Multilevel Twin Models
| Autor: | Conor V. Dolan, Elsje van Bergen, Michael D. Hunter, Z. Tamimy, S. T. Kevenaar, E.L. de Zeeuw, Michael C. Neale, J-J Hottenga, Dorret I. Boomsma, C.E.M. van Beijsterveldt |
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| Přispěvatelé: | LEARN! - Educational neuroscience, learning and development, Biological Psychology, APH - Mental Health, APH - Methodology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
0301 basic medicine OpenMx Genotype Statistics as Topic Twins Genetics Behavioral 030105 genetics & heredity Multilevel model Polymorphism Single Nucleotide 03 medical and health sciences Statistics Genetics Cluster Analysis Humans Region Child Cluster analysis Classical twin design Genetics (clinical) Ecology Evolution Behavior and Systematics Netherlands Original Research Mathematics Ancestry Snp data Models Genetic Height Variance (accounting) Explained variation Body Height Variable (computer science) Phenotype 030104 developmental biology Variation (linguistics) Principal component analysis Multilevel Analysis Variance components Female Genome-Wide Association Study |
| Zdroj: | Tamimy, Z, Kevenaar, S T, Hottenga, J J, Hunter, M D, de Zeeuw, E L, Neale, M C, van Beijsterveldt, C E M, Dolan, C V, van Bergen, E & Boomsma, D I 2021, ' Multilevel Twin Models : Geographical Region as a Third Level Variable ', Behavior Genetics, vol. 51, no. 3, pp. 319-330 . https://doi.org/10.1007/s10519-021-10047-x Behavior Genetics, 51(3), 319-330. Springer Behavior Genetics |
| ISSN: | 0001-8244 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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