• Title/Summary/Keyword: Dummy MDR

Search Result 5, Processing Time 0.021 seconds

Multifactor Dimensionality Reduction(MDR) Analysis by Dummy Variables (더미(dummy) 변수를 활용한 다중인자 차원 축소(MDR) 방법)

  • Lee, Jea-Young;Lee, Ho-Guen
    • The Korean Journal of Applied Statistics
    • /
    • v.22 no.2
    • /
    • pp.435-442
    • /
    • 2009
  • Multiple genes interacting is a difficult due to the limitations of parametric statistical method like as logistic regression for detection of gene effects that are dependent solely on interactions with other genes and with environmental exposures. Multifactor dimensionality reduction(MDR) statistical method by dummy variables was applied to 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.

A Comparison Study on SVM MDR and D-MDR for Detecting Gene-Gene Interaction in Continuous Data (연속형자료의 유전자 상호작용 규명을 위한 SVM MDR과 D-MDR의 방법 비교)

  • Lee, Jong-Hyeong;Lee, Jea-Young
    • Communications for Statistical Applications and Methods
    • /
    • v.18 no.4
    • /
    • pp.413-422
    • /
    • 2011
  • We have used a multifactor dimensionality reduction(MDR) method to study the major gene interaction effect in general; however, without application of the MDR method in continuous data. In light of this, many methods have been suggested such as Expanded MDR, Dummy MDR and SVM MDR. In this paper, we compare the two methods of SVM MDR and D-MDR. In addition, we identify the gene-gene interaction effect of single nucleotide polymorphisms(SNPs) associated with economic traits in Hanwoo(Korean cattle). Lastly, we discuss a new method in consideration of the advantages that the other methods present.

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
    • /
    • v.16 no.4
    • /
    • pp.579-586
    • /
    • 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.

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
    • /
    • v.23 no.4
    • /
    • pp.693-703
    • /
    • 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.

Power and major gene-gene identification of dummy multifactor dimensionality reduction algorithm (더미 다중인자 차원축소법에 의한 검증력과 주요 유전자 규명)

  • Yeo, Jungsou;La, Boomi;Lee, Ho-Guen;Lee, Seong-Won;Lee, Jea-Young
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.2
    • /
    • pp.277-287
    • /
    • 2013
  • It is important to detect the gene-gene interaction in GWAS (genome-wide association study). There have been many studies on detecting gene-gene interaction. The one is D-MDR (dummy multifoactor dimensionality reduction) method. The goal of this study is to evaluate the power of D-MDR for identifying gene-gene interaction by simulation. Also we applied the method on the identify interaction effects of single nucleotide polymorphisms (SNPs) responsible for economic traits in a Korean cattle population (real data).