Reference : A robustness study to investigate the performance of parametric and non-parametric te...
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/108440
A robustness study to investigate the performance of parametric and non-parametric tests used in Model-Based Multifactor Dimensionality Reduction Epistasis Detection.
English
Mahachie John, Jestinah mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
Gusareva, Elena mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
Van Lishout, François mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
Van Steen, Kristel mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
19-Sep-2011
Yes
No
International
20th Annual IGES Conference
18-20.09.2011
International Genetic Epidemiology Society
Heidelberg
Germany
[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
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/108440
http://geneticepi.org/2011Abstracts

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