title: GPNN: power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease.
abstract: BACKGROUND
  The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.
  
  
  RESULTS
  We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (<1%) or when interactions involved more than three loci. We tested GPNN on a real dataset comprised of Parkinson's disease cases and controls and found a two locus interaction between the DLST gene and sex.
  
  
  CONCLUSIONS
  These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.
authors.affiliation: Center for Human Genetics Research, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, TN 37232-0700, USA. alison.a.motsinger@vanderbilt.edu
authors.forename: Alison A
authors.initials: AA
authors.lastname: Motsinger
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Lee
authors.forename: George
authors.initials: G
authors.lastname: Mellick
authors.forename: Marylyn D
authors.initials: MD
authors.lastname: Ritchie
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods; D006801:Humans; D005810:Multigene Family / Q000235:genetics; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000175:diagnosis / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D025941:Protein Interaction Mapping / Q000379:methods*