Population stratification may cause an inflated type‐I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however, population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two established approaches: principal components analysis (PCA) and mixed‐effects models and assess the utility of genome‐wide (GW) and exonic variants, by simulation and using a data set from the Framingham Heart Study. Our results illustrate that although the PCs and genetic relationship matrices computed by GW and exonic markers are different, the type‐I error rate of association tests for common variants with additive effect appear to be properly controlled in the presence of population stratification. In addition, by considering single nucleotide variants (SNVs) that have different levels of confounding by population stratification, we also compare the power across multiple association approaches to account for population stratification such as PC‐based corrections and mixed‐effects models. We find that while these two methods achieve a similar power for SNVs that have a low or medium level of confounding by population stratification, mixed‐effects model can reach a higher power for SNVs highly confounded by population stratification.

中文翻译：

---

使用全基因组或外显子变异评估群体分层调整。

在评估遗传变异与结果之间的关联时，群体分层可能会导致夸大的 I 型错误和虚假关联。许多遗传关联研究现在使用外显子变异，它仅捕获基因组的 1%，然而，尚未在外显子变异的背景下评估群体分层调整。我们比较了两种既定方法的性能：主成分分析（PCA）和混合效应模型，并通过模拟和使用弗雷明汉心脏研究的数据集评估全基因组（GW）和外显子变异的效用。我们的结果表明，尽管由 GW 和外显子标记计算的 PC 和遗传关系矩阵不同，但在存在群体分层的情况下，具有加性效应的常见变异的关联测试的 I 型错误率似乎得到了适当的控制。此外，通过考虑群体分层具有不同混杂程度的单核苷酸变异（SNV），我们还比较了多种关联方法解释群体分层的功效，例如基于 PC 的校正和混合效应模型。我们发现，虽然这两种方法对于人口分层具有低或中等混杂水平的 SNV 具有相似的功效，但混合效应模型对于受人口分层高度混杂的 SNV 可以达到更高的功效。