title: An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics.
abstract: BACKGROUND
  Best practice for statistical methodology in cell-based dose-response studies has yet to be established. We examine the ability of MANOVA to detect trait-associated genetic loci in the presence of gene-gene interactions. We present a novel Bayesian nonparametric method designed to detect such interactions.
  
  
  RESULTS
  MANOVA and the Bayesian nonparametric approach show good ability to detect trait-associated genetic variants under various possible genetic models. It is shown through several sets of analyses that this may be due to marginal effects being present, even if the underlying genetic model does not explicitly contain them.
  
  
  CONCLUSIONS
  Understanding how genetic interactions affect drug response continues to be a critical goal. MANOVA and the novel Bayesian framework present a trade-off between computational complexity and model flexibility.
authors.affiliation: Center for Biomedical Informatics, Boston, Massachusetts.
authors.forename: Andrew L
authors.initials: AL
authors.lastname: Beam
authors.affiliation: Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina ; Department of Statistics, North Carolina State University, Raleigh, North Carolina.
authors.forename: Alison A
authors.initials: AA
authors.lastname: Motsinger-Reif
authors.affiliation: Department of Computer Science, North Carolina State University, Raleigh, North Carolina.
authors.forename: Jon
authors.initials: J
authors.lastname: Doyle
keywords: Bayesian nonparametric; Dose-response; Epistasis; Machine learning; Neural network