High-throughput genomic profiling instruments provide a snapshot of the simultaneous activity of molecules within cells. The resulting readouts, obtained in parallel for thousands of different functional elements in the genome, have enabled the analysis of cellular pathways at the systems level. A simple, fundamental and primary type of such an analysis consists of assessing changes across experimental conditions independently in each molecular profile. Yet, the fact that these data constitute a multivariate sample conveys the opportunity for us to gather additional insight by examining direct and indirect effects between genome elements such as genes and mutations. Graphical Markov models (GMMs), developed at the crossroads of graph theory, machine learning and statistics, are a sensible approach to pursue this goal. In this talk I will introduce GMMs and our recent work on how to use them to study the genetics of gene expression using the software qpgraph, developed in our group. I encourage the audience to bring along their laptops with the latest version of R (http://www.r-project.org) and the Bioconductor package qpgraph (http://bioconductor.org/packages/qpgraph) installed, to try to work out together some of the examples given during the talk.