title: Bayesian neural networks for detecting epistasis in genetic association studies.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.
authors.affiliation: Center for Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Andrew_Beam@hms.harvard.edu.
authors.forename: Andrew L
authors.initials: AL
authors.lastname: Beam
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA. alison_motsinger@ncsu.edu.|Department of Statistics, North Carolina State University, Raleigh, NC, USA. alison_motsinger@ncsu.edu.
authors.forename: Alison
authors.initials: A
authors.lastname: Motsinger-Reif
authors.affiliation: Department of Computer Science, North Carolina State University, Raleigh, NC, USA. Jon_Doyle@ncsu.edu.
authors.forename: Jon
authors.initials: J
authors.lastname: Doyle
mesh_terms: D001499:Bayes Theorem; D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic; D056726:Genetic Association Studies; D006801:Humans; D008957:Models, Genetic; D009161:Mycobacterium / Q000472:pathogenicity; D016571:Neural Networks, Computer; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D014376:Tuberculosis / Q000235:genetics* / Q000382:microbiology