Abstract
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.
Highlights
The classical twin model (CTM) is often approached from a structural equation modeling (SEM) framework (Bentler and Stein 1992; Boomsma and Molenaar 1986; Heath et al 1989; Neale and Cardon 1992; Rijsdijk and Sham 2002)
In a subsample of 7-year-old participants, we investigated the extent to which regional clustering may be due to genetic ancestry by including the first three genetic principal components (PCs; Hotelling 1933)
Adding a third level variable enabled us to determine whether part of the variance in children’s height can be explained by differences in geographical region
Summary
The classical twin model (CTM) is often approached from a structural equation modeling (SEM) framework (Bentler and Stein 1992; Boomsma and Molenaar 1986; Heath et al 1989; Neale and Cardon 1992; Rijsdijk and Sham 2002). In this framework, it is a one-level model with family as level. The classical twin design is based on data that have natural clustering, namely, twins are clustered within pairs For this reason, the MLM framework can accommodate the CTM (Guo and Wang 2002; McArdle and Prescott 2005; Rabe-Hesketh et al 2008; Van den Oord 2001). In this paper we aim to elaborate on these advantages, and to provide an empirical illustration of a multilevel twin model, where we study the clustering of children’s height in geographical
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