Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle
Bioinformatics Research Center, North Carolina State University, Raleigh, NC
arXiv:1404.3989 [q-bio.GN], (15 Apr 2014)


   author={Beam}, A.~L. and {Motsinger-Reif}, A. and {Doyle}, J.},

   title={"{Bayesian Neural Networks for Genetic Association Studies of Complex Disease}"},

   journal={ArXiv e-prints},




   keywords={Quantitative Biology – Genomics, Statistics – Applications, Statistics – Machine Learning},




   adsnote={Provided by the SAO/NASA Astrophysics Data System}


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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. 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 powerful at detecting causal SNPs while having the computational efficiency needed handle large datasets.
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