Odds ratio based multifactor-dimensionality reduction method for detecting gene–gene interactions

Chung, Yujin; Lee, Seung Yeoun; Elston, Robert C.; Park, Taesung

doi:10.1093/bioinformatics/btl557

Cited by 131 publications

(91 citation statements)

References 10 publications

(13 reference statements)

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“…It is important to not only understand these layers but also define them by incorporating data generated across a variety of '-omics' and use sophisticated genetic epidemiology approaches that are able to handle the exponential increment of the interacting multidimensionality of genetic factors (Ritchie et al, 2001;Chanda et al, 2007;Chung et al, 2007;Lee et al, 2007). It is highly likely that the effect of multiple genes interacting with each other is not often accounted for.…”

Section: Future Of 'Omics'-ology and Ptenmentioning

confidence: 99%

Genetic and phenotypic heterogeneity in the PTEN hamartoma tumour syndrome

2008

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Section: Future Of 'Omics'-ology and Ptenmentioning

confidence: 99%

Genetic and phenotypic heterogeneity in the PTEN hamartoma tumour syndrome

2008

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“…We reran MDR with these new constructed attributes and reported the best final model. To estimate the contribution of associated genotype combination, we calculated the OR of each genotype combination in final model using ORbased MDR (OR-MDR) (Chung et al 2007). Covariate analysis for the final model was performed using generalized MDR (GMDR) (Lou et al 2007).…”

Section: Multistep Approach Using Mdr Interaction Information and Dmentioning

confidence: 99%

“…Recently, a flexible framework and MDR provided visualization of the information gained through incorporating the interactions of genetic factors . Furthermore, odds ratio (OR) based MDR provide more information regarding the effect of a certain genotype combination on the disease risk (Chung et al 2007). A new generalized MDR, a framework based on the score of the generalized linear model, permits adjustment for covariates and handling both dichotomous and quantitative phenotypes (Lou et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Genetic interactions model among Eotaxin gene polymorphisms in asthma

et al. 2008

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Eotaxin family (Eotaxin 1,2 and 3) recruits and activates CCR3-bearing cells such as eosinophil, mast cells, and Th2 lymphocytes that play a major role in allergic disorders. We examined the polygenetic effects of the Eotaxin gene family in a Korean population. Genegene interactions were tested using a multistep approach with multifactor dimensionality reduction (MDR) method between asthmatics and normal controls. The overall best MDR model of the main effect single nucleotide polymorphisms (SNPs) included EOT2 + 1272A [ G and EOT3 + 77C [ T (model 1) [testing accuracy 0.597, cross-validation consistency (CVC) 10/10, P \ 0.001]. The overall best MDR model of the SNPs with no main effects included EOT2 + 304C [ A, EOT3 + 716A [ G, and EOT3 + 1579G [ A (model 2) (testing accuracy 0.616, CVC 10/10, P \ 0.001). Model 3 was obtained by including the MDR variables for models 1 and 2. This new composite model predicted asthma with better accuracy than either model 1 or model 2 (testing accuracy 0.643, CVC 10/10, P \ 0.001). The detection of statistical interaction models is one evidence of gene-gene interactions among Eotaxin genes, and this interaction is thought to influence the development of asthma. Although the models are limited to determining statistical interactions within a population, they may be useful for identifying groups at high risk of developing asthma.

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“…Ritchie et al applied Multifactor Dimensionality Reduction (MDR) to a sporadic breast cancer data set [5], whereas variations of MDR were also introduced for multi-loci associations [6]- [8]. MDR and its variations had good results when they were used on a small number of SNPs but they cannot be directly used on a large number of SNPs, due to the exhaustive search it performs for identifying n-SNP associations [4].…”

Section: Introductionmentioning

confidence: 99%

Clustering subjects in genetic studies with Self Organizing Maps

Aristodimou

Αντωνιάδης

Pattichis

2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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Abstract-Several machine learning techniques have been applied for finding multi-loci associations among Single Nucleotide Polymorphisms (SNPs) and a disease. In this paper it is investigated whether Self Organizing Maps (SOMs) can generate clusters associated with a disease based on the genetic patterns of subjects. A batch categorical SOM that can handle missing data was used on Genome Wide Association (GWA) data on Multiple Sclerosis (MS). The association of the clusters generated with the disease were initially tested using the Pearson's chi square test and then the weights of the top clusters were used for investigating for SNP patterns. The results of the analyses reveal statistically significant associations between the generated clusters and the disease, indicating that SOMs can be used for multi-loci associations.

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Odds ratio based multifactor-dimensionality reduction method for detecting gene–gene interactions

Abstract: The program written in R is available.

Cited by 131 publications

References 10 publications

Genetic and phenotypic heterogeneity in the PTEN hamartoma tumour syndrome

Genetic and phenotypic heterogeneity in the PTEN hamartoma tumour syndrome

Genetic interactions model among Eotaxin gene polymorphisms in asthma

Clustering subjects in genetic studies with Self Organizing Maps

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