Multilevel Twin Models: Geographical Region as a Third Level Variable

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.

Systematic evaluation of the environmental effect on depressive symptoms in late adolescence and early adulthood: exposome-wide association study and twin modeling

“…Additionally, previous twin studies found geographic confounding in the assessment of A, C, and E variance, possibly attributable to differences in genetic ancestry. Results from the Netherlands Twin Register found 1.8% of the variance in children's height was captured by regional clustering 23 . In the Netherlands, there were strong genetic differentiations between the north and south, between the east and west, and between the middle band and the rest of the country by PCA on genome-wide data 24 .…”

Be(com)ing social: Daily-life social interactions and parental bonding.

“…Because the ESM data have a hierarchical, three-level structure, with beeps nested within participants and participants nested within families, multilevel mixed-effects models were used with random intercepts (as in e.g., Vaessen et al, 2017). Multilevel modeling is a suitable method both for analyzing ESM-level data (Bolger & Laurenceau, 2013), and for clustering individuals in twin pairs (Guo & Wang, 2002; Hunter, 2021; Tamimy et al, 2021). As such, we account for the fact that observations from the same person are more similar to each other than to observations from other people, and family members (both twin and nontwin siblings) are more similar to each other than to other participants.…”

Time to Get Social: Adaptive Social Processes in the Daily Lives of Youth (Full PhD thesis)

“…As the ESM data have a hierarchical, three-level structure, with beeps nested within participants and participants nested within families, multilevel mixed-effects models were used with random intercepts (as in e.g., (Vaessen et al, 2017). Multilevel modelling is a suitable method both for analyzing ESM-level data (Bolger & Laurenceau, 2013), and for clustering individuals in twin pairs (Guo & Wang, 2002;Hunter, 2021;Tamimy et al, 2021). As such, we account for the fact that observations from the same person are more similar to each other than to observations from other people, and family members (both twin and non-twin siblings) are more similar to each other than to other participants.…”