Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.

人口分层是遗传关联研究的一个混杂因素。在分析罕见变异时，基于主成分 (PC) 和线性混合模型 (LMM) 的校正会得出相互矛盾的结论。评估这些方法的研究通常集中在有限类型的结构和大样本量上。我们通过使用真实外显子组数据的大型模拟研究以及几个大陆内部和大陆之间的分层场景，研究了几种校正方法的特性。我们考虑了不同的样本量，包括少至 50 例的情况，以解释罕见疾病的分析。大样本表明，与全球结构相比，大陆结构的分层更难解释。在考虑 50 个案例的样本时，对于少量控制（≤ 100）的 PC 和大量控制（≥ 1000）的 LMM，观察到 I 型错误的膨胀。我们还测试了一种新颖的局部置换方法 (LocPerm)，该方法在所有情况下都保持正确的 I 型错误。所有方法的权力都是等效的，指出关键问题是正确控制 I 类错误。最后，我们发现，如果使用适当的分层校正，可以通过添加大量外部对照来增加包括少量病例的分析能力。所有方法的权力都是等效的，指出关键问题是正确控制 I 类错误。最后，我们发现，如果使用适当的分层校正，可以通过添加大量外部对照来增加包括少量病例的分析能力。所有方法的权力都是等效的，指出关键问题是正确控制 I 类错误。最后，我们发现，如果使用适当的分层校正，可以通过添加大量外部对照来增加包括少量病例的分析能力。