结构化探索性数据分析的三种统计方法评估。
An evaluation of three statistics of structured exploratory data analysis.
作者信息
Kammerer C M, MacCluer J W, Bridges J M
出版信息
Am J Hum Genet. 1984 Jan;36(1):187-96.
The power of structured exploratory data analysis (SEDA) to discriminate among major genic, polygenic, and nongenetic determination of phenotypes was investigated using computer simulation. Three classes of SEDA indices (the major gene index, the offspring between parents function, and the midparent-child correlation coefficient) were evaluated. These three statistics, in combination, were reasonably sensitive in detecting the presence of a major locus and in discriminating between phenotypes with genetic effects and those with no genetic component. However, they were unable to discriminate between major genic and polygenically determined phenotypic models.
利用计算机模拟研究了结构化探索性数据分析（SEDA）区分主要基因、多基因和非基因决定表型的能力。评估了三类SEDA指数（主基因指数、亲子间子代函数和中亲-子代相关系数）。这三个统计量结合起来，在检测主要基因座的存在以及区分具有遗传效应的表型和没有遗传成分的表型方面具有相当的敏感性。然而，它们无法区分主要基因和多基因决定的表型模型。
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