bioRxiv · 10.1101/2020.11.11.377820
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 childrens height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in childrens 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 did no longer explain variation in height. Our results suggest that the phenotypic variance explained by region actually represent ancestry effects on height.
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Tamimy, Z., Kevenaar, S. T., Hottenga, J. J., Hunter, M. D., de Zeeuw, E. L., Neale, M. C., van Beijsterveldt, C. E. M., Dolan, C. V., van Bergen, E., Boomsma, D. I.. 2020-11-12. Multilevel Twin Models: Geographical Region as a Third Level Variable. https://doi.org/10.1101/2020.11.11.377820
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