Mediation analysis is widely used to understand mediating mechanisms of variables in causal inference. However, existing approaches do not consider heterogeneity in mediation effects. Mediators in different sub-populations could have opposite effects on the outcome, and could be difficult to identify under the homogenous model framework. In this talk, we propose a new mediator selection method, which can identify sub-populations and select mediators in each sub-population for heterogenous data simultaneously. We perform a multi-directional clustering analysis to determine sub-group mediators and the corresponding subjects. Specifically, to select mediators, we propose a new joint penalty which penalizes the effect of independent variable on a mediator and the effect of a mediator on the response jointly. The proposed algorithm is implemented through the convex-smooth gradient descent. Our Numerical studies show that the proposed method outperforms the existing methods for heterogeneous data. We also apply the proposed method to a PTSD genetic African Americans data. This is joint work with Fei Xue and Xiwei Tang.