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Multilevel Twin Models: Geographical Region as a Third Level Variable

By Zenab Tamimy, Sofieke T. Kevenaar, Jouke- Jan Hottenga, Michael D. Hunter, Eveline L de Zeeuw, Michael C Neale, Catharina E. M. van Beijsterveldt, Conor V. Dolan, Elsje van Bergen, Dorret I Boomsma

Posted 12 Nov 2020
bioRxiv DOI: 10.1101/2020.11.11.377820

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 did no longer explain variation in height. Our results suggest that the phenotypic variance explained by region actually represent ancestry effects on height. ### Competing Interest Statement The authors have declared no competing interest.

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