ORBi: Mahachie John Jestinah - Lower-Order Effects Adjustment in Quantitative Traits Model-Based Multifactor Dimensionality Reduction

Reference : Lower-Order Effects Adjustment in Quantitative Traits Model-Based Multifactor Dimensiona...


	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/108434

Title :	Lower-Order Effects Adjustment in Quantitative Traits Model-Based Multifactor Dimensionality Reduction
Language :	English
Author, co-author :	Mahachie John, Jestinah [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique >]
	Cattaert, Tom [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 >]
	Gusareva, Elena [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 :	5-Jan-2012
Journal title :	PLoS ONE
Publisher :	Public Library of Science
Peer reviewed :	Yes (verified by ORBi)
Audience :	International
e-ISSN :	1932-6203
City :	San Franscisco
Country :	CA
Abstract :	[en] Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2- way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on ‘‘top’’ findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using ‘‘on-the-fly’’ lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/108434
DOI :	10.1371/journal.pone.0029594
Other URL :	http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3252336/pdf/pone.0029594.pdf

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