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

Alison A. Motsinger, Stephen L. Lee, George Mellick and Marylyn D. Ritchie (2006). GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. In: Annals of Neurology: American Neurological Association 131st Annual Meeting. American Neurological Association 131st Annual Meeting, Chicago, Illinois, (630-631). 8-11th October, 2006. doi:10.1002/ana.21027


Author Alison A. Motsinger
Stephen L. Lee
George Mellick
Marylyn D. Ritchie
Title of paper GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease
Conference name American Neurological Association 131st Annual Meeting
Conference location Chicago, Illinois
Conference dates 8-11th October, 2006
Proceedings title Annals of Neurology: American Neurological Association 131st Annual Meeting   Check publisher's open access policy
Journal name Annals of Neurology   Check publisher's open access policy
Place of Publication United States
Publisher John Wiley & Sons, Inc.
Publication Year 2006
Sub-type Poster
DOI 10.1002/ana.21027
ISSN 0364-5134
Volume 60
Issue 5
Start page 630
End page 631
Total pages 2
Language eng
Abstract/Summary 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.
Subjects CX
Q-Index Code CX

Document type: Conference Paper
Collection: School of Medicine Publications
 
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Created: Wed, 15 Aug 2007, 09:44:01 EST