A simple and improved correction for population stratification in case-control studies
A simple and improved correction for population stratification in case-control studies
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DOI：
10.1086/516842
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发表时间：
2007-05-01
影响因子：
9.8
通讯作者：
Satten, Glen A.
中科院分区：
文献类型：
--
作者：
Epstein, Michael P.;Allen, Andrew S.;Satten, Glen A.
人口分层仍然是疾病标志物关联病例对照研究的一个重要问题，即使在被认为是遗传均质的人群中。坎贝尔等。 （Nature Genetics 2005; 37：868-872）通过表明分层在欧美样本中引起乳糖酶基因（LCT）（LCT）与高/短状态之间的虚假关联来说明这一点。此外，通过使用下结构信息基因座（例如，基因组控制，结构化关联和主要成分）来控制分层的现有方法无法解决这种混杂。为了解决这个问题，我们提出了一个简单的两步过程。在第一步中，我们对疾病的几率进行了建模，鉴于有关下结构信息基因座的数据（不包括测试基因座）。对于每个参与者，我们使用此模型来计算分层评分，即参与者的疾病的估计几率是使用其疾病 - odds模型中的基因座数据计算得出的。在第二步中，我们将受试者分配为按分层评分定义的地层，然后测试疾病与这些地层内的测试基因座之间的关联。即使在存在人口分层的情况下，最终的关联测试也是有效的。我们的方法在计算上是简单的，而与控制分层的现有方法相比，模型的依赖性较小。为了说明这些属性，我们将方法应用于Campbell等人的数据。并发现LCT基因座和高/短状态之间的关联。使用模拟数据，我们表明我们的方法对分层的校正比主组件或基因组控制更为适当。
Population stratification remains an important issue in case-control studies of disease-marker association, even within populations considered to be genetically homogeneous. Campbell et al. ( Nature Genetics 2005; 37: 868 - 872) illustrated this by showing that stratification induced a spurious association between the lactase gene (LCT) and tall/short status in a European American sample. Furthermore, existing approaches for controlling stratification by use of substructure-informative loci ( e. g., genomic control, structured association, and principal components) could not resolve this confounding. To address this problem, we propose a simple two-step procedure. In the first step, we model the odds of disease, given data on substructure-informative loci ( excluding the test locus). For each participant, we use this model to calculate a stratification score, which is that participant's estimated odds of disease calculated using his or her substructure- informative - loci data in the disease-odds model. In the second step, we assign subjects to strata defined by stratification score and then test for association between the disease and the test locus within these strata. The resulting association test is valid even in the presence of population stratification. Our approach is computationally simple and less model dependent than are existing approaches for controlling stratification. To illustrate these properties, we apply our approach to the data from Campbell et al. and find no association between the LCT locus and tall/short status. Using simulated data, we show that our approach yields a more appropriate correction for stratification than does principal components or genomic control.