Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity

By Marylyn DeRiggi Ritchie, Lance W. Hahn, Jason H. Moore. Genetic Epidemiology, 2003

568 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/abc781f634ae4c2b8abfe9edfe1d1af3c5ae40e4

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Citations in the corpus

Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix (2005, background): “Storey (2002) and Storey and Tibshirani (2003) proposed a variation of the FDR: the positive false discovery rate (pFDR), which is the conditional FDR given that at least one hypothesis is rejected.”
A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. (2006, methodology): “The MDR algorithm (Hahn et al., 2003) has reasonable power to detect epistasis (Ritchie et al., 2003a, b) but carries out knowledge discovery in a very specific manner using an exhaustive search and a single classifier to identify the optimal combination of polymorphisms for predicting a discrete…”
The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases (2003, background): “Simulation studies have indicated that MDR has greater than 80% power to detect interactions in the absence of main effects for a variety of epistasis models, even in the presence of significant genotyping error and/or missing data [Ritchie et al., 2003].”
A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction (2007, methodology): “…of one to five SNPs. Five-fold cross validation [Motsinger and Ritchie, 2006] was used to select an MDR model with maximum testing accuracy (i.e. most likely to generalize) and maximum cross-validation consistency as described previously [Ritchie et al., 2001, 2003; Hahn et al., 2003; Moore, 2004].”
Screening large-scale association study data: exploiting interactions using random forests (2004, methodology): “For models with genetic heterogeneity and interactions resulting in no main effect, similar to the models described by Ritchie et al. [ 10 ], the performance of random forests compares considerably more favorably to univariate tests (data not shown), since the univariate tests have no power when mai”
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions (2003, background): “Previous empirical studies demonstrate that MDR has good power for identifying high-order interactions in simulated data (Ritchie et al. , 2001, 2003).”