Computational analysis of gene-gene interactions using multifactor dimensionality reduction

  title={Computational analysis of gene-gene interactions using multifactor dimensionality reduction},
  author={Jason H. Moore},
  journal={Expert Review of Molecular Diagnostics},
  pages={795 - 803}
  • J. Moore
  • Published 1 November 2004
  • Biology
  • Expert Review of Molecular Diagnostics
Understanding the relationship between DNA sequence variations and biologic traits is expected to improve the diagnosis, prevention and treatment of common human diseases. Success in characterizing genetic architecture will depend on our ability to address nonlinearities in the genotype-to-phenotype mapping relationship as a result of gene–gene interactions, or epistasis. This review addresses the challenges associated with the detection and characterization of epistasis. A novel strategy known… 

Epistasis, complexity, and multifactor dimensionality reduction.

Computational approaches to genetic analysis that embrace, rather than ignore, the complexity of human health are reviewed, focusing on multifactor dimensionality reduction (MDR) as an approach for modeling one of these complexities: epistasis or gene-gene interaction.

Multifactor dimensionality reduction: An analysis strategy for modelling and detecting gene - gene interactions in human genetics and pharmacogenomics studies

Multifactor dimensionality reduction (MDR) is a novel and powerful statistical tool for detecting and modelling epistasis and has detected interactions in diseases such as sporadic breast cancer, multiple sclerosis and essential hypertension.

Detecting gene–gene interactions that underlie human diseases

A critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease is provided.

Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies.

MDR is a nonparametric and model-free approach that has been shown to have reasonable power to detect epistasis in both theoretical and empirical studies and its application in pharmacogenomic studies is demonstrated.

Novel methods for detecting epistasis in pharmacogenomics studies.

The overall goal of this paper is to aid researchers in developing an analysis plan that accounts for gene-gene and gene-environment in their own work.

A Robust Multifactor Dimensionality Reduction Method for Detecting Gene–Gene Interactions with Application to the Genetic Analysis of Bladder Cancer Susceptibility

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.

Analysis of Gene‐Gene Interactions

This unit begins with an historical overview of the concept of epistasis and the challenges inherent in the identification of potential gene‐gene interactions, and reviews statistical and machine learning methods for discovering epistasis in the context of genetic studies of quantitative and categorical traits.

Detection of Gene × Gene Interactions in Genome-Wide Association Studies of Human Population Data

Major issues and questions arising from genome-wide association analysis of large-scale SNP data are described and a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene × gene interactions in human data.

A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits

The proposed Quantitative MDR (QMDR) method handles continuous data by modifying MDR’s constructive induction algorithm to use a T-test to determine the best interaction model.



Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions

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.

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

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

Using simulated data, multifactor dimensionality reduction has high power to identify gene‐gene interactions in the presence of 5% genotyping error, 5% missing data, phenocopy, or a combination of both, and MDR has reduced power for some models in the Presence of 50% Phenocopy and very limited power in the absence of genetic heterogeneity.

New strategies for identifying gene-gene interactions in hypertension

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.

Machine Learning for Detecting Gene-Gene Interactions

This review discusses machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases and focuses on the following machine- learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction.

A Cellular Automata Approach to Detecting Interactions Among Single-nucleotide Polymorphisms in Complex Multifactorial Diseases

The identification and characterization of susceptibility genes for common complex multifactorial human diseases remains a statistical and computational challenge. Parametric statistical methods such

The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases

A working hypothesis is formed that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene.

Set association analysis of SNP case-control and microarray data

The set-association method combines information over SNPs by forming sums of relevant single-marker statistics and successfully addresses the "curse of dimensionality" problem - too many variables should be estimated with a comparatively small number of observations.

Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics

Results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene interactions, and compare the power of GPNN and stepwise logistic regression (SLR) for identifying gene-Gene interactions.

Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus

A two-locus interaction between the UCP2 and PPARγ genes among 23 loci in the candidate genes of Type 2 diabetes was shown using the MDR method, which showed the maximum consistency and minimum prediction error among all gene to gene interaction models evaluated.