dc.contributor.author | Motsinger, AA | |
dc.contributor.author | Lee, SL | |
dc.contributor.author | Mellick, G | |
dc.contributor.author | Ritchie, MD | |
dc.contributor.editor | Peter Newmark | |
dc.date.accessioned | 2017-05-03T14:29:00Z | |
dc.date.available | 2017-05-03T14:29:00Z | |
dc.date.issued | 2006 | |
dc.date.modified | 2008-12-04T01:38:08Z | |
dc.identifier.issn | 1471-2105 | |
dc.identifier.doi | 10.1186/1471-2105-7-39 | |
dc.identifier.uri | http://hdl.handle.net/10072/12346 | |
dc.description.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. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.format.extent | 42038 bytes | |
dc.format.extent | 398363 bytes | |
dc.format.mimetype | text/plain | |
dc.format.mimetype | application/pdf | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | BioMed Central, | |
dc.publisher.place | England | |
dc.publisher.uri | http://www.biomedcentral.com/1471-2105/7/39 | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofpagefrom | 1 | |
dc.relation.ispartofpageto | 10 | |
dc.relation.ispartofjournal | BMC bioinformatics | |
dc.relation.ispartofvolume | 7 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Mathematical sciences | |
dc.subject.fieldofresearch | Biological sciences | |
dc.subject.fieldofresearch | Information and computing sciences | |
dc.subject.fieldofresearchcode | 49 | |
dc.subject.fieldofresearchcode | 31 | |
dc.subject.fieldofresearchcode | 46 | |
dc.title | GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
dcterms.license | http://creativecommons.org/licenses/by/2.0 | |
gro.description.notepublic | Page numbers are not for citation purposes. Instead, this article has the unique article number of 39. | |
gro.rights.copyright | © 2006 Motsinger et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
gro.date.issued | 2006 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Mellick, George | |