Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Study on the Comparison between E-MDR and D-MDR in Continuous Data

연속형 데이터에서 E-MDR과 D-MDR방법 비교

  • Published : 2009.07.31
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Abstract

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.

통계모형의 상호작용 효과를 분석하기위해 비모수적인 방법인 다중인자 차원 축소(MDR)방법을 사용해 왔다. MDR 방법은 사례-대조 데이터에만 적용 할 수 있다. 본 논문에서는 Regression tree 알고리즘과 더미 변수를 활용한 회귀분석 알고리즘을 사용하여 다중 범주를 High 범주와 Low범주로 분류함으로써, MDR방법에서 연속형 데이터에 적용 할 수 없는 문제를 해결하는 방법으로 제시된 Expanded MDR방법과 Dummy MDR방법을 한우의 주요 경제형질(longissimus muscle dorsi area: LMA, carcass cold weight: CWT, average daily gain: ADG)데이터에 적용하여 한우의 경제형질에 영향을 주는 주요 SNPs 마커를 규명하고, Permutation test를 통해 그 결과를 비교한다.

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