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 (2-3% 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.
Motsinger
Alison A
AA
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
Lee
Stephen L
SL
Mellick
George
G
Ritchie
Marylyn D
MD
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*