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Exploring Bayesian Approaches to eQTL Mapping Through Probabilistic Programming.

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  • 1Department of Oncology, University of Oxford, Oxford, UK. dimitris.vavoulis@oncology.ox.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|December 19, 2019
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Summary
This summary is machine-generated.

We present a method for discovering genomic polymorphisms that influence gene expression (eQTLs) using sparse Bayesian regression. Automating model inference with Stan accelerates the process of eQTL data modeling and testing.

Keywords:
Bayesian variable selectionBlack-box Bayesian inferenceGlobal-local shrinkageHorseshoe priorProbabilistic programmingRRNA-seqStaneQTL mapping

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Computational Biology

Background:

  • Discovering genomic polymorphisms that influence gene expression, known as expression quantitative trait loci (eQTLs), is crucial for understanding gene regulation.
  • Formulating eQTL discovery as a sparse Bayesian multivariate/multiple regression problem is a common approach.
  • Implementing bespoke inference methodologies for these models can be laborious, especially when evaluating multiple candidate models.

Purpose of the Study:

  • To describe an automatic, black-box inference method for eQTL models.
  • To demonstrate the utility of Stan, a probabilistic programming language, for this purpose.
  • To accelerate the process of eQTL data modeling and testing.

Main Methods:

  • Utilizing Stan for automatic, black-box inference in sparse Bayesian multivariate/multiple regression models.
  • Applying the method to eQTL discovery.
  • Providing accessible code for implementation.

Main Results:

  • The proposed method automates complex model inference, reducing labor.
  • Stan facilitates rapid model prototyping and testing for eQTL analysis.
  • The approach accelerates the overall data modeling process for eQTL studies.

Conclusions:

  • Automatic inference using Stan simplifies and speeds up eQTL discovery.
  • Probabilistic programming languages like Stan are valuable tools for computational biology research.
  • This methodology enhances the efficiency of identifying genetic influences on gene expression.