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Model‐Based Multifactor Dimensionality Reduction for detecting epistasis in case–control data in the presence of noise
Author(s) -
Cattaert Tom,
Calle M. Luz,
Dudek Scott M.,
Mahachie John Jestinah M.,
Van Lishout François,
Urrea Victor,
Ritchie Marylyn D.,
Van Steen Kristel
Publication year - 2011
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2010.00604.x
Subject(s) - multifactor dimensionality reduction , phenocopy , epistasis , dimensionality reduction , type i and type ii errors , univariate , biology , confounding , statistics , genotype , statistical power , nonparametric statistics , genetics , computer science , computational biology , machine learning , gene , multivariate statistics , mathematics , single nucleotide polymorphism , phenotype
Summary Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data‐mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model‐Based Multifactor Dimensionality Reduction (MB‐MDR), a relatively new MDR‐based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB‐MDR to detect gene–gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB‐MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.