Bayesian indicator variable selection of multivariate response with heterogeneous sparsity for multi-trait fine mapping

As more data being collected nowadays, it is common to analyze multiple related responses from the same study. Existing variable selection methods select variables for all responses without considering that some features may only predict a subset of responses but not the rest. Motivated by the multi-trait fine mapping problem in genetics, we develop a novel Bayesian indicator variable selection method with a large number of grouped predictors targeting at multiple correlated and possibly heterogeneous responses. We showed the advantage of our method via extensive simulations and a fine mapping example to identify causal variants associated with multiple addictive behaviors.