There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance–covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available—genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, “mxGREML”, designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature’s current limitations, and our plans for its further development.
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Here, “raw data” is meant in the OpenMx sense, i.e. “not covariance-matrix input” (which is commonly used in SEM). Although less than ideal, it is possible to run an mxGREML analysis without raw genotypic or phenotypic data. The data’s owner would need to provide the user with one or more GRMs calculated from raw genotypes, and residuals for one or more phenotypes corrected for covariates. In such a case, the residuals would be what populates y, and X would consist only of constants.
mxGREML analyses of ordinal-threshold traits is the topic of a forthcoming manuscript.
As pointed out to us by an anonymous referee, one consequence of this design assumption is that it is not straightforward to incorporate regressions among endogenous variables in an mxGREML model, since doing so would require the corresponding regression coefficients to appear in both the model-expected mean vector and covariance matrix. That is a limitation inherent to REML, and is not specific to OpenMx. The referee suggested that there might be ways to circumvent this limitation, such as mean-centering manifest endogenous variables prior to mxGREML analysis; another possibility might be to conduct the desired regressions outside of OpenMx, and analyze the resulting residuals in the mxGREML model. To date, we have not explored such workarounds. One approach to endogenous-variable regression that will certainly work is to analyze y as a dataset with 1 row and np columns, using the pre-existing mxExpectationNormal() and mxFitFunctionML(), as they allow the user to freely and explicitly specify the model-expected mean vector (e.g., Eaves et al. 2014).
See below, under “Customization: Data-handling”.
As of this writing, the GREML fitfunction requires a partial derivative for all (or none) of the model’s explicit free parameters, though that requirement will be relaxed in the future. It is true that providing a derivative of V for every free parameter can require a fair amount of input from the user—see, for example, script #13 in Table 1, which has 16 free parameters.
To give the reader a sense of scale: on a computing cluster (Intel Xeon E5-2680 v4 CPU at 2.4 GHz), we recently ran script #11 (a five-timepoint latent-growth model) from Table 1, except edited to have a sample size of 4000 and to use 8 processing threads. The job used about 55 GB of memory, and OpenMx’s running time was slightly under 20 h.
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The work reported in this paper was funded by the National Institute on Drug Abuse R25DA026119.
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Robert M. Kirkpatrick, Joshua N. Pritikin, Michael D. Hunter and Michael C. Neale declare they have no conflict of interest.
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Kirkpatrick, R.M., Pritikin, J.N., Hunter, M.D. et al. Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx. Behav Genet 51, 331–342 (2021). https://doi.org/10.1007/s10519-020-10037-5
- Structural equation modeling
- Statistical methods