Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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.

Type

Journal article

Publication Date

12/06/2019

Keywords

q-bio.GN, q-bio.GN, q-bio.QM, stat.AP, stat.ME