A new paper by Motsinger et al. in BMC Bioinformatics evaluates and applies a genetic programming neural network (GPNN) approach for detecting epistasis in case-control studies. The strength of this approach is the ability to discover the optimal NN architecture as part of the modeling process.
Motsinger AA, Lee SL, Mellick G, Ritchie MD. GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinformatics. 2006 Jan 25;7(1):39 [PubMed]
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. CONCLUSION: These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.