Unraveling the complex biological mechanisms underlying human health and disease is a great challenge. With genomic data, many aspects can be investigated in great detail, such as interactions between different genetic variants as well as their effects on one or multiple traits. Modeling epistasis and pleiotropy jointly necessitates appropriate statistical methods. A suitable tool for this is C-JAMP, which is a recently proposed method based on copula functions. In this chapter, we outline C-JAMP and how it can be applied to investigate epistatic effects on multiple traits to advance our understanding of biological processes. We further discuss important aspects of this area of research, such as polygenic risk scores and ancestry-specific modeling, which we propose to include in future extensions of the software.