I am working with a high-dimensional dataset (e.g., ~150 features) and possible confounders (e.g., ~10 features to control). The goal is to identify association with an outcome variable that is categorical. Some example problems where this is relevant are genomics and protoemics. You would have a dataset where each row is an individual and columns are genes. Confounding variables include age, weight, gender, blood pressure, etc. Outcome variable would be a clinical feature, like presence or absence of a disease.
As a first step to approach this problem, I identify individual associations, i.e., perform 150 regressions, and use False Discovery Rate to find significant associations.
In addition, I am interested in identifying groups of features that together are associated with the outcome variable. I do not know a straightforward method for this kind of analysis. Association rule mining comes to mind, but I wanted to know if there are standard procedures that give associations along with their statistical significance. The hypothesis here is that usually it is not an individual gene, but groups of several genes acting together that influence the outcome. There could be more than one group which are associated, and the goal would be to rank those groups by the magnitude of their associations. One possible problems is correlation among the features.
This could sound like an exploratory question, but knowing more about possible directions would also be of great help. The reason is that although I have a fairly broad knowledge of statistics and machine learning, I do not remember seeing these problems in standard textbooks or coursework. Pointers to research papers that detail such methods are welcome too. So are ways to utilize non-traditional methods like decision trees, or neural networks to identify such associations (while being less explainable).