Graphical models for directed acyclic graphs
Embargo Date
2025-01-23
OA Version
Citation
Abstract
Graphical models are a family of models commonly used to represent the conditional independence structure among the variables of interest. Directed acyclic graphs (DAGs) provide a representation of the causal relationships and can be helpful for research in Epidemiology and other public health areas. When modeling causal relationships, issues such as effect measure modification and potential unmeasured confounders need to be considered. Recent advances in biomedical research and technology have made more data available, such as multi-omics data, biomarker profiles as well as biological pathway information. Therefore, we developed three graphical models for DAGs to better leverage these versatile data while accounting for effect measure modification and potential unmeasured confounders. First, we generalized a Bayesian graphical regression by Ni et al. (2018). We used a Gaussian copula to connect a latent variable with the multiple types of observed data. The proposed method allows for multiple data types while estimating the graph structure that depends on potential effect measure modification. Simulation studies showed that this proposed method outperforms the method by Ni et al. (2018) when there are multiple data types. Second, we extended the structural factor equation model by Zhou et al. (2021) and proposed an information-aided graphical model. The proposed method can incorporate the group information via the group Lasso penalty while accounting for the potential unmeasured confounders. Simulations demonstrated that the proposed method performs better than the original method that does not incorporate group information. Third, we additionally imposed the within-group sparsity constraint on our second method, yielding both the sparsity of groups and within-group variables while incorporating the group information. The proposed method is shown to be robust against the proportion of variables without effect in a group. We illustrated our proposed methods with data from the Framingham Heart Study to explore the relationships between metabolic syndromes, important inflammation biomarkers, and individual demographic characteristics. We also explored the gene regulatory networks of genes that are related to inflammation and adipose tissue. The findings may offer helpful insights into the mechanisms of metabolic syndrome and patient-specific health management strategies.
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Attribution-NonCommercial-NoDerivatives 4.0 International