The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Gamma Mixed Model to Improve Sib-Pair Linkage Analysis

감마 혼합 모형을 통한 반복 측정된 형제 쌍 연관 분석 사례연구

  • Kim, Jeonghwan (Department of Statistics, Inha University) ;
  • Suh, Young Ju (Department of Biomedical Sciences, College of Medicine, Inha University) ;
  • Won, Sungho (Department of Public Health Science, Seoul National University) ;
  • Nah, Jeung Weon (Department of Biostatistics, College of Medicine, Korea University) ;
  • Lee, Woojoo (Department of Statistics, Inha University)
  • Received : 2015.03.16
  • Accepted : 2015.03.31
  • Published : 2015.04.30
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Abstract

Traditionally, sib-pair linkage analysis with repeated measures has employed linear mixed models, but it suffers from the lack of power to find genetic marker loci associated with a phenotype of interest. In this paper, we use a gamma mixed model to improve sib-pair linkage analysis and compare it with a linear mixed model in terms of power and Type I error. We illustrate that the use of gamma mixed model can achieve higher power than linear mixed model with Genetic Analysis Workshop 13 data.

전통적으로 반복 측정된 형제 쌍 연관 분석에서는 선형 혼합 모형이 사용되어 왔다. 그러나 그 모형은 관심있는 표현형과 연관된 유전자좌를 찾는 것에 있어서 검정력이 문제가 되는 것으로 지적되어 왔다. 본 연구에서 우리는 이러한 검정력 문제를 개선하는 방법으로 감마 혼합 모형을 고려하였고, 검정력과 제 1종 오류의 관점에서 선형 혼합 모형과 성능을 서로 비교하여 보았다. Genetic Analysis Workshop 13에서 제공된 자료를 이용하여 살펴본 결과, 감마 혼합 모형이 검정력에 있어서 큰 이득을 볼 수 있는 것으로 나타났다.

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References

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