Created by W.Langdon from gp-bibliography.bib Revision:1.7072
The identification and characterisation 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 optimised neural network (GPNN) as a method for optimising 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.
We show that GPNN has high power to detect even relatively small genetic effects (2-3per cent heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (<1per cent) 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.
These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions.",
Genetic Programming entries for Alison A Motsinger Stephen L Lee George Mellick Marylyn D Ritchie