Skip Navigation

Publication Detail

Title: Bayesian neural networks for detecting epistasis in genetic association studies.

Authors: Beam, Andrew L; Motsinger-Reif, Alison; Doyle, Jon

Published In BMC Bioinformatics, (2014)

Abstract: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions.A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships.The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

PubMed ID: 25413600 Exiting the NIEHS site

MeSH Terms: Bayes Theorem*; Case-Control Studies; Computational Biology/methods*; Epistasis, Genetic*; Genetic Association Studies*; Humans; Models, Genetic; Mycobacterium/pathogenicity; Neural Networks (Computer)*; Polymorphism, Single Nucleotide/genetics; Tuberculosis/genetics*; Tuberculosis/microbiology

Back
to Top