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Multilevel Twin Models: Geographical Region as a Third Level Variable

Zenab Tamimy, View ORCID ProfileSofieke T. Kevenaar, View ORCID ProfileJouke Jan Hottenga, Michael D. Hunter, View ORCID ProfileEveline L. de Zeeuw, Michael C. Neale, View ORCID ProfileCatharina E.M. van Beijsterveldt, View ORCID ProfileConor V. Dolan, View ORCID ProfileElsje van Bergen, View ORCID ProfileDorret I. Boomsma
doi: https://doi.org/10.1101/2020.11.11.377820
Zenab Tamimy
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
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Sofieke T. Kevenaar
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
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Jouke Jan Hottenga
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
2Amsterdam Public Health (APH) and Amsterdam Reproduction and Development research institutes
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Michael D. Hunter
3School of Psychology, Georgia Institute of Technology, 654 Cherry Street NW, Atlanta, GA 30332-0170 USA
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Eveline L. de Zeeuw
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
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Michael C. Neale
4Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, 1-156, P.O. Box 980126, Richmond, VA 23298-0126 USA
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Catharina E.M. van Beijsterveldt
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
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Conor V. Dolan
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
2Amsterdam Public Health (APH) and Amsterdam Reproduction and Development research institutes
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Elsje van Bergen
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
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Dorret I. Boomsma
1Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
2Amsterdam Public Health (APH) and Amsterdam Reproduction and Development research institutes
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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 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 12, 2020.
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Multilevel Twin Models: Geographical Region as a Third Level Variable
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
bioRxiv 2020.11.11.377820; doi: https://doi.org/10.1101/2020.11.11.377820
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Multilevel Twin Models: Geographical Region as a Third Level Variable
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
bioRxiv 2020.11.11.377820; doi: https://doi.org/10.1101/2020.11.11.377820

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