Multilevel Twin Models: Geographical Region as a Third Level Variable

Z. Tamimy, S. T. Kevenaar, J. J. Hottenga, M. D. Hunter, E. L. de Zeeuw, M. C. Neale, C. E.M. van Beijsterveldt, C. V. Dolan, Elsje van Bergen, D. I. Boomsma

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)319-330
Number of pages12
JournalBehavior Genetics
Volume51
Issue number3
DOIs
StatePublished - May 2021

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Genetics(clinical)

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