GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

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

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
• Keywords: BMCBI 2006, Complex Multifactorial Diseases, Genetic Epidemiology, Neural Network