jalmar / openmx-models

A collection of documented twin model specifications and related structural equation models for the OpenMx software package for structural equation modelling in R

A repository of OpenMx structural equation models

This repository contains a collection of structural equation models for the OpenMx [1,2] software package in R [3]. The models provided in this repository are based on the models I use during my research and are accompanied by detailed examples on how to use the models and interpret the output. The contents of the repository is licensed under CC BY 4.0; if you make use of these models in your own research, please refer back to this GitHub repository or cite:

Teeuw, J. "A repository of OpenMx structural equation models", GitHub, release 2020-08-25, https://github.com/jalmar/openmx-models;

General instructions

You need a working version of R statistical software (available for download from r-project.org) with the OpenMx structural equation modelling sofware package (available for download from openmx.ssri.psu.edu) installed on your computer to run the OpenMx models. See the examples provided with the models for more details on how to use them in your own research.

Overview of the models

Measurement model

The measurement model can be used to obtain a reliable estimate of a construct and can reveal the "true" association between two constructs in the absence of random error in the measurements. The utility of this model was empirically evaluated for the association between functional connectivity from resting-state functional MRI and various traits in the Human Connectome Project Young Adult cohort (see Teeuw et al., in review [4]).

Upcoming releases

• The measurement model for twin studies is a special case of a common pathway model and can be used to estimate the heritability of the reliable component obtained through a measurement model. This model was used to estimate the heritability of the reliable and stable component of resting-state functional connectivity in a longitudinal adolescent twin study (see Teeuw et al., NeuroImage 2019 [5]) and in the Human Connectome Project Young Adult cohort (see Teeuw et al., in review [4]).
• The Cholesky decomposition model for twin studies can be used to estimate the genetic and environmental contributions to a trait or the genetic and environmental correlation between traits.
• The Longitudinal Cholesky decomposition model for twin studies is a special case of the Cholesky decomposition model that makes use of longitudinal repeated measures of the same trait(s), and can be used to estimate the heritability of age-related change rates of a trait or the change in heritability of the trait with age.

Release history

Release	Description
2020-08-25	Initial commit and the release of the measurement model.

Licensing

 This work is licensed under a Creative Commons Attribution 4.0 International License.

Acknowledgements

This work was supported by the Consortium on Individual Development (CID; individualdevelopment.nl) and Biobanking and BioMolecular resources Research Infrastructure in The Netherlands (BBMRI-NL 2.0; bbmri.nl).

References

1. Neale, M.C., Hunter, M.D., Pritikin, J.N., Zahery, M., Brick, T.R., Kirkpatrick, R.M., Estabrook, R., Bates, T.C., Maes, H.H., Boker, S.M. (2016). “OpenMx 2.0: Extended structural equation and statistical modeling”. Psychometrika, vol. 81(2), p. 535–549. doi:10.1007/s11336-014-9435-8.
2. OpenMx structural equation modelling package for the R statistical framework; website: https://openmx.ssri.psu.edu
3. The R Project for Statistical Computing; website: https://www.r-project.org
4. Teeuw, J., Boomsma, D.I., Hulshoff Pol, H.E., Brouwer, R.M. (in review). “Reliability modelling of resting-state functional connectivity”.
5. Teeuw, J., Brouwer, R.M., Guimarães, J.P.O.F.T., Brandner, P., Koenis, M.M.G., Swagerman, S.C., Verwoert, M., Boomsma, D.I., Hulshoff Pol, H.E. (2019). “Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls”. NeuroImage, vol. 202, 116073. doi:10.1016/j.neuroimage.2019.116073