Multilevel Twin Models: Geographical Region as a Third Level Variable.

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Bibliographic Details
Title:	Multilevel Twin Models: Geographical Region as a Third Level Variable.
Authors:	Tamimy, Z. (AUTHOR), Kevenaar, S. T. (AUTHOR), Hottenga, J. J. (AUTHOR), Hunter, M. D. (AUTHOR), de Zeeuw, E. L. (AUTHOR), Neale, M. C. (AUTHOR), van Beijsterveldt, C. E. M. (AUTHOR), Dolan, C. V. (AUTHOR), van Bergen, Elsje (AUTHOR), Boomsma, D. I. (AUTHOR)
Source:	Behavior Genetics. May2021, Vol. 51 Issue 3, p319-330. 12p.
Subjects:	Multilevel models, Genealogy, Phenotypes
Geographic Terms:	Netherlands
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. [ABSTRACT FROM AUTHOR]
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Database:	Psychology and Behavioral Sciences Collection

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	00018244
DOI:	10.1007/s10519-021-10047-x