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

doi:10.1007/s10519-021-10047-x

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

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

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 language	English
Pages (from-to)	319-330
Number of pages	12
Journal	Behavior Genetics
Volume	51
Issue number	3
Early online date	27 Feb 2021
DOIs	https://doi.org/10.1007/s10519-021-10047-x
Publication status	Published - May 2021

Bibliographical note

Funding Information:
This project is part of the Consortium on Individual Development (CID). CID is funded through the Gravitation Program of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO: 024-001-003). The Netherlands Twin Registry (NTR) is funded by ‘Netherlands Twin Registry Repository: researching the interplay between genome and environment’ (NWO: 480-15-001/674) and BBMRI-NL (NWO-184.021.007 and 184.033.111). EvB acknowledges ‘Decoding the gene-environment interplay of reading ability’ (NWO: 451-15-017). MCN, CVD and DIB acknowledge NIH grants DA-49867 and DA-018673

Publisher Copyright:
© 2021, The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

This project is part of the Consortium on Individual Development (CID). CID is funded through the Gravitation Program of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO: 024-001-003). The Netherlands Twin Registry (NTR) is funded by ‘Netherlands Twin Registry Repository: researching the interplay between genome and environment’ (NWO: 480-15-001/674) and BBMRI-NL (NWO-184.021.007 and 184.033.111). EvB acknowledges ‘Decoding the gene-environment interplay of reading ability’ (NWO: 451-15-017). MCN, CVD and DIB acknowledge NIH grants DA-49867 and DA-018673

Funders	Funder number
Netherlands Twin Register

Keywords

Ancestry
Classical twin design
Height
Multilevel model
OpenMx
Region

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title = "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 children{\textquoteright}s height in the Netherlands. Our findings show that 1.8%, of the phenotypic variance in children{\textquoteright}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.",

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

note = "Funding Information: This project is part of the Consortium on Individual Development (CID). CID is funded through the Gravitation Program of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO: 024-001-003). The Netherlands Twin Registry (NTR) is funded by {\textquoteleft}Netherlands Twin Registry Repository: researching the interplay between genome and environment{\textquoteright} (NWO: 480-15-001/674) and BBMRI-NL (NWO-184.021.007 and 184.033.111). EvB acknowledges {\textquoteleft}Decoding the gene-environment interplay of reading ability{\textquoteright} (NWO: 451-15-017). MCN, CVD and DIB acknowledge NIH grants DA-49867 and DA-018673 Publisher Copyright: {\textcopyright} 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

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doi = "10.1007/s10519-021-10047-x",

language = "English",

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AU - Tamimy, Z.

AU - Kevenaar, S. T.

AU - Hottenga, J. J.

AU - Hunter, M. D.

AU - de Zeeuw, E. L.

AU - Neale, M. C.

AU - van Beijsterveldt, C. E.M.

AU - Dolan, C. V.

AU - van Bergen, Elsje

AU - Boomsma, D. I.

N1 - Funding Information: This project is part of the Consortium on Individual Development (CID). CID is funded through the Gravitation Program of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO: 024-001-003). The Netherlands Twin Registry (NTR) is funded by ‘Netherlands Twin Registry Repository: researching the interplay between genome and environment’ (NWO: 480-15-001/674) and BBMRI-NL (NWO-184.021.007 and 184.033.111). EvB acknowledges ‘Decoding the gene-environment interplay of reading ability’ (NWO: 451-15-017). MCN, CVD and DIB acknowledge NIH grants DA-49867 and DA-018673 Publisher Copyright: © 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/5

Y1 - 2021/5

N2 - 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.

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KW - Height

KW - Multilevel model

KW - OpenMx

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JO - Behavior Genetics

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ER -

Multilevel Twin Models: Geographical Region as a Third Level Variable

Abstract

Bibliographical note

Funding

Keywords

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