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A screening methodology based on Random Forests to improve the detection of gene-gene interactions

Lizzy De Lobel, Pierre Geurts, Guy Baele, Francesc Castro-Giner, Manolis Kogevinas and Kristel Van Steen UGent (2010) EUROPEAN JOURNAL OF HUMAN GENETICS. 18(10). p.1127-1132
abstract
The search for susceptibility loci in gene-gene interactions imposes a methodological and computational challenge for statisticians because of the large dimensionality inherent to the modelling of gene-gene interactions or epistasis. In an era in which genome-wide scans have become relatively common, new powerful methods are required to handle the huge amount of feasible gene-gene interactions and to weed out false positives and negatives from these results. One solution to the dimensionality problem is to reduce data by preliminary screening of markers to select the best candidates for further analysis. Ideally, this screening step is statistically independent of the testing phase. Initially developed for small numbers of markers, the Multifactor Dimensionality Reduction (MDR) method is a nonparametric, model-free data reduction technique to associate sets of markers with optimal predictive properties to disease. In this study, we examine the power of MDR in larger data sets and compare it with other approaches that are able to identify gene-gene interactions. Under various interaction models (purely and not purely epistatic), we use a Random Forest (RF)-based prescreening method, before executing MDR, to improve its performance. We find that the power of MDR increases when noisy SNPs are first removed, by creating a collection of candidate markers with RFs. We validate our technique by extensive simulation studies and by application to asthma data from the European Committee of Respiratory Health Study II.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MULTIFACTOR-DIMENSIONALITY REDUCTION, Multifactor Dimensionality Reduction, Random Forests, prescreening, gene-gene interactions, EPISTASIS, DISEASES, HETEROGENEITY, ASTHMA, SUSCEPTIBILITY GENES, CLONING, LOCI
journal title
EUROPEAN JOURNAL OF HUMAN GENETICS
Eur. J. Hum. Genet.
volume
18
issue
10
pages
1127 - 1132
Web of Science type
Article
Web of Science id
000282089600010
JCR category
GENETICS & HEREDITY
JCR impact factor
4.38 (2010)
JCR rank
30/154 (2010)
JCR quartile
1 (2010)
ISSN
1018-4813
DOI
10.1038/ejhg.2010.48
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1081023
handle
http://hdl.handle.net/1854/LU-1081023
date created
2010-11-30 15:57:49
date last changed
2017-03-02 14:24:21
@article{1081023,
  abstract     = {The search for susceptibility loci in gene-gene interactions imposes a methodological and computational challenge for statisticians because of the large dimensionality inherent to the modelling of gene-gene interactions or epistasis. In an era in which genome-wide scans have become relatively common, new powerful methods are required to handle the huge amount of feasible gene-gene interactions and to weed out false positives and negatives from these results. One solution to the dimensionality problem is to reduce data by preliminary screening of markers to select the best candidates for further analysis. Ideally, this screening step is statistically independent of the testing phase. Initially developed for small numbers of markers, the Multifactor Dimensionality Reduction (MDR) method is a nonparametric, model-free data reduction technique to associate sets of markers with optimal predictive properties to disease. In this study, we examine the power of MDR in larger data sets and compare it with other approaches that are able to identify gene-gene interactions. Under various interaction models (purely and not purely epistatic), we use a Random Forest (RF)-based prescreening method, before executing MDR, to improve its performance. We find that the power of MDR increases when noisy SNPs are first removed, by creating a collection of candidate markers with RFs. We validate our technique by extensive simulation studies and by application to asthma data from the European Committee of Respiratory Health Study II.},
  author       = {De Lobel, Lizzy and Geurts, Pierre and Baele, Guy and Castro-Giner, Francesc and Kogevinas, Manolis and Van Steen, Kristel},
  issn         = {1018-4813},
  journal      = {EUROPEAN JOURNAL OF HUMAN GENETICS},
  keyword      = {MULTIFACTOR-DIMENSIONALITY REDUCTION,Multifactor Dimensionality Reduction,Random Forests,prescreening,gene-gene interactions,EPISTASIS,DISEASES,HETEROGENEITY,ASTHMA,SUSCEPTIBILITY GENES,CLONING,LOCI},
  language     = {eng},
  number       = {10},
  pages        = {1127--1132},
  title        = {A screening methodology based on Random Forests to improve the detection of gene-gene interactions},
  url          = {http://dx.doi.org/10.1038/ejhg.2010.48},
  volume       = {18},
  year         = {2010},
}

Chicago
De Lobel, Lizzy, Pierre Geurts, Guy Baele, Francesc Castro-Giner, Manolis Kogevinas, and Kristel Van Steen. 2010. “A Screening Methodology Based on Random Forests to Improve the Detection of Gene-gene Interactions.” European Journal of Human Genetics 18 (10): 1127–1132.
APA
De Lobel, L., Geurts, P., Baele, G., Castro-Giner, F., Kogevinas, M., & Van Steen, K. (2010). A screening methodology based on Random Forests to improve the detection of gene-gene interactions. EUROPEAN JOURNAL OF HUMAN GENETICS, 18(10), 1127–1132.
Vancouver
1.
De Lobel L, Geurts P, Baele G, Castro-Giner F, Kogevinas M, Van Steen K. A screening methodology based on Random Forests to improve the detection of gene-gene interactions. EUROPEAN JOURNAL OF HUMAN GENETICS. 2010;18(10):1127–32.
MLA
De Lobel, Lizzy, Pierre Geurts, Guy Baele, et al. “A Screening Methodology Based on Random Forests to Improve the Detection of Gene-gene Interactions.” EUROPEAN JOURNAL OF HUMAN GENETICS 18.10 (2010): 1127–1132. Print.