2008
DOI: 10.1002/gepi.20304
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An ensemble learning approach jointly modeling main and interaction effects in genetic association studies

Abstract: Complex diseases are presumed to be the results of interactions of several genes and environmental factors, with each gene only having a small effect on the disease. Thus, the methods that can account for gene-gene interactions to search for a set of marker loci in different genes or across genome and to analyze these loci jointly are critical. In this article, we propose an ensemble learning approach (ELA) to detect a set of loci whose main and interaction effects jointly have a significant association with t… Show more

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Cited by 16 publications
(16 citation statements)
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“…Use of such techniques is just beginning to emerge: ridge regression [11] has been used for distinguishing between causative and non-causative variants for quantitative phenotypes, and penalized logistic and least angle regression have been used for identifying gene-gene interactions in binary traits [7,12]. A closely-related Bayesian penalized regression procedure [13] has also been suggested for genome-wide and/or fine-mapping studies.…”
Section: Discussionmentioning
confidence: 99%
“…Use of such techniques is just beginning to emerge: ridge regression [11] has been used for distinguishing between causative and non-causative variants for quantitative phenotypes, and penalized logistic and least angle regression have been used for identifying gene-gene interactions in binary traits [7,12]. A closely-related Bayesian penalized regression procedure [13] has also been suggested for genome-wide and/or fine-mapping studies.…”
Section: Discussionmentioning
confidence: 99%
“…There are also a few Bayesian studies for modeling nonlinear, non-additive or interaction covariate effects (Chen et al, 2012; Chipman, George & McCulloch, 1998; Gustafson, 2000). Finally, there is a recent and rich literature for detecting epistasis in GWAS association studies: (Li, Horstman & Chen, 2011; Ueki & Cordell, 2012; Yung et al, 2011; Zhang et al 2008; 2010a; 2010b; 2011). However, all these existing methods, except for the one by Chen et al (2012), are not designed for analyzing time-to-event outcomes, possibly with censoring.…”
Section: Introductionmentioning
confidence: 99%
“…RSF, being derived from RF, naturally inherits many of its important properties. One of them is that, being fully non-parametric, it is model-assumption free, and, as an (ensemble) tree-based modeling approach, it is suited to adaptively discover non-monotonic, nonlinear, and non-additive or high-order interaction-effects (Ishwaran et al, 2008; Zhang et al, 2008; Li, Horstman & Chen, 2011). Therefore, these approaches provide a natural alternative to build models that bypass the need to impose parametric constraints on the underlying distributions and a way to automatically deal with high-level interactions; both of which should ultimately result in more accurate predictions and more efficient epistasis detections.…”
Section: Introductionmentioning
confidence: 99%
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“…To address this issue, many tools from the statistical and machine learning literature, developed to deal with high-dimensional search spaces, have been applied to multi-marker SNP data, for example neural networks (Ritchie et al, 2003b; North et al, 2003; Tomita et al, 2004), random forests (Breiman, 2001; Lunetta et al, 2004; Bureau et al, 2005; Chen et al, 2007), and various other methods based on partitions, trees, and splines, and ensembles of base learners (e.g. Chen et al, 2003; Cook et al, 2004; Zhang et al, 2008). Some approaches to delineate higher order interactions were specifically developed for SNP data, such as the multifactor dimensionality reduction techniques (Hahn et al, 2003; Ritchie et al, 2003a; Moore, 2004; Ritchie and Motsinger, 2005; Ritchie, 2005), the restricted partition method (Culverhouse et al, 2004, 2007), and logic regression (Kooperberg et al, 2001; Ruczinski et al, 2003, 2004).…”
Section: Introductionmentioning
confidence: 99%