Systematic removal of outliers to reduce heterogeneity in case-control association studies.
Systematic removal of outliers to reduce heterogeneity in case-control association studies.
复制标题
系统地去除异常值以减少病例对照关联研究中的异质性
作者：
Shen Y;Liu Z;Ott J
在人类病例对照关联研究中，经常存在群体异质性，并可能导致假阳性结果增加。已经提出了各种方法，并且目前正在使用这些方法来补救这种情况。我们假设异质性是由于相对较少的个体，其等位基因频率与样本的其余部分不同。对于这种情况，我们提出了一种新的方法来处理异质性，通过删除离群值在一个可控的方式。在多维标度（MDS）的c个最大主成分的坐标系中，系统地去除一个又一个最极端的离群个体，每次重新计算最大关联检验统计量。在M次清除中获得的最小p值用作我们的检验统计量，其显著性水平在随机化样本中进行评估。在功率模拟我们的方法和目前使用的三种方法，平均在几个不同的情况下，最好的方法原来是逻辑回归分析（基于所有个人）与MDS组件作为协变量。我们提出的方法排名紧随其后的逻辑回归分析与MDS组件，但领先于其他常用的方法。在对真实的数据集的分析中，我们的方法表现最好。
In human case-control association studies, population heterogeneity is often present and can lead to increased false-positive results. Various methods have been proposed and are in current use to remedy this situation. We assume that heterogeneity is due to a relatively small number of individuals whose allele frequencies differ from those of the remainder of the sample. For this situation, we propose a new method of handling heterogeneity by removing outliers in a controlled manner. In a coordinate system of the c largest principal components in multidimensional scaling (MDS), we systematically remove one after another of the most extreme outlying individuals and each time recompute the largest association test statistic. The smallest p value obtained within M removals serves as our test statistic whose significance level is assessed in randomization samples. In power simulations of our method and three methods in current use, averaged over several different scenarios, the best method turned out to be logistic regression analysis (based on all individuals) with MDS components as covariates. Our proposed method ranked closely behind logistic regression analysis with MDS components but ahead of other commonly used approaches. In analyses of real datasets our method performed best.
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