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# Synopsis
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The `grmsem` package is an open-source quantitative genetics tool that supports the modelling of genetic and residual covariance structures in samples of unrelated individuals with genome-wide genotyping information. `grmsem` allows fitting different models describing the underlying multivariate genetic architecture of quantitative traits, as captured by a genetic-relationship-matrix (GRM), using structural equation modelling (SEM) techniques and a maximum likelihood approach. Analogous to twin models, the `grmsem` package includes multiple models, such as a `Cholesky decomposition` model, an `Independent Pathway` model and the `Direct Symmetric` model, but also novel models such as a combined `Independent Pathway / Cholesky` model. A general form of these models can be automatically fitted. The user can adapt each model by changing the pre-fitted parameters. All estimates can be obtained in standardised form. Follow-up analyses include estimations of genetic correlations, bivariate heritabilities and factorial co-heritabilities. `grmsem` replaces the package `gsem`, presented in [1], because an unrelated package with the same name had been released simultaneously.
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The `grmsem` package is an open-source quantitative genetics tool that supports the modelling of genetic and residual covariance structures in samples of unrelated individuals with genome-wide genotyping information. `grmsem` allows fitting different models describing the underlying multivariate genetic architecture of quantitative traits, as captured by a genetic-relationship-matrix (GRM), using structural equation modelling (SEM) techniques and a maximum likelihood approach. Analogous to twin models, the `grmsem` package includes multiple models, such as a `Cholesky decomposition` model, an `Independent Pathway` model and the `Direct Symmetric` model, but also novel models such as a hybrid `Independent Pathway / Cholesky` model. A general form of these models can be automatically fitted. The user can adapt each model by changing the pre-fitted parameters. All estimates can be obtained in standardised form. Follow-up analyses include estimations of genetic correlations, bivariate heritabilities and factorial co-heritabilities. `grmsem` replaces the package `gsem`, presented in [1], because an unrelated package with the same name had been released simultaneously.
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The user can select pre-specified model structures, including the models
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- [Cholesky decomposition](https://gitlab.gwdg.de/beate.stpourcain/grmsem/-/wikis/Cholesky-model)
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- [Independent Pathway](https://gitlab.gwdg.de/beate.stpourcain/grmsem/-/wikis/Independent-pathway-model) (IP)
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- [Combined Independent Pathway / Cholesky](https://gitlab.gwdg.de/beate.stpourcain/grmsem/-/wikis/IPC-model) (IPC)
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- [Hybrid Independent Pathway / Cholesky](https://gitlab.gwdg.de/beate.stpourcain/grmsem/-/wikis/IPC-model) (IPC)
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- [Common Pathway](https://gitlab.gwdg.de/beate.stpourcain/grmsem/-/wikis/Common-pathway-model) (CP)
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by setting the `model` option of `grmsem.fit()` to `Cholesky`, `IP`, `IPC` and `CP` respectively. Note that the Common Pathway model is for likelihood comparisons only. In addition, the Cholesky model can be re-parametrised as
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