The classical twin model can be reparametrized as an equivalent multilevel model. The multilevel parameterization has underexplored advantages, such as the possibility to include higher-level clustering variables in which lower levels are nested. When this higher-level clustering is not modeled, its variance is captured by the common environmental variance component. In this paper we illustrate the application of a 3-level multilevel model to twin data by analyzing the regional clustering of 7-year-old children’s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children’s height is attributable to regional clustering, which is 7% of the variance explained by between-family or common environmental components. Since regional clustering may represent ancestry, we also investigate the effect of region after correcting for genetic principal components, in a subsample of participants with genome-wide SNP data. After correction, region no longer explained variation in height. Our results suggest that the phenotypic variance explained by region might represent ancestry effects on height.

经典孪生模型可以重新参数化为等效的多级模型。多级参数化具有未充分探索的优势，例如可以包含嵌套较低级别的较高级别聚类变量。当这个更高层次的聚类没有被建模时，它的方差被公共环境方差分量捕获。在本文中，我们通过分析荷兰 7 岁儿童身高的区域聚类来说明 3 级多级模型在双胞胎数据中的应用。我们的研究结果表明，儿童身高表型变异的 1.8% 可归因于区域聚类，这是由家庭间或共同环境成分解释的变异的 7%。由于区域聚类可能代表血统，我们还在具有全基因组 SNP 数据的参与者子样本中研究了在校正遗传主成分后区域的影响。修正后，区域不再解释高度的变化。我们的结果表明，由区域解释的表型变异可能代表祖先对身高的影响。