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

@article{Ritchie2003PowerOM,
  title={Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity},
  author={Marylyn DeRiggi Ritchie and Lance W. Hahn and Jason H. Moore},
  journal={Genetic Epidemiology},
  year={2003},
  volume={24}
}
The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously… 
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543 Citations

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A Robust Multifactor Dimensionality Reduction (RMDR) method is proposed that performs constructive induction using a Fisher's Exact Test rather than a predetermined threshold and is applied to the detection of gene–gene interactions in genotype data from a population‐based study of bladder cancer in New Hampshire.
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A multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes is developed.
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References

SHOWING 1-10 OF 22 REFERENCES
Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.
One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly…
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions
TLDR
A multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes is developed.
New strategies for identifying gene-gene interactions in hypertension
TLDR
The general problem of identifying gene-gene interactions is reviewed and several traditional and several newer methods that are being used to assess complex genetic interactions in essential hypertension are described.
The effect that genotyping errors have on the robustness of common linkage-disequilibrium measures.
TLDR
It is found that genotyping error rates as small as 3% can have serious affects on these LD measures, depending on the allele frequencies and the assumed error model, and the robustness of D', r, Q, and d, in the presence ofgenotyping errors is compared.
Application Of Genetic Algorithms To The Discovery Of Complex Models For Simulation Studies In Human Genetics
TLDR
This paper presents a strategy for identifying complex genetic models for simulation studies that utilizes genetic algorithms and demonstrates that the genetic algorithm approach routinely identifies interesting and useful penetrance functions in a human-competitve manner.
Context-dependent genetic effects in hypertension
  • S. Kardia
  • Biology
    Current hypertension reports
  • 2000
TLDR
A review of the hypertension genetic literature reveals substantial evidence for the importance of both genetic and environmental contexts on the mapping between single locus polymorphisms and risk of disease and suggests the current reliance on simple single gene studies to elucidate the complex etiology of hypertension needs to undergo some kind of transformation.
Symbolic discriminant analysis of microarray data in autoimmune disease
New laboratory technologies such as DNA microarrays have made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for…
The future of genetic case-control studies.
Classification methods for confronting heterogeneity.
TLDR
In addition to the straightforward application of classification and regression trees to define more homogeneous subsets of the data on which to conduct further analysis, developments incorporating linkage analysis into the definition of the regression trees (Shannon et al., 2000) are discussed.
Tree‐based recursive partitioning methods for subdividing sibpairs into relatively more homogeneous subgroups
TLDR
Simulation studies found that partitioning sibpairs into homogeneous subgroups is feasible and significantly increases the power to detect linkage, thus demonstrating the practical utility and potential this new methodology holds.
...
...