Article (Scientific journals)
Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.
Mahachie John, Jestinah; Van Lishout, François; Van Steen, Kristel
2011In European Journal of Human Genetics, 19, p. 696-703
Peer Reviewed verified by ORBi
 

Files


Full Text
article.pdf
Author preprint (568.73 kB)
Request a copy

All documents in ORBi are protected by a user license.

Send to



Details



Abstract :
[en] Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the power is lowest in the presence of phenotypic mixtures or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical power estimates reduce even further with main effects corrections, but at the same time, false-positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis.European Journal of Human Genetics advance online publication, 16 March 2011; doi:10.1038/ejhg.2011.17.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Mahachie John, Jestinah ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
Van Lishout, François ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
Van Steen, Kristel  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Bioinformatique
Language :
English
Title :
Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data.
Publication date :
2011
Journal title :
European Journal of Human Genetics
ISSN :
1018-4813
eISSN :
1476-5438
Publisher :
Nature Publishing Group, London, United Kingdom
Volume :
19
Pages :
696-703
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 12 April 2011

Statistics


Number of views
115 (21 by ULiège)
Number of downloads
2 (2 by ULiège)

Scopus citations®
 
28
Scopus citations®
without self-citations
16
OpenCitations
 
27

Bibliography


Similar publications



Contact ORBi