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# Synopsis
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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 [@StPourcain2018], 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 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 [@StPourcain2018], because an unrelated package with the same name had been released simultaneously.
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The user can select $\Lambda_A$ and $\Lambda_E$ according to pre-specified model structures, including the models
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The user can select pre-specified model structures, including the models
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- Cholesky decomposition
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- Cholesky decomposition
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- Independent Pathway (IP)
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- Independent Pathway (IP)
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... | @@ -12,7 +12,7 @@ by setting the `model` option of `grmsem.fit()` to `Cholesky`, `IP`, `IPC` or `C |
... | @@ -12,7 +12,7 @@ by setting the `model` option of `grmsem.fit()` to `Cholesky`, `IP`, `IPC` or `C |
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- Direct Symmetric (DS)
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- Direct Symmetric (DS)
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model, estimating genetic and residual covariances directly, using the `model` option `DS`. `grmsem` fits, like GREML, all available data to the model. Each model can be adapted by the user by setting free parameters and starting values. Although $k$, the number of different phenotypes is not restricted, in principle, computational demands will typically set a limit based on $k\cdot n\approx30,000$ for Cholesky decomposition models; models using fewer parameters can handle larger $k\cdot n$.
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model, estimating genetic and residual covariances directly, using the `model` option `DS`. `grmsem` fits, like GREML, all available data to the model. Each model can be adapted by the user by setting free parameters and starting values.
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# Download and installation
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# Download and installation
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## Package
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## Package
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