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A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype.

Lee S, Kim Y, Kwon MS, Park T - Biomed Res Int (2015)

Bottom Line: Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases.One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions.In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Republic of Korea.

ABSTRACT
Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.

No MeSH data available.


Related in: MedlinePlus

Comparison of the power of Cox-MDR, qCox-MDR, AFT-MDR, and qAFT-MDR for a Cox model when γ = 0.0. *MAF: minor allele frequency; h2: heritability; Cp: censoring proportion.
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Related In: Results  -  Collection


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fig3: Comparison of the power of Cox-MDR, qCox-MDR, AFT-MDR, and qAFT-MDR for a Cox model when γ = 0.0. *MAF: minor allele frequency; h2: heritability; Cp: censoring proportion.

Mentions: Figures 3 and 4 show the power of Cox-MDR, qCox-MDR, AFT-MDR, and qAFT-MDR for a Cox model and the log-normal distribution, respectively, when the effect size of the adjusted covariate is γ = 0.0. The power of these four methods performs similarly when the covariate effect is γ = 1.0. In addition, the power of these four methods for the log-normal distribution is almost the same as that for Weibull distribution though not shown here.


A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype.

Lee S, Kim Y, Kwon MS, Park T - Biomed Res Int (2015)

Comparison of the power of Cox-MDR, qCox-MDR, AFT-MDR, and qAFT-MDR for a Cox model when γ = 0.0. *MAF: minor allele frequency; h2: heritability; Cp: censoring proportion.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4538337&req=5

fig3: Comparison of the power of Cox-MDR, qCox-MDR, AFT-MDR, and qAFT-MDR for a Cox model when γ = 0.0. *MAF: minor allele frequency; h2: heritability; Cp: censoring proportion.
Mentions: Figures 3 and 4 show the power of Cox-MDR, qCox-MDR, AFT-MDR, and qAFT-MDR for a Cox model and the log-normal distribution, respectively, when the effect size of the adjusted covariate is γ = 0.0. The power of these four methods performs similarly when the covariate effect is γ = 1.0. In addition, the power of these four methods for the log-normal distribution is almost the same as that for Weibull distribution though not shown here.

Bottom Line: Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases.One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions.In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Republic of Korea.

ABSTRACT
Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.

No MeSH data available.


Related in: MedlinePlus