Analysis of expression quantitative trait loci (eQTL) provides a means for detecting transcriptional regulatory relationships at a genome-wide scale. Here we explain the eQTL analysis pipeline, we introduce publicly available tools for the statistical analysis, and we discuss issues that might complicate the eQTL mapping process. The detection and interpretation of eQTL requires careful consideration of a range of potentially confounding effects. Particularly population substructure and batch effects may lead to the detection of many false-positive eQTL if not accounted for. Traditionally, most eQTL mapping methods only check for the correlation of single loci with gene expression. In order to detect (epistatic) interactions between distant genetic loci one has to take into account several loci simultaneously. Here, we present the Random Forest regression method as a way of accounting for interacting loci. Next, we introduce analysis methods aiding the biological interpretation of detected eQTL. For example, the notion of local (cis) and distant (trans) eQTL has been very useful for interpreting the causes and implications of eQTL in many studies. In addition, Bayesian networks have been used extensively to infer causal relationships among eQTL and between eQTL and other genetic associations (e.g. disease associated loci). Also, the integration of eQTL with complementary information such as physical protein interaction data may significantly improve statistical power and provide insight into possible molecular mechanisms linking the regulator to its target gene. The eQTL approach is potentially very powerful for the analysis of regulatory pathways affecting disease susceptibility and other relevant traits. However, careful analysis is required to unleash its full potential.