  The discovery of genomic polymorphisms influencing gene expression (also
known as expression quantitative trait loci or eQTLs) can be formulated as a
sparse Bayesian multivariate/multiple regression problem. An important aspect
in the development of such models is the implementation of bespoke inference
methodologies, a process which can become quite laborious, when multiple
candidate models are being considered. We describe automatic, black-box
inference in such models using Stan, a popular probabilistic programming
language. The utilisation of systems like Stan can facilitate model prototyping
and testing, thus accelerating the data modelling process. The code described
in this chapter can be found at https://github.com/dvav/eQTLBookChapter.
