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

Marylyn D. Ritchie, Alison A. Motsinger

Research output: Contribution to journalReview article

64 Citations (Scopus)

Abstract

In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. 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. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.

Original languageEnglish (US)
Pages (from-to)823-834
Number of pages12
JournalPharmacogenomics
Volume6
Issue number8
DOIs
StatePublished - Dec 1 2005

Fingerprint

Multifactor Dimensionality Reduction
Gene-Environment Interaction
Genes
Genotype
Disease Susceptibility
Theoretical Models
Technology
Pharmacogenomic Testing
Pharmaceutical Preparations

All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Genetics
  • Pharmacology

Cite this

@article{0d672e1290d64e4a807bc8e112b9425e,
title = "Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies",
abstract = "In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. 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. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.",
author = "Ritchie, {Marylyn D.} and Motsinger, {Alison A.}",
year = "2005",
month = "12",
day = "1",
doi = "10.2217/14622416.6.8.823",
language = "English (US)",
volume = "6",
pages = "823--834",
journal = "Pharmacogenomics",
issn = "1462-2416",
publisher = "Future Medicine Ltd.",
number = "8",

}

Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies. / Ritchie, Marylyn D.; Motsinger, Alison A.

In: Pharmacogenomics, Vol. 6, No. 8, 01.12.2005, p. 823-834.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies

AU - Ritchie, Marylyn D.

AU - Motsinger, Alison A.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. 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. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.

AB - In the quest for discovering disease susceptibility genes, the reality of gene-gene and gene-environment interactions creates difficult challenges for many current statistical approaches. In an attempt to overcome limitations with current disease gene detection methods, the multifactor dimensionality reduction (MDR) approach was previously developed. In brief, MDR is a method that reduces the dimensionality of multilocus information to identify polymorphisms associated with an increased risk of disease. This approach takes multilocus genotypes and develops a model for defining disease risk by pooling high-risk genotype combinations into one group and low-risk combinations into another. Cross-validation and permutation testing are used to identify optimal models. While this approach was initially developed for studies of complex disease, it is also directly applicable to pharmacogenomic studies where the outcome variable is drug treatment response/nonresponse or toxicity/no toxicity. 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. This computational technology is described in detail in this review, and its application in pharmacogenomic studies is demonstrated.

UR - http://www.scopus.com/inward/record.url?scp=28444434756&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=28444434756&partnerID=8YFLogxK

U2 - 10.2217/14622416.6.8.823

DO - 10.2217/14622416.6.8.823

M3 - Review article

C2 - 16296945

AN - SCOPUS:28444434756

VL - 6

SP - 823

EP - 834

JO - Pharmacogenomics

JF - Pharmacogenomics

SN - 1462-2416

IS - 8

ER -