• Title/Summary/Keyword: Expanded-MDR(E-MDR)

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Statistical Interaction for Major Gene Combinations (우수 유전자 조합 선별을 위한 통계적 상호작용 방법비교)

  • Lee, Jea-Young;Lee, Yong-Won;Choi, Young-Jin
    • The Korean Journal of Applied Statistics
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    • v.23 no.4
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    • pp.693-703
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    • 2010
  • Diseases of human or economical traits of cattles are occured by interaction of genes. We introduce expanded multifactor dimensionality reduction(E-MDR), dummy multifactor dimensionality reduction(D-MDR) and SNPHarvester which are developed to find interaction of genes. We will select interaction of outstanding gene combinations and select final best genotype groups.

A Study on the Comparison between E-MDR and D-MDR in Continuous Data (연속형 데이터에서 E-MDR과 D-MDR방법 비교)

  • Lee, Jea-Young;Lee, Ho-Guen
    • Communications for Statistical Applications and Methods
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    • v.16 no.4
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    • pp.579-586
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    • 2009
  • We have used multifactor dimensionality reduction(MDR) method to study interaction effect of statistical model in general. But MDR method cannot be applied in all cases. It can be applied to the only case-control data. So, two methods are suggested E-MDR and D-MDR method using regression tree algorithm and dummy variables. We applied the methods on the identify interaction effects of single nucleotide polymorphisms(SNPs) responsible for longissimus mulcle dorsi area(LMA), carcass cold weight(CWT) and average daily gain(ADG) in a Hanwoo beef cattle population. Finally, we compare the results using permutation test.

EFMDR-Fast: An Application of Empirical Fuzzy Multifactor Dimensionality Reduction for Fast Execution

  • Leem, Sangseob;Park, Taesung
    • Genomics & Informatics
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    • v.16 no.4
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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.

Study Gene Interaction Effect Based on Expanded Multifactor Dimensionality Reduction Algorithm (확장된 다중인자 차원축소 (E-MDR) 알고리즘에 기반한 유전자 상호작용 효과 규명)

  • Lee, Jea-Young;Lee, Ho-Guen;Lee, Yong-Won
    • The Korean Journal of Applied Statistics
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    • v.22 no.6
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    • pp.1239-1247
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    • 2009
  • Study the gene about economical characteristic of human disease or domestic animal is a matter of grave interest, preserve and elevation of gene of Korea cattle is key subject. Studies have been done on the gene of Korea cattle using EST based SNP map, but it is based on statistical model, therefore there are difference between real position and statistical position. These problems are solved using both EST_based SNP map and Gene on sequence by Lee et al. (2009b). We have used multifactor dimensionality reduction(MDR) method to study interaction effect of statistical model in general. But MDR method cannot be applied in all cases. It can be applied to the only case-control data. So, method is suggested E-MDR method using CART algorithm. Also we identified interaction effects of single nucleotide polymorphisms(SNPs) responsible for average daily gain(ADG) and marbling score(MS) using E-MDR method.

Power of Expanded Multifactor Dimensionality Reduction with CART Algorithm (CART 알고리즘을 활용한 확장된 다중인자 차원축소방법의 검정력 평가)

  • Lee, Jea-Young;Lee, Jong-Hyeong;Lee, Ho-Guen
    • Communications for Statistical Applications and Methods
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    • v.17 no.5
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    • pp.667-678
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    • 2010
  • It is important to detect the gene-gene interaction in GWAS(Genome-Wide Association Study). There are many studies about detecting gene-gene interaction. The one is Multifactor dimensionality reduction method. But MDR method is not applied continuous data and expanded multifactor dimensionality reduction(E-MDR) method is suggested. The goal of this study is to evaluate the power of E-MDR for identifying gene-gene interaction by simulation. Also we applied the method on the identify interaction e ects of single nucleotid polymorphisms(SNPs) responsible for economic traits in a Korean cattle population (real data).

The Criticality Analysis of Spent Fuel Pool with Consolidated Fuel in KNU 9 & 10 (조밀화 집합체로 중간저장하는 경우 원자력 발전소 9, 10호기의 사용 후 핵연료 저장조의 임계분석)

  • Jae, Moo-Sung;Park, Goon-Cherl;Chung, Chang-Hyun;Jang, Jong-Hwa
    • Nuclear Engineering and Technology
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    • v.20 no.1
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    • pp.27-34
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    • 1988
  • Since the lack of the spent fuel storage capcity has been expected for all Korean nuclear power plants in the mid-1990s, the maximum density rack (MDR) with consolidated fuels can be proposed to overcome the shortage of the storage capacity in KNU 9 & 10 which have most limited capacities. To ensure the safety when the alternatives are applied in the KNU 9 & 10, the multiplication factor are calculated with varying the rack pitch and the thickness of consolidated storage box by the AMPX-KENO IV codes. The computing system is verified by the benchmark calculation with criticality experiments for arrays of consolidated fuel modules, which was reported by B & W in 1981. Also an abnormal condition, i.e. malposition accident, is simulated. The results indicate that the KNU 9 & 10 storage pools with consolidated fuel are safe in the view of the criticality. Thus the storage capacity can be expanded from 9/3 cores into 27/3 cores even with considering equipments and cooling spaces.

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