Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.
G3 (Bethesda). 2021 Jun 17;11(6). doi: 10.1093/g3journal/jkab057.
The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.
有效样本量（ESS）是一种用于总结样本中相关程度的指标，尤其在基于线性混合模型预测全基因组关联研究（GWAS）的统计功效时非常有用。在这里，我们为数量性状的混合模型 GWAS 引入了 ESS 的解析形式，并将其与最近提出的经验估计量联系起来。使用我们的框架，我们推导出了相关和不相关样本分析以及边际遗传和基因-环境交互作用检验的 ESS 近似值。我们进行了模拟以验证我们的近似值，并提供了各种情况下统计功效的定量视角，包括由于家族相关性导致的功效损失和由于多基因信号条件导致的功效增益。我们的分析还表明，相关个体中基因-环境交互作用 GWAS 的功效强烈取决于家族结构和暴露分布。最后，我们在 UK Biobank 数据上进行了一系列混合模型 GWAS，并证实了模拟结果。我们特别发现，由于 UK Biobank 中的家族相关性导致的预期功效下降可以忽略不计。
