# Exploring Bayesian approaches to eQTL mapping through probabilistic programming

## TL;DR

This paper demonstrates how probabilistic programming with Stan can streamline Bayesian eQTL mapping by enabling automatic inference, thus accelerating model development and testing in genomic studies.

## Contribution

It introduces a framework for automatic, black-box Bayesian inference in eQTL mapping models using Stan, simplifying the development process.

## Key findings

- Facilitates rapid model prototyping and testing.
- Enables automatic inference in complex Bayesian models.
- Accelerates genomic data analysis workflows.

## Abstract

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.