In the last few years genome-wide association studies have revealed over 10,000 genetic risk factors for disease. For many disorders it is now clear that there are dozens of variants involved, precluding development of drugs that target each of the causal genes in side these loci. However, since per disease these variants typically affect a limited number of pathways, devising strategies to uncover the ‘key driver’ genes and pathways for these diseases might provide leads for pharmaceutical intervention. By combining co-regulation networks (Fehrmann et al, NG 2015), trans-eQTLs (Westra et al, NG 2015), trans-meQTLs (Bonder et al, BioRXiv 2015) and novel analytical methods (Depict et al, Nature Communications 2015, Zhernakova et al, BioRXiv 2015) we believe these key driver genes might be uncovered. I will discuss these approaches and will describe how we employ machine learning to answer the questions that we work on.