• ETRI Journal
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• 제38권1호
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• pp.206-215
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• 2016

The detection of gene-gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the "curse of dimensionality" -that is, it works well for two- or three-way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10-way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre-evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.

• 응용통계연구
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• 제21권1호
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• pp.53-63
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• 2008

일반적으로 인간의 질병과 가축의 경제적인 특성은 하나의 유전자가 아닌 여러 유전자의 상호작용으로 일어난다고 믿고 있다. 따라서 본 연구에서는 세대를 거듭할수록 대립유전자의 유전이 안정적으로 발생되어지고 개체의 기능적인 유전적 가치를 직접적으로 추정할 수 있는 single nucleotide polymorphism(SNP)을 한우의 경제적 특성인도체중(carcass cold weight)에 대하여 모수적인 방법인 ANOVA와 비모수적인 방법인 multifactor dimensionality reduction(MDR)을 이용하여 하나의 유전자의 효과와 두 개의 유전자의 상호작용 효과를 비교하였다. ANOVA에서는 하나의 유전자 SNP1이 도체중에 유의한 효과가 있었고 상호작용 효과에서는 도체중에 유의한 효과는 없었다. MDR에서는 하나의 유전자의 효과인 SNP1과 두 개의 유전자의 상호작용인 SNP1*SNP2의 효과가 컸으며 SNP1과 SNP1*SNP2를 비교했을 시에는 SNP1*SNP2의 효과가 더 크게 나타났다. 이는 개별 SNP유전자 보다 복합 SNP유전자의 상호작용이 경제적인 특성인 도체증에 더 영향을 준다는 것을 알 수 있었다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.31-36
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• 2005

An identification and characterization of susceptibility genes for common complex multifactorial diseases is a challengeable task, in which the effect of single genetic variation will be likely dependent on other genetic variations(gene-gene interaction) and environmental factors (gene-environment interaction). To address is issue, the multifactor dimensionality reduction (MDR) has been proposed and implemented by Ritchie et al. (2001), Moore et al. (2002), Hahn et al.(2003) and Ritchie et al. (2003). With MDR, multilocus genotypes effectively reduce the dimension of genotype predictors from n to one, which improves the identification of polymorphism combinations associated with disease risk. However, MDR cannot handle missing observations appropriately, in which missing observation is treated as an additional genotype category. This approach may suffer from a sparseness problem since when high-order interactions are considered, an additional missing category would make the contingency table cells more sparse. We propose a new MDR approach with minimum loss of sample sizes by considering missing data over all possible multifactor classes. We evaluate the proposed MDR by using the prediction errors and cross validation consistency.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 2004년도 제56차 추계 학술대회 연제집
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• pp.40.1-40.1
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• 2004

• Genomics & Informatics
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• 제16권4호
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• pp.37.1-37.3
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• 2018

Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP ($c^{{+}{+}}$ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.

• Journal of the Korean Data and Information Science Society
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• 제22권6호
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• pp.1257-1264
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• 2011

Delirium is a neuropsychiatric disorder accompanying symptoms of hallucination, drowsiness, and tremors. It has high occurrence rates among elders, heart disease patients, and burn patients. It is a medical emergency associated with increased morbidity and mortality rates. That s why early detection and prevention of delirium ar significantly important. And This mental illness like delirium occurred by complex interaction between risk factors. In this paper, we identify risk factors and interactions between these factors for delirium using multi-factor dimensionality reduction (MDR) method.

• BMB Reports
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• 제42권9호
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• pp.617-622
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• 2009

We investigated the genetic associations of ischemic stroke by identifying epistasis of its heterogeneous subtypes such as small vessel occlusion (SVO) and large artery atherosclerosis (LAA). Epistasis was analyzed with 24 genes in 207 controls and 271 patients (SVO = 110, LAA = 95) using multifactor dimensionality reduction and entropy decomposition. The multifactor dimensionality reduction analysis with any of 1- to 4-locus models showed no significant association with LAA (P > 0.05). The analysis of SVO, however, revealed a significant association in the best 3-locus model with P10L of TGF-$\beta{1}$, C1013T of SPP1, and R485K of F5 (testing balanced accuracy = 63.17%, P < 0.05). Subsequent entropy analysis also revealed that such heterogeneity was present and quite a large entropy was estimated among the 3 loci for SVO (5.43%), but only a relatively small entropy was estimated for LAA (1.81%). This suggests that the synergistic epistasis model might contribute specifically to the pathogenetsis of SVO, which implies a different etiopathogenesis of the ischemic stroke subtypes.

• Genomics & Informatics
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• 제14권4호
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• pp.166-172
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• 2016

Although a large number of genetic variants have been identified to be associated with common diseases through genome-wide association studies, there still exits limitations in explaining the missing heritability. One approach to solving this missing heritability problem is to investigate gene-gene interactions, rather than a single-locus approach. For gene-gene interaction analysis, the multifactor dimensionality reduction (MDR) method has been widely applied, since the constructive induction algorithm of MDR efficiently reduces high-order dimensions into one dimension by classifying multi-level genotypes into high- and low-risk groups. The MDR method has been extended to various phenotypes and has been improved to provide a significance test for gene-gene interactions. In this paper, we propose a simple method, called accelerated failure time (AFT) UM-MDR, in which the idea of a unified model-based MDR is extended to the survival phenotype by incorporating AFT-MDR into the classification step. The proposed AFT UM-MDR method is compared with AFT-MDR through simulation studies, and a short discussion is given.

• Communications for Statistical Applications and Methods
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• 제17권5호
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• pp.667-678
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• 2010

인간의 유전자 상호작용을 분석하기 위해 제시된 다중인자 차원축소방법은 연속형자료에는 적용할 수 없다. 그래서 이를 보완한 CART 알고리즘을 활용한 확장된 다중인자 차원축소방법이 제안되었다. 하지만 CART 알고리즘을 활용한 확장된 다중인자 차원축소방법의 검정력이 밝혀지지 않았다. 따라서 본 연구에서는 모의실험을 통하여 CART 알고리즘을 활용한 확장된 다중인자 차원축소방법의 우수한 검정력을 평가하고, 확인된 검정력을 바탕으로 실제 한우 데이터에 적용하여 한우의 경제형질에 영향을 주는 우수 유전자조합을 규명하였다.

• Communications for Statistical Applications and Methods
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• 제18권4호
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• pp.413-422
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• 2011

유전학에서 유전자 상호작용효과 규명을 위한 방법으로 비모수적인 방법인 Multifactor Dimensionality Reduction(MDR) 방법이 제안되어 현재까지 사용되고 있다. MDR 방법은 이분형 자료에 적합한 방법으로 연속형 자료에는 적용할 수 없는 단점이 있다. 이러한 한계를 극복하기 위해서 Dummy MDR(D-MDR) 방법 그리고 SVM을 활용한 MDR(SVM MDR) 방법 등이 제안 되었다. 본 논문에서는 연속형 자료에 적용 가능한 SVM MDR 방법과 D-MDR 방법을 비교하고, 실제 한우 데이터에 두 방법에 적용한다. 그리고 각 방법의 적용결과를 바탕으로 한우의 종합경제형질에 영향을 주는 유전자 상호작용 조합을 규명한다. 그리고 마지막으로 기존의 SVM MDR 방법과 D-MDR 방법의 장단점 비교를 통해서 추후 새로운 연구방향을 제시한다.