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**Synopsis**
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# Synopsis
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The grmsem package is a quantitative genetics tool supporting the modelling of multivariate genetic variance structures in quantitative data. grmsem allows fitting different models through multivariate genetic-relationship-matrix (GRM) structural equation modelling (SEM) in unrelated individuals, using a maximum likelihood approach. Specifically, it combines genome-wide genotyping information, as captured by GRMs, with twin-research-based SEM techniques. `grmsem` uses a maximum likelihood approach setting fixed effect means to zero by use of **z-standardised** phenotypes.
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The user can select different pre-specified model structures, including
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... | ... | @@ -10,11 +10,13 @@ The user can select different pre-specified model structures, including |
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by setting the `model` parameter of `gsem.fit()` to `Cholesky`, `Independent`, `IPC` or `Common` respectively. `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. Note that the likelihood for ill-specified models may not necessarily reach the global maximum and the model fit should be confirmed using different starting values. Although k, the number of different phenotypes is not restricted, in principle, computational demands will typically set a limit based on k x n \~ 30,000 for Cholesky decomposition models, where n is the number of observations per trait; models using less parameters can handle larger k x n.
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**Installation instructions** can be found [here](Installation).
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# Installation instructions
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can be found [here](Installation).
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Multivariate models with `grmsem` are time-consuming, especially with large numbers of observations per trait. To illustrate the functionality of `grmsem`, we carried out several analyses using a range of different [data sets](https://gitlab.gwdg.de/beate.stpourcain/grmsem_external), as described in detail in the vignette. An example of a large data set, with a defined genetic architecture but high run-time, is shown here.
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# Examples
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Multivariate models with `grmsem` are time-consuming, especially with large numbers of observations per trait. To illustrate the functionality of `grmsem`, we carried out several analyses using a range of different [data sets](https://gitlab.gwdg.de/beate.stpourcain/grmsem_external), as described in detail in the vignette. An example of a large data set, with a defined genetic architecture but high run-time, is shown here.
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**Example: Quad-variate Cholesky decomposition model**
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**Quad-variate Cholesky decomposition model**
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* [Data simulation](Data simulation)
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* [Model fit and output](Model fit and output)
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