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연관분석을 위한 베이지안 모형 선택: 상호상관성 변수를 중심으로

Bayesian Model Selection for Linkage Analyses: Considering Collinear Predictors

  • 서영주 (숙명여자대학교 자연과학연구소)
  • Suh, Young-Ju (The Institute of Natural Sciences, Sookmyung Women's University)
  • 발행 : 2005.11.01

초록

본 저자는 앞선 연구에서 제안한 SSVS 방법을 이용하여 한 양적형질에 대한 연관분석에 있어, QTL에 가까이 있는 관련된 표지유전자들의 위치를 정하고자 한다. 본 논문에서는 QTL에 연관되어 있고 동시에 서로 연관되어 있는 몇 가지 표지유전자들을 대상으로 하는데, 이 유전자 좌위들의 i.b.d. 값들을 상호 상관이 있는 예측변수로서 고려하여, SSVS 방법으로 분석한다. 두개의 QTL에 강하게 연관되어 있는 표지유전자들 만을 동시에 고려한 분석의 결과, QTL에 가장 가까이 위치한 표지 유전자가 다른 유전자들보다 더 분명하게 양적형질과의 관련성을 보여주었다. SSVS를 이용한 상호 상관이 있는 표지 유전자들의 분석의 결과는 전통적인 다중회귀분석을 이용한 결과와 거의 일치했다. 본 모의실험을 바탕으로, 복합 양적형질에 대하여 서로 연관된 다중의 표지유전자들을 동시에 연관분석을 수행하는 데에 SSVS 방법이 상당히 유용하다고 결론 내린다.

We identify the correct chromosome and locate the corresponding markers close to the QTL in the linkage analysis of a quantitative trait by using the SSVS method. We consider several markers linked to the QTL, as well as to each oyher and thus the i.b.d. values at these loci generate collinear predictors to be evaluated when using the SSVS approach. The results on considering only closely linked markers to two QTL simultaneously showed clear evidence in favor of the closest marker to the QTL considered over other markers. The results of the analysis of collinear markers with SSVS showeed high concordance to those obtained using traditional multiple regression. We conclude based on this simulation study that the SSVS is quite useful to identify linkage with multiple linked markers simultaneously for a complex quantitative trait.

키워드

참고문헌

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