-MDR). We quantify their effect on MB-MDR using default options, but at the same time introduce alternative options with increased performance. The better handling of imbalanced data using robust approaches [2] within a MB-MDR context is exemplified on real data for asthma-related phenotypes. 1. EJHG (2011), Early view 2. David Freedman, Statistical Models: Theory and Practice, Cambridge University Press (2000), ISBN 978-0521671057
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/108440
Other URL :	http://geneticepi.org/2011Abstracts

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