Literature DB >> 33609197

Multilevel Modeling in Classical Twin and Modern Molecular Behavior Genetics.

Michael D Hunter1.   

Abstract

For more than a decade, it has been known that many common behavior genetics models for a single phenotype can be estimated as multilevel models (e.g., van den Oord 2001; Guo and Wang 2002; McArdle and Prescott 2005; Rabe-Hesketh et al. 2007). This paper extends the current knowledge to (1) multiple phenotypes such that the method is completely general to the variance structure hypothesized, and (2) both higher and lower levels of nesting. The multi-phenotype method also allows extended relationships to be considered (see also, Bard et al. 2012; Hadfield and Nakagawa 2010). The extended relationship model can then be continuously expanded to merge with the case typically seen in the molecular genetics analyses of unrelated individuals (e.g., Yang et al. 2011). We use the multilevel form of behavior genetics models to fit a multivariate three level model that allows for (1) child level variation from unique environments and additive genetics, (2) family level variation from additive genetics and common environments, and (3) neighborhood level variation from broader geographic contexts. Finally, we provide R (R Development Core Team 2020) functions and code for multilevel specification of several common behavior genetics models using OpenMx (Neale et al. 2016).

Entities:  

Keywords:  Behavior genetics; Methodology; Multilevel modeling; Structural equation modeling

Year:  2021        PMID: 33609197     DOI: 10.1007/s10519-021-10045-z

Source DB:  PubMed          Journal:  Behav Genet        ISSN: 0001-8244            Impact factor:   2.805


  13 in total

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Authors:  Guang Guo; Jianmin Wang
Journal:  Behav Genet       Date:  2002-01       Impact factor: 2.805

2.  Mixed-effects variance components models for biometric family analyses.

Authors:  John J McArdle; Carol A Prescott
Journal:  Behav Genet       Date:  2005-09       Impact factor: 2.805

3.  Alternative common factor models for multivariate biometric analyses.

Authors:  J J McArdle; H H Goldsmith
Journal:  Behav Genet       Date:  1990-09       Impact factor: 2.805

4.  General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters.

Authors:  J D Hadfield; S Nakagawa
Journal:  J Evol Biol       Date:  2010-01-07       Impact factor: 2.411

5.  umx: Twin and Path-Based Structural Equation Modeling in R.

Authors:  Timothy C Bates; Hermine Maes; Michael C Neale
Journal:  Twin Res Hum Genet       Date:  2019-02       Impact factor: 1.587

6.  The genetical analysis of covariance structure.

Authors:  N G Martin; L J Eaves
Journal:  Heredity (Edinb)       Date:  1977-02       Impact factor: 3.821

7.  Latent variable growth within behavior genetic models.

Authors:  J J McArdle
Journal:  Behav Genet       Date:  1986-01       Impact factor: 2.805

8.  Random-effects models for longitudinal data.

Authors:  N M Laird; J H Ware
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

9.  Applying Multivariate Discrete Distributions to Genetically Informative Count Data.

Authors:  Robert M Kirkpatrick; Michael C Neale
Journal:  Behav Genet       Date:  2015-10-24       Impact factor: 2.805

10.  Using Multimodel Inference/Model Averaging to Model Causes of Covariation Between Variables in Twins.

Authors:  Hermine H Maes; Michael C Neale; Robert M Kirkpatrick; Kenneth S Kendler
Journal:  Behav Genet       Date:  2020-11-04       Impact factor: 2.805

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  1 in total

1.  Simulated nonlinear genetic and environmental dynamics of complex traits.

Authors:  Michael D Hunter; Kevin L McKee; Eric Turkheimer
Journal:  Dev Psychopathol       Date:  2022-03-03
  1 in total

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