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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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* [Cholesky decomposition](/beate.stpourcain/grmsem/-/wikis/Cholesky%20model)
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* [Independent pathway model](/beate.stpourcain/grmsem/-/wikis/Independent%20pathway%20model)
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* [Combined independent pathway / Cholesky model](/beate.stpourcain/grmsem/-/wikis/IPC%20model)
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* [Common pathway model](/beate.stpourcain/grmsem/-/wikis/Common%20pathway%20model)
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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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